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Anticipating species distributions: Handling sampling effort bias under a Bayesian framework.

Authors :
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
Rosà, Roberto
Source :
Science of the Total Environment. Apr2017, Vol. 584, p282-290. 9p.
Publication Year :
2017

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00489697
Volume :
584
Database :
Academic Search Index
Journal :
Science of the Total Environment
Publication Type :
Academic Journal
Accession number :
121672639
Full Text :
https://doi.org/10.1016/j.scitotenv.2016.12.038