Sirén Alexej P K, Hallworth Michael T, Kilborn Jillian R, Bernier Chris A, Fortin Nicholas L, Geider Katherina D, Patry Riley K, Cliché Rachel M, Prout Leighlan S, Gifford Suzanne J, Wixsom Scott, Morelli Toni Lyn, Wilson Tammy L
Department of Environmental Conservation University of Massachusetts Amherst Massachusetts USA.
Vermont Center for Ecostudies Norwich Vermont USA.
Ecol Evol. 2024 Dec 12;14(12):e70583. doi: 10.1002/ece3.70583. eCollection 2024 Dec.
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 () 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.
远程摄像头已成为研究野生动物种群的主流工具。对于那些在照片中可识别发育阶段或状态的物种，存在跟踪种群变化和估计种群统计学参数的机会。分层模型的最新进展使得能够估计那些状态已知的未标记动物随时间变化的生态状态和参数。然而，这类强大的模型一直未得到充分利用，因为它们计算量很大，且模型输出结果可能难以解释。在此，我们通过模拟展示如何使用多状态、戴尔 - 马德森（以下简称多状态DM）模型分析摄像头数据，以估计种群数量、存活率和补充率。我们评估了由相机陷阱数据产生的四种常见情况（低丰度和高丰度以及25%和50%的数据缺失），每种情况都有18种不同的样本量组合（相机位点 = 40、250；调查次数 = 4、8和12；年份 = 2、5、10），并评估了种群数量、存活率和补充率估计值的偏差和精度。我们还使用多状态DM模型分析了我们关于驼鹿（ ）的实证摄像头数据，并将推断结果与同一时间和地区的遥测研究进行比较，以评估摄像头研究跟踪驼鹿种群的准确性。大多数情况下，我们从模拟数据中恢复已知参数的准确性更高，并且对于具有更多位点、调查次数和/或年份的情况，精度有所提高。大量的数据缺失以及较少的相机位点，尤其是在高丰度情况下，会降低存活率和补充率估计的准确性和精度。我们的实证分析提供了符合生物学实际的驼鹿存活率和补充率估计值，并恢复了该地区驼鹿种群数量的模式。当发育状态清晰可辨时，多状态DM模型可用于从摄像头数据估计种群统计学参数。我们讨论了未来研究的几个方向以及使用多状态DM模型进行大规模种群监测时的注意事项。
