Camera traps and acoustic recording devices are essential tools to quantify the distribution, abundance and behavior of mobile species. Varying detection probabilities among device locations must be accounted for when analyzing such data, which is generally done using occupancy models. We introduce a Bayesian time-dependent observation model for camera trap data (Tomcat), suited to estimate relative event densities in space and time. Tomcat allows to learn about the environmental requirements and daily activity patterns of species while accounting for imperfect detection. It further implements a sparse model that deals well will a large number of potentially highly correlated environmental variables. By integrating both spatial and temporal information, we extend the notation of overlap coefficient between species to time and space to study niche partitioning. We illustrate the power of Tomcat through an application to camera trap data of eight sympatrically occurring duiker Cephalophinae species in the savanna – rainforest ecotone in the Central African Republic and show that most species pairs show little overlap. Exceptions are those for which one species is very rare, likely as a result of direct competition.

中文翻译：

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一种量化物种分布及其时空重叠的稀疏观测模型

相机陷阱和声学记录设备是量化移动物种的分布、丰度和行为的重要工具。在分析此类数据时，必须考虑设备位置之间不同的检测概率，这通常使用占用模型来完成。我们为相机陷阱数据 (Tomcat) 引入了贝叶斯时间相关观察模型，适用于估计空间和时间的相对事件密度。Tomcat 允许了解物种的环境要求和日常活动模式，同时考虑到不完善的检测。它进一步实现了一个稀疏模型，可以很好地处理大量潜在的高度相关的环境变量。通过整合空间和时间信息，我们将物种之间重叠系数的符号扩展到时间和空间来研究生态位划分。我们通过一个应用程序来说明 Tomcat 的强大功能，该应用程序可以捕获八个同域出现的小羚羊的陷阱数据热带稀树草原中的Cephalophinae物种 - 中非共和国的热带雨林过渡带，表明大多数物种对几乎没有重叠。例外是那些物种非常稀有的物种，这可能是直接竞争的结果。