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March 2020 A hierarchical Bayesian model for predicting ecological interactions using scaled evolutionary relationships
Mohamad Elmasri, Maxwell J. Farrell, T. Jonathan Davies, David A. Stephens
Ann. Appl. Stat. 14(1): 221-240 (March 2020). DOI: 10.1214/19-AOAS1296

Abstract

Identifying undocumented or potential future interactions among species is a challenge facing modern ecologists. Recent link prediction methods rely on trait data; however, large species interaction databases are typically sparse and covariates are limited to only a fraction of species. On the other hand, evolutionary relationships, encoded as phylogenetic trees, can act as proxies for underlying traits and historical patterns of parasite sharing among hosts. We show that, using a network-based conditional model, phylogenetic information provides strong predictive power in a recently published global database of host-parasite interactions. By scaling the phylogeny using an evolutionary model, our method allows for biological interpretation often missing from latent variable models. To further improve on the phylogeny-only model, we combine a hierarchical Bayesian latent score framework for bipartite graphs that accounts for the number of interactions per species with host dependence informed by phylogeny. Combining the two information sources yields significant improvement in predictive accuracy over each of the submodels alone. As many interaction networks are constructed from presence-only data, we extend the model by integrating a correction mechanism for missing interactions which proves valuable in reducing uncertainty in unobserved interactions.

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Mohamad Elmasri. Maxwell J. Farrell. T. Jonathan Davies. David A. Stephens. "A hierarchical Bayesian model for predicting ecological interactions using scaled evolutionary relationships." Ann. Appl. Stat. 14 (1) 221 - 240, March 2020. https://doi.org/10.1214/19-AOAS1296

Information

Received: 1 January 2019; Revised: 1 July 2019; Published: March 2020
First available in Project Euclid: 16 April 2020

zbMATH: 07200169
MathSciNet: MR4085091
Digital Object Identifier: 10.1214/19-AOAS1296

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 1 • March 2020
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