Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering

Phytoplankton blooms, which occur over short to seasonal time scales, threaten freshwater ecosystems. However, short-term changes in phytoplankton distributions are often overlooked, leading to underestimations in predictions and difficulties in lake management. Considering that potential information from abundant automatic monitoring datasets has not been fully explored, we developed an automated recognition method to identify diurnal variations in phytoplankton via time series clustering. The temporal and spatial characteristics of the diurnal patterns of the chlorophyll-a (Chl-a) concentration in Taihu Lake were explored. Additionally, the contributions of environmental factors to the phytoplankton distribution were analysed. The results revealed that (1) the diurnal changes in the Chl-a concentration in Taihu Lake could be divided into four main patterns, each with unique potential hotspots that reflect different ecological responses; (2) these patterns revealed high spatiotemporal heterogeneity across the lake, highlighting the complex ecological dynamics; and (3) notably, strong winds, an increase in temperature, and abrupt environmental fluctuations at the day scale were identified as critical drivers of diurnal phytoplankton patterns. This study demonstrates the potential of employing unsupervised clustering algorithms for identifying the diurnal dynamics of the phytoplankton distribution under complex influences, which can aid in optimizing the management and monitoring of shallow lakes.

Abstract licence: CC BY-NC

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