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 frame-work, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.
ROCCHINI Duccio;
GARZON-LOPEZ Carol;
MARCANTONIO Matteo;
AMICI Valerio;
BACARO Giovanni;
BASTIN Lucy;
BRUMMITT Neil;
CHIARUCCI Alessandro;
FOODY Giles M.;
HAUFFE Heidi;
HE Kate;
RICOTTA Carlo;
RIZZOLI Annamaria;
ROSA Roberto;
2017-04-06
ELSEVIER SCIENCE BV
JRC105627
0048-9697,
http://www.sciencedirect.com/science/article/pii/S0048969716327231,
https://publications.jrc.ec.europa.eu/repository/handle/JRC105627,
10.1016/j.scitotenv.2016.12.038,
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