Abstract/Summary

Mosquito-borne diseases (MBDs) pose significant public health risks, driven by environmental changes and species interactions. This thesis takes a community-based approach to understand how multiple drivers—both biotic and abiotic—shape mosquito distributions across local and continental scales, using advanced Joint Species Distribution Models (JS-DMs). In Chapter 2, I analyse mosquito communities in managed UK wetlands, showing how habitat modifications, such as changes in vegetation structure, impact mosquito populations. The study highlights that biotic interactions, including predator-prey dynamics, play a crucial role alongside environmental factors in determining community composition. Chapter 3 scales up to a European dataset, exploring how environmental factors (e.g., temperature, precipitation) and biotic interactions drive mosquito communities. Importantly, biotic interactions remain influential at large scales, challenging the assumption that environmental variables dominate. This underlines the need to integrate species interactions into disease modelling. In Chapter 4, I enhance JSDMs by incorporating species traits and phylogenetic data. Traits like thermal tolerance and host preferences improve predictions of mosquito distribution, while phylogenetic relationships provide insights into how evolutionary history shapes community dynamics. These findings offer a deeper understanding of how species traits influence disease risk. The thesis emphasises that community-based approaches provide a richer understanding of mosquito ecology than single-species models. This is crucial for improving vector surveillance and disease control strategies, particularly in anticipating how community composition impacts disease transmission. Future research should focus on understanding how communities may shape MBD potential, expanding our biological knowledge of lesser studied mosquito species, and interrogating the impact of traits and biotic interactions across different scales in their impact on shaping mosquito communities. This research advances the field by integrating multiple ecological drivers and species interactions, offering a more comprehensive framework for predicting mosquito distributions and vector-borne disease risks across mixed scales and environmental conditions.