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

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  • Lyubchich, Vyacheslav
  • Woodland, Ryan J.

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.

Suggested Citation

  • Lyubchich, Vyacheslav & Woodland, Ryan J., 2019. "Using isotope composition and other node attributes to predict edges in fish trophic networks," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 63-68.
    • Handle: RePEc:eee:stapro:v:144:y:2019:i:c:p:63-68
    DOI: 10.1016/j.spl.2018.06.001
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    References listed on IDEAS

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    1. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    2. Hunter, David R. & Handcock, Mark S. & Butts, Carter T. & Goodreau, Steven M. & Morris, Martina, 2008. "ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i03).
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