Division of Ecology and Evolution, Research School of Biology, The Australian National University, Acton, ACT, 2601, Australia.
New Phytol. 2019 May;222(3):1235-1241. doi: 10.1111/nph.15656. Epub 2019 Jan 25.
Contents Summary 1235 I. Introduction 1235 II. The many shapes of phenotypic plasticity 1236 III. Random regression mixed model framework 1237 IV. Conclusions 1240 Acknowledgements 1240 References 1240 SUMMARY: Plant biology is experiencing a renewed interest in the mechanistic underpinnings and evolution of phenotypic plasticity that calls for a re-evaluation of how we analyse phenotypic responses to a rapidly changing climate. We suggest that dissecting plant plasticity in response to increasing temperature needs an approach that can represent plasticity over multiple environments, and considers both population-level responses and the variation between genotypes in their response. Here, we outline how a random regression mixed model framework can be applied to plastic traits that show linear or nonlinear responses to temperature. Random regressions provide a powerful and efficient means of characterising plasticity and its variation. Although they have been used widely in other fields, they have only recently been implemented in plant evolutionary ecology. We outline their structure and provide an example tutorial of their implementation.
内容摘要 1235 I. 引言 1235 II. 表型可塑性的多种形态 1236 III. 随机回归混合模型框架 1237 IV. 结论 1240 致谢 1240 参考文献 1240 摘要：植物生物学正在重新关注表型可塑性的机制基础及其进化，这要求我们重新评估如何分析植物对快速变化的气候的表型响应。我们认为，剖析植物对温度升高的可塑性需要一种能够代表多种环境中可塑性的方法，并同时考虑种群水平的响应以及基因型之间在响应上的差异。在这里，我们概述了随机回归混合模型框架如何应用于表现出对温度线性或非线性响应的塑性特征。随机回归为描述可塑性及其变化提供了一种强大而有效的手段。尽管它们在其他领域得到了广泛应用，但直到最近才在植物进化生态学中实施。我们概述了它们的结构，并提供了一个实施示例教程。
