Distribution of Suitable Habitats for Soft Corals (Alcyonacea) Based on Machine Learning

Abstract

The soft coral order Alcyonacea is a common coral found in the deep sea and plays a crucial role in the deep-sea ecosystem. This study aims to predict the distribution of Alcyonacea in the western Pacific Ocean using four machine learning-based species distribution models. The performance of these models is also evaluated. The results indicate a high consistency among the prediction results of the different models. The soft coral order is primarily distributed in the Thousand Islands Basin, Japan Trench, and Thousand Islands Trench. Water depth and silicate content are identified as important environmental factors influencing the distribution of Alcyonacea. The RF, Maxent, and XGBoost models demonstrate high accuracies, with the RF model exhibiting the highest prediction accuracy. However, the Maxent model outperforms the other three models in data processing. Developing a high-resolution, high-accuracy, and high-precision habitat suitability model for soft corals can provide a scientific basis and reference for China’s exploration and research in the deep sea field and aid in the planning of protected areas in the high seas.