Estimating animal density for a community of species using information obtained only from camera-traps
Estimating animal density for a community of species using information obtained only from camera-traps
复制标题
使用仅从相机陷阱获得的信息来估计物种群落的动物密度
DOI：
10.1111/2041-210x.13930
复制
发表时间：
2022
影响因子：
6.6
通讯作者：
Wearn O
中科院分区：
文献类型：
--
作者：
Wearn O
动物密度是生态学和自然保护的一个基本参数，但它仍然难以测量。对于陆生哺乳动物和鸟类，相机陷阱极大地提高了我们收集大量物种系统数据的能力，但密度估计（除了有自然标记的物种）仍然面临着统计和后勤障碍，包括对辅助数据和大样本量的要求，以及无法纳入协变量。为了填补相机捕获器统计工具箱中的这一空白，我们在贝叶斯框架中将现有的随机相遇模型（REM）扩展到多物种情况。这种多物种REM可以纳入协变量，并为最稀有的物种提供参数估计。作为模型的输入，我们使用了相机陷阱数据中直接可用的信息。该模型为每个物种输出REM参数的后验分布——运动速度、活动水平、相机陷阱探测区的有效角度和半径以及密度。我们将该模型应用于婆罗洲现有的35个物种的数据集，这些数据集收集于原始生长和砍伐的森林中。在这里，我们加入了从图像序列中获得的动物位置数据，以估计速度和检测区域参数。该模型显示，与原生林相比，砍伐后的物种群落的移动速度和活动范围都有所下降，而活动水平则没有一致的趋势。与原生林相比，采伐后的检测区较短，但宽度相似。总体而言，尽管大多数物种在砍伐后的森林中以较高的密度单独出现，但动物密度较低。然而，与原生林相比，被砍伐森林的单位面积生物量要高得多，尤其是草食动物和杂食动物，这可能是因为地面可利用资源增加了。我们还将体重作为模型中的一个变量，揭示了体型较大的物种更活跃，有更多的可变速度，并且有更大的检测区域。在使用相机陷阱估计半树栖和穴居物种的密度时，需要谨慎，并建议对假设进行更广泛的测试。尽管如此，我们预计多物种密度估计将有非常广泛的应用。
Animal density is a fundamental parameter in ecology and conservation, and yet it has remained difficult to measure. For terrestrial mammals and birds, camera‐traps have dramatically improved our ability to collect systematic data across a large number of species, but density estimation (except for species with natural marks) is still faced with statistical and logistical hurdles, including the requirement for auxiliary data and large sample sizes, and an inability to incorporate covariates.To fill this gap in the camera‐trapper's statistical toolbox, we extended the existing Random Encounter Model (REM) to the multi‐species case in a Bayesian framework. This multi‐species REM can incorporate covariates and provides parameter estimates for even the rarest species. As input to the model, we used information directly available in the camera‐trap data. The model outputs posterior distributions for the REM parameters—movement speed, activity level, the effective angle and radius of the camera‐trap detection zone, and density—for each species. We applied this model to an existing dataset for 35 species in Borneo, collected across old‐growth and logged forest. Here, we added animal position data derived from the image sequences in order to estimate the speed and detection zone parameters.The model revealed a decrease in movement speeds, and therefore day‐range, across the species community in logged compared to old‐growth forest, whilst activity levels showed no consistent trend. Detection zones were shorter, but of similar width, in logged compared to old‐growth forest. Overall, animal density was lower in logged forest, even though most species individually occurred at higher density in logged forest. However, the biomass per unit area was substantially higher in logged compared to old‐growth forest, particularly among herbivores and omnivores, likely because of increased resource availability at ground level. We also included body mass as a variable in the model, revealing that larger‐bodied species were more active, had more variable speeds, and had larger detection zones.Caution is warranted when estimating density for semi‐arboreal and fossorial species using camera‐traps, and more extensive testing of assumptions is recommended. Nonetheless, we anticipate that multi‐species density estimation could have very broad application.