Abstract
Nestedness is a statistical measure used to interpret bipartite interaction data inseveral ecological and evolutionary contexts, e.g. biogeography (species-site relationships)and species interactions (plant-pollinator and host-parasite networks).Multiple methods have been used to evaluate nestedness, which differ in how themetrics for nestedness are determined. Furthermore, several different null modelshave been used to calculate statistical significance of nestedness scores. The profusionof measures and null models, many of which give conflicting results, is problematicfor comparison of nestedness across different studies. We developed theFALCON software package to allow easy and efficient comparison of nestednessscores and statistical significances for a given input network, using a selection of themore popular measures and null models from the current literature. FALCON currentlyincludes six measures and five null models for nestedness in binary networks,and two measures and four null models for nestedness in weighted networks. TheFALCON software is designed to be efficient and easy to use. FALCON code is offeredin three languages (R, MATLAB, Octave) and is designed to be modular andextensible, enabling users to easily expand its functionality by adding further measuresand null models. FALCON provides a robust methodology for comparing thestrength and significance of nestedness in a given bipartite network using multiplemeasures and null models. It includes an "adaptive ensemble" method to reduceundersampling of the null distribution when calculating statistical significance. Itcan work with binary or weighted input networks. FALCON is a response to theproliferation of different nestedness measures and associated null models in the literature.It allows easy and efficient calculation of nestedness scores and statisticalsignificances using different methods, enabling comparison of results from differentstudies and thereby supporting theoretical study of the causes and implications ofnestedness in different biological contexts.
Highlights
Nestedness is a statistical property of systems where two kinds of entity interact, which can be represented as bipartite networks
FALCON uses a bootstrap method to calculate the statistical significance of a given nestedness score, since the true null distributions of the test statistics are not known
6 Summary In this paper we have presented FALCON, a software tool for reliable and efficient calculation of nestedness based on a selection of popular nestedness measures and null models used in the literature
Summary
Nestedness is a statistical property of systems where two kinds of entity interact, which can be represented as bipartite networks. The concept of nestedness was first described in studies on how species distributions varied between sites[23,24,25], and later defined quantitatively as measuring the ‘amount of order/disorder’ in matrices representing the presence/absence of species in island communities[1] Used in this way, nestedness is calculated from a matrix of presence-absence data where rows are species and columns are sampling sites along some environmental or spatial gradient. A null distribution of similar matrices allows determination of both effect size (e.g. as a z-score, which is commonly used to compare different nestedness schemes26,39) and statistical significance (e.g. as a p-value giving the expected frequency of the observed score in the null distribution) This approach necessitates choice of a suitable null model and generation of a distribution of random matrices drawn from it. FALCON allows the user to decide if any sorting is performed, enabling the “context free” assumption to be relaxed (e.g. for investigation of gradient-based nestedness[39])
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