Landscape patterns are the main regulator of pond water chlorophyll α concentrations in subtropical agricultural catchments of China

Abstract

Chlorophyll α (Chl-a) is an important indicator of algal biomass and eutrophication status of surface water ecosystems. The Chl-a concentrations exhibit significant temporal fluctuations and are strongly influenced by several environmental factors, including water physicochemical parameters and landscape patterns. Although the impacts of these factors on Chl-a concentrations in lakes and reservoirs have been extensively studied, there is little knowledge of eutrophication in ponds. In this context, the Chl-a concentrations and other water physicochemical parameters, namely pH, water temperature (WT), transparency, total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD), were monitored monthly in 28 subtropical ponds in Central China in 2019. The trophic state index (TSI) was applied to evaluate the eutrophication levels of the ponds. Multivariate statistical analysis was used to quantify the relationships between the water physicochemical parameters, landscape patterns, and Chl-a concentrations. According to the obtained results, all 28 ponds were classified as hypereutrophic in June, as well as moderate eutrophic and hypereutrophic levels in May, July, and August. Temporal clustering analysis classified the study period (12 months) into three distinct temporal clusters, with different nutrient levels of the ponds in clusters. Among the water physicochemical parameters, TP explained more than 50% of the Chl-a concentration variations during the entire study period. The partial least squares-structural equation model (PLS-SEM) results indicated that the Chl-a concentrations were controlled by TP from June to August, and jointly limited by TP and TN in the remaining months. Landscape patterns indirectly affected the Chl-a concentrations by affecting the pond water TN and TP concentrations. The orchard area, pond area, Shannon's diversity index, numbers of patches, and landscape shape index were significant landscape pattern metrics, showing different contribution rates to the variations in the Chl-a concentrations of the identified temporal clusters. The results of this study demonstrate the effectiveness of reasonable catchment landscape management in mitigating eutrophication in subtropical ponds.