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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 forecast 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.

 

To date, most research on cyanobacterial blooms in freshwater lakes has focused on the pelagic life stage. However, examining the complete cyanobacterial life cycle—including benthic life stages—may be needed to accurately predict future bloom dynamics. The current expectation, derived from the pelagic life stage, is that blooms will continue to increase due to the warmer temperatures and stronger stratification associated with climate change. However, stratification and mixing have contrasting effects on different life stages: while pelagic cyanobacteria benefit from strong stratification and are adversely affected by mixing, benthic stages can benefit from increased mixing. The net effects of these potentially counteracting processes are not yet known, since most aquatic ecosystem models do not incorporate benthic stages and few empirical studies have tracked the complete life cycle over multiple years. Moreover, for many regions, climate models project both stronger stratification and increased storm-induced mixing in the coming decades; the net effects of those physical processes, even on the pelagic life stage, are not yet understood. We therefore recommend an integrated research agenda to study the dual effects of stratification and mixing on the complete cyanobacterial life cycle—both benthic and pelagic stages—using models, field observations and experiments.

 

Globally, phytoplankton abundance is increasing in lakes as a result of climate change and land‐use change. The relative importance of climate and land‐use drivers has been examined primarily for mesotrophic and eutrophic lakes. However, oligotrophic lakes show different sensitivity to climate and land‐use drivers than mesotrophic and eutrophic lakes, necessitating further exploration of the relative contribution of the two drivers of change to increased phytoplankton abundance. Here, we investigated how air temperature (a driver related to climate change) and nutrient load (a driver related to land‐use and climate change) interact to alter water quality in oligotrophic Lake Sunapee, New Hampshire, USA. We used long‐term data and the one‐dimensional hydrodynamic General Lake Model (GLM) coupled with Aquatic EcoDyanmics (AED) modules to simulate water quality. Over the 31‐year simulation, summer median chlorophyll‐aconcentration was positively associated with summer air temperature, whereas annual maximum chlorophyll‐aconcentration was positively associated with the previous 3 years of external phosphorus load. Scenario testing demonstrated a 2°C increase in air temperature significantly increased summer median chlorophyll‐aconcentration, but not annual maximum chlorophyll‐aconcentration. For both maximum and median chlorophyll‐aconcentration, doubling external nutrient loads of total nitrogen and total phosphorus at the same time, or doubling phosphorus alone, resulted in a significant increase. This study highlights the importance of aligning lake measurements with the ecosystem metrics of interest, as maximum chlorophyll‐aconcentration may be more uniquely sensitive to nutrient load and that typical summer chlorophyll‐aconcentration may increase due to warming alone.

 

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 stimulated 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. 

Abstract. The concentration of oxygen is fundamental to lake water quality and ecosystem functioning through its control over habitat availability for organisms, redox reactions, and recycling of organic material. In many eutrophic lakes, oxygen depletion in the bottom layer (hypolimnion) occurs annually during summer stratification. The temporal and spatial extent of summer hypolimnetic anoxia is determined by interactions between the lake and its external drivers (e.g., catchment characteristics, nutrient loads, meteorology) as well as internal feedback mechanisms (e.g., organic matter recycling, phytoplankton blooms). How these drivers interact to control the evolution of lake anoxia over decadal timescales will determine, in part, the future lake water quality. In this study, we used a vertical one-dimensional hydrodynamic–ecological model (GLM-AED2) coupled with a calibrated hydrological catchment model (PIHM-Lake) to simulate the thermal and water quality dynamics of the eutrophic Lake Mendota (USA) over a 37 year period. The calibration and validation of the lake model consisted of a global sensitivity evaluation as well as the application of an optimization algorithm to improve the fit between observed and simulated data. We calculated stability indices (Schmidt stability, Birgean work, stored internal heat), identified spring mixing and summer stratification periods, and quantified the energy required for stratification and mixing. To qualify which external and internal factors were most important in driving the interannual variation in summer anoxia, we applied a random-forest classifier and multiple linear regressions to modeled ecosystem variables (e.g., stratification onset and offset, ice duration, gross primary production). Lake Mendota exhibited prolonged hypolimnetic anoxia each summer, lasting between 50–60 d. The summer heat budget, the timing of thermal stratification, and the gross primary production in the epilimnion prior to summer stratification were the most important predictors of the spatial and temporal extent of summer anoxia periods in Lake Mendota. Interannual variability in anoxia was largely driven by physical factors: earlier onset of thermal stratification in combination with a higher vertical stability strongly affected the duration and spatial extent of summer anoxia. A measured step change upward in summer anoxia in 2010 was unexplained by the GLM-AED2 model. Although the cause remains unknown, possible factors include invasion by the predacious zooplankton Bythotrephes longimanus. As the heat budget depended primarily on external meteorological conditions, the spatial and temporal extent of summer anoxia in Lake Mendota is likely to increase in the near future as a result of projected climate change in the region.