Abstract
AbstractUnderstanding species–habitat relationships for endangered species is critical for their conservation. However, many studies have limited value for conservation because they fail to account for habitat associations at multiple spatial scales, anthropogenic variables, and imperfect detection. We addressed these three limitations by developing models for an endangered wetland bird, Yuma Ridgway's rail (Rallus obsoletus yumanensis), that examined how the spatial scale of environmental variables, inclusion of anthropogenic disturbance variables, and accounting for imperfect detection in validation data influenced model performance. These models identified associations between environmental variables and occupancy. We used bird survey and spatial environmental data at 2473 locations throughout the species' U.S. range to create and validate occupancy models and produce predictive maps of occupancy. We compared habitat‐based models at three spatial scales (100, 224, and 500 m radii buffers) with and without anthropogenic disturbance variables using validation data adjusted for imperfect detection and an unadjusted validation dataset that ignored imperfect detection. The inclusion of anthropogenic disturbance variables improved the performance of habitat models at all three spatial scales, and the 224‐m‐scale model performed best. All models exhibited greater predictive ability when imperfect detection was incorporated into validation data. Yuma Ridgway's rail occupancy was negatively associated with ephemeral and slow‐moving riverine features and high‐intensity anthropogenic development, and positively associated with emergent vegetation, agriculture, and low‐intensity development. Our modeling approach accounts for common limitations in modeling species–habitat relationships and creating predictive maps of occupancy probability and, therefore, provides a useful framework for other species.
Highlights
Understanding and mapping species–habitat relationships for threatened and endangered species is critical because their recovery and persistence often depend on habitat protection and restoration (Hoffmann et al 2010, Guisan et al 2013)
The best spatial scale for predicting Yuma Ridgway’s rail occupancy was the 224-m buffer surrounding survey points, and the inclusion of anthropogenic disturbance variables improved the predictive ability of occupancy models compared to those that only incorporated wetland habitat characteristics
These models identified a suite of wetland habitat characteristics and anthropogenic disturbance variables that were associated with Yuma Ridgway’s rail occupancy; the variables selected and the direction of parameter estimates for those variables were largely consistent across the three spatial scales we examined
Summary
Understanding and mapping species–habitat relationships for threatened and endangered species is critical because their recovery and persistence often depend on habitat protection and restoration (Hoffmann et al 2010, Guisan et al 2013). SDMs often fail to examine species–habitat relationships at multiple spatial scales, include anthropogenic covariates that can affect species distributions, and account for imperfect detection, especially during model evaluation (Guisan and Thuiller 2005, Araujo and Guisan 2006, Guisan et al 2013, Guillera-Arroita et al 2015). Selection of appropriate spatial grain is a critical step in building SDMs because this choice can affect the accuracy of model predictions (Guisan and Thuiller 2005, Austin 2007), the probability of species occurrence and co-occurrence (Wilson and Schmidt 2015, Harms and Dinsmore 2016), and the estimated relationships between a species’ response and environmental predictors (Thompson and McGarigal 2002, Graf et al 2005, Price et al 2005, Martin and Fahrig 2012, Laforge et al 2015). Species– habitat relationships should be considered at multiple spatial scales to determine the scale that is most relevant to species occurrence and, the most appropriate scale for producing predictive maps of habitat quality
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