Biology subjects

Kerth, G.

Publications and source records attributed to Kerth, G..

3 recordsLinked to original sources

bioRxiv2019

Data-driven modeling of group formation in the fission-fusion dynamics of Bechstein's bats

Communal roosting in Bechsteins bat colonies is characterized by the formation of several groups that use different day roosts and that regularly dissolve and re-merge (fission-fusion dynamics). Analyzing data from two colonies of different size over many years, we find that (i) the number of days bats stay in the same roost before changing follows an exponential distribution that is independent of the colony size, and (ii) the number and size of groups bats formed for roosting depend on the size of the colony such that above a critical colony size two to six groups of different sizes are formed. To model these two observations, we propose an agent-based model in which agents make their decisions about roosts based on both random and social influences. For the latter, they copy the roost preference of another agent which models the transfer of the respective information. Our model is able to reproduce both the distribution of stay length in the same roost and the emergence of groups of different sizes dependent on the colony size. Moreover, we are able to predict the critical system size at which the formation of different groups emerges without global coordination. We further comment on dynamics that bridge the roosting decisions on short time scale (less than one day) with the social structures observed at long time scales (more than one year).

ecology↗

bioRxiv2019

Quantifying individual influence in leading-following behavior of Bechstein's bats

Leading-following behavior as a way of transferring information about the location of resources is widespread in different animal societies. However, it cannot always be observed directly. Here, we develop a general method to infer leading-following events from observational data if only the discrete appearance of individuals is recorded. Our method further allows to distinguish such events from local enhancement at the resource, such as swarming behavior in case of bats, which is another widespread way of transferring information among animals. To test our methodology, we analyze longitudinal data about the roosting behavior of Bechsteins bats from two different colonies and different years. The detection of leading-following events allows us, in a second step, to construct social networks in which nodes represent individual bats and directed and weighted links the leading-following events. We analyze the topology of these networks on the level of the colony, to see whether all individuals participate in leading-following behavior. Further, based on the leading-following network we measure the importance of individuals in this leading-following behavior by calculating different centrality measures. We find that individuals can be consistently ranked regarding their influence on others. Moreover, we identify a small set of individuals that play a central role in leading other bats to roosts. Our methodology can be used to understand the leading-following behavior and the individual impact of group members on the spread of information in animal groups in general.

animal behavior and cognition↗

bioRxiv2019

Data-driven modeling of leading-following behavior in Bechstein's bats

Leading-following behaviour in Bechsteins bats transfers information about suitable roost sites from experienced to inexperienced individuals, and thus ensures communal roosting. We analyze 9 empirical data sets about individualized leading-following (L/F) events, to infer rules that likely determine the formation of L/F pairs. To test these rules, we propose five models that differ regarding the empirical information taken into account to form L/F pairs: activity of a bat in exploring possible roosts, tendency to lead and to follow. The comparison with empirical data was done by constructing social networks from the observed L/F events, on which centralities were calculated to quantify the importance of individuals in these L/F networks. The centralities from the empirical network are then compared for statistical differences with the model-generated centralities obtained from 105 model realizations. We find that two models perform well in comparison with the empirical data: One model assumes an individual tendency to lead, but chooses followers at random. The other model assumes an individual tendency to follow and chooses leaders according to their overall activity. We note that neither individual preferences for specific individuals, nor other influences such as kinship or reciprocity, are taken into account to reproduce the empirical findings.

animal behavior and cognition↗