Assessing mutualistic metacommunity capacity by integrating spatial and interaction networks

IF 1.2 4区 生物学 Q4 ECOLOGY Theoretical Population Biology Pub Date : 2024-01-14 DOI:10.1016/j.tpb.2024.01.001
Marc Ohlmann , François Munoz , François Massol , Wilfried Thuiller
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Abstract

We develop a spatially realistic model of mutualistic metacommunities that exploits the joint structure of spatial and interaction networks. Assuming that all species have the same colonisation and extinction parameters, this model exhibits a sharp transition between stable non-null equilibrium states and a global extinction state. This behaviour allows defining a threshold on colonisation/extinction parameters for the long-term metacommunity persistence. This threshold, the ‘metacommunity capacity’, extends the metapopulation capacity concept and can be calculated from the spatial and interaction networks without needing to simulate the whole dynamics. In several applications we illustrate how the joint structure of the spatial and the interaction networks affects metacommunity capacity. It results that a weakly modular spatial network and a power-law degree distribution of the interaction network provide the most favourable configuration for the long-term persistence of a mutualistic metacommunity. Our model that encodes several explicit ecological assumptions should pave the way for a larger exploration of spatially realistic metacommunity models involving multiple interaction types.

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通过整合空间网络和互动网络评估互惠元群落的能力。
我们利用空间和相互作用网络的联合结构,建立了一个现实的互惠元群落空间模型。假设所有物种都具有相同的殖民化和灭绝参数,该模型在稳定的非零平衡状态和全局灭绝状态之间呈现出急剧的过渡。这种行为允许为元群落的长期持续性定义一个殖民化/灭绝参数阈值。这个阈值,即 "元群落容量",扩展了元种群容量的概念,可以通过空间和相互作用网络计算出来,而无需模拟整个动态过程。在一些应用中,我们说明了空间和相互作用网络的联合结构如何影响元群落容量。结果表明,弱模块化的空间网络和幂律程度分布的相互作用网络为互惠元群落的长期存在提供了最有利的配置。我们的模型包含了几个明确的生态学假设,应为更广泛地探索涉及多种相互作用类型的空间现实元群落模型铺平道路。
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来源期刊
Theoretical Population Biology
Theoretical Population Biology 生物-进化生物学
CiteScore
2.50
自引率
14.30%
发文量
43
审稿时长
6-12 weeks
期刊介绍: An interdisciplinary journal, Theoretical Population Biology presents articles on theoretical aspects of the biology of populations, particularly in the areas of demography, ecology, epidemiology, evolution, and genetics. Emphasis is on the development of mathematical theory and models that enhance the understanding of biological phenomena. Articles highlight the motivation and significance of the work for advancing progress in biology, relying on a substantial mathematical effort to obtain biological insight. The journal also presents empirical results and computational and statistical methods directly impinging on theoretical problems in population biology.
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