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

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: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
?? 50811700Jl ??
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 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
Publication ISSN: 1879-1026
Last Modified: 30 Sep 2024 11:04
Date Deposited: 07 Mar 2017 11:45
Full Text Link:
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

Export / Share Citation


Statistics

Additional statistics for this record