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Data from: Learning and robustness to catch-and-release fishing in a shark social network

dataset
posted on 2022-06-10, 03:07 authored by Johann Mourier, Culum Brown, Serge Planes
Individuals can play different roles in maintaining connectivity and social cohesion in animal populations and thereby influence population robustness to perturbations. We performed a social network analysis in a reef shark population to assess the vulnerability of the global network to node removal under different scenarios. We found that the network was generally robust to the removal of nodes with high centrality. The network appeared also highly robust to experimental fishing. Individual shark catchability decreased as a function of experience, as revealed by comparing capture frequency and site presence. Altogether, these features suggest that individuals learnt to avoid capture, which ultimately increased network robustness to experimental catch-and-release. Our results also suggest that some caution must be taken when using capture–recapture models often used to assess population size as assumptions (such as equal probabilities of capture and recapture) may be violated by individual learning to escape recapture.

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Data_Network&AttributeThe DataZip file contains the following objects 1) “Attributes” gives the identity of all sighted individuals with attributes (ID, sex and size) followed by the number of time it was observed (Present), the number of times it was captured (Capture) and probability of capture (Pcap), 2) “group_by_individual” gives the matrix denoting which individuals were detected in which group at the selective survey site. The columns give the individuals, the rows give the number of the group.DataZip.zip

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