Monitoring animal populations with cameras using open, multistate, N-mixture models
Data files
Nov 12, 2024 version files 10.96 KB
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moose_data_bulls.rds
4.10 KB
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moose_data.rds
4.07 KB
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README.md
2.78 KB
Abstract
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.
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:
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first dimension of array = number of camera sites (n = 225)
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second dimension of array = number of years (n = 6; 2014 - 2019)
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third dimension of array = number of states (n = 2; “Juveniles”, “Adults”)
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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. 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
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.