## A new method for characterising shared space use networks using animal trapping data

2022
##### Authors
Wanelik, Klara M.
Journal article
Published
##### Published in
Behavioral Ecology and Sociobiology ; 76 (2022), 9. - 127. - Springer. - ISSN 0340-5443. - eISSN 1432-0762
##### Abstract
Studying the social behaviour of small or cryptic species often relies on constructing networks from sparse point-based observations of individuals (e.g. live trapping data). A common approach assumes that individuals that have been detected sequentially in the same trapping location will also be more likely to have come into indirect and/or direct contact. However, there is very little guidance on how much data are required for making robust networks from such data. In this study, we highlight that sequential trap sharing networks broadly capture shared space use (and, hence, the potential for contact) and that it may be more parsimonious to directly model shared space use. We first use empirical data to show that characteristics of how animals use space can help us to establish new ways to model the potential for individuals to come into contact. We then show that a method that explicitly models individuals’ home ranges and subsequent overlap in space among individuals (spatial overlap networks) requires fewer data for inferring observed networks that are more strongly correlated with the true shared space use network (relative to sequential trap sharing networks). Furthermore, we show that shared space use networks based on estimating spatial overlap are also more powerful for detecting biological effects. Finally, we discuss when it is appropriate to make inferences about social interactions from shared space use. Our study confirms the potential for using sparse trapping data from cryptic species to address a range of important questions in ecology and evolution.
##### Subject (DDC)
570 Biosciences, Biology
##### Cite This
ISO 690WANELIK, Klara M., Damien R. FARINE, 2022. A new method for characterising shared space use networks using animal trapping data. In: Behavioral Ecology and Sociobiology. Springer. 76(9), 127. ISSN 0340-5443. eISSN 1432-0762. Available under: doi: 10.1007/s00265-022-03222-5
BibTex
@article{Wanelik2022-09metho-59634,
year={2022},
doi={10.1007/s00265-022-03222-5},
title={A new method for characterising shared space use networks using animal trapping data},
number={9},
volume={76},
issn={0340-5443},
journal={Behavioral Ecology and Sociobiology},
author={Wanelik, Klara M. and Farine, Damien R.},
note={Article Number: 127}
}

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<dcterms:abstract xml:lang="eng">Studying the social behaviour of small or cryptic species often relies on constructing networks from sparse point-based observations of individuals (e.g. live trapping data). A common approach assumes that individuals that have been detected sequentially in the same trapping location will also be more likely to have come into indirect and/or direct contact. However, there is very little guidance on how much data are required for making robust networks from such data. In this study, we highlight that sequential trap sharing networks broadly capture shared space use (and, hence, the potential for contact) and that it may be more parsimonious to directly model shared space use. We first use empirical data to show that characteristics of how animals use space can help us to establish new ways to model the potential for individuals to come into contact. We then show that a method that explicitly models individuals’ home ranges and subsequent overlap in space among individuals (spatial overlap networks) requires fewer data for inferring observed networks that are more strongly correlated with the true shared space use network (relative to sequential trap sharing networks). Furthermore, we show that shared space use networks based on estimating spatial overlap are also more powerful for detecting biological effects. Finally, we discuss when it is appropriate to make inferences about social interactions from shared space use. Our study confirms the potential for using sparse trapping data from cryptic species to address a range of important questions in ecology and evolution.</dcterms:abstract>
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