Climate-Risk Thresholds for Fisheries Aquatic Systems in Kenya: Bayesian Hierarchical Modelling of Resilience and Food Security

Climate change threatens the productivity of Kenya's inland fisheries, yet the thresholds beyond which these aquatic systems lose resilience and compromise food security remain poorly defined. Rather than reporting empirical estimates, the analysis proposes a methodological architecture that integrates hierarchical Bayesian inference with spatial resilience theory, treating lake systems as nested within hydrological basins and climatic zones. The framework specifies how warming rates, hydrological variability and fishing pressure jointly determine the probability that a system crosses a resilience threshold, and how such crossings propagate through fish supply chains to affect food security outcomes. A geographic information systems workflow is described for constructing a composite spatial index of system vulnerability from slope, drainage density, rainfall intensity, land cover, soil permeability, elevation and distance to channels. The article argues that threshold estimation requires moving beyond single-indicator stressor-response models toward hierarchical structures that accommodate cross-scale heterogeneity, and that Bayesian posterior inference offers a principled basis for probabilistic threshold statements that can inform precautionary fisheries governance.