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在时变环境中,营养网络结构通过拮抗协同进化而出现。

Trophic network structure emerges through antagonistic coevolution in temporally varying environments.

机构信息

Institut des Sciences de l'Evolution, UMR 5554, Université Montpellier II, Place Eugène Bataillon, 34095 Montpellier Cedex 05, France.

出版信息

Proc Biol Sci. 2012 Jan 22;279(1727):299-308. doi: 10.1098/rspb.2011.0826. Epub 2011 Jun 8.

DOI:10.1098/rspb.2011.0826
PMID:21653583
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3223677/
Abstract

Understanding the mechanisms underlying ecological specialization is central to our understanding of community ecology and evolution. Although theoretical work has investigated how variable environments may affect specialization in single species, little is known about how such variation impacts bipartite network structure in antagonistically coevolving systems. Here, we develop and analyse a general model of victim-enemy coevolution that explicitly includes resource and population dynamics. We investigate how temporal environmental heterogeneity affects the evolution of specialization and associated community structure. Environmental productivity influences victim investment in resistance, which will shape patterns of specialization through its regulating effect on enemy investment in infectivity. We also investigate the epidemiological consequences of environmental variability and show that enemy population density is maximized for intermediate lengths of productive seasons, which corresponds to situations where enemies can evolve higher infectivity than victims can evolve defence. We discuss our results in the light of empirical studies, and further highlight ways in which our model applies to a range of natural systems.

摘要

理解生态特化的机制是理解群落生态学和进化的核心。尽管理论研究已经探讨了多变的环境如何影响单一物种的特化,但对于这种变化如何影响互利共生系统中的二分网络结构知之甚少。在这里,我们开发并分析了一个明确包含资源和种群动态的受害者-捕食者共同进化的一般模型。我们研究了时间环境异质性如何影响特化和相关群落结构的进化。环境生产力影响受害者对抵抗力的投资,这将通过对捕食者对感染力的投资的调节作用来塑造特化模式。我们还研究了环境变异性的流行病学后果,并表明,在生产季节的中等长度下,捕食者种群密度最大,这对应于捕食者可以进化出比受害者更高的感染力的情况,而受害者可以进化出防御能力。我们根据实证研究讨论了我们的结果,并进一步强调了我们的模型适用于一系列自然系统的方式。