A sparse observation model to quantify species distributions and their overlap in space and time

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 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.

相机陷阱与声学记录设备是量化移动物种分布、丰度及行为的关键工具。分析此类数据时，必须考虑不同设备位置间检测概率的差异，这通常通过占据模型（occupancy models）实现。本文提出一种适用于相机陷阱数据的贝叶斯时变观测模型（Tomcat），可用于估计时空维度上的相对事件密度。Tomcat能够在考虑不完全检测的前提下，探究物种的环境需求与日活动模式；此外，它还实现了一种稀疏模型，能有效处理大量潜在高度相关的环境变量。通过整合空间与时间信息，我们将物种间重叠系数的概念扩展至时空维度，以研究生态位分化。我们通过对中非共和国稀树草原-雨林交错带中8种同域分布麂羚（Cephalophinae）物种的相机陷阱数据应用，验证了Tomcat的效能，并发现大多数物种对的重叠程度较低；例外情况为其中一种物种极为稀少的配对，这可能是直接竞争的结果。