A roadmap towards predicting species interaction networks (across space and time)
A roadmap towards predicting species interaction networks (across space and time)
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DOI：
10.1098/rstb.2021.0063
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发表时间：
2021-09
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通讯作者：
T. Strydom;Michael D Catchen;Francis Banville;Dominique Caron;Gabriel Dansereau;P. Desjardins-Proulx;Norma R. Forero-Muñoz;Gracielle T. Higino;B. Mercier;Andrew Gonzalez;D. Gravel;Laura Pollock;T. Poisot
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作者：
T. Strydom;Michael D Catchen;Francis Banville;Dominique Caron;Gabriel Dansereau;P. Desjardins-Proulx;Norma R. Forero-Muñoz;Gracielle T. Higino;B. Mercier;Andrew Gonzalez;D. Gravel;Laura Pollock;T. Poisot
物种相互作用的网络支撑着许多生态系统过程，但对这些相互作用进行全面采样是困难的。相互作用在本质上随时间和空间的变化而变化，并且考虑到组成生态群落的物种数量，很难区分真阴性（两个物种从不相互作用）和假阴性（两个物种没有观察到相互作用，即使它们实际上相互作用）。评估物种之间相互作用的可能性对生态学的几个领域来说是必要的。这意味着，要预测物种之间的相互作用，并描述它们形成的生态网络的结构、变异和变化，我们需要依靠建模工具。在这里，我们提供了一个概念证明，我们展示了一个简单的神经网络模型如何在有限的数据下对物种相互作用做出准确的预测。然后，我们评估了与改进相互作用预测相关的挑战和机遇，并为明确的时空生态网络预测模型提供了一个概念性路线图。最后，我们简要介绍了开始建立这些模型所需的相关方法和工具，我们希望这些方法和工具将指导本研究计划向前发展。本文是主题“传染病宏观生态学：全球寄生虫多样性和动态”的一部分。
Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species—and to describe the structure, variation, and change of the ecological networks they form—we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.