Machine learning approaches for predicting species interactions in dynamic ecosystems

Abstract

This paper explores the application of machine learning (ML) techniques in predicting species interactions within dynamic ecosystems. Using a multi-faceted approach, we investigate the effectiveness of various ML algorithms in analyzing species interaction strengths through an example dataset. Visualizations, including bar, line, and pie charts, depict the distribution and patterns of species interactions, providing valuable insights into ecological dynamics. Additionally, a comparative analysis examines the data requirements and characteristics of four ML approaches: Generalized Linear Models (GLM), Classification and Regression Trees (CART), Artificial Neural Networks (ANN), and Evolutionary Algorithms (EA). By synthesizing information from previous studies, we elucidate the strengths and limitations of each ML approach in predicting species interactions. Furthermore, a performance evaluation of these approaches highlights their predictive capabilities across various metrics, including accuracy, precision, recall, and F1 score. Our research methodology provides a comprehensive understanding of the application of ML techniques in ecological research, laying the groundwork for future studies aiming to predict species interactions and advance our understanding of dynamic ecosystems.