Merramax: A Machine Learning Approach To Stochastic Convergence With A Multi-Variate Dataset

Using a combination of high end computing and machine learning algorithms we developed a system to interrogate climate reanalysis data in a species distribution model. The results show that this system can be used as a tool to identify key variables of interest relevant to a species and to generate a probability map of the distribution of a species of interest. This opens new avenues for statistical inference in regions with sparse observational data.