in

Stacked distribution models predict climate-driven loss of variation in leaf phenology at continental scales

  • Wright, S. Evolution in Mendelian Populations. Genetics 16, 97–159 (1931).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • DiBattista, J. D. Patterns of genetic variation in anthropogenically impacted populations. Conserv. Genet. 9, 141–156 (2008).

    Google Scholar 

  • Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).

    CAS 
    PubMed 

    Google Scholar 

  • Nei, M., Maruyama, T. & Chakraborty, R. The Bottleneck Effect and Genetic Variability in Populations. Evolution 29, 1–10 (1975).

    PubMed 

    Google Scholar 

  • Frankham, R. Stress and adaptation in conservation genetics. J. Evol. Biol. 18, 750–755 (2005).

    CAS 
    PubMed 

    Google Scholar 

  • Mimura, M. et al. Understanding and monitoring the consequences of human impacts on intraspecific variation. Evol. Appl. 10, 121–139 (2017).

    PubMed 

    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).

    CAS 
    PubMed 

    Google Scholar 

  • Hughes, A., Inouye, B., Johnson, M., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623 (2008).

    PubMed 

    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).

    PubMed 

    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).

    Google Scholar 

  • 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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wimp, G. M. et al. Conserving plant genetic diversity for dependent animal communities. Ecol. Lett. 7, 776–780 (2004).

    Google Scholar 

  • 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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).

    CAS 
    PubMed 

    Google Scholar 

  • Salo, T. & Gustafsson, C. The Effect of Genetic Diversity on Ecosystem Functioning in Vegetated Coastal Ecosystems. Ecosystems 19, 1429–1444 (2016).

    Google Scholar 

  • Zettlemoyer, M. A. & Peterson, M. L. Does Phenological Plasticity Help or Hinder Range Shifts Under Climate Change? Front. Ecol. Evol. 9, 392 (2021).

    Google Scholar 

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

    PubMed 
    PubMed Central 

    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).

    Google Scholar 

  • Excoffier, L., Foll, M. & Petit, R. J. Genetic Consequences of Range Expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).

    Google Scholar 

  • Alsos, I. G. et al. Genetic consequences of climate change for northern plants. Proc. R. Soc. B Biol. Sci. 279, 2042–2051 (2012).

    Google Scholar 

  • 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).

    CAS 
    PubMed 
    PubMed Central 

    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).

    PubMed 
    PubMed Central 

    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).

    PubMed 
    PubMed Central 

    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).

    CAS 
    PubMed 

    Google Scholar 

  • Piao, S. et al. Plant phenology and global climate change: Current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).

    Google Scholar 

  • 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).

    CAS 
    PubMed 

    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).

    PubMed 

    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).

    PubMed 
    PubMed Central 

    Google Scholar 

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

    PubMed 

    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).

    Google Scholar 

  • 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).

    Google Scholar 

  • 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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ferrier, S. & Guisan, A. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43, 393–404 (2006).

    Google Scholar 

  • 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).

    Google Scholar 

  • 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).

    Google Scholar 

  • Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).

    PubMed 

    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).

    Google Scholar 

  • 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).

    Google Scholar 

  • 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).

    CAS 
    PubMed 

    Google Scholar 

  • Huntington, T. G. CO2-induced suppression of transpiration cannot explain increasing runoff. Hydrol. Process. 22, 311–314 (2008).

    Google Scholar 

  • 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).

    Google Scholar 

  • 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).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Change 11, 543–550 (2021).

    Google Scholar 

  • 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).

    PubMed 

    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).

    Google Scholar 

  • 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).

    Google Scholar 

  • 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).

    Google Scholar 

  • Evans, L. M. et al. Geographical barriers and climate influence demographic history in narrowleaf cottonwoods. Heredity 114, 387–396 (2015).

    CAS 
    PubMed 
    PubMed Central 

    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).

    PubMed 

    Google Scholar 

  • Gotelli, N. J. & Stanton-Geddes, J. Climate change, genetic markers and species distribution modelling. J. Biogeogr. 42, 1577–1585 (2015).

    Google Scholar 

  • 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).

    PubMed 

    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).

    PubMed 

    Google Scholar 

  • Jimenez-Valverde, A. Sample Size for the evaluation of presence-absence models. Ecol. Indic. 114, 106289 (2020).

    Google Scholar 

  • 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).

    Google Scholar 

  • 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).

    CAS 
    PubMed 
    PubMed Central 

    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).

    Google Scholar 

  • 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).

    Google Scholar 

  • Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).

    Google Scholar 

  • Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).

    Google Scholar 

  • 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).

    Google Scholar 

  • Swets, J. A. Measuring the Accuracy of Diagnostic Systems. Science 240, 1285–1293 (1988).

    CAS 
    PubMed 

    Google Scholar 

  • Engler, R. et al. 21st century climate change threatens mountain flora unequally across Europe. Glob. Change Biol. 17, 2330–2341 (2011).

    Google Scholar 

  • Randin, C. F. et al. Climate change and plant distribution: local models predict high-elevation persistence. Glob. Change Biol. 15, 1557–1569 (2009).

    Google Scholar 

  • Knutti, R., Masson, D. & Gettelman, A. Climate model genealogy: Generation CMIP5 and how we got there. Geophys. Res. Lett. 40, 1194–1199 (2013).

    Google Scholar 

  • Mateo, R. G., Mokany, K. & Guisan, A. Biodiversity Models: What If Unsaturation Is the Rule? Trends Ecol. Evol. 32, 556–566 (2017).

    PubMed 
    PubMed Central 

    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.


  • Source: Ecology - nature.com

    Advancing the energy transition amidst global crises

    MIT PhD students shed light on important water and food research