Modelling time-series Aedes albopictus abundance as a forecasting tool in urban environments. (June 2023)

• Record Type: Journal Article
• Authors: Torina, Alessandra; La Russa, Francesco; Blanda, Valeria; Peralbo-Moreno, Alfonso; Casades-Martí, Laia; Di Pasquale, Liliana; Bongiorno, Carmelo; Vitale Badaco, Valeria; Toma, Luciano; Ruiz-Fons, Francisco
• Is Part Of: Ecological indicators. Volume 150(2023)
• Publication Date: 2023-06
• Subjects: Invasive species -- Meteorological modelling -- Mosquito -- Population dynamics -- Vector-borne disease; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2023.110232
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Aedes albopictus abundance determinants investigated in Sicily (Southern Italy). Predictive models fitted on weekly Ae. albopictus abundance and weather predictors. Aedes albopictus abundance is highly seasonal with peaks in late summer. The best fitted model in the cross-validation had high predictive capacity. Air moisture, temperature and wind speed are major Ae. albopictus determinants. Abstract: Aedes albopictus is an invasive mosquito species that can maintain and transmit several arboviruses causing disease in humans. Understanding the determinants of its ecology and population dynamics to predict its abundance was the main objective of this study. Adult mosquitoes were captured weekly between 2009 and 2016 with BG sentinel traps baited with BG-Lure outdoors at a collection site within the urban area of Palermo (southern Italy). In parallel, between 2012 and 2016, we monitored the uninterrupted weekly abundance of Ae. albopictus at four additional sites nearby over an area of about two hectares. Catches were collected three times per week and mosquitoes were identified morphologically. To identify the determinants of mosquito abundance, seasonal autoregressive integrated moving-average and Poisson regression models were fitted to the weekly abundance of Ae. albopictus with a series of weather predictors that potentially modulate its activity and population dynamics. The time lag of the influence of predictors was analysed to identify the intergenerational environmental determinants of Ae. albopictus population dynamics. A cross-validation of the predictive accuracy of the different models was carried out to select the best predictive model. Over 7 years we captured 12, 152 Ae. albopictus in the first trap and another 58, 710 in four years of trapping in four additional traps. Aedes albopictus abundance was highly seasonal, with activity between mid-March and late December, highest abundances between July and September, and peak abundances in autumn. The predictive potential of the best model was further externally validated with four years of data from the other four traps, showing a high predictive capacity and a very good fit of seasonality and abundance peaks. Relative humidity, vapour saturation deficit and wind speed were identified as the main determinants of the weekly abundance of Ae. albopictus . The results obtained will allow accurate prediction of the abundance of this invasive mosquito in coastal Mediterranean areas and the design of ad-hoc measures for efficient and environmentally sustainable control.