The NEON Ecological Forecasting Challenge

Thomas, R. Quinn; Boettiger, Carl; Carey, Cayelan C.; Dietze, Michael C.; Johnson, Leah R.; Kenney, Melissa A.; McLachlan, J. S.; Peters, Jody A.; Sokol, Eric R.; Willson, Alyssa M.; Woelmer, Whitney M.; Contributors, Challenge

doi:10.22541/essoar.167079499.99891914/v1

Cited by 9 publications

(10 citation statements)

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“…If you are interested in becoming involved or learning more, see http://www.neon4cast.org (Thomas et al . 2021). We aim for the Challenge to further enable new forecast innovations, provide valuable training, and spark broad engagement among ecologists.…”

Section: Figurementioning

confidence: 99%

The NEON Ecological Forecasting Challenge

Thomas

Boettiger

Carey

et al. 2023

Frontiers in Ecol & Environ

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T he 21st century continues to be characterized by major changes to the environment and the ecosystem services upon which society depends. Anticipating and responding to these changes requires that scientists explicitly forecast future conditions in real time (Dietze et al. 2018). Ecological forecasting, like weather and epidemiological forecasting, involves integrating data and models to generate quantitative predictions of the future state of ecological systems before observations are collected. The iterative cycle of creating forecasts, evaluating them with new observations, updating the models, and then making new forecasts has the potential to accelerate learning across many ecological subdisciplines. This cycle builds on openly available data, often published soon after collection, as is increasingly common in ecological observatory networks, such as the National Ecological Observatory Network (NEON). To accelerate improvements in ecological forecasting, we designed and launched the NEON Ecological Forecasting Challenge (hereafter, "Challenge") (Figure 1), an open platform for the ecological and data science communities to forecast NEON data before they are collected.The ecological forecasting community is interested in using forecasts to advance theory (Lewis et al. 2023) and in translating forecasts for natural resource management (Enquist et al. 2017). By analyzing a catalog of forecasts developed for a range of ecological systems, spatiotemporal scales, and environmental gradients, scientists can begin to address fundamental questions in ecology. The Ecological Forecasting Initiative Research Coordination Network (EFI-RCN) -funded by the US National Science Foundation (NSF) -invites the broad ecology community to help build this catalog by forecasting NEON data. NEON is a powerful platform to support such a challenge because it provides standardized data with reported uncertainties that span a range of environmental conditions and levels of biological organization across terrestrial and freshwater systems in the US.The Challenge was designed on input from academic, government, and private sectors through workshops and working groups. We call it a "Challenge" because, despite its similarities to data science competitions (Makridakis et al. 2021), we are empowering the community to do more than just submit forecasts -we are also collaboratively developing software, training materials, and best practices. In May 2020, we launched the Challenge's design at a virtual conference with over 200 attendees (Peters and Thomas 2021). Attendees prioritized five forecasting "themes" that draw on NEON data, address open science

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Section: Figurementioning

confidence: 99%

The NEON Ecological Forecasting Challenge

Thomas

Boettiger

Carey

et al. 2023

Frontiers in Ecol & Environ

Self Cite

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“…One way EFI built a community of practice around ecological forecasting was through the National Ecological Observatory Network (NEON) Forecasting Challenge, an open opportunity for groups to submit forecasts using NEON data streams ranging from carbon and water cycling to tick dynamics (Thomas et al, 2022). There are now very active chapters of EFI in the United States, Canada, Europe, and Oceania.…”

Section: F I G U R Ementioning

confidence: 99%

“…A roadmap for fulfilling this vision incorporates open-access code, reproducible workflows, increased data science education, and lower barriers to cyberinfrastructure needed for running forecasts. Furthermore, community-wide investment in standards for forecast outputs and metadata will help to enable synthesis across various approaches to forecasting (Thomas et al, 2022).…”

Section: Emerg Ing Opp Ortunitie S: Synthe S Izing Across D Is Par At...mentioning

confidence: 99%

Synthesizing forecasts to inform decision‐making and advance ecological theory

2023

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“…This would facilitate not only the independent validation of individual forecasts, but also larger efforts at cross‐forecast synthesis (Figure 1) and the testing of grand challenge questions about the patterns of predictability across ecological systems (Dietze, 2017b). It would also allow the community to generate multi‐model forecasts and to run forecast model intercomparisons, such as the National Ecological Observatory Network (NEON) Ecological Forecasting Challenge organized by the Ecological Forecasting Initiative's Research Coordination Network (EFI‐RCN) (Thomas et al, 2021). Specifically, within model intercomparison projects having community conventions makes it easier to communicate protocols to participants, to verify that submissions from participating teams are correct and complete and to facilitate analyses by ensuring contributions are interoperable.…”

Section: Introductionmentioning

confidence: 99%

“…This allows these conventions to be implemented formally, as well as for the serialization of specific forecast output and metadata formats that adhere to this convention. The adoption of community conventions in turn facilitates the development of community tools around those formats, such as the R packages and Docker containers developed around the EFI NEON challenge that support forecast submission, validation, scoring, interactive visualization, and redistribution (Thomas et al, 2021, 2023). In other cases, community conventions have facilitated the development of sophisticated community tools for model calibration, validation, sensitivity analysis, and iterative data assimilation (Fer et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A community convention for ecological forecasting: Output files and metadata version 1.0

Dietze,

Thomas,

Peters

et al. 2023

Ecosphere

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This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma‐separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross‐forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short‐term dissemination and long‐term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process.

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The NEON Ecological Forecasting Challenge

Cited by 9 publications

References 9 publications

The NEON Ecological Forecasting Challenge

The NEON Ecological Forecasting Challenge

Synthesizing forecasts to inform decision‐making and advance ecological theory

A community convention for ecological forecasting: Output files and metadata version 1.0

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