VectAbundance: a spatio-temporal database of Aedes mosquitoes observations

Modelling approaches play a crucial role in supporting local public health agencies by estimating and forecasting vector abundance and seasonality. However, the reliability of these models is contingent on the availability of standardized, high-quality data. Addressing this need, our study focuses on collecting and harmonizing egg count observations of the mosquito Aedes albopictus, obtained through ovitraps in monitoring and surveillance efforts across Albania, France, Italy, and Switzerland from 2010 to 2022. We processed the raw observations to obtain a continuous time series of ovitraps observations allowing for an extensive geographical and temporal coverage of Ae. albopictus population dynamics. The resulting post-processed observations are stored in the open-access database VectAbundance.This initiative addresses the critical need for accessible, high-quality data, enhancing the reliability of modelling efforts and bolstering public health preparednes

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

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 set of predominantly abiotic covariates. The choice of the modeling approach, i.e., the algorithm, is itself a significant source of variability, as different algorithms applied to the same dataset can yield disparate outcomes. This inter-model variability has led to the adoption of ensemble modelling techniques, among which stacked generalisation, which has recently demonstrated its capacity to produce robust results. Stacked ensemble modelling incorporates predictions from multiple base learners or models as inputs for a meta-learner. The meta-learner, in turn, assimilates these predictions and generates a final prediction by combining the information from all the base learners. In our study, we utilized a recently published dataset documenting egg abundance observations of Aedes albopictus collected using ovitraps. and a set of environmental predictors to forecast the weekly median number of mosquito eggs using a stacked machine learning model. This approach enabled us to (i) unearth the seasonal egg-laying dynamics of Ae. albopictus for 12 years; (ii) generate spatio-temporal explicit forecasts of mosquito egg abundance in regions not covered by conventional monitoring initiatives. Our work establishes a robust methodological foundation for forecasting the spatio-temporal abundance of Ae. albopictus, offering a flexible framework that can be tailored to meet specific public health needs related to this specie

Threatened and extinct amphibians and reptiles in Italian natural history collections are useful conservation tools

Natural history museums are irreplaceable tools to study and preserve the biological diversity around the globe and among the primary actors in the recognition of species and the logical repositories for their type specimens. In this paper we surveyed the consistency of the preserved specimens of amphibians and reptiles housed in the major Italian scientific collections, and verified the presence of threatened species according to the IUCN Red List, includ-ing the Extinct (EX), Extinct in the Wild (EW), Critically Endangered (CR), Endangered (EN), and Vulnerable (VU) categories. Altogether, we analyzed 39 Italian zoological collections. We confirmed the presence of one extinct reptile (Chioninia coctei) and five extinct or extinct in the wild amphibian species (Atelopus longirostris, Nectophrynoides asperginis, Pseudophilautus leucorhinus, P. nasutus, and P. variabilis). Seven CR amphibians, fourteen CR reptile species and the extinct skink C. coctei are shared by more than one institution. Museums which host the highest number of threatened and extinct amphibian species are respectively Turin (17 CR and 1 EX), Florence (13 CR and 1 EX), and Trento (15 CR and 1 EW), while for reptiles the richest museums are those from Genoa (15 CR and 1 EX), Florence (11 CR and 1 EX), and Pisa (7 CR). Finally, we discussed the utility of natural history museums and the strategies to follow for the implementation of their functionality. © Firenze University Press