Do all freshwater ponds have similar chemical traits? Evidence on water-quality characteristics and their potential to support harmful cyanotoxins

Do all freshwater ponds have similar chemical traits? 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Evidence on water-quality characteristics and their potential to support harmful cyanotoxins Morolake M. Fatunmbi, Debabrata Sahoo, Sarah A. White, Amy E. Scaroni, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9336366/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract 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. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Scientific community and society/Water resources Agriculture CyanoHABs Microcystin Nutrients Pond Assessment Water Policy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Ponds are a common landscape feature, both in rural and urban environments, serving a wide range of hydrological, ecological and socio-economic functions (Hill et al., 2021 ; Oertli & Parris, 2019 ). This study focuses on four major pond types: stormwater, irrigation, livestock, and recreational, each with different designs and primary functions. Stormwater ponds, a relatively recent landscape feature (Lusk et al., 2025 ), and are engineered for stormwater management to help mitigate flooding, regulate runoff, and improve water quality, and may be designed as either wet or dry ponds, depending on the hydrological needs (Krivtsov et al., 2022 ; Monaghan et al., 2016 ; Skovira & Bohlen, 2023 ; White et al., 2021 ). In an agricultural setting, ponds serve as a crucial water resource for crop irrigation and livestock watering (Céréghino et al., 2014 ; Sayer et al., 2012 ), while recreational ponds serve multiple purposes, including boating, fishing, and swimming (Adhikary, 2023 ; Nix et al., 2021 ; Schagerl et al., 2010 ). Additionally, ponds contribute substantially to other ecosystem functions by providing habitats for aquatic organisms and supporting biodiversity (Hassall, 2014 ). Previous studies have shown that watershed characteristics, such as land use/land cover, topography, soil, and landscape configurations, have a significant impact on the chemical and biological conditions of surface water, with implications for biodiversity and downstream water quality (Carlisle et al., 2013 ; Dodds & Oakes, 2008 ; Shi et al., 2024 ). While extensive research and regulatory frameworks exist for larger waterbodies, such as lakes, rivers, and streams, smaller water bodies, particularly ponds, have received fewer comparative studies, despite their significant influence within the landscape globally (Downing, 2010 ; S. Li et al., 2025 ; Wachholz et al., 2025 ). Unlike deep lakes, ponds are typically shallow, with a high surface area-to-water ratio, which can accelerate nutrient cycling and promote a rapid response to contaminant inputs (Halderman et al., 2021 ; Strayer & Findlay, 2010 ). Excess nutrient inputs are a significant stressor to water quality, posing risks across pond types. High concentrations of nitrogen (> 1.0mg/L) and phosphorus (> 0.020mg/L) can stimulate eutrophication, leading to excessive algal growth that ultimately reduces water column dissolved oxygen; excess algal growth is sometimes caused by species of cyanobacteria that are toxin-producing in freshwater body (Codd et al., 2005 ; Huisman et al., 2018 ; Paerl et al., 2011 ; Sahoo et al., 2024 ). Cyanotoxins pose severe health risks to humans, livestock, pets, and aquatic organisms through ingestion, inhalation and dermal absorption (Havens, 2008 ; Sahoo et al., 2024 ). Additionally, many ponds are hydrologically connected to downstream receiving water bodies, allowing contaminated water to discharge directly into larger aquatic systems (Lane et al., 2018 ; Leibowitz et al., 2018 ; Liu et al., 2024 ). Cyanotoxins are contaminants of increasing concern because, with continued urban development driving stormwater pond installation and the aging of agricultural and recreational reservoirs, cyanobacterial bloom frequency and intensity are increasing, underscoring the need for effective bloom notification and management strategies. Despite formal recognition of cyanotoxins, including microcystins, as emerging contaminants of concern on United States Environmental Protection Agency’s (US EPA) Contaminant Candidate List 5 (EPA, 2022 ), existing guidance thresholds remain non-enforceable, such as drinking and recreational water criteria (EPA, 2022 ), with no regulatory framework currently differentiating monitoring requirements or management thresholds by source waterbody type. This is particularly concerning given the widespread distribution and functional diversity of freshwater ponds across landscapes globally (Richardson et al., 2022 ). Addressing this regulatory and knowledge gap requires an empirical understanding of how water quality conditions, including cyanotoxin occurrence, vary among functionally distinct pond systems. By characterizing water quality, including indicators for cyanotoxins, across four pond types, this study provides a basis for developing risk-stratified, pond-type-specific monitoring and management criteria that current uniform guidance thresholds fail to capture. While pond systems may have similar structural components, ponds serve diverse functions, and their water quality characteristics vary according to their uses and management (Novikmec et al., 2016 ). Understanding how multiple physicochemical variables interact to influence water quality across different pond types is essential for effective monitoring, risk assessment, and management. Univariate approaches are useful for evaluating individual water quality parameters but are limited in their ability to capture interdependencies or identify patterns across multiple variables and multiple pond types (Koklu et al., 2010 ; Mohd Hashim et al., 2020 ). A multivariate approach is particularly valuable for evaluating these complex, interrelated processes, as it enables the identification of dominant water quality variables and key drivers influencing pond water quality across landscapes (de Souza Pereira et al., 2019 ; Yidana & Yidana, 2010 ; Zeng & Rasmussen, 2005 ). In this novel, comprehensive water quality study, a set of complementary multivariate methods was applied to evaluate water quality patterns in four pond types across South Carolina, USA: irrigation, livestock, recreation, and stormwater. Principal component analysis (PCA), an unsupervised technique, was used to reduce dimensionality and identify water quality patterns that most influence overall variability (Greenacre et al., 2022 ; X. Li et al., 2014 ; Wold et al., 1987 ). Permutational multivariate analysis of variance (PERMANOVA) was used to test whether physicochemical variables differed among pond types (Anderson, 2017 ). To further classify these differences, linear discriminant analysis (LDA) was applied as a supervised classification method to evaluate the degree to which pond types can be differentiated based on water quality variables and to identify the most strongly discriminating parameters for group separation (Balakrishnama & Ganapathiraju, 1998 ; Rayens, 1993 ). To address potential violations of LDA assumptions, regularized discriminant analysis (RDA) was applied to improve classification robustness (Friedman, 1989 ; Wu et al., 1996 ). The goal of this study was to use multivariate statistics to analyze trends in water quality parameters across four pond types in South Carolina. Specifically, we identified dominant patterns in pond water quality by determining which parameter combination explained the greatest variation among ponds and which parameters contributed strongly to these patterns. Additionally, we compared the methods used and evaluated whether a consistent, interpretable pattern emerged across the analytical approaches. Results Standardized Z scores were used to compare variables with different measurement units across all pond types. The median Z-score was estimated for each pond type to quantify relative deviations from the overall dataset (Figure 1). Descriptive statistics revealed substantial variability in water quality across pond types, with several physicochemical parameters differing from the overall datasets. Water samples were collected during the summer months; thus, water temperatures were similar among pond types, with mean values ranging from 30ºC to 33ºC. Livestock ponds exhibited elevated median Z-scores for total phosphorus, soluble reactive phosphorus, total nitrogen, chlorophyll-a, phycocyanin, turbidity, iron, and microcystin relative to other pond types (Figure 1). Irrigation ponds showed moderately elevated nitrogen species, conductivity, and major ions. Recreational ponds were generally characterized by lower nutrient concentrations and biological indicators. Stormwater ponds showed elevated levels of sodium and soluble reactive phosphorus compared with the overall dataset. Permutational Multivariate Analysis of Variance (PERMANOVA) A PERMANOVA was conducted using all measured water quality variables and revealed a difference in multivariate water quality among pond types ( p = 0.001, 95% CI). Pairwise comparisons indicated that all pond types differed from one another, except recreational and stormwater ponds ( p = 0.136) (Table 1). Table 1: Result of permutational multivariate analysis of variance (PERMANOVA) showing post-hoc pairwise comparisons among pond type. Group 1 Group 2 Pseudo- F P - value 1 Stormwater Recreation 1.66 0.136 2 Stormwater Livestock 11.03 0.001 3 Stormwater Irrigation 7.24 0.001 4 Recreation Livestock 6.17 0.001 5 Recreation Irrigation 5.62 0.001 6 Livestock Irrigation 8.34 0.001 Principal component analysis (PCA) PCA was conducted using all 18 measured water quality variables. The first two principal components (PCs) explained a substantial proportion of the dataset's total variability, with PC1 accounting for ~32% and PC2 for ~16%. Together, PC1 and PC 2 explained ~48% of the total variance among ponds. Inclusion of PC3 captured an increase in explained variance (up to 63%), indicating the first three components captured the majority of the variability in pond water chemistry; however, mean PCA scores revealed that PC1 and PC2 represented a dominant gradient influencing all pond types. PC1 was largely influenced positively by variables associated with ionic strength and nutrient concentrations, including specific conductivity, total nitrogen, sulfate, total phosphorus, nitrate + nitrite, calcium, and soluble reactive phosphorus (Figure 2). Moderate positive contributions were also associated with turbidity, chloride, ammonia, and phycocyanin, while iron and temperature contributed weakly and negatively. Mean PC1 scores indicated separation among pond types along this gradient, with the irrigation pond exhibiting the highest positive mean score (1.09), followed by the livestock pond (0.03). In contrast, recreation (-0.63) and stormwater ponds (-1.01) clustered at the negative end of PC1. PC2 was characterized by positive loadings for phycocyanin, microcystin, turbidity, and ammonium-N, with additional contributions from iron and sulfate. Calcium, nitrate + nitrite-N, and specific conductivity loaded negatively along this axis (Figure 2). Livestock ponds showed a positive association with PC2, exhibiting the highest mean score (1.44), distinguishing them from all other pond types on this axis. Irrigation (−0.71), recreational (−0.22), and stormwater (−0.31) ponds clustered on the negative side of PC2, reflecting a lower relative influence of cyanobacterial and toxin-related variables along this gradient. PC3 reflected opposing contributions from physical and chemical parameters. The top two positive loadings on the PC3 were temperature and dissolved oxygen variables, with sodium and chloride on the negative axis. While other pond type had a negative mean score on this gradient, livestock ponds again showed a positive mean score (0.60) on PC3 axis. Discriminant analysis Linear discriminant analysis (LDA) LDA provided a supervised perspective on pond type differentiation by maximizing group separation based on multivariate water quality characteristics for each pond type. The first two linear discriminant axes (LD1 and LD2) were retained for interpretation, as they captured the majority of between-group discrimination (Figure 3). LD1 was primarily associated with total nitrogen, specific conductivity, soluble reactive phosphorus, and iron. Sulfate and turbidity also had a moderate positive loading on LD1 axis. In contrast, negative loadings included total phosphorus, nitrate + nitrite-N, calcium, ammonium-N, and phycocyanin. These loadings indicated LD1 captured a gradient of dissolved inorganic nutrients, salinity, and redox-sensitive elements. On LD2, positive loadings were seen for sodium, chlorophyll-a, dissolved oxygen, phycocyanin, microcystin, and soluble reactive phosphorus. Negative contributions were from chloride, total phosphorus, nitrate+nitrite-N, temperature, pH, and ammonium-N. The LD2 axis loadings were mostly driven by biological and oxygen-related variables, reflecting a contrast between biological productivity indicators and nutrient forms. Together, loadings indicated that LD1 primarily reflected a nutrient–salinity gradient, while LD2 captured biological activity and oxygen dynamics. The LDA biplot (Figure 4) displays the first two linear discriminants (LD1 and LD2) and shows group separation around pond-type clusters. LD1 primarily separated livestock ponds (positive scores) from irrigation (negative scores). Livestock ponds clustered toward the positive end of LD1, driven by total nitrogen, specific conductivity, and SRP. In contrast, irrigation ponds occupied the negative end of LD1 and were associated with total phosphorus, nitrate+nitrite-N, and calcium. LD2 was mostly influenced by biological parameters, with positive loadings from chlorophyll-a, phycocyanin, microcystin, dissolved oxygen, and sodium, and negative loadings from total phosphorus, nitrate+nitrite-N, and chloride. This axis further differentiated stormwater and recreation ponds from irrigation ponds. Stormwater and recreation ponds were positioned toward the positive end of LD2, associated with higher dissolved oxygen, sodium, and moderate biological indicators (chlorophyll-a), while irrigation ponds extended toward the negative direction. Notably, vectors for phycocyanin and microcystin were shorter and oriented between the livestock and stormwater groups, indicating a partial contribution to discrimination but less dominance on LD2 than on LD1. Recreation and stormwater ellipses showed substantial overlap, particularly along LD1 and LD2, reflecting similar multivariate water quality profiles. Within-group dispersion was greatest for livestock and irrigation ponds, indicating higher variability in these land-use categories (Figure 4). Overall, LDA classification accuracy was 53%, with a five-fold cross-validation accuracy of 45%. The diagonal elements represent correct classifications, with irrigation and livestock ponds showing the highest true-positive rates (69% and 75%, respectively), although the irrigation pond had the most misclassifications as a recreational pond. Stormwater ponds were also well-classified (75%). Recreation ponds exhibited the least classification accuracy of 56%, with frequent misclassifications as stormwater or livestock (Figure 5), corroborating the LDA biplot. Overall, LDA results complemented the PCA findings by providing a supervised classification framework, revealing that while pond types are distinguishable based on chemical profiles, predictive accuracy is limited by overlaps in recreation and stormwater groups. Regularized discriminant analysis (RDA) To address potential violations of LDA assumptions, such as the equal covariance assumptions and multicollinearity among water quality variables, RDA was performed. The RDA shrinkage parameter (γ) was determined to be 0.20 (between 0 and 1) and was applied to the covariance matrices to stabilize model predictions. Overall classification accuracy improved to 0.65 (65.3%), with a 5-fold cross-validation accuracy of 0.52 (52%), indicating an improvement in model robustness by 13%. The RDA confusion matrix (Figure 6) showed enhanced classification performance across all pond types. Similar to the LDA analysis confusion matrix, the primary misclassifications occurred between irrigation and recreation ponds, and between stormwater and recreation ponds. Livestock ponds were also misclassified, most commonly as recreation ponds, reflecting overlaps in water quality among the pond types. Overall, the recreation pond showed the greatest overlap with other pond categories in the RDA classification. Discussion This novel study used multivariate statistical methods to characterize water quality patterns and assess harmful cyanobacterial bloom risks across different pond types in South Carolina. Results showed that pond purpose and water quality characteristics were the primary drivers of physicochemical variability, with distinctions observed between livestock & irrigation and recreation & stormwater systems. The poorest quality water parameters were recorded in the livestock and irrigation ponds, though driven by different watershed mechanisms. Livestock ponds showed the highest biological response among pond types, characterized by high internal nutrient cycling, turbidity, and elevated microcystin concentrations; these patterns indicated strong internal and external nutrient loading and suggest conditions highly favorable for cyanobacterial growth and toxin production (Burford et al., 2023 ; Huisman et al., 2018 ; Paerl et al., 2011 ; Søndergaard et al., 2003 ). Elevated turbidity and iron concentrations in livestock ponds indicated sediment resuspension and redox-driven nutrient release processes, commonly associated with shallow ponds further influenced by livestock entry into ponds to cool off (Duan et al., 2016 ; Sahuquillo et al., 2012 ). Microcystin concentrations in livestock ponds pose direct health risks to livestock via ingestion, potentially leading to hepatotoxicity, reduced livestock productivity, and economic losses through potential livestock death (Aklakur et al., 2023 ; Carmichael, 2001 ). Irrigation ponds were defined by a chemical gradient of high ionic strength and nutrient enrichment. These ponds exhibited moderately elevated levels of nitrogen species, conductivity, and major ions, patterns consistent with mineral enrichments commonly associated with agricultural runoff, evaporative concentration or other landscape-derived nutrient sources (Bihn et al., 2021 ; Kaushal et al., 2005 ). While irrigation ponds are often located near agricultural areas, surrounding land use varied across sites, suggesting that the observed chemical signatures may reflect a combination of agricultural runoff, watershed inputs, and internal pond processes rather than a single dominant source. Biological responses were more variable than those observed in livestock ponds, however, the elevated nutrient concentrations in irrigation ponds suggest that readily available nutrients likely support algal blooms during periods of reduced flushing. Further, nutrient availability alone did not appear to drive HAB risks (e.g., livestock vs. irrigation reservoir), indicating that HAB prevalence may be limited by other factors such as hydrologic flushing, water withdrawals, or management practices (Lundgren et al., 2013 ). Recent studies have shown that small water bodies within agricultural landscapes are significant contributors to landscape nutrient dynamics (EPA, 2021 ). Ponds near farmland exhibit elevated total nitrogen and phosphorus concentrations due to agricultural runoff and soil erosion (EPA, 2021 ; Liu et al., 2024 ). Agricultural ponds designated for irrigation can serve as reservoirs of cyanotoxins. A study in Georgia reported year-round microcystin presence (Smith et al., 2024 ), correlating with chlorophyll-a and phycocyanin patterns in irrigation and livestock watering ponds in this study. Elevated salinity and dissolved ions, a pattern frequently reported in irrigation water bodies, can also be linked to landscape interactions and evaporative concentration under crop use, which potentially reinforces the nutrient-driven algal response (EPA, 2021 ; Malakar et al., 2019 ). Recreational and stormwater ponds exhibited significant overlap in physicochemical parameters. These systems generally showed lower average nutrient and toxin levels than irrigation and livestock ponds. In stormwater ponds, this pattern may reflect their design as flow-through systems that receive episodic stormwater inputs and discharge water through the outlet structure. Recreational ponds also tended to exhibit relatively lower nutrient levels, likely due to site-specific management practices such as vegetative buffers that help stabilize pond banks and reduce soil erosion, the application of nutrient-binding products to limit phosphorus availability, and the use of aeration systems observed at some recreational pond sites. (Koreiviene et al., 2014 ). The low chlorophyll-a and phycocyanin concentrations in these systems suggest lower HAB risk under typical conditions, although these pond systems may be vulnerable to changing hydrologic or nutrient-loading regimes (O’Keeffe, 2024 ). Despite relatively lower algal biomass and toxin indicators at the time of sampling, the presence of bioavailable phosphorus in stormwater ponds underscores the role stormwater ponds may play as nutrient reservoirs or sources to downstream water bodies (Fatunmbi et al., 2025 ; Frost et al., 2019 ; Song et al., 2017 ). The similarities observed between stormwater and recreation ponds likely reflect comparable watershed influences, particularly in developed or semi-developed settings, where both pond types were common. Recreational ponds may be either constructed or preexisting natural waterbodies adapted for recreational use, and are not always designed within managed landscapes. However, when recreational and stormwater ponds are purposely built, vegetated buffers and riparian zones are commonly recommended, though not always implemented, to stabilize shorelines, intercept surface runoff, and reduce nutrient and sediment inputs (Hassall, 2014 ; Moore & Hunt, 2012 ). Stormwater ponds, although engineered for runoff control, often receive nutrient-enriched inflows from urban and suburban catchments, while recreational ponds may experience similar nutrient pressures from surrounding residential development, lawn management, and direct human activity (Nix et al., 2021 ; Serrano & DeLorenzo, 2008 ). The presence of riparian vegetation and buffer strips in both systems can moderate nutrient loading to some extent; however, their effectiveness varies widely depending on buffer width, vegetation type, and maintenance intensity (Duan et al., 2016 ; Liu et al., 2024 ). Together, these shared hydrological pathways and landscape management practices may explain the observed similarities in multivariate water quality patterns between recreational and stormwater ponds, despite their differing functional roles. The classification overlaps suggest that continuous nutrient inputs from urban runoff, under favorable hydrological and environmental conditions, can trigger sudden eutrophication events, posing risks to downstream systems and public health despite generally better physicochemical water quality. Despite the intended function of water quality improvement systems, stormwater ponds can exhibit eutrophication under strong land-use pressures. For example, Serrano & DeLorenzo, ( 2008 ) reported consistent high levels of phosphorus and chlorophyll-a, including elevated levels of microcystin toxins, in residential stormwater ponds. Stormwater ponds can become eutrophic when land-use pressures and watershed connectivity are strong drivers of water quality. Similarly, studies of stormwater wet detention basins in Florida demonstrated that urban stormwater ponds may exhibit complex interactions among nutrients, chlorophyll-a, and microcystins, implying that even systems with lower nutrient concentrations can support episodic cyanobacterial production and toxin release under certain conditions (Lane et al., 2018 ; Nicholas et al., 2016 ). Pond sediments play a critical role in regulating nutrient dynamics, in addition to external nutrient inputs. Fatunmbi et al. ( 2025 ) showed that sediments in stormwater ponds can initially function as a phosphorus sink; however, as sediment nutrient storage reaches saturation, this buffering capacity diminishes, and sediments may transition into a secondary source of both phosphorus and other sediment-associated nutrients into the water column, further enhancing the risk of eutrophication and harmful cyanobacterial bloom development. Overall, the strong relationship between specific water quality parameters (nutrient enrichment vs. phytoplankton dominance) and pond types highlights the utility of multivariate monitoring. Ponds function as effective sentinels of landscape-scale nutrient pressures; however, their shallow depth and long residence times make them highly susceptible to internal nutrient cycling and harmful cyanobacterial blooms. Effective management strategies should be tailored to specific pond functions, prioritizing exclusion fencing and sediment management for livestock ponds, while focusing on nutrient runoff reduction and buffer maintenance for irrigation, recreational, and urban stormwater systems. While stormwater regulations, policies, and improved watershed practices in the surrounding catchment area might have played a role in maintaining water quality in stormwater ponds, these practices are relatively new in the landscape compared to other types of ponds, suggesting age might be a factor controlling their water quality. If elements such as sediment in the stormwater pond are not managed properly, they may later become a source of nutrients that may promote microcystin release, exceeding freshwater quality standards. While substantial water quality research has focused on agricultural systems at the watershed scale, including nutrient dynamics and nonpoint source pollution, this study identified irrigation and livestock ponds as potential hotspots of microcystin occurrence, suggesting that more research, education and land management practices are needed on agricultural water management at the pond scale. Many agricultural ponds also predate stormwater pond infrastructure, and pond age may be a critical factor driving fluctuations in pond water quality. Hydrological connectivity of ponds to downstream aquatic systems further heightens concerns about cyanotoxin transport, posing risks under climate change pressures that favor warmer temperatures and intensified blooms (Huisman et al., 2018 ). Better management solutions to address the causes of microcystin production (e.g., nutrients) within the pond environment need to be developed. To minimize downstream transport of microcystin from these ponds and the conditions that promote its production, Management strategies should prioritize nutrient source control, hydrologic isolation where feasible, and sediment stabilization in livestock and irrigation ponds. Methods Study area South Carolina, in the southeastern United States, is geographically divided into three main ecoregions: the Blue Ridge Mountains (Upstate), the Piedmont (Midlands), and the Atlantic Coastal Plain (Low Country and Pee Dee). The state experiences a humid subtropical climate, with mild winters and heavy rainfall, mostly during the summer. South Carolina has over 50,000 wet ponds distributed across both rural and urban landscapes, serving irrigation, livestock, recreation, and stormwater functions (SCDNR, 2013 ; White et al., 2021 ). The design, construction, and regulatory oversight of these ponds vary depending on their intended use. Sampling sites (Fig. 7 ) were identified with assistance from county extension agents, specialists, personal connections, stormwater and pond management professionals, who helped secure access to ponds on agricultural lands, private properties, neighborhoods, and managed landscapes. The age of the pond was not considered while selecting the ponds. Reconnaissance was conducted utilizing Google Maps to map the inlet and outflow and ensure the ponds were accessible. Below is the general description of each pond type that was sampled. Irrigation ponds sampled were typically located within ~ 46 m of farmland, with an average surface area of 21,990 m 2 . These ponds were predominantly inline systems, formed by damming small streams and receiving their inflow from upstream and surrounding surface runoff. The most common structural features on these ponds include a pump system that supplies water directly to farm fields and an outlet structure, usually an overhead riser (Fig. 8 : Representative images of pond types investigated in this study: a) irrigation pond, b) livestock pond, c) recreation pond, d) stormwater pond). Many had a well-defined vegetative buffer, only one of the irrigation ponds had a floating wetland feature. Common ecological features of these ponds include aquatic weeds, alligators, birds, and beavers, depending on the location in the state. Livestock ponds samples were located within ~ 91 m of an active livestock farm operation, managed primarily by the farm owners, and had an average surface area of 15,134 m 2 (Fig. 8 b). Similar to irrigation ponds, most livestock ponds were constructed by damming small streams, so they function as inline or semi-inline systems, receiving inflow from adjacent streams and surface runoff. However, unlike irrigation ponds, these systems often lack a defined outlet structure, potentially increasing water residence time. While some ponds had adequate fences to restrict animal access, a few had unrestricted access for cattle, sheep and other livestock. Most of the ponds were characterized by eroded banks from animal trampling, high turbidity, and limited aquatic habitat. Recreation ponds sampled were distributed across both suburban and forested landscapes, and were mostly privately owned and managed, although some were located within parks (Fig. 8 c). They averaged 11,436 m 2 in surface area and were commonly used for activities such as swimming, fishing, and boating. These ponds typically contained ducks, turtles, various fish populations, including volunteer fish, and other species stocked by pond owners. The primary water sources included streams and surrounding runoff. A few of these ponds were reported to have been used for irrigation purposes in the past. Stormwater pond systems were the most structurally defined of the pond types sampled, with a defined inlet structure (mostly circular piped), an outlet structure, and, in some cases, additional spillways for overflow periods (Fig. 8 d). Largely well-maintained, with some of the ponds having management features such as an aeration system, floating wetlands, and vegetation buffers. With an average surface area of 2,055 m 2 , these ponds were mostly located in urban and suburban areas to control flooding, manage runoff, improve water quality, and maintain landscape aesthetics. Many stormwater ponds were maintained by incorporating features such as aeration systems, floating wetlands, and vegetative buffers. These ponds were home to quite a few aquatic species such as fish, turtles, birds, alligators etc. Water samples and analysis Physicochemical data and water samples were collected during non-storm conditions from 75 ponds across 17 counties in South Carolina, comprising of 24 irrigation, 18 livestock, 17 recreational, and 16 stormwater ponds during the summer of 2025. The summer period is the primary growing season for phytoplankton and cyanobacteria in southeastern U.S. freshwater systems when warm water temperatures, high solar radiation, and stable stratification create a favorable condition for eutrophication signals and potential harmful cyanobacterial blooms (Greenfield et al., 2019 ; Huisman et al., 2018 ; Paerl & Otten, 2013 ). Targeting this period allows for the detection of peak water quality degradation and cyanotoxin risks, which are pronounced in warmer months, as reported in small waterbodies (Greenfield et al., 2019 ). Samplings were conducted at two sections within each pond, the inlet (primary inflow structure) and the outlet (discharge structure), using the grab sampling method. At each section, water was collected into a labelled 1-L HDPE b and the outlet, using the grab sampling method. At each section, water was collected into a labelled 1-L HDPE bottle in triplicate, thoroughly mixed, and sub-sampled into a series of 60 mL and 125 mL sample bottles for laboratory analysis. Water chemistry analysis included microcystin, total phosphorus, soluble reactive phosphorus, total nitrogen, nitrate-nitrite, ammonia-n, calcium, chloride, sodium, iron, and sulfate. Physicochemical parameters including pH, water temperature, dissolved oxygen, turbidity, specific conductivity, phycocyanin, and chlorophyll-a concentrations were measured using a YSI handheld multimeter prior to water sample collection. All water samples were stored in an ice chest and kept at a temperature below 4°C while being transported back to the South Carolina Water Resources Center in Pendleton, SC. Samples were held for up to a week, stored in accordance with Arkansas Water Resources Center (AWRC) recommendations (Austin et al., 2017 ; Bradley et al., 2016), before being transported in an ice chest to the AWRC laboratory for analysis. The laboratory analyses were performed according to AWRC standard operating procedures and standard methods (see: https://awrc.uark.edu/water-quality-lab/ ). Data analysis The statistical distribution properties of the water quality variables were initially assessed using descriptive statistics on JMP (version 11.2.0). To evaluate overall differences in multivariate water chemistry among pond types, a permutational multivariate analysis of variance was conducted. PERMANOVA is a non-parametric permutation-based method that tests differences in the centroid locations of predefined groups in a multivariate space, based on a distance matrix (Anderson, 2017 ). In this study, PERMANOVA was used to assess whether pond types exhibited significant overall variation in water quality variables, using 9999 permutations at a 95% confidence interval (α = 0.05). Pairwise comparisons were further performed to identify specific distinctions between individual pond types. Principal component analysis (PCA), an unsupervised multivariate technique, was used to reduce the dimensionality of the water quality dataset, specifically to identify dominant patterns of variation among ponds (Greenacre et al., 2022 ; X. Li et al., 2014 ; Wold et al., 1987 ). PCA transforms the original correlated variables into a new, smaller set of uncorrelated axes, referred to as principal components (PCs), which are ordered by the amount of variance they explain in the dataset, typically from highest to lowest. Each PC is defined by eigenvalues, which quantify the amount of variance explained, and loadings, which describe the contribution of individual water quality variables to each PC. In this study, PCA was applied to the standardized water quality variables across all ponds to help identify patterns in variations and determine the parameter that drives the most variation among ponds. Discriminant Analysis, a supervised multivariate method, was used to evaluate how water quality variables differentiate the predefined pond type groups. It works by maximizing separation between groups while minimizing within-group variation through a linear combination of predictor variables (Rayens, 1993 ). Linear discriminant analysis (LDA) identifies these linear combinations, known as the discriminant function, which best separate the pond types. LDA assumes multivariate normality and equal covariance matrices across groups. In this study, LDA was applied to assess the degree to which pond types could be distinguished based on their phytochemical characteristics and to identify the variables contributing most strongly to group separation. However, ecological water quality data often exhibits correlated variables and unequal covariance structures across groups, which can violate the assumptions of LDA (Koklu et al., 2010 ; Wu et al., 1996 ). To address this, regularized discriminant analysis (RDA) was additionally applied. RDA serves as an intermediate approach between LDA analysis and quadratic discriminant analysis (QDA) (Friedman, 1989 ). It improves the estimation of the group covariance matrices by introducing shrinkage (regularization) to the covariance matrices, which partially pools the separate group covariances toward a common (pooled) covariance matrix and/or toward a scaled identity matrix. This shrinkage stabilizes parameter estimates, reduces overfitting, and enhances robustness, particularly in ecological datasets with highly correlated water quality variables (Friedman, 1989 ). Together, LDA and RDA were used to evaluate classification performance, quantify group separation, and identify the most influential water quality parameters distinguishing pond types. Declarations Funding Declaration This research is based upon work supported by USGS under project number 2024917. Technical Contribution No. 7331 of the Clemson University Experiment Station. Author Contribution Conceptualization: M.F., D.S., S.W., Methodology: M.F., D.S., S.W., Field Sampling: M.F., D.S., S.W., C.S., Data Analysis: M.F., D.S., S.W., A.S., D.J., C.S., Writing-original draft preparation: M.F., D.S., S.W., Writing - review and editing: M.F., D.S., S.W., A.S., D.J., C.S. Acknowledgement The authors would like to acknowledge Dr. Ibrahim Busari, Jyoti Neupane, Abdulgafar Tunde Badrudeen, Gafar Agunbiade, and Daniela Font for assistance during the sampling events. The authors would also like to thank the farmers, pond owners, Extension agents, county officials, and PI’s personal contacts for their assistance in selecting and gaining access to the ponds. Data Availability Data request can be directed to the corresponding author with access granted only upon approval from the funding agency. References Adhikary, R. K. (2023). Water Quality of Urban Lakes and Ponds for Recreational Use . September . Aklakur, M., Bakli, S., Deo, A. D., Singh, D. K., & Pailan, G. H. 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White","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"A.","lastName":"White","suffix":""},{"id":626061568,"identity":"5789b3d4-ade8-4eef-ac6f-46db02e344f9","order_by":3,"name":"Amy E. Scaroni","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"E.","lastName":"Scaroni","suffix":""},{"id":626061569,"identity":"1e0d4d75-05c6-4c92-9018-8e71e592da3e","order_by":4,"name":"Dawoon Jeong","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Dawoon","middleName":"","lastName":"Jeong","suffix":""},{"id":626061570,"identity":"fa17c76d-a420-4330-a994-d51da60a44dc","order_by":5,"name":"Calvin B. Sawyer","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Calvin","middleName":"B.","lastName":"Sawyer","suffix":""}],"badges":[],"createdAt":"2026-04-06 17:53:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9336366/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9336366/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107720962,"identity":"01cb1c47-b157-4174-a313-5f1823835a31","added_by":"auto","created_at":"2026-04-24 10:59:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72446,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing the standardized median Z-score (standard deviation of a data point above or below the population mean) of all water quality variables measured across all pond types\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9336366/v1/91db1ad27be2da6b39c3420a.png"},{"id":108180869,"identity":"3364b017-618b-490c-8479-51d4eeada9ae","added_by":"auto","created_at":"2026-04-30 08:54:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102057,"visible":true,"origin":"","legend":"\u003cp\u003eVariable loadings for the first three principal components (PC1 – PC3). Bars represent the relative contribution (loadings) of each variable to the respective principal components, with positive and negative values indicating the direction of association.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9336366/v1/6adb35bdaa3e0a3a47c50ebf.png"},{"id":107720964,"identity":"29f29f17-3288-4fc2-9e83-7541191f0344","added_by":"auto","created_at":"2026-04-24 10:59:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116756,"visible":true,"origin":"","legend":"\u003cp\u003eLinear discriminant loadings illustrating the contributions of water quality variables to LD1 and LD2\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9336366/v1/75315db44216bea94c11f04d.png"},{"id":107869583,"identity":"eab73da1-605c-4439-82ec-19e39bfc6f5a","added_by":"auto","created_at":"2026-04-27 07:37:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":129559,"visible":true,"origin":"","legend":"\u003cp\u003eLinear discriminant analysis biplot including pond type group ellipses\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9336366/v1/e35cc229de503669485c6c47.png"},{"id":107869229,"identity":"4a77fd0b-8a79-41ad-a50a-2ef41a59603c","added_by":"auto","created_at":"2026-04-27 07:36:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32925,"visible":true,"origin":"","legend":"\u003cp\u003eConfusion matrix summarizing classification performance of linear discriminant analysis for pond type assignment based on water quality variables. Rows represent true pond types, and columns represent predicted classifications.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9336366/v1/9960331bea05577904d40b51.png"},{"id":107720968,"identity":"278a3e53-b2a5-4d32-b890-5d52412cacd9","added_by":"auto","created_at":"2026-04-24 10:59:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":50313,"visible":true,"origin":"","legend":"\u003cp\u003eRegularized discriminant analysis confusion matrix showing classification outcome for pond types based on water quality characteristics.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9336366/v1/b1371a4c0104c94ddf53dbb3.png"},{"id":107868877,"identity":"6d010d7c-c3ff-40e7-88e2-770e019ae239","added_by":"auto","created_at":"2026-04-27 07:34:37","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":793917,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of sampled ponds across South Carolina state regions, categorized based on primary uses (n = 75).\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9336366/v1/2f1f1a4d89d389e44f087a30.jpeg"},{"id":107720969,"identity":"f186a5fc-16b7-4f49-a8a9-0ed3dca420e0","added_by":"auto","created_at":"2026-04-24 10:59:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":26189521,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images of pond types investigated in this study: a) irrigation pond, b) livestock pond, c) recreation pond, d) stormwater pond.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9336366/v1/4c33b776117117556823764a.png"},{"id":108491497,"identity":"af9e4446-d1bd-4cc9-af81-904e50ec1491","added_by":"auto","created_at":"2026-05-05 09:54:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":26199593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9336366/v1/fe14a9c8-8ec1-4dad-a5ef-9c6f28243b0b.pdf"},{"id":107868961,"identity":"4251d7e1-1d49-44f5-83e2-5b57cf6ae934","added_by":"auto","created_at":"2026-04-27 07:35:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":260158,"visible":true,"origin":"","legend":"","description":"","filename":"Multivariatearticlenaturesupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9336366/v1/25ecc7c196edae20ccba76b0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Do all freshwater ponds have similar chemical traits? Evidence on water-quality characteristics and their potential to support harmful cyanotoxins","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePonds are a common landscape feature, both in rural and urban environments, serving a wide range of hydrological, ecological and socio-economic functions (Hill et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oertli \u0026amp; Parris, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This study focuses on four major pond types: stormwater, irrigation, livestock, and recreational, each with different designs and primary functions. Stormwater ponds, a relatively recent landscape feature (Lusk et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and are engineered for stormwater management to help mitigate flooding, regulate runoff, and improve water quality, and may be designed as either wet or dry ponds, depending on the hydrological needs (Krivtsov et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Monaghan et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Skovira \u0026amp; Bohlen, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; White et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In an agricultural setting, ponds serve as a crucial water resource for crop irrigation and livestock watering (C\u0026eacute;r\u0026eacute;ghino et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sayer et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), while recreational ponds serve multiple purposes, including boating, fishing, and swimming (Adhikary, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nix et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schagerl et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Additionally, ponds contribute substantially to other ecosystem functions by providing habitats for aquatic organisms and supporting biodiversity (Hassall, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have shown that watershed characteristics, such as land use/land cover, topography, soil, and landscape configurations, have a significant impact on the chemical and biological conditions of surface water, with implications for biodiversity and downstream water quality (Carlisle et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dodds \u0026amp; Oakes, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While extensive research and regulatory frameworks exist for larger waterbodies, such as lakes, rivers, and streams, smaller water bodies, particularly ponds, have received fewer comparative studies, despite their significant influence within the landscape globally (Downing, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; S. Li et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wachholz et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnlike deep lakes, ponds are typically shallow, with a high surface area-to-water ratio, which can accelerate nutrient cycling and promote a rapid response to contaminant inputs (Halderman et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Strayer \u0026amp; Findlay, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Excess nutrient inputs are a significant stressor to water quality, posing risks across pond types. High concentrations of nitrogen (\u0026gt;\u0026thinsp;1.0mg/L) and phosphorus (\u0026gt;\u0026thinsp;0.020mg/L) can stimulate eutrophication, leading to excessive algal growth that ultimately reduces water column dissolved oxygen; excess algal growth is sometimes caused by species of cyanobacteria that are toxin-producing in freshwater body (Codd et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Huisman et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paerl et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sahoo et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cyanotoxins pose severe health risks to humans, livestock, pets, and aquatic organisms through ingestion, inhalation and dermal absorption (Havens, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sahoo et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, many ponds are hydrologically connected to downstream receiving water bodies, allowing contaminated water to discharge directly into larger aquatic systems (Lane et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Leibowitz et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cyanotoxins are contaminants of increasing concern because, with continued urban development driving stormwater pond installation and the aging of agricultural and recreational reservoirs, cyanobacterial bloom frequency and intensity are increasing, underscoring the need for effective bloom notification and management strategies.\u003c/p\u003e \u003cp\u003eDespite formal recognition of cyanotoxins, including microcystins, as emerging contaminants of concern on United States Environmental Protection Agency\u0026rsquo;s (US EPA) Contaminant Candidate List 5 (EPA, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), existing guidance thresholds remain non-enforceable, such as drinking and recreational water criteria (EPA, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with no regulatory framework currently differentiating monitoring requirements or management thresholds by source waterbody type. This is particularly concerning given the widespread distribution and functional diversity of freshwater ponds across landscapes globally (Richardson et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Addressing this regulatory and knowledge gap requires an empirical understanding of how water quality conditions, including cyanotoxin occurrence, vary among functionally distinct pond systems. By characterizing water quality, including indicators for cyanotoxins, across four pond types, this study provides a basis for developing risk-stratified, pond-type-specific monitoring and management criteria that current uniform guidance thresholds fail to capture.\u003c/p\u003e \u003cp\u003eWhile pond systems may have similar structural components, ponds serve diverse functions, and their water quality characteristics vary according to their uses and management (Novikmec et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Understanding how multiple physicochemical variables interact to influence water quality across different pond types is essential for effective monitoring, risk assessment, and management. Univariate approaches are useful for evaluating individual water quality parameters but are limited in their ability to capture interdependencies or identify patterns across multiple variables and multiple pond types (Koklu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mohd Hashim et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A multivariate approach is particularly valuable for evaluating these complex, interrelated processes, as it enables the identification of dominant water quality variables and key drivers influencing pond water quality across landscapes (de Souza Pereira et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yidana \u0026amp; Yidana, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zeng \u0026amp; Rasmussen, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this novel, comprehensive water quality study, a set of complementary multivariate methods was applied to evaluate water quality patterns in four pond types across South Carolina, USA: irrigation, livestock, recreation, and stormwater. Principal component analysis (PCA), an unsupervised technique, was used to reduce dimensionality and identify water quality patterns that most influence overall variability (Greenacre et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; X. Li et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wold et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Permutational multivariate analysis of variance (PERMANOVA) was used to test whether physicochemical variables differed among pond types (Anderson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To further classify these differences, linear discriminant analysis (LDA) was applied as a supervised classification method to evaluate the degree to which pond types can be differentiated based on water quality variables and to identify the most strongly discriminating parameters for group separation (Balakrishnama \u0026amp; Ganapathiraju, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Rayens, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). To address potential violations of LDA assumptions, regularized discriminant analysis (RDA) was applied to improve classification robustness (Friedman, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe goal of this study was to use multivariate statistics to analyze trends in water quality parameters across four pond types in South Carolina. Specifically, we identified dominant patterns in pond water quality by determining which parameter combination explained the greatest variation among ponds and which parameters contributed strongly to these patterns. Additionally, we compared the methods used and evaluated whether a consistent, interpretable pattern emerged across the analytical approaches.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eStandardized Z scores were used to compare variables with different measurement units across all pond types. The median Z-score was estimated for each pond type to quantify relative deviations from the overall dataset (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDescriptive statistics revealed substantial variability in water quality across pond types, with several physicochemical parameters differing from the overall datasets. Water samples were collected during the summer months; thus, water temperatures were similar among pond types, with mean values ranging from 30\u0026ordm;C to 33\u0026ordm;C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLivestock ponds exhibited elevated median Z-scores for total phosphorus, soluble reactive phosphorus, total nitrogen, chlorophyll-a, phycocyanin, turbidity, iron, and microcystin relative to other pond types (Figure 1). Irrigation ponds showed moderately elevated nitrogen species, conductivity, and major ions. Recreational ponds were generally characterized by lower nutrient concentrations and biological indicators. Stormwater ponds showed elevated levels of sodium and soluble reactive phosphorus compared with the overall dataset.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePermutational Multivariate Analysis of Variance (PERMANOVA)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA PERMANOVA was conducted using all measured water quality variables and revealed a difference in multivariate water quality among pond types (\u003cem\u003ep\u003c/em\u003e = 0.001, 95% CI). Pairwise comparisons indicated that all pond types differed from one another, except recreational and stormwater ponds (\u003cem\u003ep\u003c/em\u003e = 0.136) (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1: Result of permutational multivariate analysis of variance (PERMANOVA) showing post-hoc pairwise comparisons among pond type.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePseudo-\u003cem\u003eF\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e- value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eStormwater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eRecreation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eStormwater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eLivestock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e11.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eStormwater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eIrrigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e7.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eRecreation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eLivestock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eRecreation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eIrrigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLivestock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eIrrigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrincipal component analysis\u003c/em\u003e (PCA)\u003c/p\u003e\n\u003cp\u003ePCA was conducted using all 18 measured water quality variables. The first two principal components (PCs) explained a substantial proportion of the dataset\u0026apos;s total variability, with PC1 accounting for ~32% and PC2 for ~16%. Together, PC1 and PC 2 explained ~48% of the total variance among ponds. Inclusion of PC3 captured an increase in explained variance (up to 63%), indicating the first three components captured the majority of the variability in pond water chemistry; however, mean PCA scores revealed that PC1 and PC2 represented a dominant gradient influencing all pond types.\u003c/p\u003e\n\u003cp\u003ePC1 was largely influenced positively by variables associated with ionic strength and nutrient concentrations, including specific conductivity, total nitrogen, sulfate, total phosphorus, nitrate + nitrite, calcium, and soluble reactive phosphorus (Figure 2). Moderate positive contributions were also associated with turbidity, chloride, ammonia, and phycocyanin, while iron and temperature contributed weakly and negatively. Mean PC1 scores indicated separation among pond types along this gradient, with the irrigation pond exhibiting the highest positive mean score (1.09), followed by the livestock pond (0.03). In contrast, recreation (-0.63) and stormwater ponds (-1.01) clustered at the negative end of PC1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePC2 was characterized by positive loadings for phycocyanin, microcystin, turbidity, and ammonium-N, with additional contributions from iron and sulfate. Calcium, nitrate + nitrite-N, and specific conductivity loaded negatively along this axis (Figure 2). Livestock ponds showed a positive association with PC2, exhibiting the highest mean score (1.44), distinguishing them from all other pond types on this axis. Irrigation (\u0026minus;0.71), recreational (\u0026minus;0.22), and stormwater (\u0026minus;0.31) ponds clustered on the negative side of PC2, reflecting a lower relative influence of cyanobacterial and toxin-related variables along this gradient.\u003c/p\u003e\n\u003cp\u003ePC3 reflected opposing contributions from physical and chemical parameters. The top two positive loadings on the PC3 were temperature and dissolved oxygen variables, with sodium and chloride on the negative axis. While other pond type had a negative mean score on this gradient, livestock ponds again showed a positive mean score (0.60) on PC3 axis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDiscriminant analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLinear discriminant analysis\u0026nbsp;\u003c/em\u003e(LDA)\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLDA provided a supervised perspective on pond type differentiation by maximizing group separation based on multivariate water quality characteristics for each pond type.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe first two linear discriminant axes (LD1 and LD2) were retained for interpretation, as they captured the majority of between-group discrimination (Figure 3). LD1 was primarily associated with total nitrogen, specific conductivity, soluble reactive phosphorus, and iron. Sulfate and turbidity also had a moderate positive loading on LD1 axis. In contrast, negative loadings included total phosphorus, nitrate + nitrite-N, calcium, ammonium-N, and phycocyanin. These loadings indicated LD1 captured a gradient of dissolved inorganic nutrients, salinity, and redox-sensitive elements. On LD2, positive loadings were seen for sodium, chlorophyll-a, dissolved oxygen, phycocyanin, microcystin, and soluble reactive phosphorus. Negative contributions were from chloride, total phosphorus, nitrate+nitrite-N, temperature, pH, and ammonium-N. The LD2 axis loadings were mostly driven by biological and oxygen-related variables, reflecting a contrast between biological productivity indicators and nutrient forms. Together, loadings indicated that LD1 primarily reflected a nutrient\u0026ndash;salinity gradient, while LD2 captured biological activity and oxygen dynamics.\u003c/p\u003e\n\u003cp\u003eThe LDA biplot (Figure 4) displays the first two linear discriminants (LD1 and LD2) and shows group separation around pond-type clusters. LD1 primarily separated livestock ponds (positive scores) from irrigation (negative scores). Livestock ponds clustered toward the positive end of LD1, driven by total nitrogen, specific conductivity, and SRP. In contrast, irrigation ponds occupied the negative end of LD1 and were associated with total phosphorus, nitrate+nitrite-N, and calcium.\u003c/p\u003e\n\u003cp\u003eLD2 was mostly influenced by biological parameters, with positive loadings from chlorophyll-a, phycocyanin, microcystin, dissolved oxygen, and sodium, and negative loadings from total phosphorus, nitrate+nitrite-N, and chloride. This axis further differentiated stormwater and recreation ponds from irrigation ponds. Stormwater and recreation ponds were positioned toward the positive end of LD2, associated with higher dissolved oxygen, sodium, and moderate biological indicators (chlorophyll-a), while irrigation ponds extended toward the negative direction. Notably, vectors for phycocyanin and microcystin were shorter and oriented between the livestock and stormwater groups, indicating a partial contribution to discrimination but less dominance on LD2 than on LD1. Recreation and stormwater ellipses showed substantial overlap, particularly along LD1 and LD2, reflecting similar multivariate water quality profiles. Within-group dispersion was greatest for livestock and irrigation ponds, indicating higher variability in these land-use categories (Figure 4).\u003c/p\u003e\n\u003cp\u003eOverall, LDA classification accuracy was 53%, with a five-fold cross-validation accuracy of 45%. The diagonal elements represent correct classifications, with irrigation and livestock ponds showing the highest true-positive rates (69% and 75%, respectively), although the irrigation pond had the most misclassifications as a recreational pond. Stormwater ponds were also well-classified (75%). Recreation ponds exhibited the least classification accuracy of 56%, with frequent misclassifications as stormwater or livestock (Figure 5), corroborating the LDA biplot.\u003c/p\u003e\n\u003cp\u003eOverall, LDA results complemented the PCA findings by providing a supervised classification framework, revealing that while pond types are distinguishable based on chemical profiles, predictive accuracy is limited by overlaps in recreation and stormwater groups.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRegularized discriminant analysis\u0026nbsp;\u003c/em\u003e(RDA)\u003c/p\u003e\n\u003cp\u003eTo address potential violations of LDA assumptions, such as the equal covariance assumptions and multicollinearity among water quality variables, RDA was performed. The RDA shrinkage parameter (\u0026gamma;) was determined to be 0.20 (between 0 and 1) and was applied to the covariance matrices to stabilize model predictions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall classification accuracy improved to 0.65 (65.3%), with a 5-fold cross-validation accuracy of 0.52 (52%), indicating an improvement in model robustness by 13%. The RDA confusion matrix (Figure 6) showed enhanced classification performance across all pond types. Similar to the LDA analysis confusion matrix, the primary misclassifications occurred between irrigation and recreation ponds, and between stormwater and recreation ponds. Livestock ponds were also misclassified, most commonly as recreation ponds, reflecting overlaps in water quality among the pond types. Overall, the recreation pond showed the greatest overlap with other pond categories in the RDA classification.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis novel study used multivariate statistical methods to characterize water quality patterns and assess harmful cyanobacterial bloom risks across different pond types in South Carolina. Results showed that pond purpose and water quality characteristics were the primary drivers of physicochemical variability, with distinctions observed between livestock \u0026amp; irrigation and recreation \u0026amp; stormwater systems.\u003c/p\u003e \u003cp\u003eThe poorest quality water parameters were recorded in the livestock and irrigation ponds, though driven by different watershed mechanisms. Livestock ponds showed the highest biological response among pond types, characterized by high internal nutrient cycling, turbidity, and elevated microcystin concentrations; these patterns indicated strong internal and external nutrient loading and suggest conditions highly favorable for cyanobacterial growth and toxin production (Burford et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Huisman et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paerl et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; S\u0026oslash;ndergaard et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Elevated turbidity and iron concentrations in livestock ponds indicated sediment resuspension and redox-driven nutrient release processes, commonly associated with shallow ponds further influenced by livestock entry into ponds to cool off (Duan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sahuquillo et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Microcystin concentrations in livestock ponds pose direct health risks to livestock via ingestion, potentially leading to hepatotoxicity, reduced livestock productivity, and economic losses through potential livestock death (Aklakur et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Carmichael, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Irrigation ponds were defined by a chemical gradient of high ionic strength and nutrient enrichment. These ponds exhibited moderately elevated levels of nitrogen species, conductivity, and major ions, patterns consistent with mineral enrichments commonly associated with agricultural runoff, evaporative concentration or other landscape-derived nutrient sources (Bihn et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kaushal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). While irrigation ponds are often located near agricultural areas, surrounding land use varied across sites, suggesting that the observed chemical signatures may reflect a combination of agricultural runoff, watershed inputs, and internal pond processes rather than a single dominant source. Biological responses were more variable than those observed in livestock ponds, however, the elevated nutrient concentrations in irrigation ponds suggest that readily available nutrients likely support algal blooms during periods of reduced flushing. Further, nutrient availability alone did not appear to drive HAB risks (e.g., livestock vs. irrigation reservoir), indicating that HAB prevalence may be limited by other factors such as hydrologic flushing, water withdrawals, or management practices (Lundgren et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies have shown that small water bodies within agricultural landscapes are significant contributors to landscape nutrient dynamics (EPA, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Ponds near farmland exhibit elevated total nitrogen and phosphorus concentrations due to agricultural runoff and soil erosion (EPA, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Agricultural ponds designated for irrigation can serve as reservoirs of cyanotoxins. A study in Georgia reported year-round microcystin presence (Smith et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), correlating with chlorophyll-a and phycocyanin patterns in irrigation and livestock watering ponds in this study. Elevated salinity and dissolved ions, a pattern frequently reported in irrigation water bodies, can also be linked to landscape interactions and evaporative concentration under crop use, which potentially reinforces the nutrient-driven algal response (EPA, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Malakar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecreational and stormwater ponds exhibited significant overlap in physicochemical parameters. These systems generally showed lower average nutrient and toxin levels than irrigation and livestock ponds. In stormwater ponds, this pattern may reflect their design as flow-through systems that receive episodic stormwater inputs and discharge water through the outlet structure. Recreational ponds also tended to exhibit relatively lower nutrient levels, likely due to site-specific management practices such as vegetative buffers that help stabilize pond banks and reduce soil erosion, the application of nutrient-binding products to limit phosphorus availability, and the use of aeration systems observed at some recreational pond sites. (Koreiviene et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The low chlorophyll-a and phycocyanin concentrations in these systems suggest lower HAB risk under typical conditions, although these pond systems may be vulnerable to changing hydrologic or nutrient-loading regimes (O\u0026rsquo;Keeffe, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite relatively lower algal biomass and toxin indicators at the time of sampling, the presence of bioavailable phosphorus in stormwater ponds underscores the role stormwater ponds may play as nutrient reservoirs or sources to downstream water bodies (Fatunmbi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Frost et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The similarities observed between stormwater and recreation ponds likely reflect comparable watershed influences, particularly in developed or semi-developed settings, where both pond types were common. Recreational ponds may be either constructed or preexisting natural waterbodies adapted for recreational use, and are not always designed within managed landscapes. However, when recreational and stormwater ponds are purposely built, vegetated buffers and riparian zones are commonly recommended, though not always implemented, to stabilize shorelines, intercept surface runoff, and reduce nutrient and sediment inputs (Hassall, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Moore \u0026amp; Hunt, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStormwater ponds, although engineered for runoff control, often receive nutrient-enriched inflows from urban and suburban catchments, while recreational ponds may experience similar nutrient pressures from surrounding residential development, lawn management, and direct human activity (Nix et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Serrano \u0026amp; DeLorenzo, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The presence of riparian vegetation and buffer strips in both systems can moderate nutrient loading to some extent; however, their effectiveness varies widely depending on buffer width, vegetation type, and maintenance intensity (Duan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTogether, these shared hydrological pathways and landscape management practices may explain the observed similarities in multivariate water quality patterns between recreational and stormwater ponds, despite their differing functional roles. The classification overlaps suggest that continuous nutrient inputs from urban runoff, under favorable hydrological and environmental conditions, can trigger sudden eutrophication events, posing risks to downstream systems and public health despite generally better physicochemical water quality.\u003c/p\u003e \u003cp\u003eDespite the intended function of water quality improvement systems, stormwater ponds can exhibit eutrophication under strong land-use pressures. For example, Serrano \u0026amp; DeLorenzo, (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) reported consistent high levels of phosphorus and chlorophyll-a, including elevated levels of microcystin toxins, in residential stormwater ponds. Stormwater ponds can become eutrophic when land-use pressures and watershed connectivity are strong drivers of water quality. Similarly, studies of stormwater wet detention basins in Florida demonstrated that urban stormwater ponds may exhibit complex interactions among nutrients, chlorophyll-a, and microcystins, implying that even systems with lower nutrient concentrations can support episodic cyanobacterial production and toxin release under certain conditions (Lane et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nicholas et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Pond sediments play a critical role in regulating nutrient dynamics, in addition to external nutrient inputs. Fatunmbi et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) showed that sediments in stormwater ponds can initially function as a phosphorus sink; however, as sediment nutrient storage reaches saturation, this buffering capacity diminishes, and sediments may transition into a secondary source of both phosphorus and other sediment-associated nutrients into the water column, further enhancing the risk of eutrophication and harmful cyanobacterial bloom development.\u003c/p\u003e \u003cp\u003eOverall, the strong relationship between specific water quality parameters (nutrient enrichment vs. phytoplankton dominance) and pond types highlights the utility of multivariate monitoring. Ponds function as effective sentinels of landscape-scale nutrient pressures; however, their shallow depth and long residence times make them highly susceptible to internal nutrient cycling and harmful cyanobacterial blooms. Effective management strategies should be tailored to specific pond functions, prioritizing exclusion fencing and sediment management for livestock ponds, while focusing on nutrient runoff reduction and buffer maintenance for irrigation, recreational, and urban stormwater systems.\u003c/p\u003e \u003cp\u003eWhile stormwater regulations, policies, and improved watershed practices in the surrounding catchment area might have played a role in maintaining water quality in stormwater ponds, these practices are relatively new in the landscape compared to other types of ponds, suggesting age might be a factor controlling their water quality. If elements such as sediment in the stormwater pond are not managed properly, they may later become a source of nutrients that may promote microcystin release, exceeding freshwater quality standards. While substantial water quality research has focused on agricultural systems at the watershed scale, including nutrient dynamics and nonpoint source pollution, this study identified irrigation and livestock ponds as potential hotspots of microcystin occurrence, suggesting that more research, education and land management practices are needed on agricultural water management at the pond scale. Many agricultural ponds also predate stormwater pond infrastructure, and pond age may be a critical factor driving fluctuations in pond water quality.\u003c/p\u003e \u003cp\u003eHydrological connectivity of ponds to downstream aquatic systems further heightens concerns about cyanotoxin transport, posing risks under climate change pressures that favor warmer temperatures and intensified blooms (Huisman et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Better management solutions to address the causes of microcystin production (e.g., nutrients) within the pond environment need to be developed. To minimize downstream transport of microcystin from these ponds and the conditions that promote its production, Management strategies should prioritize nutrient source control, hydrologic isolation where feasible, and sediment stabilization in livestock and irrigation ponds.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eSouth Carolina, in the southeastern United States, is geographically divided into three main ecoregions: the Blue Ridge Mountains (Upstate), the Piedmont (Midlands), and the Atlantic Coastal Plain (Low Country and Pee Dee). The state experiences a humid subtropical climate, with mild winters and heavy rainfall, mostly during the summer. South Carolina has over 50,000 wet ponds distributed across both rural and urban landscapes, serving irrigation, livestock, recreation, and stormwater functions (SCDNR, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; White et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The design, construction, and regulatory oversight of these ponds vary depending on their intended use.\u003c/p\u003e \u003cp\u003eSampling sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) were identified with assistance from county extension agents, specialists, personal connections, stormwater and pond management professionals, who helped secure access to ponds on agricultural lands, private properties, neighborhoods, and managed landscapes. The age of the pond was not considered while selecting the ponds. Reconnaissance was conducted utilizing Google Maps to map the inlet and outflow and ensure the ponds were accessible. Below is the general description of each pond type that was sampled.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eIrrigation ponds\u003c/em\u003e sampled were typically located within ~\u0026thinsp;46 m of farmland, with an average surface area of 21,990 m\u003csup\u003e2\u003c/sup\u003e. These ponds were predominantly inline systems, formed by damming small streams and receiving their inflow from upstream and surrounding surface runoff. The most common structural features on these ponds include a pump system that supplies water directly to farm fields and an outlet structure, usually an overhead riser (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e: Representative images of pond types investigated in this study: a) irrigation pond, b) livestock pond, c) recreation pond, d) stormwater pond). Many had a well-defined vegetative buffer, only one of the irrigation ponds had a floating wetland feature. Common ecological features of these ponds include aquatic weeds, alligators, birds, and beavers, depending on the location in the state.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLivestock ponds\u003c/em\u003e samples were located within ~\u0026thinsp;91 m of an active livestock farm operation, managed primarily by the farm owners, and had an average surface area of 15,134 m\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Similar to irrigation ponds, most livestock ponds were constructed by damming small streams, so they function as inline or semi-inline systems, receiving inflow from adjacent streams and surface runoff. However, unlike irrigation ponds, these systems often lack a defined outlet structure, potentially increasing water residence time. While some ponds had adequate fences to restrict animal access, a few had unrestricted access for cattle, sheep and other livestock. Most of the ponds were characterized by eroded banks from animal trampling, high turbidity, and limited aquatic habitat.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRecreation ponds\u003c/em\u003e sampled were distributed across both suburban and forested landscapes, and were mostly privately owned and managed, although some were located within parks (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). They averaged 11,436 m\u003csup\u003e2\u003c/sup\u003e in surface area and were commonly used for activities such as swimming, fishing, and boating. These ponds typically contained ducks, turtles, various fish populations, including volunteer fish, and other species stocked by pond owners. The primary water sources included streams and surrounding runoff. A few of these ponds were reported to have been used for irrigation purposes in the past.\u003c/p\u003e \u003cp\u003e \u003cem\u003eStormwater pond\u003c/em\u003e systems were the most structurally defined of the pond types sampled, with a defined inlet structure (mostly circular piped), an outlet structure, and, in some cases, additional spillways for overflow periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed). Largely well-maintained, with some of the ponds having management features such as an aeration system, floating wetlands, and vegetation buffers. With an average surface area of 2,055 m\u003csup\u003e2\u003c/sup\u003e, these ponds were mostly located in urban and suburban areas to control flooding, manage runoff, improve water quality, and maintain landscape aesthetics. Many stormwater ponds were maintained by incorporating features such as aeration systems, floating wetlands, and vegetative buffers. These ponds were home to quite a few aquatic species such as fish, turtles, birds, alligators etc.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWater samples and analysis\u003c/h2\u003e \u003cp\u003ePhysicochemical data and water samples were collected during non-storm conditions from 75 ponds across 17 counties in South Carolina, comprising of 24 irrigation, 18 livestock, 17 recreational, and 16 stormwater ponds during the summer of 2025. The summer period is the primary growing season for phytoplankton and cyanobacteria in southeastern U.S. freshwater systems when warm water temperatures, high solar radiation, and stable stratification create a favorable condition for eutrophication signals and potential harmful cyanobacterial blooms (Greenfield et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huisman et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paerl \u0026amp; Otten, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Targeting this period allows for the detection of peak water quality degradation and cyanotoxin risks, which are pronounced in warmer months, as reported in small waterbodies (Greenfield et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Samplings were conducted at two sections within each pond, the inlet (primary inflow structure) and the outlet (discharge structure), using the grab sampling method. At each section, water was collected into a labelled 1-L HDPE b and the outlet, using the grab sampling method. At each section, water was collected into a labelled 1-L HDPE bottle in triplicate, thoroughly mixed, and sub-sampled into a series of 60 mL and 125 mL sample bottles for laboratory analysis. Water chemistry analysis included microcystin, total phosphorus, soluble reactive phosphorus, total nitrogen, nitrate-nitrite, ammonia-n, calcium, chloride, sodium, iron, and sulfate. Physicochemical parameters including pH, water temperature, dissolved oxygen, turbidity, specific conductivity, phycocyanin, and chlorophyll-a concentrations were measured using a YSI handheld multimeter prior to water sample collection. All water samples were stored in an ice chest and kept at a temperature below 4\u0026deg;C while being transported back to the South Carolina Water Resources Center in Pendleton, SC. Samples were held for up to a week, stored in accordance with Arkansas Water Resources Center (AWRC) recommendations (Austin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bradley et al., 2016), before being transported in an ice chest to the AWRC laboratory for analysis. The laboratory analyses were performed according to AWRC standard operating procedures and standard methods (see: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://awrc.uark.edu/water-quality-lab/\u003c/span\u003e\u003cspan address=\"https://awrc.uark.edu/water-quality-lab/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe statistical distribution properties of the water quality variables were initially assessed using descriptive statistics on JMP (version 11.2.0). To evaluate overall differences in multivariate water chemistry among pond types, a permutational multivariate analysis of variance was conducted. PERMANOVA is a non-parametric permutation-based method that tests differences in the centroid locations of predefined groups in a multivariate space, based on a distance matrix (Anderson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, PERMANOVA was used to assess whether pond types exhibited significant overall variation in water quality variables, using 9999 permutations at a 95% confidence interval (α\u0026thinsp;=\u0026thinsp;0.05). Pairwise comparisons were further performed to identify specific distinctions between individual pond types.\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA), an unsupervised multivariate technique, was used to reduce the dimensionality of the water quality dataset, specifically to identify dominant patterns of variation among ponds (Greenacre et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; X. Li et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wold et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). PCA transforms the original correlated variables into a new, smaller set of uncorrelated axes, referred to as principal components (PCs), which are ordered by the amount of variance they explain in the dataset, typically from highest to lowest. Each PC is defined by eigenvalues, which quantify the amount of variance explained, and loadings, which describe the contribution of individual water quality variables to each PC. In this study, PCA was applied to the standardized water quality variables across all ponds to help identify patterns in variations and determine the parameter that drives the most variation among ponds.\u003c/p\u003e \u003cp\u003eDiscriminant Analysis, a supervised multivariate method, was used to evaluate how water quality variables differentiate the predefined pond type groups. It works by maximizing separation between groups while minimizing within-group variation through a linear combination of predictor variables (Rayens, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Linear discriminant analysis (LDA) identifies these linear combinations, known as the discriminant function, which best separate the pond types. LDA assumes multivariate normality and equal covariance matrices across groups. In this study, LDA was applied to assess the degree to which pond types could be distinguished based on their phytochemical characteristics and to identify the variables contributing most strongly to group separation. However, ecological water quality data often exhibits correlated variables and unequal covariance structures across groups, which can violate the assumptions of LDA (Koklu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). To address this, regularized discriminant analysis (RDA) was additionally applied.\u003c/p\u003e \u003cp\u003eRDA serves as an intermediate approach between LDA analysis and quadratic discriminant analysis (QDA) (Friedman, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). It improves the estimation of the group covariance matrices by introducing shrinkage (regularization) to the covariance matrices, which partially pools the separate group covariances toward a common (pooled) covariance matrix and/or toward a scaled identity matrix. This shrinkage stabilizes parameter estimates, reduces overfitting, and enhances robustness, particularly in ecological datasets with highly correlated water quality variables (Friedman, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTogether, LDA and RDA were used to evaluate classification performance, quantify group separation, and identify the most influential water quality parameters distinguishing pond types.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Declaration\u003c/h2\u003e\n\u003cp\u003eThis research is based upon work supported by USGS under project number 2024917. Technical Contribution No. 7331 of the Clemson University Experiment Station.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization: M.F., D.S., S.W., Methodology: M.F., D.S., S.W., Field Sampling: M.F., D.S., S.W., C.S., Data Analysis: M.F., D.S., S.W., A.S., D.J., C.S., Writing-original draft preparation: M.F., D.S., S.W., Writing - review and editing: M.F., D.S., S.W., A.S., D.J., C.S.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors would like to acknowledge Dr. Ibrahim Busari, Jyoti Neupane, Abdulgafar Tunde Badrudeen, Gafar Agunbiade, and Daniela Font for assistance during the sampling events. The authors would also like to thank the farmers, pond owners, Extension agents, county officials, and PI\u0026rsquo;s personal contacts for their assistance in selecting and gaining access to the ponds.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData request can be directed to the corresponding author with access granted only upon approval from the funding agency.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdhikary, R. K. (2023). \u003cem\u003eWater Quality of Urban Lakes and Ponds for Recreational Use\u003c/em\u003e. \u003cem\u003eSeptember\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eAklakur, M., Bakli, S., Deo, A. D., Singh, D. K., \u0026amp; Pailan, G. H. (2023). 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Multivariate Statistical Characterization of Water Quality in Lake Lanier, Georgia, USA. \u003cem\u003eJournal of Environmental Quality\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(6), 1980\u0026ndash;1991. https://doi.org/10.2134/jeq2004.0337\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Agriculture, CyanoHABs, Microcystin, Nutrients, Pond Assessment, Water Policy","lastPublishedDoi":"10.21203/rs.3.rs-9336366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9336366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWorldwide, 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 \u0026micro;g L⁻\u0026sup1;). 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.\u003c/p\u003e","manuscriptTitle":"Do all freshwater ponds have similar chemical traits? Evidence on water-quality characteristics and their potential to support harmful cyanotoxins","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 10:59:37","doi":"10.21203/rs.3.rs-9336366/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-19T11:58:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T05:44:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T08:51:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T16:14:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289609344388688744779615851296862510776","date":"2026-04-18T09:19:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212010152400489290492755017634772891759","date":"2026-04-17T14:21:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154767585692451024959132657905220855039","date":"2026-04-16T09:29:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T09:06:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-16T08:13:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T06:48:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T06:48:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-06T17:46:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c51959a-f3a1-46c8-8d32-957a81178fc0","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-19T11:58:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T05:44:18+00:00","index":29,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T08:51:43+00:00","index":28,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T16:14:54+00:00","index":27,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":66627458,"name":"Biological sciences/Ecology"},{"id":66627459,"name":"Earth and environmental sciences/Ecology"},{"id":66627460,"name":"Earth and environmental sciences/Environmental sciences"},{"id":66627461,"name":"Earth and environmental sciences/Hydrology"},{"id":66627462,"name":"Scientific community and society/Water resources"}],"tags":[],"updatedAt":"2026-05-19T12:11:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 10:59:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9336366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9336366","identity":"rs-9336366","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}