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1. State-of-the-art review of near-term freshwater forecasting literature published between 2017 and 2022

   https://doi.org/10.6073/pasta/949164a64de132ff3bbb7b92d2e1c729  

   Lofton, Mary E.; Howard, Dexter W.; Thomas, R. Quinn; Carey, Cayelan C. (January 2023, Environmental Data Initiative)

   This data publication includes code and results from a systematic literature review on the current state of near-term forecasting of freshwater quality. The review aimed to address the following questions: (1) Freshwater variables, scales, models, and skill: Which freshwater variables and temporal scales are most commonly targeted for near-term forecasts, and what modeling methods are most commonly employed to develop these forecasts? How is the accuracy of freshwater quality forecasts assessed, and how accurate are they? How is uncertainty typically incorporated into water quality forecast output? (2) Forecast infrastructure and workflows: Are iterative, automated workflows commonly employed in near-term freshwater quality forecasting? How are forecasts validated and archived? (3) Human dimensions: What is the stated motivation for development of most near-term freshwater quality forecasts, and who are the most common end users (if any)? How are end users engaged in forecast development? An initial search was conducted for published papers presenting freshwater quality forecasts from 1 January 2017 to 17 February 2022 in the Web of Science Core Collection. Results were subsequently analyzed in three stages. First, paper titles were screened for relevance. Second, an initial screen was conducted to assess whether each paper presented a near-term freshwater quality forecast. Third, papers that passed the initial screen were analyzed using a standardized matrix to assess the state of near-term freshwater quality forecasting and identify areas of recent progress and ongoing challenges. Additional details regarding the systematic literature search and review are presented in the Methods section of the metadata. 

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2. The power of forecasts to advance ecological theory

   https://doi.org/10.1111/2041-210X.13955  

   Lewis, Abigail S.; Rollinson, Christine R.; Allyn, Andrew J.; Ashander, Jaime; Brodie, Stephanie; Brookson, Cole B.; Collins, Elyssa; Dietze, Michael C.; Gallinat, Amanda S.; Juvigny‐Khenafou, Noel; et al (January 2022, Methods in Ecology and Evolution)

   1. Ecological forecasting provides a powerful set of methods for predicting short- and long-term change in living systems. Forecasts are now widely produced, enabling proactive management for many applied ecological problems. However, despite numerous calls for an increased emphasis on prediction in ecology, the potential for forecasting to accelerate ecological theory development remains underrealized. 2. Here, we provide a conceptual framework describing how ecological forecasts can energize and advance ecological theory. We emphasize the many opportunities for future progress in this area through increased forecast development, comparison and synthesis. 3. Our framework describes how a forecasting approach can shed new light on existing ecological theories while also allowing researchers to address novel questions. Through rigorous and repeated testing of hypotheses, forecasting can help to refine theories and understand their generality across systems. Meanwhile, synthesizing across forecasts allows for the development of novel theory about the relative predictability of ecological variables across forecast horizons and scales. 4. We envision a future where forecasting is integrated as part of the toolset used in fundamental ecology. By outlining the relevance of forecasting methods to ecological theory, we aim to decrease barriers to entry and broaden the community of researchers using forecasting for fundamental ecological insight. 

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3. Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

   https://doi.org/10.1002/ecs2.4752  

   Wander, Heather L; Thomas, R Quinn; Moore, Tadhg N; Lofton, Mary E; Breef‐Pilz, Adrienne; Carey, Cayelan C (February 2024, Ecosphere)

   Abstract Ecosystems around the globe are experiencing changes in both the magnitude and fluctuations of environmental conditions due to land use and climate change. In response, ecologists are increasingly using near‐term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near‐term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost‐prohibitive or impossible for forecasting ecological variables that lack high‐frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using Forecasting Lake and Reservoir Ecosystems (FLARE), an open source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1‐ to 35‐day‐ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1‐day‐ahead forecast root mean square error (RMSE) of 0.81°C, mean 7‐day RMSE of 1.15°C, and mean 35‐day RMSE of 1.94°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1‐ to 7‐day‐ahead horizons, but weekly data assimilation resulted in the most skillful forecasts at 8‐ to 35‐day‐ahead horizons. Within a year, forecasts with weekly data assimilation consistently outperformed forecasts with daily data assimilation after the 8‐day forecast horizon during mixed spring/autumn periods and 5‐ to 14‐day‐ahead horizons during the summer‐stratified period, depending on depth. Our results suggest that lower frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high‐frequency sensor data. 

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4. What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge

   https://doi.org/10.1002/eap.70004  

   Olsson, Freya; Carey, Cayelan C; Boettiger, Carl; Harrison, Gregory; Ladwig, Robert; Lapeyrolerie, Marcus F; Lewis, Abigail_S L; Lofton, Mary E; Montealegre‐Mora, Felipe; Rabaey, Joseph S; et al (January 2025, Ecological Applications)

   Near‐term, iterative ecological forecasts can be used to help understand and proactively manage ecosystems. To date, more forecasts have been developed for aquatic ecosystems than other ecosystems worldwide, likely motivated by the pressing need to conserve these essential and threatened ecosystems and increasing the availability of high‐frequency data. Forecasters have implemented many different modeling approaches to forecast freshwater variables, which have demonstrated promise at individual sites. However, a comprehensive analysis of the performance of varying forecast models across multiple sites is needed to understand broader controls on forecast performance. Forecasting challenges (i.e., community‐scale efforts to generate forecasts while also developing shared software, training materials, and best practices) present a useful platform for bridging this gap to evaluate how a range of modeling methods perform across axes of space, time, and ecological systems. Here, we analyzed forecasts from the aquatics theme of the National Ecological Observatory Network (NEON) Forecasting Challenge hosted by the Ecological Forecasting Initiative. Over 100,000 probabilistic forecasts of water temperature and dissolved oxygen concentration for 1–30 days ahead across seven NEON‐monitored lakes were submitted in 2023. We assessed how forecast performance varied among models with different structures, covariates, and sources of uncertainty relative to baseline null models. A similar proportion of forecast models were skillful across both variables (34%–40%), although more individual models outperformed the baseline models in forecasting water temperature (10 models out of 29) than dissolved oxygen (6 models out of 15). These top performing models came from a range of classes and structures. For water temperature, we found that forecast skill degraded with increases in forecast horizons, process‐based models, and models that included air temperature as a covariate generally exhibited the highest forecast performance, and that the most skillful forecasts often accounted for more sources of uncertainty than the lower performing models. The most skillful forecasts were for sites where observations were most divergent from historical conditions (resulting in poor baseline model performance). Overall, the NEON Forecasting Challenge provides an exciting opportunity for a model intercomparison to learn about the relative strengths of a diverse suite of models and advance our understanding of freshwater ecosystem predictability. 

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5. Transferability of ecological forecasting models to novel biotic conditions in a long‐term experimental study

   https://doi.org/10.1002/ecy.4406  

   Dumandan, Patricia_Kaye T; Simonis, Juniper L; Yenni, Glenda M; Ernest, S_K Morgan; White, Ethan P (November 2024, Ecology)

   Abstract Ecological forecasting models play an increasingly important role for managing natural resources and assessing our fundamental knowledge of processes driving ecological dynamics. As global environmental change pushes ecosystems beyond their historical conditions, the utility of these models may depend on their transferability to novel conditions. Because species interactions can alter resource use, timing of reproduction, and other aspects of a species' realized niche, changes in biotic conditions, which can arise from community reorganization events in response to environmental change, have the potential to impact model transferability. Using a long‐term experiment on desert rodents, we assessed model transferability under novel biotic conditions to better understand the limitations of ecological forecasting. We show that ecological forecasts can be less accurate when the models generating them are transferred to novel biotic conditions and that the extent of model transferability can depend on the species being forecast. We also demonstrate the importance of incorporating uncertainty into forecast evaluation with transferred models generating less accurate and more uncertain forecasts. These results suggest that how a species perceives its competitive landscape can influence model transferability and that when uncertainties are properly accounted for, transferred models may still be appropriate for decision making. Assessing the extent of the transferability of forecasting models is a crucial step to increase our understanding of the limitations of ecological forecasts. 

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