RT Journal Article
SR Electronic
T1 A sparse observation model to quantify species interactions in time and space
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 815027
DO 10.1101/815027
A1 Ait Kaci Azzou, Sadoune
A1 Singer, Liam
A1 Aebischer, Thierry
A1 Wolf, Beat
A1 Wegmann, Daniel
YR 2020
UL http://biorxiv.org/content/early/2020/07/21/815027.abstract
AB 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.Synthesis and applications. 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.Competing Interest StatementThe authors have declared no competing interest.