Harmful algal blooms (HABs) threaten lake ecosystems globally, requiring assessment tools that identify environmental controls and prioritize interventions. Traditional Habitat Suitability Index (HSI) models rely on fixed expert-based thresholds and lack diagnostic capabilities, limiting decision-support utility. We developed the Tolerance-Driven Aquatic Habitat Model (TDAHM) to address these limitations through two innovations: (1) monthly-varying thresholds calibrated against satellite-observed distributions via differential evolution, establishing empirical HSI-biomass relationships, and (2) dual diagnostics identifying limiting factors and management priorities. Applied to Taihu Lake, China (2016–2018) using 19 monitoring stations and MODIS imagery, TDAHM achieved validation correlation r = 0.70 (50% error reduction) against satellite-derived bloom areas, with monthly thresholds ranging 0.72–0.91. Limiting factor analysis identified NH₃-N (49.4%) and DO (18.7%) as dominant bottlenecks following Liebig's Law, while management priority analysis revealed TP (39.7%), WT (32.0%), and TN (21.7%) as primary contributors based on weighted HSI contributions. This discrepancy provides complementary management perspectives: limiting factors identify acute local bottlenecks, while management priorities highlight strategic targets for lake-wide improvement. TDAHM transforms HSI from descriptive mapping to quantitative decision support, providing factor-specific targets using typical monitoring infrastructure. The framework balances traditional HSI simplicity with diagnostic capabilities unavailable in conventional approaches, offering operationally feasible guidance for lake management programs globally. • Monthly-calibrated thresholds improved seasonal representation compared to fixed-threshold HSI approaches. • Dual diagnostic outputs identify both limiting factors (current bottlenecks) and management priority factors (strategic targets). • NH₃-N dominated as limiting factor (49.4%), while TP showed highest management priority (39.7%), suggesting differentiated intervention strategies.
Xu et al. (Wed,) studied this question.