Application of habitat association models across regions: Useful explanatory power retained in wetland bird case study

Abstract

Species often exhibit regionally specific habitat associations, so habitat association models developed in one region might not be accurate or even appropriate for other regions. Three programs to survey wetland‐breeding birds covering (respectively) Great Lakes coastal wetlands, inland Great Lakes wetlands, and the Prairie Pothole Region offer an opportunity to test whether regionally specific models of habitat use by wetland‐obligate breeding birds are transferrable across regions. We first developed independent, regional population density models for four species of wetland‐obligate birds: Pied‐billed Grebe (Podilymbus podiceps), Virginia Rail (Rallus limicola), Sora (Porzana carolina), and American Bittern (Botaurus lentiginosus). We then used adjusted pseudo‐R2 values to compare the amount of variation explained by each model when applied to data collected in each of the three regions. Although certain habitat characteristics, such as emergent vegetation and wetland area, were consistently important across regions, models for each species differed by region—both in variables selected for inclusion and often in the directionality of relationships for common variables—indicating that habitat associations for these species are regionally specific. When we applied a model developed in one region to data collected in another region, we found that explanatory power was reduced in most (71%) models. Therefore, we suggest that ecological analyses should emphasize regionally specific habitat association models whenever possible. Nonetheless, models created from inland Great Lakes wetland data had higher median explanatory power when applied to other regions, and the amount of explanatory power lost by other transferred models was relatively small. Thus, while regionally specific habitat association models are preferable, in the absence of reliable regional data, habitat association models developed in one region may be applied to another region, but the results need to be cautiously interpreted. Additionally, we found that median explanatory power was higher when local‐scale habitat characteristics were included in the models, indicating that regionally specific models should ideally be based on a combination of local‐ and landscape‐scale habitat characteristics. Conservation practitioners can leverage such regionally specific models and associated monitoring data to help prioritize areas for management activities that contribute to regional conservation efforts.