Sirén, A.P.K., Michael Hallworth, J. Kilborn, C.A. Bernier, N.L. Fortin, K.D. Gieder, R. Patry, R.M. Cliché, L.S. Prout, S. Gifford, S. Wixsom, T.L. Morelli, T.L. Wilson. 2024. Monitoring animal populations with cameras using open, multistate, N-mixture models. Ecology and Evolution.
Abstract
Remote cameras have become a mainstream tool for studying wildlife populations. For species whose developmental stages 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 stages are known. However, this powerful class of models has been underutilized as 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 open, multistate, N-mixture (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 to track 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 accuracy and precision of survival and recruitment. Our empirical analysis provided biologically realistic estimates of moose survival and recruitment and recovered the known pattern of moose abundance across the region. Multistate DM models can be used for estimating demographic parameters from camera data when developmental stages are clearly identifiable. We discuss several avenues for future research and caveats for using these models for large-scale population monitoring.