Bayesian Spatiotemporal Assessment of Environmental Sustainability Indicators and Ecosystem Resilience

This study develops an original empirical Bayesian spatiotemporal assessment of the relationship between environmental sustainability indicators and ecosystem resilience. The central purpose is to quantify how annual variations in vegetation condition, water quality, and carbon sequestration are associated with recovery rates following drought disturbance. The study treats resilience as a measurable spatiotemporal outcome rather than a purely conceptual property of ecological systems. The analysis uses annual empirical data for 50 spatial units observed over 15 years from 2010 to 2024. The dataset represents a mixed landscape of semi-natural vegetation, agricultural mosaics, riparian corridors, and peri-urban ecological zones. Bayesian hierarchical models are specified to incorporate spatial random effects, temporal autoregressive dependence, and spatiotemporal interaction terms. The findings indicate that higher NDVI, improved water quality, and greater carbon sequestration are positively associated with faster ecosystem recovery after drought. The strongest posterior association is observed for NDVI, followed by carbon sequestration and water quality. Spatial heterogeneity is substantial, showing that the same sustainability profile may correspond to different resilience outcomes across ecological units. Model comparison favors the Bayesian spatiotemporal interaction model over spatial-only and temporal-only alternatives. The preferred model achieves lower DIC and WAIC values, narrower posterior predictive uncertainty, and improved recovery-rate prediction in spatial units with strong temporal variability. Posterior summaries show stable convergence and credible intervals that support the interpretation of a positive sustainability-resilience relationship. The article contributes a structured empirical framework for integrating environmental sustainability indicators with ecosystem resilience assessment. The study demonstrates how Bayesian models can combine remotely sensed indicators, field-derived metrics, spatial dependence, and uncertainty quantification. The proposed framework supports environmental monitoring, resilience assessment, and evidence-based sustainability planning.