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Key Points We observed post‐wildfire increases in nutrients, dissolved organic carbon, sediments, and acidity and reduced water clarity in lakes Water quality responses were often persistent or cumulative throughout the summer, especially for lakes with tributaries from burned areas High‐severity and shoreline burns resulted in a nearly two‐fold increase in total phosphorus concentration compared to control lakes

Abstract Lake ecosystems, as integrators of watershed and climate stressors, are sentinels of change. However, there is an inherent time-lag between stressors and whole-lake response. Aquatic metabolism, including gross primary production (GPP) and respiration (R), of stream–lake transitional zones may bridge the time-lag of lake response to allochthonous inputs. In this study, we used high-frequency dissolved oxygen data and inverse modeling to estimate daily rates of summer epilimnetic GPP and R in a nutrient-limited oligotrophic lake at two littoral sites located near different major inflows and at a pelagic site. We examined the relative importance of stream variables in comparison to meteorological and in-lake predictors of GPP and R. One of the inflow streams was substantially warmer than the other and primarily entered the lake’s epilimnion, whereas the colder stream primarily mixed into the metalimnion or hypolimnion. Maximum GPP and R rates were 0.2–2.5 mg O 2 L −1  day −1 (9–670%) higher at littoral sites than the pelagic site. Ensemble machine learning analyses revealed that > 30% of variability in daily littoral zone GPP and R was attributable to stream depth and stream–lake transitional zone mixing metrics. The warm-stream inflow likely stimulated littoral GPP and R, while the cold-stream inflow only stimulatedmore »littoral zone GPP and R when mixing with the epilimnion. The higher GPP and R observed near inflows in our study may provide a sentinel-of-the-sentinel signal, bridging the time-lag between stream inputs and in-lake processing, enabling an earlier indication of whole-lake response to upstream stressors.« less

Abstract. Outgassing of carbon dioxide (CO2) from freshwater ecosystems comprises 12 %–25 % of the total carbon flux from soils and bedrock. This CO2 is largely derived from both biodegradation and photodegradation of terrestrial dissolved organic carbon (DOC) entering lakes from wetlands and soils in the watersheds of lakes. In spite of the significance of these two processes in regulating rates of CO2 outgassing, their relative importance remains poorly understood in lake ecosystems. In this study, we used groundwater from the watersheds of one subtropical and three temperate lakes of differing trophic status to simulate the effects of increases in terrestrial DOC from storm events. We assessed the relative importance of biodegradation and photodegradation in oxidizing DOC to CO2. We measured changes in DOC concentration, colored dissolved organic carbon (specificultraviolet absorbance – SUVA320; spectral slope ratio – Sr), dissolved oxygen, and dissolved inorganic carbon (DIC) in short-term experiments from May–August 2016. In all lakes, photodegradationled to larger changes in DOC and DIC concentrations and opticalcharacteristics than biodegradation. A descriptive discriminant analysisshowed that, in brown-water lakes, photodegradation led to the largestdeclines in DOC concentration. In these brown-water systems, ∼ 30 % of the DOC was processed by sunlight, and a minimum of 1 % was photomineralized. Inmore »addition to documenting the importance of photodegradation in lakes, these results also highlight how lakes in the future may respond to changes in DOC inputs.« less

Lakes process both terrestrial and aquatic organic matter, and the relative contribution from each source is often measured via ecosystem metabolism and terrestrial resource use in the food web (i.e., consumer allochthony). Yet, ecosystem metabolism and consumer allochthony are rarely considered together, despite possible interactions and potential for them to respond to the same lake characteristics. In this study, we compiled global datasets of lake gross primary production (GPP), ecosystem respiration (ER), and zooplankton allochthony to compare the strength and shape of relationships with physicochemical characteristics across a broad set of lakes. GPP was positively related to total phosphorus (TP) in lakes with intermediate TP concentrations (11–75 μg L⁻¹) and was highest in lakes with intermediate dissolved organic carbon (DOC) concentrations. While ER and GPP were strongly positively correlated, decoupling occurred at high DOC concentrations. Lastly, allochthony had a unimodal relationship with TP and related variably to DOC. By integrating metabolism and allochthony, we identified similar change points in GPP and zooplankton allochthony at intermediate DOC (4.5–10 mg L⁻¹) and TP (8–20 μg L⁻¹) concentrations, indicating that allochthony and GPP may be coupled and inversely related. The ratio of DOC:nutrients also helped to identify conditions where lake organic matter processing responded more to autochthonous ormore »allochthonous organic matter sources. As lakes globally face eutrophication and browning, predicting how lake organic matter processing will respond requires an updated paradigm that incorporates nonlinear dynamics and interactions.

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Near‐term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state‐space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin‐producing cyanobacterium,Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near‐term (1‐ to 4‐week) forecasts ofG. echinulatadensities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4‐week forecastmore »horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1‐week‐ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long‐term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

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