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

Ait Kaci Azzou, Sadoune (Université de Fribourg, Fribourg, Switzerland ; Swiss Institute of Bioinformatics, Fribourg, Switzerland) ; Singer, Liam (Université de Fribourg, Fribourg, Switzerland ; Swiss Institute of Bioinformatics, Fribourg, Switzerland) ; Aebischer, Thierry (Université de Fribourg, Fribourg, Switzerland ; Swiss Institute of Bioinformatics, Fribourg, Switzerland) ; Caduff, Madleina (Université de Fribourg, Fribourg, Switzerland ; Swiss Institute of Bioinformatics, Fribourg, Switzerland) ; Wolf, Beat (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Wegmann, Daniel (Université de Fribourg, Fribourg, Switzerland ; Swiss Institute of Bioinformatics, Fribourg, Switzerland)

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.


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
iCoSys - Institut des systèmes complexes
Date:
2021-02
Pagination:
13 p.
Published in:
Ecography
Numeration (vol. no.):
2021, vol. 44, pp. 1-13
DOI:
ISSN:
0906-7590
Appears in Collection:



 Record created 2021-03-23, last modified 2021-03-23

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)