The marine plastic litter pollution is a worldwide growing environmental concern. Despite its negative effects on marine ecosystems, the phenomenon is still not well-known at global and local scale. This work aims at assessing the spatio-temporal distribution of plastic litter amounts found at the sea-floor in a region on the central Mediterranean (Ionian sea). Inspired by species distribution models, we propose a two-parts model to accommodate the excess of zeros and the spatio-temporal correlation characterizing abundance monitoring data. A common spatial effect that links the plastic abundances and the probabilities of occurrences is impremented with the Stochastic Partial Differential Equation approach extended to a non -stationary barrier model. The INLA methodology allows to efficiently perform Bayesian inference to fit complex spatio-temporal models including effects of environmental covariates and enables to investigate the assemblages of plastic litter over the study region.

(2019). An INLA spatio-temporal model for zero-inflated marine plastic litter abundance [poster communication - poster]. Retrieved from http://hdl.handle.net/10446/146842

An INLA spatio-temporal model for zero-inflated marine plastic litter abundance

2019-01-01

Abstract

The marine plastic litter pollution is a worldwide growing environmental concern. Despite its negative effects on marine ecosystems, the phenomenon is still not well-known at global and local scale. This work aims at assessing the spatio-temporal distribution of plastic litter amounts found at the sea-floor in a region on the central Mediterranean (Ionian sea). Inspired by species distribution models, we propose a two-parts model to accommodate the excess of zeros and the spatio-temporal correlation characterizing abundance monitoring data. A common spatial effect that links the plastic abundances and the probabilities of occurrences is impremented with the Stochastic Partial Differential Equation approach extended to a non -stationary barrier model. The INLA methodology allows to efficiently perform Bayesian inference to fit complex spatio-temporal models including effects of environmental covariates and enables to investigate the assemblages of plastic litter over the study region.
2019
Calculli, C.; Pollice, A.; Paradinas, I.; Sion, L.; Maiorano, P.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/146842
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