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物候不匹配驱动繁殖时间可塑性的海拔选择,但不驱动其坡度选择,在野生鸣禽中。

Phenological mismatch drives selection on elevation, but not on slope, of breeding time plasticity in a wild songbird.

机构信息

Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), 6700, AB Wageningen, The Netherlands.

出版信息

Evolution. 2019 Feb;73(2):175-187. doi: 10.1111/evo.13660. Epub 2018 Dec 21.

Abstract

Phenotypic plasticity is an important mechanism for populations to respond to fluctuating environments, yet may be insufficient to adapt to a directionally changing environment. To study whether plasticity can evolve under current climate change, we quantified selection and genetic variation in both the elevation (RN ) and slope (RN ) of the breeding time reaction norm in a long-term (1973-2016) study population of great tits (Parus major). The optimal RN (the caterpillar biomass peak date regressed against the temperature used as cue by great tits) changed over time, whereas the optimal RN did not. Concordantly, we found strong directional selection on RN , but not RN , of egg-laying date in the second third of the study period; this selection subsequently waned, potentially due to increased between-year variability in optimal laying dates. We found individual and additive genetic variation in RN but, contrary to previous studies on our population, not in RN . The predicted and observed evolutionary change in RN was, however, marginal, due to low heritability and the sex limitation of laying date. We conclude that adaptation to climate change can only occur via micro-evolution of RN but this will necessarily be slow and potentially hampered by increased variability in phenotypic optima.

摘要

表型可塑性是种群应对环境波动的重要机制,但可能不足以适应环境的定向变化。为了研究在当前气候变化下可塑性是否能够进化,我们在一个大山雀(Parus major)的长期(1973-2016 年)研究种群中,量化了繁殖时间反应规范的海拔(RN)和坡度(RN)的选择和遗传变异。最佳 RN(毛毛虫生物量峰值日期回归到大山雀用作线索的温度)随时间发生了变化,而最佳 RN 没有变化。一致地,我们在研究的后三分之二时间里发现了对产卵日期的 RN 的强烈定向选择,但不是对 RN 的选择;这种选择随后减弱,可能是由于最佳产卵日期的年际变异性增加。我们发现了 RN 的个体和加性遗传变异,但与我们之前对该种群的研究相反,RN 没有遗传变异。由于低遗传力和产卵日期的性别限制,RN 的预测和观察到的进化变化是微不足道的。我们得出的结论是,对气候变化的适应只能通过 RN 的微观进化来实现,但这必然是缓慢的,并且可能会受到表型最优值增加的阻碍。

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