Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets

• Published in: Ecological informatics Vol. 56; p. 101055
• Main Authors: Pasini, Antonello, Amendola, Stefano, Giacomi, Angelo, Calderini, Pietro, Barlozzari, Giulia, Macrì, Gladia, Pombi, Marco, Gabrielli, Simona
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2020
• Subjects: Climatic influences; Neural networks; Phlebotomine sand flies; Population dynamics; Climatic influences; Phlebotomine sand flies; Population dynamics; Neural networks
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2020.101055

In recent years, meteo-climatic changes contributed to the geographical expansion and modifications of habitat that become more suitable to phlebotomine vectors. Among these vectors, the role of Sergentomyia minuta in the circulation of mammalian leishmaniases has been recently discussed. Here we apply a neural network model (specifically developed for modelling relationships among variables in small datasets) to estimate the population abundance of S. minuta starting from meteo-climatic variables only, during three capturing seasons (2014–2016) in an Italian site. The results show that we are able to explain a wide majority of the variance in the data of population density (R2 = 0.632). This is obtained through the application of a neural model driven in input by averaged mean temperature, relative humidity and temperature at 10 cm belowground during oviposition, larval and adult stages. A modelling pruning activity shows the major role of humidity in driving the number of captures, but also an important nonlinear role of temperature, which highlights the importance of possible heat waves on population density of S. minuta. •A neural network model successfully estimates abundance of Sergentomyia minuta starting from meteo-climatic data.•The NN model explains a wide majority of the variance in the data and performs better than multilinear regression•Our method permits to show specific linear and nonlinear influences of meteo-climatic variables on S. minuta abundance.•The roles of humidity and possible heat waves on S. minuta abundance are highlighted.