Abstract
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 duikerCephalophinaespecies 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.
Highlights
Thanks to their automated and non-intrusive nature of observation, camera traps, acoustic recorders and other devices that allow for continuous recording of animal observations have become an essential part of many wildlife monitoring efforts, especially those that aim at quantifying the distribution, abundance and behavior of mobile species (O’Brien et al 2010, Burton et al 2015, Caravaggi et al 2017)
We here introduce Tomcat, a time-dependent observation model for camera trap data, that extends currently used occupancy models in three important ways: First, we propose to quantify the rate at which animals pass through a specific location, rather than occupancy
Let Λj(τ) be the event density at time τ: the rate at which a device at location j = 1,...,J takes observations of a particular species at the time of the day τ ∈ [0,T], T = 24 h. We assume that this rate is affected by three processes: 1) the average rate l j at which individuals pass through location j, 2) the daily activity patterns (t) that reflect differences in activity throughout the day and 3) the probability pj with which an individual passing through location j is recorded: T
Summary
Thanks to their automated and non-intrusive nature of observation, camera traps, acoustic recorders and other devices that allow for continuous recording of animal observations have become an essential part of many wildlife monitoring efforts, especially those that aim at quantifying the distribution, abundance and behavior of mobile species (O’Brien et al 2010, Burton et al 2015, Caravaggi et al 2017). The inference of these biological characteristics is not trivial due to the confounding factor of detection, which may vary greatly among recording locations. Variation in the rates at which a species is recorded (e.g. the photographic rate) may reflect differences in local abundance, but. Might just as well reflect differences in the probabilities with which individuals are detected, or more likely a combination of both (reviewed by Burton et al 2015, Sollmann 2018). Since the probabilities of detection and occupation are confounded, they can not be inferred for each site individually. Detection probabilities are either assumed to be constant across sites or, more commonly, assumed to be a function of environmental covariates governed by hierarchical parameters (MacKenzie et al 2002)
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