Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat.

ABSTRACT: Highly mobile species, such as migratory birds, respond to seasonal and interannual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species-distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We develo

SUBMITTER: Conlisk EE 

PROVIDER: S-EPMC9286402 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature