Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model

Abstract Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a...

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Main Authors:	Daniele Da Re, Giovanni Marini, Carmelo Bonannella, Fabrizio Laurini, Mattia Manica, Nikoleta Anicic, Alessandro Albieri, Paola Angelini, Daniele Arnoldi, Federica Bertola, Beniamino Caputo, Claudio De Liberato, Alessandra della Torre, Eleonora Flacio, Alessandra Franceschini, Francesco Gradoni, Përparim Kadriaj, Valeria Lencioni, Irene Del Lesto, Francesco La Russa, Riccardo Paolo Lia, Fabrizio Montarsi, Domenico Otranto, Gregory L’Ambert, Annapaola Rizzoli, Pasquale Rombolà, Federico Romiti, Gionata Stancher, Alessandra Torina, Enkelejda Velo, Chiara Virgillito, Fabiana Zandonai, Roberto Rosà
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-01-01
Series:	Scientific Reports
Subjects:	Arthropod Forecast Invasive species Mosquito Population dynamics Time-series.
Online Access:	https://doi.org/10.1038/s41598-025-87554-y
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Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model

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