Explainable artificial intelligence reveals environmental constraints in seagrass distribution

Abstract

Seagrass is a globally vital marine resource that plays an essential global role in combating climate change, protecting coastlines, ensuring food security, and enriching biodiversity. However, global climate change and human activities have led to dramatic environmental changes severely affecting seagrass growth and development. Therefore, it is crucial to understand accurately how environmental changes, affect seagrass distribution. In this study, we selected the seagrass distribution area in Hainan, China, as the study area and proposed an ensemble model combining five machine learning models to predict the potential distribution of seagrass. Fifteen environmental variables were entered into the model, and the study results showed that the ensemble model provided the highest accuracy (Area Under Curve (AUC) = 0.91). The environmental variables were then classified into regional and site explanations with the help of explainable artificial intelligence (XAI) methods. The difference in the contribution of regional and site environmental variables is demonstrated, and the model provides a more reasonable explanation for the site. The Shapley value (SHAP) and Partial dependency plot (PDP) analysis methods explain the importance of environmental variables in the seagrass distribution model and the effect of multiple environmental variable interactions on the prediction results, which implies opening the black box model for machine learning. More evidence that explainable artificial intelligence can explain the effects of environmental variables in seagrass distribution models will help to improve environmental understanding in seagrass conservation processes.

Keywords

• Seagrass
• Explainable artificial intelligence
• SHAP
• Species distribution model
• Ensemble Model