Habitat use as an indicator of adaptive capacity to climate change

Aim: Populations of cold-adapted species at the trailing edges of geographic ranges are particularly vulnerable to the negative effects of climate change from the combination of exposure to warm temperatures and high sensitivity to heat. Many of these species are predicted to decline under future climate scenarios, but they could persist if they can adapt to warming climates either physiologically or behaviorally. We aim to understand local variation in contemporary habitat use and use this information to identify signs of adaptive capacity. We focus on moose (Alces alces), a charismatic species of conservation and public interest.

Location: The northeastern United States, along the trailing edge of the moose geographic range in North America.

Methods: We compiled data on occurrences and habitat use of moose from remote cameras and GPS collars across the northeastern United States. We use these data to build habitat suitability models at local and regional spatial scales, and then to predict future habitat suitability under climate change. We also use fine-scale GPS data to model relationships between habitat use and temperature on a daily temporal scale and to predict future habitat use.

Results: We find that habitat suitability for moose will decline under a range of climate change scenarios. However, moose across the region differ in their use of climatic and habitat space, indicating that they could exhibit adaptive capacity. We also find evidence for behavioral responses to weather, where moose increase their use of forested wetland habitats in warmer places and/or times.

Main Conclusions: Our results suggest that there will be significant shifts in moose distribution due to climate change. However, if there is spatial variation in thermal tolerance, trailing-edge populations could adapt to climate change. We highlight that prioritizing certain habitats for conservation (i.e., thermal refuges) could be crucial for this adaptation.

Methods
The GBIF data download is associated with the following DOI: https://doi.org/10.15468/dd.4mzn9b