VTechWorks Repository :: Browsing by Author "Woelmer, Whitney M."

Browsing by Author "Woelmer, Whitney M."

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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.
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.
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.
Assessing opportunities and inequities in undergraduate ecological forecasting education
Willson, Alyssa M.; Gallo, Hayden; Peters, Jody A.; Abeyta, Antoinette; Watts, Nievita Bueno; Carey, Cayelan C.; Moore, Tadhg N.; Smies, Georgia; Thomas, R. Quinn; Woelmer, Whitney M.; McLachlan, Jason S. (Wiley, 2023-05)
Conducting ecological research in a way that addresses complex, real-world problems requires a diverse, interdisciplinary and quantitatively trained ecology and environmental science workforce. This begins with equitably training students in ecology, interdisciplinary science, and quantitative skills at the undergraduate level. Understanding the current undergraduate curriculum landscape in ecology and environmental sciences allows for targeted interventions to improve equitable educational opportunities. Ecological forecasting is a sub-discipline of ecology with roots in interdisciplinary and quantitative science. We use ecological forecasting to show how ecology and environmental science undergraduate curriculum could be evaluated and ultimately restructured to address the needs of the 21(st) century workforce. To characterize the current state of ecological forecasting education, we compiled existing resources for teaching and learning ecological forecasting at three curriculum levels: online resources; US university courses on ecological forecasting; and US university courses on topics related to ecological forecasting. We found persistent patterns (1) in what topics are taught to US undergraduate students at each of the curriculum levels; and (2) in the accessibility of resources, in terms of course availability at higher education institutions in the United States. We developed and implemented programs to increase the accessibility and comprehensiveness of ecological forecasting undergraduate education, including initiatives to engage specifically with Native American undergraduates and online resources for learning quantitative concepts at the undergraduate level. Such steps enhance the capacity of ecological forecasting to be more inclusive to undergraduate students from diverse backgrounds and expose more students to quantitative training.
Building, applying, and communicating ecosystem understanding via freshwater forecasts over time and space
Woelmer, Whitney M. (Virginia Tech, 2023-09-05)
Accelerating rates of change in ecosystems globally heighten the need for improved predictions of future ecological conditions. Freshwater lakes and reservoirs, which provide numerous ecosystem services, are particularly threatened by global change stressors and have already exhibited substantial changes to their physical, chemical, and biological functioning. Thus, to provide useful predictive tools for managing freshwater resources in the face of global change, we must improve our ability to build, apply, and communicate understanding of lake and reservoir ecosystem dynamics. To address this, I first built ecosystem understanding by conducting multiple whole-ecosystem surveys to quantify the spatial and temporal variability of biogeochemistry in two reservoirs over a year. We found that temporal heterogeneity was higher than spatial heterogeneity for most biogeochemical variables, with the stream-reservoir interface as a consistent hotspot of biogeochemical processing. Second, I applied ecosystem understanding by producing ecological forecasts of physical (water temperature), chemical (dissolved oxygen), and biological (chlorophyll-a) variables across three waterbodies using diverse modeling methods. I developed daily, weekly, and fortnightly forecasts of chlorophyll-a at two drinking water reservoirs using a Bayesian linear model, and found process uncertainty dominated total forecast uncertainty. Additionally, I produced forecasts of water temperature and dissolved oxygen in an oligotrophic lake using a hydrodynamic-ecosystem model and found that water temperature was more predictable than oxygen despite variable performance over depth and between years. Across these two forecasting studies, forecast skill relative to a null model varied among water quality metrics: water temperature forecasts outperformed the null model up to 11 days ahead, oxygen forecasts outperformed the null model up to 2 days ahead, and chlorophyll-a forecasts outperformed the null model up to 14 days ahead. Third, to communicate forecasts for decision-making, I developed an educational module for undergraduate ecology students which taught important concepts on visualization and decision science. Following completion of the module, students' ability to identify methods for uncertainty communication increased significantly, as well as their understanding of the benefits of ecological forecasting. Overall, my dissertation provides insight into how reservoirs function in global biogeochemical cycles, the predictability of multiple water quality variables, and deepens our understanding of how to communicate ecosystem science for improved management and protection of ecosystems.
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.
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.
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.
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.
Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module
Moore, Tadhg N.; Thomas, R. Quinn; Woelmer, Whitney M.; Carey, Cayelan C. (MDPI, 2022-06-30)
Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts.
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.
Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the US
Thomas, R. Quinn; McClure, Ryan P.; Moore, Tadhg N.; Woelmer, Whitney M.; Boettiger, Carl; Figueiredo, Renato J.; Hensley, Robert T.; Carey, Cayelan C. (Wiley, 2023-04)
The US National Ecological Observatory Network's (NEON's) standardized monitoring program provides an unprecedented opportunity for comparing the predictability of ecosystems. To harness the power of NEON data for examining environmental predictability, we scaled a near-term, iterative, water temperature forecasting system to all six NEON lakes in the conterminous US. We generated 1-day-ahead to 35-days-ahead forecasts using a process-based hydrodynamic model that was updated with observations as they became available. Among lakes, forecasts were more accurate than a null model up to 35-days-ahead, with an aggregated 1-day-ahead root-mean square error (RMSE) of 0.61 degrees C and a 35-days-ahead RMSE of 2.17 degrees C. Water temperature forecast accuracy was positively associated with lake depth and water clarity, and negatively associated with fetch and catchment size. The results of our analysis suggest that lake characteristics interact with weather to control the predictability of thermal structure. Our work provides some of the first probabilistic forecasts of NEON sites and a framework for examining continental-scale predictability.
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.
The NEON Ecological Forecasting Challenge
Thomas, R. Quinn; Boettiger, Carl; Carey, Cayelan C.; Dietze, Michael C.; Johnson, Leah R.; Kenney, Melissa A.; McLachlan, Jason S.; Peters, Jody A.; Sokol, Eric R.; Weltzin, Jake F.; Willson, Alyssa; Woelmer, Whitney M. (Wiley, 2023-04)
Predicting Spring Phenology in Deciduous Broadleaf Forests: NEON Phenology Forecasting Community Challenge
Wheeler, Kathryn I.; Dietze, Michael C.; LeBauer, David; Peters, Jody A.; Richardson, Andrew D.; Ross, Arun A.; Thomas, R. Quinn; Zhu, Kai; Bhat, Uttam; Munch, Stephan; Buzbee, Raphaela Floreani; Chen, Min; Goldstein, Benjamin; Guo, Jessica; Hao, Dalei; Jones, Chris; Kelly-Fair, Mira; Liu, Haoran; Malmborg, Charlotte; Neupane, Naresh; Pal, Debasmita; Shirey, Vaughn; Song, Yiluan; Steen, McKalee; Vance, Eric A.; Woelmer, Whitney M.; Wynne, Jacob H.; Zachmann, Luke (Elsevier, 2024-01-01)
Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative's National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate when, where, and for what model type phenology forecast skill is highest. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts.
Uncertainty in projections of future lake thermal dynamics is differentially driven by lake and global climate models
Wynne, Jacob H.; Woelmer, Whitney M.; Moore, Tadhg N.; Thomas, R. Quinn; Weathers, Kathleen C.; Carey, Cayelan C. (PeerJ, 2023-06-02)
Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics.

Browsing by Author "Woelmer, Whitney M."

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