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Journal of Geographical Sciences >

2023 , Vol. 33 >Issue 1: 151 - 168

DOI: https://doi.org/10.1007/s11442-023-2077-y

Divergent responses of Qinghai spruce (Picea crassifolia) to recent warming along elevational gradients in the central Qilian Mountains, Northwest China

  • ZHANG Weiguo , 1, 2 ,
  • GOU Xiaohua 1, 2 ,
  • ZHANG Fen 1, 2 ,
  • LIU Wenhuo 1, 3 ,
  • ZHANG Yun 4 ,
  • GAO Linlin 1, 2
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  • 1. Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
  • 2. Gansu Liancheng Forest Ecosystem Field Observation and Research Station, Lanzhou University, Lanzhou 730000, China
  • 3. State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, CAS, Lanzhou 730000, China
  • 4. National Plateau Wetlands Research Center, Southwest Forestry University, Kunming 650224, China
*Gou Xiaohua (1970-), Professor, E-mail:

Zhang Weiguo (1990-), PhD, E-mail:

Received date: 2022-04-25

  Accepted date: 2022-08-11

  Online published: 2023-01-16

Supported by

National Natural Science Foundation of China(41790422)

The Second Tibetan Plateau Scientific Expedition and Research Program(STEP)

No.2019QZKK0301(2019QZKK0301)

The National Key Research & Development (R&D) Program of China(2019YFC0507401)

National Natural Science Foundation of China(41801018)

The 111 Project(BP0618001)

The Foundation for Excellent Youth Scholars of “Northwest Institute of Eco-Environment and Resources”, CAS(FEYS2019004)

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Abstract

Understanding the radial growth trends of trees and their response to recent warming along elevation gradients is crucial for assessing how forests will be impacted by future climate change. Here, we collected 242 tree-ring cores from five plots across the Qinghai spruce (Picea crassifolia Kom.) forest belt (2600-3350 m a.s.l.) in the central Qilian Mountains, Northwest China, to study trends in the radial growth of trees and their response to climate factors with variable elevation. All the sampled P. crassifolia chronologies showed an increasing trend in the radial growth of trees at higher altitudes (3000-3350 m), whereas the radial growth of trees at lower altitudes (2600-2800 m) has decreased in recent decades. The radial growth of trees was limited by precipitation at lower elevations (L, ML), but mainly by temperature at higher elevation sites (MH, H, TL). Climate warming has caused an unprecedented increase in the radial growth of P. crassifolia at higher elevations. Our results suggest that ongoing climate warming is beneficial to forest ecosystems at high elevations but restricts the growth of forest ecosystems at low elevations.

Key words： global warming; altitudinal gradient; Qinghai spruce; tree radial growth; dendroecology

Cite this article

ZHANG Weiguo , GOU Xiaohua , ZHANG Fen , LIU Wenhuo , ZHANG Yun , GAO Linlin . Divergent responses of Qinghai spruce (Picea crassifolia) to recent warming along elevational gradients in the central Qilian Mountains, Northwest China[J]. Journal of Geographical Sciences, 2023 , 33(1) : 151 -168 . DOI: 10.1007/s11442-023-2077-y

1 Introduction

The impact of global change on terrestrial ecosystems has become a critical research topic in recent decades. The dominant feature of current global change is global-scale warming (Solomon et al., 2007; Collins et al., 2013). Temperatures across the globe have risen dramatically since the 20th century: the global mean temperature is currently about 1°C higher than that before the Industrial Revolution, and is expected to reach or exceed 1.5°C during the next two decades (IPCC, 2021). The temperature of the Qilian Mountains has shown a significant increasing trend, which is consistent with global warming (Zhu et al., 2008; Lin et al., 2017).
Global warming has affected forest ecosystems, including the forest structure and functions, tree mortality, and tree line dynamics, in the past (Bonan, 2008; Zhang et al., 2020; de Wergifosse et al., 2022). Studying the impacts of climate change on the radial growth of trees can provide a scientific basis for effectively managing forest ecosystems with future climate change (Williams et al., 2013; Liang et al., 2016; Panthi et al., 2018). The radial growth of trees is significantly affected by environmental factors (Zhang et al., 2017b; de Wergifosse et al., 2022; Zeng et al., 2022). For example, variable altitude often leads to a change in hydrothermal conditions, which can affect the growth of trees (Sun et al., 2021). Therefore, the elevational gradient is an important environmental factor for studying the relationship between tree growth and the climate (Huo et al., 2017).
It is widely acknowledged that temperature affects tree growth in high-altitude regions, whereas tree growth is primarily limited by moisture conditions in low-altitude regions (Fritts, 1976; Mäkinen et al., 2002; Salzer et al., 2009; Zhang et al., 2015). However, some studies have shown that there is a relatively consistent relationship between tree growth and the climate along elevation gradients (Liang et al., 2010; Gao et al., 2013; He et al., 2013; Lyu et al., 2016; Wang et al., 2016). In contrast, other research has shown that there are complex non-linear relationships between tree growth and the climate at different altitudes (Kienast et al., 1987; Yang et al., 2013). Consequently, the response of tree growth to the climate along elevation gradients is still controversial. Using tree-ring chronologies to distinguish how climate change has affected radial growth at different elevations within a forest is an effective method of forest management.
The Qilian Mountains are one of the main areas of dendroclimatology research in China (Yang et al., 2013; Wang et al., 2021; Zhang et al., 2021a; 2021b). Various studies on the response of tree radial growth to climate change have been conducted in this region in recent years. However, the results are far from conclusive in defining the climatic factors that regulate tree growth along an elevation gradient (Gou et al., 2005; Wang et al., 2016; Gao et al., 2017; Zhang et al., 2017a). Zhang et al. (2017a) found that radial growth of P. crassifolia was positively affected by precipitation at low altitudes and was temperature controlled at high altitudes. Gao et al. (2017) suggested that P. crassifolia had similar growth trends and climate-growth relationships along elevation gradients. Gou et al. (2005) and Wang et al. (2016) found that the climate-growth response became weaker with increasing altitude. These discrepancies show that the response of tree radial growth to climate change can be very complex with different elevations. The use of different sampling areas and incomplete elevation gradients may be the cause of these dissimilar results.
In this study, we established five chronologies of P. crassifolia by using width data from tree rings along an elevational gradient in the central Qilian Mountains. The sample sites included the timberline and lower limits of the P. crassifolia distribution, covering a complete elevation gradient from 2600-3350 m a.s.l. Based on these dendrochronological methods, we studied the response of radial growth to climate change and identified the main climatic factors that influence the radial growth of P. crassifolia at different altitudes. This research provides a scientific basis for predicting the evolution of forest ecosystems in the central Qilian Mountains and valuable information for informing effective forest protection and management in the district.

2 Materials and methods

2.1 Study areas

The Qilian Mountains are located on the northeast edge of the Tibetan Plateau, where the climate is affected by the Westerlies and the East Asian Monsoon (Zhang et al., 2017b). Our study area has a typical plateau continental climate, and temperature and precipitation change significantly with altitude (Chen et al., 2008). The sample sites were located in the upper reaches of the Heihe River in the central Qilian Mountains (Figure 1), which are the main distribution areas of P. crassifolia (Zhang et al., 2017a). The description of the sampling sites is shown in Table 1. P. crassifolia is mainly distributed on shady and semi-shady slopes at altitudes from 2600 to 3300 m a.s.l in this region, with minimal human disturbance and good forest health.
Figure 1 Location of the sampling sites of Picea crassifolia in the central Qilian Mountains and the meteorological station nearby (L: Low altitude sampling site; ML: Middle-Low altitude sampling site; MH: Middle-High altitude sampling site; H: High altitude sampling site; TL: Timberline sampling site)
Table 1 Description of the sampling sites of P. crassifolia in the central Qilian Mountains
Sites Longitude (E) Latitude (N) Altitude (m a.s.l.) Slope (°) Aspect (°)
L 100.35° 38.51° 2602 31 25
ML 100.29° 38.55° 2811 31 57
MH 100.27° 38.52° 3029 22 350
H 100.28° 38.52° 3218 31 358
TL 100.31° 38.53° 3354 28 45

L: Low altitude sampling site, ML: Middle-Low altitude sampling site; MH: Middle-High altitude sampling site, H: High altitude sampling site, TL: Timberline sampling site.

2.2 Climate data

Meteorological data were obtained from the Chinese Meteorological Data Service Center (http://data.cma.cn/). To best represent the regional climate, we selected the mean climate data of Zhangye (38°56′N, 100°26′E, 1483 m a.s.l.) and Qilian (38°11′N, 100°15′E, 2787 m a.s.l.) meteorological stations. Based on data integrity, we selected 1957 to 2016 as the study period and four climatic variables as environmental factors, including the monthly mean temperature (Tmean), monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin), and monthly total precipitation (Pre). The study area has an annual mean temperature of 4.3℃, with the hottest (July) and coldest (January) months being 17.6℃ and -11.3℃, respectively. The annual mean precipitation is 269.1 mm, 88.5% of which occurs from May to September (Figure 2a). From 1957 to 2016, the annual mean temperature (0.0321℃ yr-1, Figure 2c, p<0.0001), the annual mean maximum temperature (0.0245℃ yr-1, Figure 2d, p<0.0001), the annual mean minimum temperature (0.0390℃ yr-1, Figure 2e, p<0.0001) and the annual total precipitation (0.6239 mm yr-1, Figure 2b, p<0.05) showed a significant increasing trend. We used the Standardized Precipitation-Evapotranspiration Index (SPEI), which is a drought index that is widely used for drought assessment and reconstruction (Su et al., 2021). The SPEI-12 (SPEI at a 12-month scale) was used in this study, which was derived from the Royal Netherlands Meteorological Institute Climate Explorer (http://climexp.knmi.nl/) (Vicente-Serrano et al., 2010).
Figure 2 Climate data from the Zhangye and Qilian meteorological stations (1957-2016) (a. Monthly mean climate data; b. The trend of annual total precipitation; c. The trend of annual mean temperature; d. The trend of annual mean maximum temperature; e. The trend of annual mean minimum temperature)

2.3 Sampling and chronology development

In October 2019 and September 2020, five sample plots (30 m×30 m at L, ML, MH, and H sites; 30 m ×150 m at TL site) were established for tree-ring sampling. Due to the number of tree-ring samples and the large differences in tree age in the quadrat, we selected 25 trees with the oldest age at each site to remove the influence of tree age and sample size on the results. Two (or one) cores per tree were collected from opposite directions for most of the sampled trees.
The tree-ring cores were pretreated using standard protocols (Stokes and Smiley, 1968). The tree-ring cores were progressively sanded by finer sandpaper until the surface was clearly visible. Tree-ring widths were measured using the Velmex measuring system and a stereo microscope with a precision of 1 μm. The COFECHA program (Holmes, 1983) was used to check the quality of cross-dating. The ARSTAN program (Cook and Holmes, 1986) was employed to standardize the measured tree-ring width series. Negative exponential curves or straight lines were used to detrend the tree-ring width series. A cubic smoothing spline of 67% of the series length was used in a few cases if the previous two curves did not provide a good fit. Finally, we developed standard (STD) chronologies of P. crassifolia with 242 cores from 125 trees at five sampling sites, which were used to analyze growth-climate relationships (Figure 3).
Figure 3 Standard chronologies and the number of cores at the five altitude sites

2.4 Statistical analysis

Correlation analyses were carried out in SPSS19.0 software (SPSS Inc., Chicago, USA). Considering the potential impact of the previous year’s climate factors on tree growth, the climate data from April of the previous year to October of the current year were selected for correlation analysis for radial growth. Redundancy analysis (RDA) was also used to verify and analyze climate-growth relationships using CANOCO 5 software (Šmilauer and Lepš, 2014). As a multivariate environmental gradient analysis, RDA evaluates climate-growth relationships using regression and principal component analysis of tree-ring chronologies and climatic factors (Zhang et al., 2017b). R (R Development Core Team, 2014) and SigmaPlot 10.0. (Systat Software, Inc., San Jose, CA, USA) were used to visualize the data.

3 Results

3.1 Statistical characteristics of the five chronologies

The statistical characteristics of the five chronologies of P.crassifolia in the central Qilian Mountains are shown in Table 2. The statistical characteristics between the lower (L and ML) and higher (MH, H, and TL) elevation chronologies were significantly different. The lower elevation sites had higher mean correlations among all radii (R1), between trees (R2), and within trees (R3) compared with the higher elevation sites, indicating that their tree-ring series were more consistent with each other. As a result, the first eigenvector (PC1), signal-to-noise ratio (SNR), and expressed population signal (EPS) of the lower elevation sites were also greater than the high elevation sites. In contrast, the chronologies at higher elevation sites had higher first-order autocorrelations (AC1) but lower mean sensitivity (MS) and standard deviations (SD) than the lower elevation sites, indicating that the higher elevation chronologies had more low-frequency changes but less high-frequency information. Regardless of the rate, the expressed population signal (EPS) values were all greater than 0.9, indicating that the chronologies reflect their basic characteristics and are suitable for dendrochronological studies (Wigley et al., 1984).
Table 2 Statistical characteristics of the five standard chronologies
Low elevation High elevation
Chronology L ML MH H TL
Sample depth (trees/cores) 25/50 25/50 25/49 25/48 25/45
Time span (BC) 1862-2019 1877-2019 1786-2019 1776-2019 1782-2018
MS 0.250 0.375 0.119 0.102 0.150
SD 0.383 0.544 0.187 0.196 0.274
Common period 1949-2019 1922-2019 1865-2019 1945-2018 1944-2018
AC1 0.741 0.675 0.707 0.773 0.798
R1 0.577 0.643 0.171 0.342 0.253
R2 0.787 0.841 0.344 0.572 0.423
R3 0.572 0.639 0.167 0.337 0.249
SNR 64.190 84.665 9.272 23.354 13.177
EPS 0.985 0.988 0.903 0.959 0.929
PC1 0.604 0.668 0.377 0.381 0.399
EPS>0.85 (starting year/cores) 1878/5 1887/4 1847/28 1810/11 1870/17

MS is the mean sensitivity, SD is the standard deviation, AC1 is the first-order autocorrelation, R1 is the mean correlation among all radii, R2 is the mean correlation between trees, R3 is the mean correlation within trees, SNR is the signal-to-noise ratio, and EPS is the expressed population signal.

There were significant differences between the higher and lower elevation chronologies in the central Qilian Mountains. The interannual variation of lower elevation chronologies was larger than that of higher elevation chronologies (Figure 3). As shown in Table 3, the relationships between the higher and lower chronologies were much weaker than those within the same group at both high and low altitude sites during the common reliable period from 1887-2018.
Table 3 Correlation coefficients between STD chronologies during the common period from 1887-2018
Chronology L ML MH H
ML 0.593**
MH -0.051 0.212*
H 0.243** -0.001 0.442**
TL 0.084 0.205* 0.594** 0.721**

Note: * represents p<0.05，** represents p<0.01

3.2 Correlation analysis between the climate and tree-ring chronologies

The response of tree radial growth to climate was significantly different between the different altitude sites, similar to the characteristics of different altitude chronologies. The results of the correlation analysis showed that the chronologies at higher sites (except MH) were significantly positively correlated with temperature for most months (Tmean, Tmax, and Tmin), especially for the growing season temperature (May to September). The chronologies were not significantly correlated with the temperature at the MH site, although the correlation mode with temperature was the same as that of H and TL. There was a significant negative correlation between the chronologies at low altitude sites and the temperature for most months. The relationship between the previous July-August and the current summer (June-August) was the most significant (Figures 4a, 4c, and 4e).
Figure 4 Correlations between the tree-ring index and the climate calculated from original and first-differenced data during the period from 1957-2016. The correlation analysis of the tree-ring index and Tmean (a), Tmax (c), Tmin (e), Pre (g), SPEI-12 (i). The first-order difference analysis of the tree-ring index and Tmean (b), Tmax (d), Tmin (f), Pre (h), SPEI-12 (j). Tmean: monthly mean temperature, Tmax: monthly mean maximum temperature, Tmin: monthly mean minimum temperature, Pre: monthly total precipitation, SPEI-12: monthly mean Standardized Precipitation-Evapotranspiration Index at a 12-month scale. P represents the previous year, C represents the current year, and the number represents the month, e.g., P9 represents September of the previous year, and C5 represents May of the current year. C5C7 represents May to July of the current year, and P7C6 represents July of the previous year to June of the current year. +: significant (p<0.05) positive correlation，-: significant (p<0.05) negative correlation.
To remove the influence of common trends on both climate variables and tree-ring width chronologies, correlation analysis was also calculated for the first-order differences. The results showed that the chronologies at higher sites were positively correlated with the temperatures from July to August of the current year, which may also cause a significant negative correlation with the temperature from the previous year’s July to August. The results of the first-order differences correlation analysis were generally consistent with that of the correlation analysis, except for the positive correlation with the previous June and the current January temperatures (Figures 4b, 4d and 4f).
Correlation and first-order difference analysis showed that the chronologies at higher sites were mainly not correlated with precipitation. In contrast, the chronologies at the lower sites were mainly positively correlated with precipitation, and the relationship with the previous July to June was the most significant (Figures 4g and 4h).
The chronologies at the lower sites were positively correlated with precipitation and negatively correlated with temperature, reflecting that tree radial growth was mainly limited by moisture (Fritts, 1976). The results of the correlation analysis and first-order difference correlation analysis all showed that the chronologies were significantly positively correlated with the monthly SPEI-12 at lower elevation sites, especially for the SPEI-12 of the current May to July. The growth-SPEI-12 relationships further indicate that the tree radial growth was mainly restricted by moisture in low elevation regions. On the contrary, the correlations between the chronologies at higher sites and SPEI-12 were weak, indicating that moisture had a negligible effect on tree radial growth in high-altitude regions (Figures 4i and 4j).

3.3 Redundancy analysis between climate and tree-ring chronologies

The first two ordination axes in RDA explained 46.8% of the total variance (Figure 5). Axis I accounted for 39.8% and had positive loading for the chronologies at lower sites and negative loadings for the chronologies at higher sites. Axis II accounted for 7.0% and had positive loading for TL, H, and L chronologies and negative loadings for MH and ML chronologies. The previous September precipitation and current June SPEI-12 were positively correlated with Axis I, whereas other climate factors showed a negative correlation. The climate factors were positively correlated with Axis II, except for the previous August Tmax (Figure 5).
Figure 5 The RDA analysis between the climate and tree-ring chronologies. Only significant climate factors (p<0.05) are presented. The longer the climate factor vector, the greater its relative contribution. The shorter perpendicular line between the chronology point and the climatic vector (itself, or the extension line) indicates a higher correlation. The chronology and vectors pointing in the same direction show a positive correlation and vice versa. Pre represents precipitation; Tmean represents monthly mean temperature; Tmax represents monthly maximum temperature; Tmin represents monthly minimum temperature, SPEI represents Standardized Precipitation-Evapotranspiration Index at a 12-month scale, t-1 represents the previous year, and the number indicates the month. L-STD indicates the low site chronology; ML-STD indicates the middle-low site chronology; MH-STD indicates the middle-high site chronology; H-STD indicates the high site chronology; TL-STD indicates the treeline site chronology.
It can be seen from the RDA results that moisture had a greater effect on tree radial growth in low elevation regions. The angle between the vector of the chronologies (L and ML) and the water vector (previous September precipitation and current June SPEI-12) was less than the three temperature vectors (previous June Tmin, previous August Tmax, and current summer Tmean). Conversely, temperature significantly affected tree radial growth in high altitude regions and was closely related to the previous June Tmin, previous August Tmax, and current summer Tmean.

3.4 Altitudinal trends and dynamics of the growth-climate relationship

It can be seen from Figure 6 that temperature and moisture regulate radial growth along an elevation gradient in the central Qilian Mountains. The temperature (Tmean, Tmax, Tmin) from the current May to September changed from a significant negative correlation to a significant positive correlation with increasing altitude. In contrast, the effect of the current May to July SPEI-12 and the previous July to current June precipitation turned from a significant positive correlation to an insignificant correlation.
Figure 6 Altitudinal trends in correlation coefficients of the tree-ring index with climate during the period from 1957-2016
A moving correlation analysis based on 31-year windows showed that the positive correlation between tree radial growth at high altitude and temperature was strengthened, and the relationship between tree radial growth at low altitude and moisture was relatively stable over the study period (Figure 7).
Figure 7 A 31-year moving correlation analysis of the tree-ring index along elevation gradients with C5C9 mean temperature (a), C5C9 mean maximum temperature (b), C5C9 mean minimum temperature (c), P7C6 total precipitation (d), and C5C7 mean SPEI-12 (e)

4 Discussion

4.1 Altitudinal differences of climate signals in tree radial growth

The results of correlation analysis and RDA showed that climatic factors have different impacts on radial growth at different elevations in the central Qilian Mountains. Tree radial growth was mainly restricted by moisture at lower elevations, while tree radial growth at higher elevation sites was more affected by temperature. These results are consistent with several previous studies (Fritts, 1976; Mäkinen et al., 2002; Salzer et al., 2009). Similar results have also been reported in the Central Himalaya (Rai et al., 2020), the Southeastern Tibetan Plateau (Sun et al., 2021), and central Europe (Jevšenak et al., 2021).
In low-altitude regions, radial growth was mainly affected by precipitation from the previous July to the current June and SPEI-12 from May to July of the current year. Long-term drought stress and moisture deficit were the key factors controlling declines in tree growth (Fang et al., 2021). Soil moisture availability likely does not meet the needs required for extensive tree growth at low altitudes, which reduces the photosynthesis rate (Jiao et al., 2016). Thus, moisture becomes the main limiting factor for the radial growth of trees in these low-elevation regions. At the same time, higher temperatures can cause higher transpiration and evaporation, which enhances soil moisture loss and results in drought stress that restricts tree radial growth (Shao et al., 2010; Gaire et al., 2020). In addition, higher nighttime temperatures can enhance tree respiration, consuming accumulated organic matter (Huang et al., 2010; Wehr et al., 2016). The negative correlation between tree radial growth and temperature further demonstrates the remarkable influence of drought stress on tree growth induced by high temperatures in low-altitude regions (Walker and Johnstone, 2014). In contrast, higher altitude sites had lower temperatures and less evapotranspiration, meaning that drought stress was not detected (Sun et al., 2021). The finding that tree radial growth of P. crassifolia is mainly limited by the drought conditions within a habitat has also been reported in other regions of the Qilian Mountains (Wang et al., 2020).
The results of the correlation analysis showed that the radial growth of P. crassifolia at the high elevation sites was mainly influenced by the temperature of the current growing season (May to September). The first-order difference correlation analysis reflected the effect of temperature on tree growth from July to August of the current year. Radial growth was limited by low temperatures at high altitudes, while high-temperature conditions effectively enhanced radial tree growth at high elevation sites (Wilmking et al., 2004). The rate of cell division in cambium will also accelerate with increasing temperatures (Fritts, 1976). Furthermore, a higher temperature may prolong growth durations and induce a higher tree growth rate (Rossi et al., 2008). The positive effect of temperature on radial growth has also been reported for spruce (Picea purpurea and Picea baifouriana) in the eastern Tibetan Plateau (Guo et al., 2018).
The effect of moisture on tree radial growth was weaker at the high-altitude sites. The relationship between radial growth and precipitation or the SPEI-12 mainly showed significant negative or insignificant correlations. During the growing season, increases in precipitation may lead to lower solar insolation on the ground, which could reduce photosynthetic active radiation and temperature, resulting in low tree radial growth (Fan et al., 2009; Gaire et al., 2020). Therefore, the negative correlation between tree radial growth and precipitation at high elevation indirectly reflects the positive correlation with temperature.
The effects of temperature and moisture on tree radial growth at different altitudes show complex trends (Figure 6). With increasing altitude, the relationship between the current May to September temperature and the radial growth of P. crassifolia gradually changed from a significant negative correlation to a significant positive correlation. The relationships between the radial growth of P. crassifolia and the previous July to current June precipitation and current May to July SPEI-12 gradually changed from a significant positive correlation to an insignificant correlation with increasing altitude. These patterns further indicate the influence of hydrothermal changes caused by altitude on the growth of trees (Sun et al., 2021). Our results are consistent with the idea that trees tend to be more constrained by moisture at low latitudes (Wang et al., 2006), while trees are more sensitive to temperature at high latitudes (Sidor et al., 2015). The change in the growth-climate relationship with increasing altitude has been reported in the central Hengduan Mountains (Fan et al., 2009), western Tianshan Mountains (Huo et al., 2017), and Slovenia (Jevšenak et al., 2021). Moreover, the relationship between the tree-ring index and tree growth relative to the climate at different altitudes has been shown to be determined by altitude rather than tree species (Cai et al., 2020).

4.2 The effects of different climate signals on tree growth trends

The growth trends of P. crassifolia were significantly different at the different elevation sites. In recent decades, the higher elevation tree ring width chronologies have increased significantly compared to a decreasing trend for the lower elevation chronologies. Similar findings have been reported from the southern Tibetan Plateau (Kharal et al., 2017). This divergent radial growth pattern of P. crassifolia suggests the factors that affect the trend of radial growth vary substantially depending on the elevation. We hypothesized that temperature would be a limiting factor affecting the radial growth of P. crassifolia due to low temperatures at the alpine timberline.
The higher elevation chronologies showed a significant positive correlation with the temperature. In current scenarios of global warming, the radial growth of P. crassifolia may benefit from warming at the higher elevation sites (Panthi et al., 2018), with faster production of xylem cells and a longer duration of cambium activity (Begum et al., 2013; Rossi et al., 2014). Tree growth has accelerated in the southeast Tibetan Plateau (Liang et al., 2009), Europe (Martínez-Vilalta et al., 2008), and North America (Salzer et al., 2009), where tree growth was previously limited by temperature.
In contrast, radial growth is mainly limited by drought at low altitudes, which may increase in severity with global warming (Panthi et al., 2018). Theoretically, with the increase in precipitation, tree growth should show an increasing trend. However, since the lower elevation chronologies were significantly negatively correlated with temperature, the inhibitory effect of temperature, drought stress, or moisture stress caused by high temperature on tree radial growth at low altitudes is likely to be more significant (Walker and Johnstone, 2014). This finding indicates that the tree growth of P. crassifolia is mainly limited by local moisture availability at low altitudes in the central Qilian Mountains. The decrease in tree growth due to heat-induced drought stress has also been observed in inner Asia (Liu et al., 2013), southwestern Europe (Férriz et al., 2021), and Alaska (Driscoll et al., 2005).

4.3 Prediction of future climate change impacts on tree radial growth

Regional precipitation patterns have also undergone significant changes caused by recent warming of global temperatures (IPCC, 2021). As an important part of terrestrial ecosystems, tree growth is highly sensitive to climate change (Deslauriers et al., 2007; Liu et al., 2020). Due to differences in the response of tree radial growth to climate factors, trees display different growth changes, such as growth acceleration (Salzer et al., 2009), slowing (Zhang et al., 2018), decline (Liu et al., 2013), and even mortality (Williams et al., 2013), which have important impacts on the forest ecosystem.
Under the representative concentration pathway (RCP) 8.5, where temperatures increase linearly from 2011 to 2100 with a warming magnitude of 5.36°C, precipitation changes are not evident during 2011-2100 for the far-term period relative to 1971-2000 in the Qilian Mountains (Liu et al., 2022). Since the radial growth of P. crassifolia at lower altitudes was significantly negatively correlated with temperature, and significantly positively correlated with moisture, assuming that the temperature increases continuously and precipitation changes are not evident, the radial growth at low altitude locations may decrease in the future. Previous studies on tree radial growth in arid areas have also found a similar phenomenon (Liu et al., 2013). However, in high-altitude cold environments, tree radial growth is usually enhanced by warming. Within a certain range, the higher the temperature, the higher the photosynthetic rate and the faster the tree growth rate (Cai et al., 2020). The relationship between the tree-ring index and temperature was strengthened at high altitudes (Figure 7), indicating that temperature increases will be conducive to the radial growth of P. crassifolia at higher altitudes in the future. However, if the temperature increases beyond a certain threshold, moisture may become a limiting factor for tree growth at high elevations in the central Qilian Mountains (Song et al., 2020).

5 Conclusions

We implemented a dendroecological study from five locations in the central Qilian Mountains to evaluate P. crassifolia radial growth trends and climatic responses along an altitudinal gradient. We found that the radial growth of trees has increased at the higher elevation sites but decreased at lower elevation sites during recent decades. The results of the tree growth response to climate change at five different elevations showed that tree radial growth was mainly limited by the temperature at higher altitude sites. In comparison, the tree radial growth was mainly controlled by moisture at lower altitude sites. Climate warming has promoted an unprecedented increase in the radial growth of trees at higher altitudes in the central Qilian Mountains, while tree radial growth at lower altitudes is limited by warming-induced droughts and exhibits a declining trend. The results indicate that future climate warming may promote radial growth of P. crassifolia at higher altitudes but decrease radial growth of P. crassifolia at lower altitudes in the central Qilian Mountains, leading to contrasting changes in forest ecosystems within this region. Therefore, assessments of the future impacts of climate warming on forest ecosystems should account for the divergent responses of radial growth of trees to recent warming along different elevation gradients.

We thank Qipeng Sun, Yiran Zhang, Zhenyu Tu, Dingcai Yin, Haowen Fan, Haijiang Yang, Kaixuan Yang, Rui Bian, Miaomiao Du, Kai Wang, Jie Liu, Jingqing Xia, Lanya Liu and Fangjingcheng Zhu for their kind field and laboratory works.

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