Genomic data are increasingly being integrated into macroecological forecasting, offering an evolutionary perspective that has been largely missing from global change biogeography. Genomic offset, which quantifies the disruption of genotype–environment associations under environmental change, allows for the incorporation of intraspecific climate‐associated genomic differentiation into forecasts of habitat suitability. Gradient Forest (GF) is a commonly used approach to estimate genomic offset; however, major hurdles in the application of GF‐derived genomic offsets are (1) an inability to interpret their absolute magnitude in an ecologically meaningful way and (2) uncertainty in how their implications compare with those of species‐level approaches like Ecological Niche Models (ENMs). Here, we assess the climate change vulnerability of red spruce (
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.
more » « less- NSF-PAR ID:
- 10449411
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Change Biology
- Volume:
- 25
- Issue:
- 3
- ISSN:
- 1354-1013
- Page Range / eLocation ID:
- p. 775-793
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Location Atlantic Forest, Brazil.
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