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Environmental and Ecological Statistics

, Volume 25, Issue 1, pp 71–93 | Cite as

Bayesian estimation of species relative abundances and habitat preferences using opportunistic data

  • Camille CoronEmail author
  • Clément Calenge
  • Christophe Giraud
  • Romain Julliard
Article

Abstract

We develop a new statistical procedure to monitor relative species abundances and their respective preferences for different habitat types, using opportunistic data. Following Giraud et al. (Biometrics 72(2):649–658, 2015), we combine the opportunistic data with some standardized data in order to correct the bias inherent to the opportunistic data collection. Species observations are modeled by Poisson distributions whose parameters quantify species abundances and habitat preferences, and are estimated using Bayesian computations. Our main contributions are (i) to tackle the bias induced by habitat selection behaviors, (ii) to handle data where the habitat type associated to each observation is unknown, (iii) to estimate probabilities of selection of habitat for the species. As an illustration, we estimate common bird species habitat preferences and abundances in the region of Aquitaine (France).

Keywords

Citizen science Estimation of species relative abundances Habitat selection Opportunistic data Resource selection function 

Notes

Acknowledgements

The authors would like to thank Benjamin Auder for his contribution to the computer code for data treatment. We sincerely thank Denis Roux and all observers from the ACT network. We also thank the managers of both programs Open image in new window Faune d’Aquitaine Open image in new window and STOC-EPS, as well as the observers participating to these programs. This work was partially funded by public grants as part of the “Investissement d’avenir” project, reference ANR-11-LABX-0056-LMH, LabEx LMH, and reference ANR-10-CAMP-0151-02, Fondation Mathématiques Jacques Hadamard, by the Chair “Modélisation Mathématique et Biodiversité” of VEOLIA-Ecole Polytechnique-MNHN-F.X, and by the Mission for Interdisciplinarity at CNRS.

Supplementary material

10651_2018_398_MOESM1_ESM.pdf (267 kb)
Supplementary material 1 (pdf 267 KB)
10651_2018_398_MOESM2_ESM.pdf (239 kb)
Supplementary material 2 (pdf 238 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratoire de Mathématiques d’Orsay, Univ. Paris-Sud, CNRSUniversité Paris-SaclayOrsayFrance
  2. 2.Office National de la Chasse et de la Faune SauvageLe Perray en YvelinesFrance
  3. 3.CESCO, UMR CNRS 7204, Muséum National d’Histoire NaturelleParisFrance

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