Data from: 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
requirements for standard precision (CV = 0.35) and stringent standards
(CV = 0.2). RAD-REST produced unbiased estimates with appropriate coverage
probabilities. For 200-camera arrays, analyzing 100 detection records per
species achieved standard precision (CV = 0.35) with only one hour of
analysis time. Achieving CV = 0.2 with 400-camera networks requires
analyzing approximately 7% of total records. We present a specific
protocol for integration into Snapshot, a globally expanding annual camera
trap survey program.