Incorporating local adaptation into forecasts of species' distribution and abundance under climate change.

Journal Article (Review;Journal Article)

Populations of many species are genetically adapted to local historical climate conditions. Yet most forecasts of species' distributions under climate change have ignored local adaptation (LA), which may paint a false picture of how species will respond across their geographic ranges. We review recent studies that have incorporated intraspecific variation, a potential proxy for LA, into distribution forecasts, assess their strengths and weaknesses, and make recommendations for how to improve forecasts in the face of LA. The three methods used so far (species distribution models, response functions, and mechanistic models) reflect a trade-off between data availability and the ability to rigorously demonstrate LA to climate. We identify key considerations for incorporating LA into distribution forecasts that are currently missing from many published studies, including testing the spatial scale and pattern of LA, the confounding effects of LA to nonclimatic or biotic drivers, and the need to incorporate empirically based dispersal or gene flow processes. We suggest approaches to better evaluate these aspects of LA and their effects on species-level forecasts. In particular, we highlight demographic and dynamic evolutionary models as promising approaches to better integrate LA into forecasts, and emphasize the importance of independent model validation. Finally, we urge closer examination of how LA will alter the responses of central vs. marginal populations to allow stronger generalizations about changes in distribution and abundance in the face of LA.

Full Text

Duke Authors

Cited Authors

  • Peterson, ML; Doak, DF; Morris, WF

Published Date

  • March 2019

Published In

Volume / Issue

  • 25 / 3

Start / End Page

  • 775 - 793

PubMed ID

  • 30597712

Electronic International Standard Serial Number (EISSN)

  • 1365-2486

International Standard Serial Number (ISSN)

  • 1354-1013

Digital Object Identifier (DOI)

  • 10.1111/gcb.14562


  • eng