The evolution of labile traits in sex- and age-structured populations.
The evolution of labile traits in sex- and age-structured populations.
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性别和年龄结构化人群中不稳定特征的演变。
DOI：
10.1111/1365-2656.12483
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发表时间：
2016-03
期刊：
影响因子：
--
通讯作者：
Rees M
中科院分区：
文献类型：
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作者：
Childs DZ;Sheldon BC;Rees M
许多数量性状是不稳定的（例如，体细胞生长率、生殖时间和投资），由于行为适应、发育过程和对环境的可塑性反应而在整个生命周期中变化。在种群水平上，选择可以改变这些特征在年龄组和世代之间的分布。尽管越来越多的理论研究探索不稳定性状的进化动力学，但尚未开发出将此类性状纳入人口统计模型的数据驱动框架。积分投影模型（IPM）越来越多地被用来了解不稳定的字符，生活史和种群动态之间的相互作用的变化。IPM方法的一个局限性是它依赖于亲本和后代性状之间的表型关联来捕获遗传。然而，已经确定的是，许多不同的过程可能会驱动这些关联，目前，关于如何在IPM框架中建模微观进化动力学还没有明确的共识。我们展示了如何将不稳定性状遗传的定量遗传模型嵌入到类似于标准IPM的年龄结构、两性模型中。常用的统计工具，如GLM及其混合模型对应物，然后可以用于模型参数化。我们通过开发一个简单的产卵日期进化模型来说明这种方法，该模型使用大山雀（Parus major）种群的数据进行参数化。我们展示了我们的框架可以用来项目的联合动态物种的性状和人口密度。然后，我们开发了一个简单的扩展的年龄结构的价格方程（ASPE）的两个性别的人群，并应用它来检查不同的过程中的平均表型和育种值的变化的年龄特异性的贡献。我们在这里概述的数据驱动的框架有可能促进更深入地了解选择的性质及其在焦点性状通过个体发育，行为适应和表型可塑性在一生中变化的环境中的后果，以及提供不稳定性状变异的理论和实证研究之间的潜在桥梁。
Many quantitative traits are labile (e.g. somatic growth rate, reproductive timing and investment), varying over the life cycle as a result of behavioural adaptation, developmental processes and plastic responses to the environment. At the population level, selection can alter the distribution of such traits across age classes and among generations. Despite a growing body of theoretical research exploring the evolutionary dynamics of labile traits, a data‐driven framework for incorporating such traits into demographic models has not yet been developed. Integral projection models (IPMs) are increasingly being used to understand the interplay between changes in labile characters, life histories and population dynamics. One limitation of the IPM approach is that it relies on phenotypic associations between parents and offspring traits to capture inheritance. However, it is well‐established that many different processes may drive these associations, and currently, no clear consensus has emerged on how to model micro‐evolutionary dynamics in an IPM framework. We show how to embed quantitative genetic models of inheritance of labile traits into age‐structured, two‐sex models that resemble standard IPMs. Commonly used statistical tools such as GLMs and their mixed model counterparts can then be used for model parameterization. We illustrate the methodology through development of a simple model of egg‐laying date evolution, parameterized using data from a population of Great tits (Parus major). We demonstrate how our framework can be used to project the joint dynamics of species' traits and population density. We then develop a simple extension of the age‐structured Price equation (ASPE) for two‐sex populations, and apply this to examine the age‐specific contributions of different processes to change in the mean phenotype and breeding value. The data‐driven framework we outline here has the potential to facilitate greater insight into the nature of selection and its consequences in settings where focal traits vary over the lifetime through ontogeny, behavioural adaptation and phenotypic plasticity, as well as providing a potential bridge between theoretical and empirical studies of labile trait variation.