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<p>Worldwide, freshwater ponds (irrigation, livestock, recreational and stormwater) are poorly studied compared to other lentic systems. To characterize multivariate water quality patterns across four pond types, we sampled 75 ponds in three South Carolina ecoregions in summer 2025 under non-storm conditions. Eighteen physicochemical and biological parameters were quantified. Multivariate analysis techniques (e.g., permutational multivariate analysis (PERMANOVA), linear discriminant analysis, and regularized discriminant analysis) identified dominant gradients, tested for differences, and quantified classification accuracy. PERMANOVA revealed that water quality varied among pond types. Ponds were separated into two groups. Irrigation and livestock ponds were associated with elevated nutrient and ion concentrations. Recreational and stormwater ponds generally exhibited lower or near-average values. Livestock ponds were the most eutrophic, characterized by higher nutrient concentrations and elevated microcystin levels, suggesting greater potential for harmful cyanobacterial blooms (HCBs). In contrast, water quality characteristics in recreational and stormwater ponds were similar, with microcystin concentrations below recreational guidance levels (8&#xa0;µg L<sup>−1</sup>). Pond type influenced nutrient dynamics and the risk of HCBs. The multivariate framework effectively identified key water quality drivers. Study results can inform pond-scale management, downstream water quality strategies and relevant policies in other parts of the world with similar pond types.</p>

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Freshwater pond function drives contrasting water quality and influences cyanotoxin bloom potential

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Morolake M. Fatunmbi,
  • Debabrata Sahoo,
  • Sarah A. White,
  • Amy E. Scaroni,
  • Dawoon Jeong,
  • Calvin B. Sawyer

摘要

Worldwide, freshwater ponds (irrigation, livestock, recreational and stormwater) are poorly studied compared to other lentic systems. To characterize multivariate water quality patterns across four pond types, we sampled 75 ponds in three South Carolina ecoregions in summer 2025 under non-storm conditions. Eighteen physicochemical and biological parameters were quantified. Multivariate analysis techniques (e.g., permutational multivariate analysis (PERMANOVA), linear discriminant analysis, and regularized discriminant analysis) identified dominant gradients, tested for differences, and quantified classification accuracy. PERMANOVA revealed that water quality varied among pond types. Ponds were separated into two groups. Irrigation and livestock ponds were associated with elevated nutrient and ion concentrations. Recreational and stormwater ponds generally exhibited lower or near-average values. Livestock ponds were the most eutrophic, characterized by higher nutrient concentrations and elevated microcystin levels, suggesting greater potential for harmful cyanobacterial blooms (HCBs). In contrast, water quality characteristics in recreational and stormwater ponds were similar, with microcystin concentrations below recreational guidance levels (8 µg L−1). Pond type influenced nutrient dynamics and the risk of HCBs. The multivariate framework effectively identified key water quality drivers. Study results can inform pond-scale management, downstream water quality strategies and relevant policies in other parts of the world with similar pond types.