Anticipating species distributions: handling sampling effort bias under a Bayesian framework

Duccio Rocchini*, Carol X. Garzon-Lopez, Matteo Marcantonio, Valerio Amici, Giovanni Bacaro, Lucy Bastin, Neil Brummitt, Alessandro Chiarucci, Giles M. Foody, Heidi C. Hauffe, Kate S. He, Carlo Ricotta, Annapaola Rizzoli, Roberto Rosà

*Corresponding author for this work

Research output: Contribution to journalArticle


Anticipating species distributions in space and time is necessary for effective biodiversity conservation and for prioritising management interventions. This is especially true when considering invasive species. In such a case, anticipating their spread is important to effectively plan management actions. However, considering uncertainty in the output of species distribution models is critical for correctly interpreting results and avoiding inappropriate decision-making. In particular, when dealing with species inventories, the bias resulting from sampling effort may lead to an over- or under-estimation of the local density of occurrences of a species. In this paper we propose an innovative method to i) map sampling effort bias using cartogram models and ii) explicitly consider such uncertainty in the modeling procedure under a Bayesian framework, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.

Original languageEnglish
Pages (from-to)282-290
Number of pages9
JournalScience of the Total Environment
Early online date7 Feb 2017
Publication statusPublished - 15 Apr 2017

Bibliographical note

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


  • anticipation
  • Bayesian theorem
  • sampling effort bias
  • species distribution modeling
  • uncertainty

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