A machine learning approach to investigate the reasons behind species extinction

• Using EcoSim, 49 features that could contribute to species extinction were found.
• The constructed prediction model predicts species extinction with high accuracy.
• Population fluctuation and compactness are important factors in species extinction.
• Genetic distance and socialization rate are also important in species extinction.
• Birth rate and energy at time of death are effective in predicting extinction.

Species extinction is one of the most important phenomena in conservation biology. Many factors are involved in the disappearance of species, including stochastic population fluctuations, habitat change, resource depletion, and inbreeding. Due to the complexity of the interactions between these various factors and the lengthy time period required to make empirical observations, studying the phenomenon of species extinction can prove to be very difficult in nature. On the other hand, an investigation of the various features involved in species extinction using individual-based simulation modeling and machine learning techniques can be accomplished in a reasonably short period of time. Thus, the aim of this paper is to investigate multiple factors involved in species extinction using computer simulation modeling. We apply several machine learning techniques to the data generated by EcoSim, a predator–prey ecosystem simulation, in order to select the most prominent features involved in species extinction, along with extracting rules that outline conditions that have the potential to be used for predicting extinction. In particular, we used five feature selection methods resulting in the selection of 25 features followed by a reduction of these to 14 features using correlation analysis. Each of the remaining features was placed in one of three broad categories, viz., genetic, environmental, or demographic. The experimental results suggest that factors such as population fluctuation, reproductive age, and genetic distance are important in the occurrence of species extinction in EcoSim, similar to what is observed in nature. We argue that the study of the behavior of species through Individual-Based Modeling has the potential to give rise to new insights into the central factors involved in extinction for real ecosystems. This approach has the potential to help with the detection of early signals of species extinction that could in turn lead to conservation policies to help prevent extinction.