Increased adoption of best practices in ecological forecasting enables comparisons of forecastability

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Abigail S. L. Lewis, Whitney M. Woelmer, Heather L. Wander, Dexter W. Howard, John W. Smith, Ryan Paul McClure, Mary E. Lofton, Nicholas W. Hammond, Rachel S. Corrigan, R. Quinn Thomas


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-year forecast horizon) ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and compare forecast accuracy across scales and variables. Our results indicate 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, a number of 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 day 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.



Ecology and Evolutionary Biology, Life Sciences, Terrestrial and Aquatic Ecology


data assimilation, decision support, Ecological predictability, Forecast automation, Forecast evaluation, Forecast horizon, Forecast uncertainty, Iterative forecasting, Near-term forecast, Null model, open science, Uncertainty partitioning


Published: 2021-04-27 23:11

Last Updated: 2021-10-06 02:56

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Data and Code Availability Statement:
Data and metadata are temporarily archived in the Environmental Data Initiative staging repository and will be published to the full repository upon manuscript acceptance: