Long-term spatial and temporal dynamics of ecosystem health in Lake Taihu: Insights from multi-scale field observations and holistic assessments

Understanding ecosystem health dynamics is essential for sustainable management of freshwater ecosystem under the combined pressures of eutrophication, climate change, and human disturbance. Here, we introduce a multi-indicator assessment framework integrating the trophic level index (TLI), eco-exergy (Ex), structural ecoexergy (Exst), ecological buffering capacity (beta(TP)(phyto)), and the ratio of zooplankton biomass to phytoplankton biomass (BZ/BA). Using Lake Taihu, one of China's largest shallow lakes, as a case study, we analyzed long-term monitoring data from 2005 to 2022 to reveal spatiotemporal patterns of ecosystem health. The results showed a distinct temporal three-phases in ecosystem health: high levels in 2005-2007, sustained deterioration from 2008 to 2018, and progressive recovery after 2018. Spatially, the eastern regions maintained better health, whereas northern and western regions, particularly Gonghu Bay, exhibited poorer or more unstable conditions. Among the indicators, Exst and TLI contributed most prominently to health variation, while the long-term declines in BZ/ BA, Ex, and Exst reflected structural degradation under eutrophication. These findings suggest that harmful algal blooms, heightened phosphorus sensitivity, and weakened top-down control by zooplankton were the main drivers of instability. This study therefore highlights the urgency of adaptive governance strategies. Beyond Lake Taihu, the proposed framework offers a potential diagnostic tool for diverse lake ecosystems, advancing understanding of shallow lake dynamics and contribute an analytical basis and strategic perspective to global freshwater governance.