Spatiotemporal hierarchical modelling of species richness and occupancy using camera trap data
Spatiotemporal hierarchical modelling of species richness and occupancy using camera trap data
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DOI：
10.1111/1365-2664.12399
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发表时间：
2015-04-01
影响因子：
5.7
通讯作者：
Powell, George V. N.
中科院分区：
文献类型：
--
作者：
Tobler, Mathias W.;Hartley, Alfonso Zuniga;Powell, George V. N.
1.在过去的二十年里，世界各地进行了大量的相机陷阱调查，相机陷阱被认为是清点和监测中大型陆生脊椎动物的理想工具。然而，很少有研究分析社区层面的相机陷阱数据。我们开发了一个多时段多物种占有率模型，该模型允许我们结合多个相机陷阱调查(SESSIONS)的数据来获得物种丰富度和占有率的估计。通过在会话级别估计物种的存在，并将每个物种和会话的检测概率和占用率建模为嵌套随机效应，我们可以改进每个会话的参数估计，特别是对于具有稀疏数据的物种。我们开发了我们的模型的两种变体：一种是二元潜态模型，另一种是使用Royle-Nichols公式来描述探测概率和丰度之间的关系。我们将这两个模型应用于来自秘鲁东南部的8个相机陷阱调查数据，其中包括6个研究地点、263个相机站点和17423个相机日。这些地点包括保护区、伐木特许权和巴西坚果特许权。我们将栖息地(陆地与泛滥平原)作为入住率的协变量，并将步道与非步道作为检测的协变量。摄像机间的异质性对于我们的数据来说是一个严重的问题，我们模型的Royle-Nichols变量比二进制状态变量更适合。这两个模型都得出了类似的物种丰富度估计，表明大多数遗址都包含完整的大型哺乳动物群落。不同物种之间的检测概率和占有率值比物种内不同阶段的检测概率和占有率更大。3个物种表现出栖息地偏好，4个物种表现出对踪迹的偏好或回避。合成与应用。我们的多时段多物种占有率模型为大型数据集的物种丰富度和占有率提供了改进的估计。我们的模型非常适合于整合大量的相机陷阱数据集，以调查与自然或人为因素有关的哺乳动物群落分布和组成的区域和/或时间模式，或监测一段时间内的哺乳动物群落。
1. Over the last two decades, a large number of camera trap surveys have been carried out around the world and camera traps have been proposed as an ideal tool for inventorying and monitoring medium to large-sized terrestrial vertebrates. However, few studies have analysed camera trap data at the community level.2. We developed a multi-session multi-species occupancy model that allows us to obtain estimates for species richness and occupancy combining data from multiple camera trap surveys (sessions). By estimating species presence at the session-level and modelling detection probability and occupancy for each species and sessions as nested random effects, we could improve parameter estimates for each session, especially for species with sparse data. We developed two variants of our model: one was a binary latent states model while the other used a Royle-Nichols formulation for the relationship between detection probability and abundance.3. We applied both models to data from eight camera trap surveys from south-eastern Peru including six study sites, 263 camera stations and 17 423 camera days. Sites covered protected areas, a logging concession and Brazil nut concessions. We included habitat (terra firme vs. floodplain) as a covariate for occupancy and trail vs. off-trail as a covariate for detection.4. Among-camera heterogeneity was a serious problem for our data and the Royle-Nichols variant of our model had a much better fit than the binary-state variant. Both models resulted in similar species richness estimates showing that most of the sites contained intact large mammal communities. Detection probabilities and occupancy values were more variable across species than across sessions within species. Three species showed a habitat preference and four species showed preference or avoidance of trails.5. Synthesis and applications. Our multi-session multi-species occupancy model provides improved estimates for species richness and occupancy for a large data set. Our model is ideally suited for integrating large numbers of camera trap data sets to investigate regional and/ or temporal patterns in the distribution and composition of mammal communities in relation to natural or anthropogenic factors or to monitor mammal communities over time.