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Abstract Long-term declines in lake hypolimnetic dissolved oxygen (DO) have been attributed to eutrophication, reduced water clarity, or rising temperatures. DO dynamics in human-made reservoirs may also be influenced by their distinct characteristics (for example, hydrology) and by the high levels of watershed inputs (suspended sediments, nutrients) these systems may receive, particularly in agricultural landscapes. We used a 31 year dataset in a reservoir that has experienced agricultural land management change to ask: (1) What are the long-term trends in two hypolimnetic DO metrics (DO concentration in early summer and summer anoxic factor), and (2) what are the key drivers of these metrics?. We used linear regressions to assess temporal trends, and exhaustive variable selection to identify drivers. Potential drivers included metrics of watershed discharge, temperature, stability, and potential productivity (chlorophyll, nonvolatile suspended sediments; NVSS). We found that deepwater early summer DO concentrations decreased, but there was no temporal trend for anoxic factor. Deepwater DO was best predicted by surface temperature, with warming temperatures related to lower DO. However, the top five models performed similarly, and all included a temperature or stratification metric. Higher stability was related to lower DO. For anoxic factor, the top two models performed similarly with stability, surface temperature, and NVSS identified. Anoxic factor increased with higher surface temperature, lower NVSS, and higher stability. Our findings suggest that DO dynamics were linked to previously recognized drivers (for example, temperature), as well as NVSS, a driver that is rarely acknowledged and may reflect land use and management within the watershed. 

ABSTRACT Although nitrogen and phosphorus deficiency of algal blooms have been the focus of substantial attention, organic nutrients can limit algal growth in aquatic systems. Growing evidence indicates thiamine (vitamin B₁) can influence the community of primary producers in marine systems, but comparatively little is known about the effect of thiamine on freshwater algal productivity.We conducted 106 nutrient deficiency experiments with water from 39 Ohio lakes of varying trophic status during the growing seasons (April–October) of 2008–2009. Specifically, we tested the response of phytoplankton biomass (as chlorophylla, chl‐a) relative to controls to added nitrogen (N), phosphorus (P), thiamine (Th), or combinations of N + P and N + P + Th. Next, we compared the chl‐agrowth response of treatment/control to published thresholds based on frequentist approaches and compared the conclusions with Bayesian model results that focused on probability of a response.Although N + P addition was consistently associated with the largest chl‐aresponse, we found evidence of a thiamine influence on phytoplankton growth in some experiments. The Bayesian approach suggested thiamine may become more limiting as the growing season progresses. By late in the growing season, there was an 85% probability of a positive algal growth response to thiamine addition.Understanding the role of thiamine or other overlooked nutrients is not likely to alter the prevailing understanding of nutrient deficiency in freshwater ecosystems. However, we present evidence that freshwater phytoplankton may experience thiamine deficiency and suggest limnologists consider thiamine when exploring resource deficiencies. 

Abstract In lakes, the rates of gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP) are often controlled by resource availability. Herein, we explore how catchment vs. within lake predictors of metabolism compare using data from 16 lakes spanning 39°N to 64°N, a range of inflowing streams, and trophic status. For each lake, we combined stream loads of dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP) with lake DOC, TN, and TP concentrations and high frequencyin situmonitoring of dissolved oxygen. We found that stream load stoichiometry indicated lake stoichiometry for C : N and C : P (r² = 0.74 andr² = 0.84, respectively), but not for N : P (r² = 0.04). As we found a strong positive correlation between TN and TP, we only used TP in our statistical models. For the catchment model, GPP and R were best predicted by DOC load, TP load, and load N : P (R² = 0.85 andR² = 0.82, respectively). For the lake model, GPP and R were best predicted by TP concentrations (R² = 0.86 andR² = 0.67, respectively). The inclusion of N : P in the catchment model, but not the lake model, suggests that both N and P regulate metabolism and that organisms may be responding more strongly to catchment inputs than lake resources. Our models predicted NEP poorly, though it is unclear why. Overall, our work stresses the importance of characterizing lake catchment loads to predict metabolic rates, a result that may be particularly important in catchments experiencing changing hydrologic regimes related to global environmental change. 

Abstract Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. In lakes, pelagic gross primary productivity (GPP) is strongly controlled by inputs of nutrients and dissolved organic matter. Although past studies have developed process models of this nutrient‐color paradigm (NCP), broad empirical tests of these models are scarce. We used data from 58 globally distributed, mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. The model includes three state variables–dissolved phosphorus, terrestrial dissolved organic carbon (DOC), and phytoplankton biomass–and generates realistic predictions for equilibrium rates of pelagic GPP. We calibrated our model using a Bayesian data assimilation technique on a subset of lakes where DOC and total phosphorus (TP) loads were known. We then asked how well the calibrated model performed with a larger set of lakes. Revised parameter estimates from the updated model aligned well with existing literature values. Observed GPP varied nonlinearly with both inflow DOC and TP concentrations in a manner consistent with increasing light limitation as DOC inputs increased and decreasing nutrient limitation as TP inputs increased. Furthermore, across these diverse lake ecosystems, model predictions of GPP were highly correlated with observed values derived from high‐frequency sensor data. The GPP predictions using the updated parameters improved upon previous estimates, expanding the utility of a process model with simplified assumptions for water column mixing. Our analysis provides a model structure that may be broadly useful for understanding current and future patterns in lake primary production.