Open AccessA sparse observation model to quantify species distributions and their overlap in space and time
Author(s) -
Ait Kaci Azzou Sadoune,
Singer Liam,
Aebischer Thierry,
Caduff Madleina,
Wolf Beat,
Wegmann Daniel
Publication year - 2021
Publication title -
ecography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.973
H-Index - 128
eISSN - 1600-0587
pISSN - 0906-7590
DOI - 10.1111/ecog.05411
Subject(s) - occupancy , ecotone , camera trap , environmental niche modelling , species distribution , computer science , abundance (ecology) , niche differentiation , bayesian inference , bayesian probability , ecological niche , niche , ecology , habitat , artificial intelligence , biology
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.