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

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. Significance statement Characterising animal social networks requires repeated (co-)observations of individuals. Collecting sufficient data to characterise the connections among individuals represents a major challenge when studying cryptic organisms—such as small rodents. This study draws from existing spatial mark-recapture data to inspire an approach that constructs networks by estimating space use overlap (representing the potential for contact). We then use simulations to demonstrate that the method provides consistently higher correlations between inferred (or observed) networks and the true underlying network compared to current approaches and requires fewer observations to reach higher correlations. We further demonstrate that these improvements translate to greater network accuracy and to more power for statistical hypothesis testing.

Effects of food supplementation and helminth removal on space use and spatial overlap in wild rodent populations

“…We combined capture data across individuals and across months to define an average seasonal space use for each functional group. In this way, the behavior of animals captured many times fills in the 'missing' data for animals captured only once or twice and minimizes the effect of low capture frequency on network structure (Wanelik & Farine, 2022). Further, by using overlapping space-use to define connections in the spatial overlap networks, spatial overlap between voles is also less dependent on their specific trapped locations.…”

“…The distances from a vole’s seasonal centroid to every trap in the trapping grid were pooled across voles, grouped by the combination of season*sex*reproductive status*food treatment*helminth treatment (n groups = 32). For each group, we fitted a negative sigmoidal curve (Equation 1; fitted using a Bernoulli GLM, where 1 indicated an animal was caught in a given trap, 0 indicated it was not; as developed by Wanelik & Farine, 2022) to characterize the average space use of a vole in that group: where space use was defined as the declining probability P of capturing a vole an increasing distance ( d ) from its seasonal centroid as determined by: a (describing the size of the space use radius), b (describing how steeply the capture probability declines as distance increases), and d (the logarithmic distance from the centroid). Thus, space use was separately characterized in the summer and autumn for each treatment for voles of each functional group.…”

“…The amount of pairwise space-use overlap between the two voles ( E 1,2 ) was estimated by taking the lower of the two voles’ capture probabilities at each grid point ( x, y ) and summing them across all grid points and similarly summing the higher of the two capture probabilities across all grid points. The summed minimums were then divided by the summed maximums to get a ratio indicating how similar the two voles’ capture probabilities were across space (Equation 2, developed by Wanelik & Farine, 2022): The more similar the space use of the two voles, the ratio ( E 1,2 ) approaches 1 (the two voles have similar capture probabilities at each grid point, indicating more complete overlap), whereas the more different their space use, the ratio approaches 0 (where one vole has high capture probability, the other has low capture probability, indicating very little overlap).…”

“…Seasonal vole space use was approximated within a season by aggregating capture location data among individuals by population subgroups to characterize the average space use of an individual in each group (Wanelik & Farine, 2022). This approach allows space use and spatial overlap to be estimated for individuals, even if they are caught only once or only in one trap and helps minimize uncertainty in space-use estimates created by the relatively short observation periods and limited observations per individual.…”

“…Overlap between all pairs of voles observed in a month at a site was estimated based on the overlap in the two voles' space use (as developed by Wanelik & Farine, 2022). Specifically, we calculated the amount of pairwise space-use overlap by first predicting the (independent) probabilities of capturing vole 1 and vole 2 (using Eqn.…”