Machine Learning and Its Applications in Studying the Geographical Distribution of Ants

Abstract

Traditional species distribution modelling relies on the links between species and their environments, but often such information is unavailable or unreliable. The objective of our research is to take a machine learning (ML) approach to estimate ant species richness in data-poor countries based on published data on the broader distribution of described ant species. ML is a novel black box method that does not consider functional links between species and their environment. Its prediction accuracy is limited only by the quality and quantity of species records data. ML modelling is applied to calculate the global distribution of ant species richness and achieves 71.78% (decision tree), 70.62% (random forest), 71.09% (logistic regression), and 75.18% (neural network) testing accuracy. The results show that in some West African countries, the species predicted by ML are 1.99 times as many as the species currently recorded. These West African countries have many ant species but lack observational data, and policymakers may be overlooking areas that require protection.