From Proverb to Policy: Local Knowledge as a Community-Embedded Early Warning and Governance Signal for Harmful Algal Blooms in Lake Victoria, Kenya

Harmful algal blooms (HABs) are an escalating socio-ecological hazard in freshwater systems, threatening water security, public health, and livelihoods globally. In low-income contexts such as Lake Victoria, existing technocratic early warning systems (EWS) remain constrained by analytical, infrastructural, and communication limitations, while systematically underutilizing community-based knowledge systems. This study investigates the extent to which local ecological knowledge (LEK) constitutes a functional HAB early warning system and how it can be integrated into formal risk governance frameworks. Data were collected through 11 focus group discussions (FGDs) conducted across Kenyan Lake Victoria landing sites, involving fishers, traders, community members, Beach Management Units, and research/management officials. Qualitative data were analysed using iterative thematic analysis in Quirkos, complemented by semi-quantitative coding and visualization using Python to assess cross-site patterns. Results demonstrate a rich indigenous HAB taxonomy (e.g., Tuoro, Mulukhi, Riboha) with strong ecological alignment, alongside consistent seasonality patterns with convergence during rainfall onset (March, September), indicating nutrient-driven bloom initiation. Communities identified multi-sensory ecological indicators (colour, odour, fish behaviour) and critical bloom phases (e.g., “oil-like” decomposition) linked to peak toxicity. Thematic analysis revealed dominance of governance, knowledge, and trust (110 codes) and HAB identification (104 codes), alongside a clear risk perception gap between water and fish safety (3–5 point difference). Network and cross-site analyses showed Tuoro as a universal term and highlighted linguistic–ecological diversity across sites, while impact assessments revealed divergence between community ecological concerns (fish kills) and institutional priorities (water utility). Findings expose critical gaps in current governance systems, including weak feedback loops and policy absence, but also demonstrate that LEK provides high-resolution, real-time monitoring capacity that directly addresses limitations of FAO-led technocratic frameworks and strengthens all four components of the Sendai Framework for Disaster Risk Reduction. The study recommends a hybrid, participatory EWS model integrating community detection with scientific validation, supported by structured communication pathways and localized advisories. These findings have broad applicability for HAB risk management in similar low-resource settings, offering a scalable pathway toward inclusive, anticipatory, and context-responsive early warning systems.