How useful are genomic data for predicting maladaptation to future climate?

Autor: Lind BM; Centre for Forest Conservation Genetics and Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada., Candido-Ribeiro R; Centre for Forest Conservation Genetics and Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada., Singh P; Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada., Lu M; Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada., Obreht Vidakovic D; Centre for Forest Conservation Genetics and Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada., Booker TR; Department of Zoology, University of British Columbia, Vancouver, British Columbia, Canada., Whitlock MC; Department of Zoology, University of British Columbia, Vancouver, British Columbia, Canada., Yeaman S; Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada., Isabel N; Canada Research Chair in Forest Genomics, Centre for Forest Research and Institute for Systems and Integrative Biology, Université Laval, Québec, Quebec, Canada.; Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Québec, Quebec, Canada., Aitken SN; Centre for Forest Conservation Genetics and Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
Jazyk: angličtina
Zdroj: Global change biology [Glob Chang Biol] 2024 Apr; Vol. 30 (4), pp. e17227.
DOI: 10.1111/gcb.17227
Abstrakt: Methods using genomic information to forecast potential population maladaptation to climate change or new environments are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare the validation of maladaptation estimates derived from two methods-Gradient Forests (GF offset ) and the risk of non-adaptedness (RONA)-using exome capture pool-seq data from 35 to 39 populations across three conifer taxa: two Douglas-fir varieties and jack pine. We evaluate sensitivity of these algorithms to the source of input loci (markers selected from genotype-environment associations [GEA] or those selected at random). We validate these methods against 2- and 52-year growth and mortality measured in independent transplant experiments. Overall, we find that both methods often better predict transplant performance than climatic or geographic distances. We also find that GF offset and RONA models are surprisingly not improved using GEA candidates. Even with promising validation results, variation in model projections to future climates makes it difficult to identify the most maladapted populations using either method. Our work advances understanding of the sensitivity and applicability of these approaches, and we discuss recommendations for their future use.
(© 2024 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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