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International Journal of Biometeorology

, Volume 62, Issue 9, pp 1645–1655 | Cite as

Later springs green-up faster: the relation between onset and completion of green-up in deciduous forests of North America

  • Stephen Klosterman
  • Koen Hufkens
  • Andrew D. Richardson
Original Paper

Abstract

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.

Keywords

Phenology Green-up Forest productivity Ecosystem model North America 

Notes

Acknowledgements

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.

Supplementary material

484_2018_1564_MOESM1_ESM.eps (23 kb)
ESM 1 Relation between modeled spring (April-May-June) NPP, driven by observed phenology, and date of budburst for red oak trees at Harvard Forest (slope = -4.5 ± 1.1 gCm-2d-1; r = -0.69, p < 0.001) (EPS 23.1 kb)
484_2018_1564_MOESM2_ESM.docx (40 kb)
ESM 2 (DOCX 39.7 kb)

References

  1. Aber J, Federer C (1992) A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems. Oecologia 92:463–474CrossRefGoogle Scholar
  2. Aber JD, Reich PB, Goulden ML (1996) Extrapolating leaf CO2 exchange to the canopy: a generalized model of forest photosynthesis compared with measurements by eddy correlation. Oecologia 106:257–265.  https://doi.org/10.1007/BF00328606 CrossRefGoogle Scholar
  3. Archetti M, Richardson AD, O’Keefe J, Delpierre N (2013) Predicting climate change impacts on the amount and duration of autumn colors in a new England Forest. PLoS One 8:e57373.  https://doi.org/10.1371/journal.pone.0057373 CrossRefGoogle Scholar
  4. Cannell M, Smith R (1983) Thermal time, chill days and prediction of budburst in Picea sitchensis. J Appl Ecol 20:951–963CrossRefGoogle Scholar
  5. Chuine I (2000) A unified model for budburst of trees. J Theor Biol 207:337–347.  https://doi.org/10.1006/jtbi.2000.2178 CrossRefGoogle Scholar
  6. Donnelly A, Yu R, Caffarra A, Hanes J, Liang L, Desai AR, Liu L, Schwartz MD (2017) Interspecific and interannual variation in the duration of spring phenophases in a northern mixed forest. Agric For Meteorol 243:55–67.  https://doi.org/10.1016/j.agrformet.2017.05.007 CrossRefGoogle Scholar
  7. Dragoni D, Schmid HP, Wayson CA et al (2011) Evidence of increased net ecosystem productivity associated with a longer vegetated season in a deciduous forest in south-central Indiana, USA. Glob Chang Biol 17:886–897.  https://doi.org/10.1111/j.1365-2486.2010.02281.x CrossRefGoogle Scholar
  8. Duveneck MJ, Thompson JR (2017) Climate change imposes phenological trade-offs on forest net primary productivity. J Geophys Res Biogeosci 122:2298–2313.  https://doi.org/10.1002/2017JG004025 CrossRefGoogle Scholar
  9. Fitzjarrald D, Acevedo O (2001) Climatic consequences of leaf presence in the eastern United States. J Clim 14:598–614CrossRefGoogle Scholar
  10. Gallagher JN (1979) Field studies of cereal leaf growth: I. Initiation and expansion in relation to temperature and ontogeny. J Exp Bot 30:625–636.  https://doi.org/10.1093/jxb/30.4.625 CrossRefGoogle Scholar
  11. Goulden ML, Munger JW, Fan SM et al (1996) Exchange of Carbon Dioxide by a Deciduous Forest : Response to Interannual Climate Variability. Science (80) 271:1576.  https://doi.org/10.1126/science.271.5255.1576 CrossRefGoogle Scholar
  12. Granier C, Tardieu F (1998) Is thermal time adequate for expressing the effects of temperature on sunflower leaf development? Plant Cell Environ 21:695–703.  https://doi.org/10.1046/j.1365-3040.1998.00319.x CrossRefGoogle Scholar
  13. Hunter A, Lechowicz M (1992) Predicting the timing of budburst in temperate trees. J Appl Ecol 29:597–604CrossRefGoogle Scholar
  14. Hwang T, Song C, Vose JM, Band LE (2011) Topography-mediated controls on local vegetation phenology estimated from MODIS vegetation index. Landsc Ecol 26:541–556.  https://doi.org/10.1007/s10980-011-9580-8 CrossRefGoogle Scholar
  15. Jeong S-J, Medvigy D, Shevliakova E, Malyshev S (2012) Uncertainties in terrestrial carbon budgets related to spring phenology. J Geophys Res 117:1–17.  https://doi.org/10.1029/2011JG001868 CrossRefGoogle Scholar
  16. Keenan TF, Gray J, Friedl MA, Toomey M, Bohrer G, Hollinger DY, Munger JW, O’Keefe J, Schmid HP, Wing IS, Yang B, Richardson AD (2014) Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat Clim Chang 4:598–604.  https://doi.org/10.1038/nclimate2253 CrossRefGoogle Scholar
  17. Klosterman S, Richardson A (2017a) Observing spring and fall phenology in a deciduous Forest with aerial drone imagery. Sensors 17:2852.  https://doi.org/10.3390/s17122852 CrossRefGoogle Scholar
  18. Klosterman S, Richardson AD (2017b) Landscape phenology from unmanned aerial vehicle photography at Harvard Forest since 2013. Harvard Forest data archive: HF294. http://harvardforest.fas.harvard.edu:8080/exist/apps/datasets/showData.html?id=hf294. Accessed 1 Dec 2017
  19. Klosterman ST, Hufkens K, Gray JM, Melaas E, Sonnentag O, Lavine I, Mitchell L, Norman R, Friedl MA, Richardson AD (2014) Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11:4305–4320.  https://doi.org/10.5194/bg-11-4305-2014 CrossRefGoogle Scholar
  20. Klosterman S, Melaas E, Wang J et al (2018) Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography. Agric For Meteorol 248:397–407.  https://doi.org/10.1016/J.AGRFORMET.2017.10.015 CrossRefGoogle Scholar
  21. Körner C (2017) A matter of tree longevity. Science 355:130–131.  https://doi.org/10.1126/science.aal2449 CrossRefGoogle Scholar
  22. Melaas EK, Friedl MA, Richardson AD (2016) Multiscale modeling of spring phenology across deciduous forests in the eastern United States. Glob Chang Biol 22:792–805.  https://doi.org/10.1111/gcb.13122 CrossRefGoogle Scholar
  23. Menzel A, Sparks TH, Estrella N et al (2006) European phenological response to climate change matches the warming pattern. Glob Chang Biol 12:1969–1976.  https://doi.org/10.1111/j.1365-2486.2006.01193.x CrossRefGoogle Scholar
  24. Migliavacca M, Sonnentag O, Keenan TF, Cescatti A, O'Keefe J, Richardson AD (2012) On the uncertainty of phenological responses to climate change and its implication for terrestrial biosphere models. Biogeosci Discuss 9:879–926.  https://doi.org/10.5194/bgd-9-879-2012 CrossRefGoogle Scholar
  25. Munger W, Wofsy S, Barford C, Urbanski S (2017) Canopy-atmosphere exchange of carbon, water and energy at Harvard Forest EMS tower since 1991. Harvard Forest data archive: HF004. http://harvardforest.fas.harvard.edu:8080/exist/apps/datasets/showData.html?id=hf004. Accessed 10 Dec 2017
  26. O’Keefe J (2015) Phenology of woody species at Harvard Forest since 1990. Harvard forest data archive: HF003. http://harvardforest.fas.harvard.edu:8080/exist/apps/datasets/showData.html?id=hf003. Accessed 10 Dec 2017
  27. Peñuelas J, Rutishauser T, Filella I (2009) Phenology feedbacks on climate change. Science (80) 324:887–888.  https://doi.org/10.1126/science.1173004 CrossRefGoogle Scholar
  28. Pettorelli N, Pelletier F, von Hardenberg A et al (2007) Early onset of vegetation growth vs. rapid green-up: impacts on juvenile mountain ungulates. Ecology 88:381–390.  https://doi.org/10.1890/06-0875 CrossRefGoogle Scholar
  29. Piao S, Friedlingstein P, Ciais P et al (2007) Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob Biogeochem Cycles 21.  https://doi.org/10.1029/2006GB002888
  30. Piao S, Ciais P, Friedlingstein P, Peylin P, Reichstein M, Luyssaert S, Margolis H, Fang J, Barr A, Chen A, Grelle A, Hollinger DY, Laurila T, Lindroth A, Richardson AD, Vesala T (2008) Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451:49–52.  https://doi.org/10.1038/nature06444 CrossRefGoogle Scholar
  31. Plard F, Gaillard J-M, Coulson T, Hewison AJM, Delorme D, Warnant C, Bonenfant C (2014) Mismatch between birth date and vegetation phenology slows the demography of roe deer. PLoS Biol 12:e1001828.  https://doi.org/10.1371/journal.pbio.1001828 CrossRefGoogle Scholar
  32. Richardson AD, O’Keefe J (2009) Phenological differences between understory and Overstory. In: Noormets A (ed) Phenology of ecosystem processes. Springer, New York, pp 87–117CrossRefGoogle Scholar
  33. Richardson AD, Bailey AS, Denny EG et al (2006) Phenology of a northern hardwood forest canopy. Glob Chang Biol 12:1174–1188.  https://doi.org/10.1111/j.1365-2486.2006.01164.x CrossRefGoogle Scholar
  34. Richardson AD, Braswell BH, Hollinger DY, Jenkins JP, Ollinger SV (2009) Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol Appl 19:1417–1428.  https://doi.org/10.1890/08-2022.1 CrossRefGoogle Scholar
  35. Richardson AD, Anderson RS, Arain MA, Barr AG, Bohrer G, Chen G, Chen JM, Ciais P, Davis KJ, Desai AR, Dietze MC, Dragoni D, Garrity SR, Gough CM, Grant R, Hollinger DY, Margolis HA, McCaughey H, Migliavacca M, Monson RK, Munger JW, Poulter B, Raczka BM, Ricciuto DM, Sahoo AK, Schaefer K, Tian H, Vargas R, Verbeeck H, Xiao J, Xue Y (2012) Terrestrial biosphere models need better representation of vegetation phenology: results from the north American carbon program site synthesis. Glob Chang Biol 18:566–584.  https://doi.org/10.1111/j.1365-2486.2011.02562.x CrossRefGoogle Scholar
  36. Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013) Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric For Meteorol 169:156–173.  https://doi.org/10.1016/j.agrformet.2012.09.012 CrossRefGoogle Scholar
  37. Richardson AD, Hufkens K, Milliman T, Aubrecht DM, Chen M, Gray JM, Johnston MR, Keenan TF, Klosterman ST, Kosmala M, Melaas EK, Friedl MA, Frolking S (2018) Tracking vegetation phenology across diverse north American biomes using PhenoCam imagery. Sci Data 5:180028.  https://doi.org/10.1038/sdata.2018.28 CrossRefGoogle Scholar
  38. Schwartz MD, Ahas R, Aasa A (2006) Onset of spring starting earlier across the northern hemisphere. Glob Chang Biol 12:343–351.  https://doi.org/10.1111/j.1365-2486.2005.01097.x CrossRefGoogle Scholar
  39. Sonnentag O, Hufkens K, Teshera-Sterne C, Young AM, Friedl M, Braswell BH, Milliman T, O’Keefe J, Richardson AD (2012) Digital repeat photography for phenological research in forest ecosystems. Agric For Meteorol 152:159–177.  https://doi.org/10.1016/j.agrformet.2011.09.009 CrossRefGoogle Scholar
  40. Vitasse Y, Porté AJ, Kremer A, Michalet R, Delzon S (2009) Responses of canopy duration to temperature changes in four temperate tree species: relative contributions of spring and autumn leaf phenology. Oecologia 161:187–198.  https://doi.org/10.1007/s00442-009-1363-4 CrossRefGoogle Scholar
  41. Westergaard-Nielsen A, Lund M, Pedersen SH, Schmidt NM, Klosterman S, Abermann J, Hansen BU (2017) Transitions in high-Arctic vegetation growth patterns and ecosystem productivity tracked with automated cameras from 2000 to 2013. Ambio 46:39–52.  https://doi.org/10.1007/s13280-016-0864-8 CrossRefGoogle Scholar
  42. Yu R, Schwartz MD, Donnelly A, Liang L (2016) An observation-based progression modeling approach to spring and autumn deciduous tree phenology. Int J Biometeorol 60:335–349.  https://doi.org/10.1007/s00484-015-1031-9 CrossRefGoogle Scholar

Copyright information

© ISB 2018

Authors and Affiliations

  1. 1.Department of Organismic and Evolutionary BiologyHarvard UniversityCambridgeUSA
  2. 2.UMR 1391 ISPA – Interactions Sol Plante AtmosphèreINRAVillenave D’OrnonFrance
  3. 3.Faculty of Bioscience EngineeringGhent UniversityGhentBelgium
  4. 4.School of Informatics, Computing, and Cyber SystemsNorthern Arizona UniversityFlagstaffUSA
  5. 5.Center for Ecosystem Science and SocietyNorthern Arizona UniversityFlagstaffUSA

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