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

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

Journal

Ecological applications : a publication of the Ecological Society of America
ISSN: 1051-0761
Titre abrégé: Ecol Appl
Pays: United States
ID NLM: 9889808

Informations de publication

Date de publication:
03 2022
Historique:
revised: 21 07 2021
received: 19 04 2021
accepted: 05 10 2021
pubmed: 21 11 2021
medline: 12 3 2022
entrez: 20 11 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 34800082
doi: 10.1002/eap.2500
pmc: PMC9285336
doi:

Substances chimiques

Chlorophyll 1406-65-1

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2500

Informations de copyright

© 2021 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America.

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Auteurs

Abigail S L Lewis (ASL)

Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA.

Whitney M Woelmer (WM)

Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA.

Heather L Wander (HL)

Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA.

Dexter W Howard (DW)

Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA.

John W Smith (JW)

Department of Statistics, Virginia Tech, Blacksburg, Virginia, USA.

Ryan P McClure (RP)

Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA.

Mary E Lofton (ME)

Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA.

Nicholas W Hammond (NW)

Department of Geosciences, Virginia Tech, Blacksburg, Virginia, USA.

Rachel S Corrigan (RS)

Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, Virginia, USA.

R Quinn Thomas (RQ)

Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, Virginia, USA.

Cayelan C Carey (CC)

Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA.

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