Describing variation of species interaction networks across space and time promises a better understanding of how species communities respond to global change. To understand this variation, it has been suggested to partition network dissimilarity into one component driven by species turnover, that is, changes in community composition, and another component reflecting rewiring, that is, flexibility of interactions among shared species. The latter makes a strong case for investing the enormous effort in empirically recording interactions, instead of simply building networks based on community data. Here, I present a flexible R function (available in the R package bipartite) to calculate network dissimilarity and its components, with binary and quantitative networks. With this new tool, I compare two published methods for partitioning network dissimilarity, using conceptual examples, published plant–pollinator networks, and a set of simulations. This comparison highlights that the method that has received most attention overestimates the importance of rewiring for total network dissimilarity. In contrast, an earlier-proposed method is derived from additive partitioning of the sets of interactions and thus accurately represents the two dissimilarity components. Furthermore, I argue that the term rewiring is not well defined in network ecology and that there are reasons why both methods overestimate the importance of rewiring, in particular with quantitative networks. The availability of a unified function to calculate multiple aspects of network dissimilarity will foster its critical application to characterize network dynamics and to identify underlying drivers. Studies on network dissimilarity and rewiring will have to be more careful in the choice of method and its interpretation.

中文翻译：

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物种相互作用网络的不同：如何划分重新布线和物种周转组件

描述跨空间和时间的物种相互作用网络的变化，有望更好地了解物种群落如何应对全球变化。为了理解这种变化，有人建议将网络差异分为由物种周转驱动的一个组成部分，即群落组成的变化，以及另一个反映重新布线的组成部分，即共享物种之间相互作用的灵活性。后者为投入大量精力以经验记录交互而不是简单地基于社区数据构建网络提供了强有力的理由。在这里，我提出了一个灵活的 R 函数（在 R 包 bipartite 中可用）来计算网络相异性及其组件，以及二进制和定量网络。有了这个新工具，我使用概念示例、已发布的植物传粉者网络和一组模拟来比较两种已发布的用于划分网络差异的方法。这种比较突出表明，最受关注的方法高估了重新布线对整个网络差异的重要性。相比之下，较早提出的方法是从交互集的加法划分派生而来的，因此可以准确地表示两个不同的组件。此外，我认为重新布线这个术语在网络生态学中没有得到很好的定义，并且这两种方法都高估了重新布线的重要性，特别是对于定量网络而言，这是有原因的。计算网络差异的多个方面的统一函数的可用性将促进其在表征网络动态和识别潜在驱动因素方面的关键应用。对网络差异性和重新布线的研究在方法的选择及其解释上必须更加谨慎。