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Browsing by Author "Cottingham, Kathryn L."

Now showing 1 - 5 of 5
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    Cross-scale Perspectives: Integrating Long-term and High-frequency Data into Our Understanding of Communities and Ecosystems
    Carey, Cayelan C.; Cottingham, Kathryn L. (Ecological Society of America, 2016-01)
    Ecologists are amassing extensive data sets that include both long-term records documenting trends and variability in natural systems on inter-annual to decadal time scales and sensor-based measurements on minute to subhourly scales for extended periods (Hampton et al. 2013). Together, these long- term and high- frequency data are contributing to our ecological understanding. Although there have been several previous ESA sessions that have explored the insights provided by either long-term data or high frequency data, to our knowledge this organized oral session provided one of the first opportunities to synthesize the lessons learned from leveraging both long-term data and high-frequency approaches.
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    Cyanobacteria as biological drivers of lake nitrogen and phosphorus cycling
    Cottingham, Kathryn L.; Ewing, Holly A.; Greer, Meredith L.; Carey, Cayelan C.; Weathers, Kathleen C. (Ecological Society of America, 2015-01)
    Here we draw attention to the potential for pelagic bloom-forming cyanobacteria to have substantial effects on nutrient cycling and ecosystem resilience across a wide range of lakes. Specifically, we hypothesize that cyanobacterial blooms can influence lake nutrient cycling, resilience, and regime shifts by tapping into pools of nitrogen (N) and phosphorus (P) not usually accessible to phytoplankton. The ability of many cyanobacterial taxa to fix dissolved N-2 gas is a well-known potential source of N, but some taxa can also access pools of P in sediments and bottom waters. Both of these nutrients can be released to the water column via leakage or mortality, thereby increasing nutrient availability for other phytoplankton and microbes. Moreover, cyanobacterial blooms are not restricted to high nutrient (eutrophic) lakes: blooms also occur in lakes with low nutrient concentrations, suggesting that changes in nutrient cycling and ecosystem resilience mediated by cyanobacteria could affect lakes across a gradient of nutrient concentrations. We used a simple model of coupled N and P cycles to explore the effects of cyanobacteria on nutrient dynamics and resilience. Consistent with our hypothesis, parameters reflecting cyanobacterial modification of N and P cycling alter the number, location, and/or stability of model equilibria. In particular, the model demonstrates that blooms of cyanobacteria in low-nutrient conditions can facilitate a shift to the high-nutrient state by reducing the resilience of the low-nutrient state. This suggests that cyanobacterial blooms warrant attention as potential drivers of the transition from a low-nutrient, clear-water regime to a high-nutrient, turbid-water regime, a prediction of particular concern given that such blooms are reported to be increasing in many regions of the world due in part to global climate change.
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    The cyanobacterium Gloeotrichia echinulata increases the stability and network complexity of phytoplankton communities
    Carey, Cayelan C.; Brown, Bryan L.; Cottingham, Kathryn L. (Wiley-Blackwell, 2017-07-07)
    Changes in the abundance of a taxon can have large effects on communities, particularly if that taxon is a strong interactor. These changes may arise as a consequence of environmental change, recruitment from dormant stages, or quirks of population dynamics, and have effects that ripple through a community interaction network. We hypothesized that cyanobacteria, which are increasing in many freshwater lakes globally, may be strong interactors because they can exert large and persistent effects on the biomass and composition of other phytoplankton. To test this hypothesis, we evaluated how the phytoplankton community responded to different densities of Gloeotrichia echinulata, a large colonial cyanobacterium increasingly observed in low‐nutrient lakes in northeastern North America, in an in situ mesocosm experiment. We observed that many phytoplankton taxa, especially diatoms and green algae, responded primarily to increased nutrient availability (a result of Gloeotrichia's nitrogen fixation and translocation of phosphorus from the sediments), while a few taxa (two euglenophytes, one dinoflagellate, and one cyanobacterium) responded to both the direct and indirect effects of Gloeotrichia. Surprisingly, Gloeotrichia reduced the compositional variability of the phytoplankton community relative to the non‐Gloeotrichia control treatment; there was no effect on the aggregate temporal variability of total non‐Gloeotrichia biovolume. Moreover, experimentally increased densities of Gloeotrichia coincided with increasing complexity of the phytoplankton community in network analyses of taxon co‐occurrences, as indicated by significantly greater network density and transitivity and shorter path lengths. Taken together, these findings suggest that Gloeotrichia may be a strongly interacting species in low‐nutrient lakes, with the potential to increase the resilience of phytoplankton communities to future disturbance by increasing compositional stability and network complexity.
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    Differential Responses of Maximum Versus Median Chlorophyll‐a to Air Temperature and Nutrient Loads in an Oligotrophic Lake Over 31 Years
    Ward, Nicole K.; Steele, Bethel G.; Weathers, Kathleen C.; Cottingham, Kathryn L.; Ewing, Holly A.; Hanson, Paul C.; Carey, Cayelan C. (AGU, 2020-05-28)
    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‐a concentration was positively associated with summer air temperature, whereas annual maximum chlorophyll‐a concentration 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‐a concentration, but not annual maximum chlorophyll‐a concentration. For both maximum and median chlorophyll‐a concentration, 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‐a concentration may be more uniquely sensitive to nutrient load and that typical summer chlorophyll‐a concentration may increase due to warming alone.
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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.