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

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.

[1]  S. Quegan,et al.  Spring phenology in boreal Eurasia over a nearly century time scale , 2008 .

[2]  Nathaniel A. Brunsell,et al.  Vegetation Phenology in Global Change Studies , 2003 .

[3]  Bernard Cazelles,et al.  Porcupine Feeding Scars and Climatic Data Show Ecosystem Effects of the Solar Cycle , 2004, The American Naturalist.

[4]  H. Mooney,et al.  Shifting plant phenology in response to global change. , 2007, Trends in ecology & evolution.

[5]  田中 勝人.  D. B. Percival and A. T. Walden: Wavelet Methods for Time Series Analysis, Camb. Ser. Stat. Probab. Math., 4, Cambridge Univ. Press, 2000年，xxvi + 594ページ． , 2009 .

[6]  M. Paluš,et al.  Nonlinear Processes in Geophysics Northern Hemisphere patterns of phase coherence between solar / geomagnetic activity and NCEP / NCAR and ERA 40 near-surface air temperature in period 7 – 8 years oscillatory modes , 2011 .

[7]  A. Menzel,et al.  Trends in phenological phases in Europe between 1951 and 1996 , 2000, International journal of biometeorology.

[8]  J. Schaber,et al.  Responses of spring phenology to climate change , 2004 .

[9]  C. Parmesan.  Influences of species, latitudes and methodologies on estimates of phenological response to global warming , 2007 .

[10]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[11]  Daniel Doktor,et al.  Influence of heterogeneous landscapes on computed green-up dates based on daily AVHRR NDVI observations , 2009 .

[12]  Maria Joana Soares,et al.  Using wavelets to decompose the time–frequency effects of monetary policy , 2008 .

[13]  Andrew G. Bunn,et al.  A dendrochronology program library in R (dplR) , 2008 .

[14]  T. Sparks,et al.  An examination of the relationship between flowering times and temperature at the national scale using long-term phenological records from the UK , 2000, International journal of biometeorology.

[15]  Donald B. Percival,et al.  An introduction to wavelet analysis with applications to vegetation time series , 2004 .

[16]  P. Wareing.  Growth Studies in Woody Species V. Photoperiodism in Dormant Buds of Fagus sylvatica L. , 1953 .

[17]  Effects of changes in spring temperature on flowering dates of woody plants across China , 2006 .

[18]  Annette Menzel,et al.  Altered geographic and temporal variability in phenology in response to climate change , 2006 .

[19]  A. Menzel.  Plant Phenological Anomalies in Germany and their Relation to Air Temperature and NAO , 2003 .

[20]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[21]  N. Mariappan.  Net Primary Productivity Estimation of Eastern Ghats using Multispectral MODIS Data , 2010 .

[22]  Chong-yu Xu,et al.  Wavelet-based characterization of water level behaviors in the Pearl River estuary, China , 2010 .

[23]  Jürgen Kurths,et al.  Synchronization - A Universal Concept in Nonlinear Sciences , 2001, Cambridge Nonlinear Science Series.

[24]  Dario Papale,et al.  Temporal variability of the NPP-GPP ratio at seasonal and interannual time scales in a temperate beech forest , 2011 .

[25]  P. Webster,et al.  Interdecadal changes in the ENSO-monsoon system , 1999 .

[26]  Jonas Ardö,et al.  Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems , 2011 .

[27]  Chong-Yu Xu,et al.  Temporal and spatial patterns of low-flow changes in the Yellow River in the last half century , 2010 .

[28]  T. Rötzer,et al.  Response of tree phenology to climate change across Europe , 2001 .

[29]  J. Vik,et al.  Wavelet analysis of ecological time series , 2008, Oecologia.

[30]  I. Kang,et al.  Wavelet characterization of eucalypt flowering and the influence of climate , 2011, Environmental and Ecological Statistics.

[31]  M. D. Schwartz.  Phenology: An Integrative Environmental Science , 2003, Tasks for Vegetation Science.

[32]  Jianhua Xu,et al.  The dynamic of vegetation coverage and its response to climate factors in Inner Mongolia, China , 2012, Stochastic Environmental Research and Risk Assessment.

[33]  M. Luis,et al.  Tree-ring variation, wood formation and phenology of beech (Fagus sylvatica) from a representative site in Slovenia, SE Central Europe , 2008, Trees.

[34]  Geoffrey M. Henebry,et al.  Vegetation Phenology in Global Change Studies , 2013 .

[35]  N. Breda,et al.  Contrasting distribution and seasonal dynamics of carbohydrate reserves in stem wood of adult ring-porous sessile oak and diffuse-porous beech trees. , 2002, Tree physiology.

[36]  E. Post,et al.  Phenological sequences reveal aggregate life history response to climatic warming. , 2008, Ecology.

[37]  C. Gough,et al.  Phenological and Temperature Controls on the Temporal Non-Structural Carbohydrate Dynamics of Populus grandidentata and Quercus rubra , 2010 .

[38]  Petr Tichavský,et al.  Shifts of seasons at the European mid‐latitudes: Natural fluctuations correlated with the North Atlantic Oscillation , 2005 .

[39]  S. Goward,et al.  Vegetation canopy PAR absorptance and the normalized difference vegetation index - An assessment using the SAIL model , 1992 .

[40]  Irene Lena Hudson,et al.  Interdisciplinary approaches: towards new statistical methods for phenological studies , 2010 .

[41]  Jürgen Kurths,et al.  Testing for Phase Synchronization , 2004, Int. J. Bifurc. Chaos.

[42]  R. Primack,et al.  How well do first flowering dates measure plant responses to climate change? The effects of population size and sampling frequency , 2008 .

[43]  Eugenio Rodriguez,et al.  Studying Single-Trials of phase Synchronous Activity in the Brain , 2000, Int. J. Bifurc. Chaos.

[44]  Jürgen Kurths,et al.  Synchronization: Phase locking and frequency entrainment , 2001 .

[45]  Michael R. Chernick,et al.  Wavelet Methods for Time Series Analysis , 2001, Technometrics.

[46]  Milan Paluš,et al.  Quasi-biennial oscillations extracted from the monthly NAO index and temperature records are phase-synchronized , 2006 .

[47]  Anthony J McMichael,et al.  Nonstationary Influence of El Niño on the Synchronous Dengue Epidemics in Thailand , 2005, PLoS medicine.

[48]  E. Schulze,et al.  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 , 2006, Trees.

[49]  Bernard Cazelles,et al.  Detection of imperfect population synchrony in an uncertain world , 2003 .

[50]  E. Foufoula‐Georgiou,et al.  Wavelet analysis for geophysical applications , 1997 .

[51]  O. Bjørnstad,et al.  Travelling waves and spatial hierarchies in measles epidemics , 2001, Nature.