Abstract Species at risk of extinction are not uniformly distributed in space. Concentrations of threatened species may occur where threatening processes are intense, in refuges from those processes, or in areas of high species diversity. However, there have been few attempts to identify the processes that explain the distribution of at‐risk species. Here, we identified the relative importance of biological traits, environmental factors, and anthropogenic stressors in driving the spatial patterns of both total and at‐risk species richness of North American mammals and birds. Environmental factors are the predominant drivers of both total and at‐risk species richness. Strikingly, the directions of variable relationships differ substantially between models of total and at‐risk species richness. Understanding how environmental gradients differentially drive variation in total and at‐risk species richness can inform conservation action. Moreover, our approach can predict shifts in at‐risk species concentrations in response to projected environmental change and anthropogenic stressors.
Results from the pretreatment habitat factor analysis using the nine local-scale forest structure variables measured at four study sites in Arizona and New Mexico.
Variation in the distribution of species richness as a result of introduced errors of omission and commission in the Gap Analysis database for Oregon was evaluated using Monte Carlo simulations. Random errors, assumed to be independent of a species' distribution, and boundary errors, assumed to be dependent on the species' distribution, were simulated using ten rodent species. Error rates of omission and commission equal to 5 and 20 percent were used in the simulations. Indications are that predictions of species richness within a Gap Analysis database can be very sensitive to both types of errors with sensitivity to random error being much greater. Implications are that the inclusion of error modeling in applied GIS databases is critical to spatially explicit conservation recommendations.
Wetlands generally provide significant ecosystem services and function as important harbors of biodiversity. To ensure that these habitats are conserved, an efficient means of identifying wetlands at risk of conversion is needed, especially in the southern United States where the rate of wetland loss has been highest in recent decades. We used multivariate adaptive regression splines to develop a model to predict the risk of wetland habitat loss as a function of wetland features and landscape context. Fates of wetland habitats from 1992 to 1997 were obtained from the National Resources Inventory for the U.S. Forest Service's Southern Region, and land-cover data were obtained from the National Land Cover Data. We randomly selected 70% of our 40 617 observations to build the model (n = 28 432), and randomly divided the remaining 30% of the data into five Test data sets (n = 2437 each). The wetland and landscape variables that were important in the model, and their relative contributions to the model's predictive ability (100 = largest, 0 = smallest), were land-cover/land-use of the surrounding landscape (100.0), size and proximity of development patches within 570 m (39.5), land ownership (39.1), road density within 570 m (37.5), percent woody and herbaceous wetland cover within 570 m (27.8), size and proximity of development patches within 5130 m (25.7), percent grasslands/herbaceous plants and pasture/hay cover within 5130 m (21.7), wetland type (21.2), and percent woody and herbaceous wetland cover within 1710 m (16.6). For the five Test data sets, Kappa statistics (0.40, 0.50, 0.52, 0.55, 0.56; P < 0.0001), area-under-the-receiver-operating-curve (AUC) statistics (0.78, 0.82, 0.83, 0.83, 0.84; P < 0.0001), and percent correct prediction of wetland habitat loss (69.1, 80.4, 81.7, 82.3, 83.1) indicated the model generally had substantial predictive ability across the South. Policy analysts and land-use planners can use the model and associated maps to prioritize at-risk wetlands for protection, evaluate wetland habitat connectivity, predict future conversion of wetland habitat based on projected land-use trends, and assess the effectiveness of wetland conservation programs.
Mapping of biodiversity elements to expose gaps in conservation networks has become a common strategy in nature-reserve design. We review a set of critical assumptions and issues that influence the interpretation and implementation of gap analysis, including: (1) the assumption that a subset of taxa can be used to indicate overall diversity patterns, and (2) the impact of uncertainty and error propagation in reserve design. We focus our review on species diversity patterns and use data from peer-reviewed literature or extant state-level databases to test specific predictions implied by these assumptions. Support for the biodiversity indicator assumption was varied. Patterns of diversity as reflected in species counts, coincidence of hot spots, and representativeness were not generally concordant among different taxa, with the degree of concordance depending on the measure of diversity used, the taxa examined, and the scale of analysis. Simulated errors in predicting the occurrence of individual species indicated that substantial differences in reserve-boundary recommendations could occur when uncertainty is incorporated into the analysis. Furthermore, focusing exclusively on vegetation and species distribution patterns in conservation planning will contribute to reserve-design uncertainty unless the processes behind the patterns are understood. To deal with these issues, reserve planners should base reserve design on the best available, albeit incomplete, data; should attempt to define those ecological circumstances when the indicator assumption is defensible; should incorporate uncertainty explicitly in mapped displays of biodiversity elements; and should simultaneously consider pattern and process in reserve-design problems.
The spatial and temporal scope of environmental change anticipated during the next century as a result of climate change presents unprecedented challenges for fish and wildlife management. The Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC 2007) suggested impacts from climate change on natural systems will be more grave than earlier projections. Recent reports on emissions, glacial melting, and sea level rise (Kintisch 2009) intimate that even the 2007 IPCC report is conservative in its assessment. The challenges posed by climate change cut across all aspects of land and resource management - difficult decisions will need to be made in the areas of agency policy, scientific research, and prioritization of resource management actions.
