A Conceptual Framework for the Assessment of Cumulative Exposure to Air Pollution at a Fine Spatial Scale

PubMed Central

Wahida, Kihal-Talantikite; Padilla, Cindy M.; Denis, Zmirou-Navier; Olivier, Blanchard; Géraldine, Le Nir; Philippe, Quenel; Séverine, Deguen

2016-01-01

Many epidemiological studies examining long-term health effects of exposure to air pollutants have characterized exposure by the outdoor air concentrations at sites that may be distant to subjects’ residences at different points in time. The temporal and spatial mobility of subjects and the spatial scale of exposure assessment could thus lead to misclassification in the cumulative exposure estimation. This paper attempts to fill the gap regarding cumulative exposure assessment to air pollution at a fine spatial scale in epidemiological studies investigating long-term health effects. We propose a conceptual framework showing how major difficulties in cumulative long-term exposure assessment could be surmounted. We then illustrate this conceptual model on the case of exposure to NO2 following two steps: (i) retrospective reconstitution of NO2 concentrations at a fine spatial scale; and (ii) a novel approach to assigning the time-relevant exposure estimates at the census block level, using all available data on residential mobility throughout a 10- to 20-year period prior to that for which the health events are to be detected. Our conceptual framework is both flexible and convenient for the needs of different epidemiological study designs. PMID:26999170

A Conceptual Framework for the Assessment of Cumulative Exposure to Air Pollution at a Fine Spatial Scale.

PubMed

Wahida, Kihal-Talantikite; Padilla, Cindy M; Denis, Zmirou-Navier; Olivier, Blanchard; Géraldine, Le Nir; Philippe, Quenel; Séverine, Deguen

2016-03-15

Many epidemiological studies examining long-term health effects of exposure to air pollutants have characterized exposure by the outdoor air concentrations at sites that may be distant to subjects' residences at different points in time. The temporal and spatial mobility of subjects and the spatial scale of exposure assessment could thus lead to misclassification in the cumulative exposure estimation. This paper attempts to fill the gap regarding cumulative exposure assessment to air pollution at a fine spatial scale in epidemiological studies investigating long-term health effects. We propose a conceptual framework showing how major difficulties in cumulative long-term exposure assessment could be surmounted. We then illustrate this conceptual model on the case of exposure to NO₂ following two steps: (i) retrospective reconstitution of NO₂ concentrations at a fine spatial scale; and (ii) a novel approach to assigning the time-relevant exposure estimates at the census block level, using all available data on residential mobility throughout a 10- to 20-year period prior to that for which the health events are to be detected. Our conceptual framework is both flexible and convenient for the needs of different epidemiological study designs.

Fine-Scale Exposure to Allergenic Pollen in the Urban Environment: Evaluation of Land Use Regression Approach.

PubMed

Hjort, Jan; Hugg, Timo T; Antikainen, Harri; Rusanen, Jarmo; Sofiev, Mikhail; Kukkonen, Jaakko; Jaakkola, Maritta S; Jaakkola, Jouni J K

2016-05-01

Despite the recent developments in physically and chemically based analysis of atmospheric particles, no models exist for resolving the spatial variability of pollen concentration at urban scale. We developed a land use regression (LUR) approach for predicting spatial fine-scale allergenic pollen concentrations in the Helsinki metropolitan area, Finland, and evaluated the performance of the models against available empirical data. We used grass pollen data monitored at 16 sites in an urban area during the peak pollen season and geospatial environmental data. The main statistical method was generalized linear model (GLM). GLM-based LURs explained 79% of the spatial variation in the grass pollen data based on all samples, and 47% of the variation when samples from two sites with very high concentrations were excluded. In model evaluation, prediction errors ranged from 6% to 26% of the observed range of grass pollen concentrations. Our findings support the use of geospatial data-based statistical models to predict the spatial variation of allergenic grass pollen concentrations at intra-urban scales. A remote sensing-based vegetation index was the strongest predictor of pollen concentrations for exposure assessments at local scales. The LUR approach provides new opportunities to estimate the relations between environmental determinants and allergenic pollen concentration in human-modified environments at fine spatial scales. This approach could potentially be applied to estimate retrospectively pollen concentrations to be used for long-term exposure assessments. Hjort J, Hugg TT, Antikainen H, Rusanen J, Sofiev M, Kukkonen J, Jaakkola MS, Jaakkola JJ. 2016. Fine-scale exposure to allergenic pollen in the urban environment: evaluation of land use regression approach. Environ Health Perspect 124:619-626; http://dx.doi.org/10.1289/ehp.1509761.

Using fine-scale fuel measurements to assess wildland fuels, potential fire behavior and hazard mitigation treatments in the southeastern USA

Treesearch

Roger D. Ottmar; John I. Blake; William T. Crolly

2012-01-01

The inherent spatial and temporal heterogeneity of fuel beds in forests of the southeastern United States may require fine scale fuel measurements for providing reliable fire hazard and fuel treatment effectiveness estimates. In a series of five papers, an intensive, fine scale fuel inventory from the Savanna River Site in the southeastern United States is used for...

Fine-Scale Habitat Segregation between Two Ecologically Similar Top Predators.

PubMed

Palomares, Francisco; Fernández, Néstor; Roques, Severine; Chávez, Cuauhtemoc; Silveira, Leandro; Keller, Claudia; Adrados, Begoña

2016-01-01

Similar, coexisting species often segregate along the spatial ecological axis. Here, we examine if two top predators (jaguars and pumas) present different fine-scale habitat use in areas of coexistence, and discuss if the observed pattern can be explained by the risk of interference competition between them. Interference competition theory predicts that pumas should avoid habitats or areas used by jaguars (the dominant species), and as a consequence should present more variability of niche parameters across study areas. We used non-invasive genetic sampling of faeces in 12 different areas and sensor satellite fine-scale habitat indices to answer these questions. Meta-analysis confirmed differences in fine-scale habitat use between jaguars and pumas. Furthermore, average marginality of the realized niches of pumas was more variable than those of jaguars, and tolerance (a measure of niche breadth) was on average 2.2 times higher in pumas than in jaguars, as expected under the interference competition risk hypothesis. The use of sensor satellite fine-scale habitat indices allowed the detection of subtle differences in the environmental characteristics of the habitats used by these two similar top predators, which, as a rule, until now were recorded using the same general habitat types. The detection of fine spatial segregation between these two top predators was scale-dependent.

Cortical feedback signals generalise across different spatial frequencies of feedforward inputs.

PubMed

Revina, Yulia; Petro, Lucy S; Muckli, Lars

2017-09-22

Visual processing in cortex relies on feedback projections contextualising feedforward information flow. Primary visual cortex (V1) has small receptive fields and processes feedforward information at a fine-grained spatial scale, whereas higher visual areas have larger, spatially invariant receptive fields. Therefore, feedback could provide coarse information about the global scene structure or alternatively recover fine-grained structure by targeting small receptive fields in V1. We tested if feedback signals generalise across different spatial frequencies of feedforward inputs, or if they are tuned to the spatial scale of the visual scene. Using a partial occlusion paradigm, functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis (MVPA) we investigated whether feedback to V1 contains coarse or fine-grained information by manipulating the spatial frequency of the scene surround outside an occluded image portion. We show that feedback transmits both coarse and fine-grained information as it carries information about both low (LSF) and high spatial frequencies (HSF). Further, feedback signals containing LSF information are similar to feedback signals containing HSF information, even without a large overlap in spatial frequency bands of the HSF and LSF scenes. Lastly, we found that feedback carries similar information about the spatial frequency band across different scenes. We conclude that cortical feedback signals contain information which generalises across different spatial frequencies of feedforward inputs. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Coupling fine-scale root and canopy structure using ground-based remote sensing

Treesearch

Brady Hardiman; Christopher Gough; John Butnor; Gil Bohrer; Matteo Detto; Peter Curtis

2017-01-01

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in...

Lack of sex-biased dispersal promotes fine-scale genetic structure in alpine ungulates

Treesearch

Gretchen H. Roffler; Sandra L. Talbot; Gordon Luikart; George K. Sage; Kristy L. Pilgrim; Layne G. Adams; Michael K. Schwartz

2014-01-01

Identifying patterns of fine-scale genetic structure in natural populations can advance understanding of critical ecological processes such as dispersal and gene flow across heterogeneous landscapes. Alpine ungulates generally exhibit high levels of genetic structure due to female philopatry and patchy configuration of mountain habitats. We assessed the spatial scale...

Modelling Soil-Landscapes in Coastal California Hills Using Fine Scale Terrestrial Lidar

NASA Astrophysics Data System (ADS)

Prentice, S.; Bookhagen, B.; Kyriakidis, P. C.; Chadwick, O.

2013-12-01

Digital elevation models (DEMs) are the dominant input to spatially explicit digital soil mapping (DSM) efforts due to their increasing availability and the tight coupling between topography and soil variability. Accurate characterization of this coupling is dependent on DEM spatial resolution and soil sampling density, both of which may limit analyses. For example, DEM resolution may be too coarse to accurately reflect scale-dependent soil properties yet downscaling introduces artifactual uncertainty unrelated to deterministic or stochastic soil processes. We tackle these limitations through a DSM effort that couples moderately high density soil sampling with a very fine scale terrestrial lidar dataset (20 cm) implemented in a semiarid rolling hillslope domain where terrain variables change rapidly but smoothly over short distances. Our guiding hypothesis is that in this diffusion-dominated landscape, soil thickness is readily predicted by continuous terrain attributes coupled with catenary hillslope segmentation. We choose soil thickness as our keystone dependent variable for its geomorphic and hydrologic significance, and its tendency to be a primary input to synthetic ecosystem models. In defining catenary hillslope position we adapt a logical rule-set approach that parses common terrain derivatives of curvature and specific catchment area into discrete landform elements (LE). Variograms and curvature-area plots are used to distill domain-scale terrain thresholds from short range order noise characteristic of very fine-scale spatial data. The revealed spatial thresholds are used to condition LE rule-set inputs, rendering a catenary LE map that leverages the robustness of fine-scale terrain data to create a generalized interpretation of soil geomorphic domains. Preliminary regressions show that continuous terrain variables alone (curvature, specific catchment area) only partially explain soil thickness, and only in a subset of soils. For example, at spatial scales up 20, curvature explains 40% of soil thickness variance among soils <3 m deep, while soils >3 m deep show no clear relation to curvature. To further demonstration our geomorphic segmentation approach, we apply it to DEM domains where diffusion processes are less dominant than in our primary study area. Classified landform map derived from fine scale terrestrial lidar. Color classes depict hydrogeomorphic process domains in zero order watersheds.

The complex roles of space and environment in structuring functional, taxonomic and phylogenetic beta diversity of frogs in the Atlantic Forest

PubMed Central

Luiz, Amom Mendes; Sawaya, Ricardo J.

2018-01-01

Ecological communities are complex entities that can be maintained and structured by niche-based processes such as environmental conditions, and spatial processes such as dispersal. Thus, diversity patterns may be shaped simultaneously at different spatial scales by very distinct processes. Herein we assess whether and how functional, taxonomic, and phylogenetic beta diversities of frog tadpoles are explained by environmental and/or spatial predictors. We implemented a distance–based redundancy analysis to explore variation in components of beta diversity explained by pure environmental and pure spatial predictors, as well as their interactions, at both fine and broad spatial scales. Our results indicated important but complex roles of spatial and environmental predictors in structuring phylogenetic, taxonomic and functional beta diversities. The pure fine-scales spatial fraction was more important in structuring all beta diversity components, especially to functional and taxonomical spatial turnover. Environmental variables such as canopy cover and vegetation structure were important predictors of all components, but especially to functional and taxonomic beta diversity. We emphasize that distinct factors related to environment and space are affecting distinct components of beta diversity in different ways. Although weaker, phylogenetic beta diversity, which is structured more on biogeographical scales, and thus can be represented by spatially structured processes, was more related to broad spatial processes than other components. However, selected fine-scale spatial predictors denoted negative autocorrelation, which may be revealing the existence of differences in unmeasured habitat variables among samples. Although overall important, local environmental-based processes explained better functional and taxonomic beta diversity, as these diversity components carry an important ecological value. We highlight the importance of assessing different components of diversity patterns at different scales by spatially explicit models in order to improve our understanding of community structure and help to unravel the complex nature of biodiversity. PMID:29672575

Ultra-Fine Scale Spatially-Integrated Mapping of Habitat and Occupancy Using Structure-From-Motion.

PubMed

McDowall, Philip; Lynch, Heather J

2017-01-01

Organisms respond to and often simultaneously modify their environment. While these interactions are apparent at the landscape extent, the driving mechanisms often occur at very fine spatial scales. Structure-from-Motion (SfM), a computer vision technique, allows the simultaneous mapping of organisms and fine scale habitat, and will greatly improve our understanding of habitat suitability, ecophysiology, and the bi-directional relationship between geomorphology and habitat use. SfM can be used to create high-resolution (centimeter-scale) three-dimensional (3D) habitat models at low cost. These models can capture the abiotic conditions formed by terrain and simultaneously record the position of individual organisms within that terrain. While coloniality is common in seabird species, we have a poor understanding of the extent to which dense breeding aggregations are driven by fine-scale active aggregation or limited suitable habitat. We demonstrate the use of SfM for fine-scale habitat suitability by reconstructing the locations of nests in a gentoo penguin colony and fitting models that explicitly account for conspecific attraction. The resulting digital elevation models (DEMs) are used as covariates in an inhomogeneous hybrid point process model. We find that gentoo penguin nest site selection is a function of the topography of the landscape, but that nests are far more aggregated than would be expected based on terrain alone, suggesting a strong role of behavioral aggregation in driving coloniality in this species. This integrated mapping of organisms and fine scale habitat will greatly improve our understanding of fine-scale habitat suitability, ecophysiology, and the complex bi-directional relationship between geomorphology and habitat use.

Breed locally, disperse globally: Fine-scale genetic structure despite landscape-scale panmixia in a fire-specialist

Treesearch

Jennifer C. Pierson; Fred W. Allendorf; Pierre Drapeau; Michael K. Schwartz

2013-01-01

An exciting advance in the understanding of metapopulation dynamics has been the investigation of how populations respond to ephemeral patches that go 'extinct' during the lifetime of an individual. Previous research has shown that this scenario leads to genetic homogenization across large spatial scales. However, little is known about fine-scale genetic...

Fine-scale spatial genetic structure of common and declining bumble bees across an agricultural landscape.

PubMed

Dreier, Stephanie; Redhead, John W; Warren, Ian A; Bourke, Andrew F G; Heard, Matthew S; Jordan, William C; Sumner, Seirian; Wang, Jinliang; Carvell, Claire

2014-07-01

Land-use changes have threatened populations of many insect pollinators, including bumble bees. Patterns of dispersal and gene flow are key determinants of species' ability to respond to land-use change, but have been little investigated at a fine scale (<10 km) in bumble bees. Using microsatellite markers, we determined the fine-scale spatial genetic structure of populations of four common Bombus species (B. terrestris, B. lapidarius, B. pascuorum and B. hortorum) and one declining species (B. ruderatus) in an agricultural landscape in Southern England, UK. The study landscape contained sown flower patches representing agri-environment options for pollinators. We found that, as expected, the B. ruderatus population was characterized by relatively low heterozygosity, number of alleles and colony density. Across all species, inbreeding was absent or present but weak (FIS  = 0.01-0.02). Using queen genotypes reconstructed from worker sibships and colony locations estimated from the positions of workers within these sibships, we found that significant isolation by distance was absent in B. lapidarius, B. hortorum and B. ruderatus. In B. terrestris and B. pascuorum, it was present but weak; for example, in these two species, expected relatedness of queens founding colonies 1 m apart was 0.02. These results show that bumble bee populations exhibit low levels of spatial genetic structure at fine spatial scales, most likely because of ongoing gene flow via widespread queen dispersal. In addition, the results demonstrate the potential for agri-environment scheme conservation measures to facilitate fine-scale gene flow by creating a more even distribution of suitable habitats across landscapes. © 2014 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.

Fine-Scale Mapping by Spatial Risk Distribution Modeling for Regional Malaria Endemicity and Its Implications under the Low-to-Moderate Transmission Setting in Western Cambodia

PubMed Central

Okami, Suguru; Kohtake, Naohiko

2016-01-01

The disease burden of malaria has decreased as malaria elimination efforts progress. The mapping approach that uses spatial risk distribution modeling needs some adjustment and reinvestigation in accordance with situational changes. Here we applied a mathematical modeling approach for standardized morbidity ratio (SMR) calculated by annual parasite incidence using routinely aggregated surveillance reports, environmental data such as remote sensing data, and non-environmental anthropogenic data to create fine-scale spatial risk distribution maps of western Cambodia. Furthermore, we incorporated a combination of containment status indicators into the model to demonstrate spatial heterogeneities of the relationship between containment status and risks. The explanatory model was fitted to estimate the SMR of each area (adjusted Pearson correlation coefficient R2 = 0.774; Akaike information criterion AIC = 149.423). A Bayesian modeling framework was applied to estimate the uncertainty of the model and cross-scale predictions. Fine-scale maps were created by the spatial interpolation of estimated SMRs at each village. Compared with geocoded case data, corresponding predicted values showed conformity [Spearman’s rank correlation r = 0.662 in the inverse distance weighed interpolation and 0.645 in ordinal kriging (95% confidence intervals of 0.414–0.827 and 0.368–0.813, respectively), Welch’s t-test; Not significant]. The proposed approach successfully explained regional malaria risks and fine-scale risk maps were created under low-to-moderate malaria transmission settings where reinvestigations of existing risk modeling approaches were needed. Moreover, different representations of simulated outcomes of containment status indicators for respective areas provided useful insights for tailored interventional planning, considering regional malaria endemicity. PMID:27415623

CONSTRAINTS ON SPATIAL VARIATIONS IN THE FINE-STRUCTURE CONSTANT FROM PLANCK

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Bryan, Jon; Smidt, Joseph; De Bernardis, Francesco

2015-01-01

We use the cosmic microwave background (CMB) anisotropy data from Planck to constrain the spatial fluctuations of the fine-structure constant α at a redshift of 1100. We use a quadratic estimator to measure the four-point correlation function of the CMB temperature anisotropies and extract the angular power spectrum fine-structure constant spatial variations projected along the line of sight at the last scattering surface. At tens of degree angular scales and above, we constrain the fractional rms fluctuations of the fine-structure constant to be (δα/α){sub rms} < 3.4 × 10{sup –3} at the 68% confidence level. We find no evidence formore » a spatially varying α at a redshift of 10{sup 3}.« less

Fine-Scale Spatial Heterogeneity in the Distribution of Waterborne Protozoa in a Drinking Water Reservoir.

PubMed

Burnet, Jean-Baptiste; Ogorzaly, Leslie; Penny, Christian; Cauchie, Henry-Michel

2015-09-23

The occurrence of faecal pathogens in drinking water resources constitutes a threat to the supply of safe drinking water, even in industrialized nations. To efficiently assess and monitor the risk posed by these pathogens, sampling deserves careful design, based on preliminary knowledge on their distribution dynamics in water. For the protozoan pathogens Cryptosporidium and Giardia, only little is known about their spatial distribution within drinking water supplies, especially at fine scale. Two-dimensional distribution maps were generated by sampling cross-sections at meter resolution in two different zones of a drinking water reservoir. Samples were analysed for protozoan pathogens as well as for E. coli, turbidity and physico-chemical parameters. Parasites displayed heterogeneous distribution patterns, as reflected by significant (oo)cyst density gradients along reservoir depth. Spatial correlations between parasites and E. coli were observed near the reservoir inlet but were absent in the downstream lacustrine zone. Measurements of surface and subsurface flow velocities suggest a role of local hydrodynamics on these spatial patterns. This fine-scale spatial study emphasizes the importance of sampling design (site, depth and position on the reservoir) for the acquisition of representative parasite data and for optimization of microbial risk assessment and monitoring. Such spatial information should prove useful to the modelling of pathogen transport dynamics in drinking water supplies.

FINE SCALE AIR QUALITY MODELING USING DISPERSION AND CMAQ MODELING APPROACHES: AN EXAMPLE APPLICATION IN WILMINGTON, DE

EPA Science Inventory

Characterization of spatial variability of air pollutants in an urban setting at fine scales is critical for improved air toxics exposure assessments, for model evaluation studies and also for air quality regulatory applications. For this study, we investigate an approach that su...

Multi-scale interactions between local hydrography, seabed topography, and community assembly on cold-water coral reefs

NASA Astrophysics Data System (ADS)

Henry, L.-A.; Moreno Navas, J.; Roberts, J. M.

2013-04-01

We investigated how interactions between hydrography, topography and species ecology influence the assembly of species and functional traits across multiple spatial scales of a cold-water coral reef seascape. In a novel approach for these ecosystems, we used a spatially resolved complex three-dimensional flow model of hydrography to help explain assembly patterns. Forward-selection of distance-based Moran's eigenvector mapping (dbMEM) variables identified two submodels of spatial scales at which communities change: broad-scale (across reef) and fine-scale (within reef). Variance partitioning identified bathymetric and hydrographic gradients important in creating broad-scale assembly of species and traits. In contrast, fine-scale assembly was related more to processes that created spatially autocorrelated patches of fauna, such as philopatric recruitment in sessile fauna, and social interactions and food supply in scavenging detritivores and mobile predators. Our study shows how habitat modification of reef connectivity and hydrography by bottom fishing and renewable energy installations could alter the structure and function of an entire cold-water coral reef seascape.

Downscaling SMAP Soil Moisture Using Geoinformation Data and Geostatistics

NASA Astrophysics Data System (ADS)

Xu, Y.; Wang, L.

2017-12-01

Soil moisture is important for agricultural and hydrological studies. However, ground truth soil moisture data for wide area is difficult to achieve. Microwave remote sensing such as Soil Moisture Active Passive (SMAP) can offer a solution for wide coverage. However, existing global soil moisture products only provide observations at coarse spatial resolutions, which often limit their applications in regional agricultural and hydrological studies. This paper therefore aims to generate fine scale soil moisture information and extend soil moisture spatial availability. A statistical downscaling scheme is presented that incorporates multiple fine scale geoinformation data into the downscaling of coarse scale SMAP data in the absence of ground measurement data. Geoinformation data related to soil moisture patterns including digital elevation model (DEM), land surface temperature (LST), land use and normalized difference vegetation index (NDVI) at a fine scale are used as auxiliary environmental variables for downscaling SMAP data. Generalized additive model (GAM) and regression tree are first conducted to derive statistical relationships between SMAP data and auxiliary geoinformation data at an original coarse scale, and residuals are then downscaled to a finer scale via area-to-point kriging (ATPK) by accounting for the spatial correlation information of the input residuals. The results from standard validation scores as well as the triple collocation (TC) method against soil moisture in-situ measurements show that the downscaling method can significantly improve the spatial details of SMAP soil moisture while maintain the accuracy.

Spectral characteristics of background error covariance and multiscale data assimilation

DOE PAGES

Li, Zhijin; Cheng, Xiaoping; Gustafson, Jr., William I.; ...

2016-05-17

The steady increase of the spatial resolutions of numerical atmospheric and oceanic circulation models has occurred over the past decades. Horizontal grid spacing down to the order of 1 km is now often used to resolve cloud systems in the atmosphere and sub-mesoscale circulation systems in the ocean. These fine resolution models encompass a wide range of temporal and spatial scales, across which dynamical and statistical properties vary. In particular, dynamic flow systems at small scales can be spatially localized and temporarily intermittent. Difficulties of current data assimilation algorithms for such fine resolution models are numerically and theoretically examined. Ourmore » analysis shows that the background error correlation length scale is larger than 75 km for streamfunctions and is larger than 25 km for water vapor mixing ratios, even for a 2-km resolution model. A theoretical analysis suggests that such correlation length scales prevent the currently used data assimilation schemes from constraining spatial scales smaller than 150 km for streamfunctions and 50 km for water vapor mixing ratios. Moreover, our results highlight the need to fundamentally modify currently used data assimilation algorithms for assimilating high-resolution observations into the aforementioned fine resolution models. Lastly, within the framework of four-dimensional variational data assimilation, a multiscale methodology based on scale decomposition is suggested and challenges are discussed.« less

Ultra-Fine Scale Spatially-Integrated Mapping of Habitat and Occupancy Using Structure-From-Motion

PubMed Central

McDowall, Philip; Lynch, Heather J.

2017-01-01

Organisms respond to and often simultaneously modify their environment. While these interactions are apparent at the landscape extent, the driving mechanisms often occur at very fine spatial scales. Structure-from-Motion (SfM), a computer vision technique, allows the simultaneous mapping of organisms and fine scale habitat, and will greatly improve our understanding of habitat suitability, ecophysiology, and the bi-directional relationship between geomorphology and habitat use. SfM can be used to create high-resolution (centimeter-scale) three-dimensional (3D) habitat models at low cost. These models can capture the abiotic conditions formed by terrain and simultaneously record the position of individual organisms within that terrain. While coloniality is common in seabird species, we have a poor understanding of the extent to which dense breeding aggregations are driven by fine-scale active aggregation or limited suitable habitat. We demonstrate the use of SfM for fine-scale habitat suitability by reconstructing the locations of nests in a gentoo penguin colony and fitting models that explicitly account for conspecific attraction. The resulting digital elevation models (DEMs) are used as covariates in an inhomogeneous hybrid point process model. We find that gentoo penguin nest site selection is a function of the topography of the landscape, but that nests are far more aggregated than would be expected based on terrain alone, suggesting a strong role of behavioral aggregation in driving coloniality in this species. This integrated mapping of organisms and fine scale habitat will greatly improve our understanding of fine-scale habitat suitability, ecophysiology, and the complex bi-directional relationship between geomorphology and habitat use. PMID:28076351

CMAQ MODELING FOR AIR TOXICS AT FINE SCALES: A PROTOTYPE STUDY

EPA Science Inventory

Toxic air pollutants (TAPs) or hazardous air pollutants (HAPs) exhibit considerable spatial and temporal variability across urban areas. Therefore, the ability of chemical transport models (CTMs), e.g. Community Multi-scale Air Quality (CMAQ), to reproduce the spatial and tempor...

Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

PubMed Central

Madritch, Michael D.; Kingdon, Clayton C.; Singh, Aditya; Mock, Karen E.; Lindroth, Richard L.; Townsend, Philip A.

2014-01-01

Fine-scale biodiversity is increasingly recognized as important to ecosystem-level processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales. PMID:24733949

Using High Spatial Resolution Satellite Imagery to Map Forest Burn Severity Across Spatial Scales in a Pine Barrens Ecosystem

NASA Technical Reports Server (NTRS)

Meng, Ran; Wu, Jin; Schwager, Kathy L.; Zhao, Feng; Dennison, Philip E.; Cook, Bruce D.; Brewster, Kristen; Green, Timothy M.; Serbin, Shawn P.

2017-01-01

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (less than or equal to 5 m) from very-high-resolution (VHR) data. We assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal - pre- and post-fire event - WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). This work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the less than 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.

Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meng, Ran; Wu, Jin; Schwager, Kathy L.

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severitymore » was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.« less

Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

DOE PAGES

Meng, Ran; Wu, Jin; Schwager, Kathy L.; ...

2017-01-21

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severitymore » was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.« less

Integrating High-Resolution Datasets to Target Mitigation Efforts for Improving Air Quality and Public Health in Urban Neighborhoods

PubMed Central

Shandas, Vivek; Voelkel, Jackson; Rao, Meenakshi; George, Linda

2016-01-01

Reducing exposure to degraded air quality is essential for building healthy cities. Although air quality and population vary at fine spatial scales, current regulatory and public health frameworks assess human exposures using county- or city-scales. We build on a spatial analysis technique, dasymetric mapping, for allocating urban populations that, together with emerging fine-scale measurements of air pollution, addresses three objectives: (1) evaluate the role of spatial scale in estimating exposure; (2) identify urban communities that are disproportionately burdened by poor air quality; and (3) estimate reduction in mobile sources of pollutants due to local tree-planting efforts using nitrogen dioxide. Our results show a maximum value of 197% difference between cadastrally-informed dasymetric system (CIDS) and standard estimations of population exposure to degraded air quality for small spatial extent analyses, and a lack of substantial difference for large spatial extent analyses. These results provide the foundation for improving policies for managing air quality, and targeting mitigation efforts to address challenges of environmental justice. PMID:27527205

Rooting strategies in a subtropical savanna: a landscape-scale three-dimensional assessment.

PubMed

Zhou, Yong; Boutton, Thomas W; Wu, X Ben; Wright, Cynthia L; Dion, Anais L

2018-04-01

In resource-limited savannas, the distribution and abundance of fine roots play an important role in acquiring essential resources and structuring vegetation patterns and dynamics. However, little is known regarding the three-dimensional distribution of fine roots in savanna ecosystems at the landscape scale. We quantified spatial patterns of fine root density to a depth of 1.2 m in a subtropical savanna landscape using spatially specific sampling. Kriged maps revealed that fine root density was highest at the centers of woody patches, decreased towards the canopy edges, and reached lowest values within the grassland matrix throughout the entire soil profile. Lacunarity analyses indicated that spatial heterogeneities of fine root density decreased continuously to a depth of 50 cm and then increased in deeper portions of the soil profile across this landscape. This vertical pattern might be related to inherent differences in root distribution between trees/shrubs and herbaceous species, and the presence/absence of an argillic horizon across this landscape. The greater density of fine roots beneath woody patches in both upper and lower portions of the soil profile suggests an ability to acquire disproportionately more resources than herbaceous species, which may facilitate the development and persistence of woody patches across this landscape.

Fine-Scale Spatial Heterogeneity in the Distribution of Waterborne Protozoa in a Drinking Water Reservoir

PubMed Central

Burnet, Jean-Baptiste; Ogorzaly, Leslie; Penny, Christian; Cauchie, Henry-Michel

2015-01-01

Background: The occurrence of faecal pathogens in drinking water resources constitutes a threat to the supply of safe drinking water, even in industrialized nations. To efficiently assess and monitor the risk posed by these pathogens, sampling deserves careful design, based on preliminary knowledge on their distribution dynamics in water. For the protozoan pathogens Cryptosporidium and Giardia, only little is known about their spatial distribution within drinking water supplies, especially at fine scale. Methods: Two-dimensional distribution maps were generated by sampling cross-sections at meter resolution in two different zones of a drinking water reservoir. Samples were analysed for protozoan pathogens as well as for E. coli, turbidity and physico-chemical parameters. Results: Parasites displayed heterogeneous distribution patterns, as reflected by significant (oo)cyst density gradients along reservoir depth. Spatial correlations between parasites and E. coli were observed near the reservoir inlet but were absent in the downstream lacustrine zone. Measurements of surface and subsurface flow velocities suggest a role of local hydrodynamics on these spatial patterns. Conclusion: This fine-scale spatial study emphasizes the importance of sampling design (site, depth and position on the reservoir) for the acquisition of representative parasite data and for optimization of microbial risk assessment and monitoring. Such spatial information should prove useful to the modelling of pathogen transport dynamics in drinking water supplies. PMID:26404350

Fine-grained sediment spatial distribution on the basis of a geostatistical analysis: Example of the eastern Bay of the Seine (France)

NASA Astrophysics Data System (ADS)

Méar, Y.; Poizot, E.; Murat, A.; Lesueur, P.; Thomas, M.

2006-12-01

The eastern Bay of the Seine (English Channel) was the subject in 1991 of a sampling survey of superficial sediments. Geostatistic tools were used to examine the complexity of the spatial distribution of the fine-grained fraction (<50 μm). A central depocentre of fine sediments (i.e. content up to 50%) oriented in a NW-SE direction in a muddy coastal strip, in a very high energy hydrodynamical situation due to storm swells and its megatidal setting, is for the first time recognised and discussed. Within this sedimentary unit, the distribution of the fine fraction is very heterogeneous, with mud patches of less than 4000 m diameter; the boundary between these mud patches and their substratum is very sharp. The distribution of this fine fraction appears to be controlled by an anticyclonic eddy located off the Pays de Caux. Under the influence of this, the suspended material expelled from the Seine estuary moves along the coast and swings off Antifer harbour, towards the NW. It is trapped within this eddy because of the settling of suspended particulate matter. Both at a general scale and a local scale the morphology (whether inherited or due to modern processes) has a strong influence on the spatial distribution of the fine fraction. At the general scale, the basin-like shape of the area facilitates the silting, and the presence of the submarine dunes, called "Ridins d'Antifer", clearly determines the northern limit of the muddy zone. At a local scale, the same influence is obvious: paleovalleys trap the fine sediments, whereas isolated sand dunes and ripples limit the silting. This duality of role of the morphology is therefore one of the reasons why the muddy surface is extremely heterogeneous spatially. The presence of an important population of suspension feeding echinoderm, the brittle-star Ophiothrix fragilis Abildgaard, has led to a local increase in the silting, and to the modification of the physicochemical and sedimentological parameters. A complex relationship is shown to occur between the amount of fine fraction and the number of brittle-stars (ind. m -2). Classical statistical methods are not appropriate to study the spatial distribution of the mud fraction, because the spatial component of the percentage of the distribution is not integrated in the analysis. On the other hand, this is the main property of the geostatistic concepts. The use of geostatistic tools within a strict and clearly identified procedure enables the proposal of an accurate cartography. Further application of the proposed protocol (based on a semivariographic study and a conditional simulation interpolation) for surficial sediments mapping will help explain spatial and temporal variations of fine-grained fraction. Then assessments of sedimentation and erosion stages allow highlighting signature of environmental processes.

Towards a More Biologically-meaningful Climate Characterization: Variability in Space and Time at Multiple Scales

NASA Astrophysics Data System (ADS)

Christianson, D. S.; Kaufman, C. G.; Kueppers, L. M.; Harte, J.

2013-12-01

Sampling limitations and current modeling capacity justify the common use of mean temperature values in summaries of historical climate and future projections. However, a monthly mean temperature representing a 1-km2 area on the landscape is often unable to capture the climate complexity driving organismal and ecological processes. Estimates of variability in addition to mean values are more biologically meaningful and have been shown to improve projections of range shifts for certain species. Historical analyses of variance and extreme events at coarse spatial scales, as well as coarse-scale projections, show increasing temporal variability in temperature with warmer means. Few studies have considered how spatial variance changes with warming, and analysis for both temporal and spatial variability across scales is lacking. It is unclear how the spatial variability of fine-scale conditions relevant to plant and animal individuals may change given warmer coarse-scale mean values. A change in spatial variability will affect the availability of suitable habitat on the landscape and thus, will influence future species ranges. By characterizing variability across both temporal and spatial scales, we can account for potential bias in species range projections that use coarse climate data and enable improvements to current models. In this study, we use temperature data at multiple spatial and temporal scales to characterize spatial and temporal variability under a warmer climate, i.e., increased mean temperatures. Observational data from the Sierra Nevada (California, USA), experimental climate manipulation data from the eastern and western slopes of the Rocky Mountains (Colorado, USA), projected CMIP5 data for California (USA) and observed PRISM data (USA) allow us to compare characteristics of a mean-variance relationship across spatial scales ranging from sub-meter2 to 10,000 km2 and across temporal scales ranging from hours to decades. Preliminary spatial analysis at fine-spatial scales (sub-meter to 10-meter) shows greater temperature variability with warmer mean temperatures. This is inconsistent with the inherent assumption made in current species distribution models that fine-scale variability is static, implying that current projections of future species ranges may be biased -- the direction and magnitude requiring further study. While we focus our findings on the cross-scaling characteristics of temporal and spatial variability, we also compare the mean-variance relationship between 1) experimental climate manipulations and observed conditions and 2) temporal versus spatial variance, i.e., variability in a time-series at one location vs. variability across a landscape at a single time. The former informs the rich debate concerning the ability to experimentally mimic a warmer future. The latter informs space-for-time study design and analyses, as well as species persistence via a combined spatiotemporal probability of suitable future habitat.

System and method for the detection of anomalies in an image

DOEpatents

Prasad, Lakshman; Swaminarayan, Sriram

2013-09-03

Preferred aspects of the present invention can include receiving a digital image at a processor; segmenting the digital image into a hierarchy of feature layers comprising one or more fine-scale features defining a foreground object embedded in one or more coarser-scale features defining a background to the one or more fine-scale features in the segmentation hierarchy; detecting a first fine-scale foreground feature as an anomaly with respect to a first background feature within which it is embedded; and constructing an anomalous feature layer by synthesizing spatially contiguous anomalous fine-scale features. Additional preferred aspects of the present invention can include detecting non-pervasive changes between sets of images in response at least in part to one or more difference images between the sets of images.

Clonal growth and fine-scale genetic structure in tanoak (Notholithocarpus densiflorus: Fagaceae)

Treesearch

Richard S. Dodd; Wasima Mayer; Alejandro Nettel; Zara Afzal-Rafii

2013-01-01

The combination of sprouting and reproduction by seed can have important consequences on fine-scale spatial distribution of genetic structure (SGS). SGS is an important consideration for species’ restoration because it determines the minimum distance among seed trees to maximize genetic diversity while not prejudicing locally adapted genotypes. Local environmental...

Deriving spatial trends of air pollution at a neighborhood-scale through mobile monitoring

EPA Science Inventory

Abstract: Measuring air pollution in real-time using an instrumented vehicle platform has been an emerging strategy to resolve air pollution trends at a very fine spatial scale (10s of meters). Achieving second-by-second data representative of urban air quality trends requires a...

DNA Barcoding Reveals Hidden Diversity of Sand Flies (Diptera: Psychodidae) at Fine and Broad Spatial Scales in Brazilian Endemic Regions for Leishmaniasis.

PubMed

Rodrigues, Bruno Leite; Carvalho-Costa, Luís Fernando; Pinto, Israel de Souza; Rebêlo, José Manuel Macário

2018-03-17

Sand fly (Diptera: Psychodidae) taxonomy is complex and time-consuming, which hampers epidemiological efforts directed toward controlling leishmaniasis in endemic regions such as northeastern Brazil. Here, we used a fragment of the mitochondrial cytochrome c oxidase I (COI) gene to identify sand fly species in Maranhão State (northeastern Brazil) and to assess cryptic diversity occurring at different spatial scales. For this, we obtained 148 COI sequences of 15 sand fly species (10 genera) from Maranhão (fine spatial scale), and joined them to COI sequences from other Brazilian localities (distant about 2,000 km from Maranhão, broad spatial scale) available in GenBank. We revealed cases of cryptic diversity in sand flies both at fine (Lutzomyia longipalpis (Lutz and Neiva) and Evandromyia termitophila (Martins, Falcão and Silva)) and broad spatial scales (Migonemyia migonei (França), Pressatia choti (Floch and Abonnenc), Psychodopygus davisi (Root), Sciopemyia sordellii (Shannon and Del Ponte), and Bichromomyia flaviscutellata (Mangabeira)). We argue that in the case of Bi. flaviscutellata, the cryptic diversity is associated with a putative new species. Cases in which DNA taxonomy was not as effective as morphological identification possibly involved recent speciation and/or introgressive hybridization, highlighting the need for integrative approaches to identify some sand fly species. Finally, we provide the first barcode sequences for four species (Brumptomyia avellari (Costa Lima), Evandromyia infraspinosa (Mangabeira), Evandromyia evandroi (Costa Lima and Antunes), and Psychodopygus complexus (Mangabeira)), which will be useful for further molecular identification of neotropical species.

Spatial Modeling and Uncertainty Assessment of Fine Scale Surface Processes Based on Coarse Terrain Elevation Data

NASA Astrophysics Data System (ADS)

Rasera, L. G.; Mariethoz, G.; Lane, S. N.

2017-12-01

Frequent acquisition of high-resolution digital elevation models (HR-DEMs) over large areas is expensive and difficult. Satellite-derived low-resolution digital elevation models (LR-DEMs) provide extensive coverage of Earth's surface but at coarser spatial and temporal resolutions. Although useful for large scale problems, LR-DEMs are not suitable for modeling hydrologic and geomorphic processes at scales smaller than their spatial resolution. In this work, we present a multiple-point geostatistical approach for downscaling a target LR-DEM based on available high-resolution training data and recurrent high-resolution remote sensing images. The method aims at generating several equiprobable HR-DEMs conditioned to a given target LR-DEM by borrowing small scale topographic patterns from an analogue containing data at both coarse and fine scales. An application of the methodology is demonstrated by using an ensemble of simulated HR-DEMs as input to a flow-routing algorithm. The proposed framework enables a probabilistic assessment of the spatial structures generated by natural phenomena operating at scales finer than the available terrain elevation measurements. A case study in the Swiss Alps is provided to illustrate the methodology.

Doubly stochastic Poisson process models for precipitation at fine time-scales

NASA Astrophysics Data System (ADS)

Ramesh, Nadarajah I.; Onof, Christian; Xie, Dichao

2012-09-01

This paper considers a class of stochastic point process models, based on doubly stochastic Poisson processes, in the modelling of rainfall. We examine the application of this class of models, a neglected alternative to the widely-known Poisson cluster models, in the analysis of fine time-scale rainfall intensity. These models are mainly used to analyse tipping-bucket raingauge data from a single site but an extension to multiple sites is illustrated which reveals the potential of this class of models to study the temporal and spatial variability of precipitation at fine time-scales.

A three-dimensional spatial mapping approach to quantify fine-scale heterogeneity among leaves within canopies1

PubMed Central

Wingfield, Jenna L.; Ruane, Lauren G.; Patterson, Joshua D.

2017-01-01

Premise of the study: The three-dimensional structure of tree canopies creates environmental heterogeneity, which can differentially influence the chemistry, morphology, physiology, and/or phenology of leaves. Previous studies that subdivide canopy leaves into broad categories (i.e., “upper/lower”) fail to capture the differences in microenvironments experienced by leaves throughout the three-dimensional space of a canopy. Methods: We use a three-dimensional spatial mapping approach based on spherical polar coordinates to examine the fine-scale spatial distributions of photosynthetically active radiation (PAR) and the concentration of ultraviolet (UV)-absorbing compounds (A300) among leaves within the canopies of black mangroves (Avicennia germinans). Results: Linear regressions revealed that interior leaves received less PAR and produced fewer UV-absorbing compounds than leaves on the exterior of the canopy. By allocating more UV-absorbing compounds to the leaves on the exterior of the canopy, black mangroves may be maximizing UV-protection while minimizing biosynthesis of UV-absorbing compounds. Discussion: Three-dimensional spatial mapping provides an inexpensive and portable method to detect fine-scale differences in environmental and biological traits within canopies. We used it to understand the relationship between PAR and A300, but the same approach can also be used to identify traits associated with the spatial distribution of herbivores, pollinators, and pathogens. PMID:29188145

Network analysis reveals multiscale controls on streamwater chemistry

USGS Publications Warehouse

McGuire, Kevin J.; Torgersen, Christian E.; Likens, Gene E.; Buso, Donald C.; Lowe, Winsor H.; Bailey, Scott W.

2014-01-01

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks.

Network analysis reveals multiscale controls on streamwater chemistry

PubMed Central

McGuire, Kevin J.; Torgersen, Christian E.; Likens, Gene E.; Buso, Donald C.; Lowe, Winsor H.; Bailey, Scott W.

2014-01-01

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks. PMID:24753575

Network analysis reveals multiscale controls on streamwater chemistry.

PubMed

McGuire, Kevin J; Torgersen, Christian E; Likens, Gene E; Buso, Donald C; Lowe, Winsor H; Bailey, Scott W

2014-05-13

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks.

Up, Down, and All Around: Scale-Dependent Spatial Variation in Rocky-Shore Communities of Fildes Peninsula, King George Island, Antarctica

PubMed Central

Valdivia, Nelson; Díaz, María J.; Holtheuer, Jorge; Garrido, Ignacio; Huovinen, Pirjo; Gómez, Iván

2014-01-01

Understanding the variation of biodiversity along environmental gradients and multiple spatial scales is relevant for theoretical and management purposes. Hereby, we analysed the spatial variability in diversity and structure of intertidal and subtidal macrobenthic Antarctic communities along vertical environmental stress gradients and across multiple horizontal spatial scales. Since biotic interactions and local topographic features are likely major factors for coastal assemblages, we tested the hypothesis that fine-scale processes influence the effects of the vertical environmental stress gradients on the macrobenthic diversity and structure. We used nested sampling designs in the intertidal and subtidal habitats, including horizontal spatial scales ranging from few centimetres to 1000s of metres along the rocky shore of Fildes Peninsula, King George Island. In both intertidal and subtidal habitats, univariate and multivariate analyses showed a marked vertical zonation in taxon richness and community structure. These patterns depended on the horizontal spatial scale of observation, as all analyses showed a significant interaction between height (or depth) and the finer spatial scale analysed. Variance and pseudo-variance components supported our prediction for taxon richness, community structure, and the abundance of dominant species such as the filamentous green alga Urospora penicilliformis (intertidal), the herbivore Nacella concinna (intertidal), the large kelp-like Himantothallus grandifolius (subtidal), and the red crustose red alga Lithothamnion spp. (subtidal). We suggest that in coastal ecosystems strongly governed by physical factors, fine-scale processes (e.g. biotic interactions and refugia availability) are still relevant for the structuring and maintenance of the local communities. The spatial patterns found in this study serve as a necessary benchmark to understand the dynamics and adaptation of natural assemblages in response to observed and predicted environmental changes in Antarctica. PMID:24956114

Demeter-W

DOE Office of Scientific and Technical Information (OSTI.GOV)

2017-09-27

Demeter-W, an open-access software written in Python, consists of extensible module packages. It is developed with statistical downscaling algorithms, to spatially and temporally downscale water demand data into finer scale. The spatial resolution will be downscaled from region/basin scale to grid (0.5 geographic degree) scale and the temporal resolution will be downscaled from year to month. For better understanding of the driving forces and patterns for global water withdrawal, the researchers is able to utilize Demeter-W to reconstruct the data sets to examine the issues related to water withdrawals at fine spatial and temporal scales.

Measurement of spatial and temporal variation in volatile hazardous air pollutants in Tacoma, Washington, using a mobile membrane introduction mass spectrometry (MIMS) system.

PubMed

Davey, Nicholas G; Fitzpatrick, Cole T E; Etzkorn, Jacob M; Martinsen, Morten; Crampton, Robert S; Onstad, Gretchen D; Larson, Timothy V; Yost, Michael G; Krogh, Erik T; Gilroy, Michael; Himes, Kathy H; Saganić, Erik T; Simpson, Christopher D; Gill, Christopher G

2014-09-19

The objective of this study was to use membrane introduction mass spectrometry (MIMS), implemented on a mobile platform, in order to provide real-time, fine-scale, temporally and spatially resolved measurements of several hazardous air pollutants. This work is important because there is now substantial evidence that fine-scale spatial and temporal variations of air pollutant concentrations are important determinants of exposure to air pollution and adverse health outcomes. The study took place in Tacoma, WA during periods of impaired air quality in the winter and summer of 2008 and 2009. Levels of fine particles were higher in winter compared to summer, and were spatially uniform across the study area. Concentrations of vapor phase pollutants measured by membrane introduction mass spectrometry (MIMS), notably benzene and toluene, had relatively uniform spatial distributions at night, but exhibited substantial spatial variation during the day-daytime levels were up to 3-fold higher at traffic-impacted locations compared to a reference site. Although no direct side-by-side comparison was made between the MIMS system and traditional fixed site monitors, the MIMS system typically reported higher concentrations of specific VOCs, particularly benzene, ethylbenzene and naphthalene, compared to annual average concentrations obtained from SUMA canisters and gas chromatographic analysis at the fixed sites.

Fine-scale natal homing and localized movement as shaped by sex and spawning habitat in Chinook salmon: Insights from spatial autocorrelation analysis of individual genotypes

Treesearch

H. M. Neville; D. J. Isaak; J. B. Dunham; R. F. Thurow; B. E. Rieman

2006-01-01

Natal homing is a hallmark of the life history of salmonid fishes, but the spatial scale of homing within local, naturally reproducing salmon populations is still poorly understood. Accurate homing (paired with restricted movement) should lead to the existence of finescale genetic structuring due to the spatial clustering of related individuals on spawning grounds....

Estimation of Actual Crop ET of Paddy Using the Energy Balance Model SMARET and Validation with Field Water Balance Measurements and a Crop Growth Model (ORYZA)

NASA Astrophysics Data System (ADS)

Nallasamy, N. D.; Muraleedharan, B. V.; Kathirvel, K.; Narasimhan, B.

2014-12-01

Sustainable management of water resources requires reliable estimates of actual evapotranspiration (ET) at fine spatial and temporal resolution. This is significant in the case of rice based irrigation systems, one of the major consumers of surface water resources and where ET forms a major component of water consumption. However huge tradeoff in the spatial and temporal resolution of satellite images coupled with lack of adequate number of cloud free images within a growing season act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. The scale at which ET is determined is decided by the spatial and temporal scale of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which form inputs to energy balance models. In this context, the current study employed disaggregation algorithms (NL-DisTrad and DisNDVI) to generate time series of LST and NDVI images at fine resolution. The disaggregation algorithms aimed at generating LST and NDVI at finer scale by integrating temporal information from concurrent coarse resolution data and spatial information from a single fine resolution image. The temporal frequency of the disaggregated images is further improved by employing composite images of NDVI and LST in the spatio-temporal disaggregation method. The study further employed half-hourly incoming surface insolation and outgoing long wave radiation obtained from the Indian geostationary satellite (Kalpana-1) to convert the instantaneous ET into daily ET and subsequently to the seasonal ET, thereby improving the accuracy of ET estimates. The estimates of ET were validated with field based water balance measurements carried out in Gadana, a subbasin predominated by rice paddy fields, located in Tamil Nadu, India.

Spatial averaging of a dissipative particle dynamics model for active suspensions

NASA Astrophysics Data System (ADS)

Panchenko, Alexander; Hinz, Denis F.; Fried, Eliot

2018-03-01

Starting from a fine-scale dissipative particle dynamics (DPD) model of self-motile point particles, we derive meso-scale continuum equations by applying a spatial averaging version of the Irving-Kirkwood-Noll procedure. Since the method does not rely on kinetic theory, the derivation is valid for highly concentrated particle systems. Spatial averaging yields stochastic continuum equations similar to those of Toner and Tu. However, our theory also involves a constitutive equation for the average fluctuation force. According to this equation, both the strength and the probability distribution vary with time and position through the effective mass density. The statistics of the fluctuation force also depend on the fine scale dissipative force equation, the physical temperature, and two additional parameters which characterize fluctuation strengths. Although the self-propulsion force entering our DPD model contains no explicit mechanism for aligning the velocities of neighboring particles, our averaged coarse-scale equations include the commonly encountered cubically nonlinear (internal) body force density.

Selecting habitat to survive: the impact of road density on survival in a large carnivore.

PubMed

Basille, Mathieu; Van Moorter, Bram; Herfindal, Ivar; Martin, Jodie; Linnell, John D C; Odden, John; Andersen, Reidar; Gaillard, Jean-Michel

2013-01-01

Habitat selection studies generally assume that animals select habitat and food resources at multiple scales to maximise their fitness. However, animals sometimes prefer habitats of apparently low quality, especially when considering the costs associated with spatially heterogeneous human disturbance. We used spatial variation in human disturbance, and its consequences on lynx survival, a direct fitness component, to test the Hierarchical Habitat Selection hypothesis from a population of Eurasian lynx Lynx lynx in southern Norway. Data from 46 lynx monitored with telemetry indicated that a high proportion of forest strongly reduced the risk of mortality from legal hunting at the home range scale, while increasing road density strongly increased such risk at the finer scale within the home range. We found hierarchical effects of the impact of human disturbance, with a higher road density at a large scale reinforcing its negative impact at a fine scale. Conversely, we demonstrated that lynx shifted their habitat selection to avoid areas with the highest road densities within their home ranges, thus supporting a compensatory mechanism at fine scale enabling lynx to mitigate the impact of large-scale disturbance. Human impact, positively associated with high road accessibility, was thus a stronger driver of lynx space use at a finer scale, with home range characteristics nevertheless constraining habitat selection. Our study demonstrates the truly hierarchical nature of habitat selection, which aims at maximising fitness by selecting against limiting factors at multiple spatial scales, and indicates that scale-specific heterogeneity of the environment is driving individual spatial behaviour, by means of trade-offs across spatial scales.

CONTINUOUS SPATIAL MAPPING FROM VESSELS: RESULTS AND EXPERIENCE USING VARIOUS SENSORS FOR WATER AND SEDIMENTS IN THE GREAT LAKES

EPA Science Inventory

U.S. EPA research has been exploring the use of vessel-towed sensor and underway acoustic technologies in an effort to develop spatial mapping tools and insights for a next generation of Great Lakes monitoring. Technologies allow fine-scale (meters) to meso-scale (100s of kilome...

A hierarchical spatial framework for forest landscape planning.

Treesearch

Pete Bettinger; Marie Lennette; K. Norman Johnson; Thomas A. Spies

2005-01-01

A hierarchical spatial framework for large-scale, long-term forest landscape planning is presented along with example policy analyses for a 560,000 ha area of the Oregon Coast Range. The modeling framework suggests utilizing the detail provided by satellite imagery to track forest vegetation condition and for representation of fine-scale features, such as riparian...

Microclimate predicts within-season distribution dynamics of montane forest birds

Treesearch

Sarah J.K. Frey; Adam S. Hadley; Matthew G. Betts; Mark Robertson

2016-01-01

Aim: Climate changes are anticipated to have pervasive negative effects on biodiversity and are expected to necessitate widespread range shifts or contractions. Such projections are based upon the assumptions that (1) species respond primarily to broad-scale climatic regimes, or (2) that variation in climate at fine spatial scales is less relevant at coarse spatial...

Dynamics of land change in India: a fine-scale spatial analysis

NASA Astrophysics Data System (ADS)

Meiyappan, P.; Roy, P. S.; Sharma, Y.; Jain, A. K.; Ramachandran, R.; Joshi, P. K.

2015-12-01

Land is scarce in India: India occupies 2.4% of worlds land area, but supports over 1/6th of worlds human and livestock population. This high population to land ratio, combined with socioeconomic development and increasing consumption has placed tremendous pressure on India's land resources for food, feed, and fuel. In this talk, we present contemporary (1985 to 2005) spatial estimates of land change in India using national-level analysis of Landsat imageries. Further, we investigate the causes of the spatial patterns of change using two complementary lines of evidence. First, we use statistical models estimated at macro-scale to understand the spatial relationships between land change patterns and their concomitant drivers. This analysis using our newly compiled extensive socioeconomic database at village level (~630,000 units), is 100x higher in spatial resolution compared to existing datasets, and covers over 200 variables. The detailed socioeconomic data enabled the fine-scale spatial analysis with Landsat data. Second, we synthesized information from over 130 survey based case studies on land use drivers in India to complement our macro-scale analysis. The case studies are especially useful to identify unobserved variables (e.g. farmer's attitude towards risk). Ours is the most detailed analysis of contemporary land change in India, both in terms of national extent, and the use of detailed spatial information on land change, socioeconomic factors, and synthesis of case studies.

Signatures of Penumbral Magnetic Fields at Very High Spatial Resolution

NASA Astrophysics Data System (ADS)

Langhans, K.

2006-12-01

Full Stokes spectro-polarimetry, together with refined techniques to interpret the measurements and continual modeling efforts, have improved our understanding of sunspot penumbrae in the last years. In spite of this progress, an improvement in the spatial resolution of the observations is clearly needed to establish in a more direct way the fine structure of the penumbra. The discovery of dark penumbral cores by tet{l3 Sc02} suggests that we are starting to resolve the fundamental scales of the penumbra. Spectro-polarimetric measurements that are sensitive to the magnetic field in both the photosphere and higher layers, and obtained at a spatial resolution approaching 0.1 arcsec, may therefore allow us to draw firm conclusions about the fine scale organization of penumbral magnetic fields. In this paper I will discuss recent polarization measurements at very high spatial resolution, trying to reconcile the different scenarios put forward to explain the structure of the penumbra.

Influences of geomorphology and geology on alpine treeline in the American West - More important than climatic influences?

USGS Publications Warehouse

Butler, D.R.; Malanson, G.P.; Walsh, S.J.; Fagre, D.B.

2007-01-01

The spatial distribution and pattern of alpine treeline in the American West reflect the overarching influences of geological history, lithology and structure, and geomorphic processes and landforms, and geologic and geomorphic factors—both forms and processes—can control the spatiotemporal response of the ecotone to climate change. These influences occur at spatial scales ranging from the continental scale to fine scale processes and landforms at the slope scale. Past geomorphic influences, particularly Pleistocene glaciation, have also left their impact on treeline, and treelines across the west are still adjusting to post-Pleistocene conditions within Pleistocene-created landforms. Current fine scale processes include solifluction and changes on relict solifluction and digging by animals. These processes should be examined in detail in future studies to facilitate a better understanding of where individual tree seedlings become established as a primary response of the ecotone to climate change.

Orientation decoding depends on maps, not columns

PubMed Central

Freeman, Jeremy; Brouwer, Gijs Joost; Heeger, David J.; Merriam, Elisha P.

2011-01-01

The representation of orientation in primary visual cortex (V1) has been examined at a fine spatial scale corresponding to the columnar architecture. We present functional magnetic resonance imaging (fMRI) measurements providing evidence for a topographic map of orientation preference in human V1 at a much coarser scale, in register with the angular-position component of the retinotopic map of V1. This coarse-scale orientation map provides a parsimonious explanation for why multivariate pattern analysis methods succeed in decoding stimulus orientation from fMRI measurements, challenging the widely-held assumption that decoding results reflect sampling of spatial irregularities in the fine-scale columnar architecture. Decoding stimulus attributes and cognitive states from fMRI measurements has proven useful for a number of applications, but our results demonstrate that the interpretation cannot assume decoding reflects or exploits columnar organization. PMID:21451017

The impact of mating systems and dispersal on fine-scale genetic structure at maternally, paternally and biparentally inherited markers.

PubMed

Shaw, Robyn E; Banks, Sam C; Peakall, Rod

2018-01-01

For decades, studies have focused on how dispersal and mating systems influence genetic structure across populations or social groups. However, we still lack a thorough understanding of how these processes and their interaction shape spatial genetic patterns over a finer scale (tens-hundreds of metres). Using uniparentally inherited markers may help answer these questions, yet their potential has not been fully explored. Here, we use individual-level simulations to investigate the effects of dispersal and mating system on fine-scale genetic structure at autosomal, mitochondrial and Y chromosome markers. Using genetic spatial autocorrelation analysis, we found that dispersal was the major driver of fine-scale genetic structure across maternally, paternally and biparentally inherited markers. However, when dispersal was restricted (mean distance = 100 m), variation in mating behaviour created strong differences in the comparative level of structure detected at maternally and paternally inherited markers. Promiscuity reduced spatial genetic structure at Y chromosome loci (relative to monogamy), whereas structure increased under polygyny. In contrast, mitochondrial and autosomal markers were robust to differences in the specific mating system, although genetic structure increased across all markers when reproductive success was skewed towards fewer individuals. Comparing males and females at Y chromosome vs. mitochondrial markers, respectively, revealed that some mating systems can generate similar patterns to those expected under sex-biased dispersal. This demonstrates the need for caution when inferring ecological and behavioural processes from genetic results. Comparing patterns between the sexes, across a range of marker types, may help us tease apart the processes shaping fine-scale genetic structure. © 2017 John Wiley & Sons Ltd.

A Modeling Framework for Improved Characterization of Near-Road Exposure at Fine Scales

EPA Science Inventory

Traffic-related air pollutants could cause adverse health impact to communities near roadways. To estimate the population risk and locate "hotspots" in the near-road environment, quantifying the exposure at a fine spatial resolution is essential. A new state-of-the-art ...

Untangling the contribution of aspect, drainage position and elevation to the spatial variability of fine surface fuels in south east Australian forests

NASA Astrophysics Data System (ADS)

Sheridan, Gary; nyman, petter; Duff, Tom; Baillie, Craig; Bovill, William; Lane, Patrick; Tolhurst, Kevin

2015-04-01

The prediction of fuel moisture content is important for estimating the rate of spread of wildfires, the ignition probability of firebrands, and for the efficient scheduling of prescribed fire. The moisture content of fine surface fuels varies spatially at large scales (10's to 100's km) due to variation in meteorological variables (eg. temperature, relative humidity, precipitation). At smaller scales (100's of metres) in steep topography spatial variability is attributed to topographic influences that include differences in radiation due to aspect and slope, differences in precipitation, temperature and relative humidity due to elevation, and differences in soil moisture due to hillslope drainage position. Variable forest structure and canopy shading adds further to the spatial variability in surface fuel moisture. In this study we aim to combine daily 5km resolution gridded weather data with 20m resolution DEM and vegetation structure data to predict the spatial variability of fine surface fuels in steep topography. Microclimate stations were established in south east Australia to monitor surface fine fuel moisture continuously (every 15 minutes) using newly developed instrumented litter packs, in addition to temperature and relative humidity measurements inside the litter pack, and measurement of precipitation and energy inputs above and below the forest canopy. Microclimate stations were established across a gradient of aspect (5 stations), drainage position (7 stations), elevation (15 stations), and canopy cover conditions (6 stations). The data from this extensive network of microclimate stations across a broad spectrum of topographic conditions is being analysed to enable the downscaling of gridded weather data to spatial scales that are relevant to the connectivity of wildfire fuels and to the scheduling and outcome of prescribed fires. The initial results from the first year of this study are presented here.

A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2017-07-01

Use of General Circulation Model (GCM) precipitation and evapotranspiration sequences for hydrologic modelling can result in unrealistic simulations due to the coarse scales at which GCMs operate and the systematic biases they contain. The Bias Correction Spatial Disaggregation (BCSD) method is a popular statistical downscaling and bias correction method developed to address this issue. The advantage of BCSD is its ability to reduce biases in the distribution of precipitation totals at the GCM scale and then introduce more realistic variability at finer scales than simpler spatial interpolation schemes. Although BCSD corrects biases at the GCM scale before disaggregation; at finer spatial scales biases are re-introduced by the assumptions made in the spatial disaggregation process. Our study focuses on this limitation of BCSD and proposes a rank-based approach that aims to reduce the spatial disaggregation bias especially for both low and high precipitation extremes. BCSD requires the specification of a multiplicative bias correction anomaly field that represents the ratio of the fine scale precipitation to the disaggregated precipitation. It is shown that there is significant temporal variation in the anomalies, which is masked when a mean anomaly field is used. This can be improved by modelling the anomalies in rank-space. Results from the application of the rank-BCSD procedure improve the match between the distributions of observed and downscaled precipitation at the fine scale compared to the original BCSD approach. Further improvements in the distribution are identified when a scaling correction to preserve mass in the disaggregation process is implemented. An assessment of the approach using a single GCM over Australia shows clear advantages especially in the simulation of particularly low and high downscaled precipitation amounts.

[Response of fine roots to soil nutrient spatial heterogeneity].

PubMed

Wang, Qingcheng; Cheng, Yunhuan

2004-06-01

The spatial heterogeneity is the complexity and variation of systems or their attributes, and the heterogeneity of soil nutrients is ubiquitous in all natural ecosystems. The scale of spatial heterogeneity varies considerably among different ecosystems, from tens of centimeters to hundred meters. Some of the scales can be detected by individual plant. Because the growth of individual plants can be strongly influenced by soil heterogeneity, it follows that the inter-specific competition should also be affected. During the long process of evolution, plants developed various plastic responses with their root system, including morphological, physiological and mycorrhizal plasticity, to maximize the nutrient acquisition from heterogeneous soil resources. Morphological plasticity, an adjustment in root system spatial allocation and architecture in response to spatial heterogeneous distribution of available soil resources, has been most intensively studied, and root proliferation in nutrient rich patches has been certified for many species. The species that do respond may have an increased rate of nutrient uptake, leading to a competitive advantage. Scale and precision are two important features employed in describing the size and foraging behavior of root system. It was hypothesized that scale and precision is negatively related, i. e., the species with high scale of root system tend to be a less precise forager. The outcomes of different research work have been diverse, far from reaching a consensus. Species with high scale are not necessarily less precise in fine root allocation, and vice versa. The proliferation of fine root in enriched micro-sites is species dependent, and also affected by other factors, such as patch attributes (size and nutrients concentration), nutrients, and overall soil fertility. Beside root proliferation in nutrient enriched patches, plants can also adapt themselves to the heterogeneous soil environment by altering other root characteristics such as fine root diameter, branch angle, length, and spatial architecture of root system. Physiological and mycorrhizal plasticity can add some influence on the morphological plasticity to some extent, but they are less studied. Roots located in different patches can quickly regulate their nutrient uptake kinetics within different nutrient patches, and increase overall nutrient uptake. Physiological response may, to certain extent, reduce morphological response, and is meaningful for plant growth on soils with frequently changing spatial and temporal heterogeneity. Mycorrhizal plasticity has been least studied so far. Some researches revealed that mycorrhiza, rather than fine root, proliferated in enriched patches. But, it is not the case with other studies. The proliferation of mycorrhiza within enriched patches is more profitable in term of carbon invest. The effect of fine root proliferation on nutrient uptake is complex, depending on ion mobility and whether or not neighboring plant exists. The influence of root plasticity on the growth of plants is species specific. Some species (sensitive species) gain growth benefit, while others don't. The ability of an individual plant to response to heterogeneous resources has significant effect on its competitive ability and its fate within the community, and eventually shapes the composition and structure of the community.

Fine-scale genetic structure in populations of the Chagas' disease vector Triatoma infestans (Hemiptera, Reduvidae).

PubMed

Pérez de Rosas, Alicia R; Segura, Elsa L; Fusco, Octavio; Guiñazú, Adolfo L Bareiro; García, Beatriz A

2013-03-01

Fine scale patterns of genetic structure and dispersal in Triatoma infestans populations from Argentina was analysed. A total of 314 insects from 22 domestic and peridomestic sites from the locality of San Martín (Capayán department, Catamarca province) were typed for 10 polymorphic microsatellite loci. The results confirm subdivision of T. infestans populations with restricted dispersal among sampling sites and suggest inbreeding and/or stratification within the different domestic and peridomestic structures. Spatial correlation analysis showed that the scale of structuring is approximately of 400 m, indicating that active dispersal would occur within this distance range. It was detected difference in scale of structuring among sexes, with females dispersing over greater distances than males. This study suggests that insecticide treatment and surveillance should be extended within a radius of 400 m around the infested area, which would help to reduce the probability of reinfestation by covering an area of active dispersal. The inferences made from fine-scale spatial genetic structure analyses of T. infestans populations has demonstrated to be important for community-wide control programs, providing a complementary approach to help improve vector control strategies.

Genetic analysis reveals efficient sexual spore dispersal at a fine spatial scale in Armillaria ostoyae, the causal agent of root-rot disease in conifers.

PubMed

Dutech, Cyril; Labbé, Frédéric; Capdevielle, Xavier; Lung-Escarmant, Brigitte

Armillaria ostoyae (sometimes named Armillaria solidipes) is a fungal species causing root diseases in numerous coniferous forests of the northern hemisphere. The importance of sexual spores for the establishment of new disease centres remains unclear, particularly in the large maritime pine plantations of southwestern France. An analysis of the genetic diversity of a local fungal population distributed over 500 ha in this French forest showed genetic recombination between genotypes to be frequent, consistent with regular sexual reproduction within the population. The estimated spatial genetic structure displayed a significant pattern of isolation by distance, consistent with the dispersal of sexual spores mostly at the spatial scale studied. Using these genetic data, we inferred an effective density of reproductive individuals of 0.1-0.3 individuals/ha, and a second moment of parent-progeny dispersal distance of 130-800 m, compatible with the main models of fungal spore dispersal. These results contrast with those obtained for studies of A. ostoyae over larger spatial scales, suggesting that inferences about mean spore dispersal may be best performed at fine spatial scales (i.e. a few kilometres) for most fungal species. Copyright © 2017 British Mycological Society. Published by Elsevier Ltd. All rights reserved.

Effects of Fine-Scale Landscape Variability on Satellite-Derived Land Surface Temperature Products Over Sparse Vegetation Canopies

NASA Astrophysics Data System (ADS)

Powell, R. L.; Goulden, M.; Peterson, S.; Roberts, D. A.; Still, C. J.

2015-12-01

Temperature is a primary environmental control on biological systems and processes at a range of spatial and temporal scales, from controlling biochemical processes such as photosynthesis to influencing continental-scale species distribution. The Landsat satellite series provides a long record (since the mid-1980s) of relatively high spatial resolution thermal infrared (TIR) imagery, from which we derive land surface temperature (LST) grids. Here, we investigate fine spatial resolution factors that influence Landsat-derived LST over a spectrally and spatially heterogeneous landscape. We focus on paired sites (inside/outside a 1994 fire scar) within a pinyon-juniper scrubland in Southern California. The sites have nearly identical micro-meteorology and vegetation species composition, but distinctly different vegetation abundance and structure. The tower at the unburned site includes a number of in-situ imaging tools to quantify vegetation properties, including a thermal camera on a pan-tilt mount, allowing hourly characterization of landscape component temperatures (e.g., sunlit canopy, bare soil, leaf litter). We use these in-situ measurements to assess the impact of fine-scale landscape heterogeneity on estimates of LST, including sensitivity to (i) the relative abundance of component materials, (ii) directional effects due to solar and viewing geometry, (iii) duration of sunlit exposure for each compositional type, and (iv) air temperature. To scale these properties to Landsat spatial resolution (~100-m), we characterize the sub-pixel composition of landscape components (in addition to shade) by applying spectral mixture analysis (SMA) to the Landsat Operational Land Imager (OLI) spectral bands and test the sensitivity of the relationships established with the in-situ data at this coarser scale. The effects of vegetation abundance and cover height versus other controls on satellite-derived estimates of LST will be assessed by comparing estimates at the burned vs. unburned sites across multiple seasons (~30 dates).

Modeling dynamics of western juniper under climate change in a semiarid ecosystem

NASA Astrophysics Data System (ADS)

Shrestha, R.; Glenn, N. F.; Flores, A. N.

2013-12-01

Modeling future vegetation dynamics in response to climate change and disturbances such as fire relies heavily on model parameterization. Fine-scale field-based measurements can provide the necessary parameters for constraining models at a larger scale. But the time- and labor-intensive nature of field-based data collection leads to sparse sampling and significant spatial uncertainties in retrieved parameters. In this study we quantify the fine-scale carbon dynamics and uncertainty of juniper woodland in the Reynolds Creek Experimental Watershed (RCEW) in southern Idaho, which is a proposed critical zone observatory (CZO) site for soil carbon processes. We leverage field-measured vegetation data along with airborne lidar and timeseries Landsat imagery to initialize a state-and-transition model (VDDT) and a process-based fire-model (FlamMap) to examine the vegetation dynamics in response to stochastic fire events and climate change. We utilize recently developed and novel techniques to measure biomass and canopy characteristics of western juniper at the individual tree scale using terrestrial and airborne laser scanning techniques in RCEW. These fine-scale data are upscaled across the watershed for the VDDT and FlamMap models. The results will immediately improve our understanding of fine-scale dynamics and carbon stocks and fluxes of woody vegetation in a semi-arid ecosystem. Moreover, quantification of uncertainty will also provide a basis for generating ensembles of spatially-explicit alternative scenarios to guide future land management decisions in the region.

Hi-C First Results

NASA Technical Reports Server (NTRS)

Cirtain, Jonathan

2013-01-01

Hi-C obtained the highest spatial and temporal resolution observatoins ever taken in the solar corona. Hi-C reveals dynamics and structure at the limit of its temporal and spatial resolution. Hi-C observed ubiquitous fine-scale flows consistent with the local sound speed.

Quantifying stream thermal regimes at management-pertinent scales: combining thermal infrared and stationary stream temperature data in a novel modeling framework.

USGS Publications Warehouse

Vatland, Shane J.; Gresswell, Robert E.; Poole, Geoffrey C.

2015-01-01

Accurately quantifying stream thermal regimes can be challenging because stream temperatures are often spatially and temporally heterogeneous. In this study, we present a novel modeling framework that combines stream temperature data sets that are continuous in either space or time. Specifically, we merged the fine spatial resolution of thermal infrared (TIR) imagery with hourly data from 10 stationary temperature loggers in a 100 km portion of the Big Hole River, MT, USA. This combination allowed us to estimate summer thermal conditions at a relatively fine spatial resolution (every 100 m of stream length) over a large extent of stream (100 km of stream) during during the warmest part of the summer. Rigorous evaluation, including internal validation, external validation with spatially continuous instream temperature measurements collected from a Langrangian frame of reference, and sensitivity analyses, suggests the model was capable of accurately estimating longitudinal patterns in summer stream temperatures for this system Results revealed considerable spatial and temporal heterogeneity in summer stream temperatures and highlighted the value of assessing thermal regimes at relatively fine spatial and temporal scales. Preserving spatial and temporal variability and structure in abiotic stream data provides a critical foundation for understanding the dynamic, multiscale habitat needs of mobile stream organisms. Similarly, enhanced understanding of spatial and temporal variation in dynamic water quality attributes, including temporal sequence and spatial arrangement, can guide strategic placement of monitoring equipment that will subsequently capture variation in environmental conditions directly pertinent to research and management objectives.

Species Associations in a Species-Rich Subtropical Forest Were Not Well-Explained by Stochastic Geometry of Biodiversity

PubMed Central

Wang, Qinggang; Bao, Dachuan; Guo, Yili; Lu, Junmeng; Lu, Zhijun; Xu, Yaozhan; Zhang, Kuihan; Liu, Haibo; Meng, Hongjie; Jiang, Mingxi; Qiao, Xiujuan; Huang, Handong

2014-01-01

The stochastic dilution hypothesis has been proposed to explain species coexistence in species-rich communities. The relative importance of the stochastic dilution effects with respect to other effects such as competition and habitat filtering required to be tested. In this study, using data from a 25-ha species-rich subtropical forest plot with a strong topographic structure at Badagongshan in central China, we analyzed overall species associations and fine-scale species interactions between 2,550 species pairs. The result showed that: (1) the proportion of segregation in overall species association analysis at 2 m neighborhood in this plot followed the prediction of the stochastic dilution hypothesis that segregations should decrease with species richness but that at 10 m neighborhood was higher than the prediction. (2) The proportion of no association type was lower than the expectation of stochastic dilution hypothesis. (3) Fine-scale species interaction analyses using Heterogeneous Poisson processes as null models revealed a high proportion (47%) of significant species effects. However, the assumption of separation of scale of this method was not fully met in this plot with a strong fine-scale topographic structure. We also found that for species within the same families, fine-scale positive species interactions occurred more frequently and negative ones occurred less frequently than expected by chance. These results suggested effects of environmental filtering other than species interaction in this forest. (4) We also found that arbor species showed a much higher proportion of significant fine-scale species interactions (66%) than shrub species (18%). We concluded that the stochastic dilution hypothesis only be partly supported and environmental filtering left discernible spatial signals in the spatial associations between species in this species-rich subtropical forest with a strong topographic structure. PMID:24824996

Species associations in a species-rich subtropical forest were not well-explained by stochastic geometry of biodiversity.

PubMed

Wang, Qinggang; Bao, Dachuan; Guo, Yili; Lu, Junmeng; Lu, Zhijun; Xu, Yaozhan; Zhang, Kuihan; Liu, Haibo; Meng, Hongjie; Jiang, Mingxi; Qiao, Xiujuan; Huang, Handong

2014-01-01

The stochastic dilution hypothesis has been proposed to explain species coexistence in species-rich communities. The relative importance of the stochastic dilution effects with respect to other effects such as competition and habitat filtering required to be tested. In this study, using data from a 25-ha species-rich subtropical forest plot with a strong topographic structure at Badagongshan in central China, we analyzed overall species associations and fine-scale species interactions between 2,550 species pairs. The result showed that: (1) the proportion of segregation in overall species association analysis at 2 m neighborhood in this plot followed the prediction of the stochastic dilution hypothesis that segregations should decrease with species richness but that at 10 m neighborhood was higher than the prediction. (2) The proportion of no association type was lower than the expectation of stochastic dilution hypothesis. (3) Fine-scale species interaction analyses using Heterogeneous Poisson processes as null models revealed a high proportion (47%) of significant species effects. However, the assumption of separation of scale of this method was not fully met in this plot with a strong fine-scale topographic structure. We also found that for species within the same families, fine-scale positive species interactions occurred more frequently and negative ones occurred less frequently than expected by chance. These results suggested effects of environmental filtering other than species interaction in this forest. (4) We also found that arbor species showed a much higher proportion of significant fine-scale species interactions (66%) than shrub species (18%). We concluded that the stochastic dilution hypothesis only be partly supported and environmental filtering left discernible spatial signals in the spatial associations between species in this species-rich subtropical forest with a strong topographic structure.

Unmanned Aircraft Systems for Studying Spatial Abundance of Ungulates: Relevance to Spatial Epidemiology

PubMed Central

Barasona, José A.; Mulero-Pázmány, Margarita; Acevedo, Pelayo; Negro, Juan J.; Torres, María J.; Gortázar, Christian; Vicente, Joaquín

2014-01-01

Complex ecological and epidemiological systems require multidisciplinary and innovative research. Low cost unmanned aircraft systems (UAS) can provide information on the spatial pattern of hosts’ distribution and abundance, which is crucial as regards modelling the determinants of disease transmission and persistence on a fine spatial scale. In this context we have studied the spatial epidemiology of tuberculosis (TB) in the ungulate community of Doñana National Park (South-western Spain) by modelling species host (red deer, fallow deer and cattle) abundance at fine spatial scale. The use of UAS high-resolution images has allowed us to collect data to model the environmental determinants of host abundance, and in a further step to evaluate their relationships with the spatial risk of TB throughout the ungulate community. We discuss the ecological, epidemiological and logistic conditions under which UAS may contribute to study the wildlife/livestock sanitary interface, where the spatial aggregation of hosts becomes crucial. These findings are relevant for planning and implementing research, fundamentally when managing disease in multi-host systems, and focusing on risky areas. Therefore, managers should prioritize the implementation of control strategies to reduce disease of conservation, economic and social relevance. PMID:25551673

Fine-Scale Analysis Reveals Cryptic Landscape Genetic Structure in Desert Tortoises

PubMed Central

Latch, Emily K.; Boarman, William I.; Walde, Andrew; Fleischer, Robert C.

2011-01-01

Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise (Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers (power lines, roads). We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately. PMID:22132143

Fine-scale analysis reveals cryptic landscape genetic structure in desert tortoises.

PubMed

Latch, Emily K; Boarman, William I; Walde, Andrew; Fleischer, Robert C

2011-01-01

Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise (Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers (power lines, roads). We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately.

Coupling fine-scale root and canopy structure using ground-based remote sensing

DOE PAGES

Hardiman, Brady S.; Gough, Christopher M.; Butnor, John R.; ...

2017-02-21

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy LiDAR (PCL) and ground penetrating radar (GPR) along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence (i.e., correlation in the frequency of spatial variation) at multiple spatialmore » scales 10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more becoming even vertically as forests aged. In all sites, above- and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5–4 m, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Lastly, our study highlights the potential, and limitations, for fusing LiDAR and radar technologies to quantitatively couple above- and belowground ecosystem structure.« less

Coupling fine-scale root and canopy structure using ground-based remote sensing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hardiman, Brady S.; Gough, Christopher M.; Butnor, John R.

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy LiDAR (PCL) and ground penetrating radar (GPR) along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence (i.e., correlation in the frequency of spatial variation) at multiple spatialmore » scales 10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more becoming even vertically as forests aged. In all sites, above- and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5–4 m, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Lastly, our study highlights the potential, and limitations, for fusing LiDAR and radar technologies to quantitatively couple above- and belowground ecosystem structure.« less

Accounting for small scale heterogeneity in ecohydrologic watershed models

NASA Astrophysics Data System (ADS)

Bhaskar, A.; Fleming, B.; Hogan, D. M.

2016-12-01

Spatially distributed ecohydrologic models are inherently constrained by the spatial resolution of their smallest units, below which land and processes are assumed to be homogenous. At coarse scales, heterogeneity is often accounted for by computing store and fluxes of interest over a distribution of land cover types (or other sources of heterogeneity) within spatially explicit modeling units. However this approach ignores spatial organization and the lateral transfer of water and materials downslope. The challenge is to account both for the role of flow network topology and fine-scale heterogeneity. We present a new approach that defines two levels of spatial aggregation and that integrates spatially explicit network approach with a flexible representation of finer-scale aspatial heterogeneity. Critically, this solution does not simply increase the resolution of the smallest spatial unit, and so by comparison, results in improved computational efficiency. The approach is demonstrated by adapting Regional Hydro-Ecologic Simulation System (RHESSys), an ecohydrologic model widely used to simulate climate, land use, and land management impacts. We illustrate the utility of our approach by showing how the model can be used to better characterize forest thinning impacts on ecohydrology. Forest thinning is typically done at the scale of individual trees, and yet management responses of interest include impacts on watershed scale hydrology and on downslope riparian vegetation. Our approach allow us to characterize the variability in tree size/carbon reduction and water transfers between neighboring trees while still capturing hillslope to watershed scale effects, Our illustrative example demonstrates that accounting for these fine scale effects can substantially alter model estimates, in some cases shifting the impacts of thinning on downslope water availability from increases to decreases. We conclude by describing other use cases that may benefit from this approach including characterizing urban vegetation and storm water management features and their impact on watershed scale hydrology and biogeochemical cycling.

Accounting for small scale heterogeneity in ecohydrologic watershed models

NASA Astrophysics Data System (ADS)

Burke, W.; Tague, C.

2017-12-01

Spatially distributed ecohydrologic models are inherently constrained by the spatial resolution of their smallest units, below which land and processes are assumed to be homogenous. At coarse scales, heterogeneity is often accounted for by computing store and fluxes of interest over a distribution of land cover types (or other sources of heterogeneity) within spatially explicit modeling units. However this approach ignores spatial organization and the lateral transfer of water and materials downslope. The challenge is to account both for the role of flow network topology and fine-scale heterogeneity. We present a new approach that defines two levels of spatial aggregation and that integrates spatially explicit network approach with a flexible representation of finer-scale aspatial heterogeneity. Critically, this solution does not simply increase the resolution of the smallest spatial unit, and so by comparison, results in improved computational efficiency. The approach is demonstrated by adapting Regional Hydro-Ecologic Simulation System (RHESSys), an ecohydrologic model widely used to simulate climate, land use, and land management impacts. We illustrate the utility of our approach by showing how the model can be used to better characterize forest thinning impacts on ecohydrology. Forest thinning is typically done at the scale of individual trees, and yet management responses of interest include impacts on watershed scale hydrology and on downslope riparian vegetation. Our approach allow us to characterize the variability in tree size/carbon reduction and water transfers between neighboring trees while still capturing hillslope to watershed scale effects, Our illustrative example demonstrates that accounting for these fine scale effects can substantially alter model estimates, in some cases shifting the impacts of thinning on downslope water availability from increases to decreases. We conclude by describing other use cases that may benefit from this approach including characterizing urban vegetation and storm water management features and their impact on watershed scale hydrology and biogeochemical cycling.

Spatial Structure of Seagrass Suggests That Size-Dependent Plant Traits Have a Strong Influence on the Distribution and Maintenance of Tropical Multispecies Meadows

PubMed Central

Ooi, Jillian L. S.; Van Niel, Kimberly P.; Kendrick, Gary A.; Holmes, Karen W.

2014-01-01

Background Seagrass species in the tropics occur in multispecies meadows. How these meadows are maintained through species co-existence and what their ecological drivers may be has been an overarching question in seagrass biogeography. In this study, we quantify the spatial structure of four co-existing species and infer potential ecological processes from these structures. Methods and Results Species presence/absence data were collected using underwater towed and dropped video cameras in Pulau Tinggi, Malaysia. The geostatistical method, utilizing semivariograms, was used to describe the spatial structure of Halophila spp, Halodule uninervis, Syringodium isoetifolium and Cymodocea serrulata. Species had spatial patterns that were oriented in the along-shore and across-shore directions, nested with larger species in meadow interiors, and consisted of multiple structures that indicate the influence of 2–3 underlying processes. The Linear Model of Coregionalization (LMC) was used to estimate the amount of variance contributing to the presence of a species at specific spatial scales. These distances were <2.5 m (micro-scale), 2.5–50 m (fine-scale) and >50 m (broad-scale) in the along-shore; and <2.5 m (micro-scale), 2.5–140 m (fine-scale) and >140 m (broad-scale) in the across-shore. The LMC suggests that smaller species (Halophila spp and H. uninervis) were most influenced by broad-scale processes such as hydrodynamics and water depth whereas large, localised species (S. isoetifolium and C. serrulata) were more influenced by finer-scale processes such as sediment burial, seagrass colonization and growth, and physical disturbance. Conclusion In this study, we provide evidence that spatial structure is distinct even when species occur in well-mixed multispecies meadows, and we suggest that size-dependent plant traits have a strong influence on the distribution and maintenance of tropical marine plant communities. This study offers a contrast from previous spatial models of seagrasses which have largely focused on monospecific temperate meadows. PMID:24497978

Spatial structure of seagrass suggests that size-dependent plant traits have a strong influence on the distribution and maintenance of tropical multispecies meadows.

PubMed

Ooi, Jillian L S; Van Niel, Kimberly P; Kendrick, Gary A; Holmes, Karen W

2014-01-01

Seagrass species in the tropics occur in multispecies meadows. How these meadows are maintained through species co-existence and what their ecological drivers may be has been an overarching question in seagrass biogeography. In this study, we quantify the spatial structure of four co-existing species and infer potential ecological processes from these structures. Species presence/absence data were collected using underwater towed and dropped video cameras in Pulau Tinggi, Malaysia. The geostatistical method, utilizing semivariograms, was used to describe the spatial structure of Halophila spp, Halodule uninervis, Syringodium isoetifolium and Cymodocea serrulata. Species had spatial patterns that were oriented in the along-shore and across-shore directions, nested with larger species in meadow interiors, and consisted of multiple structures that indicate the influence of 2-3 underlying processes. The Linear Model of Coregionalization (LMC) was used to estimate the amount of variance contributing to the presence of a species at specific spatial scales. These distances were <2.5 m (micro-scale), 2.5-50 m (fine-scale) and >50 m (broad-scale) in the along-shore; and <2.5 m (micro-scale), 2.5-140 m (fine-scale) and >140 m (broad-scale) in the across-shore. The LMC suggests that smaller species (Halophila spp and H. uninervis) were most influenced by broad-scale processes such as hydrodynamics and water depth whereas large, localised species (S. isoetifolium and C. serrulata) were more influenced by finer-scale processes such as sediment burial, seagrass colonization and growth, and physical disturbance. In this study, we provide evidence that spatial structure is distinct even when species occur in well-mixed multispecies meadows, and we suggest that size-dependent plant traits have a strong influence on the distribution and maintenance of tropical marine plant communities. This study offers a contrast from previous spatial models of seagrasses which have largely focused on monospecific temperate meadows.

Application of ground-based LIDAR for fine-scale forest fuel modeling

Treesearch

E. Louise Loudermilk; Abhinav Singhania; Juan C. Fernandez; J. Kevin Hiers; Joseph J. O' Brien; Wendell P. Cropper Jr.; K. Clint Slatton; Robert J. Mitchell

2007-01-01

Frequent (1 to 5 year) low intensity fire regimes of longleaf pine (Pinus palustris) savannas of the Southeastern United States create a continuous fuelbed of understory grasses, forbs, flammable pine needle litter, with interstitial hardwood shrubs. Measuring the spatial heterogeneity of these fine-fuels can be difficult, requiring intensive field...

Flight paths of seabirds soaring over the ocean surface enable measurement of fine-scale wind speed and direction.

PubMed

Yonehara, Yoshinari; Goto, Yusuke; Yoda, Ken; Watanuki, Yutaka; Young, Lindsay C; Weimerskirch, Henri; Bost, Charles-André; Sato, Katsufumi

2016-08-09

Ocean surface winds are an essential factor in understanding the physical interactions between the atmosphere and the ocean. Surface winds measured by satellite scatterometers and buoys cover most of the global ocean; however, there are still spatial and temporal gaps and finer-scale variations of wind that may be overlooked, particularly in coastal areas. Here, we show that flight paths of soaring seabirds can be used to estimate fine-scale (every 5 min, ∼5 km) ocean surface winds. Fine-scale global positioning system (GPS) positional data revealed that soaring seabirds flew tortuously and ground speed fluctuated presumably due to tail winds and head winds. Taking advantage of the ground speed difference in relation to flight direction, we reliably estimated wind speed and direction experienced by the birds. These bird-based wind velocities were significantly correlated with wind velocities estimated by satellite-borne scatterometers. Furthermore, extensive travel distances and flight duration of the seabirds enabled a wide range of high-resolution wind observations, especially in coastal areas. Our study suggests that seabirds provide a platform from which to measure ocean surface winds, potentially complementing conventional wind measurements by covering spatial and temporal measurement gaps.

Flight paths of seabirds soaring over the ocean surface enable measurement of fine-scale wind speed and direction

PubMed Central

Yonehara, Yoshinari; Goto, Yusuke; Yoda, Ken; Watanuki, Yutaka; Young, Lindsay C.; Weimerskirch, Henri; Bost, Charles-André; Sato, Katsufumi

2016-01-01

Ocean surface winds are an essential factor in understanding the physical interactions between the atmosphere and the ocean. Surface winds measured by satellite scatterometers and buoys cover most of the global ocean; however, there are still spatial and temporal gaps and finer-scale variations of wind that may be overlooked, particularly in coastal areas. Here, we show that flight paths of soaring seabirds can be used to estimate fine-scale (every 5 min, ∼5 km) ocean surface winds. Fine-scale global positioning system (GPS) positional data revealed that soaring seabirds flew tortuously and ground speed fluctuated presumably due to tail winds and head winds. Taking advantage of the ground speed difference in relation to flight direction, we reliably estimated wind speed and direction experienced by the birds. These bird-based wind velocities were significantly correlated with wind velocities estimated by satellite-borne scatterometers. Furthermore, extensive travel distances and flight duration of the seabirds enabled a wide range of high-resolution wind observations, especially in coastal areas. Our study suggests that seabirds provide a platform from which to measure ocean surface winds, potentially complementing conventional wind measurements by covering spatial and temporal measurement gaps. PMID:27457932

Estimating of Soil Texture Using Landsat Imagery: a Case Study in Thatta Tehsil, Sindh

NASA Astrophysics Data System (ADS)

Khalil, Zahid

2016-07-01

Soil texture is considered as an important environment factor for agricultural growth. It is the most essential part for soil classification in large scale. Today the precise soil information in large scale is of great demand from various stakeholders including soil scientists, environmental managers, land use planners and traditional agricultural users. With the increasing demand of soil properties in fine scale spatial resolution made the traditional laboratory methods inadequate. In addition the costs of soil analysis with precision agriculture systems are more expensive than traditional methods. In this regard, the application of geo-spatial techniques can be used as an alternative for examining soil analysis. This study aims to examine the ability of Geo-spatial techniques in identifying the spatial patterns of soil attributes in fine scale. Around 28 samples of soil were collected from the different areas of Thatta Tehsil, Sindh, Pakistan for analyzing soil texture. An Ordinary Least Square (OLS) regression analysis was used to relate the reflectance values of Landsat8 OLI imagery with the soil variables. The analysis showed there was a significant relationship (p<0.05) of band 2 and 5 with silt% (R2 = 0.52), and band 4 and 6 with clay% (R2 =0.40). The equation derived from OLS analysis was then used for the whole study area for deriving soil attributes. The USDA textural classification triangle was implementing for the derivation of soil texture map in GIS environment. The outcome revealed that the 'sandy loam' was in great quantity followed by loam, sandy clay loam and clay loam. The outcome shows that the Geo-spatial techniques could be used efficiently for mapping soil texture of a larger area in fine scale. This technology helped in decreasing cost, time and increase detailed information by reducing field work to a considerable level.

Dispersal, mating events and fine-scale genetic structure in the lesser flat-headed bats.

PubMed

Hua, Panyu; Zhang, Libiao; Guo, Tingting; Flanders, Jon; Zhang, Shuyi

2013-01-01

Population genetic structure has important consequences in evolutionary processes and conservation genetics in animals. Fine-scale population genetic structure depends on the pattern of landscape, the permanent movement of individuals, and the dispersal of their genes during temporary mating events. The lesser flat-headed bat (Tylonycteris pachypus) is a nonmigratory Asian bat species that roosts in small groups within the internodes of bamboo stems and the habitats are fragmented. Our previous parentage analyses revealed considerable extra-group mating in this species. To assess the spatial limits and sex-biased nature of gene flow in the same population, we used 20 microsatellite loci and mtDNA sequencing of the ND2 gene to quantify genetic structure among 54 groups of adult flat-headed bats, at nine localities in South China. AMOVA and F(ST) estimates revealed significant genetic differentiation among localities. Alternatively, the pairwise F(ST) values among roosting groups appeared to be related to the incidence of associated extra-group breeding, suggesting the impact of mating events on fine-scale genetic structure. Global spatial autocorrelation analyses showed positive genetic correlation for up to 3 km, indicating the role of fragmented habitat and the specialized social organization as a barrier in the movement of individuals among bamboo forests. The male-biased dispersal pattern resulted in weaker spatial genetic structure between localities among males than among females, and fine-scale analyses supported that relatedness levels within internodes were higher among females than among males. Finally, only females were more related to their same sex roost mates than to individuals from neighbouring roosts, suggestive of natal philopatry in females.

Spatial variability in denitrification rates in an Oregon tidal salt marsh

EPA Science Inventory

Modeling denitrification (DeN) is particularly challenging in tidal systems, which play a vital role in buffering adjacent coastal waters from nitrogen inputs. These systems are hydrologically and biogeochemically complex, varying on fine temporal and spatial scales. As part of a...

Direct and indirect genetic and fine-scale location effects on breeding date in song sparrows.

PubMed

Germain, Ryan R; Wolak, Matthew E; Arcese, Peter; Losdat, Sylvain; Reid, Jane M

2016-11-01

Quantifying direct and indirect genetic effects of interacting females and males on variation in jointly expressed life-history traits is central to predicting microevolutionary dynamics. However, accurately estimating sex-specific additive genetic variances in such traits remains difficult in wild populations, especially if related individuals inhabit similar fine-scale environments. Breeding date is a key life-history trait that responds to environmental phenology and mediates individual and population responses to environmental change. However, no studies have estimated female (direct) and male (indirect) additive genetic and inbreeding effects on breeding date, and estimated the cross-sex genetic correlation, while simultaneously accounting for fine-scale environmental effects of breeding locations, impeding prediction of microevolutionary dynamics. We fitted animal models to 38 years of song sparrow (Melospiza melodia) phenology and pedigree data to estimate sex-specific additive genetic variances in breeding date, and the cross-sex genetic correlation, thereby estimating the total additive genetic variance while simultaneously estimating sex-specific inbreeding depression. We further fitted three forms of spatial animal model to explicitly estimate variance in breeding date attributable to breeding location, overlap among breeding locations and spatial autocorrelation. We thereby quantified fine-scale location variances in breeding date and quantified the degree to which estimating such variances affected the estimated additive genetic variances. The non-spatial animal model estimated nonzero female and male additive genetic variances in breeding date (sex-specific heritabilities: 0·07 and 0·02, respectively) and a strong, positive cross-sex genetic correlation (0·99), creating substantial total additive genetic variance (0·18). Breeding date varied with female, but not male inbreeding coefficient, revealing direct, but not indirect, inbreeding depression. All three spatial animal models estimated small location variance in breeding date, but because relatedness and breeding location were virtually uncorrelated, modelling location variance did not alter the estimated additive genetic variances. Our results show that sex-specific additive genetic effects on breeding date can be strongly positively correlated, which would affect any predicted rates of microevolutionary change in response to sexually antagonistic or congruent selection. Further, we show that inbreeding effects on breeding date can also be sex specific and that genetic effects can exceed phenotypic variation stemming from fine-scale location-based variation within a wild population. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle J.M.; Reichle, Rolf H.; Houser, Paul R.; Arsenault, Kristi R.; Verhoest, Niko E.C.; Paulwels, Valentijn R.N.

2009-01-01

An ensemble Kalman filter (EnKF) is used in a suite of synthetic experiments to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of satellite retrievals) into fine-scale (1 km) model simulations. Coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (re-gridding) to the fine-scale model resolution prior to data assimilation. In either case observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated fine-scale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the fine-scale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.

Range expansion through fragmented landscapes under a variable climate

PubMed Central

Bennie, Jonathan; Hodgson, Jenny A; Lawson, Callum R; Holloway, Crispin TR; Roy, David B; Brereton, Tom; Thomas, Chris D; Wilson, Robert J

2013-01-01

Ecological responses to climate change may depend on complex patterns of variability in weather and local microclimate that overlay global increases in mean temperature. Here, we show that high-resolution temporal and spatial variability in temperature drives the dynamics of range expansion for an exemplar species, the butterfly Hesperia comma. Using fine-resolution (5 m) models of vegetation surface microclimate, we estimate the thermal suitability of 906 habitat patches at the species' range margin for 27 years. Population and metapopulation models that incorporate this dynamic microclimate surface improve predictions of observed annual changes to population density and patch occupancy dynamics during the species' range expansion from 1982 to 2009. Our findings reveal how fine-scale, short-term environmental variability drives rates and patterns of range expansion through spatially localised, intermittent episodes of expansion and contraction. Incorporating dynamic microclimates can thus improve models of species range shifts at spatial and temporal scales relevant to conservation interventions. PMID:23701124

Sample-based synthesis of two-scale structures with anisotropy

DOE PAGES

Liu, Xingchen; Shapiro, Vadim

2017-05-19

A vast majority of natural or synthetic materials are characterized by their anisotropic properties, such as stiffness. Such anisotropy is effected by the spatial distribution of the fine-scale structure and/or anisotropy of the constituent phases at a finer scale. In design, proper control of the anisotropy may greatly enhance the efficiency and performance of synthesized structures. In this paper, we propose a sample-based two-scale structure synthesis approach that explicitly controls anisotropic effective material properties of the structure on the coarse scale by orienting sampled material neighborhoods at the fine scale. We first characterize the non-uniform orientations distribution of the samplemore » structure by showing that the principal axes of an orthotropic material may be determined by the eigenvalue decomposition of its effective stiffness tensor. Such effective stiffness tensors can be efficiently estimated based on the two-point correlation functions of the fine-scale structures. Then we synthesize the two-scale structure by rotating fine-scale structures from the sample to follow a given target orientation field. Finally, the effectiveness of the proposed approach is demonstrated through examples in both 2D and 3D.« less

Sample-based synthesis of two-scale structures with anisotropy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Xingchen; Shapiro, Vadim

A vast majority of natural or synthetic materials are characterized by their anisotropic properties, such as stiffness. Such anisotropy is effected by the spatial distribution of the fine-scale structure and/or anisotropy of the constituent phases at a finer scale. In design, proper control of the anisotropy may greatly enhance the efficiency and performance of synthesized structures. In this paper, we propose a sample-based two-scale structure synthesis approach that explicitly controls anisotropic effective material properties of the structure on the coarse scale by orienting sampled material neighborhoods at the fine scale. We first characterize the non-uniform orientations distribution of the samplemore » structure by showing that the principal axes of an orthotropic material may be determined by the eigenvalue decomposition of its effective stiffness tensor. Such effective stiffness tensors can be efficiently estimated based on the two-point correlation functions of the fine-scale structures. Then we synthesize the two-scale structure by rotating fine-scale structures from the sample to follow a given target orientation field. Finally, the effectiveness of the proposed approach is demonstrated through examples in both 2D and 3D.« less

Fine-Grained Parcellation of Brain Connectivity Improves Differentiation of States of Consciousness During Graded Propofol Sedation.

PubMed

Liu, Xiaolin; Lauer, Kathryn K; Ward, B Douglas; Roberts, Christopher J; Liu, Suyan; Gollapudy, Suneeta; Rohloff, Robert; Gross, William; Xu, Zhan; Chen, Guangyu; Binder, Jeffrey R; Li, Shi-Jiang; Hudetz, Anthony G

2017-08-01

Conscious perception relies on interactions between spatially and functionally distinct modules of the brain at various spatiotemporal scales. These interactions are altered by anesthesia, an intervention that leads to fading consciousness. Relatively little is known about brain functional connectivity and its anesthetic modulation at a fine spatial scale. Here, we used functional imaging to examine propofol-induced changes in functional connectivity in brain networks defined at a fine-grained parcellation based on a combination of anatomical and functional features. Fifteen healthy volunteers underwent resting-state functional imaging in wakeful baseline, mild sedation, deep sedation, and recovery of consciousness. Compared with wakeful baseline, propofol produced widespread, dose-dependent functional connectivity changes that scaled with the extent to which consciousness was altered. The dominant changes in connectivity were associated with the frontal lobes. By examining node pairs that demonstrated a trend of functional connectivity change between wakefulness and deep sedation, quadratic discriminant analysis differentiated the states of consciousness in individual participants more accurately at a fine-grained parcellation (e.g., 2000 nodes) than at a coarse-grained parcellation (e.g., 116 anatomical nodes). Our study suggests that defining brain networks at a high granularity may provide a superior imaging-based distinction of the graded effect of anesthesia on consciousness.

Neighborhood-Scale Spatial Models of Diesel Exhaust Concentration Profile Using 1-Nitropyrene and Other Nitroarenes

PubMed Central

Schulte, Jill K.; Fox, Julie R.; Oron, Assaf P.; Larson, Timothy V.; Simpson, Christopher D.; Paulsen, Michael; Beaudet, Nancy; Kaufman, Joel D.; Magzamen, Sheryl

2016-01-01

With emerging evidence that diesel exhaust exposure poses distinct risks to human health, the need for fine-scale models of diesel exhaust pollutants is growing. We modeled the spatial distribution of several nitrated polycyclic aromatic hydrocarbons (NPAHs) to identify fine-scale gradients in diesel exhaust pollution in two Seattle, WA neighborhoods. Our modeling approach fused land-use regression, meteorological dispersion modeling, and pollutant monitoring from both fixed and mobile platforms. We applied these modeling techniques to concentrations of 1-nitropyrene (1-NP), a highly specific diesel exhaust marker, at the neighborhood scale. We developed models of two additional nitroarenes present in secondary organic aerosol: 2-nitro-pyrene and 2-nitrofluoranthene. Summer predictors of 1-NP, including distance to railroad, truck emissions, and mobile black carbon measurements, showed a greater specificity to diesel sources than predictors of other NPAHs. Winter sampling results did not yield stable models, likely due to regional mixing of pollutants in turbulent weather conditions. The model of summer 1-NP had an R2 of 0.87 and cross-validated R2 of 0.73. The synthesis of high-density sampling and hybrid modeling was successful in predicting diesel exhaust pollution at a very fine scale and identifying clear gradients in NPAH concentrations within urban neighborhoods. PMID:26501773

Climate and Human Pressure Constraints Co-Explain Regional Plant Invasion at Different Spatial Scales

PubMed Central

García-Baquero, Gonzalo; Caño, Lidia; Biurrun, Idoia; García-Mijangos, Itziar; Loidi, Javier; Herrera, Mercedes

2016-01-01

Alien species invasion represents a global threat to biodiversity and ecosystems. Explaining invasion patterns in terms of environmental constraints will help us to assess invasion risks and plan control strategies. We aim to identify plant invasion patterns in the Basque Country (Spain), and to determine the effects of climate and human pressure on that pattern. We modeled the regional distribution of 89 invasive plant species using two approaches. First, distance-based Moran’s eigenvector maps were used to partition variation in the invasive species richness, S, into spatial components at broad and fine scales; redundancy analysis was then used to explain those components on the basis of climate and human pressure descriptors. Second, we used generalized additive mixed modeling to fit species-specific responses to the same descriptors. Climate and human pressure descriptors have different effects on S at different spatial scales. Broad-scale spatially structured temperature and precipitation, and fine-scale spatially structured human population density and percentage of natural and semi-natural areas, explained altogether 38.7% of the total variance. The distribution of 84% of the individually tested species was related to either temperature, precipitation or both, and 68% was related to either population density or natural and semi-natural areas, displaying similar responses. The spatial pattern of the invasive species richness is strongly environmentally forced, mainly by climate factors. Since individual species responses were proved to be both similarly constrained in shape and explained variance by the same environmental factors, we conclude that the pattern of invasive species richness results from individual species’ environmental preferences. PMID:27741276

Spatially explicit modeling in ecology: A review

USGS Publications Warehouse

DeAngelis, Donald L.; Yurek, Simeon

2017-01-01

The use of spatially explicit models (SEMs) in ecology has grown enormously in the past two decades. One major advancement has been that fine-scale details of landscapes, and of spatially dependent biological processes, such as dispersal and invasion, can now be simulated with great precision, due to improvements in computer technology. Many areas of modeling have shifted toward a focus on capturing these fine-scale details, to improve mechanistic understanding of ecosystems. However, spatially implicit models (SIMs) have played a dominant role in ecology, and arguments have been made that SIMs, which account for the effects of space without specifying spatial positions, have an advantage of being simpler and more broadly applicable, perhaps contributing more to understanding. We address this debate by comparing SEMs and SIMs in examples from the past few decades of modeling research. We argue that, although SIMs have been the dominant approach in the incorporation of space in theoretical ecology, SEMs have unique advantages for addressing pragmatic questions concerning species populations or communities in specific places, because local conditions, such as spatial heterogeneities, organism behaviors, and other contingencies, produce dynamics and patterns that usually cannot be incorporated into simpler SIMs. SEMs are also able to describe mechanisms at the local scale that can create amplifying positive feedbacks at that scale, creating emergent patterns at larger scales, and therefore are important to basic ecological theory. We review the use of SEMs at the level of populations, interacting populations, food webs, and ecosystems and argue that SEMs are not only essential in pragmatic issues, but must play a role in the understanding of causal relationships on landscapes.

Spatial models reveal the microclimatic buffering capacity of old-growth forests

PubMed Central

Frey, Sarah J. K.; Hadley, Adam S.; Johnson, Sherri L.; Schulze, Mark; Jones, Julia A.; Betts, Matthew G.

2016-01-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing biodiversity persistence in mountainous systems under climate warming. PMID:27152339

Spatial models reveal the microclimatic buffering capacity of old-growth forests.

PubMed

Frey, Sarah J K; Hadley, Adam S; Johnson, Sherri L; Schulze, Mark; Jones, Julia A; Betts, Matthew G

2016-04-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing biodiversity persistence in mountainous systems under climate warming.

ASSESSING THE ACCURACY OF NATIONAL LAND COVER DATASET AREA ESTIMATES AT MULTIPLE SPATIAL EXTENTS

EPA Science Inventory

Site specific accuracy assessments provide fine-scale evaluation of the thematic accuracy of land use/land cover (LULC) datasets; however, they provide little insight into LULC accuracy across varying spatial extents. Additionally, LULC data are typically used to describe lands...

Assessment of Near-Source Air Pollution at a Fine Spatial Scale Utilizing Mobile Monitoring Approach

EPA Science Inventory

Mobile monitoring is an emerging strategy to characterize spatially and temporally variable air pollution in areas near sources. EPA’s Geospatial Monitoring of Air Pollution (GMAP) vehicle – an all-electric vehicle measuring real-time concentrations of partic...

Regulation of the demographic structure in isomorphic biphasic life cycles at the spatial fine scale.

PubMed

Vieira, Vasco Manuel Nobre de Carvalho da Silva; Mateus, Marcos Duarte

2014-01-01

Isomorphic biphasic algal life cycles often occur in the environment at ploidy abundance ratios (Haploid:Diploid) different from 1. Its spatial variability occurs within populations related to intertidal height and hydrodynamic stress, possibly reflecting the niche partitioning driven by their diverging adaptation to the environment argued necessary for their prevalence (evolutionary stability). Demographic models based in matrix algebra were developed to investigate which vital rates may efficiently generate an H:D variability at a fine spatial resolution. It was also taken into account time variation and type of life strategy. Ploidy dissimilarities in fecundity rates set an H:D spatial structure miss-fitting the ploidy fitness ratio. The same happened with ploidy dissimilarities in ramet growth whenever reproductive output dominated the population demography. Only through ploidy dissimilarities in looping rates (stasis, breakage and clonal growth) did the life cycle respond to a spatially heterogeneous environment efficiently creating a niche partition. Marginal locations were more sensitive than central locations. Related results have been obtained experimentally and numerically for widely different life cycles from the plant and animal kingdoms. Spore dispersal smoothed the effects of ploidy dissimilarities in fertility and enhanced the effects of ploidy dissimilarities looping rates. Ploidy dissimilarities in spore dispersal could also create the necessary niche partition, both over the space and time dimensions, even in spatial homogeneous environments and without the need for conditional differentiation of the ramets. Fine scale spatial variability may be the key for the prevalence of isomorphic biphasic life cycles, which has been neglected so far.

Regulation of the Demographic Structure in Isomorphic Biphasic Life Cycles at the Spatial Fine Scale

PubMed Central

Vieira, Vasco Manuel Nobre de Carvalho da Silva; Mateus, Marcos Duarte

2014-01-01

Isomorphic biphasic algal life cycles often occur in the environment at ploidy abundance ratios (Haploid:Diploid) different from 1. Its spatial variability occurs within populations related to intertidal height and hydrodynamic stress, possibly reflecting the niche partitioning driven by their diverging adaptation to the environment argued necessary for their prevalence (evolutionary stability). Demographic models based in matrix algebra were developed to investigate which vital rates may efficiently generate an H:D variability at a fine spatial resolution. It was also taken into account time variation and type of life strategy. Ploidy dissimilarities in fecundity rates set an H:D spatial structure miss-fitting the ploidy fitness ratio. The same happened with ploidy dissimilarities in ramet growth whenever reproductive output dominated the population demography. Only through ploidy dissimilarities in looping rates (stasis, breakage and clonal growth) did the life cycle respond to a spatially heterogeneous environment efficiently creating a niche partition. Marginal locations were more sensitive than central locations. Related results have been obtained experimentally and numerically for widely different life cycles from the plant and animal kingdoms. Spore dispersal smoothed the effects of ploidy dissimilarities in fertility and enhanced the effects of ploidy dissimilarities looping rates. Ploidy dissimilarities in spore dispersal could also create the necessary niche partition, both over the space and time dimensions, even in spatial homogeneous environments and without the need for conditional differentiation of the ramets. Fine scale spatial variability may be the key for the prevalence of isomorphic biphasic life cycles, which has been neglected so far. PMID:24658603

Fine-scale spatial distribution of orchid mycorrhizal fungi in the soil of host-rich grasslands.

PubMed

Voyron, Samuele; Ercole, Enrico; Ghignone, Stefano; Perotto, Silvia; Girlanda, Mariangela

2017-02-01

Mycorrhizal fungi are essential for the survival of orchid seedlings under natural conditions. The distribution of these fungi in soil can constrain the establishment and resulting spatial arrangement of orchids at the local scale, but the actual extent of occurrence and spatial patterns of orchid mycorrhizal (OrM) fungi in soil remain largely unknown. We addressed the fine-scale spatial distribution of OrM fungi in two orchid-rich Mediterranean grasslands by means of high-throughput sequencing of fungal ITS2 amplicons, obtained from soil samples collected either directly beneath or at a distance from adult Anacamptis morio and Ophrys sphegodes plants. Like ectomycorrhizal and arbuscular mycobionts, OrM fungi (tulasnelloid, ceratobasidioid, sebacinoid and pezizoid fungi) exhibited significant horizontal spatial autocorrelation in soil. However, OrM fungal read numbers did not correlate with distance from adult orchid plants, and several of these fungi were extremely sporadic or undetected even in the soil samples containing the orchid roots. Orchid mycorrhizal 'rhizoctonias' are commonly regarded as unspecialized saprotrophs. The sporadic occurrence of mycobionts of grassland orchids in host-rich stands questions the view of these mycorrhizal fungi as capable of sustained growth in soil. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.

PubMed

Wilson, Adam M; Jetz, Walter

2016-03-01

Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km) monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.

Projected Future Vegetation Changes for the Northwest United States and Southwest Canada at a Fine Spatial Resolution Using a Dynamic Global Vegetation Model.

PubMed

Shafer, Sarah L; Bartlein, Patrick J; Gray, Elizabeth M; Pelltier, Richard T

2015-01-01

Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions. We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0-58.0°N latitude by 136.6-103.0°W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070-2099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas.

Local topography shapes fine-scale spatial genetic structure in the Arkansas Valley evening primrose, Oenothera harringtonii (Onagraceae).

PubMed

Rhodes, Matthew K; Fant, Jeremie B; Skogen, Krissa A

2014-01-01

Identifying factors that shape the spatial distribution of genetic variation is crucial to understanding many population- and landscape-level processes. In this study, we explore fine-scale spatial genetic structure in Oenothera harringtonii (Onagraceae), an insect-pollinated, gravity-dispersed herb endemic to the grasslands of south-central and southeastern Colorado, USA. We genotyped 315 individuals with 11 microsatellite markers and utilized a combination of spatial autocorrelation analyses and landscape genetic models to relate life history traits and landscape features to dispersal processes. Spatial genetic structure was consistent with theoretical expectations of isolation by distance, but this pattern was weak (Sp = 0.00374). Anisotropic analyses indicated that spatial genetic structure was markedly directional, in this case consistent with increased dispersal along prominent slopes. Landscape genetic models subsequently confirmed that spatial genetic variation was significantly influenced by local topographic heterogeneity, specifically that geographic distance, elevation and aspect were important predictors of spatial genetic structure. Among these variables, geographic distance was ~68% more important than elevation in describing spatial genetic variation, and elevation was ~42% more important than aspect after removing the effect of geographic distance. From these results, we infer a mechanism of hydrochorous seed dispersal along major drainages aided by seasonal monsoon rains. Our findings suggest that landscape features may shape microevolutionary processes at much finer spatial scales than typically considered, and stress the importance of considering how particular dispersal vectors are influenced by their environmental context. © The American Genetic Association 2014. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A ubiquitous method for street scale spatial data collection and analysis in challenging urban environments: mapping health risks using spatial video in Haiti

PubMed Central

2013-01-01

Background Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti. Methods Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions. Results Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these “hotspots”. Conclusions Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio-temporal dynamics of the communities. Its simplicity should also be used to encourage local participatory collaborations. PMID:23587358

A ubiquitous method for street scale spatial data collection and analysis in challenging urban environments: mapping health risks using spatial video in Haiti.

PubMed

Curtis, Andrew; Blackburn, Jason K; Widmer, Jocelyn M; Morris, J Glenn

2013-04-15

Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti. Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions. Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these "hotspots". Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio-temporal dynamics of the communities. Its simplicity should also be used to encourage local participatory collaborations.

Modeling nutrient in-stream processes at the watershed scale using Nutrient Spiralling metrics

NASA Astrophysics Data System (ADS)

Marcé, R.; Armengol, J.

2009-01-01

One of the fundamental problems of using large-scale biogeochemical models is the uncertainty involved in aggregating the components of fine-scale deterministic models in watershed applications, and in extrapolating the results of field-scale measurements to larger spatial scales. Although spatial or temporal lumping may reduce the problem, information obtained during fine-scale research may not apply to lumped categories. Thus, the use of knowledge gained through fine-scale studies to predict coarse-scale phenomena is not straightforward. In this study, we used the nutrient uptake metrics defined in the Nutrient Spiralling concept to formulate the equations governing total phosphorus in-stream fate in a watershed-scale biogeochemical model. The rationale of this approach relies on the fact that the working unit for the nutrient in-stream processes of most watershed-scale models is the reach, the same unit used in field research based on the Nutrient Spiralling concept. Automatic calibration of the model using data from the study watershed confirmed that the Nutrient Spiralling formulation is a convenient simplification of the biogeochemical transformations involved in total phosphorus in-stream fate. Following calibration, the model was used as a heuristic tool in two ways. First, we compared the Nutrient Spiralling metrics obtained during calibration with results obtained during field-based research in the study watershed. The simulated and measured metrics were similar, suggesting that information collected at the reach scale during research based on the Nutrient Spiralling concept can be directly incorporated into models, without the problems associated with upscaling results from fine-scale studies. Second, we used results from our model to examine some patterns observed in several reports on Nutrient Spiralling metrics measured in impaired streams. Although these two exercises involve circular reasoning and, consequently, cannot validate any hypothesis, this is a powerful example of how models can work as heuristic tools to compare hypotheses and stimulate research in ecology.

Demonstration of C-Tools for Community Exposure Assessment

EPA Science Inventory

The presentation describes a new community-scale tool called C-PORT to model emissions related to all port-related activities – including, but not limited to ships, trucks, cranes, etc. – and predict concentrations at fine spatial scales in the near-source environment...

Characterizing multiscale variability of zero intermittency in spatial rainfall

NASA Technical Reports Server (NTRS)

Kumar, Praveen; Foufoula-Georgiou, Efi

1994-01-01

In this paper the authors study how zero intermittency in spatial rainfall, as described by the fraction of area covered by rainfall, changes with spatial scale of rainfall measurement or representation. A statistical measure of intermittency that describes the size distribution of 'voids' (nonrainy areas imbedded inside rainy areas) as a function of scale is also introduced. Morphological algorithms are proposed for reconstructing rainfall intermittency at fine scales given the intermittency at coarser scales. These algorithms are envisioned to be useful in hydroclimatological studies where the rainfall spatial variability at the subgrid scale needs to be reconstructed from the results of synoptic- or mesoscale meteorological numerical models. The developed methodologies are demsonstrated and tested using data from a severe springtime midlatitude squall line and a mild midlatitude winter storm monitored by a meteorological radar in Norman, Oklahoma.

Demonstrating the Uneven Importance of Fine-Scale Forest Structure on Snow Distributions using High Resolution Modeling

NASA Astrophysics Data System (ADS)

Broxton, P. D.; Harpold, A. A.; van Leeuwen, W.; Biederman, J. A.

2016-12-01

Quantifying the amount of snow in forested mountainous environments, as well as how it may change due to warming and forest disturbance, is critical given its importance for water supply and ecosystem health. Forest canopies affect snow accumulation and ablation in ways that are difficult to observe and model. Furthermore, fine-scale forest structure can accentuate or diminish the effects of forest-snow interactions. Despite decades of research demonstrating the importance of fine-scale forest structure (e.g. canopy edges and gaps) on snow, we still lack a comprehensive understanding of where and when forest structure has the largest impact on snowpack mass and energy budgets. Here, we use a hyper-resolution (1 meter spatial resolution) mass and energy balance snow model called the Snow Physics and Laser Mapping (SnowPALM) model along with LIDAR-derived forest structure to determine where spatial variability of fine-scale forest structure has the largest influence on large scale mass and energy budgets. SnowPALM was set up and calibrated at sites representing diverse climates in New Mexico, Arizona, and California. Then, we compared simulations at different model resolutions (i.e. 1, 10, and 100 m) to elucidate the effects of including versus not including information about fine scale canopy structure. These experiments were repeated for different prescribed topographies (i.e. flat, 30% slope north, and south-facing) at each site. Higher resolution simulations had more snow at lower canopy cover, with the opposite being true at high canopy cover. Furthermore, there is considerable scatter, indicating that different canopy arrangements can lead to different amounts of snow, even when the overall canopy coverage is the same. This modeling is contributing to the development of a high resolution machine learning algorithm called the Snow Water Artificial Network (SWANN) model to generate predictions of snow distributions over much larger domains, which has implications for improving land surface models that do not currently resolve or parameterize fine-scale canopy structure. In addition, these findings have implications for understanding the potential of different forest management strategies (i.e. thinning) based on local topography and climate to maximize the amount and retention of snow.

Toward daily monitoring of vegetation conditions at field scale through fusing data from multiple sensors

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires remote sensing data at fine spatial and temporal resolution. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for crop and rangeland monitoring. The Landsat satellite s...

Spatially explicit animal response to composition of habitat

Treesearch

Benjamin P. Pauli; Nicholas P. McCann; Patrick A. Zollner; Robert Cummings; Jonathan H. Gilbert; Eric J. Gustafson

2013-01-01

Complex decisions dramatically affect animal dispersal and space use. Dispersing individuals respond to a combination of fine-scale environmental stimuli and internal attributes. Individual-based modeling offers a valuable approach for the investigation of such interactions because it combines the heterogeneity of animal behaviors with spatial detail. Most individual-...

Assessment of Near-Source Air Pollution at a Fine Spatial Scale Utilizing Mobile Monitoring Approach

EPA Science Inventory

Mobile monitoring is an emerging strategy to characterize spatially and temporally variable air pollution in areas near sources. EPA’s Geospatial Monitoring of Air Pollution (GMAP) vehicle – an all-electric vehicle measuring real-time concentrations of particulate and gaseous po...

Assessment of Near-Source Air Pollution at a Fine Spatial Scale Utilizing a Mobile Monitoring Approach

EPA Science Inventory

Mobile monitoring is an emerging strategy to characterize spatially and temporally variable air pollution in areas near sources. EPA’s Geospatial Monitoring of Air Pollution (GMAP) vehicle – an all-electric vehicle measuring real-time concentrations of particulate and gaseous po...

Assessment of near-source air pollution at a fine spatial scale utilizing a mobile measurement platform approach

EPA Science Inventory

Mobile monitoring is an emerging strategy to characterize spatially and temporally variable air pollution in areas near sources. EPA’s Geospatial Monitoring of Air Pollution (GMAP) vehicle, an all-electric vehicle measuring real-time concentrations of particulate and gaseous poll...

Environmental Controls on Multi-Scale Soil Nutrient Variability in the Tropics: the Importance of Land-Cover Change

NASA Astrophysics Data System (ADS)

Holmes, K. W.; Kyriakidis, P. C.; Chadwick, O. A.; Matricardi, E.; Soares, J. V.; Roberts, D. A.

2003-12-01

The natural controls on soil variability and the spatial scales at which correlation exists among soil and environmental variables are critical information for evaluating the effects of deforestation. We detect different spatial scales of variability in soil nutrient levels over a large region (hundreds of thousands of km2) in the Amazon, analyze correlations among soil properties at these different scales, and evaluate scale-specific relationships among soil properties and the factors potentially driving soil development. Statistical relationships among physical drivers of soil formation, namely geology, precipitation, terrain attributes, classified soil types, and land cover derived from remote sensing, were included to determine which factors are related to soil biogeochemistry at each spatial scale. Surface and subsurface soil profile data from a 3000 sample database collected in Rond“nia, Brazil, were used to investigate patterns in pH, phosphorus, nitrogen, organic carbon, effective cation exchange capacity, calcium, magnesium, potassium, aluminum, sand, and clay in this environment grading from closed canopy tropical forest to savanna. We focus on pH in this presentation for simplicity, because pH is the single most important soil characteristic for determining the chemical environment of higher plants and soil microbial activity. We determined four spatial scales which characterize integrated patterns of soil chemistry: less than 3 km; 3 to 10 km; 10 to 68 km; and from 68 to 550 km (extent of study area). Although the finest observable scale was fixed by the field sampling density, the coarser scales were determined from relationships in the data through coregionalization modeling, rather than being imposed by the researcher. Processes which affect soils over short distances, such as land cover and terrain attributes, were good predictors of fine scale spatial components of nutrients; processes which affect soils over very large distances, such as precipitation and geology, were better predictors at coarse spatial scales. However, this result may be affected by the resolution of the available predictor maps. Land-cover change exerted a strong influence on soil chemistry at fine spatial scales, and had progressively less of an effect at coarser scales. It is important to note that land cover, and interactions among land cover and the other predictors, continued to be a significant predictor of soil chemistry at every spatial scale up to hundreds of thousands of kilometers.

Spatial consistency of chinook salmon redd distribution within and among years in the Cowlitz River, Washington

USGS Publications Warehouse

Klett, Katherine J.C.; Torgersen, Christian E.; Henning, Julie A.; Murray, Christopher J.

2013-01-01

We investigated the spawning patterns of Chinook Salmon Oncorhynchus tshawytscha on the lower Cowlitz River, Washington, using a unique set of fine- and coarse-scale temporal and spatial data collected during biweekly aerial surveys conducted in 1991–2009 (500 m to 28 km resolution) and 2008–2009 (100–500 m resolution). Redd locations were mapped from a helicopter during 2008 and 2009 with a hand-held GPS synchronized with in-flight audio recordings. We examined spatial patterns of Chinook Salmon redd reoccupation among and within years in relation to segment-scale geomorphic features. Chinook Salmon spawned in the same sections each year with little variation among years. On a coarse scale, 5 years (1993, 1998, 2000, 2002, and 2009) were compared for reoccupation. Redd locations were highly correlated among years. Comparisons on a fine scale (500 m) between 2008 and 2009 also revealed a high degree of consistency among redd locations. On a finer temporal scale, we observed that Chinook Salmon spawned in the same sections during the first and last week. Redds were clustered in both 2008 and 2009. Regression analysis with a generalized linear model at the 500-m scale indicated that river kilometer and channel bifurcation were positively associated with redd density, whereas sinuosity was negatively associated with redd density. Collecting data on specific redd locations with a GPS during aerial surveys was logistically feasible and cost effective and greatly enhanced the spatial precision of Chinook Salmon spawning surveys.

Multiple drivers, scales, and interactions influence southern Appalachian stream salamander occupancy

Treesearch

Kristen K. Cecala; John C. Maerz; Brian J. Halstead; John R. Frisch; Ted L. Gragson; Jeffrey Hepinstall-Cymerman; David S. Leigh; C. Rhett Jackson; James T. Peterson; Catherine M. Pringle

2018-01-01

Understanding how factors that vary in spatial scale relate to population abundance is vital to forecasting species responses to environmental change. Stream and river ecosystems are inherently hierarchical, potentially resulting in organismal responses to fine‐scale changes in patch characteristics that are conditional on the watershed context. Here, we...

Assessing Clark's nutcracker seed-caching flights using maternally inherited mitochondrial DNA of whitebark pine

Treesearch

Bryce A. Richardson; Ned B. Klopfenstein; Steven J. Brunsfeld

2002-01-01

Maternally inherited mitochondrial DNA haplotypes in whitebark pine (Pinus albicaulis Engelm.) were used to examine the maternal genetic structure at three hierarchical spatial scales: fine scale, coarse scale, and interpopulation. These data were used to draw inferences into Clark’s nutcracker (Nucifraga columbiana Wilson)...

Tracking fine-scale seasonal evolution of surface water extent in Central Alaska and the Canadian Shield

NASA Astrophysics Data System (ADS)

Cooley, S. W.; Smith, L. C.; Pitcher, L. H.; Pavelsky, T.; Topp, S.

2017-12-01

Quantifying spatial and temporal variability in surface water storage at high latitudes is critical for assessing environmental sensitivity to climate change. Traditionally the tradeoff between high spatial and high temporal resolution space-borne optical imagery has limited the ability to track fine-scale changes in surface water extent. However, the recent launch of hundreds of earth-imaging CubeSats by commercial satellite companies such as Planet opens up new possibilities for monitoring surface water from space. In this study we present a comparison of seasonal evolution of surface water extent in two study areas with differing geologic, hydrologic and permafrost regimes, namely, the Yukon Flats in Central Alaska and the Canadian Shield north of Yellowknife, N.W.T. Using near-daily 3m Planet CubeSat imagery, we track individual lake surface area from break-up to freeze-up during summer 2017 and quantify the spatial and temporal variability in inundation extent. We validate our water delineation method and inundation extent time series using WorldView imagery, coincident in situ lake shoreline mapping and pressure transducer data for 19 lakes in the Northwest Territories and Alaska collected during the NASA Arctic Boreal Vulnerability Experiment (ABoVE) 2017 field campaign. The results of this analysis demonstrate the value of CubeSat imagery for dynamic surface water research particularly at high latitudes and illuminate fine-scale drivers of cold regions surface water extent.

Strontium isotopes delineate fine-scale natal origins and migration histories of Pacific salmon

USGS Publications Warehouse

Brennan, Sean R.; Zimmerman, Christian E.; Fernandez, Diego P.; Cerling, Thure E.; McPhee, Megan V.; Wooller, Matthew J.

2015-01-01

Highly migratory organisms present major challenges to conservation efforts. This is especially true for exploited anadromous fish species, which exhibit long-range dispersals from natal sites, complex population structures, and extensive mixing of distinct populations during exploitation. By tracing the migratory histories of individual Chinook salmon caught in fisheries using strontium isotopes, we determined the relative production of natal habitats at fine spatial scales and different life histories. Although strontium isotopes have been widely used in provenance research, we present a new robust framework to simultaneously assess natal sources and migrations of individuals within fishery harvests through time. Our results pave the way for investigating how fine-scale habitat production and life histories of salmon respond to perturbations—providing crucial insights for conservation.

In the absence of a "landscape of fear": How lions, hyenas, and cheetahs coexist.

PubMed

Swanson, Alexandra; Arnold, Todd; Kosmala, Margaret; Forester, James; Packer, Craig

2016-12-01

Aggression by top predators can create a "landscape of fear" in which subordinate predators restrict their activity to low-risk areas or times of day. At large spatial or temporal scales, this can result in the costly loss of access to resources. However, fine-scale reactive avoidance may minimize the risk of aggressive encounters for subordinate predators while maintaining access to resources, thereby providing a mechanism for coexistence. We investigated fine-scale spatiotemporal avoidance in a guild of African predators characterized by intense interference competition. Vulnerable to food stealing and direct killing, cheetahs are expected to avoid both larger predators; hyenas are expected to avoid lions. We deployed a grid of 225 camera traps across 1,125 km 2 in Serengeti National Park, Tanzania, to evaluate concurrent patterns of habitat use by lions, hyenas, cheetahs, and their primary prey. We used hurdle models to evaluate whether smaller species avoided areas preferred by larger species, and we used time-to-event models to evaluate fine-scale temporal avoidance in the hours immediately surrounding top predator activity. We found no evidence of long-term displacement of subordinate species, even at fine spatial scales. Instead, hyenas and cheetahs were positively associated with lions except in areas with exceptionally high lion use. Hyenas and lions appeared to actively track each, while cheetahs appear to maintain long-term access to sites with high lion use by actively avoiding those areas just in the hours immediately following lion activity. Our results suggest that cheetahs are able to use patches of preferred habitat by avoiding lions on a moment-to-moment basis. Such fine-scale temporal avoidance is likely to be less costly than long-term avoidance of preferred areas: This may help explain why cheetahs are able to coexist with lions despite high rates of lion-inflicted mortality, and highlights reactive avoidance as a general mechanism for predator coexistence.

Fine-scale population genetic structure and sex-biased dispersal in the smooth snake (Coronella austriaca) in southern England.

PubMed

Pernetta, A P; Allen, J A; Beebee, T J C; Reading, C J

2011-09-01

Human-induced alteration of natural habitats has the potential to impact on the genetic structuring of remnant populations at multiple spatial scales. Species from higher trophic levels, such as snakes, are expected to be particularly susceptible to land-use changes. We examined fine-scale population structure and looked for evidence of sex-biased dispersal in smooth snakes (Coronella austriaca), sampled from 10 heathland localities situated within a managed coniferous forest in Dorset, United Kingdom. Despite the limited distances between heathland areas (maximum <6 km), there was a small but significant structuring of populations based on eight microsatellite loci. This followed an isolation-by-distance model using both straight line and 'biological' distances between sampling sites, suggesting C. austriaca's low vagility as the causal factor, rather than closed canopy conifer forest exerting an effect as a barrier to dispersal. Within population comparisons of male and female snakes showed evidence for sex-biased dispersal, with three of four analyses finding significantly higher dispersal in males than in females. We suggest that the fine-scale spatial genetic structuring and sex-biased dispersal have important implications for the conservation of C. austriaca, and highlight the value of heathland areas within commercial conifer plantations with regards to their future management.

Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios.

PubMed

Yen, Haw; White, Michael J; Arnold, Jeffrey G; Keitzer, S Conor; Johnson, Mari-Vaughn V; Atwood, Jay D; Daggupati, Prasad; Herbert, Matthew E; Sowa, Scott P; Ludsin, Stuart A; Robertson, Dale M; Srinivasan, Raghavan; Rewa, Charles A

2016-11-01

Complex watershed simulation models are powerful tools that can help scientists and policy-makers address challenging topics, such as land use management and water security. In the Western Lake Erie Basin (WLEB), complex hydrological models have been applied at various scales to help describe relationships between land use and water, nutrient, and sediment dynamics. This manuscript evaluated the capacity of the current Soil and Water Assessment Tool (SWAT) to predict hydrological and water quality processes within WLEB at the finest resolution watershed boundary unit (NHDPlus) along with the current conditions and conservation scenarios. The process based SWAT model was capable of the fine-scale computation and complex routing used in this project, as indicated by measured data at five gaging stations. The level of detail required for fine-scale spatial simulation made the use of both hard and soft data necessary in model calibration, alongside other model adaptations. Limitations to the model's predictive capacity were due to a paucity of data in the region at the NHDPlus scale rather than due to SWAT functionality. Results of treatment scenarios demonstrate variable effects of structural practices and nutrient management on sediment and nutrient loss dynamics. Targeting treatment to acres with critical outstanding conservation needs provides the largest return on investment in terms of nutrient loss reduction per dollar spent, relative to treating acres with lower inherent nutrient loss vulnerabilities. Importantly, this research raises considerations about use of models to guide land management decisions at very fine spatial scales. Decision makers using these results should be aware of data limitations that hinder fine-scale model interpretation. Copyright © 2016 Elsevier B.V. All rights reserved.

Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios

USGS Publications Warehouse

Yen, Haw; White, Michael J.; Arnold, Jeffrey G.; Keitzer, S. Conor; Johnson, Mari-Vaughn V; Atwood, Jay D.; Daggupati, Prasad; Herbert, Matthew E.; Sowa, Scott P.; Ludsin, Stuart A.; Robertson, Dale M.; Srinivasan, Raghavan; Rewa, Charles A.

2016-01-01

Complex watershed simulation models are powerful tools that can help scientists and policy-makers address challenging topics, such as land use management and water security. In the Western Lake Erie Basin (WLEB), complex hydrological models have been applied at various scales to help describe relationships between land use and water, nutrient, and sediment dynamics. This manuscript evaluated the capacity of the current Soil and Water Assessment Tool (SWAT2012) to predict hydrological and water quality processes within WLEB at the finest resolution watershed boundary unit (NHDPlus) along with the current conditions and conservation scenarios. The process based SWAT model was capable of the fine-scale computation and complex routing used in this project, as indicated by measured data at five gaging stations. The level of detail required for fine-scale spatial simulation made the use of both hard and soft data necessary in model calibration, alongside other model adaptations. Limitations to the model's predictive capacity were due to a paucity of data in the region at the NHDPlus scale rather than due to SWAT functionality. Results of treatment scenarios demonstrate variable effects of structural practices and nutrient management on sediment and nutrient loss dynamics. Targeting treatment to acres with critical outstanding conservation needs provides the largest return on investment in terms of nutrient loss reduction per dollar spent, relative to treating acres with lower inherent nutrient loss vulnerabilities. Importantly, this research raises considerations about use of models to guide land management decisions at very fine spatial scales. Decision makers using these results should be aware of data limitations that hinder fine-scale model interpretation.

Spatial Statistical and Modeling Strategy for Inventorying and Monitoring Ecosystem Resources at Multiple Scales and Resolution Levels

Treesearch

Robin M. Reich; C. Aguirre-Bravo; M.S. Williams

2006-01-01

A statistical strategy for spatial estimation and modeling of natural and environmental resource variables and indicators is presented. This strategy is part of an inventory and monitoring pilot study that is being carried out in the Mexican states of Jalisco and Colima. Fine spatial resolution estimates of key variables and indicators are outputs that will allow the...

MODELING SCALE-DEPENDENT LANDSCAPE PATTERN, DISPERSAL, AND CONNECTIVITY FROM THE PERSPECTIVE OF THE ORGANISM

EPA Science Inventory

Effects of fine- to broad-scale patterns of landscape heterogeneity on dispersal success were examined for organisms varying in life history traits. To systematically control spatial pattern, a landscape model was created by merging physiographically-based maps of simulated land...

Using Mobile Monitoring to Assess Spatial Variability in Urban Air Pollution Levels: Opportunities and Challenges (Invited)

NASA Astrophysics Data System (ADS)

Larson, T.

2010-12-01

Measuring air pollution concentrations from a moving platform is not a new idea. Historically, however, most information on the spatial variability of air pollutants have been derived from fixed site networks operating simultaneously over space. While this approach has obvious advantages from a regulatory perspective, with the increasing need to understand ever finer scales of spatial variability in urban pollution levels, the use of mobile monitoring to supplement fixed site networks has received increasing attention. Here we present examples of the use of this approach: 1) to assess existing fixed-site fine particle networks in Seattle, WA, including the establishment of new fixed-site monitoring locations; 2) to assess the effectiveness of a regulatory intervention, a wood stove burning ban, on the reduction of fine particle levels in the greater Puget Sound region; and 3) to assess spatial variability of both wood smoke and mobile source impacts in both Vancouver, B.C. and Tacoma, WA. Deducing spatial information from the inherently spatio-temporal measurements taken from a mobile platform is an area that deserves further attention. We discuss the use of “fuzzy” points to address the fine-scale spatio-temporal variability in the concentration of mobile source pollutants, specifically to deduce the broader distribution and sources of fine particle soot in the summer in Vancouver, B.C. We also discuss the use of principal component analysis to assess the spatial variability in multivariate, source-related features deduced from simultaneous measurements of light scattering, light absorption and particle-bound PAHs in Tacoma, WA. With increasing miniaturization and decreasing power requirements of air monitoring instruments, the number of simultaneous measurements that can easily be made from a mobile platform is rapidly increasing. Hopefully the methods used to design mobile monitoring experiments for differing purposes, and the methods used to interpret those measurements will keep pace.

How is a stream impacted by burial? Examining the spatial variation within urban buried streams in Cincinnati, OH

EPA Science Inventory

While the effects of urbanization on stream ecosystems have been well-documented, little is known regarding the impact of burying streams within culverts. Our project aims to explore the ecological impacts of stream burial at a fine spatial scale. Two culverted urban streams in C...

Evaluating Sentinel-2 for Lakeshore Habitat Mapping Based on Airborne Hyperspectral Data.

PubMed

Stratoulias, Dimitris; Balzter, Heiko; Sykioti, Olga; Zlinszky, András; Tóth, Viktor R

2015-09-11

Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite's Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds.

Fine-scale spatial genetic dynamics over the life cycle of the tropical tree Prunus africana.

PubMed

Berens, D G; Braun, C; González-Martínez, S C; Griebeler, E M; Nathan, R; Böhning-Gaese, K

2014-11-01

Studying fine-scale spatial genetic patterns across life stages is a powerful approach to identify ecological processes acting within tree populations. We investigated spatial genetic dynamics across five life stages in the insect-pollinated and vertebrate-dispersed tropical tree Prunus africana in Kakamega Forest, Kenya. Using six highly polymorphic microsatellite loci, we assessed genetic diversity and spatial genetic structure (SGS) from seed rain and seedlings, and different sapling stages to adult trees. We found significant SGS in all stages, potentially caused by limited seed dispersal and high recruitment rates in areas with high light availability. SGS decreased from seed and early seedling stages to older juvenile stages. Interestingly, SGS was stronger in adults than in late juveniles. The initial decrease in SGS was probably driven by both random and non-random thinning of offspring clusters during recruitment. Intergenerational variation in SGS could have been driven by variation in gene flow processes, overlapping generations in the adult stage or local selection. Our study shows that complex sequential processes during recruitment contribute to SGS of tree populations.

Evaluating Sentinel-2 for Lakeshore Habitat Mapping Based on Airborne Hyperspectral Data

PubMed Central

Stratoulias, Dimitris; Balzter, Heiko; Sykioti, Olga; Zlinszky, András; Tóth, Viktor R.

2015-01-01

Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite’s Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds. PMID:26378538

The utility of satellite observations for constraining fine-scale and transient methane sources

NASA Astrophysics Data System (ADS)

Turner, A. J.; Jacob, D.; Benmergui, J. S.; Brandman, J.; White, L.; Randles, C. A.

2017-12-01

Resolving differences between top-down and bottom-up emissions of methane from the oil and gas industry is difficult due, in part, to their fine-scale and often transient nature. There is considerable interest in using atmospheric observations to detect these sources. Satellite-based instruments are an attractive tool for this purpose and, more generally, for quantifying methane emissions on fine scales. A number of instruments are planned for launch in the coming years from both low earth and geostationary orbit, but the extent to which they can provide fine-scale information on sources has yet to be explored. Here we present an observation system simulation experiment (OSSE) exploring the tradeoffs between pixel resolution, measurement frequency, and instrument precision on the fine-scale information content of a space-borne instrument measuring methane. We use the WRF-STILT Lagrangian transport model to generate more than 200,000 column footprints at 1.3×1.3 km2 spatial resolution and hourly temporal resolution over the Barnett Shale in Texas. We sub-sample these footprints to match the observing characteristics of the planned TROPOMI and GeoCARB instruments as well as different hypothetical observing configurations. The information content of the various observing systems is evaluated using the Fisher information matrix and its singular values. We draw conclusions on the capabilities of the planned satellite instruments and how these capabilities could be improved for fine-scale source detection.

Assessment of fine-scale resource selection and spatially explicit habitat suitability modelling for a re-introduced tiger (Panthera tigris) population in central India

PubMed Central

Sarkar, Mriganka Shekhar; Johnson, Jeyaraj A.; Sen, Subharanjan

2017-01-01

Background Large carnivores influence ecosystem functions at various scales. Thus, their local extinction is not only a species-specific conservation concern, but also reflects on the overall habitat quality and ecosystem value. Species-habitat relationships at fine scale reflect the individuals’ ability to procure resources and negotiate intraspecific competition. Such fine scale habitat choices are more pronounced in large carnivores such as tiger (Panthera tigris), which exhibits competitive exclusion in habitat and mate selection strategies. Although landscape level policies and conservation strategies are increasingly promoted for tiger conservation, specific management interventions require knowledge of the habitat correlates at fine scale. Methods We studied nine radio-collared individuals of a successfully reintroduced tiger population in Panna Tiger Reserve, central India, focussing on the species-habitat relationship at fine scales. With 16 eco-geographical variables, we performed Manly’s selection ratio and K-select analyses to define population-level and individual-level variation in resource selection, respectively. We analysed the data obtained during the exploratory period of six tigers and during the settled period of eight tigers separately, and compared the consequent results. We further used the settled period characteristics to model and map habitat suitability based on the Mahalanobis D2 method and the Boyce index. Results There was a clear difference in habitat selection by tigers between the exploratory and the settled period. During the exploratory period, tigers selected dense canopy and bamboo forests, but also spent time near villages and relocated village sites. However, settled tigers predominantly selected bamboo forests in complex terrain, riverine forests and teak-mixed forest, and totally avoided human settlements and agriculture areas. There were individual variations in habitat selection between exploratory and settled periods. Based on threshold limits of habitat selection by the Boyce Index, we established that 83% of core and 47% of buffer areas are now suitable habitats for tiger in this reserve. Discussion Tiger management often focuses on large-scale measures, but this study for the first time highlights the behaviour and fine-scale individual-specific habitat selection strategies. Such knowledge is vital for management of critical tiger habitats and specifically for the success of reintroduction programs. Our spatially explicit habitat suitability map provides a baseline for conservation planning and optimizing carrying capacity of the tiger population in this reserve. PMID:29114438

Assessment of fine-scale resource selection and spatially explicit habitat suitability modelling for a re-introduced tiger (Panthera tigris) population in central India.

PubMed

Sarkar, Mriganka Shekhar; Krishnamurthy, Ramesh; Johnson, Jeyaraj A; Sen, Subharanjan; Saha, Goutam Kumar

2017-01-01

Large carnivores influence ecosystem functions at various scales. Thus, their local extinction is not only a species-specific conservation concern, but also reflects on the overall habitat quality and ecosystem value. Species-habitat relationships at fine scale reflect the individuals' ability to procure resources and negotiate intraspecific competition. Such fine scale habitat choices are more pronounced in large carnivores such as tiger ( Panthera tigris ), which exhibits competitive exclusion in habitat and mate selection strategies. Although landscape level policies and conservation strategies are increasingly promoted for tiger conservation, specific management interventions require knowledge of the habitat correlates at fine scale. We studied nine radio-collared individuals of a successfully reintroduced tiger population in Panna Tiger Reserve, central India, focussing on the species-habitat relationship at fine scales. With 16 eco-geographical variables, we performed Manly's selection ratio and K-select analyses to define population-level and individual-level variation in resource selection, respectively. We analysed the data obtained during the exploratory period of six tigers and during the settled period of eight tigers separately, and compared the consequent results. We further used the settled period characteristics to model and map habitat suitability based on the Mahalanobis D 2 method and the Boyce index. There was a clear difference in habitat selection by tigers between the exploratory and the settled period. During the exploratory period, tigers selected dense canopy and bamboo forests, but also spent time near villages and relocated village sites. However, settled tigers predominantly selected bamboo forests in complex terrain, riverine forests and teak-mixed forest, and totally avoided human settlements and agriculture areas. There were individual variations in habitat selection between exploratory and settled periods. Based on threshold limits of habitat selection by the Boyce Index, we established that 83% of core and 47% of buffer areas are now suitable habitats for tiger in this reserve. Tiger management often focuses on large-scale measures, but this study for the first time highlights the behaviour and fine-scale individual-specific habitat selection strategies. Such knowledge is vital for management of critical tiger habitats and specifically for the success of reintroduction programs. Our spatially explicit habitat suitability map provides a baseline for conservation planning and optimizing carrying capacity of the tiger population in this reserve.

Significant demographic and fine-scale genetic structure in expanding and senescing populations of the terrestrial orchid Cymbidium goeringii (Orchidaceae).

PubMed

Chung, Mi Yoon; Nason, John D; Chung, Myong Gi

2011-12-01

Fine-scale genetic structure (FSGS) in plants is influenced by variation in spatial and temporal demographic processes. To determine how demographic structure and FSGS change with stages of population succession, we studied replicate expanding and senescing populations of the Asian terrestrial orchid Cymbidium goeringii. We used spatial autocorrelation methods (O-ring and kinship statistics) to quantify spatial demographic structure and FSGS in two expanding and two senescing populations, also measuring genetic diversity and inbreeding in each. All populations exhibited significant aggregation of individuals and FSGS at short spatial scales. In expanding populations, this finding was associated with high recruitment rates, suggesting restricted seed dispersal. In senescing populations, recruitment was minimal, suggesting alternative mechanisms of aggregation, perhaps including spatial associations with mycorrhizal fungi. All populations had significant evidence of genetic bottlenecks, and inbreeding levels were consistently high. Our results indicate that different successional stages can generate similar patterns of spatial demographic and genetic structure, but as a consequence of different processes. These results contrast with the only other study of senescence effects on population genetic structure in an herbaceous perennial, which found little to no FSGS in senescing populations. With the exception of populations subject to mass collection by orchid sellers, significant FSGS is characteristic of the 16 terrestrial orchid species examined to date. From a conservation perspective, this result suggests that inference of orchid population history will benefit from analyses of both FSGS and demographic structure in combination with other ecological field data.

Genetic structuring of northern myotis (Myotis septentrionalis) at multiple spatial scales

USGS Publications Warehouse

Johnson, Joshua B.; Roberts, James H.; King, Timothy L.; Edwards, John W.; Ford, W. Mark; Ray, David A.

2014-01-01

Although groups of bats may be genetically distinguishable at large spatial scales, the effects of forest disturbances, particularly permanent land use conversions on fine-scale population structure and gene flow of summer aggregations of philopatric bat species are less clear. We genotyped and analyzed variation at 10 nuclear DNA microsatellite markers in 182 individuals of the forest-dwelling northern myotis (Myotis septentrionalis) at multiple spatial scales, from within first-order watersheds scaling up to larger regional areas in West Virginia and New York. Our results indicate that groups of northern myotis were genetically indistinguishable at any spatial scale we considered, and the collective population maintained high genetic diversity. It is likely that the ability to migrate, exploit small forest patches, and use networks of mating sites located throughout the Appalachian Mountains, Interior Highlands, and elsewhere in the hibernation range have allowed northern myotis to maintain high genetic diversity and gene flow regardless of forest disturbances at local and regional spatial scales. A consequence of maintaining high gene flow might be the potential to minimize genetic founder effects following population declines caused currently by the enzootic White-nose Syndrome.

Merging fine and coarse resolution remotely sensed data with household-level survey data to evaluate small-scale vulnerability to climate change in West Africa

NASA Astrophysics Data System (ADS)

Grace, K.; Husak, G. J.

2016-12-01

Climate change, in the form of increasingly variable temperatures and rainfall, is anticipated to have potentially dramatic impacts on subsistence agricultural communities throughout the world. Poor people who depend on rainfall to produce food or to produce products to sell to buy food are expected to be particularly vulnerable to the negative impacts associated with climate change. Poor people have extremely limited resources that can be used to cope with weather events and these resources are even more strained when the individuals live in poor countries. While poor and rural producers are most likely to face high levels of vulnerability to food insecurity due to their dependence on rainfall for their agricultural production, annual agricultural censuses are virtually non-existent. Surveying all of the producers in a country each year is extremely costly owing to difficulties in accessing farmers and the costs associated with extensive surveys. The result, however, is very limited information on the spatial and temporal variation in production and the resulting impacts on micro-scale food insecurity and livelihood stability. In this project we use a combination of fine and coarse resolution remotely sensed data ( 1m data, 250m NDVI data and 10km rainfall data, and others) and recently collected survey data from the World Bank to estimate agricultural and land use characteristics at a fine spatial scale in Burkina Faso, Mali and Niger. The analysis will produce estimates of cultivated area that incorporate spatially dynamic climate and vegetation data but that also account for the variation in agricultural practices associated with the different ethnic and religious groups within each country. The survey data will help to calibrate the models and will also serve as a way to validate the statistical models used to estimate on the ground agricultural practices. The models will then be used to evaluate fine-scale agricultural response to climate change in the form of drying and warming.

Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions

PubMed Central

Wilson, Adam M.; Jetz, Walter

2016-01-01

Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km) monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties. PMID:27031693

Spatial identification of tributary impacts in river networks

Treesearch

Christian E. Torgersen; Robert E. Gresswell; Douglas S. Bateman; Kelly M. Burnett

2008-01-01

The ability to assess spatial patterns of ecological conditions in river networks has been confounded by difficulties of measuring and perceiving features that are essentially invisible to observers on land and to aircraft and satellites from above. The nature of flowing water, which is opaque or at best semi-transparent, makes it difficult to visualize fine-scale...

Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus)

Treesearch

Dawn M. Reding; Samuel A. Cushman; Todd E. Gosselink; William R. Clark

2013-01-01

Spatial heterogeneity can constrain the movement of individuals and consequently genes across a landscape, influencing demographic and genetic processes. In this study, we linked information on landscape composition, movement behavior, and genetic differentiation to gain a mechanistic understanding of how spatial heterogeneity may influence movement and gene flow of...

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

PubMed Central

Hu, Xintao; Zhu, Dajiang; Lv, Peili; Li, Kaiming; Han, Junwei; Wang, Lihong; Shen, Dinggang; Guo, Lei; Liu, Tianming

2014-01-01

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures. PMID:23319242

The Effects of Fine-scale Soil Moisture and Canopy Heterogeneities on Energy and Soil Water Fluxes in a Temperate Mixed Deciduous Forest

NASA Astrophysics Data System (ADS)

He, L.; Ivanov, V. Y.; Bohrer, G.; Maurer, K.; Vogel, C. S.; Moghaddam, M.

2011-12-01

Vegetation is heterogeneous at different scales, influencing spatially variable energy and water exchanges between land-surface and atmosphere. Current land surface parameterizations of large-scale models consider spatial variability at a scale of a few kilometers and treat vegetation cover as aggregated patches with uniform properties. However, the coupling mechanisms between fine-scale soil moisture, vegetation, and energy fluxes such as evapotranspiration are strongly nonlinear; the aggregation of surface variations may produce biased energy fluxes. This study aims to improve the understanding of the scale impact in atmosphere-biosphere-hydrosphere interactions, which affects predictive capabilities of land surface models. The study uses a high-resolution, physically-based ecohydrological model tRIBS + VEGGIE as a data integration tool to upscale the heterogeneity of canopy distribution resolved at a few meters to the watershed scale. The study was carried out for a spatially heterogeneous, temperate mixed forest environment of Northern Michigan located near the University of Michigan Biological Station (UMBS). Energy and soil water dynamics were simulated at the tree-canopy resolution in the horizontal plane for a small domain (~2 sq. km) located within a footprint of the AmeriFlux tower. A variety of observational data were used to constrain and confirm the model, including a 3-m profile continuous soil moisture dataset and energy flux data (measured at the AmeriFlux tower footprint). A scenario with a spatially uniform canopy, corresponding to the commonly used 'big-leaf' scheme in land surface parameterizations was used to infer the effects of coarse-scale averaging. To gain insights on how heterogeneous canopy and soil moisture interact and contribute to the domain-averaged transpiration, several scenarios of tree-scale leaf area and soil moisture spatial variability were designed. Specifically, for the same mean states, the scenarios of variability of canopy biomass account for the spatial distribution of photosynthesis (and thus the stomatal resistance), the aerodynamic and leaf boundary layer resistances as well as the differential radiation forcing due to tall tree exposure and lateral shading of short trees. The numerical experiments show that by transpiring spatially varying amounts of water, heterogeneous canopies adjust the spatial soil water state to the scaled inverse of the canopy biomass regardless of the initial moisture state. Such a spatial distribution can be further wiped out because of the differential water stress. The aggregation of canopy-scale atmosphere-biosphere-hydrosphere interactions demonstrates non-linear relationship between soil moisture and evapotranspiration, influencing domain-averaged energy fluxes.

SEARCH: Spatially Explicit Animal Response to Composition of Habitat.

PubMed

Pauli, Benjamin P; McCann, Nicholas P; Zollner, Patrick A; Cummings, Robert; Gilbert, Jonathan H; Gustafson, Eric J

2013-01-01

Complex decisions dramatically affect animal dispersal and space use. Dispersing individuals respond to a combination of fine-scale environmental stimuli and internal attributes. Individual-based modeling offers a valuable approach for the investigation of such interactions because it combines the heterogeneity of animal behaviors with spatial detail. Most individual-based models (IBMs), however, vastly oversimplify animal behavior and such behavioral minimalism diminishes the value of these models. We present program SEARCH (Spatially Explicit Animal Response to Composition of Habitat), a spatially explicit, individual-based, population model of animal dispersal through realistic landscapes. SEARCH uses values in Geographic Information System (GIS) maps to apply rules that animals follow during dispersal, thus allowing virtual animals to respond to fine-scale features of the landscape and maintain a detailed memory of areas sensed during movement. SEARCH also incorporates temporally dynamic landscapes so that the environment to which virtual animals respond can change during the course of a simulation. Animals in SEARCH are behaviorally dynamic and able to respond to stimuli based upon their individual experiences. Therefore, SEARCH is able to model behavioral traits of dispersing animals at fine scales and with many dynamic aspects. Such added complexity allows investigation of unique ecological questions. To illustrate SEARCH's capabilities, we simulated case studies using three mammals. We examined the impact of seasonally variable food resources on the weight distribution of dispersing raccoons (Procyon lotor), the effect of temporally dynamic mortality pressure in combination with various levels of behavioral responsiveness in eastern chipmunks (Tamias striatus), and the impact of behavioral plasticity and home range selection on disperser mortality and weight change in virtual American martens (Martes americana). These simulations highlight the relevance of SEARCH for a variety of applications and illustrate benefits it can provide for conservation planning.

Hybrid Multiscale Finite Volume method for multiresolution simulations of flow and reactive transport in porous media

NASA Astrophysics Data System (ADS)

Barajas-Solano, D. A.; Tartakovsky, A. M.

2017-12-01

We present a multiresolution method for the numerical simulation of flow and reactive transport in porous, heterogeneous media, based on the hybrid Multiscale Finite Volume (h-MsFV) algorithm. The h-MsFV algorithm allows us to couple high-resolution (fine scale) flow and transport models with lower resolution (coarse) models to locally refine both spatial resolution and transport models. The fine scale problem is decomposed into various "local'' problems solved independently in parallel and coordinated via a "global'' problem. This global problem is then coupled with the coarse model to strictly ensure domain-wide coarse-scale mass conservation. The proposed method provides an alternative to adaptive mesh refinement (AMR), due to its capacity to rapidly refine spatial resolution beyond what's possible with state-of-the-art AMR techniques, and the capability to locally swap transport models. We illustrate our method by applying it to groundwater flow and reactive transport of multiple species.

Distributions of soil phosphorus in China's densely populated village landscapes

Treesearch

Jiaguo Jiao; Erle C. Ellis; Ian Yesilonis; Junxi Wu; Hongqing Wang; Huixin Li; Linzhang Yang

2010-01-01

Purpose Village landscapes, which integrate small-scale agriculture with housing, forestry and a host of other land use practices, cover more than 2x106 km2 across China. Village lands tend to be managed at very fine spatial scales (≤30 m), with managers altering soil fertility and even terrain by terracing,...

The Spatial Scale and Spatial Configuration of Residential Settlement: Measuring Segregation in the Postbellum South

PubMed Central

Logan, John R.; Martinez, Matthew

2018-01-01

Studies of residential segregation typically focus on its degree without questioning its scale and configuration. We study Southern cities in 1880 to emphasize the salience of these spatial dimensions. Distance-based and sequence indices can reflect spatial patterns but with some limitations, while geocoded 100% population data make possible more informative measures. One improvement is flexibility in spatial scale, ranging from adjacent buildings to whole districts of the city. Another is the ability to map patterns in fine detail. In Southern cities we find qualitatively distinct configurations that include not only black “neighborhoods” as usually imagined, but also backyard housing, alley housing, and side streets that were predominantly black. These configurations represent the sort of symbolic boundaries recognized by urban ethnographers. By mapping residential configurations and interpreting them in light of historical accounts, our intention is to capture meanings that are too often missed by quantitative studies of segregation. PMID:29479108

Projected future vegetation changes for the northwest United States and southwest Canada at a fine spatial resolution using a dynamic global vegetation model.

USGS Publications Warehouse

Shafer, Sarah; Bartlein, Patrick J.; Gray, Elizabeth M.; Pelltier, Richard T.

2015-01-01

Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions. We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0–58.0°N latitude by 136.6–103.0°W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070–2099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas.

Projected Future Vegetation Changes for the Northwest United States and Southwest Canada at a Fine Spatial Resolution Using a Dynamic Global Vegetation Model

PubMed Central

Shafer, Sarah L.; Bartlein, Patrick J.; Gray, Elizabeth M.; Pelltier, Richard T.

2015-01-01

Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions. We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0–58.0°N latitude by 136.6–103.0°W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070–2099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas. PMID:26488750

Predation risk, elk, and aspen: tests of a behaviorally mediated trophic cascade in the Greater Yellowstone Ecosystem.

PubMed

Winnie, John A

2012-12-01

Aspen in the Greater Yellowstone Ecosystem are hypothesized to be recovering from decades of heavy browsing by elk due to a behaviorally mediated trophic cascade (BMTC). Several authors have suggested that wolves interact with certain terrain features, creating places of high predation risk at fine spatial scales, and that elk avoid these places, which creates refugia for plants. This hypothesized BMTC could release aspen from elk browsing pressure, leading to a patchy recovery in places of high risk. I tested whether four specific, hypothesized fine-scale risk factors are correlated with changes in current elk browsing pressure on aspen, or with aspen recruitment since wolf reintroduction, in the Daly Creek drainage in Yellowstone National Park, and near two aspen enclosures outside of the park boundary. Aspen were not responding to hypothesized fine-scale risk factors in ways consistent with the current BMTC hypothesis.

Fine-scale genetic structure and cryptic associations reveal evidence of kin-based sociality in the African forest elephant.

PubMed

Schuttler, Stephanie G; Philbrick, Jessica A; Jeffery, Kathryn J; Eggert, Lori S

2014-01-01

Spatial patterns of relatedness within animal populations are important in the evolution of mating and social systems, and have the potential to reveal information on species that are difficult to observe in the wild. This study examines the fine-scale genetic structure and connectivity of groups within African forest elephants, Loxodonta cyclotis, which are often difficult to observe due to forest habitat. We tested the hypothesis that genetic similarity will decline with increasing geographic distance, as we expect kin to be in closer proximity, using spatial autocorrelation analyses and Tau K(r) tests. Associations between individuals were investigated through a non-invasive genetic capture-recapture approach using network models, and were predicted to be more extensive than the small groups found in observational studies, similar to fission-fusion sociality found in African savanna (Loxodonta africana) and Asian (Elephas maximus) species. Dung samples were collected in Lopé National Park, Gabon in 2008 and 2010 and genotyped at 10 microsatellite loci, genetically sexed, and sequenced at the mitochondrial DNA control region. We conducted analyses on samples collected at three different temporal scales: a day, within six-day sampling sessions, and within each year. Spatial autocorrelation and Tau K(r) tests revealed genetic structure, but results were weak and inconsistent between sampling sessions. Positive spatial autocorrelation was found in distance classes of 0-5 km, and was strongest for the single day session. Despite weak genetic structure, individuals within groups were significantly more related to each other than to individuals between groups. Social networks revealed some components to have large, extensive groups of up to 22 individuals, and most groups were composed of individuals of the same matriline. Although fine-scale population genetic structure was weak, forest elephants are typically found in groups consisting of kin and based on matrilines, with some individuals having more associates than observed from group sizes alone.

Estimating resource acquisition and at-sea body condition of a marine predator

PubMed Central

Schick, Robert S; New, Leslie F; Thomas, Len; Costa, Daniel P; Hindell, Mark A; McMahon, Clive R; Robinson, Patrick W; Simmons, Samantha E; Thums, Michele; Harwood, John; Clark, James S

2013-01-01

Body condition plays a fundamental role in many ecological and evolutionary processes at a variety of scales and across a broad range of animal taxa. An understanding of how body condition changes at fine spatial and temporal scales as a result of interaction with the environment provides necessary information about how animals acquire resources. However, comparatively little is known about intra- and interindividual variation of condition in marine systems. Where condition has been studied, changes typically are recorded at relatively coarse time-scales. By quantifying how fine-scale interaction with the environment influences condition, we can broaden our understanding of how animals acquire resources and allocate them to body stores. Here we used a hierarchical Bayesian state-space model to estimate the body condition as measured by the size of an animal's lipid store in two closely related species of marine predator that occupy different hemispheres: northern elephant seals (Mirounga angustirostris) and southern elephant seals (Mirounga leonina). The observation model linked drift dives to lipid stores. The process model quantified daily changes in lipid stores as a function of the physiological condition of the seal (lipid:lean tissue ratio, departure lipid and departure mass), its foraging location, two measures of behaviour and environmental covariates. We found that physiological condition significantly impacted lipid gain at two time-scales – daily and at departure from the colony – that foraging location was significantly associated with lipid gain in both species of elephant seals and that long-term behavioural phase was associated with positive lipid gain in northern and southern elephant seals. In northern elephant seals, the occurrence of short-term behavioural states assumed to represent foraging were correlated with lipid gain. Lipid gain was a function of covariates in both species. Southern elephant seals performed fewer drift dives than northern elephant seals and gained lipids at a lower rate. We have demonstrated a new way to obtain time series of body condition estimates for a marine predator at fine spatial and temporal scales. This modelling approach accounts for uncertainty at many levels and has the potential to integrate physiological and movement ecology of top predators. The observation model we used was specific to elephant seals, but the process model can readily be applied to other species, providing an opportunity to understand how animals respond to their environment at a fine spatial scale. PMID:23869551

Mortality and long-term exposure to ambient air pollution: ongoing analyses based on the American Cancer Society cohort.

PubMed

Krewski, Daniel; Burnett, Richard; Jerrett, Michael; Pope, C Arden; Rainham, Daniel; Calle, Eugenia; Thurston, George; Thun, Michael

This article provides an overview of previous analysis and reanalysis of the American Cancer Society (ACS) cohort, along with an indication of current ongoing analyses of the cohort with additional follow-up information through to 2000. Results of the first analysis conducted by Pope et al. (1995) showed that higher average sulfate levels were associated with increased mortality, particularly from cardiopulmonary disease. A reanalysis of the ACS cohort, undertaken by Krewski et al. (2000), found the original risk estimates for fine-particle and sulfate air pollution to be highly robust against alternative statistical techniques and spatial modeling approaches. A detailed investigation of covariate effects found a significant modifying effect of education with risk of mortality associated with fine particles declining with increasing educational attainment. Pope et al. (2002) subsequently reported results of a subsequent study using an additional 10 yr of follow-up of the ACS cohort. This updated analysis included gaseous copollutant and new fine-particle measurements, more comprehensive information on occupational exposures, dietary variables, and the most recent developments in statistical modeling integrating random effects and nonparametric spatial smoothing into the Cox proportional hazards model. Robust associations between ambient fine particulate air pollution and elevated risks of cardiopulmonary and lung cancer mortality were clearly evident, providing the strongest evidence to date that long-term exposure to fine particles is an important health risk. Current ongoing analysis using the extended follow-up information will explore the role of ecologic, economic, and, demographic covariates in the particulate air pollution and mortality association. This analysis will also provide insight into the role of spatial autocorrelation at multiple geographic scales, and whether critical instances in time of exposure to fine particles influence the risk of mortality from cardiopulmonary and lung cancer. Information on the influence of covariates at multiple scales and of critical exposure time windows can assist policymakers in establishing timelines for regulatory interventions that maximize population health benefits.

Reproductive phenology of coastal plain Atlantic forest vegetation: comparisons from seashore to foothills.

PubMed

Staggemeier, Vanessa Graziele; Morellato, Leonor Patrícia Cerdeira

2011-11-01

The diversity of tropical forest plant phenology has called the attention of researchers for a long time. We continue investigating the factors that drive phenological diversity on a wide scale, but we are unaware of the variation of plant reproductive phenology at a fine spatial scale despite the high spatial variation in species composition and abundance in tropical rainforests. We addressed fine scale variability by investigating the reproductive phenology of three contiguous vegetations across the Atlantic rainforest coastal plain in Southeastern Brazil. We asked whether the vegetations differed in composition and abundance of species, the microenvironmental conditions and the reproductive phenology, and how their phenology is related to regional and local microenvironmental factors. The study was conducted from September 2007 to August 2009 at three contiguous sites: (1) seashore dominated by scrub vegetation, (2) intermediary covered by restinga forest and (3) foothills covered by restinga pre-montane transitional forest. We conducted the microenvironmental, plant and phenological survey within 30 transects of 25 m × 4 m (10 per site). We detected significant differences in floristic, microenvironment and reproductive phenology among the three vegetations. The microenvironment determines the spatial diversity observed in the structure and composition of the flora, which in turn determines the distinctive flowering and fruiting peaks of each vegetation (phenological diversity). There was an exchange of species providing flowers and fruits across the vegetation complex. We conclude that plant reproductive patterns as described in most phenological studies (without concern about the microenvironmental variation) may conceal the fine scale temporal phenological diversity of highly diverse tropical vegetation. This phenological diversity should be taken into account when generating sensor-derived phenologies and when trying to understand tropical vegetation responses to environmental changes.

A space-time analysis of the WikiLeaks Afghan War Diary: a resource for analyzing the conflict-health nexus.

PubMed

Curtis, Andrew; Ye, Xinyue; Hachey, Kevin; Bourdeaux, Margaret; Norris, Alison

2015-10-16

Although it is widely acknowledged that areas of conflict are associated with a high health burden, from a geospatial perspective it is difficult to establish these patterns at fine scales because of a lack of data. The release of the "WikiLeaks" Afghan War Diary (AWD) provides an interesting opportunity to advance analysis and theory into this interrelationship. This paper will apply two different space time analyses to identify patterns of improvised explosive devices (IED) detonations for the period of 2004 to 2009 in Afghanistan. There is considerable spatial and temporal heterogeneity in IED explosions, with concentrations often following transportation links. The results are framed in terms of a resource for subsequent analyses to other existing health research in Afghanistan. To facilitate this, in our discussion we present a Google Earth file of overlapping rates that can be distributed to any researcher interested in combining his/her fine scale health data with a similarly granular layer of violence. The release of the AWD presents a previously unavailable opportunity to consider how spatially detailed data about violence can be incorporated into understanding, and predicting, health related spillover effects. The AWD can enrich previous research conducted on Afghanistan, and provide a justification for future "official" data sharing at appropriately fine scales.

A Spatial Framework to Map Heat Health Risks at Multiple Scales.

PubMed

Ho, Hung Chak; Knudby, Anders; Huang, Wei

2015-12-18

In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord G(i) index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord G(i) index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.

Spatially distributed potential evapotranspiration modeling and climate projections.

PubMed

Gharbia, Salem S; Smullen, Trevor; Gill, Laurence; Johnston, Paul; Pilla, Francesco

2018-08-15

Evapotranspiration integrates energy and mass transfer between the Earth's surface and atmosphere and is the most active mechanism linking the atmosphere, hydrosphsophere, lithosphere and biosphere. This study focuses on the fine resolution modeling and projection of spatially distributed potential evapotranspiration on the large catchment scale as response to climate change. Six potential evapotranspiration designed algorithms, systematically selected based on a structured criteria and data availability, have been applied and then validated to long-term mean monthly data for the Shannon River catchment with a 50m 2 cell size. The best validated algorithm was therefore applied to evaluate the possible effect of future climate change on potential evapotranspiration rates. Spatially distributed potential evapotranspiration projections have been modeled based on climate change projections from multi-GCM ensembles for three future time intervals (2020, 2050 and 2080) using a range of different Representative Concentration Pathways producing four scenarios for each time interval. Finally, seasonal results have been compared to baseline results to evaluate the impact of climate change on the potential evapotranspiration and therefor on the catchment dynamical water balance. The results present evidence that the modeled climate change scenarios would have a significant impact on the future potential evapotranspiration rates. All the simulated scenarios predicted an increase in potential evapotranspiration for each modeled future time interval, which would significantly affect the dynamical catchment water balance. This study addresses the gap in the literature of using GIS-based algorithms to model fine-scale spatially distributed potential evapotranspiration on the large catchment systems based on climatological observations and simulations in different climatological zones. Providing fine-scale potential evapotranspiration data is very crucial to assess the dynamical catchment water balance to setup management scenarios for the water abstractions. This study illustrates a transferable systematic method to design GIS-based algorithms to simulate spatially distributed potential evapotranspiration on the large catchment systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

Alpine Ecohydrology Across Scales: Propagating Fine-scale Heterogeneity to the Catchment and Beyond

NASA Astrophysics Data System (ADS)

Mastrotheodoros, T.; Pappas, C.; Molnar, P.; Burlando, P.; Hadjidoukas, P.; Fatichi, S.

2017-12-01

In mountainous ecosystems, complex topography and landscape heterogeneity govern ecohydrological states and fluxes. Here, we investigate topographic controls on water, energy and carbon fluxes across different climatic regimes and vegetation types representative of the European Alps. We use an ecohydrological model to perform fine-scale numerical experiments on a synthetic domain that comprises a symmetric mountain with eight catchments draining along the cardinal and intercardinal directions. Distributed meteorological model input variables are generated using observations from Switzerland. The model computes the incoming solar radiation based on the local topography. We implement a multivariate statistical framework to disentangle the impact of landscape heterogeneity (i.e., elevation, aspect, flow contributing area, vegetation type) on the simulated water, carbon, and energy dynamics. This allows us to identify the sensitivities of several ecohydrological variables (including leaf area index, evapotranspiration, snow-cover and net primary productivity) to topographic and meteorological inputs at different spatial and temporal scales. We also use an alpine catchment as a real case study to investigate how the natural variability of soil and land cover affects the idealized relationships that arise from the synthetic domain. In accordance with previous studies, our analysis shows a complex pattern of vegetation response to radiation. We find also different patterns of ecosystem sensitivity to topography-driven heterogeneity depending on the hydrological regime (i.e., wet vs. dry conditions). Our results suggest that topography-driven variability in ecohydrological variables (e.g. transpiration) at the fine spatial scale can exceed 50%, but it is substantially reduced ( 5%) when integrated at the catchment scale.

Spatial consistency of Chinook salmon redd distribution within and among years in the Cowlitz River, Washington

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klett, Katherine J.; Torgersen, Christian; Henning, Julie

2013-04-28

We investigated the spawning patterns of Chinook salmon Oncorhynchus tshawytscha on the lower Cowlitz River, Washington (USA) using a unique set of fine- and coarse-scale 35 temporal and spatial data collected during bi-weekly aerial surveys conducted in 1991-2009 (500 m to 28 km resolution) and 2008-2009 (100-500 m resolution). Redd locations were mapped from a helicopter during 2008 and 2009 with a hand-held global positioning system (GPS) synchronized with in-flight audio recordings. We examined spatial patterns of Chinook salmon redd reoccupation among and within years in relation to segment-scale geomorphic features. Chinook salmon spawned in the same sections each yearmore » with little variation among years. On a coarse scale, five years (1993, 1998, 2000, 2002, and 2009) were compared for reoccupation. Redd locations were highly correlated among years resulting in a minimum correlation coefficient of 0.90 (adjusted P = 0.002). Comparisons on a fine scale (500 m) between 2008 and 2009 also revealed a high degree of consistency among redd locations (P < 0.001). On a finer temporal scale, we observed that salmon spawned in the same sections during the first and last week (2008: P < 0.02; and 2009: P < 0.001). Redds were clustered in both 2008 and 2009 (P < 0.001). Regression analysis with a generalized linear model at the 500-m scale indicated that river kilometer and channel bifurcation were positively associated with redd density, whereas sinuosity was negatively associated with redd density. Collecting data on specific redd locations with a GPS during aerial surveys was logistically feasible and cost effective and greatly enhanced the spatial precision of Chinook salmon spawning surveys.« less

Global Night-Time Lights for Observing Human Activity

NASA Technical Reports Server (NTRS)

Hipskind, Stephen R.; Elvidge, Chris; Gurney, K.; Imhoff, Mark; Bounoua, Lahouari; Sheffner, Edwin; Nemani, Ramakrishna R.; Pettit, Donald R.; Fischer, Marc

2011-01-01

We present a concept for a small satellite mission to make systematic, global observations of night-time lights with spatial resolution suitable for discerning the extent, type and density of human settlements. The observations will also allow better understanding of fine scale fossil fuel CO2 emission distribution. The NASA Earth Science Decadal Survey recommends more focus on direct observations of human influence on the Earth system. The most dramatic and compelling observations of human presence on the Earth are the night light observations taken by the Defence Meteorological System Program (DMSP) Operational Linescan System (OLS). Beyond delineating the footprint of human presence, night light data, when assembled and evaluated with complementary data sets, can determine the fine scale spatial distribution of global fossil fuel CO2 emissions. Understanding fossil fuel carbon emissions is critical to understanding the entire carbon cycle, and especially the carbon exchange between terrestrial and oceanic systems.

Reduced fine-scale spatial genetic structure in grazed populations of Dianthus carthusianorum

PubMed Central

Rico, Y; Wagner, H H

2016-01-01

Strong spatial genetic structure in plant populations can increase homozygosity, reducing genetic diversity and adaptive potential. The strength of spatial genetic structure largely depends on rates of seed dispersal and pollen flow. Seeds without dispersal adaptations are likely to be dispersed over short distances within the vicinity of the mother plant, resulting in spatial clustering of related genotypes (fine-scale spatial genetic structure, hereafter spatial genetic structure (SGS)). However, primary seed dispersal by zoochory can promote effective dispersal, increasing the mixing of seeds and influencing SGS within plant populations. In this study, we investigated the effects of seed dispersal by rotational sheep grazing on the strength of SGS and genetic diversity using 11 nuclear microsatellites for 49 populations of the calcareous grassland forb Dianthus carthusianorum. Populations connected by rotational sheep grazing showed significantly weaker SGS and higher genetic diversity than populations in ungrazed grasslands. Independent of grazing treatment, small populations showed significantly stronger SGS and lower genetic diversity than larger populations, likely due to genetic drift. A lack of significant differences in the strength of SGS and genetic diversity between populations that were recently colonized and pre-existing populations suggested that populations colonized after the reintroduction of rotational sheep grazing were likely founded by colonists from diverse source populations. We conclude that dispersal by rotational sheep grazing has the potential to considerably reduce SGS within D. carthusianorum populations. Our study highlights the effectiveness of landscape management by rotational sheep grazing to importantly reduce genetic structure at local scales within restored plant populations. PMID:27381322

Reduced fine-scale spatial genetic structure in grazed populations of Dianthus carthusianorum.

PubMed

Rico, Y; Wagner, H H

2016-11-01

Strong spatial genetic structure in plant populations can increase homozygosity, reducing genetic diversity and adaptive potential. The strength of spatial genetic structure largely depends on rates of seed dispersal and pollen flow. Seeds without dispersal adaptations are likely to be dispersed over short distances within the vicinity of the mother plant, resulting in spatial clustering of related genotypes (fine-scale spatial genetic structure, hereafter spatial genetic structure (SGS)). However, primary seed dispersal by zoochory can promote effective dispersal, increasing the mixing of seeds and influencing SGS within plant populations. In this study, we investigated the effects of seed dispersal by rotational sheep grazing on the strength of SGS and genetic diversity using 11 nuclear microsatellites for 49 populations of the calcareous grassland forb Dianthus carthusianorum. Populations connected by rotational sheep grazing showed significantly weaker SGS and higher genetic diversity than populations in ungrazed grasslands. Independent of grazing treatment, small populations showed significantly stronger SGS and lower genetic diversity than larger populations, likely due to genetic drift. A lack of significant differences in the strength of SGS and genetic diversity between populations that were recently colonized and pre-existing populations suggested that populations colonized after the reintroduction of rotational sheep grazing were likely founded by colonists from diverse source populations. We conclude that dispersal by rotational sheep grazing has the potential to considerably reduce SGS within D. carthusianorum populations. Our study highlights the effectiveness of landscape management by rotational sheep grazing to importantly reduce genetic structure at local scales within restored plant populations.

Improving predictions of large scale soil carbon dynamics: Integration of fine-scale hydrological and biogeochemical processes, scaling, and benchmarking

NASA Astrophysics Data System (ADS)

Riley, W. J.; Dwivedi, D.; Ghimire, B.; Hoffman, F. M.; Pau, G. S. H.; Randerson, J. T.; Shen, C.; Tang, J.; Zhu, Q.

2015-12-01

Numerical model representations of decadal- to centennial-scale soil-carbon dynamics are a dominant cause of uncertainty in climate change predictions. Recent attempts by some Earth System Model (ESM) teams to integrate previously unrepresented soil processes (e.g., explicit microbial processes, abiotic interactions with mineral surfaces, vertical transport), poor performance of many ESM land models against large-scale and experimental manipulation observations, and complexities associated with spatial heterogeneity highlight the nascent nature of our community's ability to accurately predict future soil carbon dynamics. I will present recent work from our group to develop a modeling framework to integrate pore-, column-, watershed-, and global-scale soil process representations into an ESM (ACME), and apply the International Land Model Benchmarking (ILAMB) package for evaluation. At the column scale and across a wide range of sites, observed depth-resolved carbon stocks and their 14C derived turnover times can be explained by a model with explicit representation of two microbial populations, a simple representation of mineralogy, and vertical transport. Integrating soil and plant dynamics requires a 'process-scaling' approach, since all aspects of the multi-nutrient system cannot be explicitly resolved at ESM scales. I will show that one approach, the Equilibrium Chemistry Approximation, improves predictions of forest nitrogen and phosphorus experimental manipulations and leads to very different global soil carbon predictions. Translating model representations from the site- to ESM-scale requires a spatial scaling approach that either explicitly resolves the relevant processes, or more practically, accounts for fine-resolution dynamics at coarser scales. To that end, I will present recent watershed-scale modeling work that applies reduced order model methods to accurately scale fine-resolution soil carbon dynamics to coarse-resolution simulations. Finally, we contend that creating believable soil carbon predictions requires a robust, transparent, and community-available benchmarking framework. I will present an ILAMB evaluation of several of the above-mentioned approaches in ACME, and attempt to motivate community adoption of this evaluation approach.

Variation in soil carbon dioxide efflux at two spatial scales in a topographically complex boreal forest

USGS Publications Warehouse

Kelsey, Katharine C.; Wickland, Kimberly P.; Striegl, Robert G.; Neff, Jason C.

2012-01-01

Carbon dynamics of high-latitude regions are an important and highly uncertain component of global carbon budgets, and efforts to constrain estimates of soil-atmosphere carbon exchange in these regions are contingent on accurate representations of spatial and temporal variability in carbon fluxes. This study explores spatial and temporal variability in soilatmosphere carbon dynamics at both fine and coarse spatial scales in a high-elevation, permafrost-dominated boreal black spruce forest. We evaluate the importance of landscape-level investigations of soil-atmosphere carbon dynamics by characterizing seasonal trends in soil-atmosphere carbon exchange, describing soil temperature-moisture-respiration relations, and quantifying temporal and spatial variability at two spatial scales: the plot scale (0–5 m) and the landscape scale (500–1000 m). Plot-scale spatial variability (average variation on a given measurement day) in soil CO2 efflux ranged from a coefficient of variation (CV) of 0.25 to 0.69, and plot-scale temporal variability (average variation of plots across measurement days) in efflux ranged from a CV of 0.19 to 0.36. Landscape-scale spatial and temporal variability in efflux was represented by a CV of 0.40 and 0.31, respectively, indicating that plot-scale spatial variability in soil respiration is as great as landscape-scale spatial variability at this site. While soil respiration was related to soil temperature at both the plot- and landscape scale, landscape-level descriptions of soil moisture were necessary to define soil respiration-moisture relations. Soil moisture variability was also integral to explaining temporal variability in soil respiration. Our results have important implications for research efforts in high-latitude regions where remote study sites make landscape-scale field campaigns challenging.

Separating the effects of environment and space on tree species distribution: from population to community.

PubMed

Lin, Guojun; Stralberg, Diana; Gong, Guiquan; Huang, Zhongliang; Ye, Wanhui; Wu, Linfang

2013-01-01

Quantifying the relative contributions of environmental conditions and spatial factors to species distribution can help improve our understanding of the processes that drive diversity patterns. In this study, based on tree inventory, topography and soil data from a 20-ha stem-mapped permanent forest plot in Guangdong Province, China, we evaluated the influence of different ecological processes at different spatial scales using canonical redundancy analysis (RDA) at the community level and multiple linear regression at the species level. At the community level, the proportion of explained variation in species distribution increased with grid-cell sizes, primarily due to a monotonic increase in the explanatory power of environmental variables. At the species level, neither environmental nor spatial factors were important determinants of overstory species' distributions at small cell sizes. However, purely spatial variables explained most of the variation in the distributions of understory species at fine and intermediate cell sizes. Midstory species showed patterns that were intermediate between those of overstory and understory species. At the 20-m cell size, the influence of spatial factors was stronger for more dispersal-limited species, suggesting that much of the spatial structuring in this community can be explained by dispersal limitation. Comparing environmental factors, soil variables had higher explanatory power than did topography for species distribution. However, both topographic and edaphic variables were highly spatial structured. Our results suggested that dispersal limitation has an important influence on fine-intermediate scale (from several to tens of meters) species distribution, while environmental variability facilitates species distribution at intermediate (from ten to tens of meters) and broad (from tens to hundreds of meters) scales.

SEASONAL MODELING OF THE EXPORT OF POLLUTANTS FROM NORTH AMERICA USING THE MULTI-SCALE AIR QUALITY SIMULATION PLATFORM (MAQSIP)

EPA Science Inventory

Attention in recent years has focused on the trans-boundary transport of ozone and fine particulate matte between the United States and Mexico and Canada and across state boundaries in the United States. In a similar manner, but on a larger spatial scale, the export of pollutant...

Fine resolution probabilistic land cover classification of landscapes in the southeastern United States

Treesearch

Joseph St. Peter; John Hogland; Nathaniel Anderson; Jason Drake; Paul Medley

2018-01-01

Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a...

The influence of multi-season imagery on models of canopy cover: A case study

Treesearch

John W. Coulston; Dennis M. Jacobs; Chris R. King; Ivey C. Elmore

2013-01-01

Quantifying tree canopy cover in a spatially explicit fashion is important for broad-scale monitoring of ecosystems and for management of natural resources. Researchers have developed empirical models of tree canopy cover to produce geospatial products. For subpixel models, percent tree canopy cover estimates (derived from fine-scale imagery) serve as the response...

Habitat associations of sympatric red-tailed hawks and northern goshawks on the Kaibab Plateau

Treesearch

Frank A. La Sorte; R. William Mannan; Richard T. Reynolds; Teryl G. Grubb

2004-01-01

We investigated habitat association of sympatric red-tailed hawks (Buteo jamaicensis) and northern goshawks (Accipiter gentilis) at 2 spatial scales centered on nest sites: (1) fine-scale patterns of forest structure and topography within 16-m radius circles (0.08 ha), and (2) midscale patterns of forested and nonforested areas,...

Kilometric Scale Modeling of the North West European Shelf Seas: Exploring the Spatial and Temporal Variability of Internal Tides

NASA Astrophysics Data System (ADS)

Guihou, K.; Polton, J.; Harle, J.; Wakelin, S.; O'Dea, E.; Holt, J.

2018-01-01

The North West European Shelf break acts as a barrier to the transport and exchange between the open ocean and the shelf seas. The strong spatial variability of these exchange processes is hard to fully explore using observations, and simulations generally are too coarse to simulate the fine-scale processes over the whole region. In this context, under the FASTNEt program, a new NEMO configuration of the North West European Shelf and Atlantic Margin at 1/60° (˜1.8 km) has been developed, with the objective to better understand and quantify the seasonal and interannual variability of shelf break processes. The capability of this configuration to reproduce the seasonal cycle in SST, the barotropic tide, and fine-resolution temperature profiles is assessed against a basin-scale (1/12°, ˜9 km) configuration and a standard regional configuration (7 km resolution). The seasonal cycle is well reproduced in all configurations though the fine-resolution allows the simulation of smaller scale processes. Time series of temperature at various locations on the shelf show the presence of internal waves with a strong spatiotemporal variability. Spectral analysis of the internal waves reveals peaks at the diurnal, semidiurnal, inertial, and quarter-diurnal bands, which are only realistically reproduced in the new configuration. Tidally induced pycnocline variability is diagnosed in the model and shown to vary with the spring neap cycle with mean displacement amplitudes in excess of 2 m for 30% of the stratified domain. With sufficiently fine resolution, internal tides are shown to be generated at numerous bathymetric features resulting in a complex pycnocline displacement superposition pattern.

Scale-dependent effects of nonnative plant invasion on host-seeking tick abundance

PubMed Central

Adalsteinsson, Solny A.; D’Amico, Vincent; Shriver, W. Gregory; Brisson, Dustin; Buler, Jeffrey J.

2016-01-01

Nonnative, invasive shrubs can affect human disease risk through direct and indirect effects on vector populations. Multiflora rose (Rosa multiflora) is a common invader within eastern deciduous forests where tick-borne disease (e.g. Lyme disease) rates are high. We tested whether R. multiflora invasion affects blacklegged tick (Ixodes scapularis) abundance, and at what scale. We sampled host-seeking ticks at two spatial scales: fine-scale, within R. multiflora-invaded forest fragments; and patch scale, among R. multiflora-invaded and R. multiflora-free forest fragments. At a fine scale, we trapped 2.3 times more ticks under R. multiflora compared to paired traps 25 m away from R. multiflora. At the patch scale, we trapped 3.2 times as many ticks in R. multiflora-free forests compared to R. multiflora-invaded forests. Thus, ticks are concentrated beneath R. multiflora within invaded forests, but uninvaded forests support significantly more ticks. Among all covariates tested, leaf litter volume was the best predictor of tick abundance; at the patch scale, R. multiflora-invaded forests had less leaf litter than uninvaded forests. We suggest that leaf litter availability at the patch-scale plays a greater role in constraining tick abundance than the fine-scale, positive effect of invasive shrubs. PMID:27088044

Seasonal variation in coastal marine habitat use by the European shag: Insights from fine scale habitat selection modeling and diet

NASA Astrophysics Data System (ADS)

Michelot, Candice; Pinaud, David; Fortin, Matthieu; Maes, Philippe; Callard, Benjamin; Leicher, Marine; Barbraud, Christophe

2017-07-01

Studies of habitat selection by higher trophic level species are necessary for using top predator species as indicators of ecosystem functioning. However, contrary to terrestrial ecosystems, few habitat selection studies have been conducted at a fine scale for coastal marine top predator species, and fewer have coupled diet data with habitat selection modeling to highlight a link between prey selection and habitat use. The aim of this study was to characterize spatially and oceanographically, at a fine scale, the habitats used by the European Shag Phalacrocorax aristotelis in the Special Protection Area (SPA) of Houat-Hœdic in the Mor Braz Bay during its foraging activity. Habitat selection models were built using in situ observation data of foraging shags (transect sampling) and spatially explicit environmental data to characterize marine benthic habitats. Observations were first adjusted for detectability biases and shag abundance was subsequently spatialized. The influence of habitat variables on shag abundance was tested using Generalized Linear Models (GLMs). Diet data were finally confronted to habitat selection models. Results showed that European shags breeding in the Mor Braz Bay changed foraging habitats according to the season and to the different environmental and energetic constraints. The proportion of the main preys also varied seasonally. Rocky and coarse sand habitats were clearly preferred compared to fine or muddy sand habitats. Shags appeared to be more selective in their foraging habitats during the breeding period and the rearing of chicks, using essentially rocky areas close to the colony and consuming preferentially fish from the Labridae family and three other fish families in lower proportions. During the post-breeding period shags used a broader range of habitats and mainly consumed Gadidae. Thus, European shags seem to adjust their feeding strategy to minimize energetic costs, to avoid intra-specific competition and to maximize access to suitable habitats and preys.

A generic method for improving the spatial interoperability of medical and ecological databases.

PubMed

Ghenassia, A; Beuscart, J B; Ficheur, G; Occelli, F; Babykina, E; Chazard, E; Genin, M

2017-10-03

The availability of big data in healthcare and the intensive development of data reuse and georeferencing have opened up perspectives for health spatial analysis. However, fine-scale spatial studies of ecological and medical databases are limited by the change of support problem and thus a lack of spatial unit interoperability. The use of spatial disaggregation methods to solve this problem introduces errors into the spatial estimations. Here, we present a generic, two-step method for merging medical and ecological databases that avoids the use of spatial disaggregation methods, while maximizing the spatial resolution. Firstly, a mapping table is created after one or more transition matrices have been defined. The latter link the spatial units of the original databases to the spatial units of the final database. Secondly, the mapping table is validated by (1) comparing the covariates contained in the two original databases, and (2) checking the spatial validity with a spatial continuity criterion and a spatial resolution index. We used our novel method to merge a medical database (the French national diagnosis-related group database, containing 5644 spatial units) with an ecological database (produced by the French National Institute of Statistics and Economic Studies, and containing with 36,594 spatial units). The mapping table yielded 5632 final spatial units. The mapping table's validity was evaluated by comparing the number of births in the medical database and the ecological databases in each final spatial unit. The median [interquartile range] relative difference was 2.3% [0; 5.7]. The spatial continuity criterion was low (2.4%), and the spatial resolution index was greater than for most French administrative areas. Our innovative approach improves interoperability between medical and ecological databases and facilitates fine-scale spatial analyses. We have shown that disaggregation models and large aggregation techniques are not necessarily the best ways to tackle the change of support problem.

Recovering the fine structures in solar images

NASA Technical Reports Server (NTRS)

Karovska, Margarita; Habbal, S. R.; Golub, L.; Deluca, E.; Hudson, Hugh S.

1994-01-01

Several examples of the capability of the blind iterative deconvolution (BID) technique to recover the real point spread function, when limited a priori information is available about its characteristics. To demonstrate the potential of image post-processing for probing the fine scale and temporal variability of the solar atmosphere, the BID technique is applied to different samples of solar observations from space. The BID technique was originally proposed for correction of the effects of atmospheric turbulence on optical images. The processed images provide a detailed view of the spatial structure of the solar atmosphere at different heights in regions with different large-scale magnetic field structures.

Statistics of spatial derivatives of velocity and pressure in turbulent channel flow

NASA Astrophysics Data System (ADS)

Vreman, A. W.; Kuerten, J. G. M.

2014-08-01

Statistical profiles of the first- and second-order spatial derivatives of velocity and pressure are reported for turbulent channel flow at Reτ = 590. The statistics were extracted from a high-resolution direct numerical simulation. To quantify the anisotropic behavior of fine-scale structures, the variances of the derivatives are compared with the theoretical values for isotropic turbulence. It is shown that appropriate combinations of first- and second-order velocity derivatives lead to (directional) viscous length scales without explicit occurrence of the viscosity in the definitions. To quantify the non-Gaussian and intermittent behavior of fine-scale structures, higher-order moments and probability density functions of spatial derivatives are reported. Absolute skewnesses and flatnesses of several spatial derivatives display high peaks in the near wall region. In the logarithmic and central regions of the channel flow, all first-order derivatives appear to be significantly more intermittent than in isotropic turbulence at the same Taylor Reynolds number. Since the nine variances of first-order velocity derivatives are the distinct elements of the turbulence dissipation, the budgets of these nine variances are shown, together with the budget of the turbulence dissipation. The comparison of the budgets in the near-wall region indicates that the normal derivative of the fluctuating streamwise velocity (∂u'/∂y) plays a more important role than other components of the fluctuating velocity gradient. The small-scale generation term formed by triple correlations of fluctuations of first-order velocity derivatives is analyzed. A typical mechanism of small-scale generation near the wall (around y+ = 1), the intensification of positive ∂u'/∂y by local strain fluctuation (compression in normal and stretching in spanwise direction), is illustrated and discussed.

Genetic differentiation over a small spatial scale of the sand fly Lutzomyia vexator (Diptera: Psychodidae).

PubMed

Neal, Allison T; Ross, Max S; Schall, Jos J; Vardo-Zalik, Anne M

2016-10-18

The geographic scale and degree of genetic differentiation for arthropod vectors that transmit parasites play an important role in the distribution, prevalence and coevolution of pathogens of human and wildlife significance. We determined the genetic diversity and population structure of the sand fly Lutzomyia vexator over spatial scales from 0.56 to 3.79 km at a study region in northern California. The study was provoked by observations of differentiation at fine spatial scales of a lizard malaria parasite vectored by Lu. vexator. A microsatellite enrichment/next-generation sequencing protocol was used to identify variable microsatellite loci within the genome of Lu. vexator. Alleles present at these loci were examined in four populations of Lu. vexator in Hopland, CA. Population differentiation was assessed using Fst and D (of Cavalli-Sforza and Edwards), and the program Structure was used to determine the degree of subdivision present. The effective population size for the sand fly populations was also calculated. Eight microsatellite markers were characterized and revealed high genetic diversity (uHe = 0.79-0.92, Na = 12-24) and slight but significant differentiation across the fine spatial scale examined (average pairwise D = 0.327; F ST  = 0.0185 (95 % bootstrapped CI: 0.0102-0.0264). Even though the insects are difficult to capture using standard methods, the estimated population size was thousands per local site. The results argue that Lu. vexator at the study sites are abundant and not highly mobile, which may influence the overall transmission dynamics of the lizard malaria parasite, Plasmodium mexicanum, and other parasites transmitted by this species.

Fine-scale human genetic structure in Western France.

PubMed

Karakachoff, Matilde; Duforet-Frebourg, Nicolas; Simonet, Floriane; Le Scouarnec, Solena; Pellen, Nadine; Lecointe, Simon; Charpentier, Eric; Gros, Françoise; Cauchi, Stéphane; Froguel, Philippe; Copin, Nane; Le Tourneau, Thierry; Probst, Vincent; Le Marec, Hervé; Molinaro, Sabrina; Balkau, Beverley; Redon, Richard; Schott, Jean-Jacques; Blum, Michael Gb; Dina, Christian

2015-06-01

The difficulties arising from association analysis with rare variants underline the importance of suitable reference population cohorts, which integrate detailed spatial information. We analyzed a sample of 1684 individuals from Western France, who were genotyped at genome-wide level, from two cohorts D.E.S.I.R and CavsGen. We found that fine-scale population structure occurs at the scale of Western France, with distinct admixture proportions for individuals originating from the Brittany Region and the Vendée Department. Genetic differentiation increases with distance at a high rate in these two parts of Northwestern France and linkage disequilibrium is higher in Brittany suggesting a lower effective population size. When looking for genomic regions informative about Breton origin, we found two prominent associated regions that include the lactase region and the HLA complex. For both the lactase and the HLA regions, there is a low differentiation between Bretons and Irish, and this is also found at the genome-wide level. At a more refined scale, and within the Pays de la Loire Region, we also found evidence of fine-scale population structure, although principal component analysis showed that individuals from different departments cannot be confidently discriminated. Because of the evidence for fine-scale genetic structure in Western France, we anticipate that rare and geographically localized variants will be identified in future full-sequence analyses.

Cloud-based computation for accelerating vegetation mapping and change detection at regional to national scales

Treesearch

Matthew J. Gregory; Zhiqiang Yang; David M. Bell; Warren B. Cohen; Sean Healey; Janet L. Ohmann; Heather M. Roberts

2015-01-01

Mapping vegetation and landscape change at fine spatial scales is needed to inform natural resource and conservation planning, but such maps are expensive and time-consuming to produce. For Landsat-based methodologies, mapping efforts are hampered by the daunting task of manipulating multivariate data for millions to billions of pixels. The advent of cloud-based...

Elk resource selection at parturition sites, Black Hills, South Dakota

Treesearch

Chadwick P. Lehman; Mark A. Rumble; Christopher T. Rota; Benjamin J. Bird; Dillon T. Fogarty; Joshua J. Millspaugh

2015-01-01

We studied elk (Cervus canadensis nelsoni) parturition sites at coarse (314-km2 and 7-km2) and fine (0.2-ha) scales in the Black Hills, South Dakota, 2011-2013, following a period of population decline and poor calf recruitment. Our objective was to test whether female elk selected parturition sites across spatial scales in association with forage, terrain...

Agent Based Modeling: Fine-Scale Spatio-Temporal Analysis of Pertussis

NASA Astrophysics Data System (ADS)

Mills, D. A.

2017-10-01

In epidemiology, spatial and temporal variables are used to compute vaccination efficacy and effectiveness. The chosen resolution and scale of a spatial or spatio-temporal analysis will affect the results. When calculating vaccination efficacy, for example, a simple environment that offers various ideal outcomes is often modeled using coarse scale data aggregated on an annual basis. In contrast to the inadequacy of this aggregated method, this research uses agent based modeling of fine-scale neighborhood data centered around the interactions of infants in daycare and their families to demonstrate an accurate reflection of vaccination capabilities. Despite being able to prevent major symptoms, recent studies suggest that acellular Pertussis does not prevent the colonization and transmission of Bordetella Pertussis bacteria. After vaccination, a treated individual becomes a potential asymptomatic carrier of the Pertussis bacteria, rather than an immune individual. Agent based modeling enables the measurable depiction of asymptomatic carriers that are otherwise unaccounted for when calculating vaccination efficacy and effectiveness. Using empirical data from a Florida Pertussis outbreak case study, the results of this model demonstrate that asymptomatic carriers bias the calculated vaccination efficacy and reveal a need for reconsidering current methods that are widely used for calculating vaccination efficacy and effectiveness.

On the Fidelity of Semi-distributed Hydrologic Model Simulations for Large Scale Catchment Applications

NASA Astrophysics Data System (ADS)

Ajami, H.; Sharma, A.; Lakshmi, V.

2017-12-01

Application of semi-distributed hydrologic modeling frameworks is a viable alternative to fully distributed hyper-resolution hydrologic models due to computational efficiency and resolving fine-scale spatial structure of hydrologic fluxes and states. However, fidelity of semi-distributed model simulations is impacted by (1) formulation of hydrologic response units (HRUs), and (2) aggregation of catchment properties for formulating simulation elements. Here, we evaluate the performance of a recently developed Soil Moisture and Runoff simulation Toolkit (SMART) for large catchment scale simulations. In SMART, topologically connected HRUs are delineated using thresholds obtained from topographic and geomorphic analysis of a catchment, and simulation elements are equivalent cross sections (ECS) representative of a hillslope in first order sub-basins. Earlier investigations have shown that formulation of ECSs at the scale of a first order sub-basin reduces computational time significantly without compromising simulation accuracy. However, the implementation of this approach has not been fully explored for catchment scale simulations. To assess SMART performance, we set-up the model over the Little Washita watershed in Oklahoma. Model evaluations using in-situ soil moisture observations show satisfactory model performance. In addition, we evaluated the performance of a number of soil moisture disaggregation schemes recently developed to provide spatially explicit soil moisture outputs at fine scale resolution. Our results illustrate that the statistical disaggregation scheme performs significantly better than the methods based on topographic data. Future work is focused on assessing the performance of SMART using remotely sensed soil moisture observations using spatially based model evaluation metrics.

Scaling field data to calibrate and validate moderate spatial resolution remote sensing models

USGS Publications Warehouse

Baccini, A.; Friedl, M.A.; Woodcock, C.E.; Zhu, Z.

2007-01-01

Validation and calibration are essential components of nearly all remote sensing-based studies. In both cases, ground measurements are collected and then related to the remote sensing observations or model results. In many situations, and particularly in studies that use moderate resolution remote sensing, a mismatch exists between the sensor's field of view and the scale at which in situ measurements are collected. The use of in situ measurements for model calibration and validation, therefore, requires a robust and defensible method to spatially aggregate ground measurements to the scale at which the remotely sensed data are acquired. This paper examines this challenge and specifically considers two different approaches for aggregating field measurements to match the spatial resolution of moderate spatial resolution remote sensing data: (a) landscape stratification; and (b) averaging of fine spatial resolution maps. The results show that an empirically estimated stratification based on a regression tree method provides a statistically defensible and operational basis for performing this type of procedure. 

Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets.

PubMed

Shi, Yue; Huang, Wenjiang; Ye, Huichun; Ruan, Chao; Xing, Naichen; Geng, Yun; Dong, Yingying; Peng, Dailiang

2018-06-11

In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast ( Magnaporthe oryzae ), and glume blight ( Phyllosticta glumarum ) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.

Study of satellite retrieved aerosol optical depth spatial resolution effect on particulate matter concentration prediction

NASA Astrophysics Data System (ADS)

Strandgren, J.; Mei, L.; Vountas, M.; Burrows, J. P.; Lyapustin, A.; Wang, Y.

2014-10-01

The Aerosol Optical Depth (AOD) spatial resolution effect is investigated for the linear correlation between satellite retrieved AOD and ground level particulate matter concentrations (PM2.5). The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) for obtaining AOD with a high spatial resolution of 1 km and provides a good dataset for the study of the AOD spatial resolution effect on the particulate matter concentration prediction. 946 Environmental Protection Agency (EPA) ground monitoring stations across the contiguous US have been used to investigate the linear correlation between AOD and PM2.5 using AOD at different spatial resolutions (1, 3 and 10 km) and for different spatial scales (urban scale, meso-scale and continental scale). The main conclusions are: (1) for both urban, meso- and continental scale the correlation between PM2.5 and AOD increased significantly with increasing spatial resolution of the AOD, (2) the correlation between AOD and PM2.5 decreased significantly as the scale of study region increased for the eastern part of the US while vice versa for the western part of the US, (3) the correlation between PM2.5 and AOD is much more stable and better over the eastern part of the US compared to western part due to the surface characteristics and atmospheric conditions like the fine mode fraction.

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies.

PubMed

Vedal, Sverre; Han, Bin; Xu, Jia; Szpiro, Adam; Bai, Zhipeng

2017-12-15

No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources.

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies

PubMed Central

Vedal, Sverre; Han, Bin; Szpiro, Adam; Bai, Zhipeng

2017-01-01

No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources. PMID:29244738

Interspecific interference competition at the resource patch scale: do large herbivores spatially avoid elephants while accessing water?

PubMed

Ferry, Nicolas; Dray, Stéphane; Fritz, Hervé; Valeix, Marion

2016-11-01

Animals may anticipate and try to avoid, at some costs, physical encounters with other competitors. This may ultimately impact their foraging distribution and intake rates. Such cryptic interference competition is difficult to measure in the field, and extremely little is known at the interspecific level. We tested the hypothesis that smaller species avoid larger ones because of potential costs of interference competition and hence expected them to segregate from larger competitors at the scale of a resource patch. We assessed fine-scale spatial segregation patterns between three African herbivore species (zebra Equus quagga, kudu Tragelaphus strepsiceros and giraffe Giraffa camelopardalis) and a megaherbivore, the African elephant Loxodonta africana, at the scale of water resource patches in the semi-arid ecosystem of Hwange National Park, Zimbabwe. Nine waterholes were monitored every two weeks during the dry season of a drought year, and observational scans of the spatial distribution of all herbivores were performed every 15 min. We developed a methodological approach to analyse such fine-scale spatial data. Elephants increasingly used waterholes as the dry season progressed, as did the probability of co-occurrence and agonistic interaction with elephants for the three study species. All three species segregated from elephants at the beginning of the dry season, suggesting a spatial avoidance of elephants and the existence of costs of being close to them. However, contrarily to our expectations, herbivores did not segregate from elephants the rest of the dry season but tended to increasingly aggregate with elephants as the dry season progressed. We discuss these surprising results and the existence of a trade-off between avoidance of interspecific interference competition and other potential factors such as access to quality water, which may have relative associated costs that change with the time of the year. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

Predicting habitat suitability for rare plants at local spatial scales using a species distribution model.

PubMed

Gogol-Prokurat, Melanie

2011-01-01

If species distribution models (SDMs) can rank habitat suitability at a local scale, they may be a valuable conservation planning tool for rare, patchily distributed species. This study assessed the ability of Maxent, an SDM reported to be appropriate for modeling rare species, to rank habitat suitability at a local scale for four edaphic endemic rare plants of gabbroic soils in El Dorado County, California, and examined the effects of grain size, spatial extent, and fine-grain environmental predictors on local-scale model accuracy. Models were developed using species occurrence data mapped on public lands and were evaluated using an independent data set of presence and absence locations on surrounding lands, mimicking a typical conservation-planning scenario that prioritizes potential habitat on unsurveyed lands surrounding known occurrences. Maxent produced models that were successful at discriminating between suitable and unsuitable habitat at the local scale for all four species, and predicted habitat suitability values were proportional to likelihood of occurrence or population abundance for three of four species. Unfortunately, models with the best discrimination (i.e., AUC) were not always the most useful for ranking habitat suitability. The use of independent test data showed metrics that were valuable for evaluating which variables and model choices (e.g., grain, extent) to use in guiding habitat prioritization for conservation of these species. A goodness-of-fit test was used to determine whether habitat suitability values ranked habitat suitability on a continuous scale. If they did not, a minimum acceptable error predicted area criterion was used to determine the threshold for classifying habitat as suitable or unsuitable. I found a trade-off between model extent and the use of fine-grain environmental variables: goodness of fit was improved at larger extents, and fine-grain environmental variables improved local-scale accuracy, but fine-grain variables were not available at large extents. No single model met all habitat prioritization criteria, and the best models were overlaid to identify consensus areas of high suitability. Although the four species modeled here co-occur and are treated together for conservation planning, model accuracy and predicted suitable areas varied among species.

Effects of seed bank disturbance on the fine-scale genetic structure of populations of the rare shrub Grevillea macleayana.

PubMed

England, P R; Whelan, R J; Ayre, D J

2003-11-01

Dispersal in most plants is mediated by the movement of seeds and pollen, which move genes across the landscape differently. Grevillea macleayana is a rare, fire-dependent Australian shrub with large seeds lacking adaptations for dispersal; yet it produces inflorescences adapted to pollination by highly mobile vertebrates (eg birds). Interpreting fine-scale genetic structure in the light of these two processes is confounded by the recent imposition of anthropogenic disturbances with potentially contrasting genetic consequences: (1) the unusual foraging behaviour of exotic honeybees and 2. widespread disturbance of the soil-stored seedbank by road building and quarrying. To test for evidence of fine-scale genetic structure within G. macleayana populations and to test the prediction that such structure might be masked by disturbance of the seed bank, we sampled two sites in undisturbed habitat and compared their genetic structure with two sites that had been strongly affected by road building using a test for spatial autocorrelation of genotypes. High selfing levels inferred from genotypes at all four sites implies that pollen dispersal is limited. Consistent with this, we observed substantial spatial clustering of genes at 10 m or less in the two undisturbed populations and argue that this reflects the predicted effects of both high selfing levels and limited seed dispersal. In contrast, at the two sites disturbed by road building, spatial autocorrelation was weak. This suggests there has been mixing of the seed bank, counteracting the naturally low dispersal and elevated selfing due to honeybees. Pollination between near neighbours with reduced relatedness potentially has fitness consequences for G. macleayana in disturbed sites.

Quantifying the imprint of mesoscale and synoptic-scale atmospheric transport on total column carbon dioxide measurements

NASA Astrophysics Data System (ADS)

Torres, A. D.; Keppel-Aleks, G.; Doney, S. C.; Feng, S.; Lauvaux, T.; Fendrock, M. A.; Rheuben, J.

2017-12-01

Remote sensing instruments provide an unprecedented density of observations of the atmospheric CO2 column average mole fraction (denoted as XCO2), which can be used to constrain regional scale carbon fluxes. Inferring fluxes from XCO2 observations is challenging, as measurements and inversion methods are sensitive to not only the imprint local and large-scale fluxes, but also mesoscale and synoptic-scale atmospheric transport. Quantifying the fine-scale variability in XCO2 from mesoscale and synoptic-scale atmospheric transport will likely improve overall error estimates from flux inversions by improving estimates of representation errors that occur when XCO2 observations are compared to modeled XCO2 in relatively coarse transport models. Here, we utilize various statistical methods to quantify the imprint of atmospheric transport on XCO2 observations. We compare spatial variations along Orbiting Carbon Observatory (OCO-2) satellite tracks to temporal variations observed by the Total Column Carbon Observing Network (TCCON). We observe a coherent seasonal cycle of both within-day temporal and fine-scale spatial variability (of order 10 km) of XCO2 from these two datasets, suggestive of the imprint of mesoscale systems. To account for other potential sources of error in XCO2 retrieval, we compare observed temporal and spatial variations of XCO2 to high-resolution output from the Weather Research and Forecasting (WRF) model run at 9 km resolution. In both simulations and observations, the Northern hemisphere mid-latitude XCO2 showed peak variability during the growing season when atmospheric gradients are largest. These results are qualitatively consistent with our expectations of seasonal variations of the imprint of synoptic and mesoscale atmospheric transport on XCO2 observations; suggesting that these statistical methods could be sensitive to the imprint of atmospheric transport on XCO2 observations.

Heavy precipitation episodes in the Western Mediterranean : Use of a semi-Lagrangian advection model for the fine-scale description of upper-level troughs

NASA Astrophysics Data System (ADS)

Gauthier, N.; Claud, C.; Funatsu, B. M.; Chaboureau, J.-P.; Argence, S.; Lambert, D.; Richard, E.; Hauchecorne, A.; Arbogast, P.; Maynard, K.

2009-09-01

Heavy precipitation events over the Mediterranean Sea are generally associated with upper-level troughs. The mesoscale structures of such troughs are however not well reproduced by the atmospheric analyses due to inappropriate spatial resolution. We propose here to use a semi-Lagrangian advection model called MIMOSA (Modélisation Isentrope du transport Méso-échelle de l'Ozone Stratosphérique par Advection) initially developed to describe stratospheric filaments, to calculate fine-scale Potential Vorticity (PV) fields on isentropic surfaces near the tropopause. After a description of MIMOSA, we will focus on the model-generated PV fields for several high impact weather cases that occurred over the Western Mediterreanean Sea. We will demonstrate the ability of MIMOSA to resolve fine scale structures of upper-level troughs considering the Algiers' flash flood, which occurred on November 2001, and then a heavy precipitation event over southeast France on the 5-6 September 2005. Finally, with a PV inversion method, we will show the impact of the fine scales PV structures as depicted by MIMOSA to improve the numerical simulation of a « hurricane » that hit Italy in September 2006, both in terms of surface pressure and precipitation forecasts.

Exploring fine-scale variability of stratospheric wind above the tropical la reunion island using rayleigh-mie doppler lidar

NASA Astrophysics Data System (ADS)

Khaykin, S. M.; Hauchecorne, A.; Cammas, J.-P.; Marqestaut, N.; Mariscal, J.-F.; Posny, F.; Payen, G.; Porteneuve, J.; Keckhut, P.

2018-04-01

A unique Rayleigh-Mie Doppler lidar capable of wind measurements in the 5-50 km altitude range is operated routinely at La Reunion island (21° S, 55° E) since 2015. We evaluate instrument's capacities in capturing fine structures in stratospheric wind profiles and their temporal and spatial variability through comparison with collocated radiosoundings and ECMWF analysis. Perturbations in the wind velocity are used to retrieve gravity wave frequency spectrum.

Determinants of the Spatial Distributions of Elemental Carbon and Particulate Matter in Eight Southern Californian Communities

PubMed Central

Urman, Robert; Gauderman, James; Fruin, Scott; Lurmann, Fred; Liu, Feifei; Hosseini, Reza; Franklin, Meredith; Avol, Edward; Penfold, Bryan; Gilliland, Frank; Brunekreef, Bert; McConnell, Rob

2014-01-01

Emerging evidence indicates that near-roadway pollution (NRP) in ambient air has adverse health effects. However, specific components of the NRP mixture responsible for these effects have not been established. A major limitation for health studies is the lack of exposure models that estimate NRP components observed in epidemiological studies over fine spatial scale of tens to hundreds of meters. In this study, exposure models were developed for fine-scale variation in biologically relevant elemental carbon (EC). Measurements of particulate matter (PM) and EC less than 2.5 μm in aerodynamic diameter (EC2.5) and of PM and EC of nanoscale size less than 0.2 μm were made at up to 29 locations in each of eight Southern California Children's Health Study communities. Regression-based prediction models were developed using a guided forward selection process to identify traffic variables and other pollutant sources, community physical characteristics and land use as predictors of PM and EC variation in each community. A combined eight-community model including only CALINE4 near-roadway dispersion-estimated vehicular emissions accounting for distance, distance-weighted traffic volume, and meteorology, explained 51% of the EC0.2 variability. Community-specific models identified additional predictors in some communities; however, in most communities the correlation between predicted concentrations from the eight-community model and observed concentrations stratified by community were similar to those for the community-specific models. EC2.5 could be predicted as well as EC0.2. EC2.5 estimated from CALINE4 and population density explained 53% of the within-community variation. Exposure prediction was further improved after accounting for between-community heterogeneity of CALINE4 effects associated with average distance to Pacific Ocean shoreline (to 61% for EC0.2) and for regional NOx pollution (to 57% for EC2.5). PM fine spatial scale variation was poorly predicted in both size fractions. In conclusion, models of exposure that include traffic measures such as CALINE4 can provide useful estimates for EC0.2 and EC2.5 on a spatial scale appropriate for health studies of NRP in selected Southern California communities. PMID:25313293

Natural Models for Autonomous Control of Spatial Navigation, Sensing, and Guidance. Part 1

DTIC Science & Technology

2012-02-13

ketocarotenoid pigment astaxanthin is deposited in the antennal scale of a stomatopod crustacean, Odontodactylus scyllarus. Positive correlation between...partial polarization and the presence of astaxanthin indicates that the antennal scale polarizes light with astaxanthin . Both the optical properties and...the fine structure of the polarizationally-active cuticle suggest that the dipole axes of the astaxanthin molecules are oriented nearly normal to the

The roles of competition and habitat in the dynamics of populations and species distributions Ecology

Treesearch

Charles B. Yackulic; Janice Reid; James D. Nichols; James E. Hines; Raymond Davis; Eric Forsman

2014-01-01

The role of competition in structuring biotic communities at fine spatial scales is well known from detailed process-based studies. Our understanding of competition’s importance at broader scales is less resolved and mainly based on static species distribution maps. Here, we bridge this gap by examining the joint occupancy dynamics of an invading species (Barred Owl,...

Generating high temporal and spatial resolution thermal band imagery using robust sharpening approach

USDA-ARS?s Scientific Manuscript database

Thermal infrared band imagery provides key information for detecting wild fires, mapping land surface energy fluxes and evapotranspiration, monitoring urban heat fluxes and drought monitoring. Thermal infrared (TIR) imagery at fine resolution is required for field scale applications. However, therma...

Comment on: Polar Plumes and Fine-scale Coronal Structures - On the Interpretation of Coronal Radio Sounding Data by Patzold and Bird

NASA Technical Reports Server (NTRS)

Woo, R.; Habbal, S. R.

1998-01-01

Radio occultation measurements, which probe electron density over a wide dynamic range with high sensitivity and high spatial and temporal resolution reveal a solar corona permeated by a hierarchy of filamentary structures.

STOCHASTIC DESCRIPTION OF SUBGRID POLLUTANT VARIABILITY IN CMAQ

EPA Science Inventory

This paper describes a tool for investigating and describing fine scale spatial variability in model concentration fields with the goal of improving the use of air quality models for driving exposure modeling to assess human risk to hazardous air pollutants or air toxics. Region...

Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data.

PubMed

Wesolowski, Amy; Buckee, Caroline O; Engø-Monsen, Kenth; Metcalf, C J E

2016-12-01

Human travel can shape infectious disease dynamics by introducing pathogens into susceptible populations or by changing the frequency of contacts between infected and susceptible individuals. Quantifying infectious disease-relevant travel patterns on fine spatial and temporal scales has historically been limited by data availability. The recent emergence of mobile phone calling data and associated locational information means that we can now trace fine scale movement across large numbers of individuals. However, these data necessarily reflect a biased sample of individuals across communities and are generally aggregated for both ethical and pragmatic reasons that may further obscure the nuance of individual and spatial heterogeneities. Additionally, as a general rule, the mobile phone data are not linked to demographic or social identifiers, or to information about the disease status of individual subscribers (although these may be made available in smaller-scale specific cases). Combining data on human movement from mobile phone data-derived population fluxes with data on disease incidence requires approaches that can tackle varying spatial and temporal resolutions of each data source and generate inference about dynamics on scales relevant to both pathogen biology and human ecology. Here, we review the opportunities and challenges of these novel data streams, illustrating our examples with analyses of 2 different pathogens in Kenya, and conclude by outlining core directions for future research. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Need for improved methods to collect and present spatial epidemiologic data for vectorborne diseases.

PubMed

Eisen, Lars; Eisen, Rebecca J

2007-12-01

Improved methods for collection and presentation of spatial epidemiologic data are needed for vectorborne diseases in the United States. Lack of reliable data for probable pathogen exposure site has emerged as a major obstacle to the development of predictive spatial risk models. Although plague case investigations can serve as a model for how to ideally generate needed information, this comprehensive approach is cost-prohibitive for more common and less severe diseases. New methods are urgently needed to determine probable pathogen exposure sites that will yield reliable results while taking into account economic and time constraints of the public health system and attending physicians. Recent data demonstrate the need for a change from use of the county spatial unit for presentation of incidence of vectorborne diseases to more precise ZIP code or census tract scales. Such fine-scale spatial risk patterns can be communicated to the public and medical community through Web-mapping approaches.

PROPERTIES AND MODELING OF UNRESOLVED FINE STRUCTURE LOOPS OBSERVED IN THE SOLAR TRANSITION REGION BY IRIS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brooks, David H.; Reep, Jeffrey W.; Warren, Harry P.

Recent observations from the Interface Region Imaging Spectrograph ( IRIS ) have discovered a new class of numerous low-lying dynamic loop structures, and it has been argued that they are the long-postulated unresolved fine structures (UFSs) that dominate the emission of the solar transition region. In this letter, we combine IRIS measurements of the properties of a sample of 108 UFSs (intensities, lengths, widths, lifetimes) with one-dimensional non-equilibrium ionization simulations, using the HYDRAD hydrodynamic model to examine whether the UFSs are now truly spatially resolved in the sense of being individual structures rather than being composed of multiple magnetic threads.more » We find that a simulation of an impulsively heated single strand can reproduce most of the observed properties, suggesting that the UFSs may be resolved, and the distribution of UFS widths implies that they are structured on a spatial scale of 133 km on average. Spatial scales of a few hundred kilometers appear to be typical for a range of chromospheric and coronal structures, and we conjecture that this could be an important clue for understanding the coronal heating process.« less

Developing and Delivering National-Scale Gridded Phenology Data Products

NASA Astrophysics Data System (ADS)

Marsh, L.; Crimmins, M.; Crimmins, T. M.; Gerst, K.; Rosemartin, A.; Switzer, J.; Weltzin, J. F.

2016-12-01

The USA National Phenology Network (USA-NPN; www.usanpn.org) is now producing and freely delivering daily maps and short-term forecasts of accumulated growing degree days and spring onset dates (based on the Extended Spring Indices) at fine spatial scale for the conterminous United States. These data products have utility for a wide range of natural resource planning and management applications, including scheduling invasive species and pest detection and control activities, determining planting dates, anticipating allergy outbreaks and planning agricultural harvest dates. Accumulated growing degree day (AGDD) maps were selected because accumulated temperature is a strong driver of phenological transitions in plants and animals, including leaf-out, flowering, fruit ripening and migration. The Extended Spring Indices (SI-x) are based on predictive climate models for lilac and honeysuckle leaf and bloom; they have been widely used to summarize changes in the timing of spring onset. The SI-x is used as a national indicator of climate change impacts by the US Global Change Research Program and the Environmental Protection Agency. The USA-NPN is a national-scale program that supports scientific advancement and decision-making by collecting, storing, and sharing phenology data and information. To best serve various audiences, the AGDD and SI-x gridded maps are available in various formats through a range of access tools, including the USA-NPN online visualization tool as well as industry standards compliant web services. We plan to expand the suite of gridded map products offered by the USA-NPN to include predictive maps of phenological transitions for additional plant and animal species at fine spatial and temporal resolution in the near future. USA-NPN invites you to use freely available daily and short-term forecast maps of accumulated growing degree days and spring onset dates at fine spatial scale for the conterminous United States.

Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

PubMed

Torres, Leigh G; Read, Andrew J; Halpin, Patrick

2008-10-01

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.

Patterns of resting state connectivity in human primary visual cortical areas: a 7T fMRI study.

PubMed

Raemaekers, Mathijs; Schellekens, Wouter; van Wezel, Richard J A; Petridou, Natalia; Kristo, Gert; Ramsey, Nick F

2014-01-01

The nature and origin of fMRI resting state fluctuations and connectivity are still not fully known. More detailed knowledge on the relationship between resting state patterns and brain function may help to elucidate this matter. We therefore performed an in depth study of how resting state fluctuations map to the well known architecture of the visual system. We investigated resting state connectivity at both a fine and large scale within and across visual areas V1, V2 and V3 in ten human subjects using a 7Tesla scanner. We found evidence for several coexisting and overlapping connectivity structures at different spatial scales. At the fine-scale level we found enhanced connectivity between the same topographic locations in the fieldmaps of V1, V2 and V3, enhanced connectivity to the contralateral functional homologue, and to a lesser extent enhanced connectivity between iso-eccentric locations within the same visual area. However, by far the largest proportion of the resting state fluctuations occurred within large-scale bilateral networks. These large-scale networks mapped to some extent onto the architecture of the visual system and could thereby obscure fine-scale connectivity. In fact, most of the fine-scale connectivity only became apparent after the large-scale network fluctuations were filtered from the timeseries. We conclude that fMRI resting state fluctuations in the visual cortex may in fact be a composite signal of different overlapping sources. Isolating the different sources could enhance correlations between BOLD and electrophysiological correlates of resting state activity. © 2013 Elsevier Inc. All rights reserved.

How does complex terrain influence responses of carbon and water cycle processes to climate variability and climate change? (Invited)

NASA Astrophysics Data System (ADS)

Bond, B. J.; Peterson, K.; McKane, R.; Lajtha, K.; Quandt, D. J.; Allen, S. T.; Sell, S.; Daly, C.; Harmon, M. E.; Johnson, S. L.; Spies, T.; Sollins, P.; Abdelnour, A. G.; Stieglitz, M.

2010-12-01

We are pursuing the ambitious goal of understanding how complex terrain influences the responses of carbon and water cycle processes to climate variability and climate change. Our studies take place in H.J. Andrews Experimental Forest, an LTER (Long Term Ecological Research) site situated in Oregon’s central-western Cascade Range. Decades of long-term measurements and intensive research have revealed influences of topography on vegetation patterns, disturbance history, and hydrology. More recent research has shown surprising interactions between microclimates and synoptic weather patterns due to cold air drainage and pooling in mountain valleys. Using these data and insights, in addition to a recent LiDAR (Light Detection and Ranging) reconnaissance and a small sensor network, we are employing process-based models, including “SPA” (Soil-Plant-Atmosphere, developed by Mathew Williams of the University of Edinburgh), and “VELMA” (Visualizing Ecosystems for Land Management Alternatives, developed by Marc Stieglitz and colleagues of the Georgia Institute of Technology) to focus on two important features of mountainous landscapes: heterogeneity (both spatial and temporal) and connectivity (atmosphere-canopy-hillslope-stream). Our research questions include: 1) Do fine-scale spatial and temporal heterogeneity result in emergent properties at the basin scale, and if so, what are they? 2) How does connectivity across ecosystem components affect system responses to climate variability and change? Initial results show that for environmental drivers that elicit non-linear ecosystem responses on the plot scale, such as solar radiation, soil depth and soil water content, fine-scale spatial heterogeneity may produce unexpected emergent properties at larger scales. The results from such modeling experiments are necessarily a function of the supporting algorithms. However, comparisons based on models such as SPA and VELMA that operate at much different spatial scales (plots vs. hillslopes) and levels of biophysical organization (individual plants vs. aggregate plant biomass) can help us to understand how and why mountainous ecosystems may have distinctive responses to climate variability and climate change.

Utilizing 1-meter Landcover Data to Assess Associations between Green Space and Stress

EPA Science Inventory

Purpose: When using remotely-sensed data to study health, researchers must identify an appropriate spatial resolution to capture potential exposures. Investigations into urban green space are often limited by the unavailability of fine-scale landcover data. We analyzed 1-meter gr...

Landscape capability predicts upland game bird abundance and occurrence

USGS Publications Warehouse

Loman, Zachary G.; Blomberg, Erik J.; DeLuca, William; Harrison, Daniel J.; Loftin, Cyndy; Wood, Petra B.

2017-01-01

Landscape capability (LC) models are a spatial tool with potential applications in conservation planning. We used survey data to validate LC models as predictors of occurrence and abundance at broad and fine scales for American woodcock (Scolopax minor) and ruffed grouse (Bonasa umbellus). Landscape capability models were reliable predictors of occurrence but were less indicative of relative abundance at route (11.5–14.6 km) and point scales (0.5–1 km). As predictors of occurrence, LC models had high sensitivity (0.71–0.93) and were accurate (0.71–0.88) and precise (0.88 and 0.92 for woodcock and grouse, respectively). Models did not predict point-scale abundance independent of the ability to predict occurrence of either species. The LC models are useful predictors of patterns of occurrences in the northeastern United States, but they have limited utility as predictors of fine-scale or route-specific abundances. 

Multi-scale investigation of shrub encroachment in southern Africa

NASA Astrophysics Data System (ADS)

Aplin, Paul; Marston, Christopher; Wilkinson, David; Field, Richard; O'Regan, Hannah

2016-04-01

There is growing speculation that savannah environments throughout Africa have been subject to shrub encroachment in recent years, whereby grassland is lost to woody vegetation cover. Changes in the relative proportions of grassland and woodland are important in the context of conservation of savannah systems, with implications for faunal distributions, environmental management and tourism. Here, we focus on southern Kruger National Park, South Africa, and investigate whether or not shrub encroachment has occurred over the last decade and a half. We use a multi-scale approach, examining the complementarity of medium (e.g. Landsat TM and OLI) and fine (e.g. QuickBird and WorldView-2) spatial resolution satellite sensor imagery, supported by intensive field survey in 2002 and 2014. We employ semi-automated land cover classification, involving a hybrid unsupervised clustering approach with manual class grouping and checking, followed by change detection post-classification comparison analysis. The results show that shrub encroachment is indeed occurring, a finding evidenced through three fine resolution replicate images plus medium resolution imagery. The results also demonstrate the complementarity of medium and fine resolution imagery, though some thematic information must be sacrificed to maintain high medium resolution classification accuracy. Finally, the findings have broader implications for issues such as vegetation seasonality, spatial transferability and management practices.

Regional Scale High Resolution δ18O Prediction in Precipitation Using MODIS EVI

PubMed Central

Huang, Cho-Ying; Wang, Chung-Ho; Lin, Shou-De; Lo, Yi-Chen; Huang, Bo-Wen; Hatch, Kent A.; Shiu, Hau-Jie; You, Cheng-Feng; Chang, Yuan-Mou; Shen, Sheng-Feng

2012-01-01

The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ18O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated δ18O are highly correlated and thus the EVI is a good predictor of precipitated δ18O. We then test the predictability of our EVI-δ18O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale δ18O predictions (annual and monthly predictabilities [r] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and δ18O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape. PMID:23029053

Spatial heterogeneity in human activities favors the persistence of wolves in agroecosystems.

PubMed

Ahmadi, Mohsen; López-Bao, José Vicente; Kaboli, Mohammad

2014-01-01

As human populations expand, there is increasing demand and pressure for land. Under this scenario, behavioural flexibility and adaptation become important processes leading to the persistence of large carnivores in human-dominated landscapes such as agroecosystems. A growing interest has recently emerged on the outcome of the coexistence between wolves and humans in these systems. It has been suggested that spatial heterogeneity in human activities would be a major environmental factor modulating vulnerability and persistence of this contentious species in agroecosystems. Here, we combined information from 35 den sites detected between 2011 and 2012 in agroecosystems of western Iran (Hamedan province), a set of environmental variables measured at landscape and fine spatial scales, and generalized linear models to identify patterns of den site selection by wolves in a highly-modified agroecosystem. On a landscape level, wolves selected a mixture of rangelands with scattered dry-farms on hillsides (showing a low human use) to locate their dens, avoiding areas with high densities of settlements and primary roads. On a fine spatial scale, wolves primarily excavated dens into the sides of elevated steep-slope hills with availability of water bodies in the vicinity of den sites, and wolves were relegated to dig in places with coarse-soil particles. Our results suggest that vulnerability of wolves in human-dominated landscapes could be compensated by the existence of spatial heterogeneity in human activities. Such heterogeneity would favor wolf persistence in agroecosystems favoring a land sharing model of coexistence between wolves and people.

Spatial Heterogeneity in Human Activities Favors the Persistence of Wolves in Agroecosystems

PubMed Central

Ahmadi, Mohsen; López-Bao, José Vicente; Kaboli, Mohammad

2014-01-01

As human populations expand, there is increasing demand and pressure for land. Under this scenario, behavioural flexibility and adaptation become important processes leading to the persistence of large carnivores in human-dominated landscapes such as agroecosystems. A growing interest has recently emerged on the outcome of the coexistence between wolves and humans in these systems. It has been suggested that spatial heterogeneity in human activities would be a major environmental factor modulating vulnerability and persistence of this contentious species in agroecosystems. Here, we combined information from 35 den sites detected between 2011 and 2012 in agroecosystems of western Iran (Hamedan province), a set of environmental variables measured at landscape and fine spatial scales, and generalized linear models to identify patterns of den site selection by wolves in a highly-modified agroecosystem. On a landscape level, wolves selected a mixture of rangelands with scattered dry-farms on hillsides (showing a low human use) to locate their dens, avoiding areas with high densities of settlements and primary roads. On a fine spatial scale, wolves primarily excavated dens into the sides of elevated steep-slope hills with availability of water bodies in the vicinity of den sites, and wolves were relegated to dig in places with coarse-soil particles. Our results suggest that vulnerability of wolves in human-dominated landscapes could be compensated by the existence of spatial heterogeneity in human activities. Such heterogeneity would favor wolf persistence in agroecosystems favoring a land sharing model of coexistence between wolves and people. PMID:25251567

Inferring landscape-scale land-use impacts on rivers using data from mesocosm experiments and artificial neural networks.

PubMed

Magierowski, Regina H; Read, Steve M; Carter, Steven J B; Warfe, Danielle M; Cook, Laurie S; Lefroy, Edward C; Davies, Peter E

2015-01-01

Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard metrics, such as substratum composition, do not necessarily relate causally to ecological impacts. Consequently, the absence of a significant relationship between a hypothesised driver and a dependent variable does not necessarily indicate the absence of a causal relationship. We conducted a gradient survey to identify impacts of catchment-scale grazing by domestic livestock on river macroinvertebrate communities. A standard correlative approach showed that community structure was strongly related to the upstream catchment area under grazing. We then used data from a stream mesocosm experiment that independently quantified the impacts of nutrients and fine sediments on macroinvertebrate communities to train artificial neural networks (ANNs) to assess the relative influence of nutrients and fine sediments on the survey sites from their community composition. The ANNs developed to predict nutrient impacts did not find a relationship between nutrients and catchment area under grazing, suggesting that nutrients were not an important factor mediating grazing impacts on community composition, or that these ANNs had no generality or insufficient power at the landscape-scale. In contrast, ANNs trained to predict the impacts of fine sediments indicated a significant relationship between fine sediments and catchment area under grazing. Macroinvertebrate communities at sites with a high proportion of land under grazing were thus more similar to those resulting from high fine sediments in a mesocosm experiment than to those resulting from high nutrients. Our study confirms that 1) fine sediment is an important mediator of land-use impacts on river macroinvertebrate communities, 2) ANNs can successfully identify subtle effects and separate the effects of correlated variables, and 3) data from small-scale experiments can generate relationships that help explain landscape-scale patterns.

Inferring Landscape-Scale Land-Use Impacts on Rivers Using Data from Mesocosm Experiments and Artificial Neural Networks

PubMed Central

Magierowski, Regina H.; Read, Steve M.; Carter, Steven J. B.; Warfe, Danielle M.; Cook, Laurie S.; Lefroy, Edward C.; Davies, Peter E.

2015-01-01

Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard metrics, such as substratum composition, do not necessarily relate causally to ecological impacts. Consequently, the absence of a significant relationship between a hypothesised driver and a dependent variable does not necessarily indicate the absence of a causal relationship. We conducted a gradient survey to identify impacts of catchment-scale grazing by domestic livestock on river macroinvertebrate communities. A standard correlative approach showed that community structure was strongly related to the upstream catchment area under grazing. We then used data from a stream mesocosm experiment that independently quantified the impacts of nutrients and fine sediments on macroinvertebrate communities to train artificial neural networks (ANNs) to assess the relative influence of nutrients and fine sediments on the survey sites from their community composition. The ANNs developed to predict nutrient impacts did not find a relationship between nutrients and catchment area under grazing, suggesting that nutrients were not an important factor mediating grazing impacts on community composition, or that these ANNs had no generality or insufficient power at the landscape-scale. In contrast, ANNs trained to predict the impacts of fine sediments indicated a significant relationship between fine sediments and catchment area under grazing. Macroinvertebrate communities at sites with a high proportion of land under grazing were thus more similar to those resulting from high fine sediments in a mesocosm experiment than to those resulting from high nutrients. Our study confirms that 1) fine sediment is an important mediator of land-use impacts on river macroinvertebrate communities, 2) ANNs can successfully identify subtle effects and separate the effects of correlated variables, and 3) data from small-scale experiments can generate relationships that help explain landscape-scale patterns. PMID:25775245

Macroecological factors shape local-scale spatial patterns in agriculturalist settlements.

PubMed

Tao, Tingting; Abades, Sebastián; Teng, Shuqing; Huang, Zheng Y X; Reino, Luís; Chen, Bin J W; Zhang, Yong; Xu, Chi; Svenning, Jens-Christian

2017-11-15

Macro-scale patterns of human systems ranging from population distribution to linguistic diversity have attracted recent attention, giving rise to the suggestion that macroecological rules shape the assembly of human societies. However, in which aspects the geography of our own species is shaped by macroecological factors remains poorly understood. Here, we provide a first demonstration that macroecological factors shape strong local-scale spatial patterns in human settlement systems, through an analysis of spatial patterns in agriculturalist settlements in eastern mainland China based on high-resolution Google Earth images. We used spatial point pattern analysis to show that settlement spatial patterns are characterized by over-dispersion at fine spatial scales (0.05-1.4 km), consistent with territory segregation, and clumping at coarser spatial scales beyond the over-dispersion signals, indicating territorial clustering. Statistical modelling shows that, at macroscales, potential evapotranspiration and topographic heterogeneity have negative effects on territory size, but positive effects on territorial clustering. These relationships are in line with predictions from territory theory for hunter-gatherers as well as for many animal species. Our results help to disentangle the complex interactions between intrinsic spatial processes in agriculturalist societies and external forcing by macroecological factors. While one may speculate that humans can escape ecological constraints because of unique abilities for environmental modification and globalized resource transportation, our work highlights that universal macroecological principles still shape the geography of current human agricultural societies. © 2017 The Author(s).

Comparing three sampling techniques for estimating fine woody down dead biomass

Treesearch

Robert E. Keane; Kathy Gray

2013-01-01

Designing woody fuel sampling methods that quickly, accurately and efficiently assess biomass at relevant spatial scales requires extensive knowledge of each sampling method's strengths, weaknesses and tradeoffs. In this study, we compared various modifications of three common sampling methods (planar intercept, fixed-area microplot and photoload) for estimating...

A geostatistical approach to identify and mitigate agricultural nitrous oxide emission hotspots

USDA-ARS?s Scientific Manuscript database

Anthropogenic emissions of nitrous oxide (N2O), a trace gas with severe environmental costs, are greatest from agricultural soils amended with nitrogen (N) fertilizer. However, accurate N2O emission estimates at fine spatial scales are made difficult by their high variability, which represents a cr...

High resolution pollutant measurements in complex urban environments using mobile monitoring

EPA Science Inventory

Measuring air pollution in real-time using an instrumented vehicle platform has been an emerging strategy to resolve air pollution trends at a very fine spatial scale (10s of meters). Achieving second-by-second data representative of urban air quality trends requires advanced in...

ACCURACY OF THE 1992 NATIONAL LAND COVER DATASET AREA ESTIMATES: AN ANALYSIS AT MULTIPLE SPATIAL EXTENTS

EPA Science Inventory

Abstract for poster presentation:

Site-specific accuracy assessments evaluate fine-scale accuracy of land-use/land-cover(LULC) datasets but provide little insight into accuracy of area estimates of LULC

classes derived from sampling units of varying size. Additiona...

Altitudinal gradients, biogeographic history and microhabitat adaptation affect fine-scale spatial genetic structure in African and Neotropical populations of an ancient tropical tree species.

PubMed

Torroba-Balmori, Paloma; Budde, Katharina B; Heer, Katrin; González-Martínez, Santiago C; Olsson, Sanna; Scotti-Saintagne, Caroline; Casalis, Maxime; Sonké, Bonaventure; Dick, Christopher W; Heuertz, Myriam

2017-01-01

The analysis of fine-scale spatial genetic structure (FSGS) within populations can provide insights into eco-evolutionary processes. Restricted dispersal and locally occurring genetic drift are the primary causes for FSGS at equilibrium, as described in the isolation by distance (IBD) model. Beyond IBD expectations, spatial, environmental or historical factors can affect FSGS. We examined FSGS in seven African and Neotropical populations of the late-successional rain forest tree Symphonia globulifera L. f. (Clusiaceae) to discriminate the influence of drift-dispersal vs. landscape/ecological features and historical processes on FSGS. We used spatial principal component analysis and Bayesian clustering to assess spatial genetic heterogeneity at SSRs and examined its association with plastid DNA and habitat features. African populations (from Cameroon and São Tomé) displayed a stronger FSGS than Neotropical populations at both marker types (mean Sp = 0.025 vs. Sp = 0.008 at SSRs) and had a stronger spatial genetic heterogeneity. All three African populations occurred in pronounced altitudinal gradients, possibly restricting animal-mediated seed dispersal. Cyto-nuclear disequilibria in Cameroonian populations also suggested a legacy of biogeographic history to explain these genetic patterns. Conversely, Neotropical populations exhibited a weaker FSGS, which may reflect more efficient wide-ranging seed dispersal by Neotropical bats and other dispersers. The population from French Guiana displayed an association of plastid haplotypes with two morphotypes characterized by differential habitat preferences. Our results highlight the importance of the microenvironment for eco-evolutionary processes within persistent tropical tree populations.

Exploring Potential of Crowdsourced Geographic Information in Studies of Active Travel and Health: Strava Data and Cycling Behaviour

NASA Astrophysics Data System (ADS)

Sun, Y.

2017-09-01

In development of sustainable transportation and green city, policymakers encourage people to commute by cycling and walking instead of motor vehicles in cities. One the one hand, cycling and walking enables decrease in air pollution emissions. On the other hand, cycling and walking offer health benefits by increasing people's physical activity. Earlier studies on investigating spatial patterns of active travel (cycling and walking) are limited by lacks of spatially fine-grained data. In recent years, with the development of information and communications technology, GPS-enabled devices are popular and portable. With smart phones or smart watches, people are able to record their cycling or walking GPS traces when they are moving. A large number of cyclists and pedestrians upload their GPS traces to sport social media to share their historical traces with other people. Those sport social media thus become a potential source for spatially fine-grained cycling and walking data. Very recently, Strava Metro offer aggregated cycling and walking data with high spatial granularity. Strava Metro aggregated a large amount of cycling and walking GPS traces of Strava users to streets or intersections across a city. Accordingly, as a kind of crowdsourced geographic information, the aggregated data is useful for investigating spatial patterns of cycling and walking activities, and thus is of high potential in understanding cycling or walking behavior at a large spatial scale. This study is a start of demonstrating usefulness of Strava Metro data for exploring cycling or walking patterns at a large scale.

Altitudinal gradients, biogeographic history and microhabitat adaptation affect fine-scale spatial genetic structure in African and Neotropical populations of an ancient tropical tree species

PubMed Central

Torroba-Balmori, Paloma; Budde, Katharina B.; Heer, Katrin; González-Martínez, Santiago C.; Olsson, Sanna; Scotti-Saintagne, Caroline; Sonké, Bonaventure; Dick, Christopher W.

2017-01-01

The analysis of fine-scale spatial genetic structure (FSGS) within populations can provide insights into eco-evolutionary processes. Restricted dispersal and locally occurring genetic drift are the primary causes for FSGS at equilibrium, as described in the isolation by distance (IBD) model. Beyond IBD expectations, spatial, environmental or historical factors can affect FSGS. We examined FSGS in seven African and Neotropical populations of the late-successional rain forest tree Symphonia globulifera L. f. (Clusiaceae) to discriminate the influence of drift-dispersal vs. landscape/ecological features and historical processes on FSGS. We used spatial principal component analysis and Bayesian clustering to assess spatial genetic heterogeneity at SSRs and examined its association with plastid DNA and habitat features. African populations (from Cameroon and São Tomé) displayed a stronger FSGS than Neotropical populations at both marker types (mean Sp = 0.025 vs. Sp = 0.008 at SSRs) and had a stronger spatial genetic heterogeneity. All three African populations occurred in pronounced altitudinal gradients, possibly restricting animal-mediated seed dispersal. Cyto-nuclear disequilibria in Cameroonian populations also suggested a legacy of biogeographic history to explain these genetic patterns. Conversely, Neotropical populations exhibited a weaker FSGS, which may reflect more efficient wide-ranging seed dispersal by Neotropical bats and other dispersers. The population from French Guiana displayed an association of plastid haplotypes with two morphotypes characterized by differential habitat preferences. Our results highlight the importance of the microenvironment for eco-evolutionary processes within persistent tropical tree populations. PMID:28771629

Negative density-dependent dispersal in the American black bear (Ursus americanus) revealed by noninvasive sampling and genotyping

PubMed Central

Roy, Justin; Yannic, Glenn; Côté, Steeve D; Bernatchez, Louis

2012-01-01

Although the dispersal of animals is influenced by a variety of factors, few studies have used a condition-dependent approach to assess it. The mechanisms underlying dispersal are thus poorly known in many species, especially in large mammals. We used 10 microsatellite loci to examine population density effects on sex-specific dispersal behavior in the American black bear, Ursus americanus. We tested whether dispersal increases with population density in both sexes. Fine-scale genetic structure was investigated in each of four sampling areas using Mantel tests and spatial autocorrelation analyses. Our results revealed male-biased dispersal pattern in low-density areas. As population density increased, females appeared to exhibit philopatry at smaller scales. Fine-scale genetic structure for males at higher densities may indicate reduced dispersal distances and delayed dispersal by subadults. PMID:22822432

Multiscale habitat selection of wetland birds in the northern Gulf Coast

USGS Publications Warehouse

Pickens, Bradley A.; King, Sammy L.

2014-01-01

The spatial scale of habitat selection has become a prominent concept in ecology, but has received less attention in coastal ecology. In coastal marshes, broad-scale marsh types are defined by vegetation composition over thousands of hectares, water-level management is applied over hundreds of hectares, and fine-scale habitat is depicted by tens of meters. Individually, these scales are known to affect wetland fauna, but studies have not examined all three spatial scales simultaneously. We investigated wetland bird habitat selection at the three scales and compared single- and multiscale models. From 2009 to 2011, we surveyed marsh birds (i.e., Rallidae, bitterns, grebes), shorebirds, and wading birds in fresh and intermediate (oligohaline) coastal marsh in Louisiana and Texas, USA. Within each year, six repeated surveys of wintering, resident, and migratory breeding birds were conducted at > 100 points (n = 304). The results revealed fine-scale factors, primarily water depth, were consistently better predictors than marsh type or management. However, 10 of 11 species had improved models with the three scales combined. Birds with a linear association with water depth were, correspondingly, most abundant with deeper fresh marsh and permanently impounded water. Conversely, intermediate marsh had a greater abundance of shallow water species, such as king rail Rallus elegans, least bittern Ixobrychus exilis, and sora Porzana carolina. These birds had quadratic relationships with water depth or no relationship. Overall, coastal birds were influenced by multiple scales corresponding with hydrological characteristics. The effects suggest the timing of drawdowns and interannual variability in spring water levels can greatly affect wetland bird abundance.

Downscaling Coarse Scale Microwave Soil Moisture Product using Machine Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.; Yan, H.

2016-12-01

Soil moisture (SM) is a key variable in partitioning and examining the global water-energy cycle, agricultural planning, and water resource management. It is also strongly coupled with climate change, playing an important role in weather forecasting and drought monitoring and prediction, flood modeling and irrigation management. Although satellite retrievals can provide an unprecedented information of soil moisture at a global-scale, the products might be inadequate for basin scale study or regional assessment. To improve the spatial resolution of SM, this work presents a novel approach based on Machine Learning (ML) technique that allows for downscaling of the satellite soil moisture to fine resolution. For this purpose, the SMAP L-band radiometer SM products were used and conditioned on the Variable Infiltration Capacity (VIC) model prediction to describe the relationship between the coarse and fine scale soil moisture data. The proposed downscaling approach was applied to a western US basin and the products were compared against the available SM data from in-situ gauge stations. The obtained results indicated a great potential of the machine learning technique to derive the fine resolution soil moisture information that is currently used for land data assimilation applications.

On the effects of scale for ecosystem services mapping

USGS Publications Warehouse

Grêt-Regamey, Adrienne; Weibel, Bettina; Bagstad, Kenneth J.; Ferrari, Marika; Geneletti, Davide; Klug, Hermann; Schirpke, Uta; Tappeiner, Ulrike

2014-01-01

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.

On the Effects of Scale for Ecosystem Services Mapping

PubMed Central

Grêt-Regamey, Adrienne; Weibel, Bettina; Bagstad, Kenneth J.; Ferrari, Marika; Geneletti, Davide; Klug, Hermann; Schirpke, Uta; Tappeiner, Ulrike

2014-01-01

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability. PMID:25549256

On the effects of scale for ecosystem services mapping.

PubMed

Grêt-Regamey, Adrienne; Weibel, Bettina; Bagstad, Kenneth J; Ferrari, Marika; Geneletti, Davide; Klug, Hermann; Schirpke, Uta; Tappeiner, Ulrike

2014-01-01

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.

Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005-2015

NASA Astrophysics Data System (ADS)

Thompson, David R.; Kahn, Brian H.; Green, Robert O.; Chien, Steve A.; Middleton, Elizabeth M.; Tran, Daniel Q.

2018-02-01

The distribution of ice, liquid, and mixed phase clouds is important for Earth's planetary radiation budget, impacting cloud optical properties, evolution, and solar reflectivity. Most remote orbital thermodynamic phase measurements observe kilometer scales and are insensitive to mixed phases. This under-constrains important processes with outsize radiative forcing impact, such as spatial partitioning in mixed phase clouds. To date, the fine spatial structure of cloud phase has not been measured at global scales. Imaging spectroscopy of reflected solar energy from 1.4 to 1.8 µm can address this gap: it directly measures ice and water absorption, a robust indicator of cloud top thermodynamic phase, with spatial resolution of tens to hundreds of meters. We report the first such global high spatial resolution survey based on data from 2005 to 2015 acquired by the Hyperion imaging spectrometer onboard NASA's Earth Observer 1 (EO-1) spacecraft. Seasonal and latitudinal distributions corroborate observations by the Atmospheric Infrared Sounder (AIRS). For extratropical cloud systems, just 25 % of variance observed at GCM grid scales of 100 km was related to irreducible measurement error, while 75 % was explained by spatial correlations possible at finer resolutions.

Spatial Patterns in Water Temperature in Pacific Northwest Rivers: Diversity at Multiple Scales and Potential Influence of Climate Change

NASA Astrophysics Data System (ADS)

Torgersen, C. E.; Fullerton, A.; Lawler, J. J.; Ebersole, J. L.; Leibowitz, S. G.; Steel, E. A.; Beechie, T. J.; Faux, R.

2016-12-01

Understanding spatial patterns in water temperature will be essential for evaluating vulnerability of aquatic biota to future climate and for identifying and protecting diverse thermal habitats. We used high-resolution remotely sensed water temperature data for over 16,000 km of 2nd to 7th-order rivers throughout the Pacific Northwest and California to evaluate spatial patterns of summertime water temperature at multiple spatial scales. We found a diverse and geographically distributed suite of whole-river patterns. About half of rivers warmed asymptotically in a downstream direction, whereas the rest exhibited complex and unique spatial patterns. Patterns were associated with both broad-scale hydroclimatic variables as well as characteristics unique to each basin. Within-river thermal heterogeneity patterns were highly river-specific; across rivers, median size and spacing of cool patches <15 °C were around 250 m. Patches of this size are large enough for juvenile salmon rearing and for resting during migration, and the distance between patches is well within the movement capabilities of both juvenile and adult salmon. We found considerable thermal heterogeneity at fine spatial scales that may be important to fish that would be missed if data were analyzed at coarser scales. We estimated future thermal heterogeneity and concluded that climate change will cause warmer temperatures overall, but that thermal heterogeneity patterns may remain similar in the future for many rivers. We demonstrated considerable spatial complexity in both current and future water temperature, and resolved spatial patterns that could not have been perceived without spatially continuous data.

Determining fine-scale migratory connectivity and habitat selection for a migratory songbird by using new GPS technology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fraser, Kevin C.; Shave, A.; Savage, A.

Migratory aerial insectivores are among the fastest declining avian group, but our understanding of these trends has been limited by poor knowledge of migratory connectivity and the identification of critical habitat across the vast distances they travel annually. Using new, archival GPS loggers, we tracked individual purple martins ( Progne subis) from breeding colonies across North America to determine precise (<10m) locations of migratory and overwintering roost locations in South America and to test hypotheses for fine-scale migratory connectivity and habitat use. We discovered weak migratory connectivity at the roost scale, and extensive, fine-scale mixing of birds in the Amazonmore » from distant (>2000 km) breeding sites, with some individuals sharing the same roosting trees. Despite vast tracts of contiguous forest in this region, birds occupied a much more limited habitat, with most (56%) roosts occurring on small habitat islands that were strongly associated with water. Only 17% of these roosts were in current protected areas. As a result, these data reflect a critical advance in our ability to remotely determine precise migratory connectivity and habitat selection across vast spatial scales, enhancing our understanding of population dynamics and enabling more effective conservation of species at risk.« less

Determining fine-scale migratory connectivity and habitat selection for a migratory songbird by using new GPS technology

DOE PAGES

Fraser, Kevin C.; Shave, A.; Savage, A.; ...

2016-07-27

Migratory aerial insectivores are among the fastest declining avian group, but our understanding of these trends has been limited by poor knowledge of migratory connectivity and the identification of critical habitat across the vast distances they travel annually. Using new, archival GPS loggers, we tracked individual purple martins ( Progne subis) from breeding colonies across North America to determine precise (<10m) locations of migratory and overwintering roost locations in South America and to test hypotheses for fine-scale migratory connectivity and habitat use. We discovered weak migratory connectivity at the roost scale, and extensive, fine-scale mixing of birds in the Amazonmore » from distant (>2000 km) breeding sites, with some individuals sharing the same roosting trees. Despite vast tracts of contiguous forest in this region, birds occupied a much more limited habitat, with most (56%) roosts occurring on small habitat islands that were strongly associated with water. Only 17% of these roosts were in current protected areas. As a result, these data reflect a critical advance in our ability to remotely determine precise migratory connectivity and habitat selection across vast spatial scales, enhancing our understanding of population dynamics and enabling more effective conservation of species at risk.« less

A tilted cold dark matter cosmological scenario

NASA Technical Reports Server (NTRS)

Cen, Renyue; Gnedin, Nickolay Y.; Kofman, Lev A.; Ostriker, Jeremiah P.

1992-01-01

A new cosmological scenario based on CDM but with a power spectrum index of about 0.7-0.8 is suggested. This model is predicted by various inflationary models with no fine tuning. This tilted CDM model, if normalized to COBE, alleviates many problems of the standard CDM model related to both small-scale and large-scale power. A physical bias of galaxies over dark matter of about two is required to fit spatial observations.

Detecting Uniform Areas for Vicarious Calibration using Landsat TM Imagery: A Study using the Arabian and Saharan Deserts

NASA Technical Reports Server (NTRS)

Hilbert, Kent; Pagnutti, Mary; Ryan, Robert; Zanoni, Vicki

2002-01-01

This paper discusses a method for detecting spatially uniform sites need for radiometric characterization of remote sensing satellites. Such information is critical for scientific research applications of imagery having moderate to high resolutions (<30-m ground sampling distance (GSD)). Previously published literature indicated that areas with the African Saharan and Arabian deserts contained extremely uniform sites with respect to spatial characteristics. We developed an algorithm for detecting site uniformity and applied it to orthorectified Landsat Thematic Mapper (TM) imagery over eight uniform regions of interest. The algorithm's results were assessed using both medium-resolution (30-m GSD) Landsat 7 ETM+ and fine-resolution (<5-m GSD) IKONOS multispectral data collected over sites in Libya and Mali. Fine-resolution imagery over a Libyan site exhibited less than 1 percent nonuniformity. The research shows that Landsat TM products appear highly useful for detecting potential calibration sites for system characterization. In particular, the approach detected spatially uniform regions that frequently occur at multiple scales of observation.

Surface roughness manifestations of deep-seated landslide processes

NASA Astrophysics Data System (ADS)

Booth, A. M.; Roering, J. J.; Lamb, M. P.

2012-12-01

In many mountainous drainage basins, deep-seated landslides evacuate large volumes of sediment from small surface areas, leaving behind a strong topographic signature that sets landscape roughness over a range of spatial scales. At long spatial wavelengths of hundreds to thousands of meters, landslides tend to inhibit channel incision and limit topographic relief, effectively smoothing the topography at this length scale. However, at short spatial wavelengths on the order of meters, deformation of deep-seated landslides generates surface roughness that allows expert mappers or automated algorithms to distinguish landslides from the surrounding terrain. Here, we directly connect the characteristic spatial wavelengths and amplitudes of this fine scale surface roughness to the underlying landslide deformation processes. We utilize the two-dimensional wavelet transform with high-resolution, airborne LiDAR-derived digital elevation models to systematically document the characteristic length scales and amplitudes of different kinematic units within slow moving earthflows, a common type of deep-seated landslide. In earthflow source areas, discrete slumped blocks generate high surface roughness, reflecting an extensional deformation regime. In earthflow transport zones, where material translates with minimal surface deformation, roughness decreases as other surface processes quickly smooth short wavelength features. In earthflow depositional toes, compression folds and thrust faults again increase short wavelength surface roughness. When an earthflow becomes inactive, roughness in all of these kinematic zones systematically decreases with time, allowing relative dating of earthflow deposits. We also document how each of these roughness expressions depends on earthflow velocity, using sub-pixel change detection software (COSI-Corr) and pairs of orthorectified aerial photographs to determine spatially extensive landslide surface displacements. In source areas, the wavelength of slumped blocks tends to correlate with velocity as predicted by a simple sliding block model, but the amplitude is insensitive to velocity, suggesting that landslide depth rather than velocity sets this characteristic block amplitude. In both transport zones and depositional toes, the amplitude of the surface roughness is higher where the longitudinal gradient in velocity is higher, confirming that differential movement generates and maintains this fine scale roughness.

Spatiotemporal models for data-anomaly detection in dynamic environmental monitoring campaigns

Treesearch

E.W. Dereszynski; T.G. Dietterich

2011-01-01

The ecological sciences have benefited greatly from recent advances in wireless sensor technologies. These technologies allow researchers to deploy networks of automated sensors, which can monitor a landscape at very fine temporal and spatial scales. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures...

Representing climate, disturbance, and vegetation interactions in landscape models

Treesearch

Robert E. Keane; Donald McKenzie; Donald A. Falk; Erica A.H. Smithwick; Carol Miller; Lara-Karena B. Kellogg

2015-01-01

The prospect of rapidly changing climates over the next century calls for methods to predict their effects on myriad, interactive ecosystem processes. Spatially explicit models that simulate ecosystem dynamics at fine (plant, stand) to coarse (regional, global) scales are indispensable tools for meeting this challenge under a variety of possible futures. A special...

Comparing large-scale hydrological model predictions with observed streamflow in the Pacific Northwest: effects of climate and groundwater

Treesearch

Mohammad Safeeq; Guillaume S. Mauger; Gordon E. Grant; Ivan Arismendi; Alan F. Hamlet; Se-Yeun Lee

2014-01-01

Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse (1/16°) and fine (1/120°) spatial resolutions were evaluated against observed streamflows from 217 watersheds. In...

Theories of Simplification and Scaling of Spatially Distributed Processes. Chapter 12

NASA Technical Reports Server (NTRS)

Levin, Simon A.; Pacala, Stephen W.

1997-01-01

The problem of scaling is at the heart of ecological theory, the essence of understanding and of the development of a predictive capability. The description of any system depends on the spatial, temporal, and organizational perspective chosen; hence it is essential to understand not only how patterns and dynamics vary with scale, but also how patterns at one scale are manifestations of processes operating at other scales. Evolution has shaped the characteristics of species in ways that result in scale displacement: Each species experiences the environment at its own unique set of spatial and temporal scales and interfaces the biota through unique assemblages of phenotypes. In this way, coexistence becomes possible, and biodiversity is enhanced. By averaging over space, time, and biological interactions, a genotype filters variation at fine scales and selects the arena in which it will face the vicissitudes of nature. Variation at finer scales is then noise, of minor importance to the survival and dynamics of the species, and consequently of minor importance in any attempt at description. In attempting to model ecological interactions in space, contributors throughout this book have struggled with a trade-off between simplification and "realistic" complexity and detail. Although the challenge of simplification is widely recognized in ecology, less appreciated is the intertwining of scaling questions and scaling laws with the process of simplification. In the context of this chapter simplification will in general mean the use of spatial or ensemble means and low-order moments to capture more detailed interactions by integrating over given areas. In this way, one can derive descriptions of the system at different spatial scales, which provides the essentials for the extraction of scaling laws by examination of how system properties vary with scale.

Fine-grained suspended sediment source identification for the Kharaa River basin, northern Mongolia

NASA Astrophysics Data System (ADS)

Rode, Michael; Theuring, Philipp; Collins, Adrian L.

2015-04-01

Fine sediment inputs into river systems can be a major source of nutrients and heavy metals and have a strong impact on the water quality and ecosystem functions of rivers and lakes, including those in semiarid regions. However, little is known to date about the spatial distribution of sediment sources in most large scale river basins in Central Asia. Accordingly, a sediment source fingerprinting technique was used to assess the spatial sources of fine-grained (<10 microns) sediment in the 15 000 km2 Kharaa River basin in northern Mongolia. Five field sampling campaigns in late summer 2009, and spring and late summer in both 2010 and 2011, were conducted directly after high water flows, to collect an overall total of 900 sediment samples. The work used a statistical approach for sediment source discrimination with geochemical composite fingerprints based on a new Genetic Algorithm (GA)-driven Discriminant Function Analysis, the Kruskal-Wallis H-test and Principal Component Analysis. The composite fingerprints were subsequently used for numerical mass balance modelling with uncertainty analysis. The contributions of the individual sub-catchment spatial sediment sources varied from 6.4% (the headwater sub-catchment of Sugnugur Gol) to 36.2% (the Kharaa II sub-catchment in the middle reaches of the study basin) with the pattern generally showing higher contributions from the sub-catchments in the middle, rather than the upstream, portions of the study area. The importance of riverbank erosion was shown to increase from upstream to midstream tributaries. The source tracing procedure provides results in reasonable accordance with previous findings in the study region and demonstrates the general applicability and associated uncertainties of an approach for fine-grained sediment source investigation in large scale semi-arid catchments. The combined application of source fingerprinting and catchment modelling approaches can be used to assess whether tracing estimates are credible and in combination such approaches provide a basis for making sediment source apportionment more compelling to catchment stakeholders and managers.

Thermal Characteristics of Urban Landscapes

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Quattrochi, Dale A.

1998-01-01

Although satellite data are very useful for analysis of the urban heat island effect at a coarse scale, they do not lend themselves to developing a better understanding of which surfaces across the city contribute or drive the development of the urban heat island effect. Analysis of thermal energy responses for specific or discrete surfaces typical of the urban landscape (e.g., asphalt, building rooftops, vegetation) requires measurements at a very fine spatial scale (i.e., less than 15 m) to adequately resolve these surfaces and their attendant thermal energy regimes. Additionally, very fine scale spatial resolution thermal infrared data, such as that obtained from aircraft, are very useful for demonstrating to planning officials, policy makers, and the general populace the benefits of the urban forest. These benefits include mitigating the urban heat island effect, making cities more aesthetically pleasing and more habitable environments, and aid in overall cooling of the community. High spatial resolution thermal data are required to quantify how artificial surfaces within the city contribute to an increase in urban heating and the benefit of cool surfaces (e.g., surface coatings that reflect much of the incoming solar radiation as opposed to absorbing it thereby lowering urban temperatures). The TRN (thermal response number) is a technique using aircraft remotely sensed surface temperatures to quantify the thermal response of urban surfaces. The TRN was used to quantify the thermal response of various urban surface types ranging from completely vegetated surfaces to asphalt and concrete parking lots for Huntsville, AL.

Need for Improved Methods to Collect and Present Spatial Epidemiologic Data for Vectorborne Diseases

PubMed Central

Eisen, Rebecca J.

2007-01-01

Improved methods for collection and presentation of spatial epidemiologic data are needed for vectorborne diseases in the United States. Lack of reliable data for probable pathogen exposure site has emerged as a major obstacle to the development of predictive spatial risk models. Although plague case investigations can serve as a model for how to ideally generate needed information, this comprehensive approach is cost-prohibitive for more common and less severe diseases. New methods are urgently needed to determine probable pathogen exposure sites that will yield reliable results while taking into account economic and time constraints of the public health system and attending physicians. Recent data demonstrate the need for a change from use of the county spatial unit for presentation of incidence of vectorborne diseases to more precise ZIP code or census tract scales. Such fine-scale spatial risk patterns can be communicated to the public and medical community through Web-mapping approaches. PMID:18258029

The basis of orientation decoding in human primary visual cortex: fine- or coarse-scale biases?

PubMed

Maloney, Ryan T

2015-01-01

Orientation signals in human primary visual cortex (V1) can be reliably decoded from the multivariate pattern of activity as measured with functional magnetic resonance imaging (fMRI). The precise underlying source of these decoded signals (whether by orientation biases at a fine or coarse scale in cortex) remains a matter of some controversy, however. Freeman and colleagues (J Neurosci 33: 19695-19703, 2013) recently showed that the accuracy of decoding of spiral patterns in V1 can be predicted by a voxel's preferred spatial position (the population receptive field) and its coarse orientation preference, suggesting that coarse-scale biases are sufficient for orientation decoding. Whether they are also necessary for decoding remains an open question, and one with implications for the broader interpretation of multivariate decoding results in fMRI studies. Copyright © 2015 the American Physiological Society.

Hope for the Forests? Habitat Resiliency Illustrated in the Face of Climate Change Using Fine-Scale Modeling

NASA Astrophysics Data System (ADS)

Flint, L. E.; Flint, A. L.; Weiss, S. B.; Micheli, E. R.

2010-12-01

In the face of rapid climate change, fine-scale predictions of landscape change are of extreme interest to land managers that endeavor to develop long term adaptive strategies for maintaining biodiversity and ecosystem services. Global climate model (GCM) outputs, which generally focus on estimated increases in air temperature, are increasingly applied to species habitat distribution models. For sensitive species subject to climate change, habitat models predict significant migration (either northward or towards higher elevations), or complete extinction. Current studies typically rely on large spatial scale GCM projections (> 10 km) of changes in precipitation and air temperature: at this scale, these models necessarily neglect subtleties of topographic shading, geomorphic expression of the landscape, and fine-scale differences in soil properties - data that is readily available at meaningful local scales. Recent advances in modeling take advantage of available soils, geology, and topographic data to construct watershed-scale scenarios using GCM inputs and result in improved correlations of vegetation distribution with temperature. For this study, future climate projections were downscaled to 270-m and applied to a physically-based hydrologic model to calculate future changes in recharge, runoff, and climatic water deficit (CWD) for basins draining into the northern San Francisco Bay. CWD was analyzed for mapped vegetation types to evaluate the range of CWD for historic time periods in comparison to future time periods. For several forest communities (including blue oak woodlands, montane hardwoods, douglas-fir, and coast redwood) existing landscape area exhibiting suitable CWD diminishes by up 80 percent in the next century, with a trend towards increased CWD throughout the region. However, no forest community loses all suitable habitat, with islands of potential habitat primarily remaining on north facing slopes and deeper soils. Creation of new suitable habitat is also predicted throughout the region. Results have direct application to management issues of habitat connectivity, forest land protection and acquisition, and active management solutions such as transplanting or assisted migration. Although this analysis considers only one driver of forest habitat distribution, consideration of hydrologic derivatives at a fine scale explains current forest community distributions and provides a far more informed perspective on potential future forest distributions. Results demonstrate the utility of fine-scale modeling and provide landscape managers and conservation agencies valuable management tools in fine-scale future forest scenarios and a framework for evaluating forest resiliency in a changing climate.

Real-time distribution of pelagic fish: combining hydroacoustics, GIS and spatial modelling at a fine spatial scale.

PubMed

Muška, Milan; Tušer, Michal; Frouzová, Jaroslava; Mrkvička, Tomáš; Ricard, Daniel; Seďa, Jaromír; Morelli, Federico; Kubečka, Jan

2018-03-29

Understanding spatial distribution of organisms in heterogeneous environment remains one of the chief issues in ecology. Spatial organization of freshwater fish was investigated predominantly on large-scale, neglecting important local conditions and ecological processes. However, small-scale processes are of an essential importance for individual habitat preferences and hence structuring trophic cascades and species coexistence. In this work, we analysed the real-time spatial distribution of pelagic freshwater fish in the Římov Reservoir (Czechia) observed by hydroacoustics in relation to important environmental predictors during 48 hours at 3-h interval. Effect of diurnal cycle was revealed of highest significance in all spatial models with inverse trends between fish distribution and predictors in day and night in general. Our findings highlighted daytime pelagic fish distribution as highly aggregated, with general fish preferences for central, deep and highly illuminated areas, whereas nighttime distribution was more disperse and fish preferred nearshore steep sloped areas with higher depth. This turnover suggests prominent movements of significant part of fish assemblage between pelagic and nearshore areas on a diel basis. In conclusion, hydroacoustics, GIS and spatial modelling proved as valuable tool for predicting local fish distribution and elucidate its drivers, which has far reaching implications for understanding freshwater ecosystem functioning.

Effects of Spatial N nutrient mobility relevant to plants, soils and microtopograhy on plant growth and soil organic matter accumulation by using coupled CLM-PFLOTRAN biogeochemical model in an Area in NGEE-Arctic Intensive Study Sites, Barrow, AK.

NASA Astrophysics Data System (ADS)

Yuan, F.; Thornton, P. E.; Tang, G.; Xu, X.; Kumar, J.; Iversen, C. M.; Bisht, G.; Hammond, G. E.; Mills, R. T.; Wullschleger, S. D.

2015-12-01

At fine-scale spatially-explicit reactive-transport (RT) and hydrological coupled modeling for likely soil nutrient N transport mechanisms driven by gradients, soil properties and micro-topography is critical to spatial distribution of plants and thus soil organic matter stocks accumulation or changes. In this study we successfully carried out a fully coupled fine-scale CLM-PFLOTRAN soil biogeochemical (BGC) RT model simulation on Titan at 2.5mx2.5m resolution for the Area C of 100mx100m in the NGEE-Arctic Intensive Study Sites, Barrow, AK. The Area spatially varies in terms of plant function types (PFT) and soil thermal-hydraulic properties associated with locally polygonal landscape features. The spatially explicit CLM-PFLOTRAN coupled RT model allows soil N nutrient mobility driven either by diffusion or by advection or both. The modeling experiments are conducted with three soil nutrient N (NH4+ and NO3-) mobility mechanisms within the CLM-PFLOTAN: no transport, diffusion only, and diffusion and advection in 3-D soils. It shows that CLM-PFLOTRAN model simulated higher SOM C density in both lower troughs and neighbored areas when transport mechanism allowed, compared to no-transport, although with similar ranges (about 0.1~20 kgC m-3). It also simulates slightly higher LAI (0.16~0.84 vs. 0.11~0.85) in growing season, especially in lower troughs and neighbored regions. It's likely because CLM-PFLOTRAN can explicitly simulate transport of nutrients and others both vertically and laterally. So it can more mechanically mimic plant root N extract caused relatively low concentration in root zone and thus allow transport from surrounding high N concentration regions. The lateral mobility also implies that N nutrient can transport from initially high-production columns to the neighbored low-production area where then production could be improved. The results suggest that taking account of locally mobility of soil N nutrients may be critical to plant growth and thus long-term soil organic carbon stocks in this polygonal coastal tundra ecosystem at fine scale. It also implies that regional or global scale modelings should consider vertical transport (2D) due to shallow soil root zones, for which a feature in CLM-PFLOTRAN is available as well.

A Mixed-dimensional Model for Determining the Impact of Permafrost Polygonal Ground Degradation on Arctic Hydrology.

NASA Astrophysics Data System (ADS)

Coon, E.; Jan, A.; Painter, S. L.; Moulton, J. D.; Wilson, C. J.

2017-12-01

Many permafrost-affected regions in the Arctic manifest a polygonal patterned ground, which contains large carbon stores and is vulnerability to climate change as warming temperatures drive melting ice wedges, polygon degradation, and thawing of the underlying carbon-rich soils. Understanding the fate of this carbon is difficult. The system is controlled by complex, nonlinear physics coupling biogeochemistry, thermal-hydrology, and geomorphology, and there is a strong spatial scale separation between microtopograpy (at the scale of an individual polygon) and the scale of landscape change (at the scale of many thousands of polygons). Physics-based models have come a long way, and are now capable of representing the diverse set of processes, but only on individual polygons or a few polygons. Empirical models have been used to upscale across land types, including ecotypes evolving from low-centered (pristine) polygons to high-centered (degraded) polygon, and do so over large spatial extent, but are limited in their ability to discern causal process mechanisms. Here we present a novel strategy that looks to use physics-based models across scales, bringing together multiple capabilities to capture polygon degradation under a warming climate and its impacts on thermal-hydrology. We use fine-scale simulations on individual polygons to motivate a mixed-dimensional strategy that couples one-dimensional columns representing each individual polygon through two-dimensional surface flow. A subgrid model is used to incorporate the effects of surface microtopography on surface flow; this model is described and calibrated to fine-scale simulations. And critically, a subsidence model that tracks volume loss in bulk ice wedges is used to alter the subsurface structure and subgrid parameters, enabling the inclusion of the feedbacks associated with polygon degradation. This combined strategy results in a model that is able to capture the key features of polygon permafrost degradation, but in a simulation across a large spatial extent of polygonal tundra.

A SPATIAL ANALYSIS OF THE FINE ROOT BIOMASS FROM STAND DATA IN THE PACIFIC NORTHWEST

EPA Science Inventory

High spatial variability of fine roots in natural forest stands makes accurate estimates of stand-level fine root biomass difficult and expensive to obtain by standard coring methods. This study uses aboveground tree metrics and spatial relationships to improve core-based estima...

Fine scale climatic and soil variability effects on plant species cover along the Front Range of Colorado, USA

NASA Astrophysics Data System (ADS)

Cumming, William Frank Preston

Fine scale studies are rarely performed to address landscape level responses to microclimatic variability. Is it the timing, distribution, and magnitude of soil temperature and moisture that affects what species emerge each season and, in turn, their resilience to fluctuations in microclimate. For this dissertation research, I evaluated the response of vegetation change to microclimatic variability within two communities over a three year period (2009-2012) utilizing 25 meter transects at two locations along the Front Range of Colorado near Boulder, CO and Golden, CO respectively. To assess microclimatic variability, spatial and temporal autocorrelation analyses were performed with soil temperature and moisture. Species cover was assessed along several line transects and correlated with microclimatic variability. Spatial and temporal autocorrelograms are useful tools in identifying the degree of dependency of soil temperature and moisture on the distance and time between pairs of measurements. With this analysis I found that a meter spatial resolution and two-hour measurements are sufficient to capture the fine scale variability in soil properties throughout the year. By comparing this to in situ measurements of soil properties and species percent cover I found that there are several plant functional types and/or species origin in particular that are more sensitive to variations in temperature and moisture than others. When all seasons, locations, correlations, and regional climate are looked at, it is the month of March that stands out in terms of significance. Additionally, of all of the vegetation types represented at these two sites C4, C3, native, non-native, and forb species seem to be the most sensitive to fluctuations in soil temperature, moisture, and regional climate in the spring season. The steady decline in percent species cover the study period and subsequent decrease in percent species cover and size at both locations may indicate that certain are unable to respond to continually higher temperatures and lower moisture availability that is inevitable with future climatic variability.

Horizontal Residual Mean Circulation: Evaluation of Spatial Correlations in Coarse Resolution Ocean Models

NASA Astrophysics Data System (ADS)

Li, Y.; McDougall, T. J.

2016-02-01

Coarse resolution ocean models lack knowledge of spatial correlations between variables on scales smaller than the grid scale. Some researchers have shown that these spatial correlations play a role in the poleward heat flux. In order to evaluate the poleward transport induced by the spatial correlations at a fixed horizontal position, an equation is obtained to calculate the approximate transport from velocity gradients. The equation involves two terms that can be added to the quasi-Stokes streamfunction (based on temporal correlations) to incorporate the contribution of spatial correlations. Moreover, these new terms do not need to be parameterized and is ready to be evaluated by using model data directly. In this study, data from a high resolution ocean model have been used to estimate the accuracy of this HRM approach for improving the horizontal property fluxes in coarse-resolution ocean models. A coarse grid is formed by sub-sampling and box-car averaging the fine grid scale. The transport calculated on the coarse grid is then compared to the transport on original high resolution grid scale accumulated over a corresponding number of grid boxes. The preliminary results have shown that the estimate on coarse resolution grids roughly match the corresponding transports on high resolution grids.

A Fine-Scale Functional Logic to Convergence from Retina to Thalamus.

PubMed

Liang, Liang; Fratzl, Alex; Goldey, Glenn; Ramesh, Rohan N; Sugden, Arthur U; Morgan, Josh L; Chen, Chinfei; Andermann, Mark L

2018-05-31

Numerous well-defined classes of retinal ganglion cells innervate the thalamus to guide image-forming vision, yet the rules governing their convergence and divergence remain unknown. Using two-photon calcium imaging in awake mouse thalamus, we observed a functional arrangement of retinal ganglion cell axonal boutons in which coarse-scale retinotopic ordering gives way to fine-scale organization based on shared preferences for other visual features. Specifically, at the ∼6 μm scale, clusters of boutons from different axons often showed similar preferences for either one or multiple features, including axis and direction of motion, spatial frequency, and changes in luminance. Conversely, individual axons could "de-multiplex" information channels by participating in multiple, functionally distinct bouton clusters. Finally, ultrastructural analyses demonstrated that retinal axonal boutons in a local cluster often target the same dendritic domain. These data suggest that functionally specific convergence and divergence of retinal axons may impart diverse, robust, and often novel feature selectivity to visual thalamus. Copyright © 2018 Elsevier Inc. All rights reserved.

Fine-scale landscape genetics of the American badger (Taxidea taxus): disentangling landscape effects and sampling artifacts in a poorly understood species

PubMed Central

Kierepka, E M; Latch, E K

2016-01-01

Landscape genetics is a powerful tool for conservation because it identifies landscape features that are important for maintaining genetic connectivity between populations within heterogeneous landscapes. However, using landscape genetics in poorly understood species presents a number of challenges, namely, limited life history information for the focal population and spatially biased sampling. Both obstacles can reduce power in statistics, particularly in individual-based studies. In this study, we genotyped 233 American badgers in Wisconsin at 12 microsatellite loci to identify alternative statistical approaches that can be applied to poorly understood species in an individual-based framework. Badgers are protected in Wisconsin owing to an overall lack in life history information, so our study utilized partial redundancy analysis (RDA) and spatially lagged regressions to quantify how three landscape factors (Wisconsin River, Ecoregions and land cover) impacted gene flow. We also performed simulations to quantify errors created by spatially biased sampling. Statistical analyses first found that geographic distance was an important influence on gene flow, mainly driven by fine-scale positive spatial autocorrelations. After controlling for geographic distance, both RDA and regressions found that Wisconsin River and Agriculture were correlated with genetic differentiation. However, only Agriculture had an acceptable type I error rate (3–5%) to be considered biologically relevant. Collectively, this study highlights the benefits of combining robust statistics and error assessment via simulations and provides a method for hypothesis testing in individual-based landscape genetics. PMID:26243136

Object-Part Attention Model for Fine-Grained Image Classification

NASA Astrophysics Data System (ADS)

Peng, Yuxin; He, Xiangteng; Zhao, Junjie

2018-03-01

Fine-grained image classification is to recognize hundreds of subcategories belonging to the same basic-level category, such as 200 subcategories belonging to the bird, which is highly challenging due to large variance in the same subcategory and small variance among different subcategories. Existing methods generally first locate the objects or parts and then discriminate which subcategory the image belongs to. However, they mainly have two limitations: (1) Relying on object or part annotations which are heavily labor consuming. (2) Ignoring the spatial relationships between the object and its parts as well as among these parts, both of which are significantly helpful for finding discriminative parts. Therefore, this paper proposes the object-part attention model (OPAM) for weakly supervised fine-grained image classification, and the main novelties are: (1) Object-part attention model integrates two level attentions: object-level attention localizes objects of images, and part-level attention selects discriminative parts of object. Both are jointly employed to learn multi-view and multi-scale features to enhance their mutual promotions. (2) Object-part spatial constraint model combines two spatial constraints: object spatial constraint ensures selected parts highly representative, and part spatial constraint eliminates redundancy and enhances discrimination of selected parts. Both are jointly employed to exploit the subtle and local differences for distinguishing the subcategories. Importantly, neither object nor part annotations are used in our proposed approach, which avoids the heavy labor consumption of labeling. Comparing with more than 10 state-of-the-art methods on 4 widely-used datasets, our OPAM approach achieves the best performance.

Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales

NASA Astrophysics Data System (ADS)

Turner, Alexander J.; Jacob, Daniel J.; Benmergui, Joshua; Brandman, Jeremy; White, Laurent; Randles, Cynthia A.

2018-06-01

Anthropogenic methane emissions originate from a large number of fine-scale and often transient point sources. Satellite observations of atmospheric methane columns are an attractive approach for monitoring these emissions but have limitations from instrument precision, pixel resolution, and measurement frequency. Dense observations will soon be available in both low-Earth and geostationary orbits, but the extent to which they can provide fine-scale information on methane sources has yet to be explored. Here we present an observation system simulation experiment (OSSE) to assess the capabilities of different satellite observing system configurations. We conduct a 1-week WRF-STILT simulation to generate methane column footprints at 1.3 × 1.3 km2 spatial resolution and hourly temporal resolution over a 290 × 235 km2 domain in the Barnett Shale, a major oil and gas field in Texas with a large number of point sources. We sub-sample these footprints to match the observing characteristics of the recently launched TROPOMI instrument (7 × 7 km2 pixels, 11 ppb precision, daily frequency), the planned GeoCARB instrument (2.7 × 3.0 km2 pixels, 4 ppb precision, nominal twice-daily frequency), and other proposed observing configurations. The information content of the various observing systems is evaluated using the Fisher information matrix and its eigenvalues. We find that a week of TROPOMI observations should provide information on temporally invariant emissions at ˜ 30 km spatial resolution. GeoCARB should provide information available on temporally invariant emissions ˜ 2-7 km spatial resolution depending on sampling frequency (hourly to daily). Improvements to the instrument precision yield greater increases in information content than improved sampling frequency. A precision better than 6 ppb is critical for GeoCARB to achieve fine resolution of emissions. Transient emissions would be missed with either TROPOMI or GeoCARB. An aspirational high-resolution geostationary instrument with 1.3 × 1.3 km2 pixel resolution, hourly return time, and 1 ppb precision would effectively constrain the temporally invariant emissions in the Barnett Shale at the kilometer scale and provide some information on hourly variability of sources.

Predator-guided sampling reveals biotic structure in the bathypelagic.

PubMed

Benoit-Bird, Kelly J; Southall, Brandon L; Moline, Mark A

2016-02-24

We targeted a habitat used differentially by deep-diving, air-breathing predators to empirically sample their prey's distributions off southern California. Fine-scale measurements of the spatial variability of potential prey animals from the surface to 1,200 m were obtained using conventional fisheries echosounders aboard a surface ship and uniquely integrated into a deep-diving autonomous vehicle. Significant spatial variability in the size, composition, total biomass, and spatial organization of biota was evident over all spatial scales examined and was consistent with the general distribution patterns of foraging Cuvier's beaked whales (Ziphius cavirostris) observed in separate studies. Striking differences found in prey characteristics between regions at depth, however, did not reflect differences observed in surface layers. These differences in deep pelagic structure horizontally and relative to surface structure, absent clear physical differences, change our long-held views of this habitat as uniform. The revelation that animals deep in the water column are so spatially heterogeneous at scales from 10 m to 50 km critically affects our understanding of the processes driving predator-prey interactions, energy transfer, biogeochemical cycling, and other ecological processes in the deep sea, and the connections between the productive surface mixed layer and the deep-water column. © 2016 The Author(s).

Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

2015-03-01

A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.

Multiscale landscape genomic models to detect signatures of selection in the alpine plant Biscutella laevigata.

PubMed

Leempoel, Kevin; Parisod, Christian; Geiser, Céline; Joost, Stéphane

2018-02-01

Plant species are known to adapt locally to their environment, particularly in mountainous areas where conditions can vary drastically over short distances. The climate of such landscapes being largely influenced by topography, using fine-scale models to evaluate environmental heterogeneity may help detecting adaptation to micro-habitats. Here, we applied a multiscale landscape genomic approach to detect evidence of local adaptation in the alpine plant Biscutella laevigata . The two gene pools identified, experiencing limited gene flow along a 1-km ridge, were different in regard to several habitat features derived from a very high resolution (VHR) digital elevation model (DEM). A correlative approach detected signatures of selection along environmental gradients such as altitude, wind exposure, and solar radiation, indicating adaptive pressures likely driven by fine-scale topography. Using a large panel of DEM-derived variables as ecologically relevant proxies, our results highlighted the critical role of spatial resolution. These high-resolution multiscale variables indeed indicate that the robustness of associations between genetic loci and environmental features depends on spatial parameters that are poorly documented. We argue that the scale issue is critical in landscape genomics and that multiscale ecological variables are key to improve our understanding of local adaptation in highly heterogeneous landscapes.

A Study on the Effects of Spatial Scale on Snow Process in Hyper-Resolution Hydrological Modelling over Mountainous Areas

NASA Astrophysics Data System (ADS)

Garousi Nejad, I.; He, S.; Tang, Q.; Ogden, F. L.; Steinke, R. C.; Frazier, N.; Tarboton, D. G.; Ohara, N.; Lin, H.

2017-12-01

Spatial scale is one of the main considerations in hydrological modeling of snowmelt in mountainous areas. The size of model elements controls the degree to which variability can be explicitly represented versus what needs to be parameterized using effective properties such as averages or other subgrid variability parameterizations that may degrade the quality of model simulations. For snowmelt modeling terrain parameters such as slope, aspect, vegetation and elevation play an important role in the timing and quantity of snowmelt that serves as an input to hydrologic runoff generation processes. In general, higher resolution enhances the accuracy of the simulation since fine meshes represent and preserve the spatial variability of atmospheric and surface characteristics better than coarse resolution. However, this increases computational cost and there may be a scale beyond which the model response does not improve due to diminishing sensitivity to variability and irreducible uncertainty associated with the spatial interpolation of inputs. This paper examines the influence of spatial resolution on the snowmelt process using simulations of and data from the Animas River watershed, an alpine mountainous area in Colorado, USA, using an unstructured distributed physically based hydrological model developed for a parallel computing environment, ADHydro. Five spatial resolutions (30 m, 100 m, 250 m, 500 m, and 1 km) were used to investigate the variations in hydrologic response. This study demonstrated the importance of choosing the appropriate spatial scale in the implementation of ADHydro to obtain a balance between representing spatial variability and the computational cost. According to the results, variation in the input variables and parameters due to using different spatial resolution resulted in changes in the obtained hydrological variables, especially snowmelt, both at the basin-scale and distributed across the model mesh.

Patterns in Temporal Variability of Temperature, Oxygen and pH along an Environmental Gradient in a Coral Reef

PubMed Central

Guadayol, Òscar; Silbiger, Nyssa J.; Donahue, Megan J.; Thomas, Florence I. M.

2014-01-01

Spatial and temporal environmental variability are important drivers of ecological processes at all scales. As new tools allow the in situ exploration of individual responses to fluctuations, ecologically meaningful ways of characterizing environmental variability at organism scales are needed. We investigated the fine-scale spatial heterogeneity of high-frequency temporal variability in temperature, dissolved oxygen concentration, and pH experienced by benthic organisms in a shallow coastal coral reef. We used a spatio-temporal sampling design, consisting of 21 short-term time-series located along a reef flat-to-reef slope transect, coupled to a long-term station monitoring water column changes. Spectral analyses revealed sharp gradients in variance decomposed by frequency, as well as differences between physically-driven and biologically-reactive parameters. These results highlight the importance of environmental variance at organismal scales and present a new sampling scheme for exploring this variability in situ. PMID:24416364

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mehrez, Loujaine; Ghanem, Roger; Aitharaju, Venkat

Design of non-crimp fabric (NCF) composites entails major challenges pertaining to (1) the complex fine-scale morphology of the constituents, (2) the manufacturing-produced inconsistency of this morphology spatially, and thus (3) the ability to build reliable, robust, and efficient computational surrogate models to account for this complex nature. Traditional approaches to construct computational surrogate models have been to average over the fluctuations of the material properties at different scale lengths. This fails to account for the fine-scale features and fluctuations in morphology, material properties of the constituents, as well as fine-scale phenomena such as damage and cracks. In addition, it failsmore » to accurately predict the scatter in macroscopic properties, which is vital to the design process and behavior prediction. In this work, funded in part by the Department of Energy, we present an approach for addressing these challenges by relying on polynomial chaos representations of both input parameters and material properties at different scales. Moreover, we emphasize the efficiency and robustness of integrating the polynomial chaos expansion with multiscale tools to perform multiscale assimilation, characterization, propagation, and prediction, all of which are necessary to construct the data-driven surrogate models required to design under the uncertainty of composites. These data-driven constructions provide an accurate map from parameters (and their uncertainties) at all scales and the system-level behavior relevant for design. While this perspective is quite general and applicable to all multiscale systems, NCF composites present a particular hierarchy of scales that permits the efficient implementation of these concepts.« less

The Relationship Between Fusion, Suppression, and Diplopia in Normal and Amblyopic Vision.

PubMed

Spiegel, Daniel P; Baldwin, Alex S; Hess, Robert F

2016-10-01

Single vision occurs through a combination of fusion and suppression. When neither mechanism takes place, we experience diplopia. Under normal viewing conditions, the perceptual state depends on the spatial scale and interocular disparity. The purpose of this study was to examine the three perceptual states in human participants with normal and amblyopic vision. Participants viewed two dichoptically separated horizontal blurred edges with an opposite tilt (2.35°) and indicated their binocular percept: "one flat edge," "one tilted edge," or "two edges." The edges varied with scale (fine 4 min arc and coarse 32 min arc), disparity, and interocular contrast. We investigated how the binocular interactions vary in amblyopic (visual acuity [VA] > 0.2 logMAR, n = 4) and normal vision (VA ≤ 0 logMAR, n = 4) under interocular variations in stimulus contrast and luminance. In amblyopia, despite the established sensory dominance of the fellow eye, fusion prevails at the coarse scale and small disparities (75%). We also show that increasing the relative contrast to the amblyopic eye enhances the probability of fusion at the fine scale (from 18% to 38%), and leads to a reversal of the sensory dominance at coarse scale. In normal vision we found that interocular luminance imbalances disturbed binocular combination only at the fine scale in a way similar to that seen in amblyopia. Our results build upon the growing evidence that the amblyopic visual system is binocular and further show that the suppressive mechanisms rendering the amblyopic system functionally monocular are scale dependent.

Fine-scale spatial climate variation and drought mediate the likelihood of reburning

Treesearch

Sean A. Parks; Marc‐Andre Parisien; Carol Miller; Lisa M. Holsinger; Larry Scott Baggett

2018-01-01

In many forested ecosystems, it is increasingly recognized that the probability of burning is substantially reduced within the footprint of previously burned areas. This self‐limiting effect of wildland fire is considered a fundamental emergent property of ecosystems and is partly responsible for structuring landscape heterogeneity (i.e., mosaics of different age...

Fine-scale genetic structure and social organization in female white-tailed deer

Treesearch

Christopher E. Comer; John C. Kilgo; Gino J. D' Angelo; Travis C. Glenn; Karl V. Miller

2005-01-01

Social behavior of white-tailed deer (Odocoileus virginianus) can have important management implications. The formation of matrilineal social groups among female deer has been documented and management strategies have been proposed based on this well-developed social structure. Using radiocollared (n = 17) and hunter or vehicle-killed (n = 21) does, we examined spatial...

Findings and Challenges in Fine-Resolution Large-Scale Hydrological Modeling

NASA Astrophysics Data System (ADS)

Her, Y. G.

2017-12-01

Fine-resolution large-scale (FL) modeling can provide the overall picture of the hydrological cycle and transport while taking into account unique local conditions in the simulation. It can also help develop water resources management plans consistent across spatial scales by describing the spatial consequences of decisions and hydrological events extensively. FL modeling is expected to be common in the near future as global-scale remotely sensed data are emerging, and computing resources have been advanced rapidly. There are several spatially distributed models available for hydrological analyses. Some of them rely on numerical methods such as finite difference/element methods (FDM/FEM), which require excessive computing resources (implicit scheme) to manipulate large matrices or small simulation time intervals (explicit scheme) to maintain the stability of the solution, to describe two-dimensional overland processes. Others make unrealistic assumptions such as constant overland flow velocity to reduce the computational loads of the simulation. Thus, simulation efficiency often comes at the expense of precision and reliability in FL modeling. Here, we introduce a new FL continuous hydrological model and its application to four watersheds in different landscapes and sizes from 3.5 km2 to 2,800 km2 at the spatial resolution of 30 m on an hourly basis. The model provided acceptable accuracy statistics in reproducing hydrological observations made in the watersheds. The modeling outputs including the maps of simulated travel time, runoff depth, soil water content, and groundwater recharge, were animated, visualizing the dynamics of hydrological processes occurring in the watersheds during and between storm events. Findings and challenges were discussed in the context of modeling efficiency, accuracy, and reproducibility, which we found can be improved by employing advanced computing techniques and hydrological understandings, by using remotely sensed hydrological observations such as soil moisture and radar rainfall depth and by sharing the model and its codes in public domain, respectively.

Spatial analysis of sunshine duration by combination of satellite and station data

NASA Astrophysics Data System (ADS)

Frei, C.; Stöckli, R.; Dürr, B.

2009-09-01

Sunshine duration can exhibit rich fine scale patterns associated with special meteorological phenomena, such as fog layers and topographically triggered clouds. Networks of climate stations are mostly too coarse and poorly representative to resolve these patterns explicitly. We present a method which combines station observations with satellite-derived cloud-cover data to produce km-scale fields of sunshine duration. The method is not relying on contemporous satellite information, hence it can be applied over climatological time scales. We apply and evaluate the combination method over the territory of Switzerland. The combination method is based on Universal Kriging. First, the satellite data (a Heliosat clear sky index from MSG, extending over a 5 year preiod) is subjected to a S-mode Principal Component (PC) Analysis. Second, a set of leading PC loadings (seasonally stratified) is introduced as external drift covariates and their optimal linear combination is estimated from the station data (70 stations). Finally, the stochastic component is an autocorrelated field with an exponential variogram, estimated climatologically for each calendar month. For Switzerland the leading PCs of the clear sky index depict familiar patterns of cloud variability which are inhereted in the combination process. The resulting sunshine duration fields exhibit fine-scale structures that are physically plausible, linked to the topography and characteristic of the regional climate. These patterns could not be inferred from station data and/or topographic predictors alone. A cross-validation reveals that the combination method explains between 80-90% of the spatial variance in winter and autumn months. In spring and summer the relative performance is lower (60-75% explained spatial variance) but absolute errors are smaller. Our presentation will also discuss some results from a climatology of the derived sunshine duration fields.

Effects of topoclimatic complexity on the composition of woody plant communities.

PubMed

Oldfather, Meagan F; Britton, Matthew N; Papper, Prahlad D; Koontz, Michael J; Halbur, Michelle M; Dodge, Celeste; Flint, Alan L; Flint, Lorriane E; Ackerly, David D

2016-01-01

Topography can create substantial environmental variation at fine spatial scales. Shaped by slope, aspect, hill-position and elevation, topoclimate heterogeneity may increase ecological diversity, and act as a spatial buffer for vegetation responding to climate change. Strong links have been observed between climate heterogeneity and species diversity at broader scales, but the importance of topoclimate for woody vegetation across small spatial extents merits closer examination. We established woody vegetation monitoring plots in mixed evergreen-deciduous woodlands that spanned topoclimate gradients of a topographically heterogeneous landscape in northern California. We investigated the association between the structure of adult and regenerating size classes of woody vegetation and multidimensional topoclimate at a fine scale. We found a significant effect of topoclimate on both single-species distributions and community composition. Effects of topoclimate were evident in the regenerating size class for all dominant species (four Quercus spp., Umbellularia californica and Pseudotsuga menziesii) but only in two dominant species (Quercus agrifolia and Quercus garryana) for the adult size class. Adult abundance was correlated with water balance parameters (e.g. climatic water deficit) and recruit abundance was correlated with an interaction between the topoclimate parameters and conspecific adult abundance (likely reflecting local seed dispersal). However, in all cases, the topoclimate signal was weak. The magnitude of environmental variation across our study site may be small relative to the tolerance of long-lived woody species. Dispersal limitations, management practices and patchy disturbance regimes also may interact with topoclimate, weakening its influence on woody vegetation distributions. Our study supports the biological relevance of multidimensional topoclimate for mixed woodland communities, but highlights that this relationship might be mediated by interacting factors at local scales. Published by Oxford University Press on behalf of the Annals of Botany Company.

Effects of topoclimatic complexity on the composition of woody plant communities

PubMed Central

Oldfather, Meagan F.; Britton, Matthew N.; Papper, Prahlad D.; Koontz, Michael J.; Halbur, Michelle M.; Dodge, Celeste; Flint, Alan L.; Flint, Lorriane E.; Ackerly, David D.

2016-01-01

Topography can create substantial environmental variation at fine spatial scales. Shaped by slope, aspect, hill-position and elevation, topoclimate heterogeneity may increase ecological diversity, and act as a spatial buffer for vegetation responding to climate change. Strong links have been observed between climate heterogeneity and species diversity at broader scales, but the importance of topoclimate for woody vegetation across small spatial extents merits closer examination. We established woody vegetation monitoring plots in mixed evergreen-deciduous woodlands that spanned topoclimate gradients of a topographically heterogeneous landscape in northern California. We investigated the association between the structure of adult and regenerating size classes of woody vegetation and multidimensional topoclimate at a fine scale. We found a significant effect of topoclimate on both single-species distributions and community composition. Effects of topoclimate were evident in the regenerating size class for all dominant species (four Quercus spp., Umbellularia californica and Pseudotsuga menziesii) but only in two dominant species (Quercus agrifolia and Quercus garryana) for the adult size class. Adult abundance was correlated with water balance parameters (e.g. climatic water deficit) and recruit abundance was correlated with an interaction between the topoclimate parameters and conspecific adult abundance (likely reflecting local seed dispersal). However, in all cases, the topoclimate signal was weak. The magnitude of environmental variation across our study site may be small relative to the tolerance of long-lived woody species. Dispersal limitations, management practices and patchy disturbance regimes also may interact with topoclimate, weakening its influence on woody vegetation distributions. Our study supports the biological relevance of multidimensional topoclimate for mixed woodland communities, but highlights that this relationship might be mediated by interacting factors at local scales. PMID:27339048

A new method for estimating carbon dioxide emissions from transportation at fine spatial scales

PubMed Central

Shu, Yuqin; Reams, Margaret

2016-01-01

Detailed estimates of carbon dioxide (CO2) emissions at fine spatial scales are useful to both modelers and decision makers who are faced with the problem of global warming and climate change. Globally, transport related emissions of carbon dioxide are growing. This letter presents a new method based on the volume-preserving principle in the areal interpolation literature to disaggregate transportation-related CO2 emission estimates from the county-level scale to a 1 km2 grid scale. The proposed volume-preserving interpolation (VPI) method, together with the distance-decay principle, were used to derive emission weights for each grid based on its proximity to highways, roads, railroads, waterways, and airports. The total CO2 emission value summed from the grids within a county is made to be equal to the original county-level estimate, thus enforcing the volume-preserving property. The method was applied to downscale the transportation-related CO2 emission values by county (i.e. parish) for the state of Louisiana into 1 km2 grids. The results reveal a more realistic spatial pattern of CO2 emission from transportation, which can be used to identify the emission ‘hot spots’. Of the four highest transportation-related CO2 emission hotspots in Louisiana, high-emission grids literally covered the entire East Baton Rouge Parish and Orleans Parish, whereas CO2 emission in Jefferson Parish (New Orleans suburb) and Caddo Parish (city of Shreveport) were more unevenly distributed. We argue that the new method is sound in principle, flexible in practice, and the resultant estimates are more accurate than previous gridding approaches. PMID:26997973

High Resolution Insights into Snow Distribution Provided by Drone Photogrammetry

NASA Astrophysics Data System (ADS)

Redpath, T.; Sirguey, P. J.; Cullen, N. J.; Fitzsimons, S.

2017-12-01

Dynamic in time and space, New Zealand's seasonal snow is largely confined to remote alpine areas, complicating ongoing in situ measurement and characterisation. Improved understanding and modeling of the seasonal snowpack requires fine scale resolution of snow distribution and spatial variability. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial and temporal variability of snow depth and water equivalent in a New Zealand alpine catchment is assessed in the Pisa Range, Central Otago. This approach yielded orthophotomosaics and digital surface models (DSM) at 0.05 and 0.15 m spatial resolution, respectively. An autumn reference DSM allowed mapping of winter (02/08/2016) and spring (10/09/2016) snow depth at 0.15 m spatial resolution, via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by comparison of snow-free regions of the spring and autumn DSMs, while accuracy of RPAS retrieved snow depth was assessed with 86 in situ snow probe measurements. Results show a mean vertical residual of 0.024 m between DSMs acquired in autumn and spring. This residual approximated a Laplace distribution, reflecting the influence of large outliers on the small overall bias. Propagation of errors associated with successive DSMs saw snow depth mapped with an accuracy of ± 0.09 m (95% c.l.). Comparing RPAS and in situ snow depth measurements revealed the influence of geo-location uncertainty and interactions between vegetation and the snowpack on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine scale spatial variability. Despite limitations accompanying RPAS photogrammetry, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological basin ( 0.5 km2), at high resolution. Resolving snowpack features associated with re-distribution and preferential accumulation and ablation, snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. Such data may enhance understanding of physical processes controlling spatial and temporal distribution of seasonal snow, and their relative importance at varying spatial and temporal scales.

Disentangling fine motor skills' relations to academic achievement: the relative contributions of visual-spatial integration and visual-motor coordination.

PubMed

Carlson, Abby G; Rowe, Ellen; Curby, Timothy W

2013-01-01

Recent research has established a connection between children's fine motor skills and their academic performance. Previous research has focused on fine motor skills measured prior to elementary school, while the present sample included children ages 5-18 years old, making it possible to examine whether this link remains relevant throughout childhood and adolescence. Furthermore, the majority of research linking fine motor skills and academic achievement has not determined which specific components of fine motor skill are driving this relation. The few studies that have looked at associations of separate fine motor tasks with achievement suggest that copying tasks that tap visual-spatial integration skills are most closely related to achievement. The present study examined two separate elements of fine motor skills--visual-motor coordination and visual-spatial integration--and their associations with various measures of academic achievement. Visual-motor coordination was measured using tracing tasks, while visual-spatial integration was measured using copy-a-figure tasks. After controlling for gender, socioeconomic status, IQ, and visual-motor coordination, and visual-spatial integration explained significant variance in children's math and written expression achievement. Knowing that visual-spatial integration skills are associated with these two achievement domains suggests potential avenues for targeted math and writing interventions for children of all ages.

Fine-scale genetic response to landscape change in a gliding mammal.

PubMed

Goldingay, Ross L; Harrisson, Katherine A; Taylor, Andrea C; Ball, Tina M; Sharpe, David J; Taylor, Brendan D

2013-01-01

Understanding how populations respond to habitat loss is central to conserving biodiversity. Population genetic approaches enable the identification of the symptoms of population disruption in advance of population collapse. However, the spatio-temporal scales at which population disruption occurs are still too poorly known to effectively conserve biodiversity in the face of human-induced landscape change. We employed microsatellite analysis to examine genetic structure and diversity over small spatial (mostly 1-50 km) and temporal scales (20-50 years) in the squirrel glider (Petaurus norfolcensis), a gliding mammal that is commonly subjected to a loss of habitat connectivity. We identified genetically differentiated local populations over distances as little as 3 km and within 30 years of landscape change. Genetically isolated local populations experienced the loss of genetic diversity, and significantly increased mean relatedness, which suggests increased inbreeding. Where tree cover remained, genetic differentiation was less evident. This pattern was repeated in two landscapes located 750 km apart. These results lend support to other recent studies that suggest the loss of habitat connectivity can produce fine-scale population genetic change in a range of taxa. This gives rise to the prediction that many other vertebrates will experience similar genetic changes. Our results suggest the future collapse of local populations of this gliding mammal is likely unless habitat connectivity is maintained or restored. Landscape management must occur on a fine-scale to avert the erosion of biodiversity.

Regional Simulations of Stratospheric Lofting of Smoke Plumes

NASA Astrophysics Data System (ADS)

Stenchikov, G. L.; Fromm, M.; Robock, A.

2006-12-01

The lifetime and spatial distribution of sooty aerosols from multiple fires that would cause major climate impact were debated in studies of climatic and environmental consequences of a nuclear war in the 1980s. The Kuwait oil fires in 1991 did not show a cumulative effect of multiple smoke plumes on large-scale circulation systems and smoke was mainly dispersed in the middle troposphere. However, recent observations show that smoke from large forest fires can be directly injected into the lower stratosphere by strong pyro-convective storms. Smoke plumes in the upper troposphere can be partially mixed into the lower stratosphere because of the same heating and lofting effect that was simulated in large-scale nuclear winter simulations with interactive aerosols. However nuclear winter simulations were conducted using climate models with grid spacing of more than 100 km, which do not account for the fine-scale dynamic processes. Therefore in this study we conduct fine-scale regional simulations of the aerosol plume using the Regional Atmospheric Modeling System (RAMS) mesoscale model which was modified to account for radiatively interactive tracers. To resolve fine-scale dynamic processes we use horizontal grid spacing of 25 km and 60 vertical layers, and initiate simulations with the NCEP reanalysis fields. We find that dense aerosol layers could be lofted from 1 to a few km per day, but this critically depends on the optical depth of aerosol layer, single scatter albedo, and how fast the plume is being diluted. Kuwaiti plumes from different small-area fires reached only 5-6 km altitude and were probably diffused and diluted in the lower and middle troposphere. A plume of 100 km spatial scale initially developed in the upper troposphere tends to penetrate into the stratosphere. Short-term cloud resolving simulations of such a plume show that aerosol heating intensifies small-scale motions that tend to mix smoke polluted air into the lower stratosphere. Regional simulations allow us to more accurately estimate the rate of lifting and spreading of aerosol clouds. But they do not reveal any dynamic processes that could prevent heating and lofting of absorbing aerosols.

Identification of fine scale and landscape scale drivers of urban aboveground carbon stocks using high-resolution modeling and mapping.

PubMed

Mitchell, Matthew G E; Johansen, Kasper; Maron, Martine; McAlpine, Clive A; Wu, Dan; Rhodes, Jonathan R

2018-05-01

Urban areas are sources of land use change and CO 2 emissions that contribute to global climate change. Despite this, assessments of urban vegetation carbon stocks often fail to identify important landscape-scale drivers of variation in urban carbon, especially the potential effects of landscape structure variables at different spatial scales. We combined field measurements with Light Detection And Ranging (LiDAR) data to build high-resolution models of woody plant aboveground carbon across the urban portion of Brisbane, Australia, and then identified landscape scale drivers of these carbon stocks. First, we used LiDAR data to quantify the extent and vertical structure of vegetation across the city at high resolution (5×5m). Next, we paired this data with aboveground carbon measurements at 219 sites to create boosted regression tree models and map aboveground carbon across the city. We then used these maps to determine how spatial variation in land cover/land use and landscape structure affects these carbon stocks. Foliage densities above 5m height, tree canopy height, and the presence of ground openings had the strongest relationships with aboveground carbon. Using these fine-scale relationships, we estimate that 2.2±0.4 TgC are stored aboveground in the urban portion of Brisbane, with mean densities of 32.6±5.8MgCha -1 calculated across the entire urban land area, and 110.9±19.7MgCha -1 calculated within treed areas. Predicted carbon densities within treed areas showed strong positive relationships with the proportion of surrounding tree cover and how clumped that tree cover was at both 1km 2 and 1ha resolutions. Our models predict that even dense urban areas with low tree cover can have high carbon densities at fine scales. We conclude that actions and policies aimed at increasing urban carbon should focus on those areas where urban tree cover is most fragmented. Copyright © 2017 Elsevier B.V. All rights reserved.

Scaling uncertainties in estimating canopy foliar maintenance respiration for black spruce ecosystems in Alaska

USGS Publications Warehouse

Zhang, X.; McGuire, A.D.; Ruess, Roger W.

2006-01-01

A major challenge confronting the scientific community is to understand both patterns of and controls over spatial and temporal variability of carbon exchange between boreal forest ecosystems and the atmosphere. An understanding of the sources of variability of carbon processes at fine scales and how these contribute to uncertainties in estimating carbon fluxes is relevant to representing these processes at coarse scales. To explore some of the challenges and uncertainties in estimating carbon fluxes at fine to coarse scales, we conducted a modeling analysis of canopy foliar maintenance respiration for black spruce ecosystems of Alaska by scaling empirical hourly models of foliar maintenance respiration (Rm) to estimate canopy foliar Rm for individual stands. We used variation in foliar N concentration among stands to develop hourly stand-specific models and then developed an hourly pooled model. An uncertainty analysis identified that the most important parameter affecting estimates of canopy foliar Rm was one that describes R m at 0??C per g N, which explained more than 55% of variance in annual estimates of canopy foliar Rm. The comparison of simulated annual canopy foliar Rm identified significant differences between stand-specific and pooled models for each stand. This result indicates that control over foliar N concentration should be considered in models that estimate canopy foliar Rm of black spruce stands across the landscape. In this study, we also temporally scaled the hourly stand-level models to estimate canopy foliar Rm of black spruce stands using mean monthly temperature data. Comparisons of monthly Rm between the hourly and monthly versions of the models indicated that there was very little difference between the estimates of hourly and monthly models, suggesting that hourly models can be aggregated to use monthly input data with little loss of precision. We conclude that uncertainties in the use of a coarse-scale model for estimating canopy foliar Rm at regional scales depend on uncertainties in representing needle-level respiration and on uncertainties in representing the spatial variability of canopy foliar N across a region. The development of spatial data sets of canopy foliar N represents a major challenge in estimating canopy foliar maintenance respiration at regional scales. ?? Springer 2006.

Correlated randomness: Some examples of exotic statistical physics

NASA Astrophysics Data System (ADS)

Stanley, H. Eugene

2005-05-01

One challenge of biology, medicine, and economics is that the systems treated by these sciences have no perfect metronome in time and no perfect spatial architecture -- crystalline or otherwise. Nonetheless, as if by magic, out of nothing but randomness one finds remarkably fine-tuned processes in time and remarkably fine-tuned structures in space. To understand this `miracle', one might consider placing aside the human tendency to see the universe as a machine. Instead, one might address the challenge of uncovering how, through randomness (albeit, as we shall see, strongly correlated randomness), one can arrive at many spatial and temporal patterns in biology, medicine, and economics. Inspired by principles developed by statistical physics over the past 50 years -- scale invariance and universality -- we review some recent applications of correlated randomness to fields that might startle Boltzmann if he were alive today.