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逐渐的可塑性改变了多变环境中的种群动态:绿藻的热驯化。

Gradual plasticity alters population dynamics in variable environments: thermal acclimation in the green alga .

机构信息

Department of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06520, USA

W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060, USA.

出版信息

Proc Biol Sci. 2018 Jan 10;285(1870). doi: 10.1098/rspb.2017.1942.

Abstract

Environmental variability is ubiquitous, but its effects on populations are not fully understood or predictable. Recent attention has focused on how rapid evolution can impact ecological dynamics via adaptive trait change. However, the impact of trait change arising from plastic responses has received less attention, and is often assumed to optimize performance and unfold on a separate, faster timescale than ecological dynamics. Challenging these assumptions, we propose that gradual plasticity is important for ecological dynamics, and present a study of the plastic responses of the freshwater green algae as it acclimates to temperature changes. First, we show that 's gradual acclimation responses can both enhance and suppress its performance after a perturbation, depending on its prior thermal history. Second, we demonstrate that where conventional approaches fail to predict the population dynamics of exposed to temperature fluctuations, a new model of gradual acclimation succeeds. Finally, using high-resolution data, we show that phytoplankton in lake ecosystems can experience thermal variation sufficient to make acclimation relevant. These results challenge prevailing assumptions about plasticity's interactions with ecological dynamics. Amidst the current emphasis on rapid evolution, it is critical that we also develop predictive methods accounting for plasticity.

摘要

环境变异性无处不在,但人们对其如何影响种群还没有完全理解或预测。最近的研究重点关注快速进化如何通过适应性特征变化来影响生态动力学。然而,由可塑性反应引起的特征变化的影响受到的关注较少,并且通常被假设为优化性能,并在与生态动力学不同的、更快的时间尺度上展开。为了挑战这些假设,我们提出逐渐的可塑性对生态动力学很重要,并对淡水绿藻在适应温度变化时的可塑性反应进行了研究。首先,我们表明,'的逐渐适应反应可以增强也可以抑制其在受到干扰后的表现,具体取决于其先前的热历史。其次,我们证明,在传统方法无法预测暴露于温度波动的种群动态的情况下,逐渐适应的新模型可以成功预测。最后,我们使用高分辨率数据表明,湖泊生态系统中的浮游植物可以经历足以使适应相关的温度变化。这些结果挑战了关于可塑性与生态动力学相互作用的普遍假设。在当前强调快速进化的背景下,开发考虑可塑性的预测方法至关重要。

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