Using isotope composition and other node attributes to predict edges in fish trophic networks

Vyacheslav Lyubchich and Ryan J. Woodland

Statistics & Probability Letters, 2019, vol. 144, issue C, 63-68

Abstract: Stable isotope analysis becomes increasingly popular in ecological modeling. With exponential random graph models and machine learning techniques, this paper shows how predator isotope information and basic physical variables become predictors for the links in a trophic network.

Keywords: Deep neural network; Exponential random graph model; Food web; Node feature; Random forest; Random network (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.spl.2018.06.001

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