ArticleEcological applications : a publication of the Ecological Society of America2020
Automated data-intensive forecasting of plant phenology throughout the United States.
Article in Ecological applications : a publication of the Ecological Society of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Ten best practices for effective phenological research.International journal of biometeorology · 2023Review
- Article
- Near-term forecasting of companion animal tick paralysis incidence: An iterative ensemble model.PLoS computational biology · 2022Article
- Iterative data-driven forecasting of the transmission and management of SARS-CoV-2/COVID-19 using social interventions at the county-level.Scientific reports · 2022Article
- A niche for null models in adaptive resource management.Ecology and evolution · 2022Article
- Pollen production for 13 urban North American tree species: Allometric equations for tree trunk diameter and crown area.Aerobiologia · 2020Article
- Automated data-intensive forecasting of plant phenology throughout the United States.Ecological applications : a publication of the Ecological Society of America · 2020Article
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Authors and funding
2 authors.
Funding
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Abstract
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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