{"doi":"10.1101/2025.11.08.687336","title":"Inferring the causes of animal social network structure from time-series data","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Behavioural ecologists aim to understand the causes of animal social structure. Connecting theoretical models of social structure with empirical observations remains, however, a for-midable challenge. While most of the current statistical methods for animal social network analysis rely on data that are aggregated over time and summarised as one behavioural dimension (\n                  <jats:italic>e.g</jats:italic>\n                  ., an adjacency-matrix), common behavioural sampling techniques (\n                  <jats:italic>e.g</jats:italic>\n                  ., focal-animal sampling) produce data in continuous time, and involve different behaviours. Furthermore, empiricists in the field are generally interested in causal inference, but lack a framework to rigorously analyse focal-animal sampling data in light of transparent causal assumptions. As a consequence, common methods are often inappropriate, and can lead to wrong biological conclusions. Here, we introduce a causal Bayesian modelling framework to empirically study the causes of social network structure from focal-animal sampling data. We start by outlining a\n                  <jats:italic>generative model</jats:italic>\n                  that encodes how biological and measurement processes jointly produce social network data in continuous time; namely, as a temporal sequence of dyadic behavioural states (\n                  <jats:italic>e.g</jats:italic>\n                  ., no body contact, social resting, grooming). Building upon the generative model, we develop a\n                  <jats:italic>statistical model</jats:italic>\n                  : a multilevel, multiplex Bayesian model that takes raw focal observations as input, and produces a posterior probability distribution for the generative parameters as output. After validating the statistical model’s performance with sparse data— common in real-world settings—we illustrate its application with an empirical data set collected in wild Assamese macaques. We notably showcase how researchers can compute probabilistic estimates for well-defined causal hypotheses about the drivers of social structure. With this work, we not only contribute novel theoretical and statistical tools to the field, but also illustrate a\n                  <jats:italic>workflow</jats:italic>\n                  that allows researchers to iteratively translate their domain expertise into a formal analytical strategy—bridging theoretical and empirical research in behavioural ecology.\n                </jats:p>","journal":null,"year":null,"id":646679,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":311074,"name":"Richard McElreath","orcid":"0000-0002-0387-5377","position":1,"is_corresponding":false},{"id":1264870,"name":"Julia Ostner","orcid":"0000-0001-6871-9976","position":2,"is_corresponding":false},{"id":681816,"name":"Daniel Redhead","orcid":"0000-0002-2809-8121","position":3,"is_corresponding":false},{"id":1264871,"name":"Oliver Schülke","orcid":"0000-0003-0028-9425","position":4,"is_corresponding":false},{"id":1455861,"name":"Ben Kawam","orcid":"0009-0006-5779-1423","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Inferring the causes of animal social network structure from time-series data","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Behavioural ecologists aim to understand the causes of animal social structure. Connecting theoretical models of social structure with empirical observations remains, however, a for-midable challenge. While most of the current statistical methods for animal social network analysis rely on data that are aggregated over time and summarised as one behavioural dimension (\n                  <jats:italic>e.g</jats:italic>\n                  ., an adjacency-matrix), common behavioural sampling techniques (\n                  <jats:italic>e.g</jats:italic>\n                  ., focal-animal sampling) produce data in continuous time, and involve different behaviours. Furthermore, empiricists in the field are generally interested in causal inference, but lack a framework to rigorously analyse focal-animal sampling data in light of transparent causal assumptions. As a consequence, common methods are often inappropriate, and can lead to wrong biological conclusions. Here, we introduce a causal Bayesian modelling framework to empirically study the causes of social network structure from focal-animal sampling data. We start by outlining a\n                  <jats:italic>generative model</jats:italic>\n                  that encodes how biological and measurement processes jointly produce social network data in continuous time; namely, as a temporal sequence of dyadic behavioural states (\n                  <jats:italic>e.g</jats:italic>\n                  ., no body contact, social resting, grooming). Building upon the generative model, we develop a\n                  <jats:italic>statistical model</jats:italic>\n                  : a multilevel, multiplex Bayesian model that takes raw focal observations as input, and produces a posterior probability distribution for the generative parameters as output. After validating the statistical model’s performance with sparse data— common in real-world settings—we illustrate its application with an empirical data set collected in wild Assamese macaques. We notably showcase how researchers can compute probabilistic estimates for well-defined causal hypotheses about the drivers of social structure. With this work, we not only contribute novel theoretical and statistical tools to the field, but also illustrate a\n                  <jats:italic>workflow</jats:italic>\n                  that allows researchers to iteratively translate their domain expertise into a formal analytical strategy—bridging theoretical and empirical research in behavioural ecology.\n                </jats:p>","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W4416124756","authors":[],"funders":[{"funder_name":"DFG","grant_id":"Research Training Group 2070 \"Understanding Social Relationships\" (Project-ID 254142454)","title":null},{"funder_name":"DFG","grant_id":"Research Training Group 2070 &quot;Understanding Social Relationships&quot; (Project-ID 254142454)","title":null},{"funder_name":"Konrad Lorenz Institute for Evolution and Cognition Research","grant_id":"","title":null},{"funder_name":"Ministry of Science and Culture of The Netherlands","grant_id":"","title":null}],"total_grants":4,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2026,"count":1}],"oa_status":"green","license":"cc-by-nc","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/11/09/2025.11.08.687336.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/11/09/2025.11.08.687336.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2025.11.08.687336","host_type":"publisher"},{"url":"https://doi.org/10.1101/2025.11.08.687336","host_type":"repository"},{"url":"https://resolver.sub.uni-goettingen.de/purl?gro-2/154004","host_type":"repository"}],"fields_of_study":["Primate Behavior and Ecology","Animal Vocal Communication and Behavior","Animal Behavior and Reproduction"],"mesh_terms":[],"keywords":["Causal structure","Bayesian network","Raw data","Causal model","Generative model","Probabilistic logic","Set (abstract data type)","Bayesian probability","Statistical model"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T14:24:24.466590Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}