Growth response of Sabina tibetica to climate factors along an elevation gradient in south Tibet

Dendrochronologia 31 (2013) 255–265

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Growth response of Sabina tibetica to climate factors along an elevation gradient in south Tibet Jingjing Liu ∗ , Chun Qin 1 , Shuyuan Kang 1 Key Laboratory of Desert and Desertification, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China

a r t i c l e

i n f o

Article history: Received 16 March 2012 Accepted 4 December 2012 Keywords: Sabina tibetica Kom. Tree ring Tree growth Elevation Climate Tibet

a b s t r a c t We examine the climate significance in tree-ring chronologies retrieved from Sabina tibetica Kom. (Tibetan juniper) at two sites ranging in elevation from 4124 to 4693 m above sea level (a.s.l.) in the Namling region, south Tibet. The study region is under the control of semi-arid plateau temperate climate. The samples were grouped into high- and low-elevation classes and standard ring-width chronologies for both classes were developed. Statistical analysis revealed a decreasing growth rate yet increasing chronology reliability with increasing elevation. Overall, correlation analyses showed that radial growth in S. tibetica at the study sites was controlled by similar climatic factors, regardless of elevation; these factors comprised early winter (November) and early summer (May–June) temperatures as well as annual precipitation (July–June). Slight differences in the correlation between tree growth along the elevation gradient and climate variables were examined. The correlations with early winter temperature varied from significantly positive at the low-elevation site to weakly positive at the high-elevation site, whereas the correlations between radial growth and early summer temperature increased from weakly negative at the low-elevation sites to strongly negative at the high-elevation sites. The abundant precipitation through the year may have masked variations in tree growth on different elevation aspects. Our results will aid future dendroclimatological studies of Namling tree rings in south Tibet and demonstrate the potential of S. tibetica Kom. for improving our understanding of environmental impacts on tree growth. © 2013 Elsevier GmbH. All rights reserved.

Introduction High-elevation mountain environments, comprising glaciers, snow, permafrost, etc., are not only the uppermost distribution zone of many vegetation species and other life forms, but are also the most sensitive and fragile environments to global climatic change (Stone, 1992; Kullman, 1993; Shugart, 1998; Thompson, 2000; Diaz et al., 2003). In mountainous areas, the radial growth of trees may be subject to environmental gradients (e.g. in air temperature and soil moisture) associated with elevation (LaMarche, 1974; Hughes and Funkhouser, 2003; Tardif et al., 2003). Climate change affects the distribution and growth dynamics of tree species at high elevations (Payette and Filion, 1985; Kullman, 2002; Camarero and Gutierrez, 2004; Devi et al., 2008). An understanding of tree growth over elevation gradients may be used

∗ Corresponding author at: 320 Donggang West Road, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China. Tel.: +86 931 4967489; fax: +86 931 4967488. E-mail address: [email protected] (J. Liu). 1 Address: 320 Donggang West Road, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China. 1125-7865/$ – see front matter © 2013 Elsevier GmbH. All rights reserved. http://dx.doi.org/10.1016/j.dendro.2012.12.001

to evaluate and predict variations in the response of forests to future climate change. A growing number of studies have demonstrated that tree growth can vary with elevation, providing insights into the variability of the growth response under a range of climatic conditions (Fritts et al., 1965; LaMarche, 1974; Hughes and Funkhouser, 2003; Zhang and Hebda, 2004; Gou et al., 2005; Massaccesi et al., 2008). Standing on the south-facing slope and occupying open forest ranging vertically from 2800 to 4500 m a.s.l., Sabina tibetica (Tibetan juniper), one of the most important tree species in western China, is distributed widely in south and east Tibet. This dominant tree species not only provides local residents with lumber but also acts as a major agent for soil and water resource conservation in mountainous areas. Despite the long history of dendrochronology, studies on the radial growth of Sabina species were first reported in the late 1980s (Wu et al., 1988, 1989, 1990; Wu, 1990). Recently, numerous studies of S. tibetica have been carried out to reconstruct climate change (Bräuning, 1994, 2001; Wang et al., 2008; Liu et al., 2010, 2011; Yang et al., 2010; Zhu et al., 2011). High-elevation forests on the Tibetan Plateau (TP) are potentially sensitive to climate change. South Tibet is characterized by semi-arid climates, allowing the distribution belt of junipers to take

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Table 1 Information of sampling sites and the instrumental data. Sampling/instrumental data sites

Latitude (◦ N)

Longitude (◦ E)

Elevation (m a.s.l.)

C/T No.

Length (year)

Time span

NMJW NMNM RKZ NAM NIM PDSI CRU

30.08 29.52 29.15 29.41 29.26 31.25 29.25–30.25

89.11 89.59 88.53 89.06 90.10 88.75 88.75–89.75

4507–4693 4124–4210 3836 4000 3809.4 – 4272–5215

77/37 62/32

636 417 55 20 37 50 47

1375–2010 1594–2010 1956–2010 1991–2010 1974–2010 1956–2005 1956–2002

NMJW and NMNM are two tree-ring sampling sites at the Namling region; RKZ, NAM, and NIM indicate Rikaze, Namling, and Nimu meteorological stations; PDSI is the closest extracted Palmer drought severity index grid from Dai et al. (2004); CRU represents extracted gridded monthly precipitation data of CRUts2.1 from Mitchell and Jones (0.5◦ × 0.5◦ , 2005; the original data can be downloaded from http://www.cru.uea.ac.uk/cru/data/hrg/); C/T No. is core/tree number.

up a wide elevation range. Historical climate (expressed as annual temperatures) has been reconstructed using tree-ring widths from the highest Tibetan junipers located in southwest Tibet (Yang et al., 2010). However, little is known about how the radial growth of these trees may vary with elevation. From a traditional perspective, tree growth at high-elevation timberline is associated with air temperature, while a positive correlation with precipitation predominates at low elevations (Fritts et al., 1965; LaMarche, 1974). In contrast to this generalized idea, several studies have observed similar growth variation patterns along the elevation ranges, and some common limiting climatic factors might synchronize tree growth at different elevations. For example, in western North America, the growth of bristlecone pine in a limited elevational band (elevation range within 150 m) of upper treeline was strongly regulated by temperature regardless of elevation (Salzer et al., 2009). Liang et al. (2010) demonstrated that the initiation of tree-ring growth in Smith firs (Abies georgei var. smithii) is controlled by common climatic signals such as July minimum temperature across a broad elevational range (elevation difference is ∼800 m) in the Sygera Mountains of the southeastern TP. Li et al. (2012) reported a high degree of similarity in faxon fir growth variation among the elevation gradients (elevation difference is ∼400 m) in western Sichuan. The main aim of this study was to represent two tree-ring width chronologies retrieved from Tibetan junipers along an elevation gradient in south Tibet and to examine the characteristics of the differences in tree growth/climate relationships. Materials and methods Study area Lying on the northern bank of the middle-upper reaches of the Yarlung Tsangpo River Valley, our study region (Fig. 1) (30.08–29.52◦ N, 89.11–89.59◦ E; 4124–4693 m a.s.l.) is located in the Namling region, south Tibet, which is between the eastern Gangdese and the Nyainqentanglha Mountains (Mts.). The terrain ascends from southwest to northeast with a typical elevation of 3790–4901 m. The Niangrequ, Tubujiapuqu and Xiangqu Rivers, and their various tributaries, flow through the mountains in this region. The highest point of the study area is the northeasternoriented Kangzhongma Summit (6043 m a.s.l.) whilst the lowest point is the confluence of the Xiangqu and Yarlung Tsangpo Rivers (3704 m a.s.l.), yielding a relative height difference of 2339 m. Exposed bedrock, loose surfaces and serious gully erosion constitute the main landscape features in the southern parts, while deep valleys and numerous of glacial lakes are found in the northern mountainous areas. The region is characterized by a semi-arid plateau temperate climate. Annual average temperature is 5.9 ◦ C and the extreme minimum/maximum temperatures are −17.7/26.5 ◦ C, respectively;

the annual sunshine is 2917 h and the coldest/hottest month is January/August. Annual average precipitation is 413 mm and the average annual evaporation is 2298 mm, with the majority of the rainfall falling from June to September. S. tibetica constitutes the main forestry in the region, and the alpine shrubby steppe soil supports flourishing grass and bush stands.

Tree-ring sampling and chronology development S. tibetica is a non-shade-tolerant species standing on the southfacing slopes between 4100 and 4600 m a.s.l. in the mountainous area of the Namling region. Field work was conducted in the autumn of 2008 and the spring of 2011. Two study sites were selected at different elevations. The NMNM site (29.52◦ N, 89.59◦ E, 4124–4210 m a.s.l.) was chosen at the lower boundary of the juniper forest, and the NMJW site (30.08 ◦ N, 89.11 ◦ E, 4507–4693 m a.s.l.) was located in forest stands at the upper elevation limits of S. tibetica in the study area (Fig. 1 and Table 1). Both the high- and low-elevation sites are subject to similar weather patterns. All trees were growing in relatively sparse or isolated conditions with infertile shallow soil, which represent the optimal conditions for the purpose of maximizing climate signals contained in the growth rings. We collected 77 cores from 37 trees at the high-elevation site (NMJW) and 62 cores from 32 trees at the low-elevation site (NMNM). Two increment cores were collected from each tree using an increment borer at breast height. All of the cores were extracted in this manner to obtain an orthogonal representation of tree growth and to reduce variation in the growth signals caused by variable micro-habitat conditions. In total, 139 increment cores were collected from 69 healthy trees (Table 1). All the cores were air-dried and mounted on grooved sticks with the transverse surfaces facing upward (Phipps, 1985). Cores were prepared with razor blades to expose ring details to the cellular level (Stokes and Smiley, 1968). Ring widths were registered with a LINTAB 6 measuring system at a resolution of 0.01 mm, and all series were cross-dated by visual inspection (Stokes and Smiley, 1968) and by statistical tests (sign-test and t-test) using the software package TSAP-Win (Rinn, 2003). For quality control, we discarded cores which were not good enough for cross-dating (Table 2) and the COFECHA (Holmes, 1983) program was further applied to statistically check the cross-dating results of the samples.

Table 2 Statistical characteristics of STD chronologies for high- and low-elevation sites. Site

AVE

MS

AC

Core/tree

MSL

Period

High elev. Low elev.

0.954 0.974

0.203 0.289

0.37 0.44

77/34 39/24

636 417

1375–2010 1594–2010

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Fig. 1. Location of the study area: H and L indicate tree-ring sampling sites at high (NMJW, 4507–4693 m a.s.l.) and low (NMNM, 4124–4210 m a.s.l.) elevations; Points 1, 2, 3 indicate the Rikaze, Namling, and Nimu meteorological stations, respectively.

The chronologies were developed using the program ARSTAN (Cook, 1985). Prior to standardization, a data-adaptive power transformation was applied to remove bias caused by so-called heteroscedasticity (Cook and Peters, 1997). The removal of the biological age trend for each series was accomplished by the calculation of ratios between the raw data and an exponential or linear growth trend curve. All detrended series were averaged to chronologies by computing the biweight robust mean (Cook and Kairiukstis, 1990). Variance stabilization (Osborn et al., 1997) was applied to adjust for changes in variance associated with declining sample size over time. Because the sample size declined in the early portion of the tree-ring chronology, a threshold of 0.85 was chosen for the expressed population signal (EPS, Wigley et al., 1984) and the mean inter-series correlations (Rbar) were calculated using a 30-year moving window with 15-year overlaps to determine the reliable time span. Several commonly used descriptive statistics were also computed. The mean sensitivity (MS) is an indicator of the relative changes in ring-width variance between consecutive years; the first-order autocorrelation (AC) expresses the influence of the previous year’s tree growth on the current year (Table 2). A common-period analysis from 1950 to 2000 was conducted for the standard chronology. R1 is the average correlation coefficient among all series, R2 is average correlation coefficient within trees and R3 is average correlation coefficient between trees. The first principal component (PC#1) represents the common variance explained by the chronology (Table 3).

Table 3 Statistical characteristics of common period analysis from 1950 to 2000 for the STD chronology at high and low elevations.

High elev. Low elev.

R1

R2

R3

EPS

PC#1

0.305 0.396

0.627 0.584

0.298 0.392

0.952 0.948

14.78% 11.83%

Climate data The closest meteorological station, Namling (29.41◦ N, 89.06◦ E, 4000 m a.s.l.), was not selected for the calculation of the tree growth/climate relationships because of its short length of observations (only 20 years recorded since 1991, Table 1). Instead we chose the Rikaze (29.15◦ N, 88.53◦ E, 3836 m a.s.l.) and Nimu (29.26◦ N, 90.10◦ E, 3809.4 m a.s.l.) stations in a nearby region, which both lie in the same climate regime as that of Namling (Figs. 1 and 2). Monthly mean temperatures (TMEAN ) and precipitation, as well as monthly mean maximum (TMAX ) and minimum (TMIN ) temperatures were extracted from the two stations (Fig. 2). We used the nearest monthly Palmer drought severity index (PDSI, Palmer, 1965) grid point, extracted from a data set with global coverage based on a 2.5◦ × 2.5◦ grid (Dai et al., 2004) spanning 1956–2005, to examine the joint effects of temperature, precipitation and soil moisture on tree growth (Table 1). We also extracted monthly precipitation data from the high-resolution climate data points of CRUts2.1 (Climate Research Unit) (0.5◦ × 0.5◦ grid, Mitchell and Jones, 2005). Data are available for CRUts2.1 from 1901, but we only used the time period 1956–2002 because there were no instrumental observations in this region prior to 1956. Mean values were averaged from four grid-boxes covering the region 29.25–30.25◦ N and 88.75–89.75◦ E (Table 1).

Evaluation of tree growth/climate relationship Correlation coefficients and response functions (Fritts, 1976) between the ring-width chronology and monthly values of TMEAN , TMAX , TMIN and precipitation from the Rikaze and Nimu meteorological stations were calculated for the period 1956/1974–2010, respectively. The response function was calculated using the software DENDROCLIM 2002 (Biondi and Waikul, 2004). Analyses were examined for a sequence of 20 months starting from March of the previous year and ending with October of the current year. Further, we included the gridded PDSI and CRU precipitation data

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Fig. 2. Monthly total precipitation (bars) and maximum (TMAX , line with triangles), mean (TMEAN , line with squares) and minimum (TMIN , line with circles) temperatures derived from the Rikaze (left) and Nimu (right) stations.

sets to examine the levels of drought stress at the study sites. The calculation periods covered 1956–2010 for Rikaze, 1974–2010 for Nimu, 1956–2005 for the PDSI data, and 1956–2002 for the CRU precipitation data. Besides using single monthly values, we also computed correlation and response functions between ring-width and different seasonal assemblages. Pearson’s correlation coefficients between ring-width indices and meteorological data were calculated for both elevation classes using the bivariate correlation function in SPSS software. Owing to its topographical complexity, the study region is strongly controlled by micro-climates. Special attention must be paid to the recent climate variability and its temporal trend. A linear regression (Y = a × X + b) was applied to estimate the rates of temperature change, where the magnitudes of the trends were calculated by the slopes (a) of the linear trends expressed in ◦ C per decade. The statistical significance of the linear trends was evaluated using the Pearson’s correlation coefficient (R, p). Finally, a response surface regression (Fritts and Wu, 1986) was conducted to examine the integrated effect of temperature and precipitation on radial growth. The resulting regression model is as follows: RWI = C0 + C1 T + C2 P + C3 T 2 + C4 TP + C5 P 2 , where RWI is the tree-ring width index, T and P are temperature and precipitation, C0 is the regression constant and C1 , C2 , C3 , C4 , C5 are the regression coefficients. Results and discussion Characteristics of chronologies at high and low elevations Maximum, upper hinge (75th quantile), mean, median (50th quantile), lower hinge (25th quantile) and the minimum of the tree-ring indices from the high- and low-elevation sites suggested that the radial growth of S. tibetica at elevations above 4100 m (NMNM) was very rapid, whereas the annual growth at the high elevation site (NMJW) near the upper Tibetan juniper distribution boundaries was slower (Fig. 3). Trees from dry forest habitats often showed higher inter-annual growth variability than trees from temperature-limited sites (Fritts, 1976; Bräuning, 2001; Liang et al., 2006). Thus, the radial growth rate of Tibetan junipers at the Namling region decreased with increasing elevation, reflecting the impact of elevation zonality on climate and the radial growth of trees. The two final valid standard chronologies from the high and low elevations are 637 and 417 years in length, spanning A.D. 1375–2010 and 1594–2010, respectively. The purpose of this study is to discuss the impact of elevation on radial tree growth, so we

Fig. 3. Comparison of raw ring-width for the high- (red) and low- (black) elevation classes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

only show the last 200 years, covering the period A.D. 1810–2010 (Fig. 4). During this interval, both chronologies showed similar inter-annual variability. Despite the lower EPS value found during 1840 (EPS = 0.75), 1855 (EPS = 0.76) and 1900 (EPS = 0.76) for the low-elevation chronology, the overall EPS value is above 0.85 for the two chronologies, indicating statistical confidence. The chronologies generally display a high year-to-year variability (as measured by the MS), which is typical for junipers growing in semiarid environments. However, chronologies from the low elevation site (NMNM) display a higher MS value (Table 2). The trees at lower elevations probably suffer more frequently from moisture stress than those growing at higher elevation sites. The high first-order autocorrelations (AC) reflect strong persistence of the ring-width chronologies, indicating a significant impact of the previous year’s climate on the current year’s ring width, probably caused by carryover effects of carbohydrates used for early wood formation (Fritts, 1976). The first-order autocorrelation (AC) and mean sensitivity (MS) of the two chronologies showed similar patterns, in which values decreased with increasing elevation (Table 2). This pattern was also reported in similar studies in the Qilian Mts. and Anyemaqen Mts. on the northeastern Tibetan Plateau (TP) (Gou et al., 2005; Peng et al., 2008; Liang et al., 2010), the central Hengduan Mts. on the southeastern TP, the north slope of the Tianshan Mts. (Guo et al., 2007), the Emei Mts. in the Sichuan Basin, eastern TP (Fang et al., 2010), and in northern central China (Fang et al., 2010; Zhang et al., 2011, 2012).

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Fig. 4. Comparison between the STD ring-width chronology at high (red) and low elevations (black). (a) The two STD chronologies and their 10-year low-pass filtered series (bold line). (b) Expressed population signal (EPS, rectangle line); the gray dashed line denotes the EPS 0.85 threshold. (c) Sample depth through time. (d and e) Age distributions for the two sites. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

The average inter-series correlation among all series (R1) and average correlation coefficient between trees (R3) for the chronology from the low-elevation site were higher than those from the high-elevation site, but the average correlation coefficient within trees (R2) was lower at the low-elevation site, revealing consistency in radial growth between trees (Table 3). The first principal component (PC#1) explained more than 11% of the total variance in all individual series, indicating that the chronologies contain strong common signals. The relatively high EPS for the common analysis period confirms that our chronologies are suitable for tree growth/climate relationship studies (Wigley et al., 1984).

have found a faster rate of increase in maximum temperatures, such as in northern India (Yadav et al., 2004) and Mexico (Brito-Castillo et al., 2009). For monthly precipitation, no obvious increasing trend was found at either Rikaze or Nimu over the past five (or three) decades (Fig. 5d and h). The two records showed similar behavior, with stronger agreement in the low-frequency domain. Distinct differences occurred during the years of 1983 and 1988 in the Rikaze precipitation series, while variations in magnitude were relatively small in the Nimu records during the common 37 years. General tree growth/climate relationship at different elevations

Regional climate variability The monthly averaged maximum (TMAX ), mean (TMEAN ) and minimum (TMIN ) temperatures for the Rikaze (1956–2010) and Nimu (1974–2010) meteorological stations were 14.94/15.84 ◦ C, 6.57/7.12 ◦ C and −1.10/−0.65 ◦ C, respectively. In terms of monthly temperatures, the values for Rikaze were similar to those of Nimu because of their close geographical locations and similar elevations (Table 1). A notable warming trend was observed in the monthly normalized anomalies at the two stations during the past few decades, where the rates of temperature change were significantly positive (p < 0.0001) for both stations (Fig. 5). For the Rikaze station, the mean temperature during the past 55 years has been significantly increasing by 0.21 ◦ C per decade (Fig. 5b). The warming trend of the maximum temperature (0.29 ◦ C per decade) was greater than that of the minimum temperature (0.22 ◦ C per decade) (Fig. 5a and c). However, the Nimu station showed the opposite pattern (Fig. 5e–g), in which the overall warming trend was mainly contributed by an increase in minimum temperatures (TMIN ) instead of maximum temperatures (TMAX ). The increase in monthly minimum temperatures is consistent with observations in most areas of China (Zhai et al., 1999; Yan et al., 2002; Liu et al., 2006a) and west Canada (Wilson and Luckman, 2003). Nevertheless, some studies

Temporal climate variability at both Rikaze and Nimu stations showed no obvious differences, so we analyzed the correlation between radial growths of Tibetan junipers at high- and lowelevation sites with the instrumental records at both of the two stations in order to make comparisons. Concerning the monthly climate variables, correlation analysis revealed that temperature (TMAX , TMEAN , and TMIN ) in November during the previous year positively correlated with tree-ring indices of S. tibetica at both high- and low-elevation sites (Figs. 6a–c and 7a, b). The only minor difference is that minimum temperature at the Nimu station displayed opposite correlation pattern (Fig. 7c). The general positive correlation between November temperature and tree growth is similar to previous studies, where juniper species have shown their primary sensitivity to be winter temperature in the Qilian Mts. (Gou et al., 2008), Anyemaqen Mts. (Peng et al., 2008) and the Wulan and Dulan area (Zhu et al., 2008; Liu et al., 2009) on the northeastern TP. In the Sygera Mts. and central Hengduan Mts. on the southeastern TP, early winter (November) temperature was also found to be the most crucial factor limiting radial growth in other tree species, for example fir (Liang et al., 2009, 2010) and spruce (Shao and Fan, 1999; Fan et al., 2009). The positive correlation between tree

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Fig. 5. January–December averaged (a and e) maximum, (b and f) mean, (c and g) minimum temperature, and (d and h) precipitation for the Rikaze station (left panel) during 1956–2010 and the Nimu station (right panel) during 1974–2010, respectively. The gray straight line shows the linear trend, and the dashed gray line represents 10-year low-pass filtered components. R and k represent the correlation coefficient and the slope of the linear regression; SD and p represent the standard deviation and significance level.

Fig. 6. Climate correlations for the STD chronologies at high (gray bar) and low (white bars) elevations with (a) mean temperatures (TMEAN ), (b) maximum temperature (TMAX ), (c) minimum temperatures (TMIN ), and (d) precipitation at the Rikaze station. Correlations for single months were calculated from prior March to current October over the 1956–2010 common periods. Correlations for seasonal mean are shown on the x-axis as prior December–February (−12/2, winter), current March–May (3/5, spring), June–August (6/8, summer), September–November (9/11, autumn), January–July (1/7, pre-growing and growing season), May–June (5/6, major growing season), April–September (4/9, warm season), prior October–March (−10/3, cold season), prior July–June (−7/6, annual), prior August–July (−8/7, annual), prior September–August (−9/8, annual), respectively. Horizontal dashed lines denote the 95% confidence levels.

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Fig. 7. Climate correlations for the STD chronologies at high (gray bar) and low (white bars) elevations with (a) mean temperatures (TMEAN ), (b) maximum temperature (TMAX ), (c) minimum temperatures (TMIN ), and (d) precipitations at the Nimu station. Correlations for single month were calculated from prior March to current October over 1974–2010 common periods. Correlations for seasonal mean were shown on x-axis as same as in Fig. 6. Horizontal dashed lines denote the 95% confidence levels.

growth and previous November temperature was also reported across the European Alps and northern latitudes (Cullen et al., 2001; Oberhuber, 2004; Büntgen et al., 2007; Lo et al., 2010). Thus, there seems to be a widespread phenomenon in high elevation mountain areas whereby positive radial growth responds to early winter (November) temperature. This ubiquitous finding suggests that warm November conditions likely support carbon storage, promote mycorrhizal root growth by maintaining soils above freezing, and favor maturation of shoots and buds against early winter stress (Oberhuber, 2004). Liang et al. (2009) and Liang and Eckstein (2009) considered that large diurnal temperature differences in November may cause frost damage to leaves and buds, and hence reduce root activity or increase the risk of frost-induced desiccation. For the Rikaze station, TMAX and TMIN ranges varied from 14.94 to −1.10 ◦ C, which can be compared with a temperature difference of 16.04 ◦ C that is reported to cause mechanical damage. Subsequent defoliation and bud mortality would deplete the reservoir of carbon and growth hormones, and thus reduce the tree’s potential for further growth and photosynthetic activity (Kozlowski and Pallardy, 1997). For monthly precipitation, positive correlations during July, August, and September of the previous year and May and June of the current year were obtained for radial growth at the two sites (Figs. 6d, 7d and 8a). The positive correlations with climate variables of the previous year are likely to be carry-over effect (Fritts, 1976). Growth variations result from the persistence of various effects into subsequent years through changes in nutrients and biological preconditioning of growth. Storage of assimilates and water from the previous growing season impacts radial growth during the current year (Kozlowski and Pallardy, 1997). The positive correlations between tree growth and August precipitation indicated the importance of water supply during the middle part of the growing season (Bräuning, 2001). With respect to the seasonal assemblages of climate variables, radial growth of Tibetan junipers across all stands at the two sites was negatively influenced by early summer temperature of the growing season (May–June), especially maximum (TMAX ) and mean (TMEAN ) temperatures (Figs. 6b and 7b). This dominant relationship was also identified on the northeastern (Zhang et al., 2003; Liu et al., 2006b; Gou et al., 2008; Peng et al., 2008; Shao et al., 2010) and

eastern (Bräuning, 2001; Liang et al., 2010; Shi et al., 2010; Zhu et al., 2011) TP. During early summer (May–June), the combination of high temperature and insufficient moisture accelerated transpiration and soil evaporation, leading to moisture stress on tree growth (Gou et al., 2008; Cai et al., 2010; Yadav et al., 2011). Studies from mid-latitude regions in Europe (Frank and Esper, 2005; Büntgen et al., 2007) implied that the weaker correlation with radial growth of other species (e.g. Larix, Abies, Pinus and Picea) may be associated with the production of cones or pollen, or both, instead of cell wall growth (Eis et al., 1965), where additional cellular division dominates metabolic activity. Intra-annual variations in tree growth during May and June probably reflect changes in the accessibility of soil water caused by severe early summer drought stress (Kahle, 2006). A striking feature of the data set was that chronologies from both the high- and low-elevation sites were positively correlated with precipitation in most months, and that the highest correlation coefficient was found between the combined radial growth for both sites and annual precipitation (July–June, Figs. 6d, 7d and 8a). This correlation pattern is consistent with earlier dendroclimatological studies in south Tibet (Liu et al., 2011, 2012) and on the northeastern TP (Shao et al., 2005; Liu et al., 2006b; Yang et al., 2011), and shows that dry conditions hamper juniper growth at this arid site, whereas humid conditions during the growth year and vegetation period favor growth. This argument is corroborated by the fact that the correlations between tree-ring indices and PDSI were positive in all months for both chronologies, but with a significant response only found in August of the preceding year at the high-elevation site (Fig. 8b). Therefore, we conclude that annual precipitation could play an important role in tree growth at the Namling region. Subtle differences in the tree growth/climate relationship between elevation classes Despite generally similar correlation patterns with climate variables across all the juniper stands at the high- and low-elevation sites, the effects of steep elevation gradients at the Namling region are nevertheless notable (Table 1). To assess any potential difference due to the changes in elevation, response of the tree

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Fig. 8. Climate correlations for the STD chronologies at high (gray bar) and low (white bars) elevations with (a) four-point averaged CRU precipitation over the 1956–2002 common and (b) PDSI over the 1956–2005 common. Correlations for single months were calculated from prior March to current October. Correlations for seasonal mean were shown on the x-axis similarly to those in Fig. 6. Horizontal dashed lines denote the 95% confidence levels.

growth/climate relationship to climatic variations needed to be analyzed. Regarding the relationship with temperature factors, S. tibetica growth at high- and low-elevation sites was positively correlated with mean and maximum temperatures in late autumn (prior October) and early winter (prior November to current January) but negatively correlated in summer (prior July and August, current May to July). Firstly, for the late autumn and early winter seasons, the correlations varied from significantly positive at the low-elevation site to weakly positive at the high-elevation site. Similar findings were obtained for lodgepole pines in southern interior British Columbia (Lo et al., 2010), beech trees in the eastern Alps (Di Filippo et al., 2007), and cluster pines in the central Pyrenees, Spain (Tardif et al., 2003). This slight difference may be an indication that S. tibetica growing at low elevation will increase its radial growth by a greater amount if there was no water stress during the previous growing season, indicating a possible role of the previous year’s reserves in supporting early radial growth the following year (Lo et al., 2010). Meanwhile, distinctive temperature difference patterns during the days and nights in early winter could cause mechanical damages to trees, as mentioned above. For the low elevation site, small temperature differences during the days and nights and the sufficient solar radiation as well as moisture support lead to less depletion and greater reserves of nutrients for radial growth in the following year. We conclude that, irrespective of which elevation it is growing in, S. tibetica’s optimum growth is dependent on high temperatures in the previous early winter season. Secondly, for the early summer season, the correlation coefficients between radial growth and temperature increased from weakly negative at the low-elevation sites to strongly negative at the high-elevation sites. Physiologically, earlywoods of Tibetan junipers largely form during the late spring to early summer season, as suggested by previous studies in north Tibet and on the southeastern TP (Bräuning and Mantwill, 2004; Wang et al., 2008). Sufficient soil moisture is critical for tree growth when evaporation and evapotranspiration are high and the trees are in full vigor (Shao et al., 2005). High temperature during the early summer would enhance soil moisture loss, which is mainly controlled by

evapotranspiration, causing cambium activity to weaken (Tian et al., 2009). Differences in site elevation did not seem to affect the strength of the relationship between radial growth in S. tibetica and precipitation, although the elevation difference was more than 300 m. To further confirm this observation, we calculated the precipitationbased continentality index for the high- and low-elevation sites according to empirical formula derived by Grams (1932) using the annual precipitation recorded at the Rikaze station (359 mm). The larger the calculated index, the greater the control of the ocean on climate. The continentality index for the high-elevation site was 4.5, whereas the value for the low-elevation site was 4.9. This result indicated that, with only slight differences between the elevation sites, both of the study region lies under a similar climate regime with little oceanic influence but relatively greater continental control. In comparison with the semi-arid areas of the northeastern TP (Liang et al., 2006), the abundant precipitation throughout the year in the Namling region may have masked differences in tree growth related to elevation aspect. In contrast with earlier hypothesis, our result was inconsistent with the classical concept of differing climatic signals captured in tree-ring data dependent on elevation. The elevational gradients introduce no discernible differences in the tree growth/climate relationships of S. tibetica in Namling, south Tibet. It is plausible that some common climatic factors, independent of stand elevation and local ecological factors, account for the high degree of variance for both of the tree-ring chronologies. Moreover, the negative correlations between tree-ring width and early summer temperature and positive correlation between ring width and precipitation in most months probably reflected that warm and dry conditions during the growing season yield poor juniper growth in the semi-arid environment of the study region, whereas high rainfall supply during the summer preceding the growth year and during the vegetation period are beneficial for radial growth of S. tibetica at the Namling region. As the correlations between tree-ring indices and PDSI seasonal assemblages show, the correlation coefficients were all significantly positive for both sites (Fig. 8b). Furthermore, the highest correlation coefficient was between the annual precipitation (July–June) and radial

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Fig. 9. Results of response surface regression analysis for STD chronologies at high- and low-elevation sites with instrumental records at the Namling station. The white points represent the tree-ring width index data for each year during 1991–2010. The regression equations were: (a) RWIhigh = 0.098 + 0.108X − 0.116Y − 0.031X2 − 0.005Y2 + 0.015XY (adjusted R2 = 0.545, F = 11.8, p < 0.05); (b) RWIlow = 0.987 + 0.078X − 0.034Y + 0.048X2 − 0.009Y2 + 0.069XY (adjusted R2 = 0.301, F = 4.87, p < 0.05); where RWIhigh/low , X, and Y denote ring-width index, May–June temperature, and July–June precipitation, respectively.

growth at both sites (Figs. 6d and 7d). If we combined all the samples, regardless of elevation aspect, the correlation coefficient with Rikaze annual precipitation was 0.66 (p < 0.01, Liu and Yang, 2011). As a result, we suggest that the annual precipitation (July–June) was the limiting factor for radial growth. The Namling tree-ring record is a suitable proxy for moisture availability reconstruction and predictions.

Combined impacts of temperature and precipitation on tree growth at different elevations A response surface regression was performed in order to quantify the incorporated impacts of temperature and precipitation on tree growth at the high- and low-elevation sites (Fig. 9). According to the analyses above, early summer (May–June) temperature and annual (July–June) precipitation at the two sites were the most influential climate factors. Consequently, it is useful to directly adopt the instrumental record derived from the closest Namling meteorological station to investigate the combined impacts of both temperature and precipitation on tree growth in S. tibetica. In order to compare the dimensionless climate factors, the temperature and precipitation data were standardized. The analysis results indicated those climatic conditions that might be optimal for the growth of S. tibetica at its upper and lower distribution boundaries, represented by the two sites assessed. At the high-elevation site (Fig. 9a), which reaches the upper distribution limit of the species, tree growth was sharply increased by greater annual precipitation but slowed down as the growing season mean temperatures decreased (adjusted R2 = 0.545, F = 11.8, p < 0.05). The highest ring-width index values at this site were associated with climatic conditions that included the mean growing season temperatures in the lower elevation group and annual precipitation values in the higher elevation group (represented by the dark red area of the response surface shown in Fig. 9a). Likewise, for the low-elevation site, tree-ring indices were significantly correlated with climate (adjusted R2 = 0.301, F = 4.87, p < 0.05). The tree growth at the low-elevation site generally increased with July–June precipitation (Fig. 9b) but decreased with May–June temperature.

Conclusions We investigated changes in the tree growth/climate relationships along an elevation gradient at the Namling region, south Tibet. Under the control of plateau semi-arid climate, the altitudinal zonality the study region is typically appertained to continental style, which had deep influence on environmental gradient and forest stands. As revealed by correlation analyses, summer temperature (May–June) and annual precipitation (July–June) exerted strong influences on tree growth at both sampling sites, showing how warm and dry conditions were apparently the most important limiting factor on tree growth in both elevation bands of the study region. Our results revealed slight differences in tree growth/climate relationships at the high- and low-elevation sites: (1) the correlations with early winter temperature varied from significantly positive at the low-elevation site to weakly positive at the high-elevation site; (2) the correlations between radial growth and summer temperature increased from weakly negative at the low-elevation sites to strongly negative at the high-elevation sites; and (3) the abundant precipitation throughout the year may have masked differences in tree growth related to elevation aspect. Our result was inconsistent with the classical concept of differing climatic signals captured in tree-ring data dependent on elevation. The Namling tree-ring record demonstrated the impacts of drought stress and benefits of moisture availability. Furthermore, the physiological explanation for the combined effects of both temperature and precipitation on radial growth needs further study, which could improve our understanding of the environmental controls on tree growth. Acknowledgements This study was jointly funded by the National Basic Research Program of China (973 Program) (No. 2010CB950104), the Chinese Academy of Sciences (CAS) 100 Talents Project (No. 29082762), and the National Natural Science Foundation of China (NSFC) (Grant No. 40871091). The authors extend thanks to two anonymous reviewers for their valuable suggestions and the recommendation of special guest editor, Dr. Liu Yu. The authors were also grateful to Shao Yajun for her helpful work in the lab.

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