Predicting current and future Aedes vexans occurrence in the Netherlands

We have created predictions of the current and future occurrence of Aedes vexans (Meigen, 1830) mosquitoes in the Netherlands. Aedes vexans can transmit many different diseases, including western and eastern equine encephalitis virus, Tahyna virus, West Nile Virus and Rift Valley Fever Virus. However, lack of occurrence maps, especially at a local scale, has hampered accurate disease modelling. We used extensive occurrence data collected by the Netherlands Centre for Monitoring of Vectors to train models using AutoML. We made future predictions for 2050 using a combination of climate scenarios (from the Dutch Meteorological Organistion) and socio-economic scenarios (the Dutch One Health SSPs). We made predictions for individual days rather than for the season as a whole, allowing us to consider future changes in seasonal dynamics. This is the first time a seasonal model for Aedes vexans has been developed and the first time future predictions have been made for this species at a national scale.

Methods
Full details can be found in our paper: Current and future habitat suitability of the floodwater mosquito Aedes vexans (Meigen, 1830) in the Netherlands.

We used occurrence data collected by The Netherlands Centre for Monitoring of Vectors (CMV). Predictors used for modelling were temperature, precipitation, soil bulk density, soil clay content, shrub density, flood risk, surface water salinity, agricultural land cover, artificial land cover, distance to nature, permanent water and permanent wetland. We tried different models using training data in different presence:absence ratios. Modelling techniques tried were random forest, generalised linear models, XGBoost, gradient boosting machines and neural networks. We used an autoML approach to select the best models using R's h20 package. The final best model was an ensemble of several other models. To make future predictions, we used climate scenarios produced by the Dutch Meteorological Organisation together with the Dutch One Health SSPs. We considered 4 different scenarios and made predictions for 2050 on a 1km grid. Our model also allowed us to predict seasonal variation in the occurrence on this species and we also predicted how this would change in the future.