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

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          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.

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          Most cited references21

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          Constructing, conducting and interpreting animal social network analysis

          Summary Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology. Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data. We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network. Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language. We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under‐exploited potential of experimental manipulations on social networks to address research questions.
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            A comparison of association indices

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              A method for testing association patterns of social animals.

              Association indices were originally developed to describe species co-occurrences, but have been used increasingly to measure associations between individuals. However, no statistical method has been published that allows one to test the extent to which the observed association index values differ from those of a randomly associating population. Here, we describe an adaptation of a test developed by Manly (1995, Ecology, 76, 1109-1115), which uses the observed association data as a basis for a computer-generated randomization. The observed pattern of association is tested against a randomly created one while retaining important features of the original data, for example group size and sighting frequency. We applied this new method to test four data sets of associations from two populations of Hector's dolphin, Cephalorhynchus hectori, using the Half-Weight Index (HWI) as an example of a measure of association. The test demonstrated that populations with similar median HWI values showed clear differences in association patterns, that is, some were associating nonrandomly whereas others were not. These results highlight the benefits of using this new testing method in order to validate the analysis of association indices. Copyright 1998 The Association for the Study of Animal Behaviour.
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                Author and article information

                Journal
                Biol. Lett.
                Biology letters
                The Royal Society
                1744-957X
                1744-9561
                Mar 2017
                : 13
                : 3
                Affiliations
                [1 ] PSL Research University: EPHE-UPVD-CNRS, USR 3278 CRIOBE, 66860 Perpignan, France johann.mourier@gmail.com.
                [2 ] Laboratoire d'excellence 'CORAIL', EPHE, PSL Research University, UPVD, CNRS, USR 3278 CRIOBE, Papetoai, Moorea, French Polynesia.
                [3 ] Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia.
                [4 ] PSL Research University: EPHE-UPVD-CNRS, USR 3278 CRIOBE, 66860 Perpignan, France.
                Article
                rsbl.2016.0824
                10.1098/rsbl.2016.0824
                5377029
                28298593
                03baf941-de05-49d3-a573-87583902b164
                © 2017 The Author(s).
                History

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

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