Nutrient Enrichment, Water Clarity, and Ecological Risk in Lakes, Ponds, and Reservoirs of the Winooski River Basin

Nutrient Enrichment, Water Clarity, and Ecological Risk in Lakes, Ponds, and Reservoirs of the Winooski River Basin | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nutrient Enrichment, Water Clarity, and Ecological Risk in Lakes, Ponds, and Reservoirs of the Winooski River Basin Sean Patrick Flynn, Sean Flynn This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9255735/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Winooski River Basin is a watershed with a long history of eutrophication and associated ecological risk. This study evaluated nutrient dynamics, trophic state, and ecological risk across lakes, ponds, and reservoirs using a combination of water quality indicators and statistical analyses. Nutrient-related Risk Quotients (RQs) were used to quantify the extent to which observed conditions exceeded established ecological thresholds, enabling comparison of phosphorus, nitrogen, chlorophyll-a, and worst-case risk conditions across waterbodies. Results demonstrated that phosphorus is the primary driver of algal biomass, as evidenced by a strong relationship between total phosphorus (TP) and chlorophyll-a (Chl-a), as well as consistent patterns across trophic indices derived from the Carlson Trophic State Index. Trophic conditions ranged from oligotrophic to eutrophic, with most sites classified as oligotrophic to mesotrophic. Principal Component Analysis (PCA) revealed a dominant eutrophication gradient defined by TP, Chl-a, and water clarity, while secondary variation was associated with physicochemical conditions including temperature and dissolved oxygen (DO). Ecological risk assessment indicated that sites with elevated nutrient concentrations exhibited higher RQ values, with the worst-case metric (RQₘₐₓ) identifying moderate to high risk in several waterbodies. Overall, results demonstrate that nutrient enrichment—particularly phosphorus—and reduced water clarity are strongly associated with variation in ecological conditions across the basin. These findings underscore the importance of targeted nutrient management and watershed-scale processes in maintaining freshwater ecosystem health. Marine and Freshwater Ecology eutrophication phosphorus chlorophyll-a trophic state ecological risk freshwater Figures Figure 1 Figure 2 Figure 3 1.0 Introduction Freshwater ecosystems are increasingly threatened by nutrient pollution, particularly excess phosphorus and nitrogen delivered from surrounding watersheds. These nutrients accumulate in lakes, ponds, and reservoirs because they are closed systems that integrate runoff from agriculture, development, and atmospheric deposition. Phosphorus is often the primary limiting nutrient in temperate freshwater systems, meaning that even small increases can trigger large increases in algal biomass (Schindler, 1977 ). Nitrogen, while more abundant, becomes limiting during summer stratification or in systems dominated by nitrogen‑fixing cyanobacteria, enabling these taxa to outcompete other phytoplankton under high‑phosphorus conditions (Paerl et al., 2016 ). Internal loading from sediments further amplifies nutrient availability, particularly in shallow or stratified systems where anoxic bottom waters release phosphorus bound to iron minerals (Søndergaard et al., 2003 ). Seasonal dynamics also shape nutrient responses: spring snowmelt delivers large nutrient pulses, summer stratification traps nutrients in bottom waters, and fall turnover redistributes accumulated nutrients throughout the water column. Together, these processes create conditions that favor rapid bloom formation and sustained eutrophication. Elevated nutrient concentrations stimulate rapid algal growth, often dominated by cyanobacteria, which respond strongly to warm temperatures and stable water columns. As Paerl and Otten ( 2013 ) describe, cyanobacterial harmful algal blooms (cHABs) arise when nutrient enrichment interacts with environmental conditions that favor buoyant, bloom‑forming taxa. Beyond reducing water clarity, nutrient‑driven blooms disrupt food‑web structure by shifting energy pathways from grazer‑based to microbial‑dominated systems, reducing the efficiency of energy transfer to higher trophic levels (Carpenter et al., 1998 ). As blooms senesce, microbial decomposition increases ecosystem respiration, often driving nighttime or bottom‑water hypoxia. These low‑oxygen conditions can cause fish kills, particularly for cold‑water or sensitive species, and disrupt benthic invertebrate communities that support fish production (Diaz & Rosenberg, 2008 ). Chronic eutrophication can also shift ecosystem metabolism toward net heterotrophy, where respiration exceeds primary production (Odum, 1956 ). Over longer timescales, repeated bloom–hypoxia cycles may push systems toward alternative stable states dominated by cyanobacteria, turbid water, and reduced biodiversity, making recovery increasingly difficult even if nutrient inputs decline (Scheffer et al., 1993 ). Smaller waterbodies are especially vulnerable because limited volume and slower flushing rates amplify the effects of nutrient inputs and accelerate ecological responses. Ecological Risk Assessment (ERA) provides a structured and widely used framework for evaluating how environmental stressors influence ecological systems. In its classical formulation, ERA follows a causal sequence in which a stressor leads to exposure, resulting in an effect, which collectively determines ecological risk (Suter, 2007 ; USEPA, 1998). Within this framework, assessment endpoints define the ecological attributes to be protected—such as maintaining suitable oxygen conditions, preventing harmful algal blooms, or preserving fish habitat (Landis & Yu, 2019 ). Exposure pathways describe how nutrients move from watersheds into waterbodies and how organisms encounter elevated concentrations, whether through direct contact, ingestion, or habitat degradation. Thresholds play a central role in ERA because they translate ecological understanding into actionable benchmarks that distinguish acceptable from unacceptable conditions. Nutrient thresholds, for example, identify concentrations above which the probability of harmful algal blooms, hypoxia, or other adverse effects increases sharply (Dodds et al., 1998 ). By comparing measured conditions to these thresholds, ERA provides a transparent, reproducible method for quantifying risk and identifying systems where nutrient enrichment is likely to impair ecological function. Despite the well‑documented ecological consequences of nutrient enrichment, relatively few studies apply formal ERA frameworks to quantify nutrient‑related risk in freshwater systems. Most regional assessments rely on concentration‑based criteria, trophic state classifications, or statistical trend analyses, which describe conditions but do not explicitly quantify ecological risk or exceedance relative to established thresholds (Dodds et al., 2009 ; Smith et al., 1999 ). Threshold‑based approaches are widely used in toxicology and contaminant risk assessment, yet they remain underutilized for nutrient stressors, even though nutrients exhibit well‑defined nonlinear responses and ecological tipping points (Dodds et al., 1998 ; Paerl et al., 2016 ). A second gap is the limited comparison of nutrient‑related vulnerability across different waterbody types. Lakes, ponds, and reservoirs are often grouped together in monitoring programs or analyzed independently, leaving uncertainty about whether these systems experience distinct levels of nutrient‑related risk (Brooks & Zhang, 2021 ). Small waterbodies, in particular, remain understudied despite their heightened sensitivity to nutrient loading, rapid response times, and disproportionate influence on regional biogeochemical cycles (Downing, 2010 ). Finally, no studies to our knowledge have applied a worst‑case nutrient risk quotient—a maximum RQ (RQₘₐₓ) that integrates multiple nutrient stressors into a single, conservative indicator of ecological vulnerability. Worst‑case or “maximum exposure” metrics are common in chemical risk assessment (Suter, 2007 ), but have rarely been adapted for nutrient‑driven eutrophication, where multiple stressors often interact to produce compounded ecological effects (Carpenter et al., 1998 ). Incorporating (RQₘₐₓ) provides a novel way to identify systems where any single nutrient exceeds ecological thresholds, offering a transparent and precautionary tool for screening waterbodies at elevated risk. To address these gaps, this study applies a set of nutrient‑related Risk Quotients (RQs) to evaluate phosphorus, nitrogen, chlorophyll‑a, and worst‑case conditions across lakes, ponds, and reservoirs. Specifically, we ask: How do nutrient concentrations and water clarity relate to ecological risk across study sites? Which nutrient stressors contribute most strongly to overall ecological risk? Does a worst-case RQ metric (RQₘₐₓ) reveal patterns not captured by individual nutrient indicators? Based on these questions, we tested the following hypotheses: H1. Nutrient concentrations and water clarity are significantly associated with ecological risk, with higher nutrient levels and reduced transparency corresponding to increased risk. H2. Nutrient stressors do not contribute equally to ecological risk, with phosphorus, nitrogen, and chlorophyll-a exerting a stronger influence. H3. The worst-case risk metric (RQₘₐₓ) reveals patterns of ecological vulnerability that are not evident from individual nutrient-based RQs alone. The goal of this study was to apply a nutrient‑focused ERA framework to evaluate the vulnerability of lakes, ponds, and reservoirs to phosphorus, nitrogen, and chlorophyll‑a enrichment. Using threshold‑based RQs, we quantified the degree to which measured conditions exceeded established ecological benchmarks and compared patterns of nutrient‑related risk across waterbody types. Together, these objectives provide a transparent, threshold‑based assessment of nutrient risk in small and mid‑sized freshwater systems and offer a comparative framework for identifying waterbodies that may warrant enhanced monitoring or management attention. 2.0 Materials and Methods 2.1 Study Site The Winooski River Basin in northwestern Vermont drains approximately 2,300 km² into Lake Champlain, making it one of the lake’s largest tributary watersheds (VT DEC, 2022; Lake Champlain Basin Program, 2018 ). The basin spans a pronounced east–west physiographic gradient, beginning in the high‑elevation Green Mountains and descending through the foothills into the lower Champlain Valley. This gradient reflects the region’s glacial history, which produced steep uplands, narrow valleys, and lowland depositional plains (Thompson & Sorenson, 2000 ; Bierman & Chapin, 2014 ). Elevations range from roughly 30 m to over 1,100 m, creating strong spatial variation in slope, soil drainage, and hydrologic response. Land use is similarly heterogeneous. Forests dominate the upper watershed, while agriculture, rural development, and expanding suburban and urban areas are concentrated in the valley bottoms and lower basin (USGS NLCD, 2019; LCBP, 2018). These patterns create spatially variable nutrient pressures, with agricultural areas contributing nonpoint phosphorus and nitrogen loads, and developed areas contributing stormwater‑driven sediment and nutrient inputs (Sharpley et al., 2012 ; Meals et al., 2010 ). The basin experiences a humid continental climate characterized by cold winters, warm summers, and evenly distributed precipitation (NOAA, 2023). Seasonal snowmelt, summer convective storms, and autumn rainfall events all influence hydrologic connectivity and nutrient transport. The Winooski River and its tributaries form a dense drainage network that integrates these climatic and land‑use influences, delivering water and associated nutrient loads to numerous lakes, ponds, and reservoirs throughout the watershed (VT DEC, 2022). This combination of steep physiographic gradients, mixed land use, and hydrologically connected surface waters makes the Winooski River Basin an ideal setting for evaluating nutrient‑related ecological risk. The basin contains waterbodies that vary widely in size, depth, watershed area, and exposure to nutrient sources, providing a natural gradient for assessing how nutrient concentrations, chlorophyll‑a, and oxygen conditions respond to differing watershed pressures. The spatial extent of the basin, along with sampling locations and major hydrologic features, is shown in (Fig. 1 ). 2.2 Data Sources Water quality data were obtained from the EPA Water Quality Portal (WQP), a national repository that aggregates monitoring records from federal, state, tribal, and local agencies (Water Quality Portal, 2024 ). The WQP compiles data from programs including the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA), state environmental departments, and regional watershed organizations. These agencies contribute standardized measurements for nutrients, chlorophyll‑a, dissolved oxygen, temperature, and related water quality parameters. Spatial datasets used for mapping and watershed characterization were obtained from the Vermont Center for Geographic Information (VCGI). These included the 1‑meter digital elevation model (DEM) and the Vermont Hydrography Dataset, which provides waterbody boundaries, tributaries, and river centerlines (VCGI, 2023a; VCGI, 2023b). Basin boundaries were derived from state‑level watershed delineations and clipped to the Winooski River Basin for spatial analysis. All datasets were downloaded in 2024 and processed to ensure consistent units, coordinate systems, and attribute structures prior to analysis. 2.3 Data Collection and Variables Water quality data were obtained from the EPA Water Quality Portal (WQP), a national repository that aggregates monitoring records from federal, state, tribal, and local agencies (Water Quality Portal, 2024 ). Data were extracted for a 15‑year period (2010–2024) to capture long‑term patterns in nutrient conditions and ecological responses across the Winooski River Basin. This multi‑year window reduces the influence of anomalous sampling years and provides a more robust representation of chronic nutrient pressures within the watershed. The dataset included key indicators of nutrient enrichment and ecological condition: depth (m), chlorophyll‑a (µg/L), dissolved oxygen (mg/L), total nitrogen (µg/L), and total phosphorus (µg/L). These variables represent both nutrient inputs (nitrogen and phosphorus) and biological or ecological responses (chlorophyll‑a, dissolved oxygen, and depth‑related stratification effects), offering a comprehensive basis for evaluating nutrient‑related ecological risk in freshwater systems (Dodds & Smith, 2016 ; Wetzel, 2001 ). The relevance of these indicators is heightened within the Winooski River Basin, where steep physiographic gradients, mixed land use, and hydrologically connected surface waters create strong spatial variation in nutrient pressures and ecological responses. Agricultural areas contribute nonpoint nitrogen and phosphorus loads, while developed areas influence stormwater‑driven nutrient and sediment inputs (Sharpley et al., 2012 ; Meals et al., 2010 ). As a result, chlorophyll‑a and dissolved oxygen conditions can vary substantially among waterbodies depending on watershed position, elevation, and land‑use context. These variables therefore serve as effective metrics for assessing nutrient stress across the basin’s diverse lakes, ponds, and reservoirs. All water quality records were screened for completeness, units were standardized, and only sampling events with full parameter coverage were retained. For sites with multiple measurements across the 15‑year period, values were averaged to produce a single representative record per site, ensuring consistency across analyses and reducing temporal sampling bias. 2.4 Field and Lab Methods Water quality measurements were collected by multiple federal, state, tribal, and local monitoring programs contributing data to the EPA Water Quality Portal (WQP). The WQP aggregates standardized monitoring records from agencies including the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA), state environmental departments, and regional watershed organizations (Water Quality Portal, 2024 ). Although sampling protocols vary slightly among agencies, all reported measurements adhere to EPA‑approved field and laboratory methods, ensuring consistency and comparability across datasets. Field measurements included depth (m) and temperature (°C), typically collected using calibrated multiparameter sondes or depth probes following EPA field protocols for in‑situ water quality monitoring (EPA, 2017 ). Dissolved oxygen (DO) concentrations (mg/L) were measured using optical or membrane‑based DO sensors, with calibration and quality assurance procedures consistent with EPA Method 360.1 and Standard Methods 4500‑O. Laboratory analyses for nutrient parameters followed EPA‑approved colorimetric and spectrophotometric methods. Total phosphorus (TP) was quantified using persulfate digestion followed by colorimetric detection (EPA Method 365.1 or 365.3). Total nitrogen (TN) was measured using persulfate digestion with ultraviolet or colorimetric detection (EPA Method 351.2). Chlorophyll‑a concentrations (µg/L) were determined using acetone extraction and fluorometric or spectrophotometric analysis following EPA Method 445.0. All contributing agencies follow routine quality assurance and quality control (QA/QC) procedures, including field blanks, calibration checks, laboratory controls, and method detection limit verification. These standardized procedures ensure that measurements of TP, TN, chlorophyll‑a, DO, depth, and temperature are comparable across sites and years, supporting robust long‑term ecological assessment. 2.5 Spatial Analysis Spatial analyses were conducted in QGIS 3.44 (QGIS Development Team, 2024 ). A 1‑meter resolution digital elevation model (DEM) from the Vermont Center for Geographic Information (VCGI) was used to characterize topographic variation and watershed context across the Winooski River Basin (VCGI, 2023a). The DEM was clipped to the basin boundary and rendered using a grayscale elevation ramp to provide terrain context without obscuring hydrologic features. Hydrologic layers, including waterbodies, tributaries, and the mainstem Winooski River, were obtained from the Vermont Hydrography Dataset (VCGI, 2023b). All spatial layers were projected into the NAD83 / Vermont State Plane Meters coordinate system to ensure consistent spatial alignment. Sampling locations were imported as point features and overlaid on the DEM and hydrology layers to visualize the spatial distribution of monitoring sites relative to watershed position, elevation gradients, and surface‑water connectivity. All cartographic products were produced in the QGIS Print Layout environment. The final map includes the basin boundary, hydrologic network, waterbody polygons, sampling locations, and a grayscale DEM background, providing a spatial framework for interpreting nutrient concentrations, chlorophyll‑a, and dissolved oxygen patterns across the watershed. 2.6 Temporal Framework Water quality records were compiled across a 15‑year period (2010–2024) to capture long‑term variability in nutrient conditions and ecological responses within the Winooski River Basin. This temporal window encompasses a wide range of hydrologic conditions, including wet and dry years, seasonal extremes, and interannual climate variability, providing a robust basis for evaluating chronic nutrient pressures rather than short‑term fluctuations. Because data were contributed by multiple agencies reporting to the EPA Water Quality Portal, sampling frequency varied among sites and years. To ensure comparability across locations, all measurements were screened for completeness, and only years with full parameter coverage were retained. For sites with multiple observations within the 15‑year period, values were averaged to produce a single representative record per site. This approach reduces the influence of anomalous sampling events and aligns with long‑term ecological assessment practices commonly used in nutrient‑risk evaluation. The resulting dataset reflects multi‑year mean conditions for total phosphorus, total nitrogen, chlorophyll‑a, dissolved oxygen, depth, and temperature, providing a stable foundation for subsequent spatial, statistical, and risk‑based analyses. 2.7 Data Processing and Software All data processing and preparation were conducted using a combination of R (Version 4.x; R Core Team, 2024) and QGIS 3.44 (QGIS Development Team, 2024 ). Raw water quality records downloaded from the EPA Water Quality Portal were imported into R for cleaning, filtering, and standardization. Duplicate entries, incomplete records, and measurements lacking essential parameters (total phosphorus, total nitrogen, chlorophyll‑a, dissolved oxygen, depth, or temperature) were removed. Units were standardized across agencies to ensure comparability, and parameter names were harmonized to a consistent naming convention. Temporal filtering was applied to retain only observations collected between 2010 and 2024. For sites with multiple measurements within this 15‑year period, values were averaged to produce a single representative record per site. This approach reduces temporal sampling bias and aligns with long‑term ecological assessment practices. Summary statistics, data validation, and exploratory visualizations were generated in R. Spatial data processing, including clipping, projection, and integration of sampling locations with hydrologic and elevation layers, was performed in QGIS. Attribute tables were joined to spatial layers to link water quality measurements with their corresponding geographic features. Final datasets were exported as CSV and GeoPackage files for use in spatial analysis, risk calculations, and statistical modeling.This combined workflow ensured that all water quality, spatial, and temporal data were consistently formatted, quality‑checked, and ready for subsequent ecological risk assessment. 2.8 Formulas Together, these processing steps produced a fully standardized dataset suitable for quantitative evaluation of nutrient conditions across the Winooski River Basin. With all spatial, temporal, and water‑quality variables harmonized, the next stage of the analysis involved applying a set of established ecological risk and water‑quality formulas. These formulas quantify nutrient enrichment, biological response, and overall ecological risk, providing the mathematical foundation for the indicators and comparisons presented in the Results. The following section outlines the equations used in this study and the rationale behind each metric. Carlson’s Trophic State Index (TSI) The trophic status of each waterbody was evaluated using Carlson’s Trophic State Index (TSI), a widely applied metric that relates nutrient concentrations and algal biomass to overall lake productivity (Carlson, 1977 ). TSI values were calculated using chlorophyll‑a (Chl‑a), total phosphorus (TP), and Secchi Depth (SD): $$\:TSI(Chl-a)=9.81*ln(Chl-a)+30.6$$ 1 $$\:TSI\left(TP\right)=14.42*ln\left(TP\right)+4.15$$ 2 $$\:TSI\left(SD\right)=60-14.41ln\left(SD\right)$$ 3 where Chl‑a and TP are expressed in µg/L, and SD is expressed in meters and ln⁡ denotes the natural logarithm. TSI values were interpreted using standard trophic classifications: TSI 50 → eutrophic to hypereutrophic (high productivity) Differences between TSI(Chl‑a), TSI(TP), and TSI(SD) were examined to assess potential nutrient limitation or mismatches between nutrient availability and algal response. Nutrient Limitation (TN:TP Ratio) Nutrient limitation was evaluated using the molar ratio of total nitrogen to total phosphorus (TN:TP), following the Redfield Ratio framework and freshwater adaptations (Wetzel, 2001 ): $$\:TN:TP=\frac{TN/14.01}{TP/30.97}$$ 4 where TN and TP are expressed in µg/L, and 14.01 and 30.97 are the molar masses of nitrogen and phosphorus (g/mol), respectively. Interpretation followed established freshwater thresholds: TN:TP 20 → phosphorus limitation 10–20 → potential co‑limitation This ratio was used to interpret whether nutrient imbalance influenced observed chlorophyll‑a concentrations and trophic state. Risk Quotient (RQ) Ecological risk associated with nutrient concentrations was assessed using the Risk Quotient (RQ) framework, which compares measured environmental concentrations to established threshold values (EPA, 2008 ): $$\:RQ=\frac{MEC}{PNEC}$$ 5 where: MEC = measured environmental concentration (e.g., TP, TN, Chl‑a) PNEC = predicted no‑effect concentration derived from regulatory criteria or literature RQ values were interpreted using standard ecological risk thresholds: RQ 1.0 → Potential ecological risk (threshold exceedance) Separate RQs were calculated for TP, TN and Chl‑a. RQ max RQ max \(\:=max\left(RQ\right(TP),(RQ\left(TN\right),\left(RQ\right(Chl)\) (6) The highest nutrient‑related risk at each site defines the worst‑case ecological vulnerability. 2.9 Statistical Analysis All statistical analyses were conducted in RStudio (Version 4.5.2). Summary statistics, including mean, standard deviation, and range, were calculated for all variables. Water quality parameters were grouped into nutrient variables (total phosphorus [TP] and total nitrogen [TN]), biological response variables (chlorophyll-a [Chl-a]), physicochemical variables (dissolved oxygen [DO], temperature, and depth), and derived indices including the Carlson Trophic State Index (TSI) and nutrient-specific Risk Quotients (RQ). Relationships among variables were evaluated using Pearson correlation analysis to quantify linear associations between nutrient concentrations, algal biomass, trophic status, and ecological risk. Linear regression models were used to assess directional relationships between nutrient drivers (TP, TN) and response variables (Chl-a and TSI). Principal Component Analysis (PCA) was conducted on standardized (z-score) variables to identify dominant gradients in water quality and to reduce dimensionality among correlated parameters. PCA loadings and biplots were used to interpret multivariate structure and to evaluate whether nutrient enrichment, trophic state, and physicochemical conditions aligned along shared axes of variation. Statistical significance was assessed at α = 0.05 for all analyses. 3.0 Results and Discussion 3.1 Summary Statistics Nutrient and physicochemical conditions varied across sampling sites, indicating spatial heterogeneity within the system (Table 1 ). Table 1 Summary Statistics of Selected Variables Variable Min Max Mean SD Chl-a 1.23 40.7 7.73 11.1 DO 6.71 11 8.81 1.46 Secchi Depth 1.42 9.91 4.21 2.7 Temperature 6.4 15.5 11.1 2.84 TN 0.18 0.4 0.288 0.0698 TP 8.91 48.4 16.4 11.1 Chlorophyll-a (Chl-a) concentrations ranged from 1.23 to 40.7 µg/L, with a mean of 7.73 µg/L, reflecting a broad gradient in algal biomass and primary productivity. Total phosphorus (TP) concentrations exhibited substantial variability (8.91–48.4 mg/L; mean = 16.4 mg/L), suggesting that some sites experience elevated phosphorus enrichment. In contrast, total nitrogen (TN) concentrations were more constrained (0.18–0.40 mg/L; mean = 0.288 mg/L), indicating comparatively lower variability across the basin. Dissolved oxygen (DO) concentrations ranged from 6.71 to 11.0 mg/L (mean = 8.81 mg/L), indicating generally well-oxygenated conditions. Water temperature varied from 6.4 to 15.5°C (mean = 11.1°C), while Secchi depth ranged from 1.42 to 9.91 m (mean = 4.21 m), reflecting variability in water clarity and light penetration. Differences in temperature and water clarity suggest variation in physical conditions that may influence mixing dynamics, nutrient availability, and biological response. Overall, the greater variability observed in TP and chlorophyll-a relative to TN indicates that phosphorus availability and algal biomass are key factors structuring water quality conditions within the basin. Additionally, the wide range in Secchi depth suggests that water clarity varies substantially among sites, potentially reflecting both algal productivity and non-algal turbidity influences. These patterns provide the basis for evaluating trophic state dynamics and nutrient limitation across the basin. 3.2 TSI Values Trophic state conditions across the study sites were evaluated using the Carlson Trophic State Index. TSI values derived from chlorophyll-a ranged from 31.3 to 59.6, while TSI based on total phosphorus ranged from 31.7 to 54.4, indicating conditions spanning from oligotrophic to mesotrophic, with one site approaching eutrophic status. In contrast, TSI values calculated from Secchi depth were consistently higher, ranging from 40.0 to 57.0, suggesting reduced water clarity across multiple sites. The majority of sites fell within oligotrophic to mesotrophic classifications based on nutrient concentrations and algal biomass. However, one site exhibited elevated TSI values for both chlorophyll-a and total phosphorus, indicating localized eutrophic conditions likely associated with increased nutrient enrichment. Notably, TSI(SD) values were frequently higher than corresponding TSI(Chl-a) and TSI(TP) values, indicating that water transparency was lower than expected based solely on algal biomass. This pattern suggests that factors other than phytoplankton, such as suspended sediments or non-algal turbidity, may contribute to reduced clarity in certain waterbodies. These findings highlight the influence of watershed processes on light availability and reinforce the importance of considering multiple trophic indicators when assessing ecological conditions. Site-level TSI values are provided in Supplementary Table S1. 3.3 TN:TP TN:TP molar ratios ranged from 6.81 to 37.07 across the 12 sampled waterbodies. One site fell below the nitrogen‑limitation threshold (TN:TP < 10), four sites were within the co‑limitation range (10–20), and the remaining seven sites exceeded 20, indicating phosphorus limitation. Overall, most waterbodies exhibited TN:TP ratios consistent with phosphorus‑limited conditions, suggesting that phosphorus availability is the primary constraint on algal biomass across the study area. Site-specific TN:TP ratios and nutrient limitation categories are provided in Supplementary Table S2. 3.4 RQ/RQ max Table 2 Risk Level by Site Site RQ (TP) RQ (TN) RQ (Chl-a) RQmax Risk Level 1 0.58 0.85 0.44 0.85 Negligible 2 0.71 0.67 0.18 0.71 Negligible 3 0.5 0.61 0.26 0.61 Negligible 4 1.33 0.73 2 2 High Exceedance 5 0.66 0.82 0.26 0.82 Negligible 6 1.05 0.91 1 1.05 High Exceedance 7 0.72 0.55 0.31 0.72 Negligible 8 0.56 1.12 1.54 1.54 High Exceedance 9 0.98 1.09 0.42 1.09 High Exceedance 10 0.54 0.94 0.58 0.94 Negligible 11 2.69 1 5.81 5.81 High Exceedance 12 0.6 1.21 0.46 1.21 High Exceedance Note: Risk Quotient Total Phosphorus (RQP): Calculated as (Measured TP / Threshold). The Level of Concern (LOC) threshold is set at 18 µg/L (VT DEC, 2017). Risk Quotient Total Nitrogen (RQN): Calculated as (Measured TN / Threshold). The Level of Concern (LOC) threshold is set at 0.33 µg/L (U.S. EPA, 2001). Risk Quotient Chlorophyll (RQChl): Calculated as (Measured Chl-a / Threshold). The Level of Concern (LOC) threshold is set at 7 µg/L (VT DEC, 2017). Risk Quotient (RQ) values varied across nutrients and chlorophyll‑a, with exceedances occurring in all three metrics. RQ (TP) ranged from 0.50 to 2.69, with three sites exceeding the threshold value of 1. RQ (TN) values were more consistently elevated, ranging from 0.55 to 1.21, and five sites exceeded the threshold. RQ(Chl) showed the greatest variability, spanning 0.18 to 5.81, with four sites above 1, including one site with a markedly high value (RQ = 5.81). These patterns indicate that nitrogen‑related risk was the most widespread across the study area, while chlorophyll‑a exhibited the strongest individual exceedances, suggesting localized biomass responses that may reflect internal loading, hydrologic retention, or other site‑specific factors. RQ max values, representing the worst‑case nutrient or biomass risk at each site, ranged from 0.61 to 5.81. Six of the twelve sites exceeded the threshold value of 1, indicating high nutrient‑related risk, while the remaining six sites fell below the threshold. The highest RQmax value (5.81) occurred at a site where chlorophyll‑a strongly exceeded its threshold, reflecting an intense localized biomass response. Other exceedances were driven by elevated nitrogen or phosphorus RQs, depending on the site. Overall, RQmax revealed a clear split between low‑risk and high‑risk waterbodies, highlighting spatial heterogeneity in nutrient and biomass stress across the study area. 3.5 Correlation Pearson correlation analysis revealed clear relationships among nutrient concentrations, algal biomass, and physicochemical conditions across the study sites (Table 3 ). Table 3 Correlation Matrix of Selected Variables Variable TP TN Chl DO Temp Secchi Depth TP — TN 0.142 — Chl .938* 0.246 — DO −.144 −.264 0.036 — Temp 0.341 0.099 0.329 −.109 — Secchi Depth −.158 −.377 −.273 −.507 −.383 — Note : Values represent Pearson correlation coefficients. *p < 0.05. Only the lower triangle is shown. Total phosphorus (TP) exhibited a strong positive correlation with chlorophyll-a (r = 0.938, p < 0.05), indicating that phosphorus is the primary driver of algal productivity within the system. In contrast, total nitrogen (TN) showed a much weaker relationship with chlorophyll-a (r = 0.246), suggesting a comparatively limited role in controlling algal biomass. Secchi depth showed negative relationships with multiple variables, including chlorophyll-a (r = -0.273), temperature (r = -0.383), and dissolved oxygen (r = -0.507), indicating that reduced water clarity is associated with increased biological activity and changing physicochemical conditions. The moderate negative correlation between Secchi depth and temperature suggests that warmer conditions may contribute to decreased transparency, potentially through enhanced biological production or increased suspended material. Dissolved oxygen exhibited weak correlations with most variables, including chlorophyll-a (r = 0.036), suggesting that oxygen dynamics are influenced more by physical processes such as mixing and temperature rather than direct biological production alone. Temperature showed moderate positive relationships with both TP and chlorophyll-a, indicating that warmer conditions may enhance nutrient availability and biological response. 3.6 Regression A simple linear regression showed that total phosphorus significantly predicted chlorophyll‑a concentrations, F(1,10) = 73.44, p<.001, with TP explaining 88% of the variance in Chl‑a ( R2 = .88 ). The model indicated that chlorophyll‑a increased by 0.94 µg/L for every 1 µg/L increase in TP ( β = 0.94, p<.001 ) (Fig. 2 ). Chlorophyll‑a was strongly and positively related to total phosphorus. The regression model was highly significant ( F(1,10) = 73.44, p<.001 ) and explained 88% of the variation in chlorophyll‑a. TP was a strong predictor ( β = 0.94, p<.001 ), indicating that algal biomass increased nearly one‑to‑one with phosphorus concentrations. This relationship reflects a tight coupling between phosphorus availability and phytoplankton biomass across the sampled waterbodies. 3.7 PCA Principal component analysis revealed clear multivariate structure among the environmental variables (Table 4 ). Table 4 PCA explained by Variables Component Eigenvalue Proportion of Variance Cumulative Variance PC1 2.4 0.4 0.4 PC2 1.48 0.246 0.646 PC3 1.11 0.184 0.831 PC4 0.83 0.139 0.969 PC5 0.16 0.027 0.996 PC6 0.02 0.004 1 Note. Eigenvalues were calculated as the squared standard deviations of each principal component. Components with eigenvalues greater than 1 (PC1–PC3) were retained for interpretation. The first three components explained 83.1% of the total variance. PC1 (40.0%) represented a strong eutrophication gradient, with high positive loadings for total phosphorus, chlorophyll‑a, and temperature, and a negative loading for Secchi depth, indicating that nutrient‑rich, warm, turbid sites clustered together. PC2 (24.6%) captured a clarity–oxygen gradient, with Secchi depth loading positively and dissolved oxygen loading negatively. PC3 (18.4%) was dominated by total nitrogen, reflecting a secondary nutrient axis independent of the TP–Chl relationship. Together, these components describe distinct ecological gradients in productivity, water clarity, and nitrogen availability across the sampled sites. The biplot displays site scores (points) and variable loadings (arrows) for the first two principal components, which together explain 64.6% of the total variance. PC1 (40.0%) represents a strong eutrophication gradient, with positive loadings for total phosphorus, chlorophyll‑a, and temperature, and a negative loading for Secchi depth. PC2 (24.6%) reflects a clarity–oxygen gradient, with Secchi depth loading positively and dissolved oxygen loading negatively. Arrow length indicates the strength of each variable’s contribution to the ordination. 4.0 Synthesis and Hypothesis Evaluation The integration of univariate, bivariate, and multivariate analyses provides a comprehensive understanding of nutrient dynamics, trophic status, and ecological risk across the sampled waterbodies. Collectively, the results demonstrate that ecological condition is strongly structured by nutrient availability, biological response, and physicochemical controls, with clear implications for nutrient management within the basin. H1 , which proposed that nutrient concentrations and water clarity are significantly associated with ecological risk, was supported. Strong relationships between total phosphorus and chlorophyll-a, as well as consistent negative associations between Secchi depth and key variables, indicate that increased nutrient availability and reduced water clarity correspond to elevated ecological risk. These patterns were evident across correlation analysis, linear regression, and principal component analysis, where nutrient enrichment and reduced transparency aligned along dominant gradients of variation. Together, these findings confirm that both nutrient inputs and light availability play central roles in structuring ecological conditions. H2 , which proposed that nutrient stressors do not contribute equally to ecological risk, was also supported. Total phosphorus emerged as the primary driver of algal biomass and trophic state, as evidenced by its strong positive relationship with chlorophyll-a and its dominant influence within the multivariate structure. In contrast, total nitrogen exhibited weaker associations with biological response variables, suggesting a secondary role in controlling productivity. Chlorophyll-a, as an integrative indicator of system response, displayed the greatest variability and the highest individual risk exceedances. These results demonstrate that phosphorus exerts a disproportionately strong influence on ecological condition relative to other stressors. H3 , which proposed that the worst-case risk metric (RQₘₐₓ) reveals patterns of ecological vulnerability not captured by individual nutrient-based RQs, was strongly supported. While individual RQ values often indicated negligible or moderate risk, the RQₘₐₓ metric identified multiple sites with elevated ecological risk by capturing the highest exceedance among nutrients and biological response indicators. This approach revealed a clearer separation between low-risk and high-risk waterbodies and highlighted sites where localized nutrient enrichment or biological response may otherwise be underestimated. As such, RQₘₐₓ provides a more integrative and precautionary assessment of ecological condition. Overall, the results indicate that ecological risk within the study area is driven by interactions among nutrient enrichment, algal biomass, and water clarity. Phosphorus plays a central role in regulating these dynamics, while water clarity reflects both biological production and watershed influences. The application of both individual RQs and the RQₘₐₓ metric enhances the ability to detect spatial heterogeneity in ecological risk and provides a robust framework for identifying vulnerable waterbodies. These findings underscore the importance of targeted nutrient management strategies, particularly those focused on phosphorus reduction, to mitigate ecological risk and maintain water quality across diverse freshwater systems. 5.0 Conclusion This study evaluated nutrient dynamics, trophic state, and ecological risk across waterbodies within the Winooski River Basin using a combination of water quality indicators, trophic indices, and statistical analyses. Results consistently demonstrated that phosphorus is the primary driver of algal biomass and trophic condition, as evidenced by strong relationships between total phosphorus and chlorophyll-a, as well as alignment across trophic state metrics derived from the Carlson Trophic State Index. Trophic state conditions ranged from oligotrophic to mesotrophic, with localized instances approaching eutrophic conditions, indicating spatial variability in nutrient enrichment across the basin. Nutrient limitation analysis suggested that the system is generally co-limited to slightly phosphorus-limited, reinforcing the dominant role of phosphorus in controlling primary productivity. Ecological risk assessment further indicated that while overall risk levels were moderate, certain sites exhibited elevated Risk Quotient values, highlighting the presence of localized hotspots of nutrient-driven ecological concern. Multivariate and statistical analyses supported these findings, identifying a primary gradient associated with nutrient enrichment and biological response, alongside secondary influences from physicochemical conditions such as temperature and dissolved oxygen. Together, these results demonstrate that water quality and ecological conditions within the basin are shaped by interactions among nutrient inputs, watershed processes, and environmental controls. From a management perspective, these findings underscore the importance of targeted phosphorus reduction strategies to mitigate eutrophication and reduce ecological risk. Efforts to control nonpoint source inputs from agricultural and developed areas, along with continued monitoring of high-risk sites, will be critical for maintaining water quality and ecological integrity within the basin. Future research should focus on temporal dynamics, including seasonal variability and episodic nutrient loading, to further refine understanding of nutrient–ecosystem interactions in freshwater systems. References Bierman PR, Chapin DM (2014) The geologic story of Vermont. Vermont Geological Survey Brooks BW, Zhang X (2021) Water quality criteria and ecological risk assessment for nutrients. Environ Sci Technol 55(1):14–29 Carlson RE (1977) A trophic state index for lakes. Limnol Oceanogr 22(2):361–369 Carpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN, Smith VH (1998) Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol Appl 8(3):559–568 Chapman PM (2002) Ecological risk assessment (ERA) and hormesis. Sci Total Environ 288(1–2):131–140 Diaz RJ, Rosenberg R (2008) Spreading dead zones and consequences for marine ecosystems. Science 321(5891):926–929 Dodds WK, Smith VH (2016) Nitrogen, phosphorus, and eutrophication in streams. Inland Waters 6(2):155–164 Dodds WK, Bouska WW, Eitzmann JL, Pilger TJ, Pitts KL, Riley AJ, Schloesser JT, Thornbrugh DJ (2009) Eutrophication of U.S. freshwaters: Analysis of potential economic damages. Environ Sci Technol 43(1):12–19 Dodds WK, Jones JR, Welch EB (1998) Suggested classification of stream trophic state: Distributions of temperate stream types by chlorophyll, total nitrogen, and phosphorus. J North Am Benthological Soc 17(3):502–517 Downing JA (2010) Emerging global role of small lakes and ponds: Little things mean a lot. Limnetica 29(1):9–24 EPA (1993) Method 351.2: Determination of total Kjeldahl nitrogen by semi–automated colorimetry. U.S. Environmental Protection Agency EPA (1993) Method 365.1: Determination of phosphorus by semi–automated colorimetry. U.S. Environmental Protection Agency EPA (1997) Methods for Chemical Analysis of Water and Wastes. U.S. Environmental Protection Agency EPA (2002) Method 445.0: In vitro determination of chlorophyll a and pheophytin a in marine and freshwater algae by fluorescence. U.S. Environmental Protection Agency EPA (2008) EPA Ecological Risk Assessment Framework. U.S. Environmental Protection Agency EPA (2017) National Water Quality Monitoring Council: Water Quality Field Methods. U.S. Environmental Protection Agency Lake Champlain Basin Program (2018) State of the Lake and ecosystem indicators report. Lake Champlain Basin Program Landis WG, Yu M–H (2019) Introduction to environmental toxicology: Molecular substructures to ecological landscapes, 5th edn. CRC Meals DW, Budd L, Moen T (2010) Lake Champlain Basin agricultural nutrient management: Lessons learned. J Great Lakes Res 36(1):135–140 NOAA National Centers for Environmental Information (2023) Climate normals for Vermont. NOAA Odum HT (1956) Primary production in flowing waters. Limnol Oceanogr 1(2):102–117 Paerl HW, Otten TG (2013) Harmful cyanobacterial blooms: Causes, consequences, and controls. Microb Ecol 65(4):995–1010 Paerl HW, Scott JT, McCarthy MJ, Newell SE, Gardner WS, Havens KE, Hoffman DK, Wilhelm SW, Wurtsbaugh WA (2016) It takes two to tango: When and where dual nutrient (N & P) reductions are needed to protect lakes and downstream ecosystems. Science 354(6312):301–302 QGIS Development Team (2024) QGIS Geographic Information System (Version 3.44). Open Source Geospatial Foundation Scheffer M, Hosper SH, Meijer M–L, Moss B, Jeppesen E (1993) Alternative equilibria in shallow lakes. Trends Ecol Evol 8(8):275–279 Schindler DW (1977) Evolution of phosphorus limitation in lakes. Science 195(4275):260–262 Sharpley AN, Kleinman PJ, Flaten D, Buda A (2012) Critical source area management of agricultural phosphorus. J Environ Qual 41(5):1071–1079 Smith VH, Tilman GD, Nekola JC (1999) Eutrophication: Impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environ Pollut 100(1–3):179–196 Standard Methods Committee (2017) Standard Methods for the Examination of Water and Wastewater . American Public Health Association Suter GW (2007) Ecological risk assessment, 2nd edn. CRC Søndergaard M, Jensen JP, Jeppesen E (2003) Role of sediment and internal loading of phosphorus in shallow lakes. Hydrobiologia 484(1–3):75–90 Thompson EH, Sorenson ER (2000) Wetland, woodland, wildland: A guide to the natural communities of Vermont. University Press of New England U.S. Environmental Protection Agency (1998) EPA/630/R–95/002F. Guidelines for ecological risk assessment. U.S. Environmental Protection Agency U.S. Geological Survey (2019) National Land Cover Database (NLCD). USGS Vermont Center for Geographic Information (2023a) Digital Elevation Model (1–m). VCGI Vermont Center for Geographic Information (2023b) Vermont Hydrography Dataset. VCGI Vermont Department of Environmental Conservation (2022) Winooski River Basin water quality management plan. VT DEC Water Quality Portal (2024) Water Quality Data Portal . https://www.waterqualitydata.us/ Wetzel RG (2001) Limnology: Lake and river ecosystems, 3rd edn. Academic Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9255735","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613900197,"identity":"f554fe75-b331-4b13-aba4-bc68673b30b1","order_by":0,"name":"Sean Patrick Flynn","email":"","orcid":"https://orcid.org/0000-0003-4879-1617","institution":"Unity Environmental University","correspondingAuthor":false,"prefix":"","firstName":"Sean","middleName":"Patrick","lastName":"Flynn","suffix":""},{"id":613900206,"identity":"1a9688fb-d4ce-4ad1-be00-99dfc42566dc","order_by":1,"name":"Sean Flynn","email":"data:image/png;base64,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","orcid":"","institution":"Unity College","correspondingAuthor":true,"prefix":"","firstName":"Sean","middleName":"","lastName":"Flynn","suffix":""}],"badges":[],"createdAt":"2026-03-29 01:08:58","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9255735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9255735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105883100,"identity":"2bd8cdab-bbef-4617-82c4-bebe71d3e70a","added_by":"auto","created_at":"2026-04-01 07:05:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45099,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of the Winooski River Basin\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Shows sampling locations, waterbodies, tributaries, and the mainstem Winooski River overlaid on a grayscale digital elevation model (DEM). The map provides spatial context for water quality sampling and nutrient‑risk assessment across the watershed.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9255735/v1/60e5ff917ff554198d1ccfa1.png"},{"id":105905389,"identity":"c3ec4865-26d3-4adc-b890-d9d95e0fa970","added_by":"auto","created_at":"2026-04-01 10:11:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75981,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear Relationship Between Total Phosphorus and Chlorophyll-a\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9255735/v1/a9a293b6da4a9937a3418d2b.png"},{"id":105883102,"identity":"486954b5-b368-48ce-8742-008f1b358e05","added_by":"auto","created_at":"2026-04-01 07:05:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal component biplot showing multivariate relationships among environmental variables.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9255735/v1/7f8d16570bb36820c47f37b2.png"},{"id":107479710,"identity":"c5844c26-f38b-4d74-8c7d-4f1ad76e4ae4","added_by":"auto","created_at":"2026-04-22 01:45:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":945183,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9255735/v1/1a2fff6c-a402-4b00-bae3-a79c49bb797a.pdf"},{"id":105883099,"identity":"2f366a5d-08a8-4a70-9268-f082c63b382e","added_by":"auto","created_at":"2026-04-01 07:05:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17462,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1S2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9255735/v1/cbe8928f2c351e1e0d5b1be3.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eNutrient Enrichment, Water Clarity, and Ecological Risk in Lakes, Ponds, and Reservoirs of the Winooski River Basin\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eFreshwater ecosystems are increasingly threatened by nutrient pollution, particularly excess phosphorus and nitrogen delivered from surrounding watersheds. These nutrients accumulate in lakes, ponds, and reservoirs because they are closed systems that integrate runoff from agriculture, development, and atmospheric deposition. Phosphorus is often the primary limiting nutrient in temperate freshwater systems, meaning that even small increases can trigger large increases in algal biomass (Schindler, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Nitrogen, while more abundant, becomes limiting during summer stratification or in systems dominated by nitrogen‑fixing cyanobacteria, enabling these taxa to outcompete other phytoplankton under high‑phosphorus conditions (Paerl et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Internal loading from sediments further amplifies nutrient availability, particularly in shallow or stratified systems where anoxic bottom waters release phosphorus bound to iron minerals (S\u0026oslash;ndergaard et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Seasonal dynamics also shape nutrient responses: spring snowmelt delivers large nutrient pulses, summer stratification traps nutrients in bottom waters, and fall turnover redistributes accumulated nutrients throughout the water column. Together, these processes create conditions that favor rapid bloom formation and sustained eutrophication.\u003c/p\u003e \u003cp\u003eElevated nutrient concentrations stimulate rapid algal growth, often dominated by cyanobacteria, which respond strongly to warm temperatures and stable water columns. As Paerl and Otten (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) describe, cyanobacterial harmful algal blooms (cHABs) arise when nutrient enrichment interacts with environmental conditions that favor buoyant, bloom‑forming taxa. Beyond reducing water clarity, nutrient‑driven blooms disrupt food‑web structure by shifting energy pathways from grazer‑based to microbial‑dominated systems, reducing the efficiency of energy transfer to higher trophic levels (Carpenter et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). As blooms senesce, microbial decomposition increases ecosystem respiration, often driving nighttime or bottom‑water hypoxia. These low‑oxygen conditions can cause fish kills, particularly for cold‑water or sensitive species, and disrupt benthic invertebrate communities that support fish production (Diaz \u0026amp; Rosenberg, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Chronic eutrophication can also shift ecosystem metabolism toward net heterotrophy, where respiration exceeds primary production (Odum, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1956\u003c/span\u003e). Over longer timescales, repeated bloom\u0026ndash;hypoxia cycles may push systems toward alternative stable states dominated by cyanobacteria, turbid water, and reduced biodiversity, making recovery increasingly difficult even if nutrient inputs decline (Scheffer et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Smaller waterbodies are especially vulnerable because limited volume and slower flushing rates amplify the effects of nutrient inputs and accelerate ecological responses.\u003c/p\u003e \u003cp\u003eEcological Risk Assessment (ERA) provides a structured and widely used framework for evaluating how environmental stressors influence ecological systems. In its classical formulation, ERA follows a causal sequence in which a stressor leads to exposure, resulting in an effect, which collectively determines ecological risk (Suter, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; USEPA, 1998). Within this framework, assessment endpoints define the ecological attributes to be protected\u0026mdash;such as maintaining suitable oxygen conditions, preventing harmful algal blooms, or preserving fish habitat (Landis \u0026amp; Yu, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Exposure pathways describe how nutrients move from watersheds into waterbodies and how organisms encounter elevated concentrations, whether through direct contact, ingestion, or habitat degradation. Thresholds play a central role in ERA because they translate ecological understanding into actionable benchmarks that distinguish acceptable from unacceptable conditions. Nutrient thresholds, for example, identify concentrations above which the probability of harmful algal blooms, hypoxia, or other adverse effects increases sharply (Dodds et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). By comparing measured conditions to these thresholds, ERA provides a transparent, reproducible method for quantifying risk and identifying systems where nutrient enrichment is likely to impair ecological function.\u003c/p\u003e \u003cp\u003eDespite the well‑documented ecological consequences of nutrient enrichment, relatively few studies apply formal ERA frameworks to quantify nutrient‑related risk in freshwater systems. Most regional assessments rely on concentration‑based criteria, trophic state classifications, or statistical trend analyses, which describe conditions but do not explicitly quantify ecological risk or exceedance relative to established thresholds (Dodds et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Threshold‑based approaches are widely used in toxicology and contaminant risk assessment, yet they remain underutilized for nutrient stressors, even though nutrients exhibit well‑defined nonlinear responses and ecological tipping points (Dodds et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Paerl et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A second gap is the limited comparison of nutrient‑related vulnerability across different waterbody types. Lakes, ponds, and reservoirs are often grouped together in monitoring programs or analyzed independently, leaving uncertainty about whether these systems experience distinct levels of nutrient‑related risk (Brooks \u0026amp; Zhang, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Small waterbodies, in particular, remain understudied despite their heightened sensitivity to nutrient loading, rapid response times, and disproportionate influence on regional biogeochemical cycles (Downing, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Finally, no studies to our knowledge have applied a worst‑case nutrient risk quotient\u0026mdash;a maximum RQ (RQₘₐₓ) that integrates multiple nutrient stressors into a single, conservative indicator of ecological vulnerability. Worst‑case or \u0026ldquo;maximum exposure\u0026rdquo; metrics are common in chemical risk assessment (Suter, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), but have rarely been adapted for nutrient‑driven eutrophication, where multiple stressors often interact to produce compounded ecological effects (Carpenter et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Incorporating (RQₘₐₓ) provides a novel way to identify systems where any single nutrient exceeds ecological thresholds, offering a transparent and precautionary tool for screening waterbodies at elevated risk.\u003c/p\u003e \u003cp\u003eTo address these gaps, this study applies a set of nutrient‑related Risk Quotients (RQs) to evaluate phosphorus, nitrogen, chlorophyll‑a, and worst‑case conditions across lakes, ponds, and reservoirs. Specifically, we ask:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHow do nutrient concentrations and water clarity relate to ecological risk across study sites?\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWhich nutrient stressors contribute most strongly to overall ecological risk?\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDoes a worst-case RQ metric (RQₘₐₓ) reveal patterns not captured by individual nutrient indicators?\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBased on these questions, we tested the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1.\u003c/b\u003e Nutrient concentrations and water clarity are significantly associated with ecological risk, with higher nutrient levels and reduced transparency corresponding to increased risk.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2.\u003c/b\u003e Nutrient stressors do not contribute equally to ecological risk, with phosphorus, nitrogen, and chlorophyll-a exerting a stronger influence.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3.\u003c/b\u003e The worst-case risk metric (RQₘₐₓ) reveals patterns of ecological vulnerability that are not evident from individual nutrient-based RQs alone.\u003c/p\u003e \u003cp\u003eThe goal of this study was to apply a nutrient‑focused ERA framework to evaluate the vulnerability of lakes, ponds, and reservoirs to phosphorus, nitrogen, and chlorophyll‑a enrichment. Using threshold‑based RQs, we quantified the degree to which measured conditions exceeded established ecological benchmarks and compared patterns of nutrient‑related risk across waterbody types. Together, these objectives provide a transparent, threshold‑based assessment of nutrient risk in small and mid‑sized freshwater systems and offer a comparative framework for identifying waterbodies that may warrant enhanced monitoring or management attention.\u003c/p\u003e"},{"header":"2.0 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Site\u003c/h2\u003e \u003cp\u003eThe Winooski River Basin in northwestern Vermont drains approximately 2,300 km\u0026sup2; into Lake Champlain, making it one of the lake\u0026rsquo;s largest tributary watersheds (VT DEC, 2022; Lake Champlain Basin Program, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The basin spans a pronounced east\u0026ndash;west physiographic gradient, beginning in the high‑elevation Green Mountains and descending through the foothills into the lower Champlain Valley. This gradient reflects the region\u0026rsquo;s glacial history, which produced steep uplands, narrow valleys, and lowland depositional plains (Thompson \u0026amp; Sorenson, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Bierman \u0026amp; Chapin, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Elevations range from roughly 30 m to over 1,100 m, creating strong spatial variation in slope, soil drainage, and hydrologic response.\u003c/p\u003e \u003cp\u003eLand use is similarly heterogeneous. Forests dominate the upper watershed, while agriculture, rural development, and expanding suburban and urban areas are concentrated in the valley bottoms and lower basin (USGS NLCD, 2019; LCBP, 2018). These patterns create spatially variable nutrient pressures, with agricultural areas contributing nonpoint phosphorus and nitrogen loads, and developed areas contributing stormwater‑driven sediment and nutrient inputs (Sharpley et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Meals et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe basin experiences a humid continental climate characterized by cold winters, warm summers, and evenly distributed precipitation (NOAA, 2023). Seasonal snowmelt, summer convective storms, and autumn rainfall events all influence hydrologic connectivity and nutrient transport. The Winooski River and its tributaries form a dense drainage network that integrates these climatic and land‑use influences, delivering water and associated nutrient loads to numerous lakes, ponds, and reservoirs throughout the watershed (VT DEC, 2022).\u003c/p\u003e \u003cp\u003eThis combination of steep physiographic gradients, mixed land use, and hydrologically connected surface waters makes the Winooski River Basin an ideal setting for evaluating nutrient‑related ecological risk. The basin contains waterbodies that vary widely in size, depth, watershed area, and exposure to nutrient sources, providing a natural gradient for assessing how nutrient concentrations, chlorophyll‑a, and oxygen conditions respond to differing watershed pressures. The spatial extent of the basin, along with sampling locations and major hydrologic features, is shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Sources\u003c/h2\u003e \u003cp\u003eWater quality data were obtained from the EPA Water Quality Portal (WQP), a national repository that aggregates monitoring records from federal, state, tribal, and local agencies (Water Quality Portal, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The WQP compiles data from programs including the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA), state environmental departments, and regional watershed organizations. These agencies contribute standardized measurements for nutrients, chlorophyll‑a, dissolved oxygen, temperature, and related water quality parameters.\u003c/p\u003e \u003cp\u003eSpatial datasets used for mapping and watershed characterization were obtained from the Vermont Center for Geographic Information (VCGI). These included the 1‑meter digital elevation model (DEM) and the Vermont Hydrography Dataset, which provides waterbody boundaries, tributaries, and river centerlines (VCGI, 2023a; VCGI, 2023b). Basin boundaries were derived from state‑level watershed delineations and clipped to the Winooski River Basin for spatial analysis.\u003c/p\u003e \u003cp\u003eAll datasets were downloaded in 2024 and processed to ensure consistent units, coordinate systems, and attribute structures prior to analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection and Variables\u003c/h2\u003e \u003cp\u003eWater quality data were obtained from the EPA Water Quality Portal (WQP), a national repository that aggregates monitoring records from federal, state, tribal, and local agencies (Water Quality Portal, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Data were extracted for a 15‑year period (2010\u0026ndash;2024) to capture long‑term patterns in nutrient conditions and ecological responses across the Winooski River Basin. This multi‑year window reduces the influence of anomalous sampling years and provides a more robust representation of chronic nutrient pressures within the watershed.\u003c/p\u003e \u003cp\u003eThe dataset included key indicators of nutrient enrichment and ecological condition: depth (m), chlorophyll‑a (\u0026micro;g/L), dissolved oxygen (mg/L), total nitrogen (\u0026micro;g/L), and total phosphorus (\u0026micro;g/L). These variables represent both nutrient inputs (nitrogen and phosphorus) and biological or ecological responses (chlorophyll‑a, dissolved oxygen, and depth‑related stratification effects), offering a comprehensive basis for evaluating nutrient‑related ecological risk in freshwater systems (Dodds \u0026amp; Smith, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wetzel, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relevance of these indicators is heightened within the Winooski River Basin, where steep physiographic gradients, mixed land use, and hydrologically connected surface waters create strong spatial variation in nutrient pressures and ecological responses. Agricultural areas contribute nonpoint nitrogen and phosphorus loads, while developed areas influence stormwater‑driven nutrient and sediment inputs (Sharpley et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Meals et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). As a result, chlorophyll‑a and dissolved oxygen conditions can vary substantially among waterbodies depending on watershed position, elevation, and land‑use context. These variables therefore serve as effective metrics for assessing nutrient stress across the basin\u0026rsquo;s diverse lakes, ponds, and reservoirs.\u003c/p\u003e \u003cp\u003eAll water quality records were screened for completeness, units were standardized, and only sampling events with full parameter coverage were retained. For sites with multiple measurements across the 15‑year period, values were averaged to produce a single representative record per site, ensuring consistency across analyses and reducing temporal sampling bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Field and Lab Methods\u003c/h2\u003e \u003cp\u003eWater quality measurements were collected by multiple federal, state, tribal, and local monitoring programs contributing data to the EPA Water Quality Portal (WQP). The WQP aggregates standardized monitoring records from agencies including the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA), state environmental departments, and regional watershed organizations (Water Quality Portal, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although sampling protocols vary slightly among agencies, all reported measurements adhere to EPA‑approved field and laboratory methods, ensuring consistency and comparability across datasets.\u003c/p\u003e \u003cp\u003eField measurements included depth (m) and temperature (\u0026deg;C), typically collected using calibrated multiparameter sondes or depth probes following EPA field protocols for in‑situ water quality monitoring (EPA, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Dissolved oxygen (DO) concentrations (mg/L) were measured using optical or membrane‑based DO sensors, with calibration and quality assurance procedures consistent with EPA Method 360.1 and Standard Methods 4500‑O.\u003c/p\u003e \u003cp\u003eLaboratory analyses for nutrient parameters followed EPA‑approved colorimetric and spectrophotometric methods. Total phosphorus (TP) was quantified using persulfate digestion followed by colorimetric detection (EPA Method 365.1 or 365.3). Total nitrogen (TN) was measured using persulfate digestion with ultraviolet or colorimetric detection (EPA Method 351.2). Chlorophyll‑a concentrations (\u0026micro;g/L) were determined using acetone extraction and fluorometric or spectrophotometric analysis following EPA Method 445.0.\u003c/p\u003e \u003cp\u003eAll contributing agencies follow routine quality assurance and quality control (QA/QC) procedures, including field blanks, calibration checks, laboratory controls, and method detection limit verification. These standardized procedures ensure that measurements of TP, TN, chlorophyll‑a, DO, depth, and temperature are comparable across sites and years, supporting robust long‑term ecological assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Spatial Analysis\u003c/h2\u003e \u003cp\u003eSpatial analyses were conducted in QGIS 3.44 (QGIS Development Team, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A 1‑meter resolution digital elevation model (DEM) from the Vermont Center for Geographic Information (VCGI) was used to characterize topographic variation and watershed context across the Winooski River Basin (VCGI, 2023a). The DEM was clipped to the basin boundary and rendered using a grayscale elevation ramp to provide terrain context without obscuring hydrologic features.\u003c/p\u003e \u003cp\u003eHydrologic layers, including waterbodies, tributaries, and the mainstem Winooski River, were obtained from the Vermont Hydrography Dataset (VCGI, 2023b). All spatial layers were projected into the NAD83 / Vermont State Plane Meters coordinate system to ensure consistent spatial alignment. Sampling locations were imported as point features and overlaid on the DEM and hydrology layers to visualize the spatial distribution of monitoring sites relative to watershed position, elevation gradients, and surface‑water connectivity.\u003c/p\u003e \u003cp\u003eAll cartographic products were produced in the QGIS Print Layout environment. The final map includes the basin boundary, hydrologic network, waterbody polygons, sampling locations, and a grayscale DEM background, providing a spatial framework for interpreting nutrient concentrations, chlorophyll‑a, and dissolved oxygen patterns across the watershed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Temporal Framework\u003c/h2\u003e \u003cp\u003eWater quality records were compiled across a 15‑year period (2010\u0026ndash;2024) to capture long‑term variability in nutrient conditions and ecological responses within the Winooski River Basin. This temporal window encompasses a wide range of hydrologic conditions, including wet and dry years, seasonal extremes, and interannual climate variability, providing a robust basis for evaluating chronic nutrient pressures rather than short‑term fluctuations. Because data were contributed by multiple agencies reporting to the EPA Water Quality Portal, sampling frequency varied among sites and years. To ensure comparability across locations, all measurements were screened for completeness, and only years with full parameter coverage were retained. For sites with multiple observations within the 15‑year period, values were averaged to produce a single representative record per site. This approach reduces the influence of anomalous sampling events and aligns with long‑term ecological assessment practices commonly used in nutrient‑risk evaluation. The resulting dataset reflects multi‑year mean conditions for total phosphorus, total nitrogen, chlorophyll‑a, dissolved oxygen, depth, and temperature, providing a stable foundation for subsequent spatial, statistical, and risk‑based analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Data Processing and Software\u003c/h2\u003e \u003cp\u003eAll data processing and preparation were conducted using a combination of R (Version 4.x; R Core Team, 2024) and QGIS 3.44 (QGIS Development Team, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Raw water quality records downloaded from the EPA Water Quality Portal were imported into R for cleaning, filtering, and standardization. Duplicate entries, incomplete records, and measurements lacking essential parameters (total phosphorus, total nitrogen, chlorophyll‑a, dissolved oxygen, depth, or temperature) were removed. Units were standardized across agencies to ensure comparability, and parameter names were harmonized to a consistent naming convention. Temporal filtering was applied to retain only observations collected between 2010 and 2024. For sites with multiple measurements within this 15‑year period, values were averaged to produce a single representative record per site. This approach reduces temporal sampling bias and aligns with long‑term ecological assessment practices. Summary statistics, data validation, and exploratory visualizations were generated in R. Spatial data processing, including clipping, projection, and integration of sampling locations with hydrologic and elevation layers, was performed in QGIS. Attribute tables were joined to spatial layers to link water quality measurements with their corresponding geographic features. Final datasets were exported as CSV and GeoPackage files for use in spatial analysis, risk calculations, and statistical modeling.This combined workflow ensured that all water quality, spatial, and temporal data were consistently formatted, quality‑checked, and ready for subsequent ecological risk assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Formulas\u003c/h2\u003e \u003cp\u003eTogether, these processing steps produced a fully standardized dataset suitable for quantitative evaluation of nutrient conditions across the Winooski River Basin. With all spatial, temporal, and water‑quality variables harmonized, the next stage of the analysis involved applying a set of established ecological risk and water‑quality formulas. These formulas quantify nutrient enrichment, biological response, and overall ecological risk, providing the mathematical foundation for the indicators and comparisons presented in the Results. The following section outlines the equations used in this study and the rationale behind each metric.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCarlson\u0026rsquo;s Trophic State Index (TSI)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe trophic status of each waterbody was evaluated using Carlson\u0026rsquo;s Trophic State Index (TSI), a widely applied metric that relates nutrient concentrations and algal biomass to overall lake productivity (Carlson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). TSI values were calculated using chlorophyll‑a (Chl‑a), total phosphorus (TP), and Secchi Depth (SD):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:TSI(Chl-a)=9.81*ln(Chl-a)+30.6$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:TSI\\left(TP\\right)=14.42*ln\\left(TP\\right)+4.15$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:TSI\\left(SD\\right)=60-14.41ln\\left(SD\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Chl‑a and TP are expressed in \u0026micro;g/L, and SD is expressed in meters and ln⁡ denotes the natural logarithm.\u003c/p\u003e \u003cp\u003eTSI values were interpreted using standard trophic classifications:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTSI\u0026thinsp;\u0026lt;\u0026thinsp;40 \u0026rarr; oligotrophic (low productivity)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTSI 40\u0026ndash;50 \u0026rarr; mesotrophic (moderate productivity)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTSI\u0026thinsp;\u0026gt;\u0026thinsp;50 \u0026rarr; eutrophic to hypereutrophic (high productivity)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eDifferences between TSI(Chl‑a), TSI(TP), and TSI(SD) were examined to assess potential nutrient limitation or mismatches between nutrient availability and algal response.\u003c/p\u003e \u003cp\u003e \u003cb\u003eNutrient Limitation (TN:TP Ratio)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNutrient limitation was evaluated using the molar ratio of total nitrogen to total phosphorus (TN:TP), following the Redfield Ratio framework and freshwater adaptations (Wetzel, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2001\u003c/span\u003e):\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:TN:TP=\\frac{TN/14.01}{TP/30.97}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere TN and TP are expressed in \u0026micro;g/L, and 14.01 and 30.97 are the molar masses of nitrogen and phosphorus (g/mol), respectively.\u003c/p\u003e \u003cp\u003eInterpretation followed established freshwater thresholds:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTN:TP\u0026thinsp;\u0026lt;\u0026thinsp;10 \u0026rarr; nitrogen limitation\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTN:TP\u0026thinsp;\u0026gt;\u0026thinsp;20 \u0026rarr; phosphorus limitation\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e10\u0026ndash;20 \u0026rarr; potential co‑limitation\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis ratio was used to interpret whether nutrient imbalance influenced observed chlorophyll‑a concentrations and trophic state.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk Quotient (RQ)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEcological risk associated with nutrient concentrations was assessed using the Risk Quotient (RQ) framework, which compares measured environmental concentrations to established threshold values (EPA, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e):\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:RQ=\\frac{MEC}{PNEC}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMEC\u0026thinsp;=\u0026thinsp;measured environmental concentration (e.g., TP, TN, Chl‑a)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePNEC\u0026thinsp;=\u0026thinsp;predicted no‑effect concentration derived from regulatory criteria or literature\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eRQ values were interpreted using standard ecological risk thresholds:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRQ\u0026thinsp;\u0026lt;\u0026thinsp;0.1 \u0026rarr; Negligible Risk\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ\u0026thinsp;\u0026gt;\u0026thinsp;1.0 \u0026rarr; Potential ecological risk (threshold exceedance)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSeparate RQs were calculated for TP, TN and Chl‑a.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRQ\u003c/b\u003e \u003csub\u003emax\u003c/sub\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRQ\u003csub\u003emax\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=max\\left(RQ\\right(TP),(RQ\\left(TN\\right),\\left(RQ\\right(Chl)\\)\u003c/span\u003e\u003c/span\u003e (6)\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe highest nutrient‑related risk at each site defines the worst‑case ecological vulnerability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted in RStudio (Version 4.5.2). Summary statistics, including mean, standard deviation, and range, were calculated for all variables. Water quality parameters were grouped into nutrient variables (total phosphorus [TP] and total nitrogen [TN]), biological response variables (chlorophyll-a [Chl-a]), physicochemical variables (dissolved oxygen [DO], temperature, and depth), and derived indices including the Carlson Trophic State Index (TSI) and nutrient-specific Risk Quotients (RQ). Relationships among variables were evaluated using Pearson correlation analysis to quantify linear associations between nutrient concentrations, algal biomass, trophic status, and ecological risk. Linear regression models were used to assess directional relationships between nutrient drivers (TP, TN) and response variables (Chl-a and TSI). Principal Component Analysis (PCA) was conducted on standardized (z-score) variables to identify dominant gradients in water quality and to reduce dimensionality among correlated parameters. PCA loadings and biplots were used to interpret multivariate structure and to evaluate whether nutrient enrichment, trophic state, and physicochemical conditions aligned along shared axes of variation.\u003c/p\u003e \u003cp\u003eStatistical significance was assessed at α\u0026thinsp;=\u0026thinsp;0.05 for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results and Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Summary Statistics\u003c/h2\u003e\n \u003cp\u003eNutrient and physicochemical conditions varied across sampling sites, indicating spatial heterogeneity within the system (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary Statistics of Selected Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChl-a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e6.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecchi Depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e11.1\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\u003eChlorophyll-a (Chl-a) concentrations ranged from 1.23 to 40.7 \u0026micro;g/L, with a mean of 7.73 \u0026micro;g/L, reflecting a broad gradient in algal biomass and primary productivity. Total phosphorus (TP) concentrations exhibited substantial variability (8.91\u0026ndash;48.4 mg/L; mean\u0026thinsp;=\u0026thinsp;16.4 mg/L), suggesting that some sites experience elevated phosphorus enrichment. In contrast, total nitrogen (TN) concentrations were more constrained (0.18\u0026ndash;0.40 mg/L; mean\u0026thinsp;=\u0026thinsp;0.288 mg/L), indicating comparatively lower variability across the basin.\u003c/p\u003e\n \u003cp\u003eDissolved oxygen (DO) concentrations ranged from 6.71 to 11.0 mg/L (mean\u0026thinsp;=\u0026thinsp;8.81 mg/L), indicating generally well-oxygenated conditions. Water temperature varied from 6.4 to 15.5\u0026deg;C (mean\u0026thinsp;=\u0026thinsp;11.1\u0026deg;C), while Secchi depth ranged from 1.42 to 9.91 m (mean\u0026thinsp;=\u0026thinsp;4.21 m), reflecting variability in water clarity and light penetration. Differences in temperature and water clarity suggest variation in physical conditions that may influence mixing dynamics, nutrient availability, and biological response.\u003c/p\u003e\n \u003cp\u003eOverall, the greater variability observed in TP and chlorophyll-a relative to TN indicates that phosphorus availability and algal biomass are key factors structuring water quality conditions within the basin. Additionally, the wide range in Secchi depth suggests that water clarity varies substantially among sites, potentially reflecting both algal productivity and non-algal turbidity influences. These patterns provide the basis for evaluating trophic state dynamics and nutrient limitation across the basin.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 TSI Values\u003c/h2\u003e\n \u003cp\u003eTrophic state conditions across the study sites were evaluated using the Carlson Trophic State Index. TSI values derived from chlorophyll-a ranged from 31.3 to 59.6, while TSI based on total phosphorus ranged from 31.7 to 54.4, indicating conditions spanning from oligotrophic to mesotrophic, with one site approaching eutrophic status. In contrast, TSI values calculated from Secchi depth were consistently higher, ranging from 40.0 to 57.0, suggesting reduced water clarity across multiple sites.\u003c/p\u003e\n \u003cp\u003eThe majority of sites fell within oligotrophic to mesotrophic classifications based on nutrient concentrations and algal biomass. However, one site exhibited elevated TSI values for both chlorophyll-a and total phosphorus, indicating localized eutrophic conditions likely associated with increased nutrient enrichment.\u003c/p\u003e\n \u003cp\u003eNotably, TSI(SD) values were frequently higher than corresponding TSI(Chl-a) and TSI(TP) values, indicating that water transparency was lower than expected based solely on algal biomass. This pattern suggests that factors other than phytoplankton, such as suspended sediments or non-algal turbidity, may contribute to reduced clarity in certain waterbodies. These findings highlight the influence of watershed processes on light availability and reinforce the importance of considering multiple trophic indicators when assessing ecological conditions. Site-level TSI values are provided in Supplementary Table S1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 TN:TP\u003c/h2\u003e\n \u003cp\u003eTN:TP molar ratios ranged from 6.81 to 37.07 across the 12 sampled waterbodies. One site fell below the nitrogen‑limitation threshold (TN:TP\u0026thinsp;\u0026lt;\u0026thinsp;10), four sites were within the co‑limitation range (10\u0026ndash;20), and the remaining seven sites exceeded 20, indicating phosphorus limitation. Overall, most waterbodies exhibited TN:TP ratios consistent with phosphorus‑limited conditions, suggesting that phosphorus availability is the primary constraint on algal biomass across the study area. Site-specific TN:TP ratios and nutrient limitation categories are provided in Supplementary Table S2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 RQ/RQ\u003csub\u003emax\u003c/sub\u003e\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRisk Level by Site\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSite\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRQ (TP)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRQ (TN)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRQ (Chl-a)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRQmax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRisk Level\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegligible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegligible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegligible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Exceedance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegligible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Exceedance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegligible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Exceedance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Exceedance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegligible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Exceedance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Exceedance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: Risk Quotient Total Phosphorus (RQP): Calculated as (Measured TP / Threshold). The Level of Concern (LOC) threshold is set at 18 \u0026micro;g/L (VT DEC, 2017). Risk Quotient Total Nitrogen (RQN): Calculated as (Measured TN / Threshold). The Level of Concern (LOC) threshold is set at 0.33 \u0026micro;g/L (U.S. EPA, 2001). Risk Quotient Chlorophyll (RQChl): Calculated as (Measured Chl-a / Threshold). The Level of Concern (LOC) threshold is set at 7 \u0026micro;g/L (VT DEC, 2017).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eRisk Quotient (RQ) values varied across nutrients and chlorophyll‑a, with exceedances occurring in all three metrics. RQ (TP) ranged from 0.50 to 2.69, with three sites exceeding the threshold value of 1. RQ (TN) values were more consistently elevated, ranging from 0.55 to 1.21, and five sites exceeded the threshold. RQ(Chl) showed the greatest variability, spanning 0.18 to 5.81, with four sites above 1, including one site with a markedly high value (RQ\u0026thinsp;=\u0026thinsp;5.81). These patterns indicate that nitrogen‑related risk was the most widespread across the study area, while chlorophyll‑a exhibited the strongest individual exceedances, suggesting localized biomass responses that may reflect internal loading, hydrologic retention, or other site‑specific factors. RQ\u003csub\u003emax\u003c/sub\u003e values, representing the worst‑case nutrient or biomass risk at each site, ranged from 0.61 to 5.81. Six of the twelve sites exceeded the threshold value of 1, indicating high nutrient‑related risk, while the remaining six sites fell below the threshold. The highest RQmax value (5.81) occurred at a site where chlorophyll‑a strongly exceeded its threshold, reflecting an intense localized biomass response. Other exceedances were driven by elevated nitrogen or phosphorus RQs, depending on the site. Overall, RQmax revealed a clear split between low‑risk and high‑risk waterbodies, highlighting spatial heterogeneity in nutrient and biomass stress across the study area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Correlation\u003c/h2\u003e\n \u003cp\u003ePearson correlation analysis revealed clear relationships among nutrient concentrations, algal biomass, and physicochemical conditions across the study sites (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation Matrix of Selected Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDO\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSecchi Depth\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.938*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecchi Depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Values represent Pearson correlation coefficients. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Only the lower triangle is shown.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTotal phosphorus (TP) exhibited a strong positive correlation with chlorophyll-a (r\u0026thinsp;=\u0026thinsp;0.938, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that phosphorus is the primary driver of algal productivity within the system. In contrast, total nitrogen (TN) showed a much weaker relationship with chlorophyll-a (r\u0026thinsp;=\u0026thinsp;0.246), suggesting a comparatively limited role in controlling algal biomass.\u003c/p\u003e\n \u003cp\u003eSecchi depth showed negative relationships with multiple variables, including chlorophyll-a (r = -0.273), temperature (r = -0.383), and dissolved oxygen (r = -0.507), indicating that reduced water clarity is associated with increased biological activity and changing physicochemical conditions. The moderate negative correlation between Secchi depth and temperature suggests that warmer conditions may contribute to decreased transparency, potentially through enhanced biological production or increased suspended material.\u003c/p\u003e\n \u003cp\u003eDissolved oxygen exhibited weak correlations with most variables, including chlorophyll-a (r\u0026thinsp;=\u0026thinsp;0.036), suggesting that oxygen dynamics are influenced more by physical processes such as mixing and temperature rather than direct biological production alone. Temperature showed moderate positive relationships with both TP and chlorophyll-a, indicating that warmer conditions may enhance nutrient availability and biological response.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Regression\u003c/h2\u003e\n \u003cp\u003eA simple linear regression showed that total phosphorus significantly predicted chlorophyll‑a concentrations, F(1,10)\u0026thinsp;=\u0026thinsp;73.44, p\u0026lt;.001, with TP explaining 88% of the variance in Chl‑a ( R2 = .88 ). The model indicated that chlorophyll‑a increased by 0.94 \u0026micro;g/L for every 1 \u0026micro;g/L increase in TP ( \u0026beta;\u0026thinsp;=\u0026thinsp;0.94, p\u0026lt;.001 ) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eChlorophyll‑a was strongly and positively related to total phosphorus. The regression model was highly significant ( F(1,10)\u0026thinsp;=\u0026thinsp;73.44, p\u0026lt;.001 ) and explained 88% of the variation in chlorophyll‑a. TP was a strong predictor ( \u0026beta;\u0026thinsp;=\u0026thinsp;0.94, p\u0026lt;.001 ), indicating that algal biomass increased nearly one‑to‑one with phosphorus concentrations. This relationship reflects a tight coupling between phosphorus availability and phytoplankton biomass across the sampled waterbodies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 PCA\u003c/h2\u003e\n \u003cp\u003ePrincipal component analysis revealed clear multivariate structure among the environmental variables (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePCA explained by Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEigenvalue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of Variance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCumulative Variance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote. Eigenvalues were calculated as the squared standard deviations of each principal component. Components with eigenvalues greater than 1 (PC1\u0026ndash;PC3) were retained for interpretation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe first three components explained 83.1% of the total variance. PC1 (40.0%) represented a strong eutrophication gradient, with high positive loadings for total phosphorus, chlorophyll‑a, and temperature, and a negative loading for Secchi depth, indicating that nutrient‑rich, warm, turbid sites clustered together. PC2 (24.6%) captured a clarity\u0026ndash;oxygen gradient, with Secchi depth loading positively and dissolved oxygen loading negatively. PC3 (18.4%) was dominated by total nitrogen, reflecting a secondary nutrient axis independent of the TP\u0026ndash;Chl relationship. Together, these components describe distinct ecological gradients in productivity, water clarity, and nitrogen availability across the sampled sites.\u003c/p\u003e\n \u003cp\u003eThe biplot displays site scores (points) and variable loadings (arrows) for the first two principal components, which together explain 64.6% of the total variance. PC1 (40.0%) represents a strong eutrophication gradient, with positive loadings for total phosphorus, chlorophyll‑a, and temperature, and a negative loading for Secchi depth. PC2 (24.6%) reflects a clarity\u0026ndash;oxygen gradient, with Secchi depth loading positively and dissolved oxygen loading negatively. Arrow length indicates the strength of each variable\u0026rsquo;s contribution to the ordination.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4.0 Synthesis and Hypothesis Evaluation","content":"\u003cp\u003eThe integration of univariate, bivariate, and multivariate analyses provides a comprehensive understanding of nutrient dynamics, trophic status, and ecological risk across the sampled waterbodies. Collectively, the results demonstrate that ecological condition is strongly structured by nutrient availability, biological response, and physicochemical controls, with clear implications for nutrient management within the basin.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1\u003c/b\u003e, which proposed that nutrient concentrations and water clarity are significantly associated with ecological risk, was supported. Strong relationships between total phosphorus and chlorophyll-a, as well as consistent negative associations between Secchi depth and key variables, indicate that increased nutrient availability and reduced water clarity correspond to elevated ecological risk. These patterns were evident across correlation analysis, linear regression, and principal component analysis, where nutrient enrichment and reduced transparency aligned along dominant gradients of variation. Together, these findings confirm that both nutrient inputs and light availability play central roles in structuring ecological conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2\u003c/b\u003e, which proposed that nutrient stressors do not contribute equally to ecological risk, was also supported. Total phosphorus emerged as the primary driver of algal biomass and trophic state, as evidenced by its strong positive relationship with chlorophyll-a and its dominant influence within the multivariate structure. In contrast, total nitrogen exhibited weaker associations with biological response variables, suggesting a secondary role in controlling productivity. Chlorophyll-a, as an integrative indicator of system response, displayed the greatest variability and the highest individual risk exceedances. These results demonstrate that phosphorus exerts a disproportionately strong influence on ecological condition relative to other stressors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3\u003c/b\u003e, which proposed that the worst-case risk metric (RQₘₐₓ) reveals patterns of ecological vulnerability not captured by individual nutrient-based RQs, was strongly supported. While individual RQ values often indicated negligible or moderate risk, the RQₘₐₓ metric identified multiple sites with elevated ecological risk by capturing the highest exceedance among nutrients and biological response indicators. This approach revealed a clearer separation between low-risk and high-risk waterbodies and highlighted sites where localized nutrient enrichment or biological response may otherwise be underestimated. As such, RQₘₐₓ provides a more integrative and precautionary assessment of ecological condition.\u003c/p\u003e \u003cp\u003eOverall, the results indicate that ecological risk within the study area is driven by interactions among nutrient enrichment, algal biomass, and water clarity. Phosphorus plays a central role in regulating these dynamics, while water clarity reflects both biological production and watershed influences. The application of both individual RQs and the RQₘₐₓ metric enhances the ability to detect spatial heterogeneity in ecological risk and provides a robust framework for identifying vulnerable waterbodies.\u003c/p\u003e \u003cp\u003eThese findings underscore the importance of targeted nutrient management strategies, particularly those focused on phosphorus reduction, to mitigate ecological risk and maintain water quality across diverse freshwater systems.\u003c/p\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eThis study evaluated nutrient dynamics, trophic state, and ecological risk across waterbodies within the Winooski River Basin using a combination of water quality indicators, trophic indices, and statistical analyses. Results consistently demonstrated that phosphorus is the primary driver of algal biomass and trophic condition, as evidenced by strong relationships between total phosphorus and chlorophyll-a, as well as alignment across trophic state metrics derived from the Carlson Trophic State Index.\u003c/p\u003e \u003cp\u003eTrophic state conditions ranged from oligotrophic to mesotrophic, with localized instances approaching eutrophic conditions, indicating spatial variability in nutrient enrichment across the basin. Nutrient limitation analysis suggested that the system is generally co-limited to slightly phosphorus-limited, reinforcing the dominant role of phosphorus in controlling primary productivity. Ecological risk assessment further indicated that while overall risk levels were moderate, certain sites exhibited elevated Risk Quotient values, highlighting the presence of localized hotspots of nutrient-driven ecological concern.\u003c/p\u003e \u003cp\u003eMultivariate and statistical analyses supported these findings, identifying a primary gradient associated with nutrient enrichment and biological response, alongside secondary influences from physicochemical conditions such as temperature and dissolved oxygen. Together, these results demonstrate that water quality and ecological conditions within the basin are shaped by interactions among nutrient inputs, watershed processes, and environmental controls.\u003c/p\u003e \u003cp\u003eFrom a management perspective, these findings underscore the importance of targeted phosphorus reduction strategies to mitigate eutrophication and reduce ecological risk. Efforts to control nonpoint source inputs from agricultural and developed areas, along with continued monitoring of high-risk sites, will be critical for maintaining water quality and ecological integrity within the basin. Future research should focus on temporal dynamics, including seasonal variability and episodic nutrient loading, to further refine understanding of nutrient\u0026ndash;ecosystem interactions in freshwater systems.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBierman PR, Chapin DM (2014) The geologic story of Vermont. Vermont Geological Survey\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrooks BW, Zhang X (2021) Water quality criteria and ecological risk assessment for nutrients. Environ Sci Technol 55(1):14\u0026ndash;29\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlson RE (1977) A trophic state index for lakes. Limnol Oceanogr 22(2):361\u0026ndash;369\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN, Smith VH (1998) Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol Appl 8(3):559\u0026ndash;568\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapman PM (2002) Ecological risk assessment (ERA) and hormesis. Sci Total Environ 288(1\u0026ndash;2):131\u0026ndash;140\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiaz RJ, Rosenberg R (2008) Spreading dead zones and consequences for marine ecosystems. Science 321(5891):926\u0026ndash;929\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDodds WK, Smith VH (2016) Nitrogen, phosphorus, and eutrophication in streams. 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U.S. Environmental Protection Agency\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLake Champlain Basin Program (2018) State of the Lake and ecosystem indicators report. Lake Champlain Basin Program\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandis WG, Yu M\u0026ndash;H (2019) Introduction to environmental toxicology: Molecular substructures to ecological landscapes, 5th edn. CRC\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeals DW, Budd L, Moen T (2010) Lake Champlain Basin agricultural nutrient management: Lessons learned. J Great Lakes Res 36(1):135\u0026ndash;140\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNOAA National Centers for Environmental Information (2023) Climate normals for Vermont. NOAA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdum HT (1956) Primary production in flowing waters. Limnol Oceanogr 1(2):102\u0026ndash;117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaerl HW, Otten TG (2013) Harmful cyanobacterial blooms: Causes, consequences, and controls. Microb Ecol 65(4):995\u0026ndash;1010\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaerl HW, Scott JT, McCarthy MJ, Newell SE, Gardner WS, Havens KE, Hoffman DK, Wilhelm SW, Wurtsbaugh WA (2016) It takes two to tango: When and where dual nutrient (N \u0026amp; P) reductions are needed to protect lakes and downstream ecosystems. Science 354(6312):301\u0026ndash;302\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQGIS Development Team (2024) QGIS Geographic Information System (Version 3.44). Open Source Geospatial Foundation\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScheffer M, Hosper SH, Meijer M\u0026ndash;L, Moss B, Jeppesen E (1993) Alternative equilibria in shallow lakes. Trends Ecol Evol 8(8):275\u0026ndash;279\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchindler DW (1977) Evolution of phosphorus limitation in lakes. Science 195(4275):260\u0026ndash;262\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharpley AN, Kleinman PJ, Flaten D, Buda A (2012) Critical source area management of agricultural phosphorus. J Environ Qual 41(5):1071\u0026ndash;1079\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith VH, Tilman GD, Nekola JC (1999) Eutrophication: Impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environ Pollut 100(1\u0026ndash;3):179\u0026ndash;196\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStandard Methods Committee (2017) \u003cem\u003eStandard Methods for the Examination of Water and Wastewater\u003c/em\u003e. American Public Health Association\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuter GW (2007) Ecological risk assessment, 2nd edn. CRC\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;ndergaard M, Jensen JP, Jeppesen E (2003) Role of sediment and internal loading of phosphorus in shallow lakes. Hydrobiologia 484(1\u0026ndash;3):75\u0026ndash;90\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson EH, Sorenson ER (2000) Wetland, woodland, wildland: A guide to the natural communities of Vermont. University Press of New England\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Environmental Protection Agency (1998) EPA/630/R\u0026ndash;95/002F. Guidelines for ecological risk assessment. U.S. Environmental Protection Agency\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Geological Survey (2019) National Land Cover Database (NLCD). USGS\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVermont Center for Geographic Information (2023a) Digital Elevation Model (1\u0026ndash;m). VCGI\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVermont Center for Geographic Information (2023b) Vermont Hydrography Dataset. VCGI\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVermont Department of Environmental Conservation (2022) Winooski River Basin water quality management plan. VT DEC\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWater Quality Portal (2024) \u003cem\u003eWater Quality Data Portal\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.waterqualitydata.us/\u003c/span\u003e\u003cspan address=\"https://www.waterqualitydata.us/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWetzel RG (2001) Limnology: Lake and river ecosystems, 3rd edn. Academic\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"eutrophication, phosphorus, chlorophyll-a, trophic state, ecological risk, freshwater","lastPublishedDoi":"10.21203/rs.3.rs-9255735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9255735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Winooski River Basin is a watershed with a long history of eutrophication and associated ecological risk. This study evaluated nutrient dynamics, trophic state, and ecological risk across lakes, ponds, and reservoirs using a combination of water quality indicators and statistical analyses. Nutrient-related Risk Quotients (RQs) were used to quantify the extent to which observed conditions exceeded established ecological thresholds, enabling comparison of phosphorus, nitrogen, chlorophyll-a, and worst-case risk conditions across waterbodies.\u003c/p\u003e \u003cp\u003eResults demonstrated that phosphorus is the primary driver of algal biomass, as evidenced by a strong relationship between total phosphorus (TP) and chlorophyll-a (Chl-a), as well as consistent patterns across trophic indices derived from the Carlson Trophic State Index. Trophic conditions ranged from oligotrophic to eutrophic, with most sites classified as oligotrophic to mesotrophic. Principal Component Analysis (PCA) revealed a dominant eutrophication gradient defined by TP, Chl-a, and water clarity, while secondary variation was associated with physicochemical conditions including temperature and dissolved oxygen (DO).\u003c/p\u003e \u003cp\u003eEcological risk assessment indicated that sites with elevated nutrient concentrations exhibited higher RQ values, with the worst-case metric (RQₘₐₓ) identifying moderate to high risk in several waterbodies. Overall, results demonstrate that nutrient enrichment\u0026mdash;particularly phosphorus\u0026mdash;and reduced water clarity are strongly associated with variation in ecological conditions across the basin. These findings underscore the importance of targeted nutrient management and watershed-scale processes in maintaining freshwater ecosystem health.\u003c/p\u003e","manuscriptTitle":"Nutrient Enrichment, Water Clarity, and Ecological Risk in Lakes, Ponds, and Reservoirs of the Winooski River Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 07:05:50","doi":"10.21203/rs.3.rs-9255735/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e53dc14-d2bc-47d9-b1f0-5fa68cd90822","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65325276,"name":"Marine and Freshwater Ecology"}],"tags":[],"updatedAt":"2026-04-01T07:05:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 07:05:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9255735","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9255735","identity":"rs-9255735","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}