DOI: 10.5061/dryad.b8gtht7h3
title: Inferring predator-prey interactions from camera traps: A Bayesian co-abundance modelling approach

description: Predator-prey dynamics are a fundamental part of ecology, but directly
 studying interactions has proven difficult. The proliferation of camera
 trapping has enabled the collection of large datasets on wildlife, but
 researchers face hurdles inferring interactions from observational data.
 Recent advances in hierarchical co-abundance models infer species
 interactions while accounting for two species’ detection probabilities,
 shared responses to environmental covariates, and propagate uncertainty
 throughout the entire modelling process. However, current approaches
 remain unsuitable for interacting species whose natural densities differ
 by an order of magnitude and have contrasting detection probabilities,
 such as predator-prey interactions, which introduce zero-inflation and
 overdispersion in count histories. Here we developed a Bayesian
 hierarchical N-mixture co-abundance model that is suitable for inferring
 predator-prey interactions. We accounted for excessive zeros in count
 histories using an informed zero-inflated Poisson distribution in the
 abundance formula and accounted for overdispersion in count histories by
 including a random effect per sampling unit and sampling occasion in the
 detection probability formula. We demonstrate that models with these
 modifications outperform alternative approaches, improve model
 goodness-of-fit, and overcome parameter convergence failures. We highlight
 its utility using 20 camera trapping datasets from 10 tropical forest
 landscapes in Southeast Asia and estimate four predator-prey relationships
 between tigers, clouded leopards, and muntjac and sambar deer. Tigers had
 a negative effect on muntjac abundance, providing support for top-down
 regulation, while clouded leopards had a positive effect on muntjac and
 sambar deer, likely driven by shared responses to unmodelled covariates
 like hunting. This Bayesian co-abundance modelling approach to quantify
 predator-prey relationships is widely applicable across species,
 ecosystems, and sampling approaches, and may be useful in forecasting
 cascading impacts following widespread predator declines. Taken together,
 this approach facilitates a nuanced and mechanistic understanding of
 food-web ecology.
description: This dataset is a subset of 20 large systematic camera trapping
 sessions conducted across 10 landscapes in Southeast Asian primary
 tropical forests. The manuscript describes a new method of analyzing
 camera trap data to infer predator-prey species interactions and is well
 described in the manuscript. The camera trap data has already been
 converted to count history matrices and spatial covariates have already
 been generated, and both are saved as .csv files. The repository also
 contains completed co-abundance modes which are saved as .RDS
 files. 
description: The repository contains a .zip file that contains all R code,
 datasets (.csv files), and completed co-abundance models (.RDS files) to
 run your own co-abundance model and generate the same figures included in
 the manuscript. Individual data files (e.g., count_history_matrix) and
 completed models (e.g., OG models) are saved in sub-folders within the
 .zip file. In addition to the .zip file, all individual files within the
 compressed .zip file have been added to the repository to help facilitate
 examination of individual data files. The repository contains a very
 detailed README file that includes a series of questions to determine if
 running a co-abundance model is the right option for you and detailed
 descriptions of each data type. The R script included in the repository is
 well ##hashed## out and explained clearly throughout the
 document.