Browsing by Author "Lofton, Mary E."

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  • A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills
    Lofton, Mary E.; Moore, Tadhg N.; Woelmer, Whitney M.; Thomas, R. Quinn; Carey, Cayelan C. (Oxford University Press, 2024-10-10)
    Data science skills (e.g., analyzing, modeling, and visualizing large data sets) are increasingly needed by undergraduates in the life sciences. However, a lack of both student and instructor confidence in data science skills presents a barrier to their inclusion in undergraduate curricula. To reduce this barrier, we developed four teaching modules in the Macrosystems EDDIE (for environmental data-driven inquiry and exploration) program to introduce undergraduate students and instructors to ecological forecasting, an emerging subdiscipline that integrates multiple data science skills. Ecological forecasting aims to improve natural resource management by providing future predictions of ecosystems with uncertainty. We assessed module efficacy with 596 students and 26 instructors over 3 years and found that module completion increased students’ confidence in their understanding of ecological forecasting and instructors’ likelihood to work with long-term, high-frequency sensor network data. Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates.
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    Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting
    Carey, Cayelan C.; Woelmer, Whitney M.; Lofton, Mary E.; Figueiredo, Renato J.; Bookout, Bethany J.; Corrigan, Rachel S.; Daneshmand, Vahid; Hounshell, Alexandria G.; Howard, Dexter W.; Lewis, Abigail S. L.; McClure, Ryan P.; Wander, Heather L.; Ward, Nicole K.; Thomas, R. Quinn (2021-01-18)
    Near-term, iterative ecological forecasts with quantified uncertainty have great potential for improving lake and reservoir management. For example, if managers received a forecast indicating a high likelihood of impending impairment, they could make decisions today to prevent or mitigate poor water quality in the future. Increasing the number of automated, real-time freshwater forecasts used for management requires integrating interdisciplinary expertise to develop a framework that seamlessly links data, models, and cyberinfrastructure, as well as collaborations with managers to ensure that forecasts are embedded into decision-making workflows. The goal of this study is to advance the implementation of near-term, iterative ecological forecasts for freshwater management. We first provide an overview of FLARE (Forecasting Lake And Reservoir Ecosystems), a forecasting framework we developed and applied to a drinking water reservoir to assist water quality management, as a potential open-source option for interested users. We used FLARE to develop scenario forecasts simulating different water quality interventions to inform manager decision-making. Second, we share lessons learned from our experience developing and running FLARE over 2 years to inform other forecasting projects. We specifically focus on how to develop, implement, and maintain a forecasting system used for active management. Our goal is to break down the barriers to forecasting for freshwater researchers, with the aim of improving lake and reservoir management globally.
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    Anoxia decreases the magnitude of the carbon, nitrogen, and phosphorus sink in freshwaters
    Carey, Cayelan C.; Hanson, Paul C.; Thomas, R. Quinn; Gerling, Alexandra B.; Hounshell, Alexandria G.; Lewis, Abigail S.; Lofton, Mary E.; McClure, Ryan P.; Wander, Heather L.; Woelmer, Whitney M.; Niederlehner, B.R.; Schreiber, Madeline E. (Wiley, 2022-05-05)
    Oxygen availability is decreasing in many lakes and reservoirs worldwide, raising the urgency for understanding how anoxia (low oxygen) affects coupled biogeochemical cycling, which has major implications for water quality, food webs, and ecosystem functioning. Although the increasing magnitude and prevalence of anoxia has been documented in freshwaters globally, the challenges of disentangling oxygen and temperature responses have hindered assessment of the effects of anoxia on carbon, nitrogen, and phosphorus concentrations, stoichiometry (chemical ratios), and retention in freshwaters. The consequences of anoxia are likely severe and may be irreversible, necessitating ecosystem-scale experimental investigation of decreasing freshwater oxygen availability. To address this gap, we devised and conducted REDOX (the Reservoir Ecosystem Dynamic Oxygenation eXperiment), an unprecedented, 7-year experiment in which we manipulated and modeled bottom-water (hypolimnetic) oxygen availability at the whole-ecosystem scale in a eutrophic reservoir. Seven years of data reveal that anoxia significantly increased hypolimnetic carbon, nitrogen, and phosphorus concentrations and altered elemental stoichiometry by factors of 2–5× relative to oxic periods. Importantly, prolonged summer anoxia increased nitrogen export from the reservoir by six-fold and changed the reservoir from a net sink to a net source of phosphorus and organic carbon downstream. While low oxygen in freshwaters is thought of as a response to land use and climate change, results from REDOX demonstrate that low oxygen can also be a driver of major changes to freshwater biogeochemical cycling, which may serve as an intensifying feedback that increases anoxia in downstream waterbodies. Consequently, as climate and land use change continue to increase the prevalence of anoxia in lakes and reservoirs globally, it is likely that anoxia will have major effects on freshwater carbon, nitrogen, and phosphorus budgets as well as water quality and ecosystem functioning.
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    The effects of hypolimnetic anoxia on the diel vertical migration of freshwater crustacean zooplankton
    Doubek, Jonathan P.; Campbell, Kylie L.; Doubek, Kaitlyn M.; Hamre, Kathleen D.; Lofton, Mary E.; McClure, Ryan P.; Ward, Nicole K.; Carey, Cayelan C. (Ecological Society of America, 2018-07)
    Lakes and reservoirs worldwide are increasingly experiencing depletion of dissolved oxygen (anoxia) in their bottom waters (the hypolimnion) because of climate change and eutrophication, which is altering the dynamics of many freshwater ecological communities. Hypolimnetic anoxia may substantially alter the daily migration and distribution of zooplankton, the dominant grazers of phytoplankton in aquatic food webs. In waterbodies with oxic hypolimnia, zooplankton exhibit diel vertical migration (DVM), in which they migrate to the dark hypolimnion during the day to escape fish predation or ultraviolet (UV) radiation damage in the well-lit surface waters (the epilimnion). However, due to the physiologically stressful conditions of anoxic hypolimnia, we hypothesized that zooplankton may be forced to remain in the epilimnion during daylight, trading oxic stress for increased predation risk or UV radiation damage. To examine how anoxia impacts zooplankton vertical migration, distribution, biomass, and community composition over day-night periods, we conducted multiple diel sampling campaigns on reservoirs that spanned oxic, hypoxic, and anoxic hypolimnetic conditions. In addition, we sampled the same reservoirs fortnightly during the daytime to examine the vertical position of zooplankton throughout the summer stratified season. Under anoxic conditions, most zooplankton taxa were predominantly found in the epil-imnion during the day and night, did not exhibit DVM, and had lower seasonal biomass than in reservoirs with oxic hypolimnia. Only the phantom midge larva, Chaoborus spp., was consistently anoxia-tolerant. Consequently, our results suggest that hypolimnetic anoxia may alter zooplankton migration, biomass, and behavior, which may in turn exacerbate water quality degradation due to the critical role zooplankton play in freshwater ecosystems.
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    Embedding communication concepts in forecasting training increases students' understanding of ecological uncertainty
    Woelmer, Whitney M.; Moore, Tadhg N.; Lofton, Mary E.; Thomas, R. Quinn; Carey, Cayelan C. (Wiley, 2023-08)
    Communicating and interpreting uncertainty in ecological model predictions is notoriously challenging, motivating the need for new educational tools, which introduce ecology students to core concepts in uncertainty communication. Ecological forecasting, an emerging approach to estimate future states of ecological systems with uncertainty, provides a relevant and engaging framework for introducing uncertainty communication to undergraduate students, as forecasts can be used as decision support tools for addressing real-world ecological problems and are inherently uncertain. To provide critical training on uncertainty communication and introduce undergraduate students to the use of ecological forecasts for guiding decision-making, we developed a hands-on teaching module within the Macrosystems Environmental Data-Driven Inquiry and Exploration (EDDIE; MacrosystemsEDDIE.org) educational program. Our module used an active learning approach by embedding forecasting activities in an R Shiny application to engage ecology students in introductory data science, ecological modeling, and forecasting concepts without needing advanced computational or programming skills. Pre- and post-module assessment data from more than 250 undergraduate students enrolled in ecology, freshwater ecology, and zoology courses indicate that the module significantly increased students' ability to interpret forecast visualizations with uncertainty, identify different ways to communicate forecast uncertainty for diverse users, and correctly define ecological forecasting terms. Specifically, students were more likely to describe visual, numeric, and probabilistic methods of uncertainty communication following module completion. Students were also able to identify more benefits of ecological forecasting following module completion, with the key benefits of using forecasts for prediction and decision-making most commonly described. These results show promise for introducing ecological model uncertainty, data visualizations, and forecasting into undergraduate ecology curricula via software-based learning, which can increase students' ability to engage and understand complex ecological concepts.
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    Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward
    Carey, Cayelan C.; Ward, Nicole K.; Farrell, Kaitlin J.; Lofton, Mary E.; Krinos, Arianna, I.; McClure, Ryan P.; Subratie, Kensworth C.; Figueiredo, Renato J.; Doubek, Jonathan P.; Hanson, Paul C.; Papadopoulos, Philip; Arzberger, Peter (Ecological Society of America, 2019-05)
    In the era of big data, ecologists are increasingly relying on computational approaches and tools to answer existing questions and pose new research questions. These include both software applications (e.g., simulation models, databases and machine learning algorithms) and hardware systems (e.g., wireless sensor networks, supercomputing, drones and satellites), motivating the need for greater collaboration between computer scientists and ecologists. Here, we outline some synergistic opportunities for scientists in both disciplines that can be gained by building collaborations between the computer science and ecology research communities, with a focus on the benefits to ecology specifically. We also identify past contributions of computer science to ecology, including high-frequency environmental sensor technology, advanced supercomputing capacity for ecological modeling, databases for long-term and high-frequency datasets, and software programs for ecological analyses, to anticipate future potential contributions. These examples highlight the power and potential for further integration of computer science technology and ideas into the ecological research community. Finally, we translate our own experiences working together as a team of computer scientists and ecologists over the past decade into a conceptual framework with recommendations for supporting productive collaborations at the interface of the two disciplines. We specifically focus on how to apply best practices of team science for bridging computer science and ecology, which we advocate will substantially benefit ecology long-term.
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    Experimental thermocline deepening alters vertical distribution and community structure of phytoplankton in a 4-year whole-reservoir manipulation
    Lofton, Mary E.; Howard, Dexter W.; McClure, Ryan P.; Wander, Heather L.; Woelmer, Whitney M.; Hounshell, Alexandria G.; Lewis, Abigail S. L.; Carey, Cayelan C. (Wiley, 2022-11)
    Freshwater phytoplankton communities are currently experiencing multiple global change stressors, including increasing frequency and intensity of storms. An important mechanism by which storms affect lake and reservoir phytoplankton is by altering the water column's thermal structure (e.g., changes to thermocline depth). However, little is known about the effects of intermittent thermocline deepening on phytoplankton community vertical distribution and composition or the consistency of phytoplankton responses to varying frequency of these disturbances over multiple years. We conducted whole-ecosystem thermocline deepening manipulations in a small reservoir. We used an epilimnetic mixing system to experimentally deepen the thermocline via five short (24-72 hr) mixing events across two summers, inducing potential responses to storms. For comparison, we did not manipulate thermocline depth in two succeeding summers. We collected weekly depth profiles of water temperature, light, nutrients, and phytoplankton biomass as well as bottle samples to assess phytoplankton community composition. We then used time-series analysis and multivariate ordination to assess the effects of intermittent thermocline deepening due to both our experimental manipulations and naturally occurring storms on phytoplankton community structure. We observed inter-annual and intra-annual variability in phytoplankton community response to thermocline deepening. We found that peak phytoplankton biomass was significantly deeper in years with a higher frequency of thermocline deepening events (i.e., years with both manipulations and natural storms) due to altered thermal stratification and more variable depth distributions of soluble reactive phosphorus. Furthermore, we found that the depth of peak phytoplankton biomass was linked to phytoplankton community composition, with certain taxa being associated with deep or shallow biomass peaks, often according to functional traits such as optimal growth temperature, mixotrophy, and low-light tolerance. For example, Cryptomonas taxa, which are low-light tolerant and mixotrophic, were associated with deep peaks, while the cyanobacterial taxon Dolichospermum was associated with shallow peaks. Our results demonstrate that abrupt thermocline deepening due to water column mixing affects both phytoplankton depth distribution and community structure via alteration of physical and chemical gradients. In addition, our work supports previous research that phytoplankton depth distributions are related to phytoplankton community composition at inter-annual and intra-annual timescales. Variability in the inter-annual and intra-annual responses of phytoplankton to abrupt thermocline deepening indicates that antecedent conditions and the seasonal timing of surface water mixing may mediate these responses. Our findings emphasise that phytoplankton depth distributions are sensitive to global change stressors and effects on depth distributions should be taken into account when predicting phytoplankton responses to increased storms under global change.
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    Hacking Limnology Workshops and DSOS23: Growing a Workforce for the Nexus of Data Science, Open Science, and the Aquatic Sciences
    Meyer, M. F.; Harlan, M. E.; Hensley, R. T.; Zhan, Q.; Börekçi, N. S.; Bucak, T.; Cramer, A. N.; Feldbauer, J.; Ladwig, R.; Mesman, J. P.; Oleksy, I. A.; Pilla, R. M.; Zwart, J. A.; Calamita, E.; Gubbins, N. J.; Lofton, Mary E.; Maciel, D. A.; Marzolf, N. S.; Olsson, F.; Thellman, A. N.; Thomas, R. Quinn; Vlah, M. J. (Wiley, 2023-10-20)
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    Hypolimnetic Hypoxia Increases the Biomass Variability and Compositional Variability of Crustacean Zooplankton Communities
    Doubek, Jonathan P.; Campbell, Kylie L.; Lofton, Mary E.; McClure, Ryan P.; Carey, Cayelan C. (MDPI, 2019-10-19)
    In freshwater lakes and reservoirs, climate change and eutrophication are increasing the occurrence of low-dissolved oxygen concentrations (hypoxia), which has the potential to alter the variability of zooplankton seasonal dynamics. We sampled zooplankton and physical, chemical and biological variables (e.g., temperature, dissolved oxygen, and chlorophyll a) in four reservoirs during the summer stratified period for three consecutive years. The hypolimnion (bottom waters) of two reservoirs remained oxic throughout the entire stratified period, whereas the hypolimnion of the other two reservoirs became hypoxic during the stratified period. Biomass variability (measured as the coefficient of the variation of zooplankton biomass) and compositional variability (measured as the community composition of zooplankton) of crustacean zooplankton communities were similar throughout the summer in the oxic reservoirs; however, biomass variability and compositional variability significantly increased after the onset of hypoxia in the two seasonally-hypoxic reservoirs. The increase in biomass variability in the seasonally-hypoxic reservoirs was driven largely by an increase in the variability of copepod biomass, while the increase in compositional variability was driven by increased variability in the dominance (proportion of total crustacean zooplankton biomass) of copepod taxa. Our results suggest that hypoxia may increase the seasonal variability of crustacean zooplankton communities.
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    The importance of time and space in biogeochemical heterogeneity and processing along the reservoir ecosystem continuum
    Woelmer, Whitney M.; Hounshell, Alexandria G.; Lofton, Mary E.; Wander, Heather L.; Lewis, Abigail S. L.; Scott, Durelle T.; Carey, Cayelan C. (Springer, 2023-04)
    Globally significant quantities of carbon (C), nitrogen (N), and phosphorus (P) enter freshwater reservoirs each year. These inputs can be buried in sediments, respired, taken up by organisms, emitted to the atmosphere, or exported downstream. While much is known about reservoir-scale biogeochemical processing, less is known about spatial and temporal variability of biogeochemistry within a reservoir along the continuum from inflowing streams to the dam. To address this gap, we examined longitudinal variability in surface water biogeochemistry (C, N, and P) in two small reservoirs throughout a thermally stratified season. We sampled total and dissolved fractions of C, N, and P, as well as chlorophyll-a from each reservoir's major inflows to the dam. We found that heterogeneity in biogeochemical concentrations was greater over time than space. However, dissolved nutrient and organic carbon concentrations had high site-to-site variability within both reservoirs, potentially as a result of shifting biological activity or environmental conditions. When considering spatially explicit processing, we found that certain locations within the reservoir, most often the stream-reservoir interface, acted as "hotspots" of change in biogeochemical concentrations. Our study suggests that spatially explicit metrics of biogeochemical processing could help constrain the role of reservoirs in C, N, and P cycles in the landscape. Ultimately, our results highlight that biogeochemical heterogeneity in small reservoirs may be more variable over time than space, and that some sites within reservoirs play critically important roles in whole-ecosystem biogeochemical processing.
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    Increased adoption of best practices in ecological forecasting enables comparisons of forecastability
    Lewis, Abigail S. L.; Woelmer, Whitney M.; Wander, Heather L.; Howard, Dexter W.; Smith, John W.; McClure, Ryan P.; Lofton, Mary E.; Hammond, Nicholas W.; Corrigan, Rachel S.; Thomas, R. Quinn; Carey, Cayelan C. (Wiley, 2021-12-14)
    Near-term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross-ecosystem analysis of near-term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near-term (≤10-yr forecast horizon) ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near-term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near-term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.
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    Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates
    McClure, Ryan P.; Thomas, R. Quinn; Lofton, Mary E.; Woelmer, Whitney M.; Carey, Cayelan C. (Frontiers, 2021-12)
    Near-term, ecological forecasting with iterative model refitting and uncertainty partitioning has great promise for improving our understanding of ecological processes and the predictive skill of ecological models, but to date has been infrequently applied to predict biogeochemical fluxes. Bubble fluxes of methane (CH4) from aquatic sediments to the atmosphere (ebullition) dominate freshwater greenhouse gas emissions, but it remains unknown how best to make robust near-term CH4 ebullition predictions using models. Near-term forecasting workflows have the potential to address several current challenges in predicting CH4 ebullition rates, including: development of models that can be applied across time horizons and ecosystems, identification of the timescales for which predictions can provide useful information, and quantification of uncertainty in predictions. To assess the capacity of near-term, iterative forecasting workflows to improve ebullition rate predictions, we developed and tested a near-term, iterative forecasting workflow of CH4 ebullition rates in a small eutrophic reservoir throughout one open-water period. The workflow included the repeated updating of a CH4 ebullition forecast model over time with newly-collected data via iterative model refitting. We compared the CH4 forecasts from our workflow to both alternative forecasts generated without iterative model refitting and a persistence null model. Our forecasts with iterative model refitting estimated CH4 ebullition rates up to 2 weeks into the future [RMSE at 1-week ahead = 0.53 and 0.48 loge(mg CH4 m−2 d−1) at 2-week ahead horizons]. Forecasts with iterative model refitting outperformed forecasts without refitting and the persistence null model at both 1- and 2-week forecast horizons. Driver uncertainty and model process uncertainty contributed the most to total forecast uncertainty, suggesting that future workflow improvements should focus on improved mechanistic understanding of CH4 models and drivers. Altogether, our study suggests that iterative forecasting improves week-to-week CH4 ebullition predictions, provides insight into predictability of ebullition rates into the future, and identifies which sources of uncertainty are the most important contributors to the total uncertainty in CH4 ebullition predictions.
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    Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability
    Woelmer, Whitney M.; Thomas, R. Quinn; Lofton, Mary E.; McClure, Ryan P.; Wander, Heather L.; Carey, Cayelan C. (Wiley, 2022-10)
    As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1–14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.
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    Predicting phytoplankton community dynamics:  understanding water quality responses to global change
    Lofton, Mary E. (Virginia Tech, 2021-07-01)
    A fundamental focus in ecology is understanding interactions between environmental heterogeneity and ecological community structure, both of which are currently undergoing unprecedented alterations due to global change. In particular, many freshwater phytoplankton communities are experiencing multiple global change stressors, altering phytoplankton community composition, biomass, and spatial distribution. I used multiple approaches to characterize the interactions between spatial distribution and community structure of phytoplankton and quantify uncertainty in predictions of phytoplankton temporal dynamics. First, I analyzed data from 51 lakes to determine the environmental drivers of phytoplankton vertical distributions across the water column for different phytoplankton groups. I show that the relative importance of environmental drivers varies according to the functional traits of each phytoplankton group. Second, I conducted whole-ecosystem experiments in a reservoir to assess phytoplankton responses to surface water mixing events, which may become more prevalent as storms increase under global change. My results demonstrate that aggregated phytoplankton biomass has inconsistent responses to mixing over the short term, but responses of morphology-based functional groups of phytoplankton to mixing are more predictable. Third, I conducted a long-term whole-ecosystem experiment to assess phytoplankton responses to changes in water column thermal gradients which are predicted to increasingly occur under global change. I found that phytoplankton depth distributions responded similarly to thermal gradient disturbance over multiple years, and changes in depth distributions were related to changes in community composition. Fourth, I produced weekly hindcasts of phytoplankton density in a lake for two years to determine the dominant sources of uncertainty in phytoplankton density predictions. I found that better estimation of current phytoplankton density improved representation of error in phytoplankton models, and incorporation of additional life history stages to model structure may improve phytoplankton predictions. Overall, my dissertation chapters demonstrate that the vertical distribution and community structure of phytoplankton are linked, and that the interaction of phytoplankton community structure with environmental heterogeneity is more predictable over longer-term (e.g., months to years) than shorter-term (e.g., days to weeks) scales. My research emphasizes that consideration of phytoplankton community dynamics and the uncertainty associated with phytoplankton predictions are needed for freshwater management under global change.
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    Progress and opportunities in advancing near-term forecasting of freshwater quality
    Lofton, Mary E.; Howard, Dexter W.; Thomas, R. Quinn; Carey, Cayelan C. (Wiley, 2023-04)
    Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water-related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in the early stages of development (i.e., non-operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end-user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.
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    Salt dilution and flushing dynamics of an impaired agricultural−urban stream
    Lakoba, Vasiliy T.; Wind, Lauren L.; DeVilbiss, Stephen; Lofton, Mary E.; Bretz, Kristen; Weinheimer, Alaina R.; Moore, Chloe; Baciocco, Colin; Hotchkiss, Erin R.; Hession, W. Cully (2020-11-09)
    Anthropogenic freshwater salinization is increasing with global change. Rising freshwater salinity threatens ecosystem biodiversity, health, and services via toxicity to organisms and mobilization of nutrients and metals. Brining roads is one major source of freshwater salinization that continues to grow with rising urbanization. While the detrimental effects of salinization in streams are well-documented, high-frequency, temporal patterns in salt transport, particularly during winter road de-icing in mixed land use landscapes, are less understood. To address this knowledge gap, we analyzed high-frequency specific conductance as a proxy for salinity across 114 high-flow events from 2013 to 2018 in an impaired stream draining mixed agriculture−urban land use. The specific conductance was highest in winter (median = 947 μS cm−1) and decreased with first-order kinetics up to 90 days after brining (β1 = −0.003), suggesting lasting impacts of road de-icing on water quality. Although hysteresis patterns suggested a transition from distal to proximal salt sources, they showed no clear correlation of flushing versus dilution to brining events. While seasonal brining increased salinity in receiving streams, unpredictable transport dynamics reduced the efficacy of hysteresis in characterizing salt transport dynamics. Thus, the complexity of mixed land use watersheds requires more spatially and temporally explicit monitoring to characterize stream salinization dynamics.
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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.
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    Whole-Ecosystem Experiments Reveal Varying Responses of Phytoplankton Functional Groups to Epilimnetic Mixing in a Eutrophic Reservoir
    Lofton, Mary E.; McClure, Ryan P.; Chen, Shengyang; Little, John C.; Carey, Cayelan C. (MDPI, 2019-01-29)
    Water column mixing can influence community composition of pelagic phytoplankton in lakes and reservoirs. Previous studies suggest that low mixing favors cyanobacteria, while increased mixing favors green algae and diatoms. However, this shift in community dominance is not consistently achieved when epilimnetic mixers are activated at the whole-ecosystem scale, possibly because phytoplankton community responses are mediated by mixing effects on other ecosystem processes. We conducted two epilimnetic mixing experiments in a small drinking water reservoir using a bubble-plume diffuser system. We measured physical, chemical, and biological variables before, during, and after mixing and compared the results to an unmixed reference reservoir. We observed significant increases in the biomass of cyanobacteria (from 0.8 ± 0.2 to 2.4 ± 1.1 μg L−1, p = 0.008), cryptophytes (from 0.7 ± 0.1 to 1.9 ± 0.6 μg L−1, p = 0.003), and green algae (from 3.8 to 4.4 μg L−1, p = 0.15) after our first mixing event, likely due to increased total phosphorus from entrainment of upstream sediments. After the second mixing event, phytoplankton biomass did not change but phytoplankton community composition shifted from taxa with filamentous morphology to smaller, rounder taxa. Our results suggest that whole-ecosystem dynamics and phytoplankton morphological traits should be considered when predicting phytoplankton community responses to epilimnetic mixing.
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    Whole-ecosystem oxygenation experiments reveal substantially greater hypolimnetic methane concentrations in reservoirs during anoxia
    Hounshell, Alexandria G.; McClure, Ryan P.; Lofton, Mary E.; Carey, Cayelan C. (Wiley, 2020)
    Lakes and reservoirs globally produce large quantities of methane and carbon dioxide in their sediments, which accumulate in the hypolimnia (bottom waters) during thermally stratified conditions. A key parameter controlling hypolimnetic greenhouse gas concentrations is dissolved oxygen. Land use and climate change have increased hypolimnetic anoxia worldwide in lakes and reservoirs, which is expected to affect their methane and carbon dioxide concentrations. We conducted whole-ecosystem oxygenation experiments to assess the effects of oxygen concentrations on dissolved hypolimnetic greenhouse gas concentrations in comparison to a reference reservoir and calculated the maximum hypolimnetic global warming potential in both reservoirs over three summers. We observed significantly greater hypolimnetic methane under anoxic conditions but similar carbon dioxide concentrations, leading to greater hypolimnetic global warming potential of anoxic hypolimnia. Our study indicates that the global warming potential of hypolimnetic greenhouse gas concentrations may increase as the prevalence of hypolimnetic anoxia increases due to global change.