Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources

Abstract

1. The occurrence and distributions of wildlife populations and communities are shifting as a result of global changes. To evaluate whether these shifts are negatively impacting biodiversity processes, it is critical to monitor the status, trends and effects of environmental variables on entire communities. However, modelling the dynamics of multiple species simultaneously can require large amounts of diverse data, and few modelling approaches exist to simultaneously provide species and community-level inferences.2. We present an 'integrated community occupancy model' (ICOM) that unites principles of data integration and hierarchical community modelling in a single framework to provide inferences on species-specific and community occurrence dynamics using multiple data sources. The ICOM combines replicated and nonreplicated detectionnondetection data sources using a hierarchical framework that explicitly accounts for different detection and sampling processes across data sources. We use simulations to compare the ICOM to previously developed hierarchical community occupancy models and single species integrated distribution models. We then apply our model to assess the occurrence and biodiversity dynamics of foliage-gleaning birds in the White Mountain National Forest in the northeastern USA from 2010 to 2018 using three independent data sources.3. Simulations reveal that integrating multiple data sources in the ICOM increased precision and accuracy of species and community-level inferences compared to single data source models, although benefits of integration were dependent on the information content of individual data sources (e.g. amount of replication). Compared to single species models, the ICOM yielded more precise species-level estimates. Within our case study, the ICOM had the highest out-of-sample predictive performance compared to single species models and models that used only a subset of the three data sources.4. The ICOM provides more precise estimates of occurrence dynamics compared to multi-species models using single data sources or integrated single-species models.We further found that the ICOM had improved predictive performance across a broad region of interest with an empirical case study of forest birds. The ICOM offers an

spOccupancy: An R package for single‐species, multi‐species, and integrated spatial occupancy models

“…The package vignette (Supplemental Information S3) and website (https://www.jeffdoser.com/files/spoccupancy-web/) contain full details and examples on all spOccupancy model functions. We are currently working on including the following extensions within the package: (a) dynamic occupancy models (MacKenzie et al, 2003); (b) spatially varying coefficients (SVCs; Finley, 2011) in the occurrence model; (c) multi‐species integrated occupancy models (Doser, Leuenberger, et al, 2022). We expect spOccupancy will serve as a user‐friendly tool for ecologists and conservation practitioners to account for detection biases and spatial autocorrelation using large datasets (e.g.…”

“…Our spOccupancy R package fits spatially explicit single-species, multi-species integrated occupancy models (Doser, Leuenberger, et al, 2022). We expect spOccupancy will serve as a user-friendly tool for ecologists and conservation practitioners to account for detection biases and spatial autocorrelation using large datasets…”

Integrating presence-only and presence-absence data to model changes in species geographic ranges: An example of yaguarundí in Latin America

“…Third, since it is implemented in JAGS, the model can be extended in various ways. Accounting for imperfect detection (Dorazio, 2014;Koshkina et al, 2017), modelling multiple species in joint species distribution models (Doser et al, 2022;Ovaskainen & Abrego, 2020), e.g., to share information on the thinning process (Fithian et al, 2015), or accounting for false positives (Kéry & Royle, 2015) are some of the potential extensions that have recently been tested in the IDM framework (Doser et al, 2022), although not over large geographic extents. Also, for the sake of simplicity, we established two discrete temporal periods that enabled us to have balanced presence-only and presence-absence data and to produce an up-to-date map of the species' current distribution.…”

“…As an exception, Zulian et al, (2021) used data integration to model the full geographic distribution of a parrot species endemic to the tropical South American Atlantic Forest. Further, with some exceptions (Doser et al, 2022;Hertzog et al, 2021;Pagel et al, 2014), IDMs have not been used to model temporal change of distributions, although this could be their obvious application, given the scarcity of temporally replicated data. Finally, IDMs can appear complex, with a lack of user-friendly tools available; thus, their implementation can be challenging, particularly for inexperienced users.…”

Joint species distribution models with imperfect detection for high-dimensional spatial data

“…We then averaged across the three model deviance scoring rules to generate a single measure of predictive performance for the latent occurrence state. This allowed us to assess performance of the models in predicting the ecological process of interest rather than the raw detection-nondetection values (which confounds imperfect detection and true species occurrence) while accounting for model uncertainty (Doser et al, 2022).…”