Accounting for adaptive potential and migration capacity in species distribution models: conservation and management applications

Background/Question/Methods

A number of assumptions underpinning the use of species distribution models to predict biological responses to climate change are violated for temperate and boreal tree species that are widespread, long-lived, and genetically adapted to local climate conditions. To address this situation we propose a methodology to account for the potential effects of genetic structure, adaptive potential and limited migration capacity.

Similar to the widely used “no migration” and “unlimited migration” scenarios, we employ more refined biological response scenarios to evaluate the potential effects of genetic adaptation to local environments and the capacity of species to adapt and migrate. These scenarios are realized by two sets of geographic delineations that partition the species range into multiple populations, and that subdivide the study area into smaller landscape units.

Results/Conclusions

In a two case studies for western Canada, we demonstrate how the approach can be used to evaluate the adequacy of a protected areas system, and reforestation guidelines to ensure the maintenance of forest genetic resources for 48 tree species. We find that between 35% and 85% of locally adapted populations in protected areas are maintained under a median climate change scenario until the end of the century. Nevertheless, we find that on average populations already lag behind their optimal climate niche by approximately 130km in latitude, or 60m in elevation. For the 2020s we expect an average lag of approximately 310km in latitude or 140m in elevation, with the most pronounced geographic lags in the Rocky Mountains and the boreal forest.

We propose that the results of species distribution models have practical value for conservation planning if the focus is on maintenance rather than loss of suitable habitat. Accounting for genetic structure, adaptive potential, and migration capacity through best-case and worst-case scenarios provides important information to effectively allocate limited resources available for conservation action, and to guide seed transfer in reforestation programs to optimally match genotypes with anticipated planting environments.