Trees

, Volume 29, Issue 1, pp 25–34 | Cite as

Intra-annual stem radial increment response of Qilian juniper to temperature and precipitation along an altitudinal gradient in northwestern China

  • Zhangyong Wang
  • Bao Yang
  • Annie Deslauriers
  • Achim Bräuning
Original Paper

Abstract

Key message

Spring temperature is a major limiting factor at the beginning of the growing season, the timing of growth initiation can increase by about 7 days/°C. During the growing season, impacts of climate variables on radial growth are similar along an altitudinal gradient.

Abstract

Altitude is considered as an important factor affecting tree growth in mountain forest ecosystems. In this paper, the results of a 2-year field study along an altitudinal gradient in the cold and arid central Qilian Mountains, northwestern China, are reported. Twelve Qilian juniper trees (Sabina przewalskii Kom.) were monitored with high-resolution dendrometers at three altitudes ranging from 2,865 to 3,550 m. At each altitude, a local weather station was installed close to the studied trees. We identified correlations between intra-annual growth patterns derived from the Gompertz equation with local air temperature and precipitation data. The timing of growth initiation became earlier and the growing season duration increased with decreasing altitude. The onset of radial growth occurred between early May and early June, and the growing season terminated between mid-July and late August, resulting in a growing season duration that decreased from 107 to 41 days as elevation increased. June is the most important growth period at each altitude. Spring temperature, which is strongly associated with elevation, is a critical factor determining the initiation of radial growth. The timing of growth initiation was delayed by 3–4 days per 100 m elevation. When associated with the modeled altitudinal spring temperature lapse rate of −0.48 °C/100 m, the onset of the growing season increased by about 7 days/°C. However, during the growing season, daily stem radial increments showed a positive correlation with precipitation and a negative correlation with daily maximum air temperature at all altitudes. Our study provides new data revealing the basic growth processes of Qilian juniper trees and provides significant information to quantify the responses of tree growth to future global warming.

Keywords

Dendrometer Stem radial increment Sabina przewalskii Kom. Qilian Mountains Altitudinal gradient 

Introduction

Tree growth has been described as being limited by a range of different environmental factors, depending on local climate conditions. Elevation has a strong impact on local climate, but lapse rates differ between regions. It has already been stated in dendroclimatology that, in general, variations in ring width at low altitudes are strongly positively correlated with precipitation and negatively correlated with temperature, while at high altitudes the correlation with temperature is likely to reverse as temperature becomes directly correlated with growth (Fritts et al. 1965; Fritts 1976). Based on this altitude-dependent hypothesis, numerous dendroclimatological studies have indicated that climate/growth relationships may vary with elevation, following the variations in temperature and precipitation (Peterson and Peterson 2001; Mäkinen et al. 2002; Takahashi et al. 2005).

Contrary to this general hypothesis, several studies have observed similar growth variation patterns along altitudinal gradients and have identified common limiting environmental factors that might synchronize tree growth at different elevations or latitudes (Morales et al. 2004; Esper et al. 2007). For example, at both upper and lower treelines on the northeastern Qinghai-Tibetan Plateau, it was observed that Qilian juniper growth is controlled by precipitation in late spring and early summer because higher temperatures from April to June may intensify drought stress and lead to narrow tree rings (Liu et al. 2006b). The initiation of tree-ring growth of Smith firs is controlled by common climatic signals across a broad elevation range in the Sygera Mountains, southeastern Tibetan Plateau (Liang et al. 2010). For several tree species, it has been shown that tree-ring widths demonstrated similar growth responses to climatic factors at different elevations: for example Faxon fir (Abies faxoniana) in southwestern China (Li et al. 2012) and Chinese pine (Pinus tabulaeformis) in central China (Shi et al. 2012).

The Qilian Mountains in northwestern China are influenced by the Westerlies and the Asian summer monsoon. Qilian juniper (Sabina przewalskii Kom.) growing in this region is a long-lived species that has been used to develop the longest tree-ring chronology in China (Yang et al. 2014). Although this area can be regarded as a ‘hotspot’ region for dendroclimatological research in China, the relationships between tree rings and climate in the central Qilian Mountains are still a matter of debate. Earlier studies suggested that temperature has a positive effect on Qilian juniper annual growth (Liu et al. 2005, 2006a); however, recent dendroclimatological analyses showed that tree growth in this region is controlled by precipitation rather than temperature (Yang et al. 2011, 2014). Additional dendroecological studies have revealed that temperature is the most important limiting factor for tree growth at the upper tree-line, with summer temperature significantly and positively related to tree growth in both the current and previous growing seasons (Gou et al. 2012).

Despite the indisputably valuable palaeoclimatic information obtained from tree-ring widths, obtaining a tree-ring series with a higher than annual resolution has now been an ambition for several decades. However, very few studies (Wang et al. 2012; Gou et al. 2013) have made the precise measurements in China needed to assess intra-annual growth patterns, thus the effects of climatic variables on physiological processes remain uncertain. Monitoring intra-annual variability in radial growth and its association with climatic variables over different altitudes can provide insight into tree growth dynamics and improve our understanding of climate impacts on radial growth processes.

With reference to the altitude-dependent growth hypothesis predicting that radial growth of Qilian juniper and its relationships with climatic variables would vary along the altitudinal gradient, the main objective of this study was to describe the different seasonal growth patterns of Qilian juniper trees growing at three altitudes. In addition, we try to assess the different responses of daily stem radial increments to temperature and precipitation along an altitudinal gradient.

Materials and methods

Study sites

The study was conducted at the Sidalong Forestry Station, located in the central Qilian Mountains (for more details, see Wang et al. 2012). The study plots were established at three elevations along adjacent south-facing slopes (10–45 %) in a Qilian juniper (Sabina przewalskii Kom.) forest. Sites were labeled as upper altitude (UL) (38°26.64′N, 99°56.03′E, 3,550 m a.s.l.), middle altitude (ML) (38°26.62′N, 99°55.63′E, 3,200 m a.s.l.) and lower altitude (LL) (38°26.28′N, 99°55.01′E, 2,865 m a.s.l.), respectively. The UL plot extends to the mountain top and is generally considered to be close to the upper distribution limit of the species. At this altitude, abundant Qilian juniper trees live on the mountain ridge, despite the rocky environment with a very thin soil layer (mainly <10 cm). The ML plot is located on a relatively flat area in the middle of the slope, while the LL plot is located close to the lower boundary of the forest.

Dendrometer measurements

A total of 12 trees (4 per altitude) were selected for continuous growth observations. From January 2011 to December 2012, automatic high-resolution point and band dendrometers (Ecomatik, Germany; type DR and DC2, accuracy ± 2 μm) were installed at a height of 1–1.3 m on the trunk. Ten trees were measured by point dendrometers, which were mounted perpendicular to the slope. The other two trees were measured by band dendrometers. The selected trees were healthy adults with similar growth, well-developed crowns, upright stems, and without any sign of injury. The sampled trees had a mean height of 9.50 ± 2.89 m (mean ± standard deviation) and mean circumference at about breast height of 76.36 ± 10.52 cm (mean ± standard deviation). Stem radius variations were automatically recorded at 30-min intervals and saved in data loggers. To reduce the influence of expansion and shrinkage processes of the bark, the outer bark was removed without wounding the cambial zone. As the band dendrometers measured stem circumference, whereas the point dendrometers measured changes at a single point (radius) of the stems, the mean circumference data were divided by 2π for comparison, thus providing average radius changes.

Meteorological measurements

Meteorological conditions were continuously monitored at each altitude during the entire period of 2011–2012. To evaluate climatic differences along the transect, three automated weather stations (HOBO U30) were installed in open, relatively flat areas within 50 m of the sampled trees. Air temperature (probe S-THB-M002) and precipitation (probe S-RGB-M002) were automatically recorded at 30-min intervals at 2 m height and stored in a data logger (model U30-NRC).

To compare the 2011 and 2012 temperature and precipitation measurements with longer-term observations, temperature (1980–2011) and precipitation (1957–2011) data from the national weather station at Qilian (38°11′N, 100°15′E, 2,787 m a.s.l.) were used. The linear distance between the weather station and our study area is approximately 42 km.

Data analysis

One important issue is how to determine the growing season length from dendrometer measurements. Here, we defined the growing season as the period during which cell number and size increased, causing stem radial increments that could be recorded by high-resolution dendrometers. Since stem radius variations are related to changes in both radial growth and water status (Deslauriers et al. 2003b, 2007; Krepkowski et al. 2011), algorithms that are appropriate to distinguishing both sources of stem radius variability need to be considered.

Among the various sigmoidal models, the Gompertz equation is appropriate to describe growth and time relationships and is one of the most commonly applied models (Camarero et al. 1998; Deslauriers et al. 2003a). For characterizing the complete seasonal growth patterns and to avoid arbitrary choices in the initial settings of dendrometer measurements, Duchesne et al. (2012) were the first to use a formulation of the Gompertz equation that includes an additional parameter (Y0), providing a better estimate of the growing season length from dendrometer data.

May to September was selected as the approximate growing season for the subsequent analysis (Liu et al. 2006c). The Gompertz model was fitted to the daily averaged raw measurement data:
$$Y = Y_{0} + A \, \exp \, \left[ { - {\text{e}}^{{\left( {\beta - \kappa t} \right)}} } \right]$$
(1)
where Y represents the daily averaged raw measurements, Y0 is the lower asymptote, A is the upper asymptote, β is the x axis placement parameter, k is the rate of change parameter, and t is the time in days. The parameters were estimated by the ordinary least squares method with the MODEL procedure (SAS Institute, 2002), as applied by Duchesne et al. (2012). We first modeled seasonal growth patterns of individual trees (n = 4) at each altitude (n = 3) for each growing season (n = 2) to assess inter-tree variability for a total of 24 models. We then restricted our analysis to the average stem radius variations to assess seasonal growth patterns at each altitude. Timing of growth initiation and cessation was thus determined as the day of year (DOY) when modeled daily growth rates passed the threshold of 4 μm/day, which corresponded to the dendrometer accuracy.

Stem radial increments (SRIs) derived from dendrometer measurements have been widely used as representative estimates of radial growth (Deslauriers et al. 2011). To extract daily SRIs, several approaches (Downes et al. 1999; Tardif et al. 2001; Deslauriers et al. 2003b; Bouriaud et al. 2005; Bräuning et al. 2009; Krepkowski et al. 2011) may be used, since very similar time series are derived from each of these methods (Deslauriers et al. 2007). During the growing season, the daily SRIs were determined by calculating the difference between maximum values of two consecutive days for each tree. Daily SRIs were considered equal to zero when negative differences occurred, i.e., when the maximum stem radius value of the previous day was not reached during the following day. Then, the daily SRIs values were averaged for each altitude. Due to the abnormal distribution of precipitation data, nonparametric Kendall’s Tau correlation coefficients were calculated to assess the relationship between daily SRIs and climatic variables (precipitation, maximum, minimum and mean air temperature). As we were interested in the differences in growth-climate responses across the altitudinal gradient, altitude was treated as an independent variable when fitting simple linear regression functions with several dependent variables (climatic variables and seasonal growth patterns).

Results

Climate variability and gradients

The climate of the study area is characterized by warm-wet summers and cold-dry winters (Fig. 1). May to September accounted for more than 85 % of the annual precipitation totals at each altitude. Compared with previous precipitation observations at Qilian weather station (405 mm), the years 2011 (442.6 mm) and 2012 (464.4 mm) were wetter. In 2011, the temperature was slightly warmer than in 2012, especially before the start of the growing season. However, in the summer (from June to August) of 2012, the precipitation was higher than in 2011.
Fig. 1

Climatic records and stem radius variations. Top panels compare the 2011 (a) and 2012 (b) daily average temperatures at three altitudes and the 1980–2011 mean (heavy lines) obtained from the national weather station at Qilian (2,787 m a.s.l.). Shaded areas indicate the daily mean minimum and maximum temperatures averaged over the 1980–2011 period. Middle panels show the 2011 (c) and 2012 (d) monthly precipitation totals at three altitudes, relative to the 1957–2011 mean (black bars). Bottom panels show the 2011 (e) and 2012 (f) stem radius changes at three altitudes. The thin lines represent twelve sample trees, while thick lines represent altitude averages

The climatic records for 2011–2012 showed that temperature and precipitation vary across the altitudinal gradient. The altitudinal temperature trend was evident, whereas there was no apparent altitudinal precipitation trend. Using simple linear regression, mean annual temperature was estimated to decrease by 0.51 °C/100 m (P < 0.05, R2 = 0.64). However, the temperature lapse rate varied between different seasons; in the spring (from March to May), it was −0.48 °C/100 m (P < 0.01, R2 = 0.93), while in the summer (from June to August), it was −0.46 °C/100 m (P < 0.01, R2 = 0.97). On the other hand, mean annual precipitation was estimated to increase by 9.74 mm/100 m, but the regression was not significant (P = 0.114), since annual total precipitation at the intermediate site (ML) did not follow a linear increase of precipitation with elevation.

Seasonal growth patterns and their relationship with altitude

Apart from a few exceptions, the trees’ stem radius variations generally demonstrated synchronous trends (Fig. 1). The annual variability was characterized by (i) a progressive increase of stem radius beginning in late spring, (ii) a stem radius plateau in late summer and (iii) a stem radius decrease during the winter months. The common trends of the dendrometer measurements thus indicated pronounced seasonality in wood formation. Comparing tree growth rates at each altitude revealed that the LL and ML trees had similar growth rates, while UL trees showed smaller average increments.

Comparing the averaged dendrometer data for 2011 with those for 2012, stem radius at the end of April 2012 reached those of the maximum levels of 2011, although trees showed subtly different changes at the three altitudes. Along the altitudinal gradient, the mean air temperature in late April ranged from 3.46 to 6.69 °C in 2011, and ranged from 1.54 to 4.69 °C in 2012. The precipitation totals for April ranged from 10.80 to 14.20 mm in 2011, and ranged from 6.80 to 10.20 mm in 2012. Such climatic conditions likely promote the occurrence of spring rehydration. We inferred that spring rehydration was probably completed before May. Thus, the selected period from May to September was suitable to apply the Gompertz model to estimate seasonal growth patterns.

Observed and modeled stem radius variations and associated daily growth rates during May to September were illustrated (Fig. 2). Nonlinear regressions explained between 91 and 99 % of the variations in the dendrometer measurements. Several parameters describing the seasonal growth patterns were defined (Table 1). Inter-tree variability at each altitude was much higher in terms of the timing of growth cessation and growing season duration when compared with the variability of other parameters. According to the modeled results, the maximum growth rates occurred in June at each altitude. The days of maximum growth rate ranged from June 2 (DOY 153) to June 16 (DOY 167) in 2011, and around June 19 (DOY 171) in 2012. June was the main growth period, accounting for 45.22–90.38 % of the cumulative seasonal growth at different altitudes during the 2 years.
Fig. 2

Observed and modeled stem radius variations. Dendrometer raw measurements (dots) and Gompertz function modeled curves (lines) from May to September in 2011 and 2012. The dashed lines in the bottom panels represent daily growth rates equal to 4 μm/day

Table 1

Characterization of the seasonal growth patterns during the study period

Year

Altitude

Timing of growth initiation (DOY)

Timing of growth cessation (DOY)

Growing season duration (days)

Day of maximum growth (DOY)

Maximum growth rate (μm/day)

Cumulative seasonal growth (mm)

2011

UL

152 (4.3)

193 (8.1)

41 (12.3)

167 (0.6)

12.2 (8.8)

0.41 (0.1)

ML

137 (4.1)

216 (15.3)

79 (17.2)

163 (4.9)

30.5 (2.2)

1.45 (0.2)

LL

128 (4.9)

203 (13.7)

75 (15.6)

153 (4.2)

33.5 (8.4)

1.36 (0.4)

2012

UL

156 (1.0)

207 (6.5)

51 (7.0)

174 (2.1)

19.1 (3.9)

0.58 (0.1)

ML

141 (2.0)

233 (4.3)

92 (4.9)

171 (1.9)

43.0 (4.7)

2.04 (0.2)

LL

137 (2.4)

244 (12.5)

107 (14.2)

171 (2.6)

42.5 (15.1)

2.47 (0.9)

Numbers in parentheses represent inter-tree variability (standard error)

Despite the low number of available data (n = 6), linear fitting explained a higher percentage of the altitudinal dependence at the beginning and duration of the growing season (Fig. 3). The cessation of the growing season, maximum growth rate and cumulative seasonal growth were found to be less affected by altitude. The timing of growth initiation was expressed as a function of altitude; this yielded an onset delay of about 3.68 days/100 m (R2 = 0.82, P < 0.01). Associated with the modeled altitudinal spring temperature differences of about 0.48 °C/100 m, the timing of growth initiation is equivalent to a shift of about 7.67 days/°C. Duration of the growing season was estimated to decrease by 9.86 days/100 m (R2 = 0.59, P < 0.05). According to the modeled altitudinal summer temperature differences of about 0.46 °C/100 m, the growing season length is equivalent to a shift of 21.43 days/°C.
Fig. 3

Seasonal growth patterns and their associations with altitude. Simple linear regression models of the timing of growth initiation (a) and cessation (b), growing season duration (c), maximum growth rate (d), day of maximum growth (e) and cumulative seasonal growth (f) with altitude

Relationship of daily SRIs with climate

In addition to few cases, daily SRIs revealed consistent response patterns to climatic variables. Very similar growth-climate relationships can be observed along the altitudinal gradient (Fig. 4). Daily SRIs at each altitude showed significant positive correlations with precipitation. For example, in 2011, the Kendall’s Taus were equal to 0.48 at LL, 0.44 at ML and 0.42 at UL (P < 0.01). Meanwhile, daily SRIs were negatively correlated with daily maximum temperature, with Kendall’s Taus equal to −0.41 at LL, −0.34 at ML and −0.34 at UL (P < 0.01). Except for UL in 2011, significant negative associations were found between daily SRIs and daily mean temperature. Daily minimum temperature likely had no significant associations with SRI values as the results were not consistent. Also, the relationships between these coefficients and altitude were calculated (data not shown), but they were not statistically significant at P < 0.05.
Fig. 4

Nonparametric Kendall’s Tau correlation coefficients between climatic variables (Tmean: daily mean air temperature; Tmax: daily maximum air temperature; Tmin: daily minimum air temperature; Prep: daily precipitation) and daily SRIs. The bars with slashes indicate that the correlation is significant at the 0.01 level

Discussion

Comparison of seasonal growth patterns at different altitudes

The growing season durations decreased with altitude. Since temperature is the most important variable along altitudinal gradients in our study sites, this is attributed to the higher temperatures at lower altitudes. Previous studies have primarily found that temperature was the key factor controlling the cambial activity and cell production in cold environments such as boreal and montane forests (Mäkinen et al. 2003; Deslauriers and Morin 2005; Rossi et al. 2007; Deslauriers et al. 2008; Rossi et al. 2011). The timing of growth initiation was earlier at lower altitudes, which mainly due to the spring temperature variations. High spring temperatures can affect cambial activity by increasing the rate of cell division and the amount of xylem produced (Deslauriers and Morin 2005). The mean temperature of April 2011 (mean value of the three altitudes, 2.01 °C) was slightly higher than that of April 2012 (0.64 °C). As a consequence, the onset of growth was earlier in 2011 than in 2012 (Table 1), confirming the spring temperature effect on the timing of growth initiation.

The timing of growth cessation showed a higher inter-tree variability and was less well explained by altitude. Therefore, temperature seems to be an important factor, especially for determining the beginning of the phases of xylem differentiation, but has less influence in controlling the ending of the phases (Moser et al. 2010). Under the temperature increase that has occurred during the last decades, modeled results have shown that phenological events in spring were more affected than those occurring in autumn (Boulouf Lugo et al. 2012). Observations in a Canadian boreal forest also showed that air temperature and photosynthetic active radiation appeared to regulate the initiation of tree growth, while the timing of growth cessation was probably controlled by internal physiological mechanisms (Duchesne et al. 2012).

The differences in the cumulative seasonal growth at different altitudes cannot be clearly explained at present. Several authors have argued that the general trend of declining tree growth with altitude is associated with the shortening of the growing season and the reduction in mean summer temperatures at high altitudes (Carrer and Urbinati 2004; Takahashi 2010; Oladi et al. 2011). The temperature variations under the influence of elevation are likely to have an impact on radial growth of trees. In this study, the cumulative seasonal growth had no regular altitudinal response but was strongly associated with the growing season duration and maximum growth rate (data not shown). Previous studies have shown that, in cold environments, the maximum growth rates of conifers coincide with maximum day length (Rossi et al. 2006; Duchesne et al. 2012). This indicates that there are some other factors that have not yet been taken into account. The radial growth of trees simultaneously depends on tree characteristics (age, size and competition status), microenvironment (light, soil water content and nutrient availability) and species (Nabeshima et al. 2010), making relationships between cumulative seasonal growth and altitude more complex.

‘Space-for-time’

In order to assess the impacts of future climate change, information gained along an altitudinal gradient may therefore be of particular value because such gradients allow the so-called space-for-time substitution (Moser et al. 2010; Jochner et al. 2013; Mäkelä 2013). The clear altitudinal temperature trend provides a possible way to assess the mechanism of radial growth of Qilian juniper in response to future climate warming. In this study, the rates of change of the timing of tree growth onset and duration with respect to temperature were substituted by measuring changes along the altitudinal gradient. The timing of growth initiation was quantified as −3.68 days/100 m, corresponding to a change of 7.67 days/°C. This result is similar to a study conducted in the central Swiss Alps (Moser et al. 2010). The growing season length was estimated to decrease by 9.86 days/100 m and 21.43 days/°C in this study. In the boreal forest of Canada, lower rates of extension of the growing season length of 8–11 days/°C were found (Rossi et al. 2011). However, with only 2 years of observation, our models are still tentative and should not strictly be interpreted in terms of causality.

Daily SRIs and climate relationships

During the growing seasons, both temperature and precipitation affected daily SRIs more or less equally at each altitude. Daily SRIs did not show an altitude-dependent response to temperature or precipitation. Precipitation had significant positive effects on daily SRIs at all altitudes. Thus, in such a drought-prone region, the effect of precipitation on radial growth of Qilian juniper was significant. A positive effect of water supply and precipitation on intra-annual radial growth was observed in several conifer species (Tardif et al. 2001; Deslauriers et al. 2003b; Bouriaud et al. 2005; Zweifel et al. 2006). In 2012, the summer precipitation was higher than in 2011. As a consequence, the maximum growth rate and cumulative seasonal growth were higher than in 2011 at each altitude (Table 1), confirming the precipitation effect on radial growth. Furthermore, this result is in agreement with previous dendroclimatological studies conducted in the central Qilian Mountains, showing that tree-ring width has significant positive correlations with annual precipitation (Yang et al. 2011, 2014).

During the growing season, the observed negative effects of high temperature on daily SRIs can be linked to drought stress conditions (Lloyd and Fastie 2002; Wilmking et al. 2004; McMillan et al. 2008). High temperatures induce an increase in evapotranspiration and cause a decline of soil moisture, thereby intensifying water stress (Way and Sage 2008). In northwestern China, the application of the Vaganov–Shashkin model of cambial activity also indicated that precipitation during the early growing season had a significant effect on Qilian juniper radial growth; temperature promoted tree growth by extending the growing season but limited growth by warming-induced drought (Zhang et al. 2011).

Here, the relationship between radial growth and climatic variables is supported by a recent study conducted around the northeastern Tibetan Plateau (Gou et al. 2013). Their simulation results revealed that temperature might principally affect tree growth indirectly by warming-induced drought during the growing season, while precipitation in June largely determines the radial growth. Their repeated micro-sampling results also showed that the radial increment of Qilian juniper in June is the main contributor for the width of an annual ring over this region, which support our dendrometer study.

Conclusions

We hypothesized that the radial growth of Qilian juniper and its relationship with climatic variables would vary along the altitudinal gradient. This initial hypothesis was only partially supported. We found that the timing of growth initiation was earlier at lower altitudes (3–4 days per 100 m), and the duration of a tree’s growing season increased with decreasing altitude. However, during the growing season, the impacts of climate variables on radial growth were similar at different altitudes. Daily SRIs showed a significantly positive association with precipitation and a negative association with daily maximum air temperature at all altitudes.

The timing of growth cessation, the maximum growth rate, and cumulative seasonal growth were not significantly related to altitude. The mechanisms responsible for these results cannot be clearly explained at present. Since the altitudinal temperature trend is evident, we conclude that spring temperature is a major climatic factor limiting the beginning of the growing season. The timing of growth initiation can increase by about 7 days/°C. This study provides new data revealing the basic growth processes of Qilian juniper trees, and provides significant information to quantify the responses of tree growth to future global warming.

Author contribution

Zhangyong Wang collected and analyzed the data, wrote the first version of the manuscript, and contributed to method design. Bao Yang designed the project and wrote the manuscript. Annie Deslauriers and Achim Bräuning contributed to the writing of this manuscript, helped interpret data and results. All authors reviewed the paper.

Notes

Acknowledgments

The authors thank Louis Duchesne for providing the SAS procedure. The authors would like to thank Jie Wang, Jianqi Zhang, Hongyuan Ge, Wen Wang and Hongyi Wang for the maintenance of the field instruments. Thanks are also extended to Zhenhua Wang and Yi Ren for their discussions about technical problems. The authors also thank the four anonymous reviewers and the editor for their valuable comments. The study was jointly funded by the National Science Foundation of China (Grant No. 41325008) and the Interdisciplinary Innovation Team project of the Chinese Academy of Sciences (29Y329B91). Zhangyong Wang was supported by the National Science Foundation of China (Grant No. 31300412), and the Foundation for Excellent Youth Scholars of CAREERI,CAS, and the West Light Program for Talent Cultivation of Chinese Academy of Sciences. Bao Yang gratefully acknowledges the support of the K.C. Wong Education Foundation, Hong Kong.

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Boulouf Lugo J, Deslauriers A, Rossi S (2012) Duration of xylogenesis in black spruce lengthened between 1950 and 2010. Ann Bot 110:1099–1108PubMedCentralPubMedCrossRefGoogle Scholar
  2. Bouriaud O, Leban J-M, Bert D, Deleuze C (2005) Intra-annual variations in climate influence growth and wood density of Norway spruce. Tree Physiol 25:651–660PubMedCrossRefGoogle Scholar
  3. Bräuning A, Volland-Voigt F, Burchardt I, Ganzhi O, Nauß T, Peters T (2009) Climatic control of radial growth of Cedrela montana in a humid mountain rainforest in southern Ecuador. Erdkunde 63:337–345CrossRefGoogle Scholar
  4. Camarero JJ, Guerrero-Campo J, Gutiérrez E (1998) Tree-ring growth and structure of Pinus uncinata and Pinus sylvestris in the Central Spanish Pyrenees. Arct Alp Res 30:1–10CrossRefGoogle Scholar
  5. Carrer M, Urbinati C (2004) Age-dependent tree-ring growth responses to climate in Larix decidua and Pinus cembra. Ecology 85:730–740CrossRefGoogle Scholar
  6. Deslauriers A, Morin H (2005) Intra-annual tracheid production in balsam fir stems and the effect of meteorological variables. Trees 19:402–408CrossRefGoogle Scholar
  7. Deslauriers A, Morin H, Begin Y (2003a) Cellular phenology of annual ring formation of Abies balsamea in the Quebec boreal forest (Canada). Can J For Res 33:190–200CrossRefGoogle Scholar
  8. Deslauriers A, Morin H, Urbinati C, Carrer M (2003b) Daily weather response of balsam fir (Abies balsamea (L.) Mill.) stem radius increment from dendrometer analysis in the boreal forests of Québec (Canada). Trees 17:477–484CrossRefGoogle Scholar
  9. Deslauriers A, Rossi S, Anfodillo T (2007) Dendrometer and intra-annual tree growth: what kind of information can be inferred? Dendrochronologia 25:113–124CrossRefGoogle Scholar
  10. Deslauriers A, Rossi S, Anfodillo T, Saracino A (2008) Cambial phenology, wood formation and temperature thresholds in two contrasting years at high altitude in southern Italy. Tree Physiol 28:863–871PubMedCrossRefGoogle Scholar
  11. Deslauriers A, Rossi S, Turcotte A, Morin H, Krause C (2011) A three-step procedure in SAS to analyze the time series from automatic dendrometers. Dendrochronologia 29:151–161CrossRefGoogle Scholar
  12. Downes G, Beadle C, Worledge D (1999) Daily stem growth patterns in irrigated Eucalyptus globulus and E. nitens in relation to climate. Trees 14:102–111Google Scholar
  13. Duchesne L, Houle D, D’Orangeville L (2012) Influence of climate on seasonal patterns of stem increment of balsam fir in a boreal forest of Québec, Canada. Agric For Meteorol 162–163:108–114CrossRefGoogle Scholar
  14. Esper J, Frank DC, Wilson RJS, Büntgen U, Treydte K (2007) Uniform growth trends among central Asian low- and high-elevation juniper tree sites. Trees 21:141–150CrossRefGoogle Scholar
  15. Fritts HC (1976) Tree rings and climate. Academic Press, LondonGoogle Scholar
  16. Fritts HC, Smith DG, Cardis JW, Budelsky CA (1965) Tree-ring characteristics along a vegetation gradient in northern Arizona. Ecology 46:394–401CrossRefGoogle Scholar
  17. Gou X, Zhang F, Deng Y, Ettl GJ, Yang M, Gao L, Fang K (2012) Patterns and dynamics of tree-line response to climate change in the eastern Qilian Mountains, northwestern China. Dendrochronologia 30:121–126CrossRefGoogle Scholar
  18. Gou X, Zhou F, Zhang Y, Chen Q, Zhang J (2013) Forward modeling analysis of regional scale tree-ring patterns around the northeastern Tibetan Plateau, Northwest China. Biogeosciences. doi:10.5194/bgd-10-9969-2013
  19. Jochner S, Caffarra A, Menzel A (2013) Can spatial data substitute temporal data in phenological modelling? A survey using birch flowering. Tree Physiol 33:1256–1268PubMedCrossRefGoogle Scholar
  20. Krepkowski J, Bräuning A, Gebrekirstos A, Strobl S (2011) Cambial growth dynamics and climatic control of different tree life forms in tropical mountain forest in Ethiopia. Trees 25:59–70CrossRefGoogle Scholar
  21. Li ZS, Liu GH, Fu BJ, Hu CJ, Luo SZ, Liu XL, He F (2012) Anomalous temperature–growth response of Abies faxoniana to sustained freezing stress along elevational gradients in China’s Western Sichuan Province. Trees 26:1373–1388CrossRefGoogle Scholar
  22. Liang E, Wang Y, Xu Y, Liu B, Shao X (2010) Growth variation in Abies georgei var. smithii along altitudinal gradients in the Sygera Mountains, southeastern Tibetan Plateau. Trees 24:363–373CrossRefGoogle Scholar
  23. Liu X, Qin D, Shao X, Chen T, Ren J (2005) Temperature variations recovered from tree-rings in the middle Qilian Mountain over the last millennium. Sci China, Ser D Earth Sci 48:521–529Google Scholar
  24. Liu L, Shao X, Liang E, Wang L (2006a) Tree-ring records of Qilian Juniper’s growth and regeneration patterns in the central Qilian Mountains. Geogr Res 25:53–61Google Scholar
  25. Liu LS, Shao XM, Liang EY (2006b) Climate signals from tree ring chronologies of the upper and lower treelines in the Dulan region of the Northeastern Qinghai-Tibetan Plateau. J Integr Plant Biol 48:278–285CrossRefGoogle Scholar
  26. Liu X, Wang Q, Meng H (2006c) Qilian juniper. China Science and Technology Press, BeijingGoogle Scholar
  27. Lloyd AH, Fastie CL (2002) Spatial and temporal variability in the growth and climate response of treeline trees in Alaska. Clim Change 52:481–509CrossRefGoogle Scholar
  28. Mäkelä A (2013) En route to improved phenological models: can space-for-time substitution give guidance? Tree Physiol 33:1253–1255PubMedCrossRefGoogle Scholar
  29. Mäkinen H, Nöjd P, Kahle HP, Neumann U, Tveite B, Mielikäinen K, Röhle H (2002) Radial growth variation of Norway spruce (Picea abies (L.) Karst.) across latitudinal and altitudinal gradients in central and northern Europe. For Ecol Manag 171:243–259CrossRefGoogle Scholar
  30. Mäkinen H, Nöjd P, Saranpää P (2003) Seasonal changes in stem radius and production of new tracheids in Norway spruce. Tree Physiol 23:959–968PubMedCrossRefGoogle Scholar
  31. McMillan AMS, Winston GC, Goulden ML (2008) Age-dependent response of boreal forest to temperature and rainfall variability. Glob Change Biol 14:1904–1916CrossRefGoogle Scholar
  32. Morales MS, Villalba R, Grau HR, Paolini L (2004) Rainfall-controlled tree growth in high-elevation subtropical treelines. Ecology 85:3080–3089CrossRefGoogle Scholar
  33. Moser L, Fonti P, Buntgen U, Esper J, Luterbacher J, Franzen J, Frank D (2010) Timing and duration of European larch growing season along altitudinal gradients in the Swiss Alps. Tree Physiol 30:225–233PubMedCrossRefGoogle Scholar
  34. Nabeshima E, Kubo T, Hiura T (2010) Variation in tree diameter growth in response to the weather conditions and tree size in deciduous broad-leaved trees. For Ecol Manag 259:1055–1066CrossRefGoogle Scholar
  35. Oladi R, Pourtahmasi K, Eckstein D, Bräuning A (2011) Seasonal dynamics of wood formation in Oriental beech (Fagus orientalis Lipsky) along an altitudinal gradient in the Hyrcanian forest, Iran. Trees 25:425–433CrossRefGoogle Scholar
  36. Peterson DW, Peterson DL (2001) Mountain hemlock growth responds to climatic variability at annual and decadal time scales. Ecology 82:3330–3345CrossRefGoogle Scholar
  37. Rossi S, Deslauriers A, Anfodillo T, Morin H, Saracino A, Motta R, Borghetti M (2006) Conifers in cold environments synchronize maximum growth rate of tree-ring formation with day length. New Phytol 170:301–310PubMedCrossRefGoogle Scholar
  38. Rossi S, Deslauriers A, Anfodillo T, Carraro V (2007) Evidence of threshold temperatures for xylogenesis in conifers at high altitudes. Oecologia 152:1–12PubMedCrossRefGoogle Scholar
  39. Rossi S, Morin H, Deslauriers A, Plourde P-Y (2011) Predicting xylem phenology in black spruce under climate warming. Glob Change Biol 17:614–625CrossRefGoogle Scholar
  40. Shi J, Li J, Cook ER, Zhang X, Lu H (2012) Growth response of Pinus tabulaeformis to climate along an elevation gradient in the eastern Qinling Mountains, central China. Clim Res 53:157–167CrossRefGoogle Scholar
  41. Takahashi K (2010) Effects of altitude and competition on growth and mortality of the conifer Abies sachalinensis. Ecol Res 25:801–812CrossRefGoogle Scholar
  42. Takahashi K, Tokumitsu Y, Yasue K (2005) Climatic factors affecting the tree-ring width of Betula ermanii at the timberline on Mount Norikura, central Japan. Ecol Res 20:445–451CrossRefGoogle Scholar
  43. Tardif J, Flannigan M, Bergeron Y (2001) An analysis of the daily radial activity of 7 boreal tree species, northwestern Quebec. Environ Monit Assess 67:141–160PubMedCrossRefGoogle Scholar
  44. Wang Z, Yang B, Deslauriers A, Qin C, He M, Shi F, Liu J (2012) Two phases of seasonal stem radius variations of Sabina przewalskii Kom. in northwestern China inferred from sub-diurnal shrinkage and expansion patterns. Trees 26:1747–1757CrossRefGoogle Scholar
  45. Way DA, Sage RF (2008) Elevated growth temperatures reduce the carbon gain of black spruce [Picea mariana (Mill.) B.S.P.]. Glob Change Biol 14:624–636CrossRefGoogle Scholar
  46. Wilmking M, Juday GP, Barber VA, Zald HSJ (2004) Recent climate warming forces contrasting growth responses of white spruce at treeline in Alaska through temperature thresholds. Glob Change Biol 10:1724–1736CrossRefGoogle Scholar
  47. Yang B, Qin C, Bräuning A, Burchardt I, Liu J (2011) Rainfall history for the Hexi Corridor in the arid northwest China during the past 620 years derived from tree rings. Int J Climatol 31:1166–1176CrossRefGoogle Scholar
  48. Yang B, Qin C, Wang J, He M, Melvin TM, Osborn TJ, Briffa KR (2014) A 3,500-year tree-ring record of annual precipitation on the northeastern Tibetan Plateau. Proc Natl Acad Sci 111:2903–2908PubMedCentralPubMedCrossRefGoogle Scholar
  49. Zhang Y, Shao X, Xu Y, Wilmking M (2011) Process-based modeling analyses of Sabina przewalskii growth response to climate factors around the northeastern Qaidam Basin. Chin Sci Bull 56:1518–1525CrossRefGoogle Scholar
  50. Zweifel R, Zimmermann L, Zeugin F, Newbery DM (2006) Intra-annual radial growth and water relations of trees: implications towards a growth mechanism. J Exp Bot 57:1445–1459PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Zhangyong Wang
    • 1
  • Bao Yang
    • 1
  • Annie Deslauriers
    • 2
  • Achim Bräuning
    • 3
  1. 1.Key Laboratory of Desert and Desertification, Cold and Arid Regions Environmental and Engineering Research InstituteChinese Academy of SciencesLanzhouChina
  2. 2.Département des Sciences FondamentalesUniversité du Québec à ChicoutimiChicoutimiCanada
  3. 3.Institute of GeographyUniversity of Erlangen-NurembergErlangenGermany

Personalised recommendations