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- W3207170239 abstract "A long-term, yet detailed view into the social patterns of aquatic animals has been elusive. With advances in reality mining tracking technologies, a proximity-based social network (PBSN) can capture detailed spatio-temporal underwater interactions. We collected and analysed a large dataset of 108 freshwater fish from four species, tracked every few seconds over 1 year in their natural environment. We calculated the clustering coefficient of minute-by-minute PBSNs to measure social interactions, which can happen among fish sharing resources or habitat preferences (positive/neutral interactions) or in predator and prey during foraging interactions (agonistic interactions). A statistically significant coefficient compared to an equivalent random network suggests interactions, while a significant aggregated clustering across PBSNs indicates prolonged, purposeful social behaviour. Carp ( Cyprinus carpio ) displayed within- and among-species interactions, especially during the day and in the winter, while tench ( Tinca tinca ) and catfish ( Silurus glanis ) were solitary. Perch ( Perca fluviatilis ) did not exhibit significant social behaviour (except in autumn) despite being usually described as a predator using social facilitation to increase prey intake. Our work illustrates how methods for building a PBSN can affect the network's structure and highlights challenges (e.g. missing signals, different burst frequencies) in deriving a PBSN from reality mining technologies." @default.
- W3207170239 created "2021-10-25" @default.
- W3207170239 creator A5010212305 @default.
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- W3207170239 creator A5049109175 @default.
- W3207170239 creator A5064047566 @default.
- W3207170239 date "2021-10-01" @default.
- W3207170239 modified "2023-10-01" @default.
- W3207170239 title "Network analysis of intra- and interspecific freshwater fish interactions using year-around tracking" @default.
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