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Abstract 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 Pmore »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.« less

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 weremore »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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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 (CH 4 ) from aquatic sediments to the atmosphere (ebullition) dominate freshwater greenhouse gas emissions, but it remains unknown how best to make robust near-term CH 4 ebullition predictions using models. Near-term forecasting workflows have the potential to address several current challenges in predicting CH 4 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 CH 4 ebullition rates in a small eutrophic reservoir throughout one open-water period. The workflow included the repeated updating of a CH 4 ebullition forecast model over time with newly-collected data via iterative model refitting. We compared the CH 4 forecasts from our workflow to both alternative forecasts generated without iterative model refitting and a persistencemore »null model. Our forecasts with iterative model refitting estimated CH 4 ebullition rates up to 2 weeks into the future [RMSE at 1-week ahead = 0.53 and 0.48 log e (mg CH 4 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 CH 4 models and drivers. Altogether, our study suggests that iterative forecasting improves week-to-week CH 4 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 CH 4 ebullition predictions.« less

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.