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.

中文翻译：

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对整个美国的植物物候进行自动数据密集型预测。

物候学是周期性和季节性自然现象（如开花和落叶）的时间，是生态系统的一个组成部分，对环境管理、旅游和农业等人类活动产生影响。因此，对于物候事件何时发生的可行预测，有许多潜在的应用。然而，尽管可以获得具有大空间、时间和分类范围的物候数据，以及众多物候模型，但还没有植物物候的自动物种水平预测。这部分是由于建立一个集成大量气候观测和预测的系统所面临的挑战，使用这些数据来拟合模型并对大量物种进行预测，并以可解释的方式持续传播这些预测的结果。这里，我们描述了一个新的近期物候预测系统，该系统可以提前 6 个月预测美国 78 个物种的发芽、开花、成熟果实和秋天颜色的时间，并且每四天更新一次。我们利用开发该系统的经验教训，为开发大规模近期生态预报系统提供更广泛的指导，以帮助推进生态学自动预报的使用。