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

Rocchini, Duccio, Garzon-Lopez, Carol X., Marcantonio, Matteo, Amici, Valerio, Bacaro, Giovanni, Bastin, Lucy, Brummitt, Neil, Chiarucci, Alessandro, Foody, Giles M., Hauffe, Heidi C., He, Kate S., Ricotta, Carlo, Rizzoli, Annapaola and Rosà, Roberto (2017). Anticipating species distributions:handling sampling effort bias under a Bayesian framework. Science of the Total Environment, 584-58 , pp. 282-290.

Abstract

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.

Publication DOI: https://doi.org/10.1016/j.scitotenv.2016.12.038
Divisions: Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences > Engineering systems & management
Engineering & Applied Sciences > Computer science research group
Engineering & Applied Sciences > Sustainable environment research group
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Engineering & Applied Sciences
Additional Information: © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: anticipation,Bayesian theorem,sampling effort bias,species distribution modeling,uncertainty,Environmental Engineering,Environmental Chemistry,Waste Management and Disposal,Pollution
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2017-04-15
Authors: Rocchini, Duccio
Garzon-Lopez, Carol X.
Marcantonio, Matteo
Amici, Valerio
Bacaro, Giovanni
Bastin, Lucy ( 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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