Machine Learning-Based Early Warning Systems for Ecosystem Collapse: Application to Coral Reef Models

This study explores the development and application of machine learning-based early warning systems for predicting ecosystem collapse, with a specific focus on coral reef ecosystems. We compare the performance of three machine learning approaches: Random Forests (RF), Support Vector Machines (SVM), and Long Short-Term Memory networks (LSTM), in their ability to detect early signs of coral reef degradation. Using a combination of simulated data from complex ecosystem models and real-world data from the Great Barrier Reef, we demonstrate that machine learning techniques can significantly improve the accuracy and lead time of ecosystem collapse predictions. Our results show that the LSTM model outperforms other approaches, providing accurate predictions up to 24 months in advance of critical transitions. This research contributes to the field of ecological forecasting and offers new tools for marine conservation efforts.