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Automated data‐intensive forecasting of plant phenology throughout the United States

Phenology, the timing of cyclical and seasonal natural phenomena such as flowering and leaf out, is an integral part of ecological systems with impacts on human activities like environmental management, tourism, and agriculture. As a result, there are numerous potential applications for actionable p...

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Detalles Bibliográficos
Autores principales: Taylor, Shawn D., White, Ethan P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285964/
https://www.ncbi.nlm.nih.gov/pubmed/31630468
http://dx.doi.org/10.1002/eap.2025
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author Taylor, Shawn D.
White, Ethan P.
author_facet Taylor, Shawn D.
White, Ethan P.
author_sort Taylor, Shawn D.
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description Phenology, the timing of cyclical and seasonal natural phenomena such as flowering and leaf out, is an integral part of ecological systems with impacts on human activities like environmental management, tourism, and agriculture. As a result, there are numerous potential applications for actionable predictions of when phenological events will occur. However, despite the availability of phenological data with large spatial, temporal, and taxonomic extents, and numerous phenology models, there have been no automated species‐level forecasts of plant phenology. This is due in part to the challenges of building a system that integrates large volumes of climate observations and forecasts, uses that data to fit models and make predictions for large numbers of species, and consistently disseminates the results of these forecasts in interpretable ways. Here, we describe a new near‐term phenology‐forecasting system that makes predictions for the timing of budburst, flowers, ripe fruit, and fall colors for 78 species across the United States up to 6 months in advance and is updated every four days. We use the lessons learned in developing this system to provide guidance developing large‐scale near‐term ecological forecast systems more generally, to help advance the use of automated forecasting in ecology.
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spelling pubmed-92859642022-07-19 Automated data‐intensive forecasting of plant phenology throughout the United States Taylor, Shawn D. White, Ethan P. Ecol Appl Articles Phenology, the timing of cyclical and seasonal natural phenomena such as flowering and leaf out, is an integral part of ecological systems with impacts on human activities like environmental management, tourism, and agriculture. As a result, there are numerous potential applications for actionable predictions of when phenological events will occur. However, despite the availability of phenological data with large spatial, temporal, and taxonomic extents, and numerous phenology models, there have been no automated species‐level forecasts of plant phenology. This is due in part to the challenges of building a system that integrates large volumes of climate observations and forecasts, uses that data to fit models and make predictions for large numbers of species, and consistently disseminates the results of these forecasts in interpretable ways. Here, we describe a new near‐term phenology‐forecasting system that makes predictions for the timing of budburst, flowers, ripe fruit, and fall colors for 78 species across the United States up to 6 months in advance and is updated every four days. We use the lessons learned in developing this system to provide guidance developing large‐scale near‐term ecological forecast systems more generally, to help advance the use of automated forecasting in ecology. John Wiley and Sons Inc. 2019-11-25 2020-01 /pmc/articles/PMC9285964/ /pubmed/31630468 http://dx.doi.org/10.1002/eap.2025 Text en © 2019 The Authors. Ecological Applications published by Wiley Periodicals, Inc. on behalf of Ecological Society of America https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Taylor, Shawn D.
White, Ethan P.
Automated data‐intensive forecasting of plant phenology throughout the United States
title Automated data‐intensive forecasting of plant phenology throughout the United States
title_full Automated data‐intensive forecasting of plant phenology throughout the United States
title_fullStr Automated data‐intensive forecasting of plant phenology throughout the United States
title_full_unstemmed Automated data‐intensive forecasting of plant phenology throughout the United States
title_short Automated data‐intensive forecasting of plant phenology throughout the United States
title_sort automated data‐intensive forecasting of plant phenology throughout the united states
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285964/
https://www.ncbi.nlm.nih.gov/pubmed/31630468
http://dx.doi.org/10.1002/eap.2025
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