DOI: 10.5061/dryad.kprr4xhgt
title: Data from: Reducing data processing effort in camera trap density estimation: Extending the REST model by explicitly modeling animal detection processes

description: 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.
description: # 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. #### CSV Data and R Code *
 **detection_data.csv** A CSV file containing information about each video
 recording (one row per video). The columns are as follows: * Season:
 Survey round * Station: Camera station ID * DateTimeCorrected: Recording
 date and time * Species: Animal species name * Enter: Number of entries
 with a predefined focal area * Stay: Duration of stay in seconds (NA if
 not measured) * RightCens: Presence of right-censoring (1: present, 0:
 absent,  NA if not measured) * **station_data.csv** A CSV file containing
 information about each camera station (one row per station). Since this
 analysis does not include covariates, it only contains station IDs. *
 Station: Camera station ID for all locations * **Rcode.R** Code for
 density estimation and simulations using the 'ctrest' package.
 #### Code/software The data can be viewed and analyzed using R (version
 4.4.1 or higher). Required packages include: ctrest (custom package
 available from GitHub: YoshihiroNakashima/ctrest), tidyverse, MASS,
 MCMCvis, MCMCpack, and ggplot2. The ctrest package must be installed from
 GitHub using devtools. The workflow consists of: (1) loading camera trap
 data from two CSV files (detection_data.csv and station_data.csv), (2)
 data preprocessing and formatting for RAD-REST model analysis, (3) stay
 time model selection using Bayesian methods, (4) density estimation for 12
 mammal species using single-species RAD-REST models, (5) simulation data
 generation and validation, and (6) regression analysis and visualization.
 The main analysis script contains all the necessary code with detailed
 comments explaining each step. Note that the simulation section requires
 extensive computational time (potentially weeks) and uses Bayesian MCMC
 methods with specified iterations, warmup, and chain parameters."
 #### Access information Not applicable. This dataset was collected as part
 of original research and is not available at other publicly accessible
 locations. The data was not derived from other sources.