Accounting for population-level adaptation and migration remains a central challenge to predicting climate change effects on biodiversity. Assessing how climate change could disrupt local climate adaptation, resulting in maladaptation and possibly extirpation, can inform where climate change poses the greatest risks across species ranges. For the forest tree species balsam poplar (Populus balsamifera), we used climate-associated genetic loci to predict population maladaptation with and without migration, the distance to sites that minimize maladaptation, and the emergence of novel genotype–climate associations. We show that the greatest disruptions to contemporary genotype–climate associations occur along the longitudinal edges of the range, where populations are predicted to be maladapted to all future North American climates, rescue via migration is most limited and novel genotype–climate associations emerge. Our work advances beyond species-level range modelling towards the long-held goal of simultaneously estimating the contributions of maladaptation and migration to understanding the risks that populations may face from shifting climates.
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The R code and genetic data to calculate local, forward and reverse offsets are available at github.com/agougher/poplarAdaptiveOffset.
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We thank V. E. Chhatre for providing comments on an earlier draft of this manuscript. This work was supported by National Science Foundation Plant Genome Research award no. 1461868 to S.R.K. and M.C.F., and an UMCES PhD fellowship to A.V.G.
The authors declare no competing interests.
Peer review information Nature Climate Change thanks Erin Landguth, Christian Rellstab and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Geographic locations of populations used in GDM and GF models (blue circles), within balsam poplar’s range (white polygon). b, Position of populations (blue circles), and cells in balsam poplar’s range (black dots) in current North American climate space (gray circles). Red circles show the composite future climate (RCP 8.5) of North America used in predictions. Climate space is shown as the first two principal components (PCs) of current North American climate (mean diurnal range, isothermality, mean summer temperature, mean winter temperature, summer precipitation, winter precipitation), with future climates predicted into the same PCA space. b, is shown only for visualization purposes.
Extended Data Fig. 2 Red-green-blue map of local (red), forward (green), and reverse (blue) offsets.
Offset values were calculated from Gradient Forest throughout the range of balsam poplar for 2070 and RCP 8.5. Brighter cells, closer to white, have relatively high values along each of the three axes while darker cells, closer to black, have relatively lower values. b-d, Bivariate scattergrams of (a), with 1:1 lines. Individual maps used in (a) are shown in Extended Data Fig. 4.
Extended Data Fig. 3 Local, forward, and reverse offsets from generalized dissimilarity models for balsam poplar.
a & b, Local genetic offset, (c & d) forward offset, and (e & f) reverse offset from a generalized dissimilarity model for RCP 4.5 (first column; a, c, e) and RCP 8.5 (second column; b, d, f) for 2070. Note the non-linear color scale.
a & b, Local genetic offset, (c & d) forward offset, and (e & f) reverse offset from a Gradient Forest model for RCP 4.5 (first column; a, c, e) and RCP 8.5 (second column; b, d, f) for 2070.
Distance and (b) initial bearing were calculated from the focal cell to the location in future North American climate (2070, RCP 8.5) that minimizes predicted offset from a Gradient Forest model. Polar histogram in (b) shows the log10 number of cells in each bearing bin.
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Gougherty, A.V., Keller, S.R. & Fitzpatrick, M.C. Maladaptation, migration and extirpation fuel climate change risk in a forest tree species. Nat. Clim. Chang. 11, 166–171 (2021). https://doi.org/10.1038/s41558-020-00968-6
Evolutionary Applications (2021)
Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests
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