An improved habitat suitability model with dual diagnostic outputs for identifying limiting factors and management priorities: Application to algal blooms in Taihu Lake

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.