Pettigrew Pascal, Sigouin Daniel, St-Laurent Martin-Hugues
Département de Biologie Chimie et Géographie Centre for Forest Research Université du Québec à Rimouski Rimouski QC Canada.
Forillon National Park Gaspé QC Canada.
Ecol Evol. 2021 May 7;11(12):7879-7889. doi: 10.1002/ece3.7619. eCollection 2021 Jun.
The use of camera traps in ecology helps affordably address questions about the distribution and density of cryptic and mobile species. The random encounter model (REM) is a camera-trap method that has been developed to estimate population densities using unmarked individuals. However, few studies have evaluated its reliability in the field, especially considering that this method relies on parameters obtained from collared animals (., average speed, in km/h), which can be difficult to acquire at low cost and effort. Our objectives were to (1) assess the reliability of this camera-trap method and (2) evaluate the influence of parameters coming from different populations on density estimates. We estimated a reference density of black bears () in Forillon National Park (Québec, Canada) using a spatial capture-recapture estimator based on hair-snag stations. We calculated average speed using telemetry data acquired from four different bear populations located outside our study area and estimated densities using the REM. The reference density, determined with a Bayesian spatial capture-recapture model, was 2.87 individuals/10km [95% CI: 2.41-3.45], which was slightly lower (although not significatively different) than the different densities estimated using REM (ranging from 4.06-5.38 bears/10km depending on the average speed value used). Average speed values obtained from different populations had minor impacts on REM estimates when the difference in average speed between populations was low. Bias in speed values for slow-moving species had more influence on REM density estimates than for fast-moving species. We pointed out that a potential overestimation of density occurs when average speed is underestimated, that is, using GPS telemetry locations with large fix-rate intervals. Our study suggests that REM could be an affordable alternative to conventional spatial capture-recapture, but highlights the need for further research to control for potential bias associated with speed values determined using GPS telemetry data.
在生态学中使用相机陷阱有助于以经济实惠的方式解决有关隐秘和移动物种的分布与密度问题。随机相遇模型（REM）是一种相机陷阱方法，已被开发用于使用未标记个体估计种群密度。然而，很少有研究评估其在野外的可靠性，特别是考虑到该方法依赖于从佩戴项圈的动物获得的参数（例如，平均速度，单位为公里/小时），而这些参数可能难以低成本、低工作量地获取。我们的目标是：（1）评估这种相机陷阱方法的可靠性；（2）评估来自不同种群的参数对密度估计的影响。我们使用基于毛发捕捉站的空间捕获 - 重捕估计器，估计了加拿大魁北克省福里永国家公园黑熊（ ）的参考密度。我们使用从位于我们研究区域之外的四个不同熊种群获取的遥测数据计算平均速度，并使用随机相遇模型估计密度。通过贝叶斯空间捕获 - 重捕模型确定的参考密度为2.87只/10平方公里[95%置信区间：2.41 - 3.45]，这略低于（尽管无显著差异）使用随机相遇模型估计的不同密度（根据所使用的平均速度值，范围为4.06 - 5.38只/10平方公里）。当种群之间的平均速度差异较小时，从不同种群获得的平均速度值对随机相遇模型估计的影响较小。与快速移动的物种相比，慢速移动物种的速度值偏差对随机相遇模型密度估计的影响更大。我们指出，当平均速度被低估时，即使用固定率间隔较大的GPS遥测位置时，可能会出现密度的潜在高估。我们的研究表明，随机相遇模型可能是传统空间捕获 - 重捕方法的一种经济实惠的替代方法，但强调需要进一步研究以控制与使用GPS遥测数据确定的速度值相关的潜在偏差。
