Phenological Differences Between Understory and Overstory

A Case Study Using the Long-term Harvard Forest Records
  • Andrew D. Richardson
  • John O’Keefe


The timing of phenological events varies both among species, and also among individuals of the same species. Here we use a 12-year record of spring and autumn phenology for 33 woody species at the Harvard Forest to investigate these differences. Specifically, we focus on patterns of leaf budburst, expansion, coloration and fall, in the context of differences between canopy and understory species, and between canopy and understory individuals of the same species. Many understory species appear to adopt a strategy of “phenological escape” in spring but not autumn, taking advantage of the high-light period in spring before canopy development. For all but a few of these species, the spring escape period is very brief. Relationships between canopy and understory conspecifics (i.e. individuals of the same species) varied among species, with leaf budburst and leaf fall occurring earlier in understory individuals of some species, but later in other species. We fit standard models of varying complexity to the budburst time series for each species to investigate whether biological responses to environmental cues differed among species. While there was no clear consensus model, Akaike’s Information Criterion (AIC) indicated that a simple two-parameter “Spring warming” model was best supported by the data for more than a third of all species, and well supported for two-thirds of all species. More highly-parameterized models involving various chilling requirements (e.g., Alternating, Parallel or Sequential chilling) were less well supported by the data. Species-specific model parameterization suggested that responses to both chilling and forcing temperatures vary among species. While there were no obvious differences in this regard between canopy and understory species, or between early- and late-budburst species, these results imply that species can be expected to differ in their responses to future climate change.


Sugar Maple Leaf Coloration Understory Species Canopy Species Yellow Birch 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The National Science Foundation Long-Term Ecological Research (LTER) program supported the research at Harvard Forest. A.D.R. acknowledges support from the Northeastern States Research Cooperative. We thank Jeremy Fisher, Brenden McNeil, Alan Barr, Abraham Miller-Rushing, Jake Weltzin, and Asko Noormets for helpful comments on drafts of this chapter. This is a contribution of the Northeast Regional Phenology Network (NE-RPN).


  1. Akaike, H. (1973) Information theory and an extension of the maximum likelihood principle. In: B.N. Petrov and F. Csaki (Eds.), Proceedings of the 2nd International Symposium on Information Theory, Akademiai Kiado, Budapest, pp. 267–281. (Reproduced in: S. Kotz and N.L. Johnson (2003), Breakthroughs in Statistics, Vol. I, Foundations and Basic Theory. Springer, New York, pp. 610–624.)Google Scholar
  2. Anderson, D.R., Burnham, K.P. and Thompson, W.L. (2000) Null hypothesis testing: problems, prevalance and an alternative. J. Wildlife Manage. 64, 912–923.CrossRefGoogle Scholar
  3. Augspurger, C.K. (2004) Developmental versus environmental control of early leaf phenology in juvenile Ohio buckeye (Aesculus glabra). Can. J. Bot. 82, 31–36.CrossRefGoogle Scholar
  4. Augspurger, C.K. and Bartlett, E.A. (2003) Differnces in leaf phenology between juvenile and adult trees in a temperate deciduous forest. Tree Physiol. 23, 517–525.Google Scholar
  5. Augspurger, C.K., Cheeseman, J.M. and Salk, C.F. (2005) Light gains and physiological capacity of understorey woody plants during phenological avoidance of canopy shade. Funct. Ecol. 19, 537–547.CrossRefGoogle Scholar
  6. Baldocchi, D., Hutchison, B., Matt, D. and McMillen, R. (1984) Seasonal variations in the radiation regime within an oak-hickory forest. Agric. For. Meteorol. 33, 177–191.CrossRefGoogle Scholar
  7. Baldochi, D., Hutchison, B., Matt, D. and McMillen, R. (1986) Seasonal variation in the statistics of photosynthetically active radiation penetration in an oak-hickory forest. Agric. For. Meteorol. 36, 343–361.CrossRefGoogle Scholar
  8. Barr, A.G., Black, T.A., Hogg, E.H., Kljun, N., Morgenstern, K. and Nesic, Z. (2004) Inter-annual variability in the leaf area index of a boreal aspen-hazelnut forest in relation to net ecosystem production. Agric. For. Meteorol. 126, 237–255.CrossRefGoogle Scholar
  9. Boardman, N.K. (1977) Comparative photosynthesis of sun and shade plants. Ann. Rev. Plant Physiol. 28, 355–377.CrossRefGoogle Scholar
  10. Burnham, K.P. and Anderson, D.R. (2002) Model selection and multimodel inference: a practical information-theoretic approach (2nd ed.). Springer, New York.Google Scholar
  11. Cannell, M. and Smith, R. (1983) Thermal time, chill days and prediction of budburst in Picea sitchensis. J. Appl. Ecol. 20, 951–963.CrossRefGoogle Scholar
  12. Chazdon, R.L. and Pearcy, R.W. (1991) The importance of sunflecks for forest understory plants. BioScience 41, 760–766.CrossRefGoogle Scholar
  13. Chen, W.J., Black, T.A., Yang, P.C., Barr, A.G., Neumann, H.H., Nesic, Z., Blanken, P.D., Novak, M.D., Eley, J., Ketler, R.J. and Cuenca, R. (1999) Effects of climatic variability on the annual carbon sequestration by a boreal aspen forest. Global Change Biol. 5, 41–53.CrossRefGoogle Scholar
  14. Chuine, I. (2000) A unified model for budburst of trees. J. of Theor. Biol. 207, 337–347.CrossRefGoogle Scholar
  15. Chuine, I., Cambon, G. and Comtois, P. (2000) Scaling phenology from the local to the regional level: advances from species-specific phenological models. Global Change Biol. 6, 943–952.CrossRefGoogle Scholar
  16. Chuine, I., Cour, P. and Rousseau, D.D. (1998) Fitting models predicting dates of flowering of temperate-zone trees using simulated annealing. Plant Cell Environ. 21, 455–466.CrossRefGoogle Scholar
  17. Chuine, I., Cour, P. and Rousseau, D.D. (1999) Selecting models to predict the timing of flowering of temperate trees: implications for tree phenology modelling. Plant Cell Environ. 22, 1–13.CrossRefGoogle Scholar
  18. Crawley, M.J. (1997) Life history and environment. In: M.J. Crawley (Ed.) Plant Ecology. Blackwell Science, Oxford, pp. 73–131.Google Scholar
  19. dePamphilis, C.W. and Neufeld, H.S. (1989) Phenology and ecophysiology of Aesculus sylvatica, a vernal understory tree. Can. J. Bot. 67, 2161–2167.CrossRefGoogle Scholar
  20. Fisher, J.I., Mustard, J.F. and Vadeboncoeur, M.A. (2006) Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote Sens. Environ. 100, 265–279.CrossRefGoogle Scholar
  21. Fisher, J.I., Richardson, A.D. and Mustard, J.F. (2007) Phenology model from surface meteorology does not capture satellite-based greenup estimations. Global Change Biol. 13, 707–721.CrossRefGoogle Scholar
  22. Gill, D.S., Amthor, J.S. and Bormann, F.H. (1998) Leaf phenology, photosynthesis, and the persistence of saplings and shrubs in a mature northern hardwood forest. Tree Physiol. 18, 281–289.Google Scholar
  23. Gu, L., Hanson, P.J., Post, W.M., Kaiser, D.P., Yang, B., Nemani, R., Pallardy, S.G., Meyers, T., 2008. The 2007 eastern US spring freeze: Increased cold damage in a warming world? BioScience, 58, 253–262.CrossRefGoogle Scholar
  24. Hänninen, H. (1995) Effects of climatic change on trees from cool and temperate regions: an ecophysiological approach to modeling of bud burst phenology. Can. J. Bot. 73, 183–199.CrossRefGoogle Scholar
  25. Harrington, R.A., Brown, B.J. and Reich, P.B. (1989) Ecophysiology of exotic and native shrubs in Southern Wisconsin. Oecologia 80, 356–367.CrossRefGoogle Scholar
  26. Hull, J.C. (2002) Photosynthetic induction dynamics to sunflecks of four deciduous forest understory herbs with different phenologies. Int. J. Plant Sci. 163, 913–924.CrossRefGoogle Scholar
  27. Hunter, A.F. and Lechowicz, M.J. (1992) Predicting the timing of budburst in temperate trees. J. Appl. Ecol. 29, 597–604.CrossRefGoogle Scholar
  28. Hurvich, C.M. and Tsai, C.L. (1990) Model selection for least absolute deviations regression in small samples. Stat. Prob. Lett. 9, 259–265.CrossRefGoogle Scholar
  29. Jolly, W.M., Nemani, R. and Running, S.W. (2004) Enhancement of understory productivity by asynchroouse phenology with overstory competitiors in a temperate deciduous forest. Tree Physiol. 24, 1069–1071.Google Scholar
  30. Kato, S. and Komiyama, A. (2002) Spatial and seasonal heterogeneity in understory light conditions caused by differential leaf flushing of deciduous overstory trees. Ecol. Res. 17, 687–693.CrossRefGoogle Scholar
  31. Kramer, K. (1994) A modelling analysis of the effects of climatic warming on the probability of spring frost damage to tree species in the Netherlands and Germany. Plant Cell Environ. 17, 367–377.CrossRefGoogle Scholar
  32. Lassoie, J.P., Dougherty, P.M., Reich, P.B., Hinckley, T.M., Metcalf, C.M. and Dina, S.J. (1983) Ecophysiological investigations of understory eastern red cedar in central Missouri. Ecology 63, 1355–1366.CrossRefGoogle Scholar
  33. Lechowicz, M.J. (1984) Why do temperate deciduous trees leaf out at different times? Adaptation and ecology of forest communities. Am. Nat. 124, 821–842.CrossRefGoogle Scholar
  34. Lichtenthaler, H.K., Buschmann, C., Döll, M., Fietz, H.J., Bach, T., Kozel, U., Meier, D. and Rahmsdorf, U. (1981) Photosynthetic activity, chloroplast ultrastructure and leaf characteristics of high-light and low-light plants and of sun and shade leaves. Photosynth. Res. 2, 115–141.CrossRefGoogle Scholar
  35. Lovett, G.M., Burns, D.A., Driscoll, C.T., Jenkins, J.C., Mitchell, M.J., Rustad, L., Shanley, J.B., Likens, G.E. and Haeuber, R. (2007) Who needs environmental monitoring? Front. Ecol. Environ. 5, 253–260.CrossRefGoogle Scholar
  36. Mahall, B.E. and Bormann, F.H. (1978) A quantitativ description of the vegetative phenology of herbs in a northern hardwood forest. Bot. Gaz. 139, 467–481.CrossRefGoogle Scholar
  37. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E. (1953) Equations of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092.CrossRefGoogle Scholar
  38. Morecroft, M.D., Stokes, V.J. and Morison, J.I.L. (2003) Seasonal changes in the photosynthetic capacity of canopy oak (Quercus robur) leaves: the impact of slow development on annual carbon uptake. Int. J. Biometeor. 47, 221–226.CrossRefGoogle Scholar
  39. Morisette, J.T., Richardson, A.D., Knapp, A.K., Fisher, J.I., Graham, E., Abatzoglou, J., Wilson, B.E., Breshears, D.D., Henebry, G.M., Hanes, J.M. and Liang, L. (2009) Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st Century. Front. Ecol. Environ., doi: 10.1890/070217.Google Scholar
  40. Motulsky, H.J. and Christopoulos, A. (2003) Fitting models to biological data using linear and nonlinear regression. A practical guide to curve fitting. GraphPad Software, Inc., San Diego, CA.Google Scholar
  41. Muller, R.N. (1978) The phenology, growth and ecosystem dynamics of Erythronium americanum in the northern hardwood forest. Ecol. Monogr. 48, 1–20.CrossRefGoogle Scholar
  42. Murray, M.B., Cannell, M.G.R. and Smith, R.I. (1989) Date of budburst of fifteen tree species in Britain following climatic warming. J. Appl. Ecol. 26, 693–700.CrossRefGoogle Scholar
  43. Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (1992) Numerical recipes in Fortran 77: The art of scientific computing. Cambridge UP, New York.Google Scholar
  44. Rathcke, B. and Lacey, E.P. (1985) Phenological patterns of terrestrial plants. Ann. Rev. Ecol. Syst. 16, 179–214.CrossRefGoogle Scholar
  45. Raulier, F. and Bernier, P.Y. (2000) Predicting the date of leaf emergence for sugar maple across its native range. Can. J. For. Res. 30, 1429–1435.CrossRefGoogle Scholar
  46. Richardson, A.D., Bailey, A.S., Denny, E.G., Martin, C.W. and O’Keefe, J. (2006) Phenology of a northern hardwood forest canopy. Global Change Biol. 12, 1174–1188.CrossRefGoogle Scholar
  47. Rothstein, D.E. and Zak, D.R. (2001) Photosynthetic adaptation and acclimation to exploit seasonal periods of direct irradiance in three temperate, deciduous-forest herbs. Funct. Ecol. 15, 722–731.CrossRefGoogle Scholar
  48. Sakai, R.K., Fitzjarrald, D.R. and Moore, K.E. (1997) Detecting leaf area and surface resistance during transition seasons. Agric. For. Meteorol. 84, 273–284.CrossRefGoogle Scholar
  49. Sarvas, R. (1974) Investigations on the annual cycle of development of forest trees. II. Autumn dormancy and winter dormancy. Communicationes Instituti Forestalis Fenniae 84, 1–101.Google Scholar
  50. Schaber, J. and Badeck, F.W. (2003) Physiology-based phenology models for forest tree species in Germany. Int. J. Biometeor. 47, 193–201.CrossRefGoogle Scholar
  51. Schlichting, C.D. (1986) The evolution of phenotypic plasticity in plants. Ann. Rev. Ecol. Syst. 17, 667–693.CrossRefGoogle Scholar
  52. Schwartz, G.D. (1978) Estimating the dimension of a model. Ann. Stat. 6, 461–464.CrossRefGoogle Scholar
  53. Schwartz, M.D. (1997) Spring index models: An aproach to connecting satellite and surface phenology. In: H. Lieth and M.D. Schwartz (Eds.), Phenology in Seasonal Climates. Backhuys Publishers, Leiden, pp. 23–38.Google Scholar
  54. Schwartz, M.D., Ahas, R. and Aasa, A. (2006) Onset of spring starting earlier across the Northern Hemisphere. Global Change Biol. 12, 343–351.CrossRefGoogle Scholar
  55. Seiwa, K. (1999a) Changes in leaf phenology are dependent on tree height in Acer mono, a deciduous broad-leaved tree. Ann. Bot. 83, 355–361.CrossRefGoogle Scholar
  56. Seiwa, K. (1999b) Ontogenetic changes in leaf phenology of Ulmus davidiana var. japonica, a deciduous broad-leaved tree. Tree Physiol. 19, 793–797.Google Scholar
  57. Sparling, J.H. (1967) Assimilation rates of some woodland herbs in Ontario. Bot. Gaz. 128, 160–168.CrossRefGoogle Scholar
  58. Sultan, S.E. (1995) Phenotypic plasticity and plant adaptation. Acta Bot. Neerlandica 44, 363–383.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Organismic and Evolutionary BiologyHarvard UniversityCambridgeMA
  2. 2.Harvard ForestPetershamMA

Personalised recommendations