Difference in tree growth responses to climate at the upper treeline: Qilian Juniper in the Anyemaqen Mountains.
ABSTRACT Three ring-width chronologies were developed from Qilian Juniper (Sabina przewalskii Kom.) at the upper treeline along a west-east gradient in the Anyemaqen Mountains. Most chronological statistics, except for mean sensitivity (MS), decreased from west to east. The first principal component (PC1) loadings indicated that stands in a similar climate condition were most important to the variability of radial growth. PC2 loadings decreased from west to east, suggesting the difference of tree-growth between eastern and western Anyemaqen Mountains. Correlations between standard chronologies and climatic factors revealed different climatic influences on radial growth along a west-east gradient in the study area. Temperature of warm season (July-August) was important to the radial growth at the upper treeline in the whole study area. Precipitation of current May was an important limiting factor of tree growth only in the western (drier) upper treeline, whereas precipitation of current September limited tree growth in the eastern (wetter) upper treeline. Response function analysis results showed that there were regional differences between tree growth and climatic factors in various sampling sites of the whole study area. Temperature and precipitation were the important factors influencing tree growth in western (drier) upper treeline. However, tree growth was greatly limited by temperature at the upper treeline in the middle area, and was more limited by precipitation than temperature in the eastern (wetter) upper treeline.
- SourceAvailable from: Fritz H. Schweingruber[show abstract] [hide abstract]
ABSTRACT: Preserving multicentennial climate variability in long tree-ring records is critically important for reconstructing the full range of temperature variability over the past 1000 years. This allows the putative "Medieval Warm Period" (MWP) to be described and to be compared with 20th-century warming in modeling and attribution studies. We demonstrate that carefully selected tree-ring chronologies from 14 sites in the Northern Hemisphere (NH) extratropics can preserve such coherent large-scale, multicentennial temperature trends if proper methods of analysis are used. In addition, we show that the average of these chronologies supports the large-scale occurrence of the MWP over the NH extratropics.Science 04/2002; 295(5563):2250-3. · 31.20 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: Although global warming over the past century has been confirmed, the response of different regions to it is still uncertain. We developed a tree-ring width chronology based on tree-ring samples from juniper trees from the Xiqing Mountains in the northeast Tibetan Plateau, the central headwater area of the Yellow River. Using this tree-ring chronology, the minimum winter half-year (October–April) temperature for the research area was reconstructed for the past 425 years. The reconstruction shows that temperature variability was minimal over past four centuries prior to the warming that began in 1941. During the 50 years from 1941 to 1990, the minimum temperature of the winter half-year increased 2.5 °C. This degree of warming relative to the past 400 years suggests that the eastern Tibetan Plateau is highly sensitive to global warming. Copyright © 2007 Royal Meteorological SocietyInternational Journal of Climatology 08/2007; 27(11):1497 - 1503. · 2.89 Impact Factor
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ABSTRACT: Based on the cross-dated tree-ring samples collected from the middle Qilian Mountain, a standard ring-width chronology had been developed, which covered the period AD 1000 to 2000. The correlations between the chronology and climatic records from the nearby meteorological stations indicated that temperature was the dominant climatic factor for tree growth at upper timberline, and the most important climatic factor for the tree growth in the area was the mean temperature from previous December to current April. The temperature variations recovered from the ring-width data showed a cold period during the “Little Ice Age” and the continuous warming during the twentieth century. Comparison between the ring-width chronology and δ 18O records from the Dunde ice core in the Qilian Mountain indicated that there was a consistent trend in both time series. A significant correlation existed between our ring-width chronology and the Northern Hemispheric temperature, suggesting that the climate changes in the Qilian Mountain were not only driven by regional factors, but also responsive to the global climate.Science in China Series D Earth Sciences 03/2005; 48(4):521-529. · 1.59 Impact Factor
Journal of Integrative Plant Biology 2008, 50 (8): 982–990
Difference in Tree Growth Responses to Climate at the
Upper Treeline: Qilian Juniper in the Anyemaqen
Jianfeng Peng1,2, Xiaohua Gou1∗, Fahu Chen1, Jinbao Li3, Puxing Liu4,
Yong Zhang1and Keyan Fang1
(1Center for Arid Environment and Paleoclimate Research (CAEP), Key Laboratory of Western China’s Environment Systems MOE,
Lanzhou University, Lanzhou 730000, China;
2Institute of Resource and Environment, Henan University, Kaifeng 475004, China;
3Tree-Ring Laboratory, Lamont-Doherty Earth Observatory, Columbia University, NY 10964, USA;
4College of Geography and Environment Sciences, Northwest Normal University, Lanzhou 730070, China)
Three ring-width chronologies were developed from Qilian Juniper (Sabina przewalskii Kom.) at the upper treeline along a
west-east gradient in the Anyemaqen Mountains. Most chronological statistics, except for mean sensitivity (MS), decreased
from west to east. The first principal component (PC1) loadings indicated that stands in a similar climate condition were
most important to the variability of radial growth. PC2 loadings decreased from west to east, suggesting the difference of
tree-growth between eastern and western Anyemaqen Mountains. Correlations between standard chronologies and climatic
factors revealed different climatic influences on radial growth along a west-east gradient in the study area. Temperature of
warm season (July–August) was important to the radial growth at the upper treeline in the whole study area. Precipitation of
current May was an important limiting factor of tree growth only in the western (drier) upper treeline, whereas precipitation
of current September limited tree growth in the eastern (wetter) upper treeline. Response function analysis results showed
that there were regional differences between tree growth and climatic factors in various sampling sites of the whole study
area. Temperature and precipitation were the important factors influencing tree growth in western (drier) upper treeline.
However, tree growth was greatly limited by temperature at the upper treeline in the middle area, and was more limited by
precipitation than temperature in the eastern (wetter) upper treeline.
Key words: climate-growth correlations; dendrochronology; Qilian juniper; treeline.
Peng J, Gou X, Chen F, Li J, Liu P, Zhang Y, Fang K (2008). Difference in tree growth responses to climate at the upper treeline: Qilian juniper in
the Anyemaqen Mountains. J. Integr. Plant Biol. 50(8), 982–990.
Available online at www.jipb.net
With the rapid development of dendrochronology, tree ring
records have become one of the most important proxies of
Received 7 Aug. 2007Accepted 13 Jan. 2008
Supported by the National Natural Science Foundation of China (NSFC)
(40671191 and 90502008), the Chinese NSFC Innovation Team Project
(40721061), Program for New Century Excellent Talents in University (NCET-
05-0888) and the Chinese 111 Project (B06026).
∗Author for correspondence.
Tel: +86 931 891 5309;
Fax: +86 931 891 2330;
C ?2008 Institute of Botany, the Chinese Academy of Sciences
climate and have been used to study climate change over
much of the globe (Mann et al. 1998; Esper et al. 2002; Mann
and Jones 2003). High-resolution proxy climate records, such
as those derived from tree-ring series, are of great value for
understanding natural background variation in environmentally
sensitive regions of China (Liu et al. 2005a).
The Tibetan Plateau (TP) is a unique geographical cell on
earth with an average elevation above 4000 m a.s.l., and
change. It is considered that the TP was a sensitive area to
of eastern Asia at the decadal scale (Tang et al. 1988). There
has been great progress in using tree-rings to study climate
change on the TP since the earliest studies in its southeastern
Difference in Tree Growth Responses983
is a native tree species growing in the northeastern TP, with
cold- and drought-tolerant taxon forming forests. Due to the
extreme growth conditions and long potential lifespan of more
chronology of China using Qilian juniper tree ring width data
(Kang et al. 1997; Zhang et al. 2003), and reconstructed and
et al. 1997; Zhang et al. 2003; Shaoet al. 2004; Liu et al. 2005b).
In order to fully understand climate variability, it is necessary
to have a good spatial and temporal coverage of climate vari-
ables. So there were lots of studies about regional differences of
horizontal environmental gradient (Jacoby et al. 1996; Br¨ auning
2001; Rigling et al. 2002; Rolland 2002; Linderholm et al. 2003;
Macias et al. 2004), and most regional studies located near
the treeline (Esper and Schweingruber 2004; Frank and Esper
2005; Lara et al. 2005; Wilmking and Juday 2005). Of course,
northern and high-latitude alpine treelines are generally thought
to be limited by temperature. However, the differences in tree
growth responses to climatic factors exist at different upper
To our knowledge, there have been no studies of Qilian
juniper growth in relation to regional difference of climatic
Tree-ring width indeces
11 years smoothing
Figure 1. The three standard chronologies developed at the treeline in Aynemaqen Mountains.
Table 1. Standard chronological characteristics
Standard chronologyCommon interval 1850−2000 detrended series
Study sitesSSS>0.8 since (core)
AC1, first order autocorrelation; DQH, Zhongtie Forestry Centre; EPS, expressed population signal; HBSH, Hebei Forestry Centre; MS, mean
sensitivity; SD, standard deviation; SNR, signal-to-noise ratio; SSS, sub-sample signal strength; YYCH, Yangyu Forestry Centre. The common
interval was set as 1850−2000.
variability using tree-ring series at the upper treeline on the
at upper and lower treelines in the Dulan area by Liu et al.
(2006). There were few or no dendrochronological studies in
Peng et al. (2007). This paper attempts to use three ring-width
chronologies developed from Qilian juniper at the upper treeline
from east to west in the Anyemaqen Mountains to analyze their
responses to climatic factors and to detect the difference of
Three ring-width chronologies were developed from Qilian ju-
niper wood at the upper treeline along a west-east gradient
in the Anyemaqen Mountains (Figure 1). The chronological
statistics are shown in Table 1. Except that MS (mean sen-
sitivity) increased from west to east (DQH-YYCH-HBSH) (DQH
(Zhongtie Forestry Centre), YYCH (Yangyu Forestry Centre)
and HBSH (Hebei Forestry Centre)), other statistics, includ-
ing SD (standard deviation), AC1 (first order autoregressive
Journal of Integrative Plant Biology
Vol. 50 No. 82008
coefficient), SNR (signal-to-noise ratio), EPS (expressed pop-
ulation signal), all decreased from west to east (DQH-YYCH-
HBSH). Higher SNR and EPS values in Table 1 indicated that
there is more climatic information in these chronologies. The
differences could be due to different climate conditions from
west to east in the Anyemaqen Mountains.
Principal component analysis (PCA) was carried out on
three standard chronologies for the common time period 1850–
2002. PC1 has the largest loading of the chronologies and
represents the greatest common variance (63.097%) in all
chronologies (Table 2). The PC1 loadings were positive for
all chronologies (Figure 2) and reflect the tendency for growth
patterns to be correlated over the whole sampling area, which
are consistent with the macroclimate environment. PC2 and
PC3 account for 25.563% and 11.034% of the total variance
(Table 2), respectively, and PC2 weighting coefficient (Fig-
ure 2) decreased (0.778-0.067-0.624) from west to east (DQH-
YYCH-HBSH). Therefore, PC2 indicated the difference of tree-
growth from west to east in the Anyemaqen Mountains. Be-
cause, temperature and precipitation of the eastern Anyemaqen
are higher than those of the western Anyemaqen, which is
Table 2. Principal component analysis derived from three tree-ring
standard chronologies (the common time period 1850−2002)
% Var. explained: 63.097 (PC1)
and 25.563 (PC2)
Figure 2. Scatterplots of the loadings derived from each standard series
of the first principal component (PC1) and PC2.
DQ, Zhongtie Forestry Centre; HBS, Hebei Forestry Centre; YYC,
Yangyu Forestry Centre.
about 4◦C in temperature and 18% in relative humidity. Also,
Table 3 showed that precipitation decreased from east to west
(Henan→Maqin→Maduo or Henan→Tongde→Xinghai) along
the Anyemaqen Mountains.
chronology, and the lowest (0.286) was between DQH and
HBSH, and only 0.453 between YYCH and HBSH.
Correlations between standard chronologies at the upper
along west-east gradients in the study area. Temperature of the
warmer season (July–August) was important to radial growth
in the whole study area. Precipitation of current May was
only an important limiting factor to tree growth at the west
(drier) sampling site, whereas precipitation of current Septem-
ber limited tree growth at the east (wetter) site (Figure 3).
Response function analysis results showed that there were
regional differences between tree growth at the upper treeline
and climatic factors in various sampling sites of the whole study
area. Temperature and precipitation were the important factors
influencing tree growth at the west (drier) treeline. However, tree
ring widths were greatly limited by temperature at treeline in the
middle area and more limited by precipitation than temperature
in the eastern region (Figure 4).
In the PCA, the first PC accounted for the greatest proportion
of the total variance in the three chronologies, the second PC
and so on. Each PC was orthogonal (unrelated) to the others
and involved a linear combination of the three chronologies. The
series of PC scores over the chronology length represented
the growth variation common to the three sites. The weight
associated with each chronology indicated information about
the characteristic growth relationship between a specific site
and the PC: the higher the weight, the closer the relationship
(Legendre and Legendre 1998; Zhang and Hebda 2004).
Similar results of correlations among standard chronologies
about level gradient study were described by Macias et al.
Table 3. Locations and mean annual temperature and annual precipita-
tion (1960−2001) of each meteorological station close to the sampling
T (◦C)P (mm)
Difference in Tree Growth Responses 985
Figure 3. Correlation of three standard chronologies with mean monthly temperature and total monthly precipitation in Xinghai/Maqin/He’nan
meteorological stations, respectively.
Vertical dot line is a dividing line between previous year and current year, and the left is previous year. Ellipse indicates correlations above 95%
(2004) in previous studies across northern Fennoscandia.
Macias thought that the lower correlation could be due to the
lower correlation could be attributed to more young trees in
eastern HBSH chronology and different climate conditions at
different level sampling sites.
As shown in Figure 3, we found that correlations between
temperature and tree growth at upper treelines were similar at
different sampling sites. Tree growth has positive and significant
correlations (above the 95% significant level) with tempera-
tures in the current warm period, but this varies with August,
July, and July–August for the DQH, YYCH, and HBSH sam-
pling site, respectively. Significant (above the 95% significant
level) and negative correlations with temperature of previous
July/August/October and current September were only found
at the DQH sampling site. All tree growth has a negative
correlation with the current April–May temperature. However,
complex correlations occurred between precipitation and tree-
growth at the upper treelines from west to east Aynemaqen.
Significant correlations (above the 95% significant level) with
current May (positive) and September (negative) were found for
the DQH sampling site, whereas the previous July (negative)
and current September (negative) were found for the HBSH
sampling site, but no significant correlations were found for the
YYCH sampling site.
Generally, May–September is a warm and humid season on
the TP (Qinghai Forest Editorial Committee 1993). Figure 5
also shows that May–September mean temperature was almost
above 5◦C and precipitation over 40mm at some nearby
weather stations. Positive correlations between tree-growth at
Journal of Integrative Plant Biology
Vol. 50No. 82008
Percentage variance of response function
Figure 4. Response results of tree-growth at the upper treeline from west to east in the Anyemaqen Mountains to climatic factors (T+P, temperature
and precipitation together; T, temperature; P, precipitation.)
DQ, Zhongtie Forestry Centre; HBS, Hebei Forestry Centre; YYC, Yangyu Forestry Centre.
Temperature (0.1 °C)
Precipitation (0.1 mm)
Figure 5. Mean monthly temperature (0.1◦C) and total monthly precipitation (0.1mm) averaged for 1960−2001 using records from the meteorological
stations in the Anyemaqen Mountains.
HN, Henan; MD, Maduo; MQ, Maqin; TD, Tongde; XH, Xinghai
upper treelines and temperature of July-August could be due
to higher temperature in this period that could be sufficient to
tree-growth at upper treelines. Similar results that high-latitude
alpine treeline is generally thought to be limited by available
warmth was reported by Wilmking and Juday (2005). Increase
in air temperature and evaporation in April–May resulted in
a shortage of water at the start of the tree growing period,
so high temperature in April–May limited tree growth at the
treeline. Generally, the upper treeline is the area with high
precipitation in the high mountains; therefore there are few
Difference in Tree Growth Responses987
significant correlations with precipitation at the three sampling
sites. However, May was the start of tree growing period on the
TP, with increases in air temperature and evaporation. High
precipitation in May had a positive effect on tree growth in
DQH, which not only provides moisture for photosynthesis but
also benefits to capture nutrients from the environment. Similar
results were reported in the south Qinghai plateau (Qin et al.
2003) and the Delingha (Shao et al. 2004) and Dulan (Liu et al.
2006) in Qinghai Province. This was apparently related to their
level sites, because these level sites are to the west of the DQH
sampling site, with a much drier climate than the DQH sampling
site. In the further east and wetter HBSH site, September
was the last tree growing period; here plentiful precipitation
was suited to lower temperatures sequentially limiting tree-
Similarly, response function analysis results of tree-growth to
climatic factors were different for different regions (Figure 4).
Explained variances of growth to climate factors (including
temperature and precipitation together, temperature and pre-
cipitation) almost had a decreasing trend from west to east.
However, only growth variances in YYCH to precipitation were
the lowest in the three regions. Figure 6 shows that temperature
was the main limiting factor to tree growth in the western DQH
and middle YYCH sampling sites. Similar results were reported
by Kang et al. (1997) in the Dulan area of Qinghai, and Wang
et al. (1982) and Liu et al. (2005b) in the Qilian Mountains.
This could be due to its location in the western part of the
study area that is far from the monsoon. At this point, it was
reasonable that precipitation was an important limiting factor of
tree growth in the western DQH sampling site. Growth variance
to temperature is less than that of precipitation in the east
regions (HBSH) (Figure 4), it was obvious that precipitation was
a more important factor on tree-growth; this could be due to a
large change of monthly precipitation resulting from the summer
Materials and Methods
The Anyemaqen Mountains are located on the fold-zone mar-
gin of the northeastern Tibetan Plateau, a transition zone of
monsoon to non-monsoon, semi-wetness to semi-aridity, and
warm zone to sub-frigid zone. It is also the core area of the
Yellow River headwaters natural reserve. This is a climate-
sensitive and complex area, and is characterized by a short
and mild summer period and a relatively long and cold winter
period. In this area, the mean annual temperature is 0.5–
3.9◦C, the warmest month mean temperature is 11.0–14.2◦C,
mean annual precipitation is about 450–620mm, 56%–62% of
the annual precipitation falls in summer due to the monsoonal
regime, while the winters are cold and dry because of the
prevalence of continental air masses from central Asia (Wang
1988). Qilian Juniper is almost exclusively found on the south-
ern slopes (including southeast-facing and southwest-facing)
of 3400–3800ma.s.l., and mainly distributes in the Hebei,
Yangyu, and Zhongtie forestry centre (Figure 6), with simple
structure within forest and monolayer pure forest. This area is
the most southern boundary and the highest boundary of Qilian
junipers (Qinghai Forest Editorial Committee 1993). There-
fore it is important to study this region’s sensitivity to climate
Fieldworks were carried out in June and July of 2003 and
2005. Three sampling sites near a treeline along a west-east
transect were found in DQH (Zhongtie Forestry Centre), YYCH
(Yangyu Forestry Centre) and HBSH (Hebei Forestry Centre),
respectively (Figure 6). Each sampling site was adopted into
the sampling-zone with an interior high-gap of about 15 m with
20–30 of the biggest and presumably oldest trees selected for
increment core sampling. Trees were selected subjectively, with
the view of obtaining climatic signals from sensitive trees and
reducing non-climatic signals from local disturbances. One to
two cores were extracted at a height of about 1.3m (at breast
height) from each tree (Table 4).
In the laboratory, all cores were mounted in slotted wooden
boards and polished with different sandpaper of progressively
fine grit, until annual ring boundaries could be easily distin-
guished, and then were dated following the procedures of
Stokes and Smiley (1968). The ring widths were measured
using a Velmex measure system, with 0.001mm precision.
All measured tree-ring sequences were quality-checked with
the computer program COFECHA (Holmes 1983), the cores
with poor quality (e.g. fragmented or rotten) were excluded
to improve the common signals in tree-ring width sequences.
Standard and residual chronologies were developed with the
ARSTAN program (Cook and Holmes 1986) by combining
standardized tree ring series with biweighted robust estimation.
The resulting individual core index series was averaged to
produce a ring-width chronology with biological growth trends
removed while preserving variations that are likely related to
climate (Cook and Kairiukstis 1990). Our data were detrended
by fitting a negative exponential curve to the raw ring-width
data. In some cases a cubic spline equal to 67% of the series
length was also used (Cook and Kairiukstis 1990). In general,
the sample size declines in the early portion of a tree-ring
chronology; therefore we used the sub-sample signal strength
(SSS) (Wigley et al. 1984) with a threshold value of 0.80 to
Journal of Integrative Plant Biology
Vol. 50No. 82008
Figure 6. Map showing the location of the sampling sites and meteorological stations.
DQ, Zhongtie Forestry Centre; HBS, Hebei Forestry Centre; YYC, Yangyu Forestry Centre.
Table 4. Locations of the sampling sits and the number of cores of each chronology and the temporal spans of the chronologies
Site and IDLongitudeLatitudeElevation (m)Years Length (years)Mean ring-width (±SD)
Number of cores/trees
DQH, Zhongtie Forestry Centre; HBSH, Hebei Forestry Centre; YYCH, Yangyu Forestry Centre.
Difference in Tree Growth Responses989
evaluate the reliable time span of the final chronologies. In so
ring width at the upper treelines from west to east in Aynemaqen
Mountains (Figure 1).
Five meteorological stations (Maduo, Maqin, He’nan, Tongde
and Xinghai) in the study area are selected (Table 3 and
Figure 6). The mean annual temperature and precipitation
(1960–2001) are shown in Figure 4. All stations have the most
precipitation and the highest temperature in July; and each sta-
tion holds mean monthly temperature above 5◦C and monthly
precipitation above 40mm (except Maduo in May) during May–
September. Correlation analysis results of climatic records
between five meteorological stations showed that correlation
coefficients between mean monthly temperature and between
total monthly precipitation were above 98%, with remarkable
consistency. After calculating and comparing, we found that
the tree-ring chronologies are better correlated with the climatic
data from Maqin and He’nan, which are the nearest meteo-
rological station to YYC and HBS, respectively. The nearest
meteorological station to DQH is Tongde, which is unfortunately
weaker correlation with tree-ring chronology at DQH. Thus we
used the second nearest station (Xinghai) instead. Accordingly,
we used climatic records from Xinghai, Maqin and He’nan
meteorological stations for DQH, YYCH and HBSH sampling
sites in the following calibration, respectively.
Tree growth-climate relationships
In order to describe tree growth variations, PCA (principal com-
ponent analysis) (LaMarche and Fritts 1971; Brubaker 1980)
was used to examine the structure of the relationships between
the standard tree-ring series and evaluate the shared variance
among standard chronologies. PCA was conducted by using
the software PCA (Grissino-Mayer et al. 1996) for the common
interval 1850–2002 in this study.
To identify the influence of climatic factors on tree growth,
standard chronologies were compared to temperature and
precipitation by correlation functions (Blasing et al. 1984) with
DENDRO2002 software program (Biondi 2000), and response
function analysis with PRECON software program version 5.17
(Fritts 1998). In the two analyses, the relationships between
ring width and monthly climate data were examined for a
sequence of 18 months starting with May of the previous year
and ending in October of the current year in which the ring
formed.Thesignificance oftheresponse coefficientswastested
with a bootstrap method, which assesses the variability of the
coefficients based on a large number of sub-samples randomly
extracted, with replacement from the initial data set (Guiot
1991). Such random sampling and the subsequent calibration
and verification of the climate-growth model were iterated 500
times with the program PRECON (Fritts 1998).
We sincerely thank many people (Puriwa, Jianguo Huang, Dr
Qibin Zhang from Institute of Botany of the Chinese Academy
of Sciences provided and taught Precon 5.17 and PC analysis
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(Handling editor: Jianxin Sun)