Skip to main content
SpringerLink
Log in

Phase difference analysis of temperature and vegetation phenology for beech forest: a wavelet approach

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Temporal shifts in phenology or vegetation period of plants are seen as indicators of global warming with potentially severe impacts on ecosystem functioning. In spite of increasing knowledge on drivers, it is of utmost importance to disentangle the relationship between air temperatures, phenological events, potential temporal lags (phase shifts) and time scale for certain plant species. Assessing the phase shifts as well as the scale-dependent relationship between temperature and vegetation phenology requires the development of a nonlinear temporal model. Therefore, we use wavelet analysis and present a framework for identifying scale-dependent cross-phase coupling of bivariate time series. It allows the calculation of (a) scale-dependent decompositions of time series, (b) phase shifts of seasonal components in relation to the annual cycle, and (c) inter-annual phase differences between seasonal phases of different time series. The model is applied to air temperature data and remote sensing phenology data of a beech forest in Germany. Our study reveals that certain seasonal changes in amplitude and phase with respect to the normal annual rhythm of temperature and beech phenology are coupled time-delayed components, which are characterized by a time shift of about one year.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Aguiar-Conraria A, Azevedo N, Soares M (2008) Using wavelets to decompose the time–frequency effects of monetary policy. Phys A 387:2863–2878

    Article  Google Scholar 

  • Allefeld C, Kurths J (2004) Testing for phase synchronization. Int J Bifurcat Chaos 14:405–416

    Article  Google Scholar 

  • Badeck F, Bondeau A, Böttcher K, Doktor D, Lucht W, Schaber J, Sitch S (2004) Responses of spring phenology to climate change. New Phytol 162:295–309

    Article  Google Scholar 

  • Barbaroux C, Bréda N (2002) Contrasting distribution and seasonal dynamics of carbohydrate reserves in stem wood of adult ring-porous sessile oak and diffuse-porous beech trees. Tree Physiol 22:1201–1210

    Article  CAS  Google Scholar 

  • Bunn A (2008) A dendrochronology program library in R (dplR). Dendrochronologia 26:115–124

    Article  Google Scholar 

  • Campioli M, Gielen B, Göckede M, Papale D, Bouriaud O, Granier A (2011) Temporal variability of the NPP–GPP ratio at seasonal and interannual time scales in a temperate beech forest. Biogeosciences 8:2481–2492

    Article  CAS  Google Scholar 

  • Cazelles B, Stone L (2003) Detection of imperfect population synchrony in an uncertain world. J Anim Ecol 72:953–968

    Article  Google Scholar 

  • Cazelles B, Chavez M, McMichael A, Hales S (2005) Nonstationary influence of El Niño on the synchronous dengue epidemics in Thailand. PLoS Med 2:313–318

    Article  Google Scholar 

  • Cazelles B, Chavez M, Berteaux D, Ménard F, Vik J, Jenouvrier S, Stenseth N (2008) Wavelet analysis of ecological time series. Oecologia 156:287–304

    Article  Google Scholar 

  • Chmielewski F, Rötzer T (2001) Response of tree phenology to climate change across Europe. Agric For Meteorol 108:101–112

    Article  Google Scholar 

  • Cleland E, Chuine I, Menzel A, Mooney H, Schwartz M (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22:357–365

    Article  Google Scholar 

  • Čufar K, Prislan P, de Luis M, Gričar J (2008) Tree-ring variation, wood formation and phenology of beech (Fagus sylvatica) from a representative site in Slovenia, SE Central Europe. Trees 22:749–758

    Article  Google Scholar 

  • Delbart N, Picard G, Le Toan T, Kergoat L, Quegan S, Woodward I, Fedotova V (2008) Spring phenology in boreal Eurasia over a nearly century time scale. Glob Change Biol 14:603–614

    Article  Google Scholar 

  • Doktor D, Bondeau A, Koslowski D, Badeck F (2009) Influence of heterogeneous landscapes on computed green-up dates based on daily AVHRR NDVI observations. Remote Sens Environ 113:2618–2632

    Article  Google Scholar 

  • Foufoula-Georgiou E, Kumar P (1997) Wavelet analysis for geophysical applications. Rev Geophys 35(4):385–412

    Article  Google Scholar 

  • Gough CM, Flower CE, Vogel CS, Curtis PS (2010) Phenological and temperature controls on the temporal non-structural carbohydrate dynamics of Populus grandidentata and Quercus rubra. Forests 1:65–81

    Article  Google Scholar 

  • Goward S, Huemmrich K (1992) Vegetation canopy PAR absorptance and the normalized difference vegetation index: an assessment using the SAIL model. Remote Sens Environ 39:119–140

    Article  Google Scholar 

  • Grenfell B, Bjørnstad O, Kappey J (2001) Travelling waves and spatial hierarchies in measles epidemics. Nature 414:716–723

    Article  CAS  Google Scholar 

  • Hudson I (2010) Interdisciplinary approaches: towards new statistical methods for phenological studies. Clim Change 100:143–171

    Article  Google Scholar 

  • Hudson I, Keatley MR, Kang I (2011) Wavelet characterization of eucalypt flowering and the influence of climate. Environ Ecol Stat 18:513–533

    Article  Google Scholar 

  • Klvana I, Berteaux D, Cazelles B (2004) Porcupine feeding scars and climatic data show ecosystem effects of the solar cycle. Am Nat 164:283–297

    Article  Google Scholar 

  • Lachaux JP, Rodriguez E, le van Quyen M, Lutz A, Martinerie J, Varela F (2000) Studying single-trials of phase synchronous activity in the brain. Int J Bifurcat Chaos 10:2429–2439

    Google Scholar 

  • Lu P, Yu Q, Liu J, He Q (2006) Effects of changes in spring temperature on flowering dates of woody plants across China. Bot Stud 47:153–161

    Google Scholar 

  • Mariappan N (2010) Net primary productivity estimation of Eastern Ghats using multispectral MODIS data. Int J Geomat Geosci 1:406–413

    Google Scholar 

  • Menzel A (2000) Trends in phenological phases in Europe between 1951 and 1996. Int J Biometeorol 44:76–81

    Article  CAS  Google Scholar 

  • Menzel A (2003) Plant phenological anomalies in Germany and their relation to air temperature and NAO. Clim Change 57:243–263

    Article  Google Scholar 

  • Menzel A, Sparks T, Estrella N, Roy D (2006) Altered geographic and temporal variability in phenology in response to climate change. Glob Ecol Biogeogr 15:498–504

    Google Scholar 

  • Miller-Rushing A, Inouye D, Primack R (2008) How well do first flowering dates measure plant responses to climate change? The effects of population size and sampling frequency. J Ecol 96:1289–1296

    Article  Google Scholar 

  • Paluš M, Novotná D (2006) Quasi-biennial oscillations extracted from the monthly NAO index and temperature records are phase-synchronized. Nonlin Process Geophys 13:287–296

    Article  Google Scholar 

  • Paluš M, Novotná D (2011) Northern hemisphere patterns of phase coherence between solar/geomagmetic activity and NCEP/NCAR and ERA40 near-surface air temperature in period 7–8 y oscillatorymodes. Nonlin Process Geophys 18:251–260

    Article  Google Scholar 

  • Paluš M, Novotná D, Tichavský P (2005) Shifts of seasons at the European mid-latitudes: natural fluctuations correlated with the North Atlantic oscillation. Geophys Res Lett 32:L12805

    Article  Google Scholar 

  • Parmesan C (2007) Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob Change Biol 13:1860–1872

    Article  Google Scholar 

  • Percival D, Walden A (2000) Wavelet methods for time series analysis. Cambridge University Press, Cambridge

    Google Scholar 

  • Percival D, Wang M, Overland J (2004) An introduction to wavelet analysis with applications to vegetation time series. Community Ecol 5:19–30

    Article  Google Scholar 

  • Pikovsky A, Rosenblum M, Kurths J (2001) Synchronization, a universal concept in nonlinear sciences. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Post E, Pedersen C, Wilmers C, Forchhammer M (2008) Phenological sequences reveal aggregate life history response to climatic warming. Ecology 89:363–370

    Article  Google Scholar 

  • Sjöström M, Ardö J, Arneth A, Boulain N, Cappelaere B, Eklundh L, Veenendaal E (2011) Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems. Remote Sens Environ 115:1081–1089

    Article  Google Scholar 

  • Skomarkova MV, Vaganov EA, Mund M, Knohl A, Linke P, Boerner A, Schulze ED (2006) Inter-annual and seasonal variability of radial growth, wood density and carbon isotope ratios in tree rings of beech (Fagus sylvatica) growing in Germany and Italy. Trees 20:571–586

    Article  Google Scholar 

  • Sparks T, Jeffree E, Jeffree C (2000) An examination of the relationship between flowering times and temperature at the national scale using long-term phenological records from the UK. Int J Biometeorol 44:82–87

    Article  CAS  Google Scholar 

  • R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0. http://www.R-project.org}

  • Torrence C, Compo G (1998) A practical guide to wavelet analysis. Bull Am Meteorol Soc 79:61–78

    Article  Google Scholar 

  • Torrence C, Webster P (1999) Interdecadal changes in the ENSO–monsoon system. J Clim 12:2679–2690

    Article  Google Scholar 

  • Wareing P (1953) Growth studies in woody species V. Photoperiodism in dormant buds of Fagus sylvatica L. Physiol Plant 6:692–706. doi:10.1111/j.1399-3054.1953.tb08442.x

    Article  Google Scholar 

  • White M, Brunsell N, Schwartz M (2003) Vegetation phenology in global change studies. In: Schwartz M (ed) Phenology: an integrative environmental science. Kluwer Academic Publishers, Dordrecht, pp 453–466

    Chapter  Google Scholar 

  • Yang T, Xu C, Shao Q, Chen X, Lu G, Hao Z (2010) Temporal and spatial patterns of low-flow changes in the Yellow River in the last half century. Stoch Environ Res Risk Assess 24:297–309

    Article  Google Scholar 

  • Yang Y, Xu J, Hong Y, Lv G (2012) The dynamic of vegetation coverage and its response to climate factors in Inner Mongolia, China. Stoch Environ Res Risk Assess 26:357–373

    Article  Google Scholar 

  • Zhang Q, Xu C, Chen Y (2010) Wavelet-based characterization of water level behaviors in the Pearl River estuary, China. Stoch Environ Res Risk Assess 24:81–92

    Article  CAS  Google Scholar 

Download references

Acknowledgments

We acknowledge support by the EU infrastructure project “Distributed Infrastructure for EXPErimentation in Ecosystem Research—EXPEER” (project reference 262060) funded under 7th Framework Programme. We would also like to thank Maximilian Lange (UFZ) who provided assistance with data assimilation.

Author information

Authors and Affiliations

  1. Department of Community Ecology, UFZ—Helmholtz Centre for Environmental Research, Theodor-Lieser-Str. 4, 06120, Halle, Germany

    Gudrun Carl & Ingolf Kühn

  2. Department of Computational Landscape Ecology, UFZ—Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany

    Daniel Doktor

  3. Department of Meteorology, Free University of Berlin, Carl-Heinrich-Becker-Weg 6-10, 12165, Berlin, Germany

    Dirk Koslowsky

Authors
  1. Gudrun Carl
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Doktor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dirk Koslowsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ingolf Kühn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gudrun Carl.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Carl, G., Doktor, D., Koslowsky, D. et al. Phase difference analysis of temperature and vegetation phenology for beech forest: a wavelet approach. Stoch Environ Res Risk Assess 27, 1221–1230 (2013). https://doi.org/10.1007/s00477-012-0658-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-012-0658-x

Keywords

Search

Navigation