Data from: Learning increases network robustness to fishing in sharks

Individuals can play different roles in maintaining connectivity and 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. It was also highly robust to experimental fishing. Individual catchability decreased as a function of experience suggesting that individuals learnt to avoid capture, which ultimately increased network robustness to fishing pressure. Studying network topology, redundancy and individual learning can be useful for predicting conservation effectiveness. Our results also suggest that some caution must be taken when using capture-recapture models as assumptions may be violated by individual learning.