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.
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; Rosà, Roberto. - In: SCIENCE OF THE TOTAL ENVIRONMENT. - ISSN 0048-9697. - ELETTRONICO. - 584-585:(2017), pp. 282-290. [10.1016/j.scitotenv.2016.12.038]
Anticipating species distributions: Handling sampling effort bias under a Bayesian framework
ROCCHINI, DUCCIO;CHIARUCCI, ALESSANDRO;
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.