Explainable Artificial Intelligence Models for Ecological Forecasting and Conservation Decision-Making

Abstract

Abstract: Under the growing environmental threats like climate change, land-use transformation and human activity, ecological forecasting has a pivotal role in understanding and information of biodiversity dynamics, habitat changes, and ecosystem resilience. The application of predictive modeling has greatly benefited from recent advances in artificial intelligence (AI), but complex models are not easily interpretable and so are limited in their use for conservation planning and environmental decision making. The purpose of this review is to provide a thorough analysis of the status of explainable artificial intelligence (XAI) models that are used for ecological forecasting and for making ecological conservation decisions. The paper reviews the key data sources used for ecology such as remote sensing imagery, climate, wildlife sensor network, geographic information systems and biodiversity databases, as well as preprocessing techniques for reliable ecological analysis. It covers machine learning, deep learning and transformer-based forecasting methods with a focus on techniques to make machine learning models explainable to the human reader, including SHAP, LIME and attention mechanisms for transparent prediction and interpretation of features. Additionally, an ecological forecasting framework with a unified and interpretable XAI is suggested to bring together a variety of environmental data, extract ecological features, provide interpretable forecasts, and provide guidance for conservation strategies. The review examines the application of these in species distribution modelling, habitat suitability assessment and forecasting of ecosystem health, identifies any current challenges, identifies any research gaps and discusses future directions. In general, explainable AI offers reliable ecological intelligence, which improves the accuracy of predictions, the trust of stakeholders and the planning of sustainable biodiversity conservation.