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

Rocchini, D.; Garzon Lopez, C.X.; Marcantonio, M.; Amici, V.; Bacaro, G.; Bastin, L.; Brummitt, N.; Chiarucci, A.; Foody, G.M.; Hauffe, H.C.; He, K.S.; Ricotta, C.; Rizzoli, A.; Rosa', R. (2017). Anticipating species distributions: handling sampling effort bias under a Bayesian framework. SCIENCE OF THE TOTAL ENVIRONMENT, 584-585: 282-290. doi: 10.1016/j.scitotenv.2016.12.038 handle: http://hdl.handle.net/10449/42821

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

Rocchini, Duccio
Primo
;
Marcantonio, Matteo;Hauffe, Heidi Christine;Rizzoli, Annapaola;Rosa', Roberto
Ultimo
2017-01-01

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
Anticipation
Bayesian theorem
Sampling effort bias
Species distribution modeling
Uncertainty
Settore BIO/03 - BOTANICA AMBIENTALE E APPLICATA
Rocchini, D.; Garzon Lopez, C.X.; Marcantonio, M.; Amici, V.; Bacaro, G.; Bastin, L.; Brummitt, N.; Chiarucci, A.; Foody, G.M.; Hauffe, H.C.; He, K.S.; Ricotta, C.; Rizzoli, A.; Rosa', R. (2017). Anticipating species distributions: handling sampling effort bias under a Bayesian framework. SCIENCE OF THE TOTAL ENVIRONMENT, 584-585: 282-290. doi: 10.1016/j.scitotenv.2016.12.038 handle: http://hdl.handle.net/10449/42821
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/42821
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