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Importance of accounting for phylogenetic dependence in multi-species mark–recapture studies

Author

Listed:
  • Abadi, Fitsum
  • Barbraud, Christophe
  • Besson, Dominique
  • Bried, Joël
  • Crochet, Pierre-André
  • Delord, Karine
  • Forcada, Jaume
  • Grosbois, Vladimir
  • Phillips, Richard A.
  • Sagar, Paul
  • Thompson, Paul
  • Waugh, Susan
  • Weimerskirch, Henri
  • Wood, Andrew G.
  • Gimenez, Olivier

Abstract

Species in comparative demography studies often have a common phylogenetic or evolutionary ancestry and hence, they cannot fully be treated as independent samples in the statistical analysis. Although the serious implication of ignoring phylogeny has long been recognized, no attempt has been made so far to account for the lack of statistical independence due to phylogeny in multi-species mark–recapture comparative demography studies. In this paper, we propose a Bayesian hierarchical model that explicitly accounts for phylogenetic dependence among species, and to correct for imperfect detection, which is a common phenomenon in free-ranging species. We illustrate the method using individual mark–recapture data collected from 16 seabird species of the order Procellariiformes. Data on body mass and phylogeny of these species are compiled from literature. We investigate the relationship between adult survival and body mass with and without accounting for phylogeny. If we ignore phylogeny, we obtain a positive survival–body mass relationship. However, this relationship is no longer statistically significant once phylogenetic dependence is taken into account, implying that survival may actually depend on an unmeasured variable that is correlated with body mass due to a shared dependence on phylogeny. The proposed model allows the integration of multi-species mark–recapture data and phylogenetic information, and it is therefore a valuable tool in ecological and evolutionary biology.

Suggested Citation

  • Abadi, Fitsum & Barbraud, Christophe & Besson, Dominique & Bried, Joël & Crochet, Pierre-André & Delord, Karine & Forcada, Jaume & Grosbois, Vladimir & Phillips, Richard A. & Sagar, Paul & Thompson, P, 2014. "Importance of accounting for phylogenetic dependence in multi-species mark–recapture studies," Ecological Modelling, Elsevier, vol. 273(C), pages 236-241.
    • Handle: RePEc:eee:ecomod:v:273:y:2014:i:c:p:236-241
    DOI: 10.1016/j.ecolmodel.2013.11.017
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    References listed on IDEAS

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    1. Russell B. Millar, 2009. "Comparison of Hierarchical Bayesian Models for Overdispersed Count Data using DIC and Bayes' Factors," Biometrics, The International Biometric Society, vol. 65(3), pages 962-969, September.
    2. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    3. O. Gimenez & C. Crainiceanu & C. Barbraud & S. Jenouvrier & B. J. T. Morgan, 2006. "Semiparametric Regression in Capture–Recapture Modeling," Biometrics, The International Biometric Society, vol. 62(3), pages 691-698, September.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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