Predicting the geographical distribution of a species is a central topic in ecology, conservation and management of natural resources especially for invasive organisms. Invasive species can modify the structure and function of invaded ecosystems, altering their biodiversity, and causing significant economic losses locally and globally. Therefore, measuring and visualizing the uncertainty inherent in species’ potential distributions is fundamental for effective biodiversity monitoring and planning conservation interventions. This paper discusses a new Bayesian approach to mapping this uncertainty using cartograms, previously published knowledge, and presence/absence data

Rocchini, D.; Marcantonio, M.; Arhonditsis, G.; Lo Cacciato, A.; Hauffe, H.C.; He, K.S. (2019). Cartogramming uncertainty in species distribution models: a Bayesian approach. ECOLOGICAL COMPLEXITY, 38: 146-155. doi: 10.1016/j.ecocom.2019.04.002 handle: http://hdl.handle.net/10449/55213

Cartogramming uncertainty in species distribution models: a Bayesian approach

Rocchini, D.
Primo
;
Hauffe, H. C.;
2019-01-01

Abstract

Predicting the geographical distribution of a species is a central topic in ecology, conservation and management of natural resources especially for invasive organisms. Invasive species can modify the structure and function of invaded ecosystems, altering their biodiversity, and causing significant economic losses locally and globally. Therefore, measuring and visualizing the uncertainty inherent in species’ potential distributions is fundamental for effective biodiversity monitoring and planning conservation interventions. This paper discusses a new Bayesian approach to mapping this uncertainty using cartograms, previously published knowledge, and presence/absence data
Bayes’ theorem
Biodiversity
Cartograms
Species distribution models
Spatial modeling
Settore BIO/03 - BOTANICA AMBIENTALE E APPLICATA
Rocchini, D.; Marcantonio, M.; Arhonditsis, G.; Lo Cacciato, A.; Hauffe, H.C.; He, K.S. (2019). Cartogramming uncertainty in species distribution models: a Bayesian approach. ECOLOGICAL COMPLEXITY, 38: 146-155. doi: 10.1016/j.ecocom.2019.04.002 handle: http://hdl.handle.net/10449/55213
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/55213
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