Elevation-influenced variation in canopy and stem phenology of Qinghai spruce, central Qilian Mountains, northeastern Tibetan Plateau
- 41 Downloads
Canopy and stem phenology of Qinghai spruce, central Qilian Mountains, respond to different environmental factors depending on season and elevation.
To understand vegetation species response to climate change, much research has been devoted to changes in forest phenology. Results of such studies are not only of scientific interest; they are potentially of great use in forest management. This study focuses on variations in canopy and stem phenology as affected by climate and elevation. We collected data on canopy phenology (as recorded in the Normalized Differential Vegetation Index) and stem phenology [using the Vaganov–Shashkin (V–S) model] in Qinghai spruce (Picea crassifolia) growing at two sites in the central Qilian Mountains, Northeast Tibetan Plateau. One site was at a higher elevation, near the local alpine tree-line, and the other was near the local lower tree-line. At both sites, a significant correlation was found between canopy and stem spring phenology. This would seem to be mainly due to spring temperatures. No such correlation was found between canopy and stem autumn phenology. The study suggests that the main factors affecting stem growth after the beginning of growing season would be temperature and soil moisture, and that these have different effects depending on elevation. At the lower elevation, soil moisture seems to be the main factor limiting growth. At the higher elevation, temperature was the determining factor. Climate change will have different effects depending on elevation.
KeywordsSpring phenology Picea crassifolia Stem radial growth Forest management
The study was jointly funded by the National Natural Science Foundation of China (nos. 41701050, 41601051, 41520104005, 41325008), the CAS Light of West China Program, and the Foundation for Excellent Youth Scholars of the Northwest Institute of Eco-Environment and Resources, CAS.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
- Briffa KR, Jones PD (1990) Basic chronology statistics and assessment. In: Cook ER, Kairiukstis LA (eds) Methods of dendrochronology: applications in the environmental sciences. Kluwer Academic Publishers, Dordrecht, pp 137–152Google Scholar
- Cheng GD, Xiao HL, Fu BJ et al (2014) Advances in synthetic research on the eco-hydrological process of the Heihe River Basin. Adv Earth Sci 29:431–437 (in Chinese with English abstract) Google Scholar
- Deslauriers A, Fonti P, Rossi S et al (2017) Ecophysiology and plasticity of wood and phloem formation. In: Amoroso MM, Daniels LD, Baker PJ et al (eds) Dendroecology tree-ring analyses applied to ecological studies. Springer International Publishing AG, SwitzerlandGoogle Scholar
- Evans MN, Reichert BK, Kaplan A et al (2006) A forward modeling approach to paleoclimatic interpretation of tree-ring data. J Geophys Res Biogeosci 111:G03008Google Scholar
- Grissino-Mayer HD (2001) Evaluating crossdating accuracy: a manual and tutorial for the computer program COFECHA. Tree Ring Res 57:205–221Google Scholar
- Settele J, Scholes R, Betts R et al (2014) Terrestrial and inland water systems. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Climate Change 2014: impacts, adaptation, and vulnerability. part a: global and sectoral aspects. Cambridge University Press, Cambridge, pp 271–359Google Scholar
- Vaganov EA, Hughes MK, Shashkin AV (2006) Growth dynamics of tree rings. Springer, BerlinGoogle Scholar
- Wigley TML, Briffa KR, Jones PD (1984) On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. J Clim 23:201–213Google Scholar
- Yang X, Mustard JF, Tang J et al (2012) Regional-scale phenology modeling based on meteorological records and remote sensing observations. J Geophys Res Biogeosci 117:G03029Google Scholar
- Zhang JZ, Gou XH, Pederson N et al (2018) Cambial phenology in Juniperus przewalskii along different altitudinal gradients in a cold and arid region. Tree Physiol 38:840–852Google Scholar