Browsing by Author "Ward, Nicole K."

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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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    Can interactive data visualizations promote waterfront best management practices?
    Ward, Nicole K.; Sorice, Michael G.; Reynolds, Mikaila S.; Weathers, Kathleen C.; Weng, Weizhe; Carey, Cayelan C. (Taylor & Francis, 2022-01-02)
    Lake water quality management often requires private property owner engagement since land-use change generally occurs on private property. Educational components of lake management outreach must connect current property owner behaviors with future water quality. However, it may be challenging for property owners to associate their current behaviors with water quality outcomes due to the time lag between a behavior (e.g., fertilizer application) and a water quality outcome (e.g., decreased water clarity). Interactive data visualizations, characterized by user-determined selections that change visualization output, may be well suited to help property owners connect current behavior to future water quality. We tested the effectiveness of an online, interactive visualization as an educational intervention to alter property owners' perspectives related to applying lawn fertilizer and installing waterfront buffers. We used cognitive psychology measures to quantify intervention effectiveness. Since property owners' decision making may be driven by connections to their property, we also explored relationships between seasonal and permanent residents and intentions to apply fertilizer or install waterfront buffers and intervention effectiveness. Despite no significant difference in effectiveness between the interactive and noninteractive versions, the combined responses demonstrated a positive shift in behavioral beliefs and intentions related to lawn fertilizer application and waterfront buffer installation. Seasonal residents were less likely than permanent residents to apply lawn fertilizer before the intervention and more likely to shift their intentions after the intervention. This study provides evidence that brief educational interventions-regardless of their interactivity-can shift private property owner beliefs and intentions regarding lakefront property management.
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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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    Dynamics of the stream-lake transitional zone affect littoral lake metabolism
    Ward, Nicole K.; Brentrup, Jennifer A.; Richardson, David C.; Weathers, Kathleen C.; Hanson, Paul C.; Hewett, Russell J.; Carey, Cayelan C. (Springer, 2022-07)
    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.
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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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    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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    From concept to practice to policy: modeling coupled natural and human systems in lake catchments
    Cobourn, Kelly M.; Carey, Cayelan C.; Boyle, Kevin J.; Duffy, Christopher J.; Dugan, Hilary A.; Farrell, Kaitlin J.; Fitchett, Leah Lynn; Hanson, Paul C.; Hart, Julia A.; Henson, Virginia Reilly; Hetherington, Amy L.; Kemanian, Armen R.; Rudstam, Lars G.; Shu, Lele; Soranno, Patricia A.; Sorice, Michael G.; Stachelek, Joseph; Ward, Nicole K.; Weathers, Kathleen C.; Weng, Weizhe; Zhang, Yu (Ecological Society of America, 2018-05-03)
    Recent debate over the scope of the U.S. Clean Water Act underscores the need to develop a robust body of scientific work that defines the connectivity between freshwater systems and people. Coupled natural and human systems (CNHS) modeling is one tool that can be used to study the complex, reciprocal linkages between human actions and ecosystem processes. Well‐developed CNHS models exist at a conceptual level, but the mapping of these system representations in practice is limited in capturing these feedbacks. This article presents a paired conceptual–empirical methodology for functionally capturing feedbacks between human and natural systems in freshwater lake catchments, from human actions to the ecosystem and from the ecosystem back to human actions. We address extant challenges in CNHS modeling, which arise from differences in disciplinary approach, model structure, and spatiotemporal resolution, to connect a suite of models. In doing so, we create an integrated, multi‐disciplinary tool that captures diverse processes that operate at multiple scales, including land‐management decision‐making, hydrologic‐solute transport, aquatic nutrient cycling, and civic engagement. In this article, we build on this novel framework to advance cross‐disciplinary dialogue to move CNHS lake‐catchment modeling in a systematic direction and, ultimately, provide a foundation for smart decision‐making and policy.
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    Integrating fast and slow processes is essential for simulating human-freshwater interactions
    Ward, Nicole K.; Fitchett, Leah Lynn; Hart, Julia A.; Shu, Lele; Stachelek, Joseph; Weng, Weizhe; Zhang, Yu; Dugan, Hilary A.; Hetherington, Amy L.; Boyle, Kevin J.; Carey, Cayelan C.; Cobourn, Kelly M.; Hanson, Paul C.; Kemanian, Armen R.; Sorice, Michael G.; Weathers, Kathleen C. (Springer, 2019-10-01)
    Integrated modeling is a critical tool to evaluate the behavior of coupled human–freshwater systems. However, models that do not consider both fast and slow processes may not accurately reflect the feedbacks that define complex systems. We evaluated current coupled human–freshwater system modeling approaches in the literature with a focus on categorizing feedback loops as including economic and/or socio-cultural processes and identifying the simulation of fast and slow processes in human and biophysical systems. Fast human and fast biophysical processes are well represented in the literature, but very few studies incorporate slow human and slow biophysical system processes. Challenges in simulating coupled human–freshwater systems can be overcome by quantifying various monetary and non-monetary ecosystem values and by using data aggregation techniques. Studies that incorporate both fast and slow processes have the potential to improve complex system understanding and inform more sustainable decision-making that targets effective leverage points for system change.
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    A Practical Guide for Managing Interdisciplinary Teams: Lessons Learned from Coupled Natural and Human Systems Research
    Henson, V. Reilly; Cobourn, Kelly M.; Weathers, Kathleen C.; Carey, Cayelan C.; Farrell, Kaitlin J.; Klug, Jennifer L.; Sorice, Michael G.; Ward, Nicole K.; Weng, Weizhe (MDPI, 2020-07-09)
    Interdisciplinary team science is essential to address complex socio-environmental questions, but it also presents unique challenges. The scientific literature identifies best practices for high-level processes in team science, e.g., leadership and team building, but provides less guidance about practical, day-to-day strategies to support teamwork, e.g., translating jargon across disciplines, sharing and transforming data, and coordinating diverse and geographically distributed researchers. This article offers a case study of an interdisciplinary socio-environmental research project to derive insight to support team science implementation. We evaluate the project’s inner workings using a framework derived from the growing body of literature for team science best practices, and derive insights into how best to apply team science principles to interdisciplinary research. We find that two of the most useful areas for proactive planning and coordinated leadership are data management and co-authorship. By providing guidance for project implementation focused on these areas, we contribute a pragmatic, detail-oriented perspective on team science in an effort to support similar projects.
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    Predicting lake surface water phosphorus dynamics using process-guided machine learning
    Hanson, Paul C.; Stillman, Aviah B.; Jia, Xiaowei; Karpatne, Anuj; Dugan, Hilary A.; Carey, Cayelan C.; Stachelek, Joseph; Ward, Nicole K.; Zhang, Yu; Read, Jordan S.; Kumar, Vipin (2020-08-15)
    Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 mu g P L-1, 22.7 mu g P L-1, and 20.7 mu g P L-1, respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 mu g P L-1, 10.9 mu g P L-1, and 10.7 mu g P L-1. Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 mu g P L-1 and 28.0 mu g P L-1, respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.