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Ecological forecasting challenges, formalized efforts to empower the ecology community to develop near‐term iterative forecasts of yet‐to‐be‐collected data, are a powerful tool to advance predictive capacities in ecology. The National Ecological Observatory Network (NEON) Ecological Forecasting Challenge, which has processed over 82 million individual forecasts across 81 sites since 2021, exemplifies this approach. However, the cyberinfrastructure (CI) required to support such large‐scale, community‐driven forecasting efforts has not been fully described. Here, we present the design principles, technical implementation, and lessons learned from the CI developed for the NEON Challenge. This serverless, open‐source platform automates key tasks, including target data generation, probabilistic forecast submission‐processing, evaluation using proper scoring rules, and dissemination of results. The CI leverages cloud‐native technologies, including Simple Storage Service (S3)‐based storage, GitHub Actions, and Docker containers, to ensure scalability, reproducibility, and vendor‐agnostic deployment. The CI also includes tools to reduce barriers for contributors, such as R packages for forecast submission, access to weather forecasts as model drivers, and tutorials. A dashboard and searchable catalog enhance transparency and accessibility. Our experience demonstrates that forecasting challenges can serve as focal points for community engagement, education, and synthesis. The modular design of the CI enables adaptation to new observatories and evolving technologies, providing a roadmap for future ecological forecasting efforts. By documenting this infrastructure, we aim to empower researchers to launch and sustain forecasting challenges that advance ecological understanding through prediction. 

Abstract: We measured carbon dioxide and methane flux exchange with the atmosphere at the deepest site of Falling Creek Reservoir (Vinton, Virginia, USA) every 30 minutes from 04 April 2020 to 31 December 2025. Falling Creek Reservoir is a drinking water supply reservoir owned and managed by the Western Virginia Water Authority (WVWA) as a primary drinking water source. The dataset consists of micrometeorological and flux data collected using an eddy covariance system (LiCor Biosciences, Lincoln, Nebraska, USA) and analyzed with associated Eddy Pro software (Eddy Pro Version 7.0.6), including carbon dioxide, methane, and water vapor. All analysis scripts are included for data processing and quality assurance/quality control following best practices. 

AGU and ESA invite contributions to a cross-society special collection on ecological forecasting across ecosystems and scales. 

Abstract As ecology becomes a more predictive discipline, identifying the intrinsic predictability, or stochasticity, of ecosystem variables across space and time is needed to help guide the development of ecological models and forecasts. For example, if an ecological time series has high intrinsic predictability, then a high‐performing model should presumably be able to replicate its dynamics. Conversely, if an ecological variable has low intrinsic predictability, then no model—regardless of its performance—will be able to replicate its dynamics. However, despite the proliferation of ecological models and forecasts, the intrinsic predictability of ecological variables remains largely unknown. To bridge this gap, we analyzed a >4‐year time series of high‐frequency sensor data collected from replicate freshwater ecosystems to determine how intrinsic predictability (quantified as permutation entropy) differs among ecological variables, seasons, and ecosystems. We observed greater differences in predictability among ecological variables and days of year than between ecosystems. Although intrinsic predictability was generally low for all variables, it was still significantly higher than white noise, indicating complex yet predictable dynamics. We observed the highest predictability for physical ecosystem variables (e.g., water temperature) and the lowest predictability for biological variables (e.g., phytoplankton biomass), with chemical variables (e.g., dissolved oxygen) intermediate. We observed substantial seasonal differences in predictability among variables: surface water temperature and dissolved organic matter exhibited their highest levels of predictability in autumn, whereas surface chlorophyll and bottom‐layer dissolved oxygen and temperature exhibited highest predictability in summer. Periods of anoxia (low oxygen) were associated with the highest levels of predictability in dissolved oxygen over the time series. Altogether, our analysis highlights how intrinsic predictability data can both guide ecological model development and improve our understanding of how ecological predictability varies across space and time. 

Abstract Harmful phytoplankton blooms driven by climate warming and nutrient pollution are a growing threat to freshwater ecosystems worldwide. Predicting these blooms is critical for managing water resources. However, process‐based models often struggle to capture the complex nonlinear dynamics of phytoplankton. Although machine learning (ML) offers powerful predictive capabilities, its “black‐box” nature has limited its adoption for management. In this study, we applied four ensemble ML algorithms, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Gradient Boosting Machine (GBM), and Categorical Boosting (CatBoost), to model phytoplankton dynamics in two adjacent drinking‐water reservoirs. All models performed similarly; however, the XGBoost algorithm achieved the best performance in both reservoirs, with a root mean square error (RMSE) of 2.4–6.6 μg/L chlorophyll a and a Pearson correlation coefficient (r) of 0.83–0.86. To enhance the interpretability of these ML models, we applied explainable artificial intelligence (XAI) techniques, including Shapley Additive Explanations (SHAP) and partial dependence analyses (PDPs). Our XAI analysis revealed reservoir‐specific dynamics, with deep dissolved oxygen and water temperature more influential in one reservoir, whereas seasonality and water column stability were more critical in the other. We also observed opposing effects of thermal stratification, with high water temperatures and strong stratification stimulating phytoplankton in one reservoir and suppressing it in the other. Stability analyses confirmed that the SHAP explanations were robust to perturbations, boosting confidence in their interpretability. This study demonstrates the application of a stability‐constrained XAI framework to provide transparent and trustworthy ecological insights, offering a robust approach for data‐driven water quality management and forecasting. 

Abstract 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.