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 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,