Reducing data processing effort in camera trap density estimation: Extending the REST model by explicitly modeling animal detection processes

Ecological communities are increasingly unstable in the Anthropocene, requiring continuous multi-species monitoring across broad spatial scales. While camera traps offer great potential for monitoring ground-dwelling mammal densities, labor-intensive data processing constrains their application. We developed the RAD-REST (Random Encounter and Staying Time model Relying on All Detections) model, an extension of the conventional REST model, to enable community-level density monitoring with substantially reduced effort. By modeling the probabilistic process of animals entering predefined focal areas using a Dirichlet compound multinomial distribution, our model estimates densities using detection record subsets (videos/photo sequences) while leveraging all available data. We created a user-friendly R package that performs full Bayesian parameter estimation, including activity levels. Using data in the Boso Peninsula, Japan, Monte Carlo simulations determined analytical workload requirement..., , # Data from: Reducing data processing effort in camera trap density estimation: Extending the REST model by explicitly modeling animal detection processes

Dataset DOI: [10.5061/dryad.kprr4xhgt](10.5061/dryad.kprr4xhgt)

## Description of the data and file structure

This dataset was collected to estimate population density using a newly developed Random Encounter and Staying Time Model Relying on All Detection (RAD-REST model), and to validate its accuracy. We provide two CSV files containing camera trap data and analysis code for density estimation based on actual data collected from the Boso Peninsula, Japan, as well as simulation code based on these results.

### Files and variables

This data repository contains camera trap data collected from the Boso Peninsula in Japan and R code for density estimation and simulations using the newly developed \"ctrest\" R package. For detailed information
about each function, please refer to the package function help documentation and vignettes.

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