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

Johann Mourier, Culum Brown, Serge Planes

    Research output: Contribution to journalArticlepeer-review

    35 Citations (Scopus)

    Abstract

    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.
    Original languageEnglish
    Pages (from-to)1-5
    Number of pages5
    JournalBiology Letters
    Volume13
    Issue number3
    DOIs
    Publication statusPublished - 1 Mar 2017

    Keywords

    • social network topology
    • resilience
    • blacktip reef shark
    • coral reefs

    Fingerprint

    Dive into the research topics of 'Learning and robustness to catch-and-release fishing in a shark social network'. Together they form a unique fingerprint.

    Cite this