Monitoring Animal Populations With Cameras Using Open, Multistate, N-Mixture Models.

Autor: Sirén APK; Department of Environmental Conservation University of Massachusetts Amherst Massachusetts USA., Hallworth MT; Vermont Center for Ecostudies Norwich Vermont USA., Kilborn JR; Vermont Department of Fish and Wildlife Rutland Vermont USA.; New Hampshire Fish and Game Department Concord New Hampshire USA., Bernier CA; Vermont Department of Fish and Wildlife Rutland Vermont USA., Fortin NL; Vermont Department of Fish and Wildlife Rutland Vermont USA., Geider KD; Vermont Department of Fish and Wildlife Rutland Vermont USA., Patry RK; Dartmouth College Woodlands Milan New Hampshire USA., Cliché RM; United States Fish and Wildlife Service, Silvio O. Conte National Wildlife Refuge Nulhegan Basin Division Brunswick Vermont USA., Prout LS; United States Forest Service White Mountain National Forest Campton New Hampshire USA., Gifford SJ; United States Forest Service Green Mountain National Forest Mendon Vermont USA., Wixsom S; United States Forest Service Green Mountain National Forest Mendon Vermont USA., Morelli TL; Department of Environmental Conservation University of Massachusetts Amherst Massachusetts USA.; U.S. Geological Survey Northeast Climate Adaptation Science Center Amherst Massachusetts USA., Wilson TL; Department of Environmental Conservation University of Massachusetts Amherst Massachusetts USA.; U.S. Geological Survey Massachusetts Cooperative Fish and Wildlife Research Unit Amherst Massachusetts USA.
Jazyk: angličtina
Zdroj: Ecology and evolution [Ecol Evol] 2024 Dec 12; Vol. 14 (12), pp. e70583. Date of Electronic Publication: 2024 Dec 12 (Print Publication: 2024).
DOI: 10.1002/ece3.70583
Abstrakt: 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 four 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, and 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 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.
Competing Interests: The authors declare no conflicts of interest.
(© 2024 The Author(s). Ecology and Evolution published by John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)
Databáze: MEDLINE
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