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Forests 2019, 10(10), 899; https://doi.org/10.3390/f10100899

Article
Intra-Annual Radial Growth of Pinus kesiya var. langbianensis Is Mainly Controlled by Moisture Availability in the Ailao Mountains, Southwestern China
1
CAS-Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, Mengla, Yunnan 666303, China
2
Center for Plant Ecology, Core Botanical Gardens, Chinese Academy of Sciences, Xishuangbanna 666303, China
3
Ailaoshan Station of Subtropical Forest Ecosystem Studies, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Jingdong, Yunnan 676209, China
4
Institute of Geography, University of Erlangen-Nürnberg, Wetterkreuz 15, Erlangen D-91058, Germany
5
World Agroforestry Centre (ICRAF), Nairobi 00100, Kenya
*
Author to whom correspondence should be addressed.
Received: 22 September 2019 / Accepted: 8 October 2019 / Published: 11 October 2019

Abstract

:
Intra-annual monitoring of tree growth dynamics is increasingly applied to disentangle growth-change relationships with local climate conditions. However, such studies are still very limited in subtropical regions which show a wide variety of climate regimes. We monitored stem radius variations (SRV) of Pinus kesiya var. langbianensis (Szemao pine) over five years (2012–2015 and 2017) in the subtropical monsoon mountain climate of the Ailao Mountains, Yunnan Province, southwest China. On average, the stem radial growth of Szemao pine started in early March and ended in early October, and the highest growth rates occurred during May to June. Stem radius increments were synchronous with precipitation events, while tree water deficit corresponded to the drought periods. Correlation analysis and linear mixed-effects models revealed that precipitation and relative humidity are the most important limiting factors of stem radial increments, whereas air temperature and vapor pressure deficit significantly affected tree water balance and may play an important role in determining the growing season length and seasonality (i.e., duration, start, and cessation). This study reveals that moisture availability plays a major role for tree growth of P. kesiya var langbianensis in the Ailao Mountains, southwest China.
Keywords:
Ailao Mountains; climate response; pine; point dendrometer; stem radius variations; tree water deficit

1. Introduction

Global climate change already affects tree species composition, forest productivity, and ecosystem functioning across biomes [1,2]. Therefore, understanding the possible impact of changing environmental conditions on forest growth and vitality is of crucial importance [3]. Growth–climate relationships are frequently determined by empirically correlating tree-ring parameters (i.e., ring width, maximum latewood density, wood anatomical variables) to monthly climatic variables [4]. However, such empirical approaches may only capture limited information of climatic forcing on tree growth, as similar amounts of radial tree growth can be achieved with different combinations of growth rate and duration under contrasting climatic conditions during the vegetation period [5,6]. Therefore, studies on intra-annual growth dynamics may shed more light on the impact of climatic forcing on tree growth [7,8] and may allow better understanding the response of the forest ecosystem to climatic extreme events.
High-resolution dendrometers continuously measure stem radius variations (SRV), which are composed of (1) irreversible radial expansion of the stem due to newly formed sapwood and bark tissue cells, and (2) water-related contraction and expansion of wood and bark [9,10]. Stem growth is mainly driven by turgor and water potential conditions in the cambium, which triggers cell division and elongation [11]. Water-related processes of the stems are mainly driven by water potential gradients between the different compartments of the soil–tree–atmosphere system, which are mainly induced by transpiration and altered by water availability in the soil [12]. Zweifel et al. (2016) proposed a zero-growth concept (ZG model) for separating stem radius fluctuations into growth-induced irreversible expansion of the stem and reversible tree water deficit-related shrinkage of the stem. The ZG model assumes that stem radial growth is restricted to periods of stem water saturation, while stem radius variations below a previous stem radius maximum are induced by changing water status [8].
High-resolution dendrometer measurements were extensively used to analyze tree–water relationships [13], intra-annual growth [6,14], wood formation [15], and carbon fixation [16] in forest ecosystems in different climate zones. For example, moisture availability during the growing season is the most important factor limiting the radial growth of various tree species in semi-arid areas of north China [17,18,19]. However, only few studies were so far conducted in tropical and subtropical forest ecosystems. For example, precipitation and vapor pressure deficit were attributed to determine tree radial growth and drought responses in tropical dry mountain forests of east Africa [20] and South America [21,22]. High air temperatures have a negative influence on the radial increment of tree species in tropical dry forests in Bolivia, probably caused by increasing vapor pressure deficit and enhancing evapotranspiration rates [23]. Moreover, a combination of intensive monitoring of stem radial variations and quantitative wood anatomy may provide more valuable information on intra-annual growth variability [24].
Pinus kesiya var. langbianensis (Szemao pine), a regional subspecies of Pinus kesiya, is distributed naturally in the humid and sub-humid areas of Yunnan Province, southwestern China. Due to its rapid growth rate, vigorous germination, and high productivity of quality wood and resins, Szemao pine is one of the main timber plantation species in the tropical and subtropical areas of Yunnan. Despite of its economic and ecological importance, little is known about its intra-annual growth dynamics and responses to climatic factors. This, however, is of great relevance to derive reliable estimates of its productivity and resilience under projected future climate conditions in the study region, where a higher frequency of drought events is expected [25].
This study presents five years of high-resolution dendrometer measurements on P. kesiya var. langbianensis in the Ailao Mountains, southwestern China. Our specific aims were (i) to describe the inter-annual variability of growth seasonality (i.e., growth onset, cessation, and duration) and xylem anatomical traits under contrasting weather conditions, and (ii) to identify the main meteorological factors that determine daily and seasonal stem radial growth of Szemao pine.

2. Material and Methods

2.1. Study Site

The study was conducted in the Ailao Mountains of central Yunnan in southwestern China. The local climate is influenced by the Asian summer monsoon, consisting of the branches of the southwest monsoon crossing the Bay of Bengal and the southeast monsoon originating in the Pacific Ocean [26]. Meteorological data from the Ailaoshan Station for Subtropical Forest Ecosystem Studies (24°31′ north (N), 101°01′ east (E), 2480 m above sea level (a.s.l.), 1982–2015) display an annual mean temperature of 11.6 °C, with monthly minimum temperatures occurring in January (5.8 °C) and maximum temperatures in July (15.8 °C). Mean annual precipitation is 1799 mm, 65% of which (1179 mm) occurs during the summer monsoon season from June to September, and only 14% of which (258 mm) occurs during the pre-monsoon season from March to May (Figures S1 and S2, Supplementary Materials).
Covering an area of 503 km2, the Ailao Mountains encompass the largest protected area of subtropical evergreen broad-leaved forests in China. P. kesiya var. langbianensis (Szemao pine) is one of the native species selected for plantations. It was sown by airplane since the 1960s, largely replacing the natural vegetation in the middle mountain regions of Yunnan [27]. Szemao pine covers vast parts of the landscape from 1600 to 2000 m in the Ailao Mountains. Our study site is located in a ~20-year-old secondary forest of Szemao pine (24.50° N, 100.98° E, 1900 m a.s.l.). The investigated trees are characterized by a diameter at breast height (DBH) of around 15–20 cm and a height of 10–15 m. P. kesiya is the dominant tree species in the secondary forest, and Schima noronhae is the main associated broad-leaved tree species of the mixed forest.

2.2. Meteorological Data

Meteorological data were obtained from the Ailaoshan meteorological station located around 8 km away from the study site. Available climate variables include precipitation (PRE), air temperature (Ta) at 2 m height and soil temperature (Ts) at 10 cm depth, relative humidity (RH), photosynthetic active radiation (PAR), and wind speed (WS). Vapor pressure deficit (VPD) was calculated from the values of T and RH. The hourly measured climate data were averaged to daily means for further analyses.

2.3. Dendrometer Measurements

Stem radius variations (SRV) were monitored for three Szemao pines using electronic point dendrometers (DR type, Ecomatik, Munich, Germany), with a spatial resolution of 2 m. The instruments were installed on the tree trunks at breast height. The dead outmost layers of the bark were removed to reduce the influence of hygroscopic shrinking and swelling of the bark. Stem radius variations were recorded at 10-min intervals by a data logger (DL15, Ecomatik, Munich, Germany) during the period from January 2012 to December 2017. Data were not recorded properly during the growth season of 2016 due to technical problems and were, thus, not included in our analysis. Nevertheless, our data represent the longest continuous stem diameter growth records in the study region.

2.4. Data Analysis

To describe the seasonality of tree growth over years, a Gompertz model [28] was fitted to the daily means of stem radius increment, as shown in Equation (1).
Y = Y 0 + A   e x p e β k t
where Y represents the daily average stem diameter, Y0 and A are the lower and upper asymptotes, β is the x-axis placement parameter, k is the rate of change parameter, and t is day of year (DOY). Consequently, (AY0) corresponds to the total seasonal growth whereas (YtYt−1) corresponds to the growth rate (mm∙d−1). The parameters were estimated using the ordinary least squares method with the nsl function in R software (version 3.5.1) [29]. Days of growth initiation and cessation were determined when 10% and 90% of the total annual growth were attained [14].
The growth line (GRO) was defined by a moving maximum of the current and previous dendrometer measurements, while tree water deficit (TWD) was defined as the deviations of current measured stem radius from the GRO baseline. Thus, GRO covers any increase in stem radius (SR) exceeding a precedent SR maximum. TWD covers any shrinkage of the stem plus the increase in SR during periods with shrunken stems (Figure S3, Supplementary Materials). Daily sums of growth (GRO rate, mm∙d−1) and daily minimum tree water deficit (TWD, mm) were calculated for the study period.
The monthly sum of daily precipitation, GRO rate, and monthly average of TWD were compared. Daily and weekly GRO rates and TWD were correlated with corresponding climatic variables for the main vegetation period from 1 March to 31 October for 2012–2017.
Linear mixed-effects modeling (LMM) was performed in the nlme package in the R statistical software [30] to assess the relative importance of different climate factors on GRO rate and TWD. The values of GRO rate, TWD, relative humidity, and precipitation were log-transformed to fit the normal distribution. All dependent and predicted variables were scaled according to their means and standard deviations [30]. We calculated variance inflation factors (VIF) of the models containing all variables to assess collinearity among variables. To reduce the collinearity problem, we excluded those climate variables with high collinearity according to the threshold of VIF values smaller than 2.5, i.e., mean and minimum air temperature and soil temperature. We constructed a full model with Tmax, WS, PRE, RH, and PAR as fixed factors. LMMs with a random intercept associated with trees nested in years and first-order auto-correlation structures were fitted. Fixed-effects selections were based on Akaike’s information criterion (AIC), and the models with the lowest AIC values were selected. We performed the LMM model first for the full available time period and subsequently for each single year separately.

3. Results

3.1. Climate Variability

The climate of the study area is characterized by a distinct seasonality, i.e., pre-monsoon season from March to May, summer monsoon from June to September, and post-monsoon and winter season from October to February (Figure 1, Figure S2, Supplementary Materials). Climatic conditions during the studied period from 2012 to 2017 were generally warmer and drier than the long-term mean from 1982–2011 (Figure S2, Supplementary Materials). During 2012–2017, the pre-monsoon season from March to May showed lower RH and higher VPD compared to the long-term mean (Table S1, Figure S2b,d, Supplementary Materials). In 2017, VPD and RH were generally above the long-term mean, except for a short dry period during May–June. In 2014, pre-monsoon season (March–May) rainfall was only 57 mm, which is only 21.1% of the long-term mean (270 mm) (Figure S2, Supplementary Materials).

3.2. Seasonal Stem Radius Variations

Stem radius variations showed characteristic seasonal patterns (Figure 2). Tree water deficit (TWD) derived from the ZG model showed substantial stem shrinking during the pre-monsoon seasons of 2012–2015 and during the autumn–winter seasons in 2012 and 2013 (Figure 2b). In all years, irreversible stem expansion (GRO rate) was concentrated during the late pre-monsoon and summer monsoon seasons from DOY 100 to DOY 300 (Figure 2c). Gompertz nonlinear regressions explained more than 99% of the variations in the dendrometer measurements. According to the modeled results, annual cumulative growth ranged from 3.6 mm in 2014 to 6.0 mm in 2017 (Table S1, Supplementary Materials). The beginning of the growing season ranged from 25 March (DOY 85) in 2012 to 1 May (DOY 121) in 2014, while the end of the growing season ranged from 8 October (DOY 281) in 2014 to 17 October (DOY 290) in 2017. The length of the growing season ranged from 160 days in the dry year 2014 to 203 days in the comparatively wet year 2012 (Figure 3, Table S1, Supplementary Materials). The occurrence of maximum growth rates varied from 24 May in 2012 (DOY 145) to 24 June in 2014 (DOY 173).

3.3. Effects of Climate Factors on Stem Growth

Monthly summed radial increments (GRO rate) were highly synchronous with rainfall events during the main growing season, whereas periods with tree water deficit (TWD) were synchronous with drought periods during the pre-monsoon and post-monsoon seasons (Figure 4). Based on the linear mixed-effect model, precipitation and relative humidity had strong positive effects on stem radial growth (Table 1, Tables S2 and S3, Supplementary Materials). Tree water deficit (TWD) was mainly affected by daily vapor pressure deficit (VPD) (Table 1, Table S4, Supplementary Materials). Correlation analyses confirmed that daily stem radius increments (GRO rates) were highly correlated with precipitation and relatively humidity (Figure 5a), indicating that radial growth of Szemao pine is mainly limited by moisture availability during the pre-monsoon and summer monsoon season. The correlations between weekly averaged GRO rates and corresponding precipitation and RH were consistent with results from daily data; however, negative correlations between weekly GRO rates and VPD and PAR increased (Figure 5b). Correlations between monthly GRO rates and climatic variables generally were higher with precipitation and relative humidity during the early and late growing seasons (Figure 6a). Tree water deficit (TWD) correlated positively with maximum temperature and relative humidity (Figure 6b), which is related to higher water consumption and canopy transpiration under warmer and drier conditions.

4. Discussion

Our results revealed that stem radial growth of Pinus kesiya var. langbianensis mainly occurs during early March to October, with highest growth rates during May to June (Figure 2, Figure 3 and Figure 4). These results are in good accordance with seasonal domains of cambial activity of P. kesiya and P. merkusii based on wood anatomy analysis in northern Thailand [31]. Due to inter-annual climate variability, the length of the growing period varied substantially between years, which can strongly affect tree growth performance and forest carbon fixation [32].
Previous studies showed that the maximum growth rates of conifers in cold environments at high latitudes coincide with maximum day length [5,33]. In our subtropical study region, intra-annual radial growth of P. kesiya var. langbianensis was positively correlated to precipitation and humidity (Table 1, Figure 5), indicating that radial growth of Szemao pine is mainly limited by moisture availability. In a subtropical monsoon climate regime in northwestern Thailand, tree-ring widths of Pinus kesiya were significantly correlated with moisture availability during the pre-monsoon season from March to May [34]. Tree water deficit (TWD) was synchronous with drought periods during spring and autumn and correlated positively with air temperatures and VPD, indicating that water consumption through canopy transpiration increased during sunnier, warmer, and less humid conditions [12,13]. During the pre-monsoon season (March–May), relative high amplitudes and duration of stem contraction phases indicated a shortage of stem water supply (Figure 4). These results are consistent with the finding of pre-monsoon moisture limitation of stem radial growth of Pinus kesiya in northwestern Thailand [34] and in northeast India [35]. Our results underline the critical importance of this warm but dry season for the whole annual tree growth performance and carbon uptake by monsoonal forest ecosystems [32].
Precipitation and humidity were found as main limiting factors of stem growth for coniferous and broad-leaved tree species in temperate ecosystems, as well as in semi-arid regions of China [5,18,19,36,37]. The daily increment of Pinus cembra (L.) at the alpine treeline ecotone was positively correlated with changes in the tree water status controlled by relative humidity and precipitation [38]. As sufficient soil moisture availability is crucial for maintaining stem water status, cell turgor pressure and, thus, cell enlargement [9,39]. Stem hydrological processes, i.e., stem swelling, might also partially explain the strong positive effect of precipitation on stem increment [10]. The negative effect of current-day solar radiation on growth is assumed to be associated with reversible stem shrinkage rather than with irreversible stem growth alone [6].
The seasonal distribution of precipitation and water availability may have influenced the seasonal water budget and growth of the studied pine species. For example, pre-monsoon precipitation and humidity in 2014 were considerably lower than normal (Figure S2, Supplementary Materials). This resulted in a strong stem shrinkage and tree water deficit (TWD), and delayed the start of xylem growth, leading to a shorter duration of the growth period and reduced annual growth rates (Figure 2 and Figure 3). Increased transpiration leads to increasing leaf water demand, resulting in a decline in stem water potential and stomata closure, if soil water availability is low [40]. This may induce carbon starvation and negative consequences for tree growth [41,42]. During prolonged dry periods like in March–June 2014, low soil moisture availability and high evaporation demand cause a negative water budget of the trees, thereby reducing carbon supply for cambial cell production [40]. Thus, a change in the precipitation regime may substantially affect the diel phases of stem hydrological status and influence the dynamics of water depletion and replenishment, as already been observed in single-leaf pinyon pine (Pinus monophylla) in North America [43].

5. Conclusions

We studied seasonal dynamics and climatic determinants of stem radius changes of Pinus kesiya var. langbianensis in the Ailao Mountains, southwestern China. Based on five-year high-resolution dendrometer data, we demonstrated that stem increment of P. kesiya var. lanbianensis was primarily related to precipitation and humidity, and confirmed that sufficient water supply, especially during the early growing season, is crucial for maintaining tree water status, as well as xylem cell production and cell enlargement. Our results are fundamental to better understand the growth performance and carbon sequestration capacity of this economically important species under future climate change. Further simultaneous measurements of carbon storage, sap flow, and stem diameter variations will allow assessing the potential risk of drought-related growth decline or mortality of tree species from subtropical mountainous environments [44] under expected climate change.

Supplementary Materials

The following are available online at https://www.mdpi.com/1999-4907/10/10/899/s1.

Author Contributions

Z.-X.F. and A.B. conceived and designed the experiments. Z.X.F, P.-L.F., R.-Q.Y. and J.-H.Q. performed the experiments. Z.-X.F. and P.-L.F. analyzed the data and wrote the paper. A.B., J.G., and A.G. reviewed and edited the draft.

Funding

This work was funded by the National Natural Science Foundation of China (31770533, 31861133007), the National Key Research Development Program of China (2016YFC0502105), and the CAS 135 program (2017XTBG-T01). A.B. was supported by the CAS President’s International Fellowship Initiative (PIFI, 2017VCA0037).

Acknowledgments

The Ailaoshan Station for Subtropical Forest Ecosystem Studies provided meteorological data. We thank Ma Hong and Tan Meiyi for their help in the field investigation.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H.; et al. A global overview of drought and heat-induced tee mortality reveals emerging climate change risks for forest. For. Ecol. Manag. 2010, 259, 660–684. [Google Scholar] [CrossRef]
  2. Boisvenue, C.; Running, S.W. Impacts of climate change on natural forest productivity–evidence since the middle of the 20th century. Glob. Chang. Biol. 2006, 12, 862–882. [Google Scholar] [CrossRef]
  3. Saxe, H.; Cannell, M.G.R.; Johnsen, B.; Ryan, M.G.; Vourlitis, G. Tree and forest functioning in response to global warming. New Phytol. 2001, 139, 395–436. [Google Scholar] [CrossRef]
  4. Fritts, H. Tree Rings and Climate; Academic Press: New York, NY, USA, 1976; pp. 25–27. [Google Scholar]
  5. Duchesne, L.; Houle, D.; D’Orangeville, L. Influence of climate on seasonal patterns of stem increment of balsam fir in a boreal forest of Québec, Canada. Agric. For. Meteorol. 2012, 162–163, 108–114. [Google Scholar] [CrossRef]
  6. van der Maaten, E.; Bouriaud, O.; van der Maaten-Theunissen, M.; Mayer, H.; Spieker, H. Meteorological forcing of day-to-day stem radius variations of beech is highly synchronic on opposing aspects of a valley. Agric. For. Meteorol. 2013, 181, 85–93. [Google Scholar] [CrossRef]
  7. Gričar, J.; Rathgeber, C.B.K.; Fonti, P. Monitoring seasonal dynamics of wood formation. Dendrochronologia 2011, 29, 123–125. [Google Scholar] [CrossRef]
  8. Zweifel, R.; Haeni, M.; Buchmann, N.; Eugster, W. Are trees able to grow in periods of stem shrinkage? New Phytol. 2016, 211, 839–849. [Google Scholar] [CrossRef] [PubMed]
  9. Zweifel, R.; Zimmermann, L.; Zeugin, F.; Newbery, D.M. Intra-annual radial growth and water relations of trees: Implications towards a growth mechanism. J. Exp. Bot. 2006, 57, 1445–1459. [Google Scholar] [CrossRef] [PubMed]
  10. Chan, T.; Hölttä, T.; Berninger, F.; Mäkkinen, H.; Nöjd, P.; Mencuccini, M.; Nikinmaa, E. Separating water-potential induced swelling and shrinking from measured radial stem variations reveals a cambial growth and osmotic concentration signal. Plant Cell Environ. 2016, 39, 233–244. [Google Scholar] [CrossRef]
  11. Steppe, K.; Sterck, F.; Deslauriers, A. Diel growth dynamics in tree stems: Linking anatomy and ecophysiology. Trends Plant Sci. 2015, 20, 335–343. [Google Scholar] [CrossRef]
  12. Zweifel, R.; Zimmermann, L.; Newbery, D. Modelling tree water deficit from microclimate: An approach to quantifying drought stress. Tree Physiol. 2005, 25, 147–156. [Google Scholar] [CrossRef] [PubMed]
  13. Dietrich, L.; Zweifel, R.; Kahmen, A. Daily stem diameter variations can predict the canopy water status of mature temperate trees. Tree Physiol. 2018, 38, 941–952. [Google Scholar] [CrossRef] [PubMed]
  14. Oberhuber, W.; Gruber, A.; Kofler, W.; Swidrak, I. Radial stem growth in response to microclimate and soil moisture in a drought-prone mixed coniferous forest at an inner Alpine site. Eur. J. For. Res. 2014, 133, 467–479. [Google Scholar] [CrossRef] [PubMed]
  15. Drew, D.M.; Downes, G.M. The use of precision dendrometers in research on daily stem size and wood property variation: A review. Dendrochronologia 2009, 27, 159–172. [Google Scholar] [CrossRef]
  16. Cuny, H.E.; Rathgeber, C.B.K.; Frank, D.; Fonti, P.; Mäkinen, H.; Prislan, P.; Rossi, S.; del Castillo, E.M. Woody biomass production lags stem-girth increase by over one month in coniferous forests. Nat. Plants 2015, 1, 15160. [Google Scholar] [CrossRef]
  17. Wang, Z.Y.; Yang, B.; Deslauriers, A.; Bräuning, A. Intra-annual stem radial increment response of Qilian juniper to temperature and precipitation along an altitudinal gradient in northwestern China. Trees 2015, 29, 25–34. [Google Scholar] [CrossRef]
  18. Jiang, Y.; Wang, B.Q.; Dong, M.Y.; Huang, Y.M.; Wang, M.C.; Wang, B. Response of daily stem radial growth of Platycladus orientalis to environmental factors in a semi-arid area of North China. Trees 2015, 29, 87–96. [Google Scholar] [CrossRef]
  19. Zhang, R.; Yuan, Y.; Gou, X.; Zhang, T.; Zou, C.; Ji, C.; Fan, Z.; Qin, L.; Shang, H.; Li, X. Intra-annual radial growth of Schrenk spruce (Picea schrenkiana Fisch. et Mey) and its response to climate on the northern slopes of the Tianshan Mountains. Dendrochronologia 2016, 40, 36–42. [Google Scholar] [CrossRef]
  20. Krepkowski, J.; Bräuning, A.; Gebrekirstos, A.; Strobl, S. Seasonal growth dynamics and climatic control of different tree life forms in Munessa Forest (Ethiopia). Trees 2011, 25, 59–70. [Google Scholar] [CrossRef]
  21. Volland-Voigt, F.; Bräuning, A.; Ganzhi, O.; Peters, T.; Maza, H. Radial stem variations of Tabebuia chrysantha (Bignoniaceae) in different tropical forest ecosystems of southern Ecuador. Trees 2011, 25, 39–48. [Google Scholar] [CrossRef]
  22. Raffelsbauer, V.; Spannl, S.; Peña, K.; Pucha, D.A.; Steppe, K.; Bräuning, A. Tree circumference changes and species-specific growth recovery after extreme dry events in a montane rainforest in southern Ecuador. Front. Plant Sci. 2019, 10, 1–10. [Google Scholar] [CrossRef] [PubMed]
  23. Mendivelso, H.A.; Julio Camarero, J.; Gutiérrez, E.; Castañ o-Naranjo, A. Climatic influences on leaf phenology, xylogenesis and radial stem changes at hourly to monthly scales in two tropical dry forests. Agric. For. Meteorol. 2016, 216, 20–36. [Google Scholar] [CrossRef]
  24. Cocozza, C.; Palombo, C.; Tognetti, R.; Porta, N.L.; Anichini, M.; Giovannelli, A.; Emiliani, G. Monitoring intra-annual dynamics of wood formation with microcores and dendrometers in Picea abies at two different altitudes. Tree Physiol. 2016, 36, 832–846. [Google Scholar] [CrossRef] [PubMed]
  25. Liu, M.; Xu, X.; Sun, A.Y.; Wang, K.; Liu, W.; Zhang, X. Is southwestern China experiencing more frequent precipitation extremes? Environ. Res. Lett. 2014, 9, 064002. [Google Scholar] [CrossRef]
  26. Zhang, K.; Ma, Y.; Li, Y.; Liu, Y. On climatic effects of airstreams over the Ailao Mountains. Geogr. Res. 1992, 31, 461–467. [Google Scholar]
  27. Wang, H.F.; Lencinas, M.V.; Friedman, C.R.; Zhu, Z.X.; Qiu, J.X. Understory plant diversity assessment of Szemao pine (Pinus kesiya var. langbianensis) plantations in Yunnan, China. Collect. Bot. 2012, 31, 51–65. [Google Scholar] [CrossRef]
  28. Rossi, S.; Deslauriers, A.; Morin, H. Application of the Gompertz equation for the study of xylem cell development. Dendrochronologia 2003, 21, 33–39. [Google Scholar] [CrossRef]
  29. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria.
  30. Zuur, A.F.; Leno, E.N.; Walker, N.J.; Saveliev, A.A.; Smith, G.M. Mixed Effects Models and Extensions in Ecology with R; Springer: New Youk, NY, USA, 2009. [Google Scholar]
  31. Pumijumnong, N.; Wanyaphet, T. Seasonal cambial activity and tree-ring formation of Pinus merkusii and Pinus kesiya in Northern Thailand in dependence on climate. For. Ecol. Manag. 2006, 226, 279–289. [Google Scholar] [CrossRef]
  32. Wagner, F.H.; Hérault, B.; Bonal, D.; Stahl, C.; Anderson, L.O.; Baker, T.R.; Sebastian Becker, G.; Beeckman, H.; Boanerges Souza, D.; Cesar Botosso, P.; et al. Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests. Biogeosciences 2016, 13, 2537–2562. [Google Scholar] [CrossRef]
  33. Rossi, S.; Deslauriers, A.; Anfodillo, T.; Morin, H.; Saracino, A.; Motta, R.; Borghetti, M. Conifers in cold environments synchronize maximum growth rates of tree-ring formation with day length. New Phytol. 2006, 170, 301–310. [Google Scholar] [CrossRef]
  34. Pumijumnong, N.; Eckstein, D. Reconstruction of pre-monsoon weather conditions in northwestern Thailand from the tree-ring widths of Pinus merkusii and Pinus kesiya. Trees 2011, 25, 125–132. [Google Scholar] [CrossRef]
  35. Chaudhary, V.; Bhattacharyya, A. Suitability of Pinus kesiya in Shillong, Meghalaya for tree-ring analysis. Curr. Sci. 2002, 83, 1010–1015. [Google Scholar]
  36. Deslauriers, A.; Morin, H.; Urbinati, C.; Carrer, M. Daily weather response of balsam fir (Abies balsamea (L.) Mill.) stem radius increment from dendrometer analysis in the boreal forests of Québec (Canada). Trees 2003, 17, 477–484. [Google Scholar] [CrossRef]
  37. Deslauriers, A.; Rossi, S.; Anfodillo, T. Dendrometer and intra-annual tree growth: What kind of information can be inferred? Dendrochronologia 2007, 25, 113–124. [Google Scholar] [CrossRef]
  38. Gruber, A.; Zimmermann, J.; Wieser, G.; Oberhuber, W. Effects of climate variables on intra-annual stem radial increment in Pinus cembra (L.) along the alpine treeline ecotone. Ann. For. Sci. 2009, 6, 503–603. [Google Scholar] [CrossRef] [PubMed]
  39. Steppe, K.; De Pauw, D.J.; Lemeur, R.; Vanrolleghem, P.A. A mathematical model linking tree sap flow dynamics to daily stem diameter fluctuations and radial stem growth. Tree Physiol. 2006, 26, 257–273. [Google Scholar] [CrossRef]
  40. Gebrekirstos, A.; Noordwijk, M.; van Neufeldt, H.; Mitlöhner, R. Relationships of stable carbon isotopes, plant water potential and growth an approach to assess water use efficiency and growth strategies of dry land agroforestry species. Trees 2011, 25, 95–102. [Google Scholar] [CrossRef]
  41. McDowell, N.; Pockman, W.T.; Allen, C.D.; Breshears, D.D.; Cobb, N.; Kolb, T.; Plaut, J.; Sperry, J.; West, A.; Williams, D.G.; et al. Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought? New Phytol. 2008, 178, 719–739. [Google Scholar] [CrossRef]
  42. King, G.; Fonti, P.; Nievergelt, D.; Büntgen, U.; Frank, D. Climatic drivers of hourly to yearly tree radius variations along a 6 °C natural warming gradient. Agric. For. Meteorol. 2013, 168, 36–46. [Google Scholar] [CrossRef]
  43. Biondi, F.; Hartsough, P. Using automated point dendrometers to analyze tropical treeline stem growth at Nevado de Colima, Mexico. Sensors 2010, 10, 5827–5844. [Google Scholar] [CrossRef]
  44. Rosado, B.H.P.; Joly, C.A.; Burgess, S.S.O.; Oliveira, R.S.; Aidar, M.P.M. Changes in plant functional traits and water use in Atlantic rainforest: Evidence of conservative water use in spatio-temporal scales. Trees 2016, 30, 47–61. [Google Scholar] [CrossRef]
Figure 1. Daily climate conditions from the Ailao meteorological station for the period of January 2012 to December 2017: (a) daily minimum (blue), mean (black), and maximum (red) temperature; (b) photosynthetic active radiation (PAR); (c) precipitation (PRE); (d) relative humidity (RH); (e) vapor pressure deficit (VPD).
Figure 1. Daily climate conditions from the Ailao meteorological station for the period of January 2012 to December 2017: (a) daily minimum (blue), mean (black), and maximum (red) temperature; (b) photosynthetic active radiation (PAR); (c) precipitation (PRE); (d) relative humidity (RH); (e) vapor pressure deficit (VPD).
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Figure 2. (a) Cumulative daily mean stem radius variations (SRV, mm); (b) daily minimum tree water deficit (TWD); (c) daily sums of growth-induced irreversible stem expansion (GRO rate) calculated from a zero-growth (ZG) model (Zweifel et al., 2016); (d) daily stem growth rates of Pinus kesiya var. langbianensis trees modeled with a Gompertz function for the years 2012–2015 and 2017. Shaded areas indicate the standard error of the mean.
Figure 2. (a) Cumulative daily mean stem radius variations (SRV, mm); (b) daily minimum tree water deficit (TWD); (c) daily sums of growth-induced irreversible stem expansion (GRO rate) calculated from a zero-growth (ZG) model (Zweifel et al., 2016); (d) daily stem growth rates of Pinus kesiya var. langbianensis trees modeled with a Gompertz function for the years 2012–2015 and 2017. Shaded areas indicate the standard error of the mean.
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Figure 3. (a) Total annual growth (mm); (b) maximum growth rate (μm∙d−1); (c) starting day of year (DOY) of growth; (d) cessation day of year (DOY) of growth, predicted by a Gompertz model fitted on daily mean stem radius variations (SRV). The boxplots show medians, the 25th and 75th quartiles, and minimum–maximum values.
Figure 3. (a) Total annual growth (mm); (b) maximum growth rate (μm∙d−1); (c) starting day of year (DOY) of growth; (d) cessation day of year (DOY) of growth, predicted by a Gompertz model fitted on daily mean stem radius variations (SRV). The boxplots show medians, the 25th and 75th quartiles, and minimum–maximum values.
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Figure 4. Comparison between monthly sums of precipitation (mm, gray bars) and (a) monthly sums of daily growth-induced irreversible stem expansion (GRO rate, mm∙month−1, red points), and (b) monthly means of daily minimum tree water deficit (TWD, mm, blue points). Standard deviations of three individuals are also shown.
Figure 4. Comparison between monthly sums of precipitation (mm, gray bars) and (a) monthly sums of daily growth-induced irreversible stem expansion (GRO rate, mm∙month−1, red points), and (b) monthly means of daily minimum tree water deficit (TWD, mm, blue points). Standard deviations of three individuals are also shown.
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Figure 5. Correlation coefficients between daily (a) and weekly (b) means of growth-induced irreversible stem expansion (GRO rate) and corresponding climate variables for the years 2012–2015 and 2017. Gray lines indicate correlations significant at the p ≤ 0.01 level. Tmean: mean air temperature; Tmax: maximum air temperature; Tmin: minimum air temperature; TS10: mean soil temperature at 10 cm depth; WS: wind speed; PRE: precipitation; RH: relative humidity; VPD: vapor pressure deficit; PAR: photosynthetic active radiation.
Figure 5. Correlation coefficients between daily (a) and weekly (b) means of growth-induced irreversible stem expansion (GRO rate) and corresponding climate variables for the years 2012–2015 and 2017. Gray lines indicate correlations significant at the p ≤ 0.01 level. Tmean: mean air temperature; Tmax: maximum air temperature; Tmin: minimum air temperature; TS10: mean soil temperature at 10 cm depth; WS: wind speed; PRE: precipitation; RH: relative humidity; VPD: vapor pressure deficit; PAR: photosynthetic active radiation.
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Figure 6. Correlation coefficients between monthly stem growth rate (GRO rate, a) and tree water deficit (TWD, b) and climatic variables from March to October for the years 2012–2015 and 2017. Tmean: mean air temperature; PRE: precipitation; RH: relative humidity; PAR: photosynthetic active radiation. N ranges from 289 to 358, and all correlations with a coefficient above 0.18 are significant at the p < 0.01 level.
Figure 6. Correlation coefficients between monthly stem growth rate (GRO rate, a) and tree water deficit (TWD, b) and climatic variables from March to October for the years 2012–2015 and 2017. Tmean: mean air temperature; PRE: precipitation; RH: relative humidity; PAR: photosynthetic active radiation. N ranges from 289 to 358, and all correlations with a coefficient above 0.18 are significant at the p < 0.01 level.
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Table 1. Parameter estimates for the selected linear mixed-effects models fitted to explain the variations of daily sums of growth-induced irreversible stem expansion (GRO rate) and daily minimum tree water deficit (TWD). Mixed-effects models were performed separately for GRO rate and TWD, with climate variables as fixed factors and individual trees nested in years and a first-order auto-correlation structure as random factor. PRE: precipitation; RH: relative humidity; WS: wind speed; vapor pressure deficit (VPD); SE: standard error; df: degrees of freedom.
Table 1. Parameter estimates for the selected linear mixed-effects models fitted to explain the variations of daily sums of growth-induced irreversible stem expansion (GRO rate) and daily minimum tree water deficit (TWD). Mixed-effects models were performed separately for GRO rate and TWD, with climate variables as fixed factors and individual trees nested in years and a first-order auto-correlation structure as random factor. PRE: precipitation; RH: relative humidity; WS: wind speed; vapor pressure deficit (VPD); SE: standard error; df: degrees of freedom.
#VariableEstimateSEdft-Valuep-Value
GRO rateIntercept−4.050.091307−44.90<0.001
PRE0.2660.025130710.64<0.001
RH0.4010.007213075.54<0.001
TWDIntercept−3.650.155642−23.59<0.001
WS0.0970.0466422.1020.036
PRE−0.1260.049642−2.5860.009
VPD0.2990.1046422.8910.004

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