Multi‐species occupancy models: review, roadmap, and recommendations

Abstract

Recent technological and methodological advances have revolutionized wildlife monitoring. Although most biodiversity monitoring initiatives are geared towards focal species of conservation concern, researchers are increasingly studying entire communities, specifically the spatiotemporal drivers of community size and structure and interactions among species. This has resulted in the emergence of multi‐species occupancy models (MSOMs) as a promising and efficient approach for the study of community ecology. Given the potential of MSOMs for conservation and management action, it is critical to know whether study design and model assumptions are consistent with inference objectives. This is especially true for studies that are designed for a focal species but can give insights about a community. Here, we review the recent literature on MSOMs, identify areas of improvement in the multi‐species study workflow, and provide a reference model for best practices for focal species and community monitoring study design. We reviewed 92 studies published between 2009 and early 2018, spanning 27 countries and a variety of taxa. There is a consistent under‐reporting of details that are central to determining the adequacy of designs for generating data that can be used to make inferences about community‐level patterns of occupancy, including the spatial and temporal extent, types of detectors used, covariates considered, and choice of field methods and statistical tools. This reporting bias could consequently result in skewed estimates, affecting conservation actions and management plans. On the other hand, comprehensive reporting is likely to help researchers working on MSOMs assess the robustness of inferences, in addition to making strides in terms of reproducibility and reusability of data. We use our literature review to inform a roadmap with best practices for MSOM studies, from simulations to design considerations and reporting, for the collection of new data as well as those involving existing datasets.

The Company Canids Confront: Spatiotemporal Partitioning at Local Scales Facilitates Carnivore Coexistence at the Landscape Level

“…Possibly as a consequence of these challenges coupled with the resource-intensive nature of wildlife monitoring and the lag in statistical and computational tools and methods to handle ecological data, the conservation and management of carnivores has historically focused on single species. There is growing interest in studying entire communities simultaneously, paving the way for multi-species or community models in ecological research [Mackenzie, et al, 2002;Royle & Young, 2008;Royle, et al, 2009;Waddle, et al, 2010;Rota, et al, 2016;Sollmann, et al, 2011;Devarajan, et al, 2020]. An additional challenge to modeling communities is that co-occurrence alone is not sufficient to establish ecological interactions [Blanchet, et al, 2020].…”

“…The cell size thus helps account for the assumptions of geographic closure and independence in MSOMs [Devarajan, et al, 2020]. The assumption of demographic closure is also satisfied since this is a single season study.…”

“…Since little is known about the ecology of the desert fox, we assumed a home range of about 4 km 2 considering that it is intermediate in size relative to the jackal and Indian fox. The cell size thus helps account for the assumptions of geographic closure and independence in MSOMs [Devarajan, et al, 2020]. The assumption of demographic closure is also satisfied since this is a single season study.…”

“…The assumption of demographic closure is also satisfied since this is a single season study. While all four canids belong to the same guild and satisfy the crucial assumption of ecological similarity due to their relatedness within carnivores, they are easy to tell apart in the camera trap videos and hence satisfy the assumption of accurate identification, an important consideration in MSOMs [Devarajan, et al, 2020].…”

“…The MSOM (see [Waddle, et al, 2010;Rota, et al, 2016;Devarajan, et al, 2020] for more information on MSOM implementation) was implemented under a Bayesian framework with JAGS using jagsUI in R. It was parallelized for a faster run using eight cores. The JAGS code provided as supplementary information through Figshare [Devarajan, 2020b] has the parametrization for each species.…”