Later springs green-up faster: the relation between onset and completion of green-up in deciduous forests of North America
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In deciduous forests, spring leaf phenology controls the onset of numerous ecosystem functions. While most studies have focused on a single annual spring event, such as budburst, ecosystem functions like photosynthesis and transpiration increase gradually after budburst, as leaves grow to their mature size. Here, we examine the “velocity of green-up,” or duration between budburst and leaf maturity, in deciduous forest ecosystems of eastern North America. We use a diverse data set that includes 301 site-years of phenocam data across a range of sites, as well as 22 years of direct ground observations of individual trees and 3 years of fine-scale high-frequency aerial photography, both from Harvard Forest. We find a significant association between later start of spring and faster green-up: − 0.47 ± 0.04 (slope ± 1 SE) days change in length of green-up for every day later start of spring within phenocam sites, − 0.31 ± 0.06 days/day for trees under direct observation, and − 1.61 ± 0.08 days/day spatially across fine-scale landscape units. To explore the climatic drivers of spring leaf development, we fit degree-day models to the observational data from Harvard Forest. We find that the default phenology parameters of the ecosystem model PnET make biased predictions of leaf initiation (39 days early) and maturity (13 days late) for red oak, while the optimized model has biases of 1 day or less. Springtime productivity predictions using optimized parameters are closer to results driven by observational data (within 1%) than those of the default parameterization (17% difference). Our study advances empirical understanding of the link between early and late spring phenophases and demonstrates that accurately modeling these transitions is important for simulating seasonal variation in ecosystem productivity.
KeywordsPhenology Green-up Forest productivity Ecosystem model North America
SK was supported by NASA Headquarters under the NASA Earth and Space Science Fellowship Program – Grant 14-EARTH14R-23. KH received support from the French National Research Agency (ANR) in the frame of the Investments for the future Programme, within the Cluster of Excellence COTE (ANR-10-LABX-45). ADR acknowledges support for the PhenoCam network from the National Science Foundation, through the Macrosystems Biology program (EF-1065029 and EF-1702697). Additional support was received from the National Science Foundation under the Harvard Forest Long-Term Ecological Research Program (DEB-1237491) and the Harvard Center for Geographic Analysis. We thank John O’Keefe of Harvard Forest for his insightful discussions of this research.
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