度量嵌套性：不同度量的性能比较研究
Measuring Nestedness: A comparative study of the performance of different metrics
摘要：
嵌套性是在自然互惠社区中广泛观察到的相互作用网络的一种性质。尽管对这种模式的广泛兴趣，没有普遍的共识存在于如何衡量它。相反，旨在量化嵌套，基于不同的，但不一定是独立的网络属性的几个指标，共存于文献中模糊生态系统之间的比较。在这项工作中，我们提出了一个详细的批判性研究的行为，六个流行的嵌套度量和其中两个的变体。为了评估其性能，我们比较了它们之间的一个大的一组真实的网络的嵌套性，并对最大熵和极大似然空模型得到的值。我们的研究结果指出，首先，指标不排名的嵌套网络的程度普遍。此外，几个指标显示出显着的不期望的依赖关系的网络属性考虑。对这些依赖关系的研究使我们能够理解嵌套的真实值与空模型的平均值之间的一些系统性变化。本文旨在通过解释六个标准度量及其两个变体的功能，然后揭示其质量和缺陷，为读者提供如何测量嵌套模式的关键指南。通过这样做，我们还旨在将最近提出的基于最大熵的空模型的应用扩展到生态网络的大部分尚未开发的领域。最后，为了补充指南，我们提供了一个名为nullnest的完整文档存储库，它收集代码以生成空模型并使用所研究的指标计算嵌套指数-真实值和空期望。此外，该存储库还包含应用于200多个二分网络的大型数据集的空模型的主要结果。
英文摘要：
Nestedness is a property of interaction networks widely observed in natural mutualistic communities. Despite a widespread interest on this pattern, no general consensus exists on how to measure it. Instead, several metrics aiming at quantifying nestedness, based on different but not necessarily independent properties of the networks, coexist in the literature blurring the comparison between ecosystems. In this work, we present a detailed critical study of the behavior of six popular nestedness metrics and the variants of two of them. In order to evaluate their performance, we compare the obtained values of the nestedness of a large set of real networks among them and against a maximum entropy and maximum likelihood null model. Our results point out, first, that the metrics do not rank the degree of nestedness of networks universally. Furthermore, several metrics show significant undesired dependencies on the network properties considered. The study of these dependencies allows us to understand some of the systematic shifts between the real values of nestedness and the average over the null model. This paper intends to provide readers with a critical guide on how to measure nestedness patterns, by explaining the functioning of six standard metrics and two of its variants, and then disclosing its qualities and flaws. By doing so, we also aim to extend the application of the recently proposed null models based on maximum entropy to the still largely unexplored area of ecological networks. Finally, to complement the guide, we provide a fully-documented repository named nullnest which gathers the codes to produce the null model and calculate the nestedness index -- both the real value and the null expectation -- using the studied metrics. The repository contains, moreover, the main results of the null model applied to a large dataset of more than 200 bipartite networks.
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