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

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  • Additional Information
    • Source:
      Publisher: Ecological Society of America Country of Publication: United States NLM ID: 9889808 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1051-0761 (Print) Linking ISSN: 10510761 NLM ISO Abbreviation: Ecol Appl Subsets: MEDLINE
    • Publication Information:
      Publication: Washington, D.C. : Ecological Society of America
      Original Publication: Tempe, AZ : The Society, 1991-
    • Subject Terms:
    • Abstract:
      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-yr forecast horizon) ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated 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, some 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 d 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.
      (© 2021 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America.)
    • References:
      Ecol Appl. 2022 Mar;32(2):e2500. (PMID: 34800082)
      Ecol Appl. 2011 Jul;21(5):1429-42. (PMID: 21830693)
      Ecol Appl. 2017 Oct;27(7):2048-2060. (PMID: 28646611)
      Sci Data. 2016 Mar 15;3:160018. (PMID: 26978244)
      Ecol Appl. 2020 Sep;30(6):e02120. (PMID: 32159900)
      JAMA. 1990 Mar 9;263(10):1385-9. (PMID: 2406472)
      Ecol Appl. 2019 Jan;29(1):e01822. (PMID: 30362295)
      Proc Natl Acad Sci U S A. 2018 Feb 13;115(7):1424-1432. (PMID: 29382745)
      PeerJ. 2018 Feb 8;6:e4278. (PMID: 29441230)
      Ecol Lett. 2015 Jul;18(7):597-611. (PMID: 25960188)
      Biometrika. 2011 Dec;98(4):995-999. (PMID: 23049133)
      Science. 2001 Jul 27;293(5530):657-60. (PMID: 11474103)
      Science. 2011 Feb 11;331(6018):703-5. (PMID: 21311007)
      Ecol Appl. 2019 Mar;29(2):e01850. (PMID: 30821885)
      Water Res. 2020 Sep 1;182:115959. (PMID: 32531494)
      Interface Focus. 2012 Apr 6;2(2):241-54. (PMID: 23565336)
      Nat Hum Behav. 2017 Jan 10;1:0021. (PMID: 33954258)
      Biochem Med (Zagreb). 2017 Oct 15;27(3):030201. (PMID: 29180912)
      BMC Biol. 2014 Apr 04;12:22. (PMID: 24708669)
      Ecol Appl. 2014 Mar;24(2):346-62. (PMID: 24689146)
    • Contributed Indexing:
      Keywords: 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
    • Accession Number:
      1406-65-1 (Chlorophyll)
    • Publication Date:
      Date Created: 20211120 Date Completed: 20220311 Latest Revision: 20220731
    • Publication Date:
      20240105
    • Accession Number:
      PMC9285336
    • Accession Number:
      10.1002/eap.2500
    • Accession Number:
      34800082