Automated data-intensive forecasting of plant phenology throughout the United States.
Ecol Appl
; 30(1): e02025, 2020 01.
Article
de En
| MEDLINE
| ID: mdl-31630468
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.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Changement climatique
/
Climat
Type d'étude:
Prognostic_studies
Pays/Région comme sujet:
America do norte
Langue:
En
Journal:
Ecol Appl
Année:
2020
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique
Pays de publication:
États-Unis d'Amérique