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Nestedness is a property of interaction networks widely observed in naturalmutualistic communities. Despite a widespread interest on this pattern, nogeneral consensus exists on how to measure it. Instead, several metrics aimingat quantifying nestedness, based on different but not necessarily independentproperties of the networks, coexist in the literature blurring the comparisonbetween ecosystems. In this work, we present a detailed critical study of thebehavior of six popular nestedness metrics and the variants of two of them. Inorder to evaluate their performance, we compare the obtained values of thenestedness of a large set of real networks among them and against a maximumentropy and maximum likelihood null model. Our results point out, first, thatthe metrics do not rank the degree of nestedness of networks universally.Furthermore, several metrics show significant undesired dependencies on thenetwork properties considered. The study of these dependencies allows us tounderstand some of the systematic shifts between the real values of nestednessand the average over the null model. This paper intends to provide readers witha critical guide on how to measure nestedness patterns, by explaining thefunctioning of six standard metrics and two of its variants, and thendisclosing its qualities and flaws. By doing so, we also aim to extend theapplication of the recently proposed null models based on maximum entropy tothe still largely unexplored area of ecological networks. Finally, tocomplement the guide, we provide a fully-documented repository named nullnestwhich gathers the codes to produce the null model and calculate the nestednessindex -- both the real value and the null expectation -- using the studiedmetrics. The repository contains, moreover, the main results of the null modelapplied to a large dataset of more than 200 bipartite networks.

Measuring Nestedness: A comparative study of the performance of different metrics