Anderson Jill T, DeMarche Megan L, Denney Derek A, Breckheimer Ian, Santangelo James, Wadgymar Susana M
Department of Genetics and Odum School of Ecology, University of Georgia, Athens, GA, USA.
Department of Plant Biology, University of Georgia, Athens, GA, USA.
Science. 2025 May;388(6746):525-531. doi: 10.1126/science.adr1010. Epub 2025 May 1.
Climate change increasingly drives local population dynamics, shifts geographic distributions, and threatens persistence. Gene flow and rapid adaptation could rescue declining populations yet are seldom integrated into forecasts. We modeled eco-evolutionary dynamics under preindustrial, contemporary, and projected climates using up to 9 years of fitness data from 102,272 transplants (115 source populations) of in five common gardens. Climate change endangers locally adapted populations and reduces genotypic variation in long-term population growth rate, suggesting limited adaptive potential. Upslope migration could stabilize high-elevation populations and preserve low-elevation ecotypes, but unassisted gene flow modeled with genomic data is too spatially restricted. Species distribution models failed to capture current dynamics and likely overestimate persistence under intermediate emissions scenarios, highlighting the importance of modeling evolutionary processes.
气候变化日益推动着当地种群动态变化，改变地理分布，并威胁到物种的存续。基因流动和快速适应本可拯救数量下降的种群，但在预测中却很少被纳入考虑。我们利用来自五个共同花园中102272次移植（115个源种群）长达9年的适合度数据，对工业化前、当代和预测气候条件下的生态进化动态进行了建模。气候变化危及当地适应的种群，并降低长期种群增长率中的基因型变异，这表明适应潜力有限。向上坡迁移可稳定高海拔种群并保留低海拔生态型，但基于基因组数据建模的自然基因流动在空间上受到的限制过大。物种分布模型未能捕捉当前动态，并且很可能高估了中等排放情景下的物种存续情况，这凸显了对进化过程进行建模的重要性。
