Dendrochronology-based stand growth estimation of Larix olgensis forest in relation with climate on the eastern slope of Changbai Mountain, NE China

Abstract

The eastern slope of Changbai Mountain is characterized by pure larch forest (Larix olgensis) with little human disturbance. Response of tree growth to climate in this area remains unknown. Meanwhile, little is known about how climate variations affect the biomass increase which could be recognized as a three-dimensional tree growth index. The objective of this study is to investigate the climate effects on the radial and biomass growth of larch on eastern slope of Changbai Mountain. Tree-ring width chronologies and mean annual biomass increment were established using tree-ring data. We used correlation analysis and multiple regression analysis to explore the relationship between larch growth and climatic factors from 1957 to 2009. Results show that tree-ring growth and mean annual biomass increment were primarily and significantly affected by previous year climatic variables with slight difference among months. Temperatures were more consistently and strongly correlated to the chronologies and mean annual biomass increment than was precipitation. Temperature is the main factor limiting larch growth on Changbai Mountain and the ongoing climate warming may accelerate the growth of the species. The current stand biomass of the area was 240.72 Mg·ha^-1 and the annual stand biomass increment in 2009 was 2.91 Mg·ha^-1. In conclusion, the old-growth forest in the study area is still accumulating carbon efficiently.

Keywords

Larix olgensis / tree ring / climatic factors / biomass / China

Introduction

Tree growth is affected by the combination of environmental factors which include climate, fire, and competition among trees, soil characteristics, anthropogenic disturbances, and other (Fritts, 1976). Tree rings provide valuable information related to these environmental influences, especially climate (Wu, 1990). Due to their high sensitivity to climate change, high accuracy of age-dating and long time span, tree rings play an important role in determining the past climate variations (Blasing and Duvick, 1984; Esper et al., 2002; Fan et al., 2008; Liang et al., 2008). Changbai Mountain, which lies in northeast China, is characterized by forests and mountains. The northern slope of Changbai Mountain is covered by a vertical zonation of different temperate forests along the altitudinal gradient, which represent the major forest types in Northeast China (Yu et al., 2011). However, the vegetation of the eastern slope of the mountain has no such apparent vertical distributions. This region is characterized by pure larch forest, ranging from bottom to the tree line (elevation, 2000 m) of the mountain (Dai et al., 2008). In recent years, dendrochronological studies have been intensively conducted on the northern slope (Yang and Xie, 1994; Yu et al., 2005; Gao et al., 2009; Yu et al., 2011). Since this area is in a pristine status with little human disturbance, it is ideal for investigating tree growth-climate interactions. Previous studies on the growth-climate relationships of larch (Larix olgensis) in the region have suggested that radial growth is sensitive to climate variations and they are more affected by temperature than by precipitation. Also, the response of tree growth to climatic factors varied significantly along altitudinal gradient (Shao and Wu, 1997; Yu et al., 2005).

However, to our knowledge, the response of larch growth to climate has not been reported yet on the eastern slope of Changbai Mountain, and their sensitivity to climatic factors is unknown. Meanwhile, the existing dendroclimatology studies in other regions of the Changbai Mountain mainly focused on the correlation between tree-ring width chronologies and climatic factors, while little is known about how climate variations affect the biomass increase which could be recognized as a three-dimensional tree growth index. Some existing studies have already discussed the response of basal area increment (BAI) to climatic factors (Moore et al., 2006; Sánchez-Salguero et al., 2010; Linares et al., 2011). Previous studies conducted in North America and central Japan also demonstrated generally good agreement between measurements of net ecosystem production (NEP), using eddy covariance techniques, and biometric measurements of woody tissue increments (Curtis et al., 2002; Gough et al., 2008; Ohtsuka et al., 2009). In this study, in addition to radial increment, the role of biomass in growth-climate relationship will also be emphasized by investigating primary productivity in response to climate variation. Furthermore, since we have obtained the biomass data, the annual stand-level above-ground carbon storage can be also estimated, which could help understanding the carbon storage capability of the forest on the eastern slope of Changbai Mountain.

The aims of this study were to investigate the tree-growth/climate relationships and to assess the carbon sequestration ability of larch forest on the eastern slope of Changbai Mountain. To achieve these objectives, we (ⅰ) developed the tree-ring width chronology and calculated the mean annual biomass increment, (ⅱ) identified critical climatic factors that affect larch growth, (ⅲ) analyzed the radial and biomass growing process, and (ⅳ) estimated the stand biomass and annual biomass growth of this old-growth forest.

Materials and methods

Study area

The study was conducted on the eastern slope of Changbai Mountain (42°03′58″ N, 128°15′26″ E, 1450 m a.s.l.) in northeast China. The climate belongs to the temperate continental climate, characterized by long, cold winters and relatively short, warm and moist summers. The temperature and precipitation vary seasonally. According to long term meteorological data (A.D. 1957-2009), mean annual temperature ranges from 2.68°C to 5.36°C, with mean January temperature -15.52°C and mean July temperature 20.09°C. Mean annual precipitation ranges from 574 to 1190 mm, and about 80% of the total annual precipitation occurs during the growing season from May to September (Fig. 1). The northern slope of Changbai Mountain is characterized by obvious vertical distribution of four altitudinal vegetation zones that is mainly caused by the variation of hydro-thermal conditions along the altitudinal gradient (Zhao et al., 2004; Yu et al., 2011). The eastern slope of the mountain, by contrast, is dominated by pure larch forest, due to the devastating damage of a great volcanic eruption to the vegetation 800 years ago (Dai et al., 2008). The volcanic eruption has led to the extinction of vegetation several times before larch forest was established as the dominate species in the area. Our study was carried out at the primary forest at altitude 1450 m. The slope of this area is less than 5 degrees.

Tree-ring sampling and chronology development

Increment cores were collected from larch trees in the summer of 2010. One core was taken from each sample tree at breast height with an increment borer. A total of 90 trees were sampled.

Cores were mounted, sanded and dated following traditional process (Stokes and Smiley, 1968). Firstly, cores were mounted on grooved wooden boards and sanded with a series of sandpaper grits up to 600 meshes until ring-width patterns were highlighted (Orvis and Grissino-Mayer, 2002). Then we measured the ring width using a LINTAB 6.0 measuring system with a resolution of 0.01 mm. The measured ring-sequences were plotted and the patterns of wide and narrow rings were cross-dated among trees using TSAP-Win program (Rinn, 2005). The quality of the measurement and cross-dating was examined by the program COFECHA (Holmes, 1983). Finally, 51 out of 82 measured cores were selected for inclusion in tree-ring chronology construction based on the performance of cross-dating verification.

Each ring-width series was detrended by fitting a negative exponential curve, straight line, or a horizontal line with negative slope using the program ARSTAN (Cook and Holmes, 1986). Two versions of chronology were obtained. The first version is standard chronology (STD) from which low frequency information was retained, the second version is residual chronology (RES) from which significant low-order persistence has been removed (Fig. 2). To evaluate the reliability of the tree-ring series, several statistics were calculated, including mean sensitivity, standard deviation, and first order autocorrelation (Fritts, 1976). A common interval analysis for the two chronologies from 1880 to 2009 was also performed. The correlation between trees, signal-to-noise ratio, expressing population signal (EPS) (Wigley et al., 1984), and the variance explained by the first principal component of the correlation matrix for a common time interval were calculated using ARSTAN (Cook, 1985).

Biomass growth

Field survey was conducted at established one-ha plot during the summer of 2010. Individual and stand biomass were estimated based on the field data investigated. The plot was a pure stand with few small individuals of other species, and the biomass was estimated with an equation for larch. The allometric equation was constructed based on the following process: Firstly, we calculated the stem volume of the larch based on the authorized local (Heilongjiang Province) two-way tree volume model.

(1)V=a_{\mathrm{1}}\times D^{b\mathrm{1}}\times H^{c\mathrm{1}},

where V is the larch stem volume; D is the diameter at breast height; and H is the tree height; a₁=5.016824E-05, b₁=1.7582889, c₁=1.149665.

Secondly, we calculated the trunk biomass based on the stem volume and the wood basic density of larch (Gong et al., 2000).Thirdly, we estimated the crown biomass based on the stem volume-crown biomass relationship. Finally, the aboveground biomass can be obtained by adding the trunk biomass and crown biomass together. Biomass equation (Eq. 2, R²=0.9976, p<0.001) was fit using data from 51 trees.

(2)B=\mathrm{0.6183}{D}^{\mathrm{1.9729}},

where B is individual aboveground biomass, and D is the diameter at breast height.

The annual biomass increment was calculated by the following equation:

(3)W_n=B_n-B_{n-\mathrm{1}},

where W_n is the annual biomass increment of year n, B_n is the individual aboveground biomass of year n.

The annual biomass increment sequence of each tree was standardized to remove long-term growth trends and maximize the common climatic signal between trees. Biomass chronology for Larix olgensis was derived by averaging the growth indices for each year among different trees (Fig. 3).

Climate data

Monthly temperature (maximum, mean, and minimum) and precipitation data over the period A.D. 1953-2009 were obtained from the nearest weather station. The data extending from previous May to current September were used for the analysis, as the climate conditions of the previous year may have effects on tree-ring growth of the current year (Fritts, 1976). Given that the climatic variables may have cumulative or long term effects, the relationship between seasonal periods of climate data and tree growth were also examined by means of Pearson correlation analysis. Based on the growth habit of larch on Changbai Mountain, the monthly data were divided into 5 periods, including previous growing season (PG, from previous June to pervious September), Fall (F, previous October and November), Winter (W, from current January to current March), Spring (S, current April and May) and current growing season (CG, from current June to current September) (Yu et al., 2005). Palmer Drought Severity Index (PDSI) and accumulated temperature were also adopted in this study as composite climate variables. The PDSI data (Dai et al., 2004) for A.D. 1957-2005 were obtained from the grid point between 127°-130° N, 40°-42.5° E.

Statistical analysis

All the time series climate data were proved to be valid based on the Mann–Kendall and Double-Mass tests. The relationships between tree-ring index, biomass index and climate variables were examined using Pearson correlation analysis and multiple regression analysis for the period A.D. 1953-2009. For multiple regression analysis, regression variables were selected from those climatic variables significantly correlated (p<0.05) with residual tree-ring index chronology and (or) mean annual biomass growth. A forward stepwise regression was then performed for a variable to enter into the final model. Collinearity diagnosis was performed to reduce collinearity among variables. These analysis were performed with the software package SPSS v16.0 (SPSS Inc., Chicago, IL, USA) with a significance level of 0.05.

Results

Chronology characteristics

According to common interval analysis, the mean correlation between cores reached 0.45 and 0.43, respectively, for standard and residual chronology, suggesting that the radial growth trend of individual larch trees was rather similar (Table 1). The expressed population signal (EPS) is an indicator of chronology reliability and series replication. An EPS value 0.85 is assumed to be the threshold of the signal strength of a chronology (Wigley et al., 1984). Both of our chronologies had EPS>0.85 for the common interval of 1880-2009. For both chronologies, the first principal component explained a statistically significant proportion (approximately 45%) of the total variance, which indicates that radial growth of larch was mainly affected by some key environmental factors. The high signal-to-noise ratio further suggests that larch was suitable for dendroclimatic analyses. Mean sensitivity, standard deviation and first-order autocorrelation were frequently used to judge the probable dendroclimatic value of tree-ring chronologies (Fritts, 1976; Cook, 1985). Taking all these statistics into account, RES chronology was selected for further dendroclimatic analysis in this study.

Larch growing process

We have carefully chosen three sample trees to analyze the growing process in both diameter and biomass. These three trees were of the same age, and the increment cores were with piths. The average DBH (22.4 cm) of the selected three trees were as the same as the stand’s (22.6 cm). Our analysis demonstrated that these three sample trees were highly consistent in growth pattern. The growth processes of diameter were in line with the trend seen in biomass increment (Fig. 4). Generally, larch grew at a relatively high speed during the juvenile period of 0-40 years. The growth rate decreased slowly over the half-mature period of 41-70 years, reaching to a trough around 70 years, and it remained at a low level throughout the near-mature and mature periods of 71-140 years. There was an upwards trend in growth after the stand entered into the over-matured period of 141 years to present. The rapid growth of the over-mature stand illustrates that this old-growth forest was still accumulating carbon efficiently.

The stand average age, height, density, and volume were 170 a, 24 m, 1023 stems·ha^-1 and 413 m³·ha^-1 respectively. The current stand biomass was 240.72 Mg·ha^-1. The increment in diameter and individual biomass for 2009 were 0.156 cm and 5.123 kg respectively. The stand biomass increase for this year was 2.91 Mg·ha^-1 (or 1.455 tons of carbon was sequestrated in 2009).

Climate-growth relationships

RES chronology and climate

Correlation analysis between ring-width chronologies and monthly climate variables for the period A.D. 1953-2009 were presented in Fig. 5. The radial growth of larch was significantly positively correlated with the mean temperature in May, the minimum temperature in July and previous August, and the maximum temperature in the previous June and July, but was significantly negatively correlated with the maximum temperature in the previous October and December. Ring growth of larch was significantly positively correlated with the precipitation in July and the previous September, but was significantly negatively correlated with that in March. There was a consistent positive correlation between larch radial growth and PDSI, which takes into account the combined effects of moisture and temperature (Alley, 1985).

Correlation between seasonal climatic factors and larch radial growth was in accordance with the results of monthly analysis, but the correlation coefficients were lower than those of monthly climatic factors. All previous growing season climate factors had positive effects on ring-width growth, while the temperature in the previous fall and winter had a consistent negative effect. Previous winter precipitation also had a significant negative effect on tree-ring growth. The mean and maximum temperatures of current spring were significantly positively correlated with radial growth. From the seasonal correlation analysis results, it can also be found that temperature may have a stronger influence on the radial growth than precipitation.

Biomass growth and climate

Correlation between biomass growth and climatic factors was basically in accordance with the results of ring-width chronology. Biomass growth was significantly positively correlated with the mean temperature in the previous June and current May, and the minimum temperature in previous and current July. It was also significantly positively correlated with the maximum temperature in the previous June. However, the maximum temperature and precipitation in the previous December had significant negative effects on biomass growth (Fig. 6). Consistent positive correlation was also found between biomass growth and PDSI.

Models of tree growth

Through multiple linear regression analysis, the following two regression equations were developed:

(4)

where RES is the residual ring-width index, X₁ is previous July maximum temperature (°C), and X₂ is previous September PDSI.

(5)

where ABI is the annual biomass increment (kg·a^-1), X₃ is previous July minimum temperature (°C), and X₄ is previous December maximum temperature (°C).

Paired t-test shows that there was no significant difference between the simulated and observed values of RES (p = 0.894) and ABI (p = 0.913). Multivariate models explained 32% and 45.4% of growth variance, respectively. For both models, most of the variables selected were from the previous year, and were mostly related to temperature. This further illustrates that larch growth was mainly affected by climate variables from the previous year, and temperature had more influence on tree growth than precipitation.

Discussion

Radial growth and mean annual biomass increment were primarily and significantly affected by climate variables of previous year, with slight difference among months. This is in accordance with a report (Lo et al., 2010b) in Canada, which examined the relationships between climate and radial growth of three coniferous trees along major elevation gradient in southern interior British Columbia (western Canada). Temperatures were more consistently and strongly correlated to the chronologies and mean annual biomass increment than precipitation, suggesting that temperature is the main factor limiting larch growth on the eastern slope of Changbai Mountain. Several studies carried out on the northern slope of Changbai Mountain (Shao and Wu, 1997; Yu et al., 2005) and Canada (Lo et al., 2010b; Miyamoto et al., 2010) came to similar conclusions. Regression analysis shows that multivariate models could explain limited proportion of variance in larch growth, which is in a good agreement with the study performed by Yu et al. (2007) on the northern slope of Changbai Mountain. This fact demonstrates that while climate variability could explain a considerable proportion of interannual changes in tree growth at a population-wide scale, other site and stand factors are as important as climate, or possibly even more so (Lo et al., 2010b).

Growth response to temperature

Temperature affects photosynthesis in trees by regulating respiration, transpiration, and gas exchange, and high temperatures tend to favor respiration over net carbon assimilation (Kozlowski, 1971; Foster and Brooks, 2001). Extremely high temperatures may harm the growth of trees because of increased moisture stress (Harley et al., 2011), respiration, and accelerated transpiration (Henderson and Grissino-Mayer, 2009; Yu et al., 2011). On Changbai Mountain, temperature rebound from below ice point to 0°C at the end of May, and it reaches 5°C in June, which is the threshold for tree growth (Lindsey and Newman, 1956; Vaganov et al., 2006). The growing season generally lasts to the end of August or the beginning of September. There was a consistent trend in our study that the high temperature in previous growth season (particularly June and July) would benefit larch growth. This is largely due to the fact that a favorable thermal condition at the early stage of tree growth is essential to cambium activity and tracheid production and enlargement (Vaganov et al., 2006), and a warm summer could result in a large carbohydrate reserve for the next growing season (Yu et al., 2007; Zhu et al., 2009;Yu et al., 2011). The high temperature prior to the growing season also had a positive effect on larch growth, because a higher temperature during this period is helpful for terminating dormancy and advancing the tree growing period (Dang et al., 2007). In addition, higher temperature would also promote tree growth by enhancing water and mineral salts absorption in root area (Kimmins, 1987; Litton et al., 2007).

Larch growth shows a negative response to the previous fall temperature. Similar findings were reported for longleaf pine (Pinus palustris) in Florida (Foster and Brooks, 2001; Henderson and Grissino-Mayer, 2009). Possibly, cool October temperatures could reflect an early winter and dormant season, and if the trees ceased their annual growth prematurely, additional photosynthate could be stored for immediate growth at the start of the next growing season (Henderson and Grissino-Mayer, 2009).

In contrast to the positive effects of the June mean and maximum temperature, the minimum temperature in this month had an adverse impact on larch growth (Fig. 5), which might be related to plant diseases caused by active pathogens. The average June minimum temperature for the period A.D. 1957-2009 is 3.4°C, while 4°C is the activation temperature for plant pathogenic fungi (Xu, 2009). A higher minimum temperature in June would be more likely to promote the activity of these pathogenic fungi and harm the trees, which are still in their early stage of growth during this time of a year.

Growth response to precipitation

In contrast to the temperature-growth relationship, correlation between larch growth and precipitation was weak. Considering that relatively high annual precipitation could be received and most rainfall occurs during the growing season, water is unlikely to become a limiting factor for larch growth in the study area. December precipitation in the previous year had a significant adverse influence on larch growth. This is probably because snow (the most common form of winter precipitation on Changbai Mountain) could cause damage to branches or buds and reduce the growth in the following year. Other studies have also shown a negative association between winter precipitation and lodgepole pine growing in North America (Vaganov et al., 2006; Case and Peterson, 2007). A negative correlation was also found between current March precipitation and larch growth. Presumably, the reason lies in that high spring precipitation would postpone the thaw of seasonally frozen soil and delay the coming of the growing season (Vaganov et al., 1999; Kirdyanov et al., 2003).

Growth response to PDSI

There was a persistent positive relationship between PDSI and larch growth, and the strongest correlations occurred in the previous August-September. Other research has obtained similar results (Foster and Brooks, 2001; Henderson and Grissino-Mayer, 2009). Because PDSI integrate the available water content of the soil, temperature, and precipitation, the composite nature of the variables more closely reflects the conditions required for tree growth than precipitation or temperature alone (Su and Wang, 2007; Henderson and Grissino-Mayer, 2009). PDSI is perhaps the most widely used regional drought index (Alley, 1985). Since most of the water sustaining tree growth is provided by roots which absorb moisture from surrounding soil, the drought/wet variation of soil has a constant positive effect on larch growth. Results also show clearly that climatic conditions of the previous year have more influence on larch growth on the eastern slope of Changbai Mountain.

Biomass’s role in growth-climate analysis

Standard chronology and residual chronology represent the radial growth process of trees. While standard chronology reflects the low-frequency variation of past climate, residual chronology reveals the high-frequency variation where significant low-order persistence has been removed (Cook, 1985; Shao and Wu, 1994). They both have been widely used in dendroclimatology studies, but to date reports on the relationship between biomass growth and climatic factors are very rare. Only several studies have been conducted to check the relationship between the basal area increment (BAI) and climatic factors (Moore et al., 2006; Sánchez-Salguero et al., 2010; Linares et al., 2011). According to Gough et al. (2008), the long-term (>5 years) biometric and meteorological NEP estimates of a North America mixed-deciduous forest can be cross-validated. Ohtsuka et al. (2009) also proved the possible link between multiyear biometric-based net primary production (NPP) and eddy covariance-based NEP in a deciduous forest in Japan. These studies have demonstrated, from another perspective, the internal relation between biomass growth and climatic factors. We have identified the relationship between the annual biomass increment and larch growth, which was in a good agreement with the results of radial growth-climate analysis. In other words, the climate effect on larch growth was well verified in this case study, and the biomass growth’s role in dendroclimatology studies is worthy of further research and testing.

The effects of climate change on larch growth

Analysis of the larch growth process shows that the diameter and biomass growing processes were consistent. The low growth rate observed during the near-mature and mature periods might be caused by competition among trees, as well as the programmed plant senescence. The increasing growth rate as the larch stand progressed towards the over-matured stage is an interesting and important result. This is probably caused by the elimination of competitive trees, as well as the climate warming trend since decades ago (Fig. 4).

According to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4), global average temperatures increased by 0.74°C for the past 100 years from 1906 to 2005, and the global average surface temperature would increase by 1.1°C to 6.4°C at the end of 21^st century (Alley et al., 2007). China also witnessed an apparently warming trend over the last century, with the average surface temperature rising about 0.5°C to 0.8°C. If the climate pattern on Changbai Mountain continues this warming trend into the future, the temperature in the study area would meet a trend rate of 0.26 °C·10a^-1. The multivariate models (Eqs. 4 and 5) based on regression analysis show clearly that climate warming would promote the growth of trees in the study area, which has also been reflected in the recent annual biomass growing process (Fig. 4). Some other studies carried out on the northern slope of Changbai Mountain (Wu and Shao, 1996) and on the central Henduan Mountain (Fan et al., 2008) in southwest China showed similar conclusions. However, it should be noted that climate is not the only factor regulating tree growth. Some other non-climate factors such as soils, competition, diseases, insects, and fire are equally or more important (Lo et al., 2010a). Therefore, although growth-climate analysis predicts an increase in growth, the uncertainty remains high. Further studies should be conducted to develop more complex, process-based models that take into account a greater proportion of the key determinants governing forest growth (Kimmins et al., 2008; Lo et al., 2010a).

The biomass growing process of larch further indicates that the old-growth forest in the study area still has a strong capability of carbon accumulation in the context of global warming. It is a long-standing view that ageing forests are carbon neutral and cease to accumulate carbon, but based on literature analysis, Luyssaert et al. (2008) demonstrated that old-growth forests can continue to accumulate carbon. They estimated that 15 percent of the global forest area (unmanaged primary forest containing the remaining old-growth forests), which is currently not considered when offsetting increasing atmospheric carbon dioxide concentrations, provides at least 10 percent of the global net ecosystem productivity (NEP). Our study has in some way verified this conclusion.

Conclusions

Our study demonstrated that tree-ring chronology and tree-ring based biomass chronology of larch can provide useful information on past forest growth dynamics, climate variations, and carbon sequestration potential on the eastern slope of Changbai Mountain. Larch growth is primarily and significantly affected by the previous year climatic variables with slight difference among months. Since adequate precipitation can be obtained, water information expressed in tree rings is rather small. Temperature is the main factor limiting larch growth on Changbai Mountain and the ongoing climate warming may accelerate the growth of the species. The old-growth forest in the study area is still accumulating carbon efficiently. However, although growth-climate analysis predicts an increase in larch growth, the uncertainty remains high. More environmental factors like soil moisture condition should be taken into account in future study to improve our understanding of forest growth dynamics.

References

[1] Alley R, Berntsen T, Bindoff N L, Chen Z, Chidthaisong A, Friedlingstein P, Gregory J, Hegerl G, Heimann M, Hewitson B (2007). Climate Change, 2007: the physical science basis. Summary for Policy Makers. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland

[2] Alley W M (1985). The Palmer Drought Severity Index as a measure of hydrologic drought. JAWRA, 21(1): 105–114

[3] Blasing T J, Duvick D (1984). Reconstruction of precipitation history in North American corn belt using tree rings. Nature, 307(5947): 143–145

[4] Case M J, Peterson D L (2007). Growth-climate relations of lodgepole pine in the North Cascades National Park, Washington. Northwest Sci, 81(1): 62–75

[5] Cook E R (1985). A time-series analysis approach to tree-ring standardization. Dissertation for Ph.D Degree. University of Arizona, Tucson

[6] Cook E R, Holmes R L (1986). User manual for ARSTAN Laboratory of Tree Ring Research. University of Arizona, Tucson

[7] Curtis P S, Hanson P J, Bolstad P, Barford C, Randolph J C, Schmid H P, Wilson K B (2002). Biometric and eddy-covariance based estimates of annual carbon storage in five eastern North American deciduous forests. Agric Meteorol, 113(1–4): 3–19

[8] Dai A, Trenberth K E, Qian T (2004). A global dataset of Palmer Drought Severity Index for 1870–2002: relationship with soil moisture and effects of surface warming. J Hydrometeorol, 5(6): 1117–1130

[9] Dai L, Wu Y, Han S, Li Y (2008). Effects of great volcanic eruption on historical vegetation succession on eastern slope of Changbai Mountains. Chin J Ecol, 27(10): 1771–1778 (in Chinese with English abstract)

[10] Dang H S, Jiang M X, Zhang Q F, Zhang Y J (2007). Growth responses of subalpine fir (Abies fargesii) to climate variability in the Qinling Mountain, China. For Ecol Manag, 240(1–3): 143–150

[11] Esper J, Cook E R, Schweingruber F H (2002). Low-frequency signals in long tree-ring chronologies for reconstructing past temperature variability. Science, 295(5563): 2250–2253

[12] Fan Z X, Brauning A, Cao K F (2008). Annual temperature reconstruction in the central Hengduan Mountains, China, as deduced from tree rings. Dendrochronologia, 26(2): 97–107

[13] Foster T E, Brooks J R (2001). Long-term trends in growth of Pinus palustris and Pinus elliottii along a hydrological gradient in central Florida. Can J For Res, 31(10): 1661–1670

[14] Fritts H C (1976). Tree Rings and Climate. London: Academic Press

[15] Gao L S, Zhao X H, Wang X M (2009). Correlations between meteorological factors and growth of Pinus koraiensis after fired. Acta Ecol Sin, 29(11): 5963–5970 (in Chinese with English abstract)

[16] Gong R M, He L Z, Liu Y L, Xu L Y (2000). Effect of Temperature on Shrinkage and Density of Larch Wood from Man-made Forests. China Forest Products Industry, 27(1): 14–16 (in Chinese with English abstract)

[17] Gough C M, Vogel C S, Schmid H P, Su H B, Curtis P S (2008). Multi-year convergence of biometric and meteorological estimates of forest carbon storage. Agric Meteorol, 148(2): 158–170

[18] Harley G L, Grissino-Mayer H D, Horn S P (2011). The dendrochronology of Pinus elliottii in the lower Florida Keys: chronology development and climate response. Tree-Ring Research, 67(1): 39–50

[19] Henderson J P, Grissino-Mayer H D (2009). Climate-tree growth relationships of longleaf pine (Pinus palustris Mill.) in the Southeastern Coastal Plain, USA. Dendrochronologia, 27(1): 31–43

[20] Holmes RL (1983). Computer-assisted quality control in tree-ring dating and measurement. Tree-ring Bull, 43(1): 69–78

[21] Kimmins J P (1987). Forest Ecology. New York: Macmillan Publishing Company

[22] Kimmins J P, Blanco J A, Seely B, Welham C, Scoullar K (2008). Complexity in modelling forest ecosystems: how much is enough? For Ecol Manage, 256(10): 1646–1658

[23] Kirdyanov A, Hughes M, Vaganov E, Schweingruber F, Silkin P (2003). The importance of early summer temperature and date of snow melt for tree growth in the Siberian Subarctic. Trees (Berl), 17(1): 61–69

[24] Kozlowski T T (1971). Growth and Development of Trees. Vol. II. Cambial Growth, Root Growth and Reproduction Growth. New York: Academic Press

[25] Liang E, Shao X, Qin N (2008). Tree-ring based summer temperature reconstruction for the source region of the Yangtze River on the Tibetan Plateau. Global Planet Change, 61(3–4): 313–320

[26] Linares J C, Delgado-Huertas A, Carreira J A (2011). Climatic trends and different drought adaptive capacity and vulnerability in a mixed Abies pinsapo–Pinus halepensis forest. Clim Change, 105(1–2): 67–90

[27] Lindsey A A, Newman J E (1956). Use of official weather data in spring time: temperature analysis of an Indiana phenological record. Ecology, 37(4): 812–823

[28] Litton C M, Raich J W, Ryan M G (2007). Carbon allocation in forest ecosystems. Glob Change Biol, 13(10): 2089–2109

[29] Lo Y H, Blanco J A, Kimmins J P (2010a). A word of caution when projecting future shifts of tree species ranges. For Chron, 86: 312–316

[30] Lo Y H, Blanco J A, Seely B, Welham C, Kimmins J P (2010b). Relationships between climate and tree radial growth in interior British Columbia, Canada. For Ecol Manage, 259(5): 932–942

[31] Luyssaert S, Schulze E D, Börner A, Knohl A, Hessenmöller D, Law B E, Ciais P, Grace J (2008). Old-growth forests as global carbon sinks. Nature, 455(7210): 213–215

[32] Miyamoto Y, Griesbauer H P, Scott Green D (2010). Growth responses of three coexisting conifer species to climate across wide geographic and climate ranges in Yukon and British Columbia. For Ecol Manage, 259(3): 514–523

[33] Moore D J P, Aref S, Ho R M, Pippen J S, Hamilton J G, de Lucia E H (2006). Annual basal area increment and growth duration of Pinus taeda in response to eight years of free-air carbon dioxide enrichment. Glob Change Biol, 12(8): 1367–1377

[34] Ohtsuka T, Saigusa N, Koizumi H (2009). On linking multiyear biometric measurements of tree growth with eddy covariance-based net ecosystem production. Glob Change Biol, 15(4): 1015–1024

[35] Orvis K H, Grissino-Mayer H D (2002). Standardizing the reporting of abrasive papers used to surface tree-ring samples. Tree-Ring Research, 58(1–2): 47–50

[36] Rinn F (2005). TSAP-Win. Time Series Analysis and Presentation for Dendrochronology and Related Applications. Version 0.53 for Microsoft Windows. User Reference

[37] Sánchez-Salguero R, Navarro R M, Camarero J J, Fernández-Cancio A (2010). Drought-induced growth decline of Aleppo and maritime pine forests in south-eastern Spain. ForSyst, 19(3): 458–470

[38] Shao X M, Wu X D (1994). Tree-ring chronologies for Pinus armandi Franch from Huashan, China. Acta Geogr Sin, 49(2): 174–181 (in Chinese with English abstract)

[39] Shao X M, Wu X D (1997). Reconstruction of climate change on Changbai Mountain, Northeast China using tree-ring data. Quatern Sci, 1: 76–85 (in Chinese with English abstract)

[40] Stokes M A, Smiley T L (1968). An Introduction to Tree-Ring Dating. Chicago & London: University of Chicago Press

[41] Su M F, Wang H J (2007). Relationship and its instability of ENSO—Chinese variations in droughts and wet spells. Sci China Ser D, 50(1): 145–152

[42] Vaganov E A, Hughes M K, Kirdyanov A V, Schweingruber F H, Silkin P P (1999). Influence of snowfall and melt timing on tree growth in subarctic Eurasia. Nature, 400(6740): 149–151

[43] Vaganov E A, Hughes M K, Shashkin A V (2006). Growth Dynamics of Conifer Tree Rings: Images of Past and Future Environments. Berlin/Heidalberg: Springer Verlag

[44] Wigley T M L, Briffa K R, Jones P D (1984). On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. J Clim Appl Meteorol, 23(2): 201–213

[45] Wu X D (1990). Application of tree ring analysis to the study on environment variation. Quatern Sci, 10(2): 188–196 (in Chinese with English abstract)

[46] Wu X D, Shao X M (1996). A preliminary study on impact of climate change on tree growth using tree ring-width data. Acta Geogr Sin, 51: 92–101 (in Chinese with English abstract)

[47] Xu Z G (2009). General Plant Pathology. Beijing: Higher Education Press (in Chinese)

[48] Yang H X, Xie H S (1994). Study on the reconstruction of disturbance history of Pinus koraiensis mixed forest in Changbai Mountain. Chin J Plant Ecol, 18(3): 201–208 (in Chinese with English abstract)

[49] Yu D, Wang G G, Dai L, Wang Q (2007). Dendroclimatic analysis of Betula ermanii forests at their upper limit of distribution in Changbai Mountain, Northeast China. For Ecol Manage, 240(1–3): 105–113

[50] Yu D, Wang Q, Wang Y, Zhou W, Ding H, Fang X, Jiang S, Dai L (2011). Climatic effects on radial growth of major tree species on Changbai Mountain. Ann Sci, 68(5): 921–933

[51] Yu D, Wang S, Tang L, Dai L, Wang Q, Wang S (2005). Relationship between tree-ring chronology of Larix olgensis in Changbai Mountains and the climate change. Chinese Journal of Applied Ecology, 16(1): 14–20 (in Chinese with English abstract)

[52] Zhao S Q, Fang J Y, Zong Z J, Zhu B, Shen H H (2004). Composition, structure and species diversity of plant communities along an altitudinal gradient on the northern slope of Mt. Changbai, Northeast China. Biodivers Sci, 12(1): 164–173

[53] Zhu H F, Fang X Q, Shao X M, Yin Z Y (2009). Tree ring-based February–April temperature reconstruction for Changbai Mountainin Northeast China and its implication for East Asian winter monsoon. Clim Past, 5(4): 661–666

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg