Novel Machine Learning Methods for Estimating the Spatial Distribution of Species

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My project focuses on the development of novel machine learning methods for predicting the geographic distribution of species. These methods will be trained on large-scale, crowdsourced datasets, and will focus on species for which we have the least data available. The resulting outputs will provide a greater understanding about the ranges of previously unmapped species. Improving our knowledge of the distribution of rare species may help with conservation efforts, as this helps to identify areas that are important for species survival and can inform the design of protected areas. The developed models will also be used to modify predictions from image classifiers to help disambiguate between species that mostly appear in different regions (e.g. different parts of the world). My proposed research will avail of recent advances in deep learning and will allow information about different species to be captured inside of the same model. This has several advantages over existing methods such as better predictions for rarer species, better temporal modelling, and the ability to use these models to determine the best sites to sample for future data collection.Novel methodology that will be developed in the course of this project include innovative ways to make best use of the noisy and spatially biased data available for this task, data efficient methods of modelling temporal changes in the distribution of species, and new methods to gather useful additional data for species that we know little about currently, guided by our present understanding of the species distribution. One main objective is using recent deep learning advances to create new models that can simultaneously represent both spatial and temporal trends in the distribution of a species, allowing us to tell both which regions a species is currently present in, and how we expect that to change in the future, which may assist in conservation applications. Another objective is to develop new machine learning methods to best make use of the unusual "presence only" data commonly collected for this task, where only locations that a species has been observed are recorded and "absences" where the species is not present must be inferred from the presence data.