DOI: 10.5061/dryad.tqjq2bw76
title: Monitoring animal populations with cameras using open, multistate, N-mixture models

description: Remote cameras have become a mainstream tool for studying wildlife
 populations. For species whose developmental stages or states are
 identifiable in photographs, there are opportunities for tracking
 population changes and estimating demographic rates. Recent developments
 in hierarchical models allow for the estimation of ecological states and
 rates over time for unmarked animals whose states are known. However, this
 powerful class of models has been underutilized because they are
 computationally intensive, and model outputs can be difficult to
 interpret. Here, we use simulation to show how camera data can be analyzed
 with multistate, Dail-Madsen (hereafter multistate DM) models to estimate
 abundance, survival, and recruitment. We evaluated 4 commonly encountered
 scenarios arising from camera trap data (low and high abundance and 25%
 and 50% missing data) each with 18 different sample size combinations
 (camera sites = 40, 250; surveys = 4, 8, 12; and years = 2, 5, 10) and
 evaluated the bias and precision of abundance, survival, and recruitment
 estimates. We also analyzed our empirical camera data on moose (Alces
 alces) with multistate DM models and compared inference with telemetry
 studies from the same time and region to assess the accuracy of camera
 studies in tracking moose populations. Most scenarios recovered the known
 parameters from our simulated data with higher accuracy and increased
 precision for scenarios with more sites, surveys, and/or years. Large
 amounts of missing data and fewer camera sites, especially at higher
 abundances, reduced the accuracy and precision of survival and
 recruitment. Our empirical analysis provided biologically realistic
 estimates of moose survival and recruitment and recovered the pattern of
 moose abundance across the region. Multistate DM models can be used for
 estimating demographic parameters from camera data when developmental
 states are clearly identifiable. We discuss several avenues for future
 research and caveats for using multistate DM models for large-scale
 population monitoring.
description: For the simulation analysis, data were generated using base
 simulation functions in R (see code) and there are no traditional field
 data associated with this part of the manuscript. The
 dataset (moose_data.rds) accompanies the manuscript: "Monitoring
 animal populations with cameras using open, multistate, N-mixture
 models". It is an rds file that includes counts of adult female and
 juvenile moose (Alces alces) captured on remote cameras. The file is a
 4-dimensional table that includes sites (n = 225 [indexed as 257]), years
 (n = 6), age classes (n = 2; adult and juveniles), and surveys per year (n
 = 4). We have also included another file (moose_data_bulls.rds) that
 includes counts of adult female and male moose as well as juveniles. These
 data were not formally analyzed but mentioned in the discussion as a
 dataset for readers to explore using multistate DM models. The data were
 collected by Dr. Alexej Siren and the other co-authors (see dataset
 authors) in Vermont and New Hampshire, USA, from 9 January 2014 to 9
 August 2019. Data were examined for logical errors in processing step 1.
 Logical errors that could not be reconciled by observer agreement
 (majority), were assigned as 0s in the database, which is consistent with
 camera data standards. All missing data (not recorded in the field) was
 assigned an NA  value. Data were collected at random sites in Vermont and
 New Hampshire. Care should be taken when extrapolating values outside of
 the geographical domain. The counts of adult female and juvenile moose are
 affected by local weather conditions and seasonal phenology, therefore raw
 counts do not represent a census or absolute measure of absence or
 abundance.
description: # Monitoring animal populations with cameras using open, multistate,
 N-mixture models
 [https://doi.org/10.5061/dryad.tqjq2bw76](https://doi.org/10.5061/dryad.tqjq2bw76) Our dataset allows the user to replicate the results of the study (see description below). **We politely request to be contacted by parties interested in data reuse from the empirical moose study to discuss collaboration**. ## Description of the data and file structure The simulation file `Multi-State DM Simulation.Rmd` does not contain external data (see below). The empirical moose data (moose_data.rds) is a 4-dimensional array that contains count data of juvenile and adult female moose from 2014 to 2019. The structure of the data is as follows: * first dimension of array = number of camera sites (n = 225) * second dimension of array = number of years (n = 6; 2014 - 2019) * third dimension of array = number of states (n = 2; "Juveniles", "Adults") * fourth dimension of array = number of monthly surveys per year (n = 4; "May", "Jun", "Jul". "Aug") We have also included an additional data file (moose_data_bulls.rds) that contains counts of adult bulls (males) and females(cows) that can be analyzed using the model that Zipkin et al. (2014) used for salamanders. ## Sharing/Access information Data and R code used in this study are available at Dryad (Sirén et al. 2024): [https://doi.org/10.5061/dryad.tqjq2bw76](https://doi.org/10.5061/dryad.tqjq2bw76). **We politely request to be contacted by parties interested in data reuse from the empirical moose study to discuss collaboration**. ## Code/Software The simulation code `Multi-State DM Simulation.Rmd` contains code to simulate data for a multistate DM analysis. All information about the simulations is provided in this markdown file. The 'Multi-State DM Moose - Dryad.Rmd' file is the R code for running the analysis. Both markdown files use the `jagsUI` R package, version 1.5.2, to run the models in JAGS. It uses the `open_multi_Nmix_Sim.txt` JAGS model to run the Bayesian models on the simulated data. The JAGS code `jags_model.txt` is borrowed from the Zipkin et al. (2014) salamander example. The Zipkin et al. (2014) model considers 2 juvenile stages, one of which appears to be latent, and 1 adult stage because their salamanders don't start breeding until their 3rd year of life (i.e., at 2+ years). This is relevant for the moose example because previous studies found that yearlings rarely breed. \- Zipkin, E. F., Thorson, J. T., See, K., Lynch, H. J., Grant, E. H. C., Kanno, Y., Chandler, R. B., Letcher, B. H., & Royle, J. A. (2014). Modeling structured population dynamics using data from unmarked individuals. Ecology, 95(1), 22–29. [https://doi.org/10.1890/13-1131.1](https://doi.org/10.1890/13-1131.1)