Reservoirs are globally important aquatic ecosystems that provide critical anthropogenic functions, including water storage and fisheries, yet their ecosystem metabolism remains understudied. We estimated daily lake metabolism (net ecosystem production [NEP], gross primary production [GPP], ecosystem respiration [R]) at 9 locations throughout Clinton Lake, a temperate, hypereutrophic, polymictic reservoir in Kansas, USA, throughout a growing season. Using dissolved oxygen monitoring and Bayesian hierarchical generalized additive mixed models, we assessed spatiotemporal variation in metabolism and relationships with environmental predictors, including thermal stratification, temperature, wind, and phytoplankton. Clinton Lake exhibited frequent daily alternations between autotrophy and heterotrophy, resulting in the near-zero mean NEP (0.01 mg O₂ L⁻¹ d⁻¹) for the study period. Seasonal patterns were strong, with GPP and R magnitudes increasing throughout the growing season. Temporal variation dominated spatial variation in all metabolism metrics, but GPP and R models showed modest site-level effects. Thermal stratification was associated with metabolic variability. Stronger stratification was associated with higher GPP and R, whereas changes in stratification strength corresponded to directional changes in NEP: stratification promoted autotrophy while destratification promoted heterotrophy. We suggest the coupling between metabolism and stratification is driven by abundant buoyant cyanobacteria controlling oxygen dynamics near the surface. Our results suggest that Great Plains reservoirs can be highly dynamic ecosystems where wind-driven mixing strongly influences metabolic processes. The frequent stratification–destratification cycles characteristic of polymictic systems create spatiotemporally variable ecosystem function. These findings provide a baseline understanding for monitoring changes in reservoir ecosystems and highlight the importance of physical forcing in structuring metabolism in engineered systems.

Reservoirs are globally important aquatic ecosystems that provide critical anthropogenic functions, including water storage and fisheries, yet their ecosystem metabolism remains understudied. We estimated daily lake metabolism (net ecosystem production [NEP], gross primary production [GPP], ecosystem respiration [R]) at 9 locations throughout Clinton Lake, a temperate, hypereutrophic, polymictic reservoir in Kansas, USA, throughout a growing season. Using dissolved oxygen monitoring and Bayesian hierarchical generalized additive mixed models, we assessed spatiotemporal variation in metabolism and relationships with environmental predictors, including thermal stratification, temperature, wind, and phytoplankton. Clinton Lake exhibited frequent daily alternations between autotrophy and heterotrophy, resulting in the near-zero mean NEP (0.01 mg O₂ L⁻¹ d⁻¹) for the study period. Seasonal patterns were strong, with GPP and R magnitudes increasing throughout the growing season. Temporal variation dominated spatial variation in all metabolism metrics, but GPP and R models showed modest site-level effects. Thermal stratification was associated with metabolic variability. Stronger stratification was associated with higher GPP and R, whereas changes in stratification strength corresponded to directional changes in NEP: stratification promoted autotrophy while destratification promoted heterotrophy. We suggest the coupling between metabolism and stratification is driven by abundant buoyant cyanobacteria controlling oxygen dynamics near the surface. Our results suggest that Great Plains reservoirs can be highly dynamic ecosystems where wind-driven mixing strongly influences metabolic processes. The frequent stratification–destratification cycles characteristic of polymictic systems create spatiotemporally variable ecosystem function. These findings provide a baseline understanding for monitoring changes in reservoir ecosystems and highlight the importance of physical forcing in structuring metabolism in engineered systems.

Reservoirs are globally important aquatic ecosystems that provide critical anthropogenic functions including water storage, flood control, and fisheries, yet their ecosystem metabolism remains understudied compared to natural lakes. We estimated daily lake metabolism (net ecosystem production [NEP], gross primary production [GPP], and ecosystem respiration [R]) at nine locations throughout Clinton Lake, a temperate, hypereutrophic, polymictic reservoir in Kansas, USA, during the 2024 growing season. Using dissolved oxygen monitoring and Bayesian hierarchical generalized additive mixed models, we assessed spatiotemporal variation in metabolism and relationships with environmental predictors including thermal stratification, temperature, wind, and phytoplankton pigment data. Clinton Lake exhibited dynamic metabolism with frequent daily alternations between autotrophy and heterotrophy, resulting in near-zero mean NEP (0.01 mg O₂ L⁻¹ d⁻¹) across the study period. All metabolism components showed strong seasonal patterns, with GPP and R magnitudes increasing throughout the growing season. Temporal variation dominated over spatial variation in all metabolism metrics, but modest site-level effects were present in both GPP and R models. Thermal stratification emerged as a main driver of metabolism variability. Stronger stratification was associated with higher GPP and R rates, while changes in stratification strength corresponded to directional changes in NEP: stratification events promoted autotrophy, while destratification promoted heterotrophy. We suggest the coupling between metabolism and stratification is driven by the dominance of buoyant cyanobacteria controlling oxygen dynamics near the surface. Our results suggest that Great Plains reservoirs can function as highly dynamic ecosystems where wind-driven mixing regimes precede nutrients or light for the primary control of metabolic processes. The frequent stratification-destratification cycles characteristic of polymictic systems create spatiotemporally variable ecosystem function. These findings provide critical baseline understanding for monitoring future changes in reservoir ecosystems and highlight the importance of physical forcing in structuring metabolism in engineered aquatic systems.

Kelly, AG, Harris, TD. 2024. Watershed grassland fires drive nutrient increases in replicated experimental ponds. Lake Reserv Manage. 40:303–316. Forest and grassland fires have been increasing in frequency due to anthropogenic climate change and fire suppression-focused land management practices. Fire has frequently been observed in lake watersheds, yet links between fire ecology and limnology are not well understood, especially in grassland ecosystems. We conducted a 21-d replicated whole pond experiment to determine the effects of burning on lakes within grassland ecosystems. We examined physicochemical parameters and the phytoplankton concentration and community composition in control and treatment (burned) ponds. Total phosphorus, total dissolved phosphorus, soluble reactive phosphorus, and nitrate increased significantly following burning. The total nitrogen:total phosphorus and total dissolved nitrogen:total dissolved phosphorus ratios decreased significantly following burning. Increased nutrient inputs from burned material led to greater mean phytoplankton concentrations in the treatment ponds compared to the control ponds; however, no statistically significant changes were observed in phytoplankton concentration nor the community composition. Increases in nutrient concentrations in the treatment ponds were observed within the first half of the study and were short-lived, whereas increases in phytoplankton concentrations were not observed until experiment day 21. Our results indicate that increased prevalence of fire may worsen eutrophication issues in grassland water bodies, but more research is needed to understand the persistence of these impacts in pond ecosystems.

Kelly, AG, Harris, TD. 2024. Watershed grassland fires drive nutrient increases in replicated experimental ponds. Lake Reserv Manage. 40:303–316. Forest and grassland fires have been increasing in frequency due to anthropogenic climate change and fire suppression-focused land management practices. Fire has frequently been observed in lake watersheds, yet links between fire ecology and limnology are not well understood, especially in grassland ecosystems. We conducted a 21-d replicated whole pond experiment to determine the effects of burning on lakes within grassland ecosystems. We examined physicochemical parameters and the phytoplankton concentration and community composition in control and treatment (burned) ponds. Total phosphorus, total dissolved phosphorus, soluble reactive phosphorus, and nitrate increased significantly following burning. The total nitrogen:total phosphorus and total dissolved nitrogen:total dissolved phosphorus ratios decreased significantly following burning. Increased nutrient inputs from burned material led to greater mean phytoplankton concentrations in the treatment ponds compared to the control ponds; however, no statistically significant changes were observed in phytoplankton concentration nor the community composition. Increases in nutrient concentrations in the treatment ponds were observed within the first half of the study and were short-lived, whereas increases in phytoplankton concentrations were not observed until experiment day 21. Our results indicate that increased prevalence of fire may worsen eutrophication issues in grassland water bodies, but more research is needed to understand the persistence of these impacts in pond ecosystems.

Harris TD, Graham JL. 2017. Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset. Lake Reserve Manage. 33:32-48. Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations <60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.

Harris TD, Graham JL. 2017. Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset. Lake Reserve Manage. 33:32-48. Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations <60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.