Wright, S. Evolution in Mendelian Populations. Genetics 16, 97–159 (1931).
Google Scholar
DiBattista, J. D. Patterns of genetic variation in anthropogenically impacted populations. Conserv. Genet. 9, 141–156 (2008).
Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).
Google Scholar
Nei, M., Maruyama, T. & Chakraborty, R. The Bottleneck Effect and Genetic Variability in Populations. Evolution 29, 1–10 (1975).
Google Scholar
Frankham, R. Stress and adaptation in conservation genetics. J. Evol. Biol. 18, 750–755 (2005).
Google Scholar
Mimura, M. et al. Understanding and monitoring the consequences of human impacts on intraspecific variation. Evol. Appl. 10, 121–139 (2017).
Google Scholar
Whitham, T. G. et al. A framework for community and ecosystem genetics: from genes to ecosystems. Nat. Rev. Genet. 7, 510–523 (2006).
Google Scholar
Hughes, A., Inouye, B., Johnson, M., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623 (2008).
Google Scholar
Hughes, A. R., Stachowicz, J. J. & Williams, S. L. Morphological and physiological variation among seagrass (Zostera marina) genotypes. Oecologia 159, 725–733 (2009).
Google Scholar
Schweitzer, J. A. et al. Genetically based trait in a dominant tree affects ecosystem processes: Plant genetics impact ecosystems. Ecol. Lett. 7, 127–134 (2004).
Hughes, A. R. & Stachowicz, J. J. Genetic diversity enhances the resistance of a seagrass ecosystem to disturbance. Proc. Natl Acad. Sci. USA 101, 8998–9002 (2004).
Google Scholar
Wimp, G. M. et al. Conserving plant genetic diversity for dependent animal communities. Ecol. Lett. 7, 776–780 (2004).
Reusch, T. B. H., Ehlers, A., Hämmerli, A. & Worm, B. Ecosystem recovery after climatic extremes enhanced by genotypic diversity. Proc. Natl Acad. Sci. 102, 2826–2831 (2005).
Google Scholar
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Google Scholar
Salo, T. & Gustafsson, C. The Effect of Genetic Diversity on Ecosystem Functioning in Vegetated Coastal Ecosystems. Ecosystems 19, 1429–1444 (2016).
Zettlemoyer, M. A. & Peterson, M. L. Does Phenological Plasticity Help or Hinder Range Shifts Under Climate Change? Front. Ecol. Evol. 9, 392 (2021).
Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).
Google Scholar
Yiming, L. et al. Latitudinal gradients in genetic diversity and natural selection at a highly adaptive gene in terrestrial mammals. Ecography 44, 206–218 (2021).
Excoffier, L., Foll, M. & Petit, R. J. Genetic Consequences of Range Expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).
Alsos, I. G. et al. Genetic consequences of climate change for northern plants. Proc. R. Soc. B Biol. Sci. 279, 2042–2051 (2012).
Stahl, U., Reu, B. & Wirth, C. Predicting species’ range limits from functional traits for the tree flora of North America. Proc. Natl Acad. Sci. 111, 13739–13744 (2014).
Google Scholar
Van Nuland, M. E. et al. Intraspecific trait variation across elevation predicts a widespread tree species’ climate niche and range limits. Ecol. Evol. 10, 3856–3867 (2020).
Google Scholar
Peterson, M. L., Angert, A. L. & Kay, K. M. Experimental migration upward in elevation is associated with strong selection on life history traits. Ecol. Evol. 10, 612–625 (2019).
Google Scholar
Vitasse, Y., Signarbieux, C. & Fu, Y. H. Global warming leads to more uniform spring phenology across elevations. Proc. Natl Acad. Sci. 115, 1004–1008 (2018).
Google Scholar
Piao, S. et al. Plant phenology and global climate change: Current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Chen, I.-C., Hill, J., Ohlemüller, R., Roy, D. B. & Thomas, C. Rapid Range Shifts of Species Associated with High Levels of Climate Warming. Science 333, 1024–6 (2011).
Google Scholar
Pauls, S. U., Nowak, C., Bálint, M. & Pfenninger, M. The impact of global climate change on genetic diversity within populations and species. Mol. Ecol. 22, 925–946 (2013).
Google Scholar
De Kort, H. et al. Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat. Commun. 12, 516 (2021).
Google Scholar
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: the rear edge matters. Ecol. Lett. 8, 461–467 (2005).
Google Scholar
DeMarche, 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).
Bothwell, H. M. et al. Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts. Ecol. Appl. 31, e02254 (2021).
Syfert, M. M., Brummitt, N. A., Coomes, D. A., Bystriakova, N. & Smith, M. J. Inferring diversity patterns along an elevation gradient from stacked SDMs: A case study on Mesoamerican ferns. Glob. Ecol. Conserv. 16, e00433 (2018).
Mateo, R. G., Felicísimo, Á. M., Pottier, J., Guisan, A. & Muñoz, J. Do Stacked Species Distribution Models Reflect Altitudinal Diversity Patterns? PLOS ONE 7, e32586 (2012).
Google Scholar
Ferrier, S. & Guisan, A. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43, 393–404 (2006).
Ware, I. M. et al. Climate-driven reduction of genetic variation in plant phenology alters soil communities and nutrient pools. Glob. Change Biol. 25, 1514–1528 (2019).
Endler, J. A. Geographic variation, speciation, and clines (Princeton University Press, 1977).
May, R. M. & Godfrey, J. Biological Diversity: Differences between Land and Sea [and Discussion]. Philos. Trans. Biol. Sci. 343, 105–111 (1994).
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).
Google Scholar
Van Nuland, M. E., Bailey, J. K. & Schweitzer, J. A. Divergent plant–soil feedbacks could alter future elevation ranges and ecosystem dynamics. Nat. Ecol. Evol. 1, 0150 (2017).
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B Biol. Sci. 365, 3227–3246 (2010).
Richardson, A. D. et al. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiol. 29, 321–321 (2009).
Google Scholar
Huntington, T. G. CO2-induced suppression of transpiration cannot explain increasing runoff. Hydrol. Process. 22, 311–314 (2008).
Kim, J. H. et al. Warming-Induced Earlier Greenup Leads to Reduced Stream Discharge in a Temperate Mixed Forest Catchment. J. Geophys. Res. Biogeosciences 123, 1960–1975 (2018).
Ware, I. M. et al. Climate-driven divergence in plant-microbiome interactions generates range-wide variation in bud break phenology. Commun. Biol. 4, 748 (2021).
Google Scholar
Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Change 11, 543–550 (2021).
Woolbright, S. A., Whitham, T. G., Gehring, C. A., Allan, G. J. & Bailey, J. K. Climate relicts and their associated communities as natural ecology and evolution laboratories. Trends Ecol. Evol. 29, 406–416 (2014).
Google Scholar
Naiman, R. J., Décamps, H. & McClain, M. E. Riparia: ecology, conservation, and management of streamside communities (Elsevier Academic Press, 2005).
Bayliss, S. L. J., Mueller, L. O., Ware, I. M., Schweitzer, J. A. & Bailey, J. K. Plant genetic variation drives geographic differences in atmosphere–plant–ecosystem feedbacks. Plant-Environ. Interact. 1, 166–180 (2020).
Cooke, J. E. K. & Rood, S. B. Trees of the people: the growing science of poplars in Canada and worldwide. This commentary is one of a selection of papers published in the Special Issue on Poplar Research in Canada. Can. J. Bot. 85, 1103–1110 (2007).
Evans, L. M., Allan, G. J., Meneses, N., Max, T. L. & Whitham, T. G. Herbivore host- associated genetic differentiation depends on the scale of plant genetic variation examined. Evol. Ecol. 27, 65–81 (2013).
Evans, L. M. et al. Geographical barriers and climate influence demographic history in narrowleaf cottonwoods. Heredity 114, 387–396 (2015).
Google Scholar
Hargreaves, A. L., Samis, K. E., Eckert, C. G., Schmitz, A. E. O. J. & Bronstein, E. J. L. Are Species’ Range Limits Simply Niche Limits Writ Large? A Review of Transplant Experiments beyond the Range. Am. Nat. 183, 157–173 (2014).
Google Scholar
Gotelli, N. J. & Stanton-Geddes, J. Climate change, genetic markers and species distribution modelling. J. Biogeogr. 42, 1577–1585 (2015).
Cushman, S. A. et al. Landscape genetic connectivity in a riparian foundation tree is jointly driven by climatic gradients and river networks. Ecol. Appl. 24, 1000–1014 (2014).
Google Scholar
Bothwell, H. M. et al. Conserving threatened riparian ecosystems in the American West: Precipitation gradients and river networks drive genetic connectivity and diversity in a foundation riparian tree (Populus angustifolia). Mol. Ecol. 26, 5114–5132 (2017).
Google Scholar
Jimenez-Valverde, A. Sample Size for the evaluation of presence-absence models. Ecol. Indic. 114, 106289 (2020).
Hamann, A., Wang, T., Spittlehouse, D. L. & Murdock, T. Q. A Comprehensive, High-Resolution Database of Historical and Projected Climate Surfaces for Western North America. Bull. Am. Meteorol. Soc. 94, 1307–1309 (2013).
Lucinda. M. et al. NHDPlus version 2: user guide (Horizon Systems Corporation, 2012).
ESRI. ArcMap (ESRI, 2018).
Bayliss, S. L. J., Papeş, M., Schweitzer, J. A. & Bailey, J. K. Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree. PLoS One. 17, e0274892 (2022).
Google Scholar
Elith, J. & Leathwick, J. R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Franklin, J. Mapping species distributions: spatial inference and prediction (Cambridge University Press, 2009).
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015). (1).
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).
Swets, J. A. Measuring the Accuracy of Diagnostic Systems. Science 240, 1285–1293 (1988).
Google Scholar
Engler, R. et al. 21st century climate change threatens mountain flora unequally across Europe. Glob. Change Biol. 17, 2330–2341 (2011).
Randin, C. F. et al. Climate change and plant distribution: local models predict high-elevation persistence. Glob. Change Biol. 15, 1557–1569 (2009).
Knutti, R., Masson, D. & Gettelman, A. Climate model genealogy: Generation CMIP5 and how we got there. Geophys. Res. Lett. 40, 1194–1199 (2013).
Mateo, R. G., Mokany, K. & Guisan, A. Biodiversity Models: What If Unsaturation Is the Rule? Trends Ecol. Evol. 32, 556–566 (2017).
Google Scholar
R. Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
Oksanen, J. et al. vegan: community ecology package (2020) http://CRAN.R-project.org/package=vegan.
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