The camtrapR R package: From data management to interactive ecological analysis of camera trap data

Camera trapping has become an indispensable tool in wildlife ecology, generating vast datasets that require efficient and robust analytical workflows. The R package camtrapR was originally developed for preparing camera trap data for analysis in external packages and has since become a standard tool for this purpose. The analyses themselves, however, still had to be conducted elsewhere.

Here, we present a major update that transforms camtrapR into an end-to-end analytical platform that unifies data import, exploration, covariate preparation, activity pattern estimation, occupancy modelling and spatial prediction. All new functionality is available as R functions and through surveyDashboard(), a new code-free graphical user interface that guides users through the entire analytical pipeline.

Data import supports widely used formats, including exports from the Wildlife Insights platform and datasets conforming to the Camtrap DP data standard. Exploratory tools generate species accumulation curves, maps of species detections and activity patterns for up to two species. Covariates can be extracted at camera trap sites from user-supplied rasters or online sources, providing inputs for model fitting and, as raster layers, for spatial predictions. Users can fit single-species and community occupancy models, some of the most widely adopted analytical frameworks in wildlife ecology, and map predicted occupancy across space.

Community occupancy models are fitted in a Bayesian framework using JAGS or NIMBLE. These hierarchical models estimate species- and community-level responses to environmental predictors and improve inference for rare species. The workflow includes convergence diagnostics and posterior predictive checks for model assessment and produces spatial predictions of species occupancy and species richness. The workflow can be implemented both in code and within the dashboard.

By combining a code-free interface with advanced modelling capabilities, camtrapR now makes robust camera trap data analysis accessible to a wider audience of ecologists, wildlife managers and students. This paper accompanies camtrapR version 3 and serves as the reference for its expanded functionality.