Article Radial Growth Response of Abies georgei to Climate at the Upper Timberlines in Central , Southwestern China

Dingcai Yin 1, Derong Xu 2, Kun Tian 1,3, Derong Xiao 1,4, Weiguo Zhang 1, Dacheng Sun 1, Hui Sun 1 and Yun Zhang 1,3,* 1 National Plateau Wetlands Research Center, Southwest Forestry University, 650224, China; [email protected] (D.Y.); [email protected] (K.T.); [email protected] (D.X.); [email protected] (W.Z.); [email protected] (D.S.); [email protected] (H.S.) 2 Forestry College, Southwest Forestry University, Kunming 650224, China; [email protected] 3 National Positioning Research Station for Dianchi Wetland Ecosystem, Kunming 650224, China 4 Plateau Wetlands Science Innovation Team of Province, Kunming 650224, China * Correspondence: [email protected]; Tel.: +86-871-386-4277; Fax: +86-871-386-3477

 Received: 17 August 2018; Accepted: 26 September 2018; Published: 29 September 2018 

Abstract: Climate change has an inevitable impact on tree radial growth, particularly at mountain timeberlines. To understand climate effects on conifer radial growth in the central Hengduan Mountains, and the potential impacts of future climate change on conifer forests, we studied the growth responses to climate variables in Abies georgei, the major tree species of conifer forest in the Hengduan Mountains. We collected tree ring samples from four sites near the timberlines and analyzed the relationship between principle components (PC#1) of four chronologies and climatic variables (monthly mean temperature and monthly total precipitation) by using response function analysis (RFA), redundancy analysis (RDA), and moving interval analysis (MIA). A. georgei growth was affected by both temperature (positive effects) and precipitation (negative effects). Specifically, the radial growth of A. georgei was significantly and positively correlated with current July (by 6.1%) and previous November temperature (by 17.3%) (detected by both RFA and RDA), while precipitation of current June (by 6.6%) and September (by 11.7%) inhibited tree growth (detected by RDA). More rapid warming in the most recent 20 years (1990–2010) clearly enhanced growth responses to July and November temperature, whereas the relationship was weakened for June and September precipitation, according to MIA. Under the climate trend of the study area, if the increasing temperature could offset the negative effects of excessive precipitation, A. georgei radial growth would likely benefit from warming.

Keywords: Southeastern rim of ; dendrochronology; climate change; subalpine conifer forests

1. Introduction Instrumental climate records suggest that mountain forests in high elevation areas have been subject to higher rates of temperature change than the surrounding lowlands [1]. The global mean temperature has risen by about 0.74 ± 0.18 ◦C over the past century, and it is projected to increase by 1.8–4.0 ◦C in the 21st century. During the past five decades, the area of central Hengduan Mountains has experienced a significant warming trend at a rate of 0.3 ◦C/decade [2]. This climate change has a great influence on tree regeneration, growth, and migrating abilities in high elevation areas [1,3], consequently affecting the structure, productivity, and dynamics of forests in high mountain regions [4,5]. Tree radial growth is more sensitive to climate change near the species distributional boundaries than the suitable area for its growth, such as intermediate altitude or latitude [6]. In high mountain

Forests 2018, 9, 606; doi:10.3390/f9100606 www.mdpi.com/journal/forests Forests 2018, 9, 606 2 of 14 regions, tree radial growth at the upper timberlines is generally believed to be controlled by temperatures [7–9], and both growing season and previous winter temperatures have been found to be important factors affecting tree growth at upper distributional limits [10–12]. However, some studies have shown that early growing season moisture also influenced tree radial growth in high elevation forests [13–16]. Therefore, the mechanism of tree growth response to climate is complex at upper timberlines and needs to be better understood. The Tibetan Plateau is one of the most sensitive regions to global climate change [17,18], with an average elevation of more than 4000 m above sea level, and many tree species have formed natural timberlines on Tibetan Plateau, and therefore the region is suitable for dendroclimatology study. The radial growth of Betula utilis D. Don at the upper timberlines is mainly limited by the moisture during the pre-monsoon season on Southern Tibetan Plateau [16], while the minimum temperature in July is the main factor affecting the tree growth of Abies georgei Orr. at the upper timberlines on the southeastern Tibetan Plateau [19]. Additionally, tree growth of Sabina tibetica Kom. at the upper timberlines on the northeastern Tibetan Plateau is positively correlated with temperature from October to January, and with the Palmer Drought Severity Index (PDSI) from September to June [20]. The relationships between tree growth and climate variables at the upper timberlines may vary among species even in the same region. Therefore, dendroclimatology studies in different species at different timberline sites can help to reveal the main factors affecting tree growth at the distributional limit in a region. The central Hengduan Mountains, located in the southeastern margin of the Tibetan Plateau, is a climate-sensitive region, and thus it is a typical place for dendroclimatology study [21]. Up to now, several tree species (Picea brachytyla Pritz., Tsuga dumosa D. Don and Picea likiangensis Pritz.) in this region have been used to reconstruct the past climate variations (annual temperature, spring, and June–July PDSI) [9,22–24], and several studies have analyzed the growth responses of different species to climate change [12,20]. However, the range of those studied sites were limited; moreover, a dendroclimatology study for a specific species was rare in this region, and these two issues may bring representative problems in a real understanding of the relationship between climate variables and tree redial growth. A. georgei, as a typical timberline tree species in the central Hengduan Mountains, is also an important component of subalpine ecoregions in the region due to its economic value, and its radial growth has been highly focused upon. Previous climate-growth response studies of A. georgei have been concentrated in the variations of elevational trends [19,25], while others have been conducted for climate reconstruction [22]. However, most of them were carried out in a single mountain, the study of relationships between tree growth of A. georgei and climate variables at timberlines on regional scales are rare; thus an integrated understanding of which climatic factors influencing its growth is lacking in the central Hengduan Mountains. Tree ring variations often reflect the environmental change, particularly for climate conditions in the past years [26]. Dendroclimatology, as a traditional and valid method, has widely been used to detect the long-term growth-climate relationship, and to further evaluate the impact of future climate change on tree growth and forest dynamics. In this study, we aimed at exploring key climate factors influencing radial growth of A. georgei at the upper timberlines in the central Hengduan Mountains. We hypothesized that A. georgei growth was controlled by temperature due to the cold environment at high elevations in mountain regions, and A. georgei forests would benefit from increasing temperatures. To test the hypotheses, we studied the relationships between the residual chronologies of A. georgei and climatic variables, and discussed the potential effects of future climate change on A. georgei and related forest growth.

2. Materials and Methods

2.1. Study Area The central Hengduan Mountains form the transitional zone between northwestern Yunnan, southern , and southeastern (Figure1). The Mountains extend roughly from north to Forests 2018, 9, x FOR PEER REVIEW 3 of 14

2. Materials and Methods

2.1. Study Area Forests 2018, 9, 606 3 of 14 The central Hengduan Mountains form the transitional zone between northwestern Yunnan, southern Sichuan, and southeastern Tibet (Figure 1). The Mountains extend roughly from north to south, and and they they separate separate three three major major Asian Asian rivers, rivers, namely namely Jinsha, Jinsha, Lancang, Lancang, and and Nujiang, Nujiang, forming forming one ofone the of richest the richest biodiversity biodiversity areas areas in the in theworld. world. Distinct Distinct altitudinal altitudinal belts belts and andvegetation vegetation types types are createdare created due dueto the to topographic the topographic gradients. gradients. The Theforests forests are dominated are dominated by Pinus by Pinus armandii armandii Franch.Franch. and Pinusand Pinus yunnanensis yunnanensis Franch.Franch. at elevations at elevations of 1500–2800 of 1500–2800 m. From m. From2800 to 2800 3500 to m, 3500 T. dumosa m, T. dumosa and Pinusand Pinusdensata densata are dominantare dominant species. species. Between Between 3200 and 3200 4200 and m, 4200forests m, develop, forests develop, with Larix with potaniniiLarix potaniniiBatalin., P.Batalin., likiangensisP. likiangensis, and A. ,georgei and A. being georgei thebeing most the common most common species. species.From 4200 From m 4200to the m alpine to the screes, alpine vegetationscrees, vegetation is dominated is dominated by cold temperate by cold temperate shrubs (alpine shrubs rhododendrons) (alpine rhododendrons) and meadows. and Our meadows. study sitesOur studyare located sites arein Shika located Snow in Shika Mountain Snow (SK), Mountain Potatso (SK), National Potatso Park National (PDC), Park Haba (PDC), Snow Haba Mountain Snow (HB),Mountain and Yulong (HB), and Snow Yulong Mountain Snow Mountain(YL), and sampling (YL), and stands sampling are standsnear their are neartimberlines. their timberlines.

Figure 1. The location of sampling sites and meteorological stations. SK: Shika Snow Mountain; FigurePDC: Potatso 1. The location National of Park; sampling HB: Haba sites Snowand meteorolog Mountain;ical YL: stations. Yulong SnowSK: Shika Mountain; Snow Mountain; the same below. PDC: Potatso National Park; HB: Haba Snow Mountain; YL: Yulong Snow Mountain; the same below. A. georgei is native to southwestern Sichuan, northwestern Yunnan, and southeastern Tibet, and it rangesA. fromgeorgei 3200 is native to 4100 to m southwestern in this transitional Sichuan, area. northw The speciesestern Yunnan, is shade-tolerant and southeastern and adapted Tibet, to acidand itgrey ranges soils from with 3200 humus. to 4100 Typically, m in this it forms transitional pure forests area.at The the species upper timberlinesis shade-tolerant (about and 4000 adapted m) in the to acidHengduan grey soils Mountains, with humus. while Typically, it mixed it with formsP. likiangensispure forestsand at the other upper conifers timberlines at the lower(about elevation 4000 m) (aboutin the 3200Hengduan m) forests Mountains, [27]. while it mixed with P. likiangensis and other conifers at the lower elevationInfluenced (about by3200 the m) interaction forests [27]. of maritime and continental monsoon, the climate in the central HengduanInfluenced Mountains by the hasinteraction a distinct of seasonal maritime change, and continental with a warm monsoon, and wet the summer, climate and in athe relatively central Hengduancold and dry Mountains winter. According has a distinct to theseasonal instrumental change, datawith (1960–2011)a warm and inwet Shangri-La summer, and meteorological a relatively coldstation and (27 dry◦50 0winter.N, 99◦42 According0 E, 3276.7 to m the a.s.l.), instrumental which is near data PDC (1960–2011) and SK, the in annuallyShangri-La mean meteorological temperature stationis 5.9 ◦ C(27°50 with′ N, highest 99°42′ temperature E, 3276.7 m a.s.l.), (13.6 ◦whichC) occurred is near PDC in July, and and SK, the thelowest annually (− mean3.0 ◦C) temperature in January (Figureis 5.9 °C2a). with The highest totalannual temperature precipitation (13.6 °C) is occurred 633 mm, andin July, the and amount the lowest of precipitation (−3.0 °C) fromin January June (Figureto September 2a). The accounts total annual for 73% precipitation of the total is precipitation. 633 mm, and the amount meteorological of precipitation station from (26 June◦520 N,to 100September◦130 E, 2393 accounts m a.s.l.) for 73% is the of closest the total one precipitation. to YL and HB, Lijiang with meteorological climate data from station 1951 (26°52 to 2010′ N, showing 100°13′ E,that 2393 the m annually a.s.l.) is meanthe closest temperature one to YL is and 12.8 HB,◦C inwith this climate area. June data is from the warmest1951 to 2010 month, showing with that a mean the annuallytemperature mean of 18.2temperature◦C, and January is 12.8 is°C the in coldest this area. month, June with is the a temperature warmest month, of 6.1 ◦withC (Figure a mean2b). The total annual precipitation is 964.7 mm, 81% of which falls in the period of June to September. Forests 2018, 9, x FOR PEER REVIEW 4 of 14 Forests 2018, 9, x FOR PEER REVIEW 4 of 14 temperature of 18.2 °C, and January is the coldest month, with a temperature of 6.1 °C (Figure 2b). temperatureThe total annual of 18.2 precipitation °C, and January is 964.7 is themm, coldest 81% of month, which fallswith in a temperaturethe period of ofJune 6.1 to°C September. (Figure 2b). ForestsThe total2018 ,annual9, 606 precipitation is 964.7 mm, 81% of which falls in the period of June to September.4 of 14

30 PREC a 150 b Lijiang 250 Shangri-la 30 Tmax 20 25 PRECTmean a Shangri-la 150 b Lijiang 250 TmaxTmin 20 120 2520 Tmean 200 Tmin 10 120 2015 200 90 150 10 1510 90 150 0 60 10 5 100 Temperature (℃) Temperature Temperature (℃) Temperature 0 60 5 100 Precipitation (mm) Precipitation

Precipitation (mm) Precipitation 0

30 (℃) Temperature 50 Temperature (℃) Temperature Precipitation (mm) Precipitation -10 (mm) Precipitation 0-5 30 50 -10 0 -5-10 0 1234567891011120 -10 1234567891011120 123456789101112Month 123456789101112 Month Month Month Figure 2. Climate diagrams for (a) Shangri-La (1960–2011) and (b) Lijiang (1951–2010) meteorological Figurestations. 2. ClimateClimate PREC: diagramsmonthly diagrams totalfor for ( (a aprecipitation;)) Shangri-La Shangri-La (1960–2011) (1960–2011)Tmin: monthly and and ( (bmeanb)) Lijiang Lijiang minimum (1951–2010) (1951–2010) temperature; meteorological meteorological Tmean: stations.monthly PREC:PREC: mean monthly monthlytemperature; total total precipitation; Tmax: precipitation; monthly Tmin: Tm mean monthlyin: maximummonthly mean mean minimum temperature. minimum temperature; temperature; Tmean: monthlyTmean: meanmonthly temperature; mean temperature; Tmax: monthly Tmax: mean monthly maximum mean maximum temperature. temperature. A significant warming trend has been observed in the annual mean temperature over the past six Adecades, significantsignificant at both warming Shangri-La trend and has Lijiang been observed stations. in More the annualrapid warming mean temperature occurred in over the therecent past 20 sixyears; decades, the annual at at both both mean Shangri-La Shangri-La temperature and and Lijiang Lijiang increased stations. stations. by 0.64 More More °C rapidand rapid 0.56 warming warming °C per occurred decade occurred at in Shangri-La inthe the recent recent and20 ◦ ◦ 20years;Lijiang years; the station the annual annual since mean mean 1991, temperature temperature respectively increased increased (Figure by 3a,b by 0.64 0.64). While °CC and and the 0.56 0.56annually °CC per per total decade decade precipitation at at Shangri-La Shangri-La increased and Lijiangslightly station in both since Shangri-La 1991, respectively and Lijiang (Figure before3 3a,ba,b). 1991;). WhileWhile after thethe that, annuallyannually there was totaltotal a precipitationprecipitationdecreasing trend increasedincreased in two slightlymeteorological in both Shangri-Lastations (Figure and Lijiang 3c,d). before 1991; after that, there was a decreasing trend in two meteorological stations (Figure3 3c,d).c,d).

8.0 14.5 8.0 a Shangri-La 14.5 b Lijiang 7.5 a Shangri-La 14.0 b Lijiang 7.5 7.0 14.0 Slope = 0.007 13.5 2 R = 0.059 p = 0.126 7.0 Slope = 0.029 Slope = 0.007 6.5 13.5 2 2 R = 0.059 p = 0.126 R = 0.436 p < 0.001 13.0 6.5 Slope = 0.029 6.0 2 R = 0.436 p < 0.001 13.0 6.0 12.5 Temperature/℃ 5.5 12.5

Temperature/℃ 5.5 5.0 Slope = 0.064 12.0 2 Slope = 0.056 R = 0.467 p < 0.001 2 5.0 Slope = 0.064 12.0 R = 0.353 p < 0.01 2 Slope = 0.056 4.5 R = 0.467 p < 0.001 11.5 R2 = 0.353 p < 0.01 4.5900 11.51400 c d 900 14001300 c 800 d 13001200 800 1200 700 1100 700 11001000

600 1000900 600 Precipitation/mm 900800 500 Precipitation/mm 800 500 700 400 700600 400 1960 1970 1980 1990 2000 2010 600 1950 1960 1970 1980 1990 2000 2010 1960 1970 1980Year 1990 2000 2010 1950 1960 1970 1980Year 1990 2000 2010 Year Year Figure 3. Temperature and precipitation variables for (a,c) Shangri-La (1960-2011) and (b,d) Lijiang 2 (1951-2010) meteorological stations. R indicates the proportion of the dependent variable that can be explained by independent variable in the regression model, p represents the significance level of the regression model. Forests 2018, 9, 606 5 of 14

2.2. Tree-Ring Sampling and Chronology Development Increment cores of A. georgei were collected from the timberlines at the four sites (Figure1). In total, 264 cores from 134 trees were sampled (Table1). At each site, we sampled mature and healthy living trees without insect damage at breast height (approximately 1.3 m) with an increment borer; two cores per tree were taken from opposite directions that were parallel to the contour. Moreover, in order to ensure the consistency of the climate information contained in the samples, we controlled the elevation variations to be 10 m at each site.

Table 1. Sampling sites information.

Site Longitude Latitude Elevation (m) No. (tree/radii) Aspect Slope (◦) SK 99.56 27.92 4074 35/70 W 10 PDC 100.01 27.79 3954 29/58 SW 15 HB 100.10 27.35 4105 38/76 W 16 YL 100.21 27.10 4014 27/54 NW 13 SK: Shika Snow Mountain; PDC: Potatso National Park; HB: Haba Snow Mountain; YL: Yulong Snow Mountain. The same below.

According to the standard dendrochronological methods [28], wood surfaces of cores were smoothed with sandpaper until the tree ring boundaries were clearly visible. The cores were placed under the microscope for dating work, and dated cores were scanned by an Epson scanner (Expresstion 11000XL, Seiko Epson Corporation, Nagano, Suwa, Japan) with setting parameters (full image type 24-bit and a resolution of 3200 dpi). The tree ring widths were measured on the CDendro and CooRecorder version 7.3 programs with a resolution of 0.001 mm, and all the cores were cross-dated and statistically tested by using COFFCHA program [29]. Cores with low correlations between the main sequences were rejected from further analysis. In the end, 244 cores (125 trees) were used for chronology development (Table2).

Table 2. Statistics of tree ring width residual chronologies.

Chronology SK PDC HB YL Trees/Cores 35/68 27/52 37/73 26/51 Time span 1707–2016 1761–2016 1733–2016 1750–2017 AGR 1.087 0.819 0.995 0.906 MS 0.14 0.16 0.11 0.22 EPS > 0.85 since 1768 1807 1796 1913 Common Interval Analysis (1961–2010) Trees/Cores 31/62 24/48 30/59 16/32 PC1 31.35 34.28 39.60 34.42 SNR 25.29 22.63 35.73 14.88 EPS 0.96 0.96 0.97 0.94 AGR: average annual growth rate (mm); MS: mean sensitivity; SNR: signal to noise ratio; EPS: expressed population signal; PC1: variance in first eigenvector.

Tree ring measurements were standardized by using the ARSTAN program (ARSTAN, Tucson, AZ, USA) [30] to remove the biological growth trend, as well as any other low-frequency variations due to stand dynamics, while retaining the climatic signals influencing tree growth. Cores were detrended with a cubic smoothing spline with a 50% frequency-response cut-off that was equal to 67% the series length to retain high frequency [31]. To reduce the influence of outliers in the computation of the mean chronologies, all detrended series were averaged on a site-by-site basis using the bi-weight robust mean. Residual chronologies (Figure4) were produced to remove any auto-correlation effects and to enhance the common signals. Forests 2018, 9, x FOR PEER REVIEW 6 of 14 using the bi-weight robust mean. Residual chronologies (Figure 4) were produced to remove any auto-correlationForests 2018, 9, 606 effects and to enhance the common signals. 6 of 14

1.5 80 SK sample depth ring width index 60

1.0 40

20

0.5 0 1.5 60 PDC

40 1.0 20

0.5 0 1.5 80 HB 60 Sample depth/cores Sample Tree ring width index width Tree ring 1.0 40

20

0.5 0 2 60 YL

40 1 20

0 0 1700 1750 1800 1850 1900 1950 2000

Year

Figure 4. Residual tree ring widthwidth chronologieschronologies of ofAbies Abies georgei georgeiat at four four sites sites with with an an annual annual variation variation in inring ring width width index index (black (black line), line), and and sample sample depth depth (gray (gray shading). shading). 2.3. Data Analysis 2.3. Data Analysis The monthly instrumental records of Shangri-La (1960–2011) and Lijiang (1951–2010) were The monthly instrumental records of Shangri-La (1960–2011) and Lijiang (1951–2010) were obtained from the National Meteorological Information Center (NMIC), China. Monthly mean obtained from the National Meteorological Information Center (NMIC), China. Monthly mean temperatures and monthly total precipitation were used to study the tree growth response to climate. temperatures and monthly total precipitation were used to study the tree growth response to climate. Considering the lagged effects of the climate in the previous year on tree growth, climate data Considering the lagged effects of the climate in the previous year on tree growth, climate data from from July of the prior year to October of the current year between 1961 and 2010 were selected. July of the prior year to October of the current year between 1961 and 2010 were selected. The climate– The climate–growth relationships of four sites were analyzed by using the response function analysis growth relationships of four sites were analyzed by using the response function analysis (RFA) (RFA) through theDendroClim 2002 program (DendroClim 2002, Reno, NV, USA) [32]. The response through theDendroClim 2002 program (DendroClim 2002, Reno, NV, USA) [32]. The response function analysis was a linear multiple regression technique that used the principle components of function analysis was a linear multiple regression technique that used the principle components of monthly climatic conditions to estimate the tree ring growth. monthly climatic conditions to estimate the tree ring growth. To further explore the patterns of growth response to climate change for A. georgei in the central To further explore the patterns of growth response to climate change for A. georgei in the central Hengduan Mountains, a subsequent principal component analysis was performed for the four Hengduan Mountains, a subsequent principal component analysis was performed for the four chronologies. The first component (PC#1), which accounts for 62.5% of the total variance over the chronologies. The first component (PC#1), which accounts for 62.5% of the total variance over the period 1955–2015, was presented (Table3). Average climate data from two meteorological stations period 1955–2015, was presented (Table 3). Average climate data from two meteorological stations (1960–2010) were used to analyze regional tree growth response (PC#1) to the climate. (1960–2010) were used to analyze regional tree growth response (PC#1) to the climate.

Forests 2018, 9, 606 7 of 14

Table 3. Eigenvalues of principal component analysis of the four residual chronologies for the common period 1955–2015.

Component Eigenvalue Variance Cumulative Variance (%) 1 2.501 62.529 62.529 2 0.799 19.966 82.495 3 0.413 10.329 92.824 4 0.287 7.176 100

In addition, to better understand the growth-climate relationships of A. georgei, redundancy analysis (RDA) was used to verify the results of RFA. RDA was a multivariate “direct” gradient analysis and its ordination axes were constrained to represent the linear combinations of supplied environment variables [33]; thus it can effectively describe the relationship between the tree ring width index and climate variables [34], and it has been widely used in dendroclimatology studies [35–37]. In the correlation matrix, the four residual chronologies were considered as response variables, and the years were considered as samples, while climate variables were considered as explanatory variables. Significant (p < 0.05) climate variables were selected after a forward selection using a Monte Carlo permutation test based on 499 random permutations, and RDA was conducted by the program CANOCO4.5 [38]. Moreover, in order to analyze the stability of the connection between tree growth (PC#1) and climate variables, moving interval analysis (MIA) was used to detect the relationship dynamics, with a 32-year moving window on the backward and forward evolutionary intervals, carried out with the Evolutionary and Moving Response and Correlation module in DendroClim 2002.

3. Results Four chronologies (Table2; Figure4) had a high mean sensitivity (MS), signal-to-noise ratio (SNR) and expressed population signal (EPS), which indicated that the chronologies had a high quality. Moreover, the EPS values of all four chronologies were above 0.85, suggesting that the chronologies could represent the characteristics of tree-ring width in the area, and that they could be used in this dendrochronological study. According to the results of correlation analysis (Pearson correlation) among four chronologies for the common period 1955–2015 (Table4), all chronologies were significantly ( p < 0.05 or p < 0.01) and positively correlated. The distance between the sampling sites or the elevation affected the correlation coefficients. The elevation of HB and SK were higher, and they had the highest correlation, while the distance between SK and PDC were closest, and the correlation coefficient was also high. The lower correlation coefficients between YL and the other three sites were due to longer distances.

Table 4. Person correlation coefficients between tree ring width residual chronologies of four sites for the common period 1955–2015.

SK PDC HB PDC 0.637 ** - - HB 0.704 ** 0.586 ** - YL 0.331 ** 0.265 * 0.387 ** SK: Shika Snow Mountain; PDC: Potatso National Park; HB: Haba Snow Mountain; YL: Yulong Snow Mountain. Significance level: * p < 0.05, ** p < 0.01.

The results of RFA showed that the pattern of tree growth response to climate was consistent at four sites (Figure5). The previous November and the current July temperatures positively affected tree growth, while the current June precipitation had a negative impact on tree growth, although significant correlations of the above relationships were detected at one or two out of four sites. Specifically, tree growth showed a significantly positive correlation with the previous November temperature, and a Forests 2018, 9, 606 8 of 14 Forests 2018, 9, x FOR PEER REVIEW 8 of 14 negativewhile the correlation radial growth with the showed current positive June precipitation correlations at SKwith and the YL, current respectively, July and while the the previous radial growthNovember showed temperature positive correlations at PDC and with HB, the respectively. current July and the previous November temperature at PDC and HB, respectively.

0.4 SK * Temperature 0.2 Precipitation

0.0

-0.2

-0.4 * 0.4 PDC 0.2 *

0.0

-0.2

-0.4 0.4 HB

Response coefficients 0.2 *

0.0

-0.2

-0.4 0.4 YL 0.2 *

0.0

-0.2

-0.4 * p7p8p9p10p11p1212345678910

Month FigureFigure 5. Correlation5. Correlation coefficients coefficients from thefrom response the response function analysisfunction between analysis the residualbetween chronologiesthe residual andchronologies the monthly and climate the monthly variables climate at four variables sites over at four the periodsites over 1961–2010. the period * indicates1961–2010. a significant* indicates a relationshipsignificant atrelationshipp < 0.05. p: at previous p < 0.05. year,p: previous the same year, below. the same below.

The RFA (Figure6) between PC#1 and the monthly climate variables showed that the radial growth The RFA (Figure 6) between PC#1 and the monthly climate variables showed that the radial of A. georgei was mainly affected by temperature at the upper timberlines in the area, by showing growth of A. georgei was mainly affected by temperature at the upper timberlines in the area, by positive effects of the previous November and current July temperature on its growth. showing positive effects of the previous November and current July temperature on its growth.

Forests 2018, 9, x FOR PEER REVIEW 9 of 14 ForestsForests2018 2018, 9, 9 606, x FOR PEER REVIEW 9 of9 of 14 14

0.4 Temperature 0.4 * Precipitation 0.2 * * Temperature Precipitation 0.2 * 0.0 0.0 -0.2

Response coefficients Response -0.2 -0.4 Response coefficients Response -0.4 p7p8p9p10p11p1212345678910 p7p8p9p10p11p1212345678910Month

Month Figure 6. Response function analysis between the PC#1 of four chronologies and climate variables for theFigureFigure common 6. 6.Response Response period 1961–2010. function function analysis analysis between between the the PC#1 PC#1 of of four four chronologies chronologies and and climate climate variables variables for for thethe common common period period 1961–2010. 1961–2010. The results of RDA (Figure 7) further supported the results of RFA that the temperature was the The results of RDA (Figure7) further supported the results of RFA that the temperature was the importantThe factor results influencing of RDA (Figure tree growth 7) further at the supported upper timberlines the results ofof RFAthe studied that the area. temperature Temperature was the important factor influencing tree growth at the upper timberlines of the studied area. Temperature in in importantthe previous factor November influencing and tree the growth current at theJuly u pperaccelerated timberlines tree ofgrowth the studied by 17.3% area. Temperatureand 6.1%, the previous November and the current July accelerated tree growth by 17.3% and 6.1%, respectively, respectively,in the previous while treeNovember growth andwas theinhibited current by July the currentaccelerated June tree (11.7%) growth and bySeptember 17.3% and (6.6%) 6.1%, while tree growth was inhibited by the current June (11.7%) and September (6.6%) precipitations. precipitations.respectively, In while total, treethese growth four climatic was inhibited factors accounted by the current for 41.7% June in (11.7%) tree growth. and September (6.6%) In total, these four climatic factors accounted for 41.7% in tree growth. precipitations. In total, these four climatic factors accounted for 41.7% in tree growth.

Figure 7. The redundancy analysis between the five chronologies and the climate variables for the Figurecommon 7. The period redundancy (1961–2010). analysis Only betw significanteen the (p five< 0.05) chronologies climate variables and the were climate presented. variables The for longer the commonvectorFigure of period 7. the The climate (1961–2010). redundancy factor (dotted Only analysis significant line) betw indicatedeen (p the< 0.05) the five greater climate chronologies contribution. variables and were the The presented.climate shorter variables perpendicular The longer for the vectorlinecommon between of the period climate the chronology (1961–2010). factor (dotted (triangle) Only line) significant indicate and thed climate ( thep < 0.05)greater vector climate contribution. (itself, variables or the The extensionwere shorter presented. line)perpendicular indicated The longer a linehighervector between correlation of the the climate chronology between factor (triangle) them. (dotted Chronology line) and indicatethe climate (grayd the triangle)vector greater (itself, andcontribution. climateor the extension vectors The shorter pointing line) perpendicularindicated the same a higherdirectionline correlationbetween represented the between chronology a positive them. (triangle) correlation, Chronology and and the (gray in climate opposite triangle) vector directions, and (itself, climate indicatedor the vectors extension a negativepointing line) correlation.the indicated same a directionSK:higher Shika correlationrepresented Snow Mountain; between a positive PDC: them. Potatsocorrelation, Chronology National and (gray Park; in triangle)opposite HB: Haba and directions, Snow climate Mountain; vectorsindicated YL:pointing Yulonga negative the Snow same correlation.Mountain.direction SK: PC#1:represented Shika the Snow first a component Mountain;positive correlation, ofPDC: four Potatso residual and National chronologies;in opposite Park; HB: p11T:directions, Haba November Snow indicated Mountain; temperature a negative YL: of Yulongpreviouscorrelation. Snow year; Mountain.SK: 7T: Shika July temperature SnowPC#1: Mountain;the first in current component PDC: year; Potatso 6P:of four June National residual precipitation; Park; chronologies; HB: 9P: Haba September Snow p11T: Mountain; precipitation.November YL: temperatureTheYulong same Snow below. of previous Mountain. year; PC#1: 7T: the July first temperatur componente inof fourcurrent residual year; chronologies;6P: June precipitation; p11T: November 9P: Septembertemperature precipitation. of previous The year;same below.7T: July temperature in current year; 6P: June precipitation; 9P: The results of MIA (Figure8) showed that the relationship between tree growth (PC#1) and September precipitation. The same below. climateThe variablesresults of wasMIA not (Figure stable 8) in showed the most that recent the relationship 50 years. The between positive tree effects growth of the(PC#1) previous and climateNovember Thevariables results and the was of current MIAnot stable (Figure July temperatures in 8)the showed most wererecent that strengthenedth 50e relationshipyears. The in recentpositive between 20 effects years, tree growth byof showingthe previous(PC#1) more and Novembersignificantclimate variablesand correlations the current was thannot July stable the temperatures period in the between most were recent 1962strengthened and50 years. 1980. in The Meanwhile, recent positive 20 years, theeffects by negative showing of the effects previous more of significantcurrentNovember June correlations and the September current than the wereJuly period temperatures weakened, between by were showing1962 st andrengthened no1980. significant Meanwhile, in recent correlations 20 the years, negative overby showing 1992–2010. effects moreof currentsignificant June and correlations September than were the weakened, period between by showing 1962 andno signif 1980.icant Meanwhile, correlations the overnegative 1992–2010. effects of current June and September were weakened, by showing no significant correlations over 1992–2010.

ForestsForests2018 2018, 9,, 9 606, x FOR PEER REVIEW 1010 of of 14 14

0.6 a b 1992-2010 1962-1980 p11T 0.5 7T 0.4

0.3

0.2

0.1

0.0 0.0 c d -0.1 Response coefficients Response

-0.2

-0.3

-0.4 6P 9P

-0.5 1960 1965 1970 1975 1980 1990 1995 2000 2005 2010 Year Year

FigureFigure 8. 8. MovingMoving intervalinterval analysis between PC#1 PC#1 of of four four chronologies chronologies and and climate climate variables, variables, A Awindow window with with 32-year 32-year ( a,c) backwardbackward (1962–1980)(1962–1980) andand (b(b,d,d)) forwardforward (1992–2010) (1992–2010) evolutionary evolutionary intervals.intervals. Dashed Dashed line line indicates indicates the 95%the confidence95% confidence level. A level. significantly A significantly positive relationship positive relationship occurred whenoccurred dotted when lines dotted were abovelines were significant above level,significant while le dottedvel, while lines dotted below lines the significant below the levelsignificant indicated level a significantlyindicated a significantly negative relationship. negative relationship. 4. Discussion 4. Discussion Our study showed that both temperature (previous November by +17.3% and current July by Our study showed that both temperature (previous November by +17.3% and current July by +6.1%) and precipitation (current June by −11.7% and September by −6.6%) affected A. georgei growth +6.1%) and precipitation (current June by −11.7% and September by −6.6%) affected A. georgei growth in the central Hengduan Mountains, which denied the hypothesis that tree growth at the upper in the central Hengduan Mountains, which denied the hypothesis that tree growth at the upper timberlines was only affected by temperature. However, the temperature was likely to play a more timberlines was only affected by temperature. However, the temperature was likely to play a more important role than the precipitation, according to a more stable relationship revealed by MIA, and only important role than the precipitation, according to a more stable relationship revealed by MIA, and temperature impacts on PC#1 were detected by RFA. Below, we discuss the details of these findings, only temperature impacts on PC#1 were detected by RFA. Below, we discuss the details of these and the potential effects of future climate conditions on the radial growth of A. georgei. findings, and the potential effects of future climate conditions on the radial growth of A. georgei. The November temperature in the previous year is the most important factor affecting A. georgei The November temperature in the previous year is the most important factor affecting A. georgei growth in the study area, by showing significantly positive effects on tree growth at three out of four growth in the study area, by showing significantly positive effects on tree growth at three out of four sites and high correlation coefficients in RFA for PC#1, which supported that early winter temperature sites and high correlation coefficients in RFA for PC#1, which supported that early winter was one of the dominant climate variables at the upper timberlines [39]. Warmer winter temperatures temperature was one of the dominant climate variables at the upper timberlines [39]. Warmer winter may protect the needles from frost damage [20,40]. Although the temperature of November was not temperatures may protect the needles from frost damage [20,40]. Although the temperature of the coldest in the area (Figure2), but the temperature decreased sharply from October to November, November was not the coldest in the area (Figure 2), but the temperature decreased sharply from and the minimum temperature was cold enough to harm needles and cause defoliation, which may October to November, and the minimum temperature was cold enough to harm needles and cause reduce the tree’s potential for future growth by causing low photosynthetic capture and nutrient defoliation, which may reduce the tree’s potential for future growth by causing low photosynthetic accumulation, thus restricting tree radial growth during the following year [41–43]. The positive effect capture and nutrient accumulation, thus restricting tree radial growth during the following year [41– of the previous November temperature on the tree growth of fir at the timberlines was also reported 43]. The positive effect of the previous November temperature on the tree growth of fir at the from previous studies in the central Hengduan Mountains [20,25], Western Carpathian Tatra Mountain, timberlines was also reported from previous studies in the central Hengduan Mountains [20,25], Eastern Europe [44], and North Cascade mountains in Canada [45]. Western Carpathian Tatra Mountain, Eastern Europe [44], and North Cascade mountains in Canada July temperature was another important factor that positively affected A. georgei growth in the [45]. central Hengduan Mountains. The higher temperature in current July could promote photosynthesis, July temperature was another important factor that positively affected A. georgei growth in the central Hengduan Mountains. The higher temperature in current July could promote photosynthesis,

Forests 2018, 9, 606 11 of 14 thereby increasing the formation of carbohydrates [46]. Our results supported the traditional opinion that the summer temperature was the main factor influencing tree growth at the timberlines due to the cold environment. The positive effects of July temperature on tree growth was found by previous studies carried out in the Southern Tibetan Plateau [24,47], Mount Norikura in central Japan [48], Mount Alps in Switzerland [49], and Mount Rockies in Canada [50]. The negative correlations between June precipitation and tree growth may reflect the indirect effects of temperature on radial growth. June was the transitional month for the abrupt precipitation fluctuations, although maritime monsoon from Indian Ocean brought sufficient precipitation in this month, it also increased cloud cover with comparatively lower solar radiation and temperature, which may reduce photosynthetic activity and consequently negatively affected tree growth [25]. The negative influence of June precipitation on A. georgei growth was also reported in previous studies in the Shika and Baima Snow Mountains of the study area [12,25]. Although September was the late growth stage and cambial growth was slow, climatic variations still had influences on tree growth. We speculated that excessive precipitation would reduce the oxidation reduction potential in the soil and decrease the accumulation of nutrients by limiting the activity of the roots [51].Moreover, excessive moisture in soil may decrease the net accumulation of nutrients, and thus inhibit the radial growth. The RFA and RDA both indicated that the previous November (+17.3%) and the current July (+6.1%) temperatures were the main factors affecting tree growth, which further proved that RDA could effectively quantify the relationship between the tree-ring width index and climatic variables. However, there was a difference, RDA also indicated that June (−6.6%) and September (−11.7%) precipitation also had an influence on tree growth, which was not found in RFA for PC#1. The reason for this difference might be related to the statistical theory of the two methods. The two methods should be used together to comprehend the key climatic factors affecting tree growth, which has been proved in previous studies [12,20]. The stability between tree growth and climatic variables has been highly focused in tree-ring research [52], which could provide an important reference for more accurately climate reconstruction. The promotion effects of the previous November and July temperatures on tree growth became more significant in the most recent 20 years because of the rapid increase in temperature since the 1990s. In contrast, the negative effects of June and September precipitation on tree growth were not detected in the most recent 20 years, probably due to a slight decrease trend (although not significant) in precipitation between 1990 and 2010. Stronger temperatures and weaker precipitation influences on tree growth with the warming trend have also been reported in previous dendroclimatology study in the central Hengduan Mountains [25]. Our study suggested that the growth–climate relationship might vary even over a small time scale; therefore, stability analyses of such relationships could help us better understand the dynamics of climate conditions and promote climate reconstruction accuracy. According to the regional climate model system (PRECIS), the seasonal (spring, summer, autumn, and winter) mean temperatures in Southwestern China would increase by 2.6 ◦C, 3.1 ◦C, 2.7 ◦C, and 3.1 ◦C, respectively, while the total precipitation would increase by 8%, 7%, 6%, and 8%, respectively [53]. Combined with the climate–growth relationships of A. georgei, the impact of future climate change on tree growth might be complex. If the increasing temperature could offset the negative effects of excessive precipitation, the radial growth of A. georgei would likely benefit from the future warming trend at the upper timberlines. Furthermore, indirect impacts of climate change on tree growth should be considered, such as the frequency and intensity of extreme climate events, e.g., fire, pest, drought, and wind disturbances. All these changes may lead to changes in tree growth rate and their relative abundances, and which in turn would eventually affect the structure, diversity, and position of fir forests in the central Hengduan Mountains. Forests 2018, 9, 606 12 of 14

5. Conclusions Tree cores of A. georgei were taken from four typical sites near upper timberlines in the central Hengduan Mountains; both temperature (positive effects of the previous November and the current July) and precipitation (negative effects of the current June and September) influenced its radial growth. Those four climatic factors accounted for 41.7% of the effects on tree growth, in which temperature played a more important role in tree growth based on a higher contribution (by +23.4%) than precipitation (by −18.3%). Combined with the pattern of growth–climate relationships and the prediction of future climate change, if the increasing temperature could offset the negative effects of excessive precipitation, the radial growth of A. georgei at the upper timberlines would likely benefit from the future warming.

Author Contributions: D.Y. finished the manuscript, D.X. analyzed the data, K.T. and D.X. put forward the idea of the article, W.Z., H.S. and D.S. helped field sampling, Yun Zhang modified the article. Funding: This work was funded by the National Natural Science Foundation of China (31600395), the Scientific Research Foundation of the Educational Department of Yunnan Province (2015Z136, 2018Y118) and the Plateau Wetlands Science Innovation Team of Yunnan Province (2012HC007). Acknowledgments: We would like to acknowledge the Administration of Yulong Snow Mountain and Bitahai Provincial Nature Reserve for providing permission to collect tree-ring samples. Conflicts of Interest: The authors declare no conflict of interest.

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