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共生并不意味着存在生态相互作用。

Co-occurrence is not evidence of ecological interactions.

机构信息

Département de biologie, Université de Sherbrooke, Sherbrooke, J1K 2R1, QC, Canada.

Department of Integrative of Biology, University of Guelph, Guelph, N1G 2W1, ON, Canada.

出版信息

Ecol Lett. 2020 Jul;23(7):1050-1063. doi: 10.1111/ele.13525. Epub 2020 May 19.

Abstract

There is a rich amount of information in co-occurrence (presence-absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co-occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co-occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co-occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co-occurrence, along with potential avenues to extract as much information as possible from such data.

摘要

共生(存在-缺失)数据中蕴含着大量可用于理解群落组装的信息。这一观点最初由福布斯(Forbes)(1907 年)提出,然后由戴蒙德(Diamond)(1975 年)进一步发展,并促使了许多模型方法的发展(例如,零模型分析、共生网络,以及最近的联合物种分布模型)。理论和实验证据都支持这样一种观点,即生态相互作用可能会影响共生,但是在观察数据中捕捉到相互作用信号的程度仍不清楚。现在是时候从统计发展中退一步,批判性地评估共生数据是否真的可以作为生态相互作用的替代物。在本文中,我们基于概率、抽样、食物网和共存理论提出了一系列论点,这些论点支持这样一种观点,即物种之间(或缺乏)的显著空间关联是生态相互作用的一个很差的替代物。我们讨论了共生的适当解释,以及从这些数据中提取尽可能多信息的潜在途径。

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