Browsing by Author "McCullough, Ian M."

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    Dynamic modeling of organic carbon fates in lake ecosystems
    McCullough, Ian M.; Dugan, Hilary A.; Farrell, Kaitlin J.; Morales-Williams, Ana M.; Ouyang, Zutao; Roberts, Derek C.; Scordo, Facundo; Bartlett, Sarah L.; Burke, Samantha M.; Doubek, Jonathan P.; Krivak-Tetley, Flora E.; Skaff, Nicholas K.; Summers, Jamie C.; Weathers, Kathleen C.; Hanson, Paul C. (2018-10-24)
    Lakes are active processors of organic carbon (OC) and play important roles in landscape and global carbon cycling. Allochthonous OC loads from the landscape, along with autochthonous OC loads from primary production, are mineralized in lakes, buried in lake sediments, and exported via surface or groundwater outflows. Although these processes provide a basis for a conceptual understanding of lake OC budgets, few studies have integrated these fluxes under a dynamic modeling framework to examine their interactions and relative magnitudes. We developed a simple, dynamic mass balance model for OC, and applied the model to a set of five lakes. We examined the relative magnitudes of OC fluxes and found that long-term (> 10 year) lake OC dynamics were predominantly driven by allochthonous loads in four of the five lakes, underscoring the importance of terrestrially-derived OC in northern lake ecosystems. Our model highlighted seasonal patterns in lake OC budgets, with increasing water temperatures and lake productivity throughout the growing season corresponding to a transition from burial- to respiration-dominated OC fates. Ratios of respiration to burial, however, were also mediated by the source (autochthonous vs. allochthonous) of total OC loads. Autochthonous OC is more readily respired and may therefore proportionally reduce burial under a warming climate, but allochthonous OC may increase burial due to changes in precipitation. The ratios of autochthonous to allochthonous inputs and respiration to burial demonstrate the importance of dynamic models for examining both the seasonal and inter-annual roles of lakes in landscape and global carbon cycling, particularly in a global change context. Finally, we highlighted critical data needs, which include surface water DOC observations in paired tributary and lake systems, measurements of OC burial rates, groundwater input volume and DOC, and budgets of particulate OC.
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    Salting our freshwater lakes
    Dugan, Hilary A.; Bartlett, Sarah L.; Burke, Samantha M.; Doubek, Jonathan P.; Krivak-Tetley, Flora E.; Skaff, Nicholas K.; Summers, Jamie C.; Farrell, Kaitlin J.; McCullough, Ian M.; Morales-Williams, Ana M.; Roberts, Derek C.; Ouyang, Zutao; Scordo, Facundo; Hanson, Paul C.; Weathers, Kathleen C. (NAS, 2017-03-08)
    The highest densities of lakes on Earth are in north temperate ecosystems, where increasing urbanization and associated chloride runoff can salinize freshwaters and threaten lake water quality and the many ecosystem services lakes provide. However, the extent to which lake salinity may be changing at broad spatial scales remains unknown, leading us to first identify spatial patterns and then investigate the drivers of these patterns. Significant decadal trends in lake salinization were identified using a dataset of long-term chloride concentrations from 371 North American lakes. Landscape and climate metrics calculated for each site demonstrated that impervious land cover was a strong predictor of chloride trends in Northeast and Midwest North American lakes. As little as 1% impervious land cover surrounding a lake increased the likelihood of long-term salinization. Considering that 27% of large lakes in the United States have >1% impervious land cover around their perimeters, the potential for steady and long-term salinization of these aquatic systems is high. This study predicts that many lakes will exceed the aquatic life threshold criterion for chronic chloride exposure (230 mg L⁻¹), stipulated by the US Environmental Protection Agency (EPA), in the next 50 y if current trends continue.
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    Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density
    Lofton, Mary E.; Brentrup, Jennifer A.; Beck, Whitney S.; Zwart, Jacob A.; Bhattacharya, Ruchi; Brighenti, Ludmila S.; Burnet, Sarah H.; McCullough, Ian M.; Steele, Bethel G.; Carey, Cayelan C.; Cottingham, Kathryn L.; Dietze, Michael C.; Ewing, Holly A.; Weathers, Kathleen C.; LaDeau, Shannon L. (Wiley, 2022-03)
    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 of G. echinulata densities. 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.