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 journalArticlepeer-review


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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