Geospatial species sample data (e.g., records with location information from natural history museums or annual surveys) are rarely collected optimally, yet are increasingly used for decisions concerning our biological heritage. Using computer simulations, we examined factors that could affect the performance of autologistic regression (ALR) models that predict species occurrence based on environmental variables and spatially correlated presence/absence data. We used a factorial experiment design to examine the effects of survey design, spatial contiguity, and species detection probability and applied the results of ten replications of each factorial combination to an ALR model. We used additional simulations to assess the effects of sample size and environmental data error on model performance. Predicted distribution maps were compared to simulated distribution maps, considered “truth,” and evaluated using several metrics: omission and commission error counts, residual sums of squares (RSS), and areas under receiver operating characteristic curves (AUC). Generally, model performance was better using random and stratified survey designs than when using other designs. Adaptive survey designs were an exception to this generalization under the omission error performance criterion. Surveys using rectangular quadrats, designed to emulate roadside surveys, resulted in models with better performance than those using square quadrats (using AUC, RSS, and omission error metrics) and were most similar in performance to a systematic quadrat design. Larger detection probabilities, larger sample sizes, contiguous distributions, and fewer environmental data errors generally improved model performance. Results suggest that spatially biased sample data, e.g., data collected along roads, could result in model performance near that of systematic quadrat designs even in the presence of potentially confounding factors such as contiguity of distributions, detection probability, sample size, and environmental data error.

Climate change poses major challenges for conservation and management because it alters the area, quality, and spatial distribution of habitat for natural populations. To assess species' vulnerability to climate change and target ongoing conservation investments, researchers and managers often consider the effects of projected changes in climate and land use on future habitat availability and quality and the uncertainty associated with these projections. Here, we draw on tools from hydrology and climate science to project the impact of climate change on the density of wetlands in the Prairie Pothole Region of the USA, a critical area for breeding waterfowl and other wetland-dependent species. We evaluate the potential for a trade-off in the value of conservation investments under current and future climatic conditions and consider the joint effects of climate and land use. We use an integrated set of hydrological and climatological projections that provide physically based measures of water balance under historical and projected future climatic conditions. In addition, we use historical projections derived from ten general circulation models (GCMs) as a baseline from which to assess climate change impacts, rather than historical climate data. This method isolates the impact of greenhouse gas emissions and ensures that modeling errors are incorporated into the baseline rather than attributed to climate change. Our work shows that, on average, densities of wetlands (here defined as wetland basins holding water) are projected to decline across the U.S. Prairie Pothole Region, but that GCMs differ in both the magnitude and the direction of projected impacts. However, we found little evidence for a shift in the locations expected to provide the highest wetland densities under current vs. projected climatic conditions. This result was robust to the inclusion of projected changes in land use under climate change. We suggest that targeting conservation towards wetland complexes containing both small and relatively large wetland basins, which is an ongoing conservation strategy, may also act to hedge against uncertainty in the effects of climate change.

Aim A raw count of the species encountered across surveys usually underestimates species richness. Statistical estimators are often less biased. Nonparametric estimators of species richness are widely considered the least biased, but no particular estimator has consistently performed best. This is partly a function of estimators responding differently to assemblage-level factors and survey design parameters. Our objective was to evaluate the performance of raw counts and nonparametric estimators of species richness across various assemblages and with different survey designs.Location We used both simulated and published field data. MethodsWe evaluated the bias, precision and accuracy of raw counts and 13 nonparametric estimators using simulations that systematically varied assemblage characteristics (number of species, species abundance distribution, total number of individuals, spatial configuration of individuals and species detection probability), sampling effort and survey design. Results informed the development of an estimator selection framework that we evaluated with field data.Results When averaged across assemblages, most nonparametric estimators were less negatively biased than a raw count. Estimators based on the similarity of repeated subsets of surveys were most accurate and their accumulation curves appeared to reach asymptotes fastest. Number of species, species abundance distribution and effort had the largest effects on performance, ultimately by affecting the proportion of the species pool contained in a sample. Our estimator selection framework showed promising results when applied to field data.Main conclusions A raw count of the number of species in an area is far from the best estimate of true species richness. Nonparametric estimators are less biased. Newer largely unused, estimators perform better than more well known and longer established counterparts under certain conditions. Given that there is generally a trade-off between bias and precision, we believe that estimator variance, which is often not reported when presenting species richness estimates, should always be included.

To identify areas on the landscape that may contribute to a robust network of conservation areas, we modeled the probabilities of occurrence of several en route migratory shorebirds and wintering waterfowl in the southern Great Plains of North America, including responses to changing climate. We predominantly used data from the eBird citizen-science project to model probabilities of occurrence relative to land-use patterns, spatial distribution of wetlands, and climate. We projected models to potential future climate conditions using five representative general circulation models of the Coupled Model Intercomparison Project 5 (CMIP5). We used Random shorebird probabilities of occurrence varied with species-specific general distribution pattern, migration distance, and spatial extent. Species using the western and northern portion of the study area exhibited the greatest likelihoods of decline, whereas species with more easterly occurrences, mostly long-distance migrants, had the greatest projected increases in probability of occurrence. At an ecoregional extent, differences in probabilities of shorebird occurrence ranged from −0.015 to 0.045 when averaged across climate models, with the largest increases occurring early in migration. Spatial shifts are predicted for several shorebird species. Probabilities of occurrence of wintering Mallards and Northern Pintail are predicted to increase by 0.046 and 0.061, respectively, with northward shifts projected for both species. When incorporated into partner land management decision tools, results at ecoregional extents can be used to identify wetland complexes with the greatest potential to support birds in the nonbreeding season under a wide range of future climate scenarios. K E Y W O R D Sclimate change, conservation design, migratory shorebirds, species distribution models, wintering waterfowl

Summary1. Species richness, the number of species in a defined area, is the most frequently used biodiversity measure. Despite its intuitive appeal and conceptual simplicity, species richness is often difficult to quantify, even in wellsurveyed areas, because of sampling limitations such as survey effort and species detection probability. Nonparametric estimators have generally performed better than other options, but no particular estimator has consistently performed best across variation in assemblage and survey parameters. 2. In order to evaluate estimator performances, we developed the program SimAssem. SimAssem can: (i) simulate assemblages and surveys with user-specified parameters, (ii) process existing species encounter history files, (iii) generate species richness estimates not available in other programs and (iv) format encounter history data for several other programs. 3. SimAssem can help elucidate relationships between assemblage and survey parameters and the performance of species richness estimators, thereby increasing our understanding of estimator sensitivity, improving estimator development and defining the bounds for appropriate application.