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


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.

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Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
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College of Engineering & Physical Sciences > Sustainable environment research group
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Uncontrolled Keywords: anticipation,Bayesian theorem,sampling effort bias,species distribution modeling,uncertainty,Environmental Engineering,Environmental Chemistry,Waste Management and Disposal,Pollution
Publication ISSN: 1879-1026
Last Modified: 20 May 2024 07:18
Date Deposited: 07 Mar 2017 11:45
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2017-04-15
Published Online Date: 2017-02-07
Accepted Date: 2016-12-04
Submitted Date: 2016-07-04
Authors: Rocchini, Duccio
Garzon-Lopez, Carol X.
Marcantonio, Matteo
Amici, Valerio
Bacaro, Giovanni
Bastin, Lucy (ORCID Profile 0000-0003-1321-0800)
Brummitt, Neil
Chiarucci, Alessandro
Foody, Giles M.
Hauffe, Heidi C.
He, Kate S.
Ricotta, Carlo
Rizzoli, Annapaola
Rosà, Roberto

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