Adaptive Indicator Frameworks for Ecosystem Preservation and Environmental Risk Mitigation

Environmental disasters demand structured monitoring systems capable of linking ecological responses to adaptive governance. This study proposes an integrated indicator framework designed to support ecosystem preservation and environmental risk mitigation following large-scale contamination events. The proposed framework combines multi-source environmental data, intrinsic risk classification, multivariate statistical validation, and a dashboard-based decision-support architecture. When the model was applied to Restinga ecosystems impacted by mining tailings deposition, the results revealed significant spatial heterogeneity between the monitoring stations, with ~33% of sites classified under high or critical ecological risk during at least one monitoring period. Of the metals evaluated, 46.15% were above the reference levels, while for biological response indicators such as primary productivity, a 23.53% reduction in danger alerts was observed in 2019 across the evaluated sites when comparing the rainy and dry seasons. The composite “Danger Alert” indicator was triggered in all sampling campaigns during the evaluated period, demonstrating persistent ecological pressure throughout seasonal cycles. Sensitivity analyses confirmed the robustness of the risk classifications under alternative baseline and aggregation scenarios, and an uncertainty assessment indicated stable trends across temporal variability ranges. The proposed framework enhances the interpretability of complex environmental datasets by structuring inferential ecological associations between environmental pressures and biological responses, which can then be translated into actionable governance outputs. Beyond the case study, the architecture is structurally transferable to other ecosystems, provided that ecological indicators and thresholds are contextually recalibrated. The proposed approach contributes to sustainability-oriented environmental governance by integrating statistical validation, adaptive risk thresholds, and decision-support visualization within a unified monitoring system.