The natural world is changing rapidly: It is expected that by 2100, mean temperatures will rise by 4 degrees, 450 million hectares of forest will be lost and the human population will reach 11.2 billion. It is estimated that nearly half the world’s human population is infected with least one pathogen carried by invertebrate vector species, causing an immense daily burden and re-enforcing cycles of poverty. Climate and land-use change will have a major impact on disease-carrying species over the coming decades. However, our understanding of how biodiversity and in particular vector species respond to global change is limited. This project aims to understand and predict the response of vector species to global change drivers. Following on from the methods used in the Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (PREDICTS) project, this PhD will for the first time, gather empirical evidence to uncover global patterns of vector and non-vector invertebrate community responses to land-use change and other global change drivers. The project would involve using machine learning analytics to gather data from literature and other primary data sources to generate spatial and temporal models of how vector abundances and distributions respond to current and future environmental variables.