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基于特质的生态进化动力学理论框架:种群结构、种内变异和群落组装。

A Theoretical Framework for Trait-Based Eco-Evolutionary Dynamics: Population Structure, Intraspecific Variation, and Community Assembly.

出版信息

Am Nat. 2023 Apr;201(4):501-522. doi: 10.1086/723406. Epub 2023 Feb 20.

Abstract

AbstractHow is trait diversity in a community apportioned between and within coevolving species? Disruptive selection may result in either a few species with large intraspecific trait variation (ITV) or many species with different mean traits but little ITV. Similar questions arise in spatially structured communities: heterogeneous environments could result in either a few species that exhibit local adaptation or many species with different mean traits but little local adaptation. To date, theory has been well-equipped to either include ITV or to dynamically determine the number of coexisting species, but not both. Here, we devise a theoretical framework that combines these facets and apply it to the above questions of how trait variation is apportioned within and between species in unstructured and structured populations, using two simple models of Lotka-Volterra competition. For unstructured communities, we find that as the breadth of the resource spectrum increases, ITV goes from being unimportant to crucial for characterizing the community. For spatially structured communities on two patches, we find no local adaptation, symmetric local adaptation, or asymmetric local adaptation, depending on how much the patches differ. Our framework provides a general approach to incorporate ITV in models of eco-evolutionary community assembly.

摘要

摘要

群落中的性状多样性在共同进化的物种之间和内部是如何分配的?非连续性选择可能导致少数几个物种具有较大的种内性状变异(ITV),或者许多物种具有不同的平均性状但 ITV 很小。在空间结构的群落中也会出现类似的问题:异质环境可能导致少数几个表现出局部适应性的物种,或者许多物种具有不同的平均性状但很少有局部适应性。到目前为止,理论已经很好地适应了包含 ITV 或动态确定共存物种数量这两个方面,但不能同时兼顾。在这里,我们设计了一个理论框架,结合了这两个方面,并将其应用于无结构和结构种群中物种内部和之间性状变异分配的上述问题,使用了两种简单的 Lotka-Volterra 竞争模型。对于无结构的群落,我们发现随着资源谱的宽度增加, ITV 从不重要到对群落特征的描述变得至关重要。对于两个斑块上的空间结构群落,我们发现没有局部适应性、对称局部适应性或不对称局部适应性,这取决于斑块之间的差异有多大。我们的框架为在生态进化群落组装模型中纳入 ITV 提供了一种通用方法。

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