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Phillip Staniczenko et al. on a new measure of nestedness with deep roots in mathematics

I always joke in the lab that anybody who wants to propose a new measure in ecology (or index, etc.) should pay 1,000$, 2,000$ if the new measure has an acronym. These funds could pay for graduate students to go to some conference. 

The only exception to the rule is for studies showing that two seemingly different measures are in fact the same thing. This is the case of our recent study on nestedness, published today in Nature Communications:

Phillip P. A. Staniczenko, Jason C. Kopp & Stefano Allesina

Ecologists are fascinated by the prevalence of nestedness in biogeographic and community data, where it is thought to promote biodiversity in mutualistic systems. Traditionally, nestedness has been treated in a binary sense: species and their interactions are either present or absent, neglecting information on abundances and interaction frequencies. Extending nestedness to quantitative data facilitates the study of species preferences, and we propose a new detection method that follows from a basic property of bipartite networks: large dominant eigenvalues are associated with highly nested configurations. We show that complex ecological networks are binary nested, but quantitative preferences are non-nested, indicating limited consumer overlap of favoured resources. The spectral graph approach provides a formal link to local dynamical stability analysis, where we demonstrate that nested mutualistic structures are minimally stable. We conclude that, within the binary constraint of interaction plausibility, species preferences are partitioned to avoid competition, thereby benefiting system-wide resource allocation.

We uploaded the code needed for the analysis here.

 

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