Sciences in Cold and Arid Regions ›› 2021, Vol. 13 ›› Issue (6): 496-509.doi: 10.3724/SP.J.1226.2021.20080

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Climate response and radial growth of Pinus tabulaeformis at different altitudes in Qilian Mountains

Liang Jiao1,2(),ChangLiang Qi1,RuHong Xue1,Ke Chen1,XiaoPing Liu1   

  1. 1.College of Geography and Environmental Science, Northwest Normal University, No. 967, Anning East Road, Lanzhou, Gansu 730070, China
    2.State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
  • Received:2020-08-30 Accepted:2020-11-12 Online:2021-12-31 Published:2022-01-11
  • Contact: Liang Jiao
  • Supported by:
    the National Natural Science Foundation of China(41861006);Project / Supported by State Key Laboratory of Earth Surface Processes and Resource Ecology(2020-KF04);the Research Ability Promotion Program for Young Teachers of Northwest Normal University(NWNU-LKQN2019-4)


In order to test whether the relationship between climate and the radial growth of trees is affected by altitude, altitude variability and time stability of climate-influenced radial growth of a dominant conifer, Chinese pine (Pinus tabulaeformis), in the eastern Qilian Mountains were studied against the background of climate change using dendrochronology. Results show that 1) droughts at the end of the growing season of last year and during the early and middle growing season of the current year were the main limiting factors for the radial growth of Chinese pine at two altitude gradients; this was determined by analyzing the relationship between tree-ring width chronologies and climate factors. 2) The sensitivity of the radial growth of trees to climate change gradually decreased and was affected more by drought stress at a lower altitude. 3) An unstable divergence response was observed in the radial growth at the two altitudes, in response to controlling climatic factors; this observation was based on the moving correlation analysis of growth/climate relationships, and the aggravation of drought stress caused by increasing temperature was the main reason. 4) The growth rate of Chinese pine at the two altitudes increased at first and then decreased, as measured by basal area increment (BAI) modeling. Future temperature rises may have significant effects on mountain forest ecosystems in arid and semi-arid regions. Effective management and protection measures should be taken, according to the response patterns of trees to climate change at different altitude gradients.

Key words: eastern Qilian Mountains, altitude effect, drought stress, divergence response, Pinus tabulaeformis

Figure 1

Locations of the sampling site and the nearest meteorological station"

Figure 2

Monthly (a) and interannual (b) variability and Mann–Kendall test (c) annual mean temperature, (d) annual total precipitation; (C1 represents U values for the normal time series, C2 represents U values for the retrograde time series, and the solid lines indicate the value of significance at the 0.01 level) of total precipitation and minimum, mean, and maximum temperatures during 1960-2018"

Table 1

Sampling area information"

Sampling areaLongitudeLatitudeElevation (m)Average tree height (m)Average breast diameter (cm)SlopeSlope direction
High altitude102°44′25″E36°41′18″N2,26016.033.921°North
Low altitude102°44′25″E36°41′27″N2,05514.535.718°North

Figure 3

Tree-ring width residual chronologies and sample depth (number of cores) at different altitudes (red line represents 10-year moving average; SSS >0.85 means starting year of EPS reaching or exceeding 0.85)"

Table 2

Statistical characteristics of chronologies at different altitudes (common period 1960-2018)"

Dendrochronological ParametersHigh altitudeLow altitude
Cores/trees50 (25)44 (22)
Chronology span1898-20181893-2018
Standard deviation (SD)0.1830.164
Variance in the first principal comment (PC1)0.2670.313
Mean sensitivity (MS)0.1780.147
First-order serial Autocorrelation (AC1)0.2090.355
Mean correlation for all series (R)0.1340.245
Mean correlation within-trees (R1)0.5630.261
Mean correlation between trees (R2)0.1190.240
Signal-to-noise ratio (SNR)13.45512.174
Expressed population signal (EPS)0.9310.924
First year of SSS >0.85 (number of tree)1921 (9)1938 (7)

Figure 4

Correlation results of chronologies at different altitudes and climatic factors (black solid five-pointed star reaches 0.01 significance level, black hollow five-pointed star reaches 0.05 significance level; P: previous year, C: current year)"

Figure 5

Moving correlation between chronologies and monthly climate factors (total precipitation and minimum, mean and maximum temperatures) at different altitudes (Moving window: 30 years, the black solid dots represent significance at the 0.05 level and black solid triangles at 0.01 level; P: previous year, C: current year)"

Figure 6

Interannual variability of basal area increment (BAI) at different altitudes (the solid black line represents the 10-year moving average of BAI)"

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