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

Mavrodiev, Pavlin; Fleischmann, Daniela; Kerth, Gerald; Schweitzer, Frank

doi:10.1101/843912

Cited by 4 publications

(11 citation statements)

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“…year. These recordings were pre-processed by specic algorithms that are able to detect L/F events and to distinguish them from swarming behaviour (Mavrodiev et al, 2019). As the result, 3/25 P. Mavrodiev, D. Fleischmann, G.Kerth, F. Schweitzer: Data-driven modeling of leading-following behavior in Bechstein's bats Submitted for publication we are provided with tables of consecutive L/F events (for an example see Table S1 in the Supplementary Material), which are used as an input for our study.…”

Section: Study Animals and Datamentioning

confidence: 99%

“…Shown are number of nodes (bats), number of identied L/F events (links) for dierent years. The data was inferred from collected raw data as described in (Mavrodiev et al, 2019).…”

Section: Study Animals and Datamentioning

confidence: 99%

“…In this paper, we focus on information transfer in maternity colonies of Bechstein's bats (Myotis bechsteinii). This bat has several key traits that make it particularly relevant for studying networks constructed from the L/F events (Mavrodiev et al, 2019) occurs on a time scale much shorter than the long-term social relationships detected in Bechstein's bats (Kerth et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…As a consequence identifying inuential individuals or the emergence of distinct roles is contingent on our ability to reconstruct reliably the missing information on inter-individual interactions. This problem is addressed in a forthcoming paper (Mavrodiev et al, 2019) which proposes a methodology to infer leading-following (L/F) events in Bechstein's bats from such discrete records of occurrences.In this paper, we build on data sets about these L/F events from two dierent colonies and up to ve years (see Section 2.1 for details). We demonstrate how we can infer the rules underlying the formation of L/F pairs from this data, by introducing and testing ve null-models that vary in complexity regarding the information involved.Social network theory plays an important role in our approach.…”

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

Mavrodiev

Fleischmann

Kerth

et al. 2019

Preprint

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Leading-following behaviour in Bechstein's 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 ve models that dier 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 dierences with the model-generated centralities obtained from 10 5 model realizations. We nd 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 specic individuals, nor other inuences such as kinship or reciprocity, are taken into account to reproduce the empirical ndings. * Corresponding author, fschweitzer@ethz.ch 1/25 P. Mavrodiev, D. Fleischmann, G.Kerth, F. Schweitzer:Data-driven modeling of leading-following behavior in Bechstein's bats Submitted for publication coordination problems in animal systems (Kerth and Reckardt, 2003;Kerth et al., 2006). First, the frequent switching of communal day roosts implies that group coordination and collective decision-making regarding communal roosting is vital for individuals getting grouping benets.Second, coordination must be achieved in the presence of dierent individual preferences and limited information about the suitability of roosts , which has been recognized as particularly challenging for collective decision-making (Conradt, 2011)As Bechstein's bats forage solitarily , they have only limited information of the nightly exploration behaviour of others. Moreover, some individuals are more active in exploring their habitat and thus become better informed about the location of suitable roosts than their less active colony mates (Kerth and Reckardt, 2003;Fleischmann et al., 2013). Information asymmetry and heterogeneous roosting preferences in the colony are, thus, invariable challenges in coordinating roosting behaviour and ensuring grouping benets.All these challenges not only make it dicult for the colony members to achieve group coordination, they also make it hard for us to infer which inuence, on the individual level, is relevant for the outcome. The information transfer from informed to naive individuals does not explain how such individuals nd each other. Recruitment plays an important role (Richner and Heeb, 1996;Kerth and Reckardt, 2003), but the rules by which individuals form leading-follo...

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Section: Study Animals and Datamentioning

confidence: 99%

“…Shown are number of nodes (bats), number of identied L/F events (links) for dierent years. The data was inferred from collected raw data as described in (Mavrodiev et al, 2019).…”

Section: Study Animals and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mavrodiev

Fleischmann

Kerth

et al. 2019

Preprint

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Data-driven modelling of group formation in the fission–fusion dynamics of Bechstein’s bats

Perony

Kerth

Schweitzer

2022

J. R. Soc. Interface.

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Communal roosting in Bechstein’s 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). Analysing data from two colonies of different sizes over many years, we find that (i) the number of days that 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 that 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 scales (less than 1 day) with the social structures observed at long time scales (more than 1 year).

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Modeling Social Resilience: Questions, Answers, Open Problems

Schweitzer

Andres

Casiraghi

et al. 2022

Advs. Complex Syst.

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Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, nonequilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) delimitation, i.e., narrowing down the target systems, (ii) conceptualization, i.e., identifying how to approach social organizations, (iii) formal representation using a combination of agent-based and network models, (iv) operationalization, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the robustness of social organizations and their adaptivity, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.

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

Cited by 4 publications

References 50 publications

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

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

Data-driven modelling of group formation in the fission–fusion dynamics of Bechstein’s bats

Modeling Social Resilience: Questions, Answers, Open Problems

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