Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Maladaptation, migration and extirpation fuel climate change risk in a forest tree species


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Schematic of how local, forward and reverse offsets were calculated and mapped for GDMs.
Fig. 2: RGB map of local (red), forward (green) and reverse (blue) offsets.
Fig. 3: Distance and initial bearing to locations that minimize forward offset.
Fig. 4: Effect of search distance on forward offset.

Data availability

All data are publicly available. The allele frequencies are available in ref. 14, and the climate data are available at

Code availability

The R code and genetic data to calculate local, forward and reverse offsets are available at


  1. 1.

    Aitken, S. N., Yeaman, S., Holliday, J. A., Wang, T. & Curtis-McLane, S. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol. Appl. 1, 95–111 (2008).

    Article  Google Scholar 

  2. 2.

    Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to Quaternary climate change. Science 292, 673–679 (2001).

    CAS  Article  Google Scholar 

  3. 3.

    Ikeda, D. H. et al. Genetically informed ecological niche models improve climate change predictions. Glob. Change Biol. 23, 164–176 (2017).

    Article  Google Scholar 

  4. 4.

    Maguire, K. C., Shinneman, D. J., Potter, K. M. & Hipkins, V. D. Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change. Syst. Biol. (2018).

  5. 5.

    Smith, A. B., Godsoe, W., Rodríguez-Sánchez, F., Wang, H.-H. & Warren, D. Niche estimation above and below the species level. Trends Ecol. Evol. 34, 260–273 (2019).

    Article  Google Scholar 

  6. 6.

    Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).

    CAS  Article  Google Scholar 

  7. 7.

    Wang, T., O’Neill, G. A. & Aitken, S. N. Integrating environmental and genetic effects to predict responses of tree populations to climate. Ecol. Appl. 20, 153–163 (2010).

    CAS  Article  Google Scholar 

  8. 8.

    Radeloff, V. C. et al. The rise of novelty in ecosystems. Ecol. Appl. 25, 2051–2068 (2015).

    Article  Google Scholar 

  9. 9.

    Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475–482 (2007).

    Article  Google Scholar 

  10. 10.

    Aitken, S. N. & Whitlock, M. C. Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388 (2013).

    Article  Google Scholar 

  11. 11.

    Vitt, P., Havens, K., Kramer, A. T., Sollenberger, D. & Yates, E. Assisted migration of plants: changes in latitudes, changes in attitudes. Biol. Conserv. 143, 18–27 (2010).

    Article  Google Scholar 

  12. 12.

    Williams, M. I. & Dumroese, R. K. Preparing for climate change: forestry and assisted migration. J. For. 111, 287–297 (2013).

    Google Scholar 

  13. 13.

    Keller, S. R., Levsen, N., Olson, M. S. & Tiffin, P. Local adaptation in the flowering-time gene network of balsam poplar, Populus balsamifera L. Mol. Biol. Evol. 29, 3143–3152 (2012).

    CAS  Article  Google Scholar 

  14. 14.

    Keller, S. R., Chhatre, V. E. & Fitzpatrick, M. C. Influence of range position on locally adaptive gene–environment associations in Populus flowering time genes. J. Hered. 109, 47–58 (2018).

    CAS  Article  Google Scholar 

  15. 15.

    Chuine, I. Why does phenology drive species distribution? Phil. Trans. R. Soc. B 365, 3149–3160 (2010).

    Article  Google Scholar 

  16. 16.

    Morin, X., Viner, D. & Chuine, I. Tree species range shifts at a continental scale: new predictive insights from a process-based model. J. Ecol. 96, 784–794 (2008).

    Article  Google Scholar 

  17. 17.

    Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers. Distrib. 13, 252–264 (2007).

    Article  Google Scholar 

  18. 18.

    Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).

    Article  Google Scholar 

  19. 19.

    Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).

    Article  Google Scholar 

  20. 20.

    Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).

    Article  Google Scholar 

  21. 21.

    VanDerWal, J. et al. Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. Nat. Clim. Change 3, 239–243 (2013).

    Article  Google Scholar 

  22. 22.

    Shaw, R. G. From the past to the future: considering the value and limits of evolutionary prediction. Am. Nat. 193, 1–10 (2018).

    Article  Google Scholar 

  23. 23.

    Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: the rear edge matters. Ecol. Lett. 8, 461–467 (2005).

    Article  Google Scholar 

  24. 24.

    Yun, J. et al. Influence of winter precipitation on spring phenology in boreal forests. Glob. Change Biol. 24, 5176–5187 (2018).

    Article  Google Scholar 

  25. 25.

    Fu, Y. H. et al. Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes. Glob. Change Biol. 20, 3743–3755 (2014).

    Article  Google Scholar 

  26. 26.

    Peterson, M. L., Doak, D. F. & Morris, W. F. Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Glob. Change Biol. 25, 775–793 (2019).

    Article  Google Scholar 

  27. 27.

    Atkins, K. E. & Travis, J. M. J. Local adaptation and the evolution of species’ ranges under climate change. J. Theor. Biol. 266, 449–457 (2010).

    CAS  Article  Google Scholar 

  28. 28.

    Chen, I.-C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).

    CAS  Article  Google Scholar 

  29. 29.

    Groom, Q. J. Some poleward movement of British native vascular plants is occurring, but the fingerprint of climate change is not evident. PeerJ 1, e77 (2013).

    Article  Google Scholar 

  30. 30.

    Olson, M. S. et al. The adaptive potential of Populus balsamifera L. to phenology requirements in a warmer global climate. Mol. Ecol. 22, 1214–1230 (2013).

    CAS  Article  Google Scholar 

  31. 31.

    Fitzpatrick, M., Chhatre, V., Soolanayakanahally, R. & Keller, S. Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests. Preprint at (2020).

  32. 32.

    Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).

    CAS  Article  Google Scholar 

  33. 33.

    Keller, S. R. et al. Climate-driven local adaptation of ecophysiology and phenology in balsam poplar, Populus balsamifera L. (Salicaceae). Am. J. Bot. 98, 99–108 (2011).

    Article  Google Scholar 

  34. 34.

    Alberto, F. J. et al. Potential for evolutionary responses to climate change—evidence from tree populations. Glob. Change Biol. 19, 1645–1661 (2013).

    Article  Google Scholar 

  35. 35.

    Little, E. L. Atlas of United States Trees (US Dept of Agriculture, Forest Service, 1971).

  36. 36.

    Romero-Lankao, P. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects (eds Barros, V. R. et al.) 1439–1498 (Cambridge Univ. Press, 2014).

  37. 37.

    Fetter, K. C., Gugger, P. F. & Keller, S. R. in Comparative and Evolutionary Genomics of Angiosperm Trees (eds Groover, A. & Cronk, Q.) 303–333 (Springer International, 2017);

  38. 38.

    Soolanayakanahally, R. Y., Guy, R. D., Silim, S. N., Drewes, E. C. & Schroeder, W. R. Enhanced assimilation rate and water use efficiency with latitude through increased photosynthetic capacity and internal conductance in balsam poplar (Populus balsamifera L.). Plant Cell Environ. 32, 1821–1832 (2009).

    CAS  Article  Google Scholar 

  39. 39.

    Chhatre, V. E. et al. Climatic niche predicts the landscape structure of locally adaptive standing genetic variation. Preprint at (2019).

  40. 40.

    Günther, T. & Coop, G. Robust identification of local adaptation from allele frequencies. Genetics 195, 205–220 (2013).

    Article  Google Scholar 

  41. 41.

    Frichot, E., Schoville, S. D., Bouchard, G. & François, O. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol. 30, 1687–1699 (2013).

    CAS  Article  Google Scholar 

  42. 42.

    Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

    Article  Google Scholar 

  43. 43.

    Frichot, E. & François, O. LEA: an R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).

    Article  Google Scholar 

  44. 44.

    Goudet, J. & Jombart, T. hierfstat: Estimation and tests of hierarchical F-statistics. R package version 0.04-22 (2015).

  45. 45.

    Manion, G., Lisk, M., Nieto-Lugilde, D., Mokany, K. & Fitzpatrick, M. gdm: Generalized dissimilarity modeling. R package version 1.3.11 (2018).

  46. 46.

    Hijmans, R. J. geosphere: Spherical trigonometry. R package version 1.5-10 (2019).

  47. 47.

    Gougherty, A. V., Chhatre, V. E., Keller, S. R. & Fitzpatrick, M. C. Contemporary range position predicts the range-wide pattern of genetic diversity in balsam poplar (Populus balsamifera L.). J. Biogeogr. 47, 1246–1257 (2020).

    Article  Google Scholar 

  48. 48.

    Vallejos, R., Osorio, F. & Bevilacqua, M. Spatial Relationships Between Two Georeferenced Variables: With Applications in R (Springer, 2018).

Download references


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.

Author information




A.V.G. and M.C.F. conceived the study. S.R.K. processed and provided the genetic information. A.V.G. analysed the data and led the writing, with contributions and discussion from all authors.

Corresponding author

Correspondence to Andrew V. Gougherty.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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.

Extended data

Extended Data Fig. 1 Sampled population locations in geographic and climatic space.

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.

Extended Data Fig. 4 Local, forward, and reverse offsets from Gradient Forest for balsam poplar.

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.

Extended Data Fig. 5 Distance and initial bearing to locations that minimizes forward offset.

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.

Supplementary information

Supplementary Information

Supplementary Figs. 1–12 and Tables 1 and 2.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing