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USGS2016

Inferring social structure and its drivers from refuge use in the desert tortoise, a relatively solitary species

For several species, refuges (such as burrows, dens, roosts, nests) are an essential resource for protection from predators and extreme environmental conditions. Refuges also serve as focal sites for social interactions, including mating, courtship, and aggression. Knowledge of refuge use patterns can therefore provide information about social structure, mating, and foraging success, as well as the robustness and health of wildlife populations, especially for species considered to be relatively solitary. In this study, we construct networks of burrow use to infer social associations in a threatened wildlife species typically considered solitary—the desert tortoise. We show that tortoise social networks are significantly different than null networks of random associations, and have moderate spatial constraints. We next use statistical models to identify major mechanisms behind individual-level variation in tortoise burrow use, popularity of burrows in desert tortoise habitat, and test for stressor-driven changes in refuge use patterns. We show that seasonal variation has a strong impact on tortoise burrow switching behavior. On the other hand, burrow age and topographical condition influence the number of tortoises visiting a burrow in desert tortoise habitat. Of three major population stressors affecting this species (translocation, drought, disease), translocation alters tortoise burrow switching behavior, with translocated animals visiting fewer unique burrows than residents. In a species that is not social, our study highlights the importance of leveraging refuge use behavior to study the presence of and mechanisms behind non-random social structure and individual-level variation. Our analysis of the impact of stressors on refuge-based social structure further emphasizes the potential of this method to detect environmental or anthropogenic disturbances.

Behavioral Ecology and Sociobiology↗

USGS2017

Atmospheric deposition to forests in the eastern USA

Atmospheric mercury (Hg) deposition to forests is important because half of the land cover in the eastern USA is forest. Mercury was measured in autumn litterfall and weekly precipitation samples at a total of 27 National Atmospheric Deposition Program (NADP) monitoring sites in deciduous and mixed deciduous-coniferous forests in 16 states in the eastern USA during 2007–2014. These simultaneous, uniform, repeated, annual measurements of forest Hg include the broadest area and longest time frame to date. The autumn litterfall-Hg concentrations and litterfall mass at the study sites each year were combined with annual precipitation-Hg data. Rates of litterfall-Hg deposition were higher than or equal to precipitation-Hg deposition rates in 70% of the annual data, which indicates a substantial contribution from litterfall to total atmospheric-Hg deposition. Annual litterfall-Hg deposition in this study had a median of 11.7 μg per square meter per year (μg/m 2 /yr) and ranged from 2.2 to 23.4 μg/m 2 /yr. It closely matched modeled dry-Hg deposition, based on land cover at selected NADP Hg-monitoring sites. Mean annual atmospheric-Hg deposition at forest study sites exhibited a spatial pattern partly explained by statistical differences among five forest-cover types and related to the mapped density of Hg emissions. Forest canopies apparently recorded changes in atmospheric-Hg concentrations over time because litterfall-Hg concentrations decreased year to year and litterfall-Hg concentrations were significantly higher in 2007–2009 than in 2012–2014. These findings reinforce reported decreases in Hg emissions and atmospheric elemental-Hg concentrations during this same time period. Methylmercury (MeHg) was detected in all litterfall samples at all sites, compared with MeHg detections in less than half the precipitation samples at selected sites during the study. These results indicate MeHg in litterfall is a pathway into the terrestrial food web where it can accumulate in the prey of songbirds, bats, and raptors.

Environmental Pollution↗

USGS2022

Validation of a vulnerability index of exposure to chemicals of emerging concern in surface water and sediment of Great Lakes tributaries of the United States

Widespread occurrence of emerging contaminants in Great Lakes tributaries led to the development and publication of a vulnerability index (VI) to assess the potential exposure of aquatic communities to chemicals of emerging concern (CEC) in the Great Lakes basin. The robust nature of the VI was tested to evaluate the underlying statistical model and expand the spatial domain of the index. Data collected at 131 new sampling sites (Test 1) and published data from independent studies (Test 2) were used to test the model predictions. Test 1 water and sediment samples were analyzed for the same classes of CEC chemicals and compared to the predictions for the original VI. Concentrations and numbers of unique CECs detected in water and sediment samples were similar between the original data and the two test datasets, although CECs tended to have higher detection frequencies in the original dataset compared to the Test 1 and Test 2 datasets. For example, 69 CECs were detected in ≥30% of water samples in the original dataset compared with 17 CECs in the Test 1 data and 59 in the Test 2 data. Predicted vulnerability for test sites agreed with actual vulnerability 64% of the time for water and 71% of the time for sediment. Agreement percentage results were greater when individual sites were grouped by river, with 82% agreement between predictions and actual vulnerability for water and 78% agreement for sediment. For the entire dataset, the VI ranks correlated with an independent estimate of potential biological impact. Agreement percentage was the greatest for low or high vulnerability index values but highly variable for sites that are classified as having medium vulnerability. Despite the underlying variability, there is a significant correlation (R 2 = 0.26; p < 0.01) between the VI ranking of tributaries and the independent ranking of potential negative biological impact.

Illinois, Indiana, Michigan, Minnesota, New York, ↗

USGS2000

Using multi-scale sampling and spatial cross-correlation to investigate patterns of plant species richness

Land managers need better techniques to assess exoticplant invasions. We used the cross-correlationstatistic, I YZ , to test for the presence ofspatial cross-correlation between pair-wisecombinations of soil characteristics, topographicvariables, plant species richness, and cover ofvascular plants in a 754 ha study site in RockyMountain National Park, Colorado, U.S.A. Using 25 largeplots (1000 m 2 ) in five vegetation types, 8 of 12variables showed significant spatial cross-correlationwith at least one other variable, while 6 of 12variables showed significant spatial auto-correlation. Elevation and slope showed significant spatialcross-correlation with all variables except percentcover of native and exotic species. Percent cover ofnative species had significant spatialcross-correlations with soil variables, but not withexotic species. This was probably because of thepatchy distributions of vegetation types in the studyarea. At a finer resolution, using data from ten1 m 2 subplots within each of the 1000 m 2 plots, allvariables showed significant spatial auto- andcross-correlation. Large-plot sampling was moreaffected by topographic factors than speciesdistribution patterns, while with finer resolutionsampling, the opposite was true. However, thestatistically and biologically significant spatialcorrelation of native and exotic species could only bedetected with finer resolution sampling. We foundexotic plant species invading areas with high nativeplant richness and cover, and in fertile soils high innitrogen, silt, and clay. Spatial auto- andcross-correlation statistics, along with theintegration of remotely sensed data and geographicinformation systems, are powerful new tools forevaluating the patterns and distribution of native andexotic plant species in relation to landscape structure.

Environmental Monitoring and Assessment↗

USGS2008

Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California

Aim: Several studies have found that more accurate predictive models of species' occurrences can be developed for rarer species; however, one recent study found the relationship between range size and model performance to be an artefact of sample prevalence, that is, the proportion of presence versus absence observations in the data used to train the model. We examined the effect of model type, species rarity class, species' survey frequency, detectability and manipulated sample prevalence on the accuracy of distribution models developed for 30 reptile and amphibian species. Location: Coastal southern California, USA. Methods: Classification trees, generalized additive models and generalized linear models were developed using species presence and absence data from 420 locations. Model performance was measured using sensitivity, specificity and the area under the curve (AUC) of the receiver-operating characteristic (ROC) plot based on twofold cross-validation, or on bootstrapping. Predictors included climate, terrain, soil and vegetation variables. Species were assigned to rarity classes by experts. The data were sampled to generate subsets with varying ratios of presences and absences to test for the effect of sample prevalence. Join count statistics were used to characterize spatial dependence in the prediction errors. Results: Species in classes with higher rarity were more accurately predicted than common species, and this effect was independent of sample prevalence. Although positive spatial autocorrelation remained in the prediction errors, it was weaker than was observed in the species occurrence data. The differences in accuracy among model types were slight. Main conclusions: Using a variety of modelling methods, more accurate species distribution models were developed for rarer than for more common species. This was presumably because it is difficult to discriminate suitable from unsuitable habitat for habitat generalists, and not as an artefact of the effect of sample prevalence on model estimation. ?? 2008 The Authors.

Diversity and Distributions↗

USGS2012

Water-quality assessment and macroinvertebrate data for the Upper Yampa River watershed, Colorado, 1975 through 2009

A study was initiated in 2009 by the U.S. Geological Survey (USGS), in cooperation with Routt County, the Colorado Water Conservation Board, and the City of Steamboat Springs, to compile and analyze historic water-quality data and assess water-quality conditions in the Upper Yampa River watershed (UYRW) in northwestern Colorado. Water-quality data for samples collected by federal, state, and local agencies for various periods from 1975 through 2009 were compiled and assessed for streams, lakes, reservoirs, and groundwater in the UYRW, including the Elkhead Creek subwatershed and the Yampa River watershed that is upstream from Elkhead Creek. For selected physical-property and chemical-constituent data for samples collected from surface-water sites and groundwater wells in the UYRW, this report: (1) characterizes available data through statistical summaries, (2) analyzes the spatial and temporal distribution of water-quality conditions, (3) identifies temporal trends in water quality, where possible, (4) provides comparisons to federal and state water-quality standards and recommendations, and (5) identifies factors affecting the quality of water. In addition, the availability and characteristics of macroinvertebrate data collected in the UYRW are described.

Colorado↗

USGS2014

Simulating future residential property losses from wildfire in Flathead County, Montana

Wildfire damages to private residences in the United States and elsewhere have increased as a result of expansion of the wildland-urban interface (WUI) and other factors. Understanding this unwelcome trend requires analytical frameworks that simulate how various interacting social, economic, and biophysical factors influence those damages. A methodological framework is developed for simulating expected residential property losses from wildfire [E(RLW)], which is a probabilistic monetary measure of wildfire risk to residential properties in the WUI. E(RLW) is simulated for Flathead County, Montana for five, 10-year subperiods covering the period 2010-2059, under various assumptions about future climate change, economic growth, land use policy, and forest management. Results show statistically significant increases in the spatial extent of WUI properties, the number of residential structures at risk from wildfire, and E(RLW) over the 50-year evaluation period for both the county and smaller subareas (i.e., neighborhoods and parcels). The E(RLW) simulation framework presented here advances the field of wildfire risk assessment by providing a finer-scale tool that incorporates a set of dynamic, interacting processes. The framework can be applied using other scenarios for climate change, economic growth, land use policy, and forest management, and in other areas.

Montana↗

USGS1998

Resource management of forested wetlands: Hurricane impact and recovery mapped by combining Landsat TM and NOAA AVHRR data

A temporal suite of NOAA Advanced Very High Resolution Radiometer (AVHRR) images, transformed into a vegetation biomass indicator, was combined with a single-date classification of Landsat Thematic Mapper (TM) to map the association between forest type and hurricane effects. Hurricane effects to the forested wetland included an abrupt decrease and subsequent increase in biomass. The decrease was associated with hurricane impact and the increase with an abnormal bloom in vegetation in the impacted areas. Impact severity was estimated by differencing the biomass maps before and immediately (3 days) after the hurricane. Recovery magnitude was estimated by differencing the biomass maps from immediately (3 days) after and shortly (1.5 months) after the hurricane. Regions of dominantly hardwoods suffering high to moderate impacts and of dominantly cypress-tupelos suffering low impacts identified in this study corroborated findings of earlier studies. Conversely, areas not reported in previous studies as affected were identified, and these areas showed a reverse relationship, i.e., highly impacted cypresstupelo and low or moderately impacted hardwoods. Additionally, generated proportions of hardwood, cypress-tupelo, and open (mixed) forests per each 1-km pixel (impact and recovery maps) suggest that regions containing higher percentages of cypress-tupelos were more likely to have sustained higher impacts. Visual examination of the impact map revealed a spatial covariation between increased impact magnitudes and river corridors dominated by open forest. This spatial association was corroborated by examining changes in the percentage of open forest per 1-km impact pixel; the percentage of open forest peaked at moderate to high impacts. The distribution of recovery supported the impact spatial distribution; however, the magnitudes of the two indicators of hurricane effects were not always spatially dependent. Converse to univariate statistics describing a11 forested area within the basin, higher recoveries tended to be related to higher percentages of hardwoods. Lower recoveries, on the other hand, tended to be related to forests with nearly equal percentages of hardwoods and cypress-tupelo.

Louisiana↗

USGS2018

Geomorphometry in landscape ecology: Issues of scale, physiography, and application

Topographic measures are frequently used in a variety of landscape ecology applications, in their simplest form as elevation, slope, and aspect, but increasingly more complex measures are being employed. We explore terrain metric similarity with changes in scale, both grain and extent, and examine how selecting the best measures is sensitive to changes in application. There are three types of topographic measures: 1) those that relate to orientation for approximating solar input, 2) those that capture variability in terrain configuration, and 3) those that provide metrics about landform features. Many biodiversity hotspots and predators have been found to coincide with areas of complexity, yet most complexity measures cannot differentiate between terrain steepness and uneven and broken terrain. Currently characterizing terrain in landscape-level analyses can be challenging, especially at coarser spatial resolutions but developing methods that improve landscape-level assessments include multivariate approaches and the use of neighborhood statistics. Some measures are sensitive to the spatial grain of calculation, the physiography of the landscape, and the scale of application. We show which measures have the potential to be multi-collinear, and illustrate with a case study how the selection of the best measures can change depending on the question at hand using mountain lion (Puma concolor) occurrence data. The case study showed a combination of infrequently employed metrics, such as view-shed analysis and focal statistics, outperform more commonly employed singular metrics. The use of focal statistics as a measure of topographic complexity shows promise for improving how mountain lions use terrain features.

Environment and Ecology Research↗

USGS2012

Toxicity of sediment pore water in Puget Sound (Washington, USA): a review of spatial status and temporal trends

Data from toxicity tests of the pore water extracted from Puget Sound sediments were compiled from surveys conducted from 1997 to 2009. Tests were performed on 664 samples collected throughout all of the eight monitoring regions in the Sound, an area encompassing 2,294.1 km 2 . Tests were performed with the gametes of the Pacific purple sea urchin, Strongylocentrotus purpuratus, to measure percent fertilization success as an indicator of relative sediment quality. Data were evaluated to determine the incidence, degree of response, geographic patterns, spatial extent, and temporal changes in toxicity. This is the first survey of this kind and magnitude in Puget Sound. In the initial round of surveys of the eight regions, 40 of 381 samples were toxic for an incidence of 10.5 %. Stations classified as toxic represented an estimated total of 107.1 km 2 , equivalent to 4.7 % of the total area. Percent sea urchin fertilization ranged from >100 % of the nontoxic, negative controls to 0 %. Toxicity was most prevalent and pervasive in the industrialized harbors and lowest in the deep basins. Conditions were intermediate in deep-water passages, urban bays, and rural bays. A second round of testing in four regions and three selected urban bays was completed 5–10 years following the first round. The incidence and spatial extent of toxicity decreased in two of the regions and two of the bays and increased in the other two regions and the third bay; however, only the latter change was statistically significant. Both the incidence and spatial extent of toxicity were lower in the Sound than in most other US estuaries and marine bays.

Washington↗

USGS2021

Spatial clustering of aftershocks impacts the performance of physics‐based earthquake forecasting models

I explore why physics‐based models of earthquake triggering rarely outperform statistical models in prospective testing, outside of limited spatial‐temporal windows. Pseudo‐prospective tests on suites of synthetic aftershock sequences show that a major factor is the level of unmodeled spatial clustering of the direct aftershocks triggered by the mainshock. The synthetic sequences are generated from generalized “physical” triggering models, optionally superimposed on background heterogeneity that controls the level of clustering. The statistical Epidemic Type Aftershock Sequence (ETAS) model performs relatively better the more clustered the direct aftershocks, while the true generalized “physical” model performs relatively worse. Real aftershocks appear to be sufficiently clustered to allow ETAS to perform as well as or better than physical models such as Coulomb stress triggering. A likely cause of the spatial clustering of direct aftershocks is heterogeneity of the background physical conditions, which typically is not modeled in physics‐based forecasts. This implies that the forecast performance of physical models could be substantially improved through a better understanding of the interaction between earthquake stress changes and variable background physical conditions such as stress state, fault strength, and fluid pressure.

JGR Solid Earth↗

USGS1990

Computerized stratified random site-selection approaches for design of a ground-water-quality sampling network

Computer software was written to randomly select sites for a ground-water-quality sampling network. The software uses digital cartographic techniques and subroutines from a proprietary geographic information system. The report presents the approaches, computer software, and sample applications. It is often desirable to collect ground-water-quality samples from various areas in a study region that have different values of a spatial characteristic, such as land-use or hydrogeologic setting. A stratified network can be used for testing hypotheses about relations between spatial characteristics and water quality, or for calculating statistical descriptions of water-quality data that account for variations that correspond to the spatial characteristic. In the software described, a study region is subdivided into areal subsets that have a common spatial characteristic to stratify the population into several categories from which sampling sites are selected. Different numbers of sites may be selected from each category of areal subsets. A population of potential sampling sites may be defined by either specifying a fixed population of existing sites, or by preparing an equally spaced population of potential sites. In either case, each site is identified with a single category, depending on the value of the spatial characteristic of the areal subset in which the site is located. Sites are selected from one category at a time. One of two approaches may be used to select sites. Sites may be selected randomly, or the areal subsets in the category can be grouped into cells and sites selected randomly from each cell.

Water-Resources Investigations Report↗

USGS2016

Regional effects of agricultural conservation practices on nutrient transport in the Upper Mississippi River Basin

Despite progress in the implementation of conservation practices, related improvements in water quality have been challenging to measure in larger river systems. In this paper we quantify these downstream effects by applying the empirical U.S. Geological Survey water-quality model SPARROW to investigate whether spatial differences in conservation intensity were statistically correlated with variations in nutrient loads. In contrast to other forms of water quality data analysis, the application of SPARROW controls for confounding factors such as hydrologic variability, multiple sources and environmental processes. A measure of conservation intensity was derived from the USDA-CEAP regional assessment of the Upper Mississippi River and used as an explanatory variable in a model of the Upper Midwest. The spatial pattern of conservation intensity was negatively correlated ( p = 0.003) with the total nitrogen loads in streams in the basin. Total phosphorus loads were weakly negatively correlated with conservation ( p = 0.25). Regional nitrogen reductions were estimated to range from 5 to 34% and phosphorus reductions from 1 to 10% in major river basins of the Upper Mississippi region. The statistical associations between conservation and nutrient loads are consistent with hydrological and biogeochemical processes such as denitrification. The results provide empirical evidence at the regional scale that conservation practices have had a larger statistically detectable effect on nitrogen than on phosphorus loadings in streams and rivers of the Upper Mississippi Basin.

Environmental Science & Technology↗

USGS2011

Modelling the distribution of domestic ducks in Monsoon Asia

Domestic ducks are considered to be an important reservoir of highly pathogenic avian influenza (HPAI), as shown by a number of geospatial studies in which they have been identified as a significant risk factor associated with disease presence. Despite their importance in HPAI epidemiology, their large-scale distribution in Monsoon Asia is poorly understood. In this study, we created a spatial database of domestic duck census data in Asia and used it to train statistical distribution models for domestic duck distributions at a spatial resolution of 1 km. The method was based on a modelling framework used by the Food and Agriculture Organisation to produce the Gridded Livestock of the World (GLW) database, and relies on stratified regression models between domestic duck densities and a set of agro-ecological explanatory variables. We evaluated different ways of stratifying the analysis and of combining the prediction to optimize the goodness of fit of the predictions. We found that domestic duck density could be predicted with reasonable accuracy (mean RMSE and correlation coefficient between log-transformed observed and predicted densities being 0.58 and 0.80, respectively), using a stratification based on livestock production systems. We tested the use of artificially degraded data on duck distributions in Thailand and Vietnam as training data, and compared the modelled outputs with the original high-resolution data. This showed, for these two countries at least, that these approaches could be used to accurately disaggregate provincial level (administrative level 1) statistical data to provide high resolution model distributions.

Agriculture, Ecosystems and Environment↗

USGS2018

Incorporating spatially heterogeneous infiltration capacity into hydrologic models with applications for simulating post‐wildfire debris flow initiation

Soils in post‐wildfire environments are often characterized by a low infiltration capacity with a high degree of spatial heterogeneity relative to unburned areas. Debris flows are frequently initiated by run‐off in recently burned steeplands, making it critical to develop and test methods for incorporating spatial variability in infiltration capacity into hydrologic models. We use Monte Carlo simulations of run‐off generation over a soil with a spatially heterogenous saturated hydraulic conductivity ( K s ) to derive an expression for an aerially averaged saturated hydraulic conductivity ( ) that depends on the rainfall rate, the statistical properties of K s , and the spatial correlation length scale associated with K s . The proposed method for determining is tested by simulating run‐off on synthetic topography over a wide range of spatial scales. Results provide a simplified expression for an effective saturated hydraulic conductivity that can be used to relate a distribution of small‐scale K s measurements to infiltration and run‐off generation over larger spatial scales. Finally, we use a hydrologic model based on to simulate run‐off and debris flow initiation at a recently burned catchment in the Santa Ana Mountains, CA, USA, and compare results to those obtained using an infiltration model based on the Soil Conservation Service Curve Number.

California↗

USGS2021

Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity

Climate change is impacting both the distribution and abundance of vegetation, especially in far northern latitudes. The effects of climate change are different for every plant assemblage and vary heterogeneously in both space and time. Small changes in climate could result in large vegetation responses in sensitive assemblages but weak responses in robust assemblages. But, patterns and mechanisms of sensitivity and robustness are not yet well understood, largely due to a lack of long-term measurements of climate and vegetation. Fortunately, observations are sometimes available across a broad spatial extent. We develop a novel statistical model for a multivariate response based on unknown cluster-specific effects and covariances, where cluster labels correspond to sensitivity and robustness. Our approach utilizes a prototype model for cluster membership that offers flexibility while enforcing smoothness in cluster probabilities across sites with similar characteristics. We demonstrate our approach with an application to vegetation abundance in Alaska, USA, in which we leverage the broad spatial extent of the study area as a proxy for unrecorded historical observations. In the context of the application, our approach yields interpretable site-level cluster labels associated with assemblage-level sensitivity and robustness without requiring strong a priori assumptions about the drivers of climate sensitivity.

Alaska↗

USGS2016

Spatial variation in nutrient and water color effects on lake chlorophyll at macroscales

The nutrient-water color paradigm is a framework to characterize lake trophic status by relating lake primary productivity to both nutrients and water color, the colored component of dissolved organic carbon. Total phosphorus (TP), a limiting nutrient, and water color, a strong light attenuator, influence lake chlorophyll a concentrations (CHL). But, these relationships have been shown in previous studies to be highly variable, which may be related to differences in lake and catchment geomorphology, the forms of nutrients and carbon entering the system, and lake community composition. Because many of these factors vary across space it is likely that lake nutrient and water color relationships with CHL exhibit spatial autocorrelation, such that lakes near one another have similar relationships compared to lakes further away. Including this spatial dependency in models may improve CHL predictions and clarify how well the nutrient-water color paradigm applies to lakes distributed across diverse landscape settings. However, few studies have explicitly examined spatial heterogeneity in the effects of TP and water color together on lake CHL. In this study, we examined spatial variation in TP and water color relationships with CHL in over 800 north temperate lakes using spatially-varying coefficient models (SVC), a robust statistical method that applies a Bayesian framework to explore space-varying and scale-dependent relationships. We found that TP and water color relationships were spatially autocorrelated and that allowing for these relationships to vary by individual lakes over space improved the model fit and predictive performance as compared to models that did not vary over space. The magnitudes of TP effects on CHL differed across lakes such that a 1 μg/L increase in TP resulted in increased CHL ranging from 2–24 μg/L across lake locations. Water color was not related to CHL for the majority of lakes, but there were some locations where water color had a positive effect such that a unit increase in water color resulted in a 2 μg/L increase in CHL and other locations where it had a negative effect such that a unit increase in water color resulted in a 2 μg/L decrease in CHL. In addition, the spatial scales that captured variation in TP and water color effects were different for our study lakes. Variation in TP–CHL relationships was observed at intermediate distances (~20 km) compared to variation in water color–CHL relationships that was observed at regional distances (~200 km). These results demonstrate that there are lake-to-lake differences in the effects of TP and water color on lake CHL and that this variation is spatially structured. Quantifying spatial structure in these relationships furthers our understanding of the variability in these relationships at macroscales and would improve model prediction of chlorophyll a to better meet lake management goals.

Maine, Michigan, New York, Wisconsin↗

USGS2020

A hierarchical analysis of habitat area, connectivity, and quality on amphibian diversity across spatial scales

Habitat fragmentation can alter species distributions and lead to reduced diversity at multiple scales. Yet, the literature describing fragmentation effects on biodiversity patterns is contradictory and inconclusive, possibly because most studies fail to integrate spatial scale into experimental designs and statistical analyses. As a result, it is difficult to extrapolate the effects of fragmentation to large-scaled systems in which conservation management is of immediate importance. Objectives To explore the influence of fragmentation on biodiversity across scales, we (1) estimated the effects of habitat area, connectivity, and quality at both local (i.e. community) and regional (i.e. metacommunity) scales; and (2) evaluated the direction, magnitude, and precision of these effect estimates at both spatial scales. Methods We developed a multi-region community occupancy model to analyze 13 years (2005-2017) of amphibian monitoring data within the National Capital Region, a network of U.S. National Parks. Results Overall, we found a positive effect of park size and a negative effect of isolation on species richness at the park-level (i.e. metacommunity), and generally positive effects of wetland area, connectivity, and quality on species richness at the wetland-level (i.e. community), although parameter estimates varied among species. Covariate effects were less precise, but effects sizes were larger, at the local wetland-level as compared to the larger park-level scale. Conclusions Our analysis reveals how scale can mediate interpretation of results from scientific studies, which might help explain conflicting narratives concerning the impacts of fragmentation in the published literature. Our hierarchical framework can help managers and policymakers elucidate the relevant spatial scale(s) to target conservation efforts.

Landscape Ecology↗