Climate change significantly influences tree radial growth, particularly in high-elevation forests. As a typical plateau lake in the Hengduan Mountains, Lugu Lake lacks sufficient dendrochronological research, hindering the understanding of regional conifers’ responses to climate change.
Methods
Using dendrochronological methods, we constructed residual chronologies from tree-ring width data of Larix potaninii Batalin. (Chinese larch), Picea likiangensis Franch. (Lijiang spruce) and Pinus yunnanensis Franch. (Yunnan pine) collected around Lugu Lake. We used Response Function Analysis (RFA) and Redundancy Analysis (RDA) to quantify growth–climate relationships. We further identified the key climatic drivers of radial growth for the three conifers.
Results and discussion
The radial growth of L. potaninii, P. likiangensis, and P. yunnanensis around Lugu Lake was jointly influenced by temperature and precipitation. Specifically, the mean minimum temperature (Tmin) of previous September, current January precipitation, the mean temperature (Tmean) of current May, and the mean maximum temperature (Tmax) of current September were common factors influencing the radial growth of three conifers. L. potaninii was more influenced by temperature in the early growing season (April–May) and moisture conditions in the post growing season (September–October). Elevated growing-season temperatures were detrimental to the growth of P. likiangensis. P. yunnanensis was more affected by spring drought stress and summer precipitation. Under projected warming with slightly reduced precipitation, the observed climate sensitivities suggest that growth of L. potaninii and P. likiangensis may respond differently, whereas the response of P. yunnanensis is likely more complex. RFA and RDA demonstrated consistency and could effectively complement each other in dendroclimatological studies. This study provides new tree-ring evidence from northwestern Yunnan and insights into potential future growth responses in the region under climate change.
climatic factorsdendrochronologyHengduan MountainsLugu Lakeradial growthThe author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Yunnan Provincial Joint Special Project for Basic Agricultural Research (202101BD070001-098) and the Scientific Research Fund Project of the Yunnan Provincial Department of Education (2024Y596).section-at-acceptanceForest GrowthIntroduction
Climate change is accelerating with detectable impacts on high-elevation forests. Studies have demonstrated that global surface temperatures will continue to rise at least until the middle of this century (Ma et al., 2022; Scafetta, 2024). Without substantial reductions in carbon dioxide and other greenhouse gas emissions over the next few decades, global warming is projected to exceed 1.5–2°C within this century (Masson-Delmotte et al., 2021). Climate change alters tree regeneration, growth, and migration in high-elevation regions (Pepin et al., 2015; Kharal et al., 2017). These changes can reshape the structure, productivity, and dynamics of alpine forests (Yadava et al., 2017; Cailleret et al., 2014). Meanwhile, as the main body of terrestrial ecosystems, forests play a crucial role in global carbon sink resources. Forests mitigate climate change through carbon sequestration. They also regulate regional climate, conserve water resources, and provide substantial ecological and economic benefits (Peng, 2023). Therefore, studying the impacts of climate change on forest ecosystems provides a scientific basis for developing effective mitigation and adaptation strategies (Wei et al., 2013).
Dendrochronology overcomes limitations associated with conventional meteorological observations, such as sparse station coverage and short records. By reducing non-climatic influences (e.g., genetics and age-related trends), tree-ring width indices retain key signals of past climate variability. Therefore, they are widely used to examine climate responses and reconstruct past climate (Lin et al., 1995; Fritts, 1991; Wu, 1990; Hu, 2007). Tree rings document annual growth patterns and capture interannual climatic fluctuations. This allows tree rings to be used to assess climate impacts on ecosystems and to support scenario-based discussion of potential future changes (Peng et al., 2023). In earlier investigations, the relationships between multiple tree species and climatic variables have been validated (Li et al., 2024; Yang et al., 2022; Jin et al., 2013). These efforts have further revealed that different species exhibit varied growth response mechanisms to climate change.
The Hengduan Mountains are located on the southeastern edge of the Qinghai-Tibet Plateau, with northwestern Yunnan at its core. As a critical transition zone from the plateau to lower elevations, this area exhibits extreme vertical relief and complex topography, making it a biodiversity hotspot and a region highly sensitive to climate change (Wang, 2021; Zhang et al., 2020). In recent years, scholars have used tree-ring data to investigate the responses of tree radial growth to climate change in this area, clarifying the relationships between radial growth of coniferous species and climatic factors. In the central Hengduan Mountains, Abies georgei growth was affected by both temperature (positive effects) and precipitation (negative effects) (Yin et al., 2018). Research on Tsuga dumosa in Wuliang Mountain found that water availability was the main limiting factor for radial growth (Yin et al., 2023). In addition, dendrochronological studies have been successively carried out in Laojun Mountain and Jianhu areas (Cao et al., 2020; Deng et al., 2024). However, despite these advances, dendroclimatological evidence remains scarce around Lugu Lake, a representative plateau-lake ecosystem in northwestern Yunnan with well-preserved montane forests (Canham and Murphy, 2016). Unlike climatically extreme environments (e.g., treeline or semi-arid regions), forests around Lugu Lake develop under relatively favorable hydrothermal conditions, where tree growth may be regulated by multiple climatic factors rather than a single dominant limitation. The lack of tree-ring studies in this region limits our understanding of how dominant tree species respond to climate variability under such non-extreme but ecologically important conditions.
Notably, Larix potaninii, Picea likiangensis, and Pinus yunnanensis are key constructive, dominant and representative species of subalpine forests in northwestern Yunnan. With their distinct annual ring boundaries, clear climate sensitivity, and strong chronology signals, they are preferred species for dendroclimatological research (Shen, 2019; Yuan and Yu, 2013). Furthermore, these three species have different biological characteristics, a multi-species tree-ring comparison may enable understand insights of common and species-specific response to climate change, and then understand their adaptable mechanisms. Existing studies have found that P. yunnanensis in Haba Snow Mountain was primarily limited by drought in May of the current year (Zhang et al., 2020). For L. potaninii in the middle section of the Hengduan Mountains, warm climate and sufficient heat during the early growing season (May and June) were crucial for its radial growth (Yue et al., 2023). The radial growth of P. likiangensis in Shika Snow Mountain was jointly influenced by temperature and precipitation, whereas Pinus densata showed a significant correlation only with temperature (Zhang et al., 2018). However, these studies were mostly conducted at individual sites and focused on single species, limiting direct interspecific comparisons under a shared climatic background. Accordingly, coordinated multi-species tree-ring analyses provide a direct basis for identifying shared climatic controls versus species-specific sensitivities, which is essential for assessing relative vulnerability under future climate change and for informing species-specific conservation and adaptive management aimed at maintaining conifer diversity and subalpine forest stability.
We analyzed tree-ring samples from sites around Lugu Lake, focusing on three study species: L. potaninii, P. likiangensis, and P. yunnanensis. By employing dendroecological methods, we aimed to: (1) find the interspecific differences in the climate-response patterns of the three conifers and identify the main climate factors affecting tree growth, (2) discuss the potential growth implications of these species in the context of climate change.
Materials and methodsStudy area and species
Lugu Lake is situated on the border between Yunnan and Sichuan Provinces, China, with geographical coordinates ranging from 100°43’ to 100°50’E and 27°37’ to 27°45’N. This lake extends across Ninglang County and Yanyuan County, which belong to the two aforementioned provinces (Figure 1; Xie et al., 2020). The surrounding area is characterized by tectonic erosion, denudation, and karst landforms, dominated by mid-to-high mountain tectonic erosion terrain with elevations ranging from 2,657 m to over 3,800 m (Wu, 2023). Brown soil is distributed in the area with an elevation of 2,800∼3,600 m, and the main soil types are red-brown soil, brown soil, black limestone soil, and brown felt soil. The soil above 3,600 m above sea level is dark brown, mainly including dark brown soil, humus-accumulated bleached soil, and brown felt soil.
Study area location and sampling sites. (a) Location of Lugu Lake in China; the red rectangle indicates the extent shown in (b). (b) Regional topography (DEM) around Lugu Lake showing lakes, meteorological station, CRU grids, and sampling sites; the red rectangle indicates the extent shown in (c). (c) Satellite image of the Lugu Lake area showing the sampling sites.
Composite map showing the location of Lugu Lake at the Sichuan–Yunnan border in China, with inset maps for regional context and a satellite view. Main map uses color shading for elevation and symbols to indicate lakes, a meteorological station, CRU grids, and sampling sites.
Vertical vegetation zonation is evident in the mountains surrounding Lugu Lake. Along the elevation gradient from the lake surface up to the mountain summits, the vegetation types appear in the following sequential order: cold-temperate mountain sclerophyllous evergreen broad-leaved forests, cold-temperate broad-leaved forests, warm-temperate coniferous forests, cool-temperate coniferous forests, and cold-temperate coniferous forests (Xie, 2020). Below 3,000 m around Lugu Lake, large areas of P. yunnanensis forests are distributed, with local development of Quercus delavayi forests. Between 3,000 and 3,500 m, the landscape is dominated by P. densata, P. yunnanensis, P. likiangensis and A. georgei. Above 3,500 m, Abies delavayi and L. potanini are the dominant species (Sheng, 2015). All three study tree species were dominant species at each sampling site. In the L. potaninii site, rhododendrons (e.g., Rhododendron bureavii and Rhododendron leptothrium), together with Lyonia ovalifolia and Salix takasagoalpina, were the dominant shrubs. The herb layer consisted of Viola delavayi, Saxifraga epiphylla, and Rhodiola yunnanensis. In the shrub layer of the P. likiangensis site, common species included R. bureavii and Rhododendron uvarifolium. The herb layer was dominated by Pteris dactylina, with companion species such as Impatiens delavayi and Triplostegia grandiflora. In the P. yunnanensis site, the shrub layer was dominated by cold- and drought-tolerant alpine oaks (e.g., Quercus monimotricha and Quercus pannosa), with companion species including Rhododendron decorum and Rhododendron racemosum. The herb layer included Argentina lineata, and Sedum yunnanense.
The Lugu Lake area (∼27°N) lies within a southwest monsoon climate zone but, due to its high elevation, exhibits an atypical subtropical climate (Chang et al., 2018). The region experiences abundant sunlight, relatively small annual temperature fluctuations, and distinct wet and dry seasons (Wu, 2023). The wet season spans from June to October, accounting for approximately 89% of the annual precipitation (929.7 mm), while the dry season extends from November to the following May, with January and February receiving minimal rainfall. Based on 1950–2023 data from the Lijiang Meteorological Station, the area has an annual mean temperature of 12.7°C, with maximum and minimum averages of 18.9 and 6.7°C, respectively. In addition, the Palmer Drought Severity Index (PDSI) shows a decreasing trend, suggesting a gradual shift toward drier conditions in the region (Figure 2).
Climate data from 1950 to 2023. *p < 0.05 and **p < 0.01.
Composite figure with three charts: top chart combines monthly precipitation as gray bars (highest in July and August), maximum, mean, and minimum monthly temperatures as red, blue, and green dashed lines, respectively, all peaking in July. Bottom left chart shows annual trends in minimum, mean, and maximum temperatures from 1950 to 2025, each with a slight upward trend. Bottom right chart displays annual precipitation and Palmer Drought Severity Index (PDSI) from 1950 to 2025, indicating limited long-term trend for precipitation and a slight downward trend for PDSI.Climate data
Five climate factors were used in the analysis of climate-growth relationship, including mean minimum temperature (Tmin), mean temperature (Tmean), mean maximum temperature (Tmax), precipitation (Prec), and PDSI. Tmin and Tmax for the Lijiang meteorological station (100.22°E, 26.85°N, elevation 2,380 m) were obtained from the National Oceanic and Atmospheric Administration (NOAA), covering the period 1950–2023. Considering the incomplete and short-term records from nearby meteorological stations, the Lijiang meteorological station was chosen for its long-term and continuous dataset, despite being geographically more distant. Its representativeness for the study area was evaluated and deemed acceptable, making it the most reliable data source available at present. Prec and Tmean datasets were sourced from the Climatic Research Unit (CRUTSv.4.07, https://crudata.uea.ac.uk/cru/data/hrg) with a spatial resolution of 0.5° × 0.5°. PDSI data were derived from the global CRU gridded monthly dataset,1 also at a resolution of 0.5° × 0.5°.
Tree-ring sampling and processing
Tree core sampling was carried out in the Lugu Lake area in May 2020 and March 2024. A total of 168 cores were obtained from 83 individual trees, which covered the three target species in this study (Table 1). Sampling followed standard dendrochronological protocols (Fritts, 2012). Specifically, conifers with minimal anthropogenic disturbance were selected within the study area. Cores were extracted at breast height (1.3 m above ground) using an increment borer with an inner diameter of 5.15 mm, and efforts were made to reach the pith. Each tree was cored 1–3 times from different directions. The three species were sampled from sites with comparable topographic settings (similar elevation and generally south to southeast aspects) to reduce potential non-climatic influences. All cores were placed in plastic straws and labeled with unique identifiers. Following the tree-ring processing methods described by Stokes and Smiley (Stokes and Smiley, 1996), the cores were brought to the laboratory, mounted on custom wooden troughs (1 cm thick, 1.5 cm wide) using white latex with auxiliary fixation via tape, air-dried naturally, and sanded sequentially with grit sandpaper ranging from 240 to 1,200 mesh until tree rings were clearly visible before being scanned using an EPSON Scan (Expression 11000XL) with parameters set to 24-bit full color and 3,000 dpi resolution. The tree ring widths were measured using CDendro and CooRecorder ver 7.3 software with a precision of 0.001 mm (Larsson, 2010), and the measurement results were validated by using the COFECHA program (Holmes, 1983), with poor-quality samples excluded, ultimately retaining 151 cores from 83 trees for the master chronology. Tree-ring chronologies were developed by using the ARSTAN program (Cook, 1986), with a 67% spline function applied to remove the effects of individual tree growth trends. This resulted in three types of chronologies [standard chronology (STD), residual chronology (RES), and autoregressive chronology (ARS)] for each coniferous species, and residual chronologies were used for further analyses to detect climate-growth relationships (Figure 3).
Sampling sites information.
Tree species
Latitude
Longitude
Elevation
Aspect
Slope (°)
No. (tree/core collected)
L. potaninii
27°39’44″N
100°44’15″E
3,623 m
SE
10
30/61
P. likiangensis
27°39’31″N
100°44’35″E
3,670 m
S
9
27/56
P. yunnanensis
27°39’37″N
100°44’29″E
3,575 m
S-SE
7
26/51
Residual tree-ring chronology and sample size.
Three panel line and area chart compares ring width index (line, left axis) and sample depth (shaded, right axis) over time for P. yunnanensis, L. potaninii, and P. likiangensis from eighteen hundred to two thousand ten.Data analysis
Given that tree radial growth exhibited a “lag effect” in response to climate, which means its growth is not only related to climatic factors in the current year but also influenced by those in the previous year (Fritts et al., 1965), such as previous September to December. Considering that the tree growing season typically spans from April to October, correlation analyses were conducted between the tree-ring indices of the three conifers and climatic factors, including Tmin, Tmax, Tmean, Prec, and PDSI from September of the previous year to October of the current year. Additionally, to assess the cumulative effects of climate conditions on tree growth, the same set of climatic variables was analyzed for specific phenological periods: the post growing season (September–October) of the previous year, the early growing season (April–May) of the current year, the growing season (June–August) of the current year, and the post growing season of the current year. Here, “post growing season (September–October)” refers to a late-season period potentially associated with carry-over effects on the subsequent year’s growth, rather than implying direct ring formation after growth cessation. These analyses aimed to explore the correlations between tree growth and climatic factors, as well as to identify the primary limiting factors for tree growth. Response function (RFA) was performed by using DendroClim2002 software (Biondi and Waikul, 2004). The response function first extracted principal components from climatic factors before conducting regression analysis, enabling a more accurate reflection of the extent to which sample data are influenced by environmental variables (Blasing et al., 1984). RDA in CANOCO 5 software (Ter Braak and Smilauer, 2002) was then used to further validate the relationships between tree growth and climatic factors. In total, 70 examined climate variables were included in the RDA, consisting of monthly Tmin, Tmean, Tmax, Prec and PDSI from September of the previous year to October of the current year (14 months × 5 climatic variables). As a multivariate environmental gradient analysis, RDA evaluated the associations between tree radial growth and climatic factors through regression and principal component analysis of chronologies and climatic variables (Ter Braak, 1994). Graphs were generated using Origin 2025, GraphPad Prism 10.1.2, and QGIS 3.34.
ResultsChronology statistics
Based on the analysis of the chronology characteristics and statistical features within the common interval (1950-2020) of the three conifers (Table 2), the chronology lengths of L. potaninii and P. yunnanensis were similar, measuring 121 years and 105 years, respectively, whereas that of P. likiangensis was the longest, reaching 237 years. The residual chronology of L. potaninii showed higher values in terms of mean sensitivity (MS), variance in the first eigenvector (VFE), standard deviation (SD), signal-to-noise ratio (SNR), and expressed population signal (EPS) compared with those of P. likiangensis and P. yunnanensis. This indicated that the residual chronology of L. potaninii was of better quality and contained more environmental information. Although the mean sensitivity (MS) values of the three residual chronologies were relatively low, this was consistent with the relatively favorable hydrothermal conditions in the Lugu Lake region, where tree growth was not strongly constrained by a single climatic factor. Moreover, the high values of EPS (0.89–0.96) and SNR (8.37–25.95) indicated strong common signals and reliable climatic information in the chronologies. Therefore, the residual chronologies were considered robust and suitable for subsequent climate–growth relationship analyses.
Statistics of residual chronologies and common interval analysis.
Residual chronologies
L. potaninii
P. likiangensis
P. yunnanensis
Sample no (trees/cores retained after quality control)
30/61
27/43
26/47
Chronology span/a
1899–2020
1787–2024
1918–2023
Mean sensitivity (MS)
0.13
0.07
0.08
Statistics of common interval analysis
1950–2020
Variance in the first eigenvector/% (VFE)
39.92
29.68
31.10
Standard deviation (SD)
0.12
0.06
0.07
Signal-to-noise ratio (SNR)
25.95
12.56
8.37
Expressed population signal (EPS)
0.96
0.93
0.89
Response function analysis of tree growth to climate
The response function analysis (RFA) between the residual chronologies and climatic factors (Figure 4) showed that significant relationships were identified at p < 0.05. L. potaninii was significantly and negatively correlated with September Tmin of the previous year (p < 0.05), and positively correlated with Tmin from April to May of the current year (p < 0.05). The radial growth of P. likiangensis showed a significant and negative correlation with February PDSI and September Prec of the current year (p < 0.05), while exhibited a significant and positive correlation with September Tmax of the current year (p < 0.05). For P. yunnanensis, a significant and negative correlation was found between its radial growth and January Prec, as well as May Tmean and Tmax of the current year (p < 0.05). Conversely, a significant and positive correlation was observed with November Tmin of the previous year (p < 0.05).
Correlation analyses of response function between the residual chronologies and the 630 monthly climatic factors. p: previous year, *indicates a significant correlation at p < 0.05 based on 631 bootstrap significance tests implemented by DendroClim2002.
Grouped bar chart with three panels compares the monthly correlation coefficients of climate variables—minimum, mean, and maximum temperature, precipitation, and PDSI—for L. potaninii, P. likiangensis, and P. yunnanensis. Significant correlations are indicated by asterisks, with visible month-to-month and species-specific differences.
For the results of correlation analysis between radial growth and growing-season (Table 3), significance was assessed at p < 0.05, and L. potaninii was significantly and negatively correlated with early growing season Prec of the current year (p < 0.05) and showed positive correlations with early growing season Tmin and Tmean (p < 0.05), as well as post growing season Prec and PDSI of the current year (p < 0.05). For P. likiangensis, negative and positive correlations with Tmin were found in the current growing season and post growing season, respectively (p < 0.05). P. yunnanensis exhibited a significant and negative correlation with growing season Prec of the current year (p < 0.05), and a significant and positive correlation with early growing season PDSI of the current year (p < 0.05).
Correlation analyses between the residual chronologies and the climatic factors in 643 growing season.
Growing season
L. potaninii
P. likiangensis
P. yunnanensis
Tmin
Tmean
Tmax
Prec
PDSI
Tmin
Tmean
Tmax
Prec
PDSI
Tmin
Tmean
Tmax
Prec
PDSI
Post growing season of the previous year
–0.086
–0.054
–0.034
–0.024
–0.025
0.013
–0.020
0.023
–0.016
0.036
–0.006
0.046
0.032
0.000
0.004
Early growing season of the current year
0.099*
0.114*
0.017
–0.080*
–0.059
0.053
–0.021
0.058
–0.005
–0.015
–0.002
–0.052
–0.052
0.069
0.117*
Growing season of the current year
0.038
0.016
0.064
0.054
–0.049
–0.123*
0.004
–0.010
–0.021
–0.017
–0.025
0.049
0.050
–0.092*
–0.085
Post growing season of the current year
0.002
–0.009
–0.042
0.072*
0.092*
0.115*
0.039
0.042
–0.035
–0.027
0.051
0.015
0.040
–0.020
–0.027
*Indicates a significant correlation at p < 0.05.
Redundancy analysis of tree growth to climate
The RDA results (Figure 5) showed that the first and second axes collectively explained 19.27% of the variance in the response variables, with the first axis accounting for 11.50% and the second axis for 7.77%. Among the 70 examined climate variables, four factors significantly affected tree growth (p < 0.05), and temperature-related factors outnumbered precipitation-related ones, indicating that temperature exerted a stronger influence on radial growth. September Tmin of the previous year and January Prec of the current year had a similar impact on tree growth, as they showed a significant and negative correlations with three conifers. In contrast, September Tmax of the current year exhibited a significant and positive correlation with three conifers. Current May Tmean had a positive effect on the radial growth of P. likiangensis and L. potaninii, but a negative effect on P. yunnanensis.
The RDA between three chronologies and the climate variables for the common period (1950–2020). Only significant (p < 0.05) climate variables were presented. The longer vector of the climate factor indicated the greater contribution. The shorter perpendicular line between the chronology and the climate vector (itself, or the extension line) indicated a higher correlation between them. Chronology and climate vectors pointing the same direction represented a positive correlation, and in opposite directions, indicated a negative correlation.
Biplot showing three green-labeled points, PY-RES, PL-RES, and LP-RES, distributed across Axis-1 and Axis-2; four blue vectors, labeled Tmax-9, Tmin-p9, Tmean-5, and Prec-1, radiate from the origin to represent environmental variables.DiscussionCommon responses in climate–growth relationships of study species
Previous September Tmin showed a significant negative association with the radial growth of L. potaninii, while the other two species exhibited negative but non-significant trends. These results suggest that the radial growth of L. potaninii may be influenced by both current-year and previous-year temperature conditions, indicating a lag effect (Fritts et al., 1965; Yu et al., 2005). Although trees were in the post growing season, their physiological activities (such as water transport and nutrient accumulation) still persisted (Li et al., 2014). Elevated nighttime temperatures could lead to enhanced respiration and transpiration, this is detrimental to organic matter accumulation. To meet the water demands of transpiration, trees mobilized stored water within their tissues. Thus, increased temperatures exacerbated water stress, this likely slowed tree growth (Wang et al., 2019). Similar results have been reported in tree-ring studies of Picea koraiensis in coniferous forest of northeastern China, as well as in subalpine forests of adjacent Shika Snow Mountain (Zhang et al., 2018; Yuan et al., 2020).
Regarding daytime temperature effects, RDA indicated that current September Tmax was positively associated with radial growth in all three species at the multivariate level. In contrast, RFA revealed a negative but non-significant relationship between September Tmax and the growth of L. potaninii at the monthly scale. Late-season temperature responses in P. yunnanensis was method-dependent. September corresponds to the end of the growing season, during which cambial activity may not have fully ceased in this region. Moderate increases in daytime temperature during this period may favor photosynthesis and carbon assimilation. They may also extend the effective growing season, contributing to the growth variability observed at the multivariate level (Tan et al., 2011). Similar results have been observed in studies of adjacent areas, such as Pudacuo National Park and Small Zhongdian of Shangri-La, and Miyaluo Forest in western Sichuan (Yu et al., 2017; Xu et al., 2013; Zhao et al., 2012).
Given that January precipitation had a significant negative correlation with growth, while temperature showed a positive correlation, this suggests that tree growth was limited by temperature. Winter snowfall was often accompanied by low temperatures. Together with partial freezing of surface soil moisture, these conditions can damage root and impair nutrient uptake. As a result, growth may be reduced and rings become narrower (Zhang et al., 2009; Yue et al., 2022). In addition, increased winter snowfall prolonged spring snowmelt and shortened the tree growth period, thereby restricting radial growth (Vaganov et al., 1999). The negative impacts of winter precipitation on tree growth have also been observed in studies of Larix gmelinii in northern Greater Khingan Mountains of northeastern China and Abies spectabilis in the Manaslu Range of central Himalaya (Sun et al., 2019; Gaire et al., 2020).
Both RDA and RFA results indicated that the number of temperature-related factors exceeded that of precipitation factors, further highlighting the importance of temperature for tree growth. This was consistent with the high sensitivity of alpine tree species to thermal conditions (Zhao et al., 2024).
Divergent responses among species to climatic factors
The temperature (Tmean) in current May promoted the growth of L. potaninii and P. likiangensis, particularly for L. potaninii, which was also positively influenced by Tmin and Tmean during the early growing season (April–May), highlighting the critical role of spring temperatures in tree growth. For L. potaninii, in the early growing season, leaf buds begin to sprout, and xylem cambial cells start to divide and elongate. High temperatures, combined with ample sunlight, create favorable conditions for photosynthesis, thereby increasing the formation of carbohydrates needed for branch development and bud growth (Zhang et al., 2020; Yin et al., 2018). In addition, higher temperatures can increase enzyme activity in plants and promote the activity of soil microorganisms, enabling trees to start growing earlier and have a longer growing season, thereby promoting radial growth (Bhuta et al., 2009; Tang et al., 2018). Spring warming can accelerate the onset of cambial reactivation and advance phenological stages such as budburst and shoot elongation, which are essential for earlywood formation (Rossi et al., 2007; Bhuta et al., 2009). Reports from high-altitude areas such as Haba Snow Mountain and western Sichuan have documented the positive effects of early-season temperature on tree growth (Zhang et al., 2020; Sun et al., 2010).
In contrast, moist conditions during early growing season were crucial for the growth of P. yunnanensis, and higher temperature during this period (particularly in May) inhibited its growth. The radial growth of P. yunnanensis relies on spring moisture conditions, likely due to its high water demand (Shen et al., 2025). PDSI reflected the availability of soil moisture, which directly affected cambial activity and, consequently, xylem formation (Dai et al., 2004; Gruber et al., 2010). P. yunnanensis had a high water demand at the beginning of the growing season to promote the expansion of xylem cells. However, high temperatures and low precipitation during this period (Figure 2) made soil moisture availability a key limiting factor for tree growth (Yang et al., 2018). The sensitivity of P. yunnanensis to spring drought may be attributed to its high water demand during early growth stages and limited surface moisture availability. Although it possesses a relatively deep root system, insufficient early-season precipitation combined with high evaporative demand can restrict water uptake. Additionally, its high stomatal conductance increases water loss through transpiration, exacerbating drought stress under warming conditions (Shen et al., 2025). The negative impact of spring drought on the radial growth of P. yunnanensis has also been reported in other areas of northwestern Yunnan, such as Haba Snow Mountain and Yulong Snow Mountain (Zhang et al., 2020; Yang et al., 2018).
Species-specific responses to climate variability
For L. potaninii, Prec and PDSI in the post growing season positively influenced radial growth (Table 3). Favorable moisture conditions toward the end of the growing season may promote carbon storage, bud formation, root activity, and soil moisture recharge prior to dormancy, thereby exerting carry-over effects on radial growth in the subsequent growing season (Xu et al., 2021).
Elevated temperatures during the growing season (June–August) were detrimental to the growth of P. likiangensis. On the one hand, the growing season had a high water demand. If nighttime temperatures rose without a corresponding increase in precipitation, it would increase tree transpiration, leading to a water balance disorder in the trees, thereby inhibiting the enlargement and division of cambial cells and ultimately affecting radial growth (Lu et al., 2015; Hou et al., 2024). On the other hand, higher nighttime temperatures enhanced respiration, consuming photosynthetic products and thus restricting tree growth (Zhao et al., 2016). Notably, this negative response contrasts with the positive temperature–growth relationships reported for P. likiangensis at nearby high-altitude treelines (e.g., Yulong and Haba Snow Mountains) (Yang et al., 2018; Zhang et al., 2020), suggesting a shift in climatic limiting factors: while warming can benefit colder treelines by extending the growing season (Rossi et al., 2007). At our sites, higher temperatures were more likely to increase evaporative demand and respiratory carbon costs, and may be further amplified by relatively closed-canopy competition for water (Zhang et al., 2009). Additionally, the radial growth of P. likiangensis showed a significant correlation with the entire growing season, but not with individual months (June–August), indicating the importance of the cumulative effect of temperature on the radial growth of P. likiangensis. P. yunnanensis showed a significant and negative correlation with growing-season Prec, possibly because after water accumulation in the early stage, excessive water during the growing season caused anaerobic respiration in the roots, consuming photosynthetic products and producing harmful substances such as alcohol, which affected growth (Kreuzwieser and Rennenberg, 2014). Similar conclusions have been confirmed in previous studies (Zhang et al., 2018; Li et al., 2014).
In this study, the results of RDA and RFA were largely consistent. RDA offered distinct advantages over traditional RFA, as it could more precisely delineate the response patterns, thereby identifying the critical impacts of temperature in September of the previous year, May and September of the current year, and Prec in January on the three species. In the RFA, September Tmin of the previous year exhibited negative correlations with all three species, although statistical significance was only observed for L. potaninii. Similarly, January Prec of the current year showed negative correlations with all three species, but significance was limited to P. yunnanensis, which complicated the generalization of patterns. Additionally, while RFA detected that September Prec of the current year constrained the growth of P. likiangensis, this relationship was not captured by RDA. This discrepancy highlighted both the consistency and complementarity of the two methods. Therefore, their integrated application enabled a more comprehensive and accurate identification of the primary climate factors influencing the radial growth of the three conifers.
Taken together, the results presented above demonstrate pronounced species-specific responses of the three conifers to climate variability, providing a basis for understanding their potential growth responses under future climate change. Building on this, existing climate scenario simulations allow a qualitative discussion of possible growth trajectories and associated adaptation risks. Under the RCP4.5 scenario based on the EC-EARTH model (CMIP5), previous studies suggested that the annual mean temperature in the study area may exhibit an overall upward trend, with relatively pronounced warming in April and May, while precipitation may show a slight decrease (Hazeleger et al., 2010). This model-based information is used here to provide a general climatic context rather than quantitative projections. From a qualitative perspective, reduced precipitation could potentially influence radial growth of the three conifers, particularly for P. likiangensis and P. yunnanensis. For L. potaninii, the growth response to reduced precipitation may be more complex, as potentially beneficial effects associated with reduced moisture stress could partly offset inhibitory effects. The impacts of future warming on the three species may therefore involve both positive and negative effects, potentially favoring L. potaninii and P. likiangensis, while higher temperatures may constrain the growth of P. yunnanensis. In addition, our results indicate that other temperature variables (Tmin and Tmax), especially Tmin, play important roles in regulating conifer growth. Therefore, robust projections of future tree growth would require greater consideration of Tmin and Tmax based on bias-corrected and downscaled CMIP6 multi-model ensembles, which is beyond the scope of the present study. Future research could further utilize CMIP6 multi-model ensembles, combined with various emission pathways, to provide quantitative regional projections of temperature and precipitation fluctuations. Such efforts will be essential for enhancing the precision and reliability of climate risk assessments.
Conclusion
This study shows that co-occurring conifer species in subalpine forests could share common climatic constraints while exhibiting distinct, species-specific growth sensitivities. Overall, temperature-related factors played a dominant role in regulating radial growth, whereas moisture availability exerted additional controls depending on species and season. The study highlights pronounced species-specificity in conifer growth responses to climate, even among co-occurring species. Under ongoing climate change, continued warming may alleviate cold limitation for cold-limited conifers, but it may also increase drought- and heat-related risks for moisture-limited species by intensifying evaporative demand and carbon costs. Adaptive forest management should account for species-specific climate vulnerability. For P. yunnanensis, priorities include using drought-tolerant planting material or companion species, conserving soil moisture, and enhancing dry-season water conservation. For P. likiangensis, avoiding over-thinning and protecting moist microsites may buffer heat and evaporative stress. For L. potaninii, maintaining mixed-species stands and tracking seasonal moisture can support resilience. Therefore, multi-species dendroclimatological assessments provide critical information for evaluating differential vulnerability and informing species-specific conservation and adaptive forest management strategies in mountain ecosystems. Future work integrating additional sites and physiological evidence would further strengthen regional generalization and mechanistic understanding.
Data availability statement
The original contributions presented in this study are included in this article/supplementary material, further inquiries can be directed to the corresponding author.
We are grateful to the Lugu Lake Provincial Nature Reserve Management and Protection Bureau and College of Ecology and Environment (College of Wetlands), Southwest Forestry University, for providing field investigation sampling sites and laboratory facilities for experimental processing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
ReferencesBhutaA. A.KennedyL. M.PedersonN. (2009). Climate-radial growth relationships of northern latitudinal range margin longleaf pine (Pinus palustris P. Mill.) in the Atlantic coastal plain of southeastern Virginia.Tree-Ring Res.65105–115. 10.3959/2008-17.1BiondiF.WaikulK. (2004). DENDROCLIM2002: A C++ program for statistical calibration of climate signals in tree-ring chronologies.Comput. Geosci.30303–311. 10.1016/j.cageo.2003.11.004BlasingT. J.SolomonA. M.DuvickD. N. (1984). Response functions revisited.Tree-Ring Bull.441–15.CailleretM.HeurichM.BugmannH. (2014). Reduction in browsing intensity may not compensate climate change effects on tree species composition in the Bavarian Forest National Park.For. Ecol. Manage328179–192. 10.1016/j.foreco.2014.05.030CanhamC. D.MurphyL. (2016). The demography of tree species response to climate: Sapling and canopy tree growth.Ecosphere7:e01474. 10.1002/ecs2.1474CaoR. J.YinD. C.TianK.XiaoD. R.LiZ. J.ZhangX. G. (2020). Response of radial growth of Abies georgei and Tsuga dumosa to climate change at upper distributional limits on Laojun Mountain, Lijiang, Yunnan, China.Acta Ecol. Sin.406067–6076. 10.5846/stxb201905171019ChangJ.ZhangE. L.LiuE. F.SunW. W.LangdonP. G.ShulmeisterJ. (2018). A 2500-year climate and environmental record inferred from subfossil chironomids from Lugu Lake, southwestern China.Hydrobiologia811193–206. 10.1007/s10750-017-3488-5CookE. R. (1986). Users manual for program ARSTAN.Tree-ring Chronol. West. N. Am. Calif. East. Or. North. Gt. Basin.DaiA. G.TrenberthK. E.QianT. T. (2004). A global dataset of palmer drought severity index for 1870–2002: Relationship with soil moisture and effects of surface warming.J. Hydrometeorol.51117–1130. 10.1175/JHM-386.1DengB. L.HouT. W.TanH. Y.ZhangL.YanT. (2024). Radial growth response of Pinus yunnanensis to Climate Change on Jianhu in Jianchuan, Dali.J. Green Sci. Technol.269–12. 10.3969/j.issn.1674-9944.2024.12.003FrittsH. (1991). Reconstruction Large Scale Climate Patterns from Tree-ring Data.Tucson: University of Arizona Press.FrittsH. (2012). Tree Rings and Climate.Amsterdam: Elsevier.FrittsH. C.SmithD. G.StokesM. A. (1965). The biological model for paleoclimatic interpretation of mesa verde tree-ring series1.Mem. Soc. Am. Archaeol.19101–121. 10.1017/S0081130000004457GaireN. P.FanZ. X.BräuningA.PanthiS.RanaP.ShresthaA. (2020). Abies spectabilis shows stable growth relations to temperature, but changing response to moisture conditions along an elevation gradient in the central Himalaya.Dendrochronologia60:125675. 10.1016/j.dendro.2020.125675GruberA.StroblS.VeitB.OberhuberW. (2010). Impact of drought on the temporal dynamics of wood formation in Pinus sylvestris.Tree Physiol.30490–501. 10.1093/treephys/tpq00320197285HazelegerW.SeverijnsC.SemmlerT.ŞtefãnescuS.YangS.WangX. (2010). EC-Earth: A seamless earth-system prediction approach in action.Bull. Am. Meteorol. Soc.911357–1364. 10.1175/2010BAMS2877.1HolmesR. L. (1983). Computer-assisted quality control in tree-ring dating and measurement.Tree-Ring Bull.4369–78.HouJ. N.ChenF.LiJ. R. (2024). Response of Larix sibirica radial growth to climate change in Kanas, Northern Xinjiang, China.Forests15:2137. 10.3390/f15122137HuX. R. (2007). Computer Automatic Discrimination and Extraction of Tree-ring. Master’s thesis, Nanjing: Nanjing Forestry University.JinX.XuQ.LiuS. R.JiangC. Q. (2013). Responses of the tree-ring of Abies faxoniana and Tsuga chinensis to climate factors in sub-alpine in Western Sichuan.Sci. Silave Sin.4921–26. 10.11707/j.1001-7488.20130104KharalD. K.ThapaU. K.GeorgeS. S.MeilbyH.RayamajhiS.BhujuD. R. (2017). Tree-climate relations along an elevational transect in Manang Valley, central Nepal.Dendrochronologia4157–64. 10.1016/j.dendro.2016.04.004KreuzwieserJ.RennenbergH. (2014). Molecular and physiological responses of trees to waterlogging stress.Plant Cell Environ.372245–2259. 10.1111/pce.1231024611781LarssonL. (2010). CDendro v. 7.3. Cybis Elektronik & Data AB.Sweden: Saltsjöbaden.LiT.HeX. Y.ChenZ. J. (2014). Tree-ring growth responses of Mongolian oak (Quercus mongolica) to climate change in southern Northeast: A case study in Qianshan Mountains.Chin. J. Appl. Ecol.251841–1848. 10.13287/j.1001-9332.2014.01249LiT.SunQ. Q.ZouH. F.MarschnerP. (2024). Climate sensitivity and frought legacy of tree growth in Plantation Forests in Northeast China are species- and age-dependent.Remote Sens.16:281. 10.3390/rs16020281LinX. C.YuS. Q.TangG. L. (1995). Series of average air temperature over China for the last 100-year period.Sci. Atmos. Sin.19532–543. 10.3878/j.issn.1006-9895.1995.05.02LuM.GouX. H.ZhangJ. Z.ZhangF.ManZ. H. (2015). Seasonal radial growth dynamics of Qilian Juniper and its response to environmental factors in the eastern Qilian Mountains.Quat. Sci.351201–1208. 10.11928/j.issn.1001-7410.2015.05.16MaN.JiangJ. H.HouK.LinY.VuT.RosenP. E. (2022). 21st century global and regional surface temperature projections.Earth Space Sci.9:e2022EA002662. 10.1029/2022ea002662Masson-DelmotteV.ZhaiP.PiraniA.ConnorsS. L.PéanC.BergerS. (2021). Climate change 2021: The physical science basis the Working Group I contribution to the Sixth Assessment Report.Intergov. Panel clim. Change2:2391. 10.1017/9781009157896PengM. (2023). Response of Pinus Armandii Radial Growth to Climate Change in Western Funiu Mountains.Master’s thesis, Kaifeng: Henan University.PengX.YangR. Q.YinY. L.ShankarP.XuT. L.FuP. L. (2023). Radial growth response of Pinus densata to climate factors in the Baima Snow Mountain, Northwest Yunnan.Acta Ecol. Sin.438884–8893. 10.27114/d.cnki.ghnau.2023.001426PepinN.BradleyR. S.DiazH. F.BaraerM.CaceresE. B.ForsytheN. (2015). Elevation-dependent warming in mountain regions of the world.Nat. Clim. Change5424–430. 10.1038/nclimate2563RossiS.DeslauriersA.AnfodilloT.CarraroV. (2007). Evidence of threshold temperatures for xylogenesis in conifers at high altitudes.Oecologia1521–12. 10.1007/s00442-006-0625-717165095ScafettaN. (2024). Impacts and risks of “realistic” global warming projections for the 21st century.Geosci. Front.15:101774. 10.1016/j.gsf.2023.101774ShenJ. Y.ZawZ.HuangX. B.ShangR. G.YangR. Q.LiuW. D. (2025). Context-dependent effects of drought severity and climate sensitivity on growth resilience of Pinus yunnanensis in Yunnan, SW China.Clim. Change178:142. 10.1007/s10584-025-03982-9ShenL. Q. (2019). Community Structure and Population Dynamics of Old-Growth Pinus yunnanensis Forest in Mt. Wubao, Yunlong County, Yunnan Province.Master thesis, Kunming: Yunnan University.ShengE. G. (2015). Late Holocene Climatic Changes Recorded at Lake Lugu, Northwestern Yunnan Province and their Links to Global Climate.Master’s thesis, Beijing: The University of Chinese Academy of Sciences.StokesM.SmileyT. (1996). An Introduction to Tree-Ring Dating. Tucson: University of Arizona Press.SunY.WangL. L.ChenJ.DuanJ. P.ShaoX. M.ChenK. L. (2010). Growth characteristics and response to climate change of Larix Miller tree-ring in China.Sci. China Earth Sci.40645–653. 10.1007/s11430-010-0056-5SunZ. J.ZhaoH. Y.ZhuL. J.LiZ. S.ZhangY. D.WangX. C. (2019). Differences in response of Larix gmelinii growth to rising temperature under different precipitation gradients in northern Daxing’an Mountains of northeastern China.J. Beijing For. Univ.411–14. 10.13332/j.1000-1522.20190007TanL. Y.ZhaoZ. J.KangD. W.KangW.LiJ. Q. (2011). A study on the relationship between the radial growth of Picea purpuea and the climatic factors in Wanglang National Nature Reserve.J. Sichuan Agric. Univ.2929–34. 10.3969/j.issn.1000-2650.2011.01.006TangG. Y.ZhangC. H.LiuF. Y.LiK.MaY. (2018). Effects of seasonal asymmetric warming on soil CO2 release in Karst region.Environ. Sci.391962–1970. 10.13227/j.hjkx.20170902129965024Ter BraakC. J. (1994). Canonical community ordination. Part I: Basic theory and linear methods.Ecoscience1127–140. 10.1080/11956860.1994.11682237Ter BraakC. J.SmilauerP. (2002). CANOCO Reference Manual and CanoDraw for Windows User’s Guide: Software for Canonical Community Ordination (version 4.5). Ithaca, NYVaganovE. A.HughesM. K.KirdyanovA. V.SchweingruberF. H.SilkinP. P. (1999). Influence of snowfall and melt timing on tree growth in subarctic Eurasia.Nature400149–151. 10.1038/2208741545452WangH. (2021). The relationship between radial growth of 2 conifer trees and climatic factors in the Haba Snow Mountain.J. Southwest For. Univ.4179–85. 10.11929/j.swfu.202005056WangX. C.PedersonN.ChenZ. J.LawtonK.ZhuC.HanS. J. (2019). Recent rising temperatures drive younger and southern Korean pine growth decline.Sci. Total Environ.6491105–1116. 10.1016/j.scitotenv.2018.08.39330308882WeiW. S.ShangH. M.ChenF. (2013). Review: Data acquisition methods in different periods in climatology study.Adv. Meteorol. Sci. Technol.314–23. 10.3969/j.issn.2095-1973.2013.03.002WuQ. X. (2023). Research on the current situation, changing trends, and countermeasures of the ecological environment of Lugu Lake.Art Des.280–82. 10.61369/art.5942WuX. D. (1990). Tree Rings and Climate Change.New Delhi: Meteorological Press.XieC. S. (2020). Century Temperature Reconstruction and Holocene Climatechange Cycle Analysis in Lugu Lake Area.Master’s thesis, Chengdu: Chengdu University of Technology.XieC. S.LiJ.GaoY. Y.ShiS. L.PengP. H.YangX. (2020). Tree-ring width based autumn and winter mean temperature reconstruction and its variation over the past 137 years in southwestern Sichuan Province.Quat. Sci.40252–263. 10.26986/d.cnki.gcdlc.2020.001099XuH. F.ZhangH. L.ShangH. M.WangY. H.ChenY. P. (2021). Analysis on climate response of Pseudotsuga sinensis tree-rings in Ta-pa Mountains, the Northern Boundary of Natural Belt Distribution.J. Ecol. Rural Environ.37761–768. 10.19741/j.issn.1673-4831.2020.0928XuN.WangX. C.ZhangY. D.LiuS. R. (2013). Climate-growth relationships of Abies faxoniana from different elevations at Miyaluo, western Sichuan. China.Acta Ecol. Sin.333742–3751. 10.5846/stxb201211131594YadavaA. K.SharmaY. K.DubeyB.SinghJ.SinghV.BhutiyaniM. R. (2017). Altitudinal treeline dynamics of Himalayan pine in western Himalaya. India.Quat. Int.44444–52. 10.1016/j.quaint.2016.07.032YangR. Q.FanZ. X.LiZ. S.WenQ. Z. (2018). Radial growth of Pinus yunnanensis at different elevations and their responses to climatic factors in the Yulong Snow Mountain, Northwest Yunnan, China.Acta Ecol. Sin.388983–8991. 10.5846/stxb201805311214YangR. Q.FuP. L.FanZ. X.PanthiS.GaoJ.NiuY. (2022). Growth-climate sensitivity of two pine species shows species-specific changes along temperature and moisture gradients in southwest China.Agric. For. Meteorol.318:108907. 10.1016/j.agrformet.2022.108907YinD. C.XuD. R.TianK.XiaoD. R.ZhangW. G.SunD. C. (2018). Radial growth response of Abies georgei to climate at the upper timberlines in central Hengduan Mountains. Southwestern China.Forests9:606. 10.3390/f9100606YinY. L.ZawZ.PengX. H.ZhangH.FuP. L.WangW. L. (2023). Tree rings in Tsuga dumosa reveal increasing drought variability in subtropical southwest China over the past two centuries.Palaeogeogr. Palaeoclimatol. Palaeoecol.628:111757. 10.1016/j.palaeo.2023.111757YuD. P.WangS. Z.TangL. N.DaiL. M.WangQ. L.WangS. X. (2005). Relationship between tree-ring chronology of Larix olgensis in Changbai Mountains and the climae change.Chin. J. Appl. Ecol.1614–20. 10.13287/j.1001-9332.2005.0378YuJ. L.ZhangW. G.TianK.SongW. H.LiQ. P.YangR. (2017). Response of radial growth of three conifer trees to climate change at their upper distribution limits in Potatso National Park, Shangri-La, southwestern China.J. Beijing For. Univ.3943–51. 10.13332/j.1000-1522.20160184YuanD. Y.ZhaoH. Y.LiZ. S.ZhuL. J.GuoM.ZhangY. D. (2020). Radial growth of Pinus koraiensis and Picea koraiensis response to climate change in Yichun City, Heilongjiang Province.Acta Ecol. Sin.401150–1160. 10.5846/stxb201812292845YuanF. J.YuC. Y. (2013). Distribution pattern and conservation value of Larix potaninii var. macrocarpa forest in Haba Snow Mountain Reserve.For. Inventory Plan.38, 65–68. 10.3969/j.issn.1671-3168.2013.02.014YueH.LiJ.XieS.ChenH.TianK.SunM. (2023). Relationships between climate variability and radial growth of Larix potaninii at the upper altitudinal limit in central Hengduan Mountain. Southwestern China.Forests14:1790. 10.3390/f14091790YueW. P.ChenF.YuanY. J.YuS. L.GaoZ. H.ZhaoX. E. (2022). The decline in radial growth of Larix potaninii in Northwestern Yunnan and its driving factors under the background of climate warming.Acta Ecol. Sinica422331–2341. 10.5846/stxb202011303065ZhangC. Y.GaoL. S.ZhaoY. Z.JiaY. Z.LiJ. X.ZhaoX. H. (2009). Response of radial growth to neighboring competition and climate factors in Taxus cuspidata.Chin. J. Plant Ecol.33:1177. 10.3773/j.issn.1005-264x.2009.06.018ZhangY.CaoR. J.YinJ.TianK.XiaoD. R.ZhangW. G. (2020). Radial growth response of major conifers to climate change on Haba Snow Mountain, Southwestern China.Dendrochronologia60:125682. 10.1016/J.DENDRO.2020.125682ZhangY.YinD. C.SunM.LiL. P.TianK.ZhangW. G. (2018). Radial growth response of two conifers to temperature and precipitation at upper forest limits in Shika Snow Mountain, Northwestern Yunnan Plateau.Acta Ecol. Sin.382442–2449. 10.5846/stxb201703260521ZhaoG. Q.XinZ. B.LiuJ. H.LiuY. L.HuangY. Z.LiZ. S. (2024). Response of radial growth of Sabina squamata to climatic change in the source region of Lancang river.Acta Ecol. Sin.44235–245. 10.20103/j.stxb.202210102868ZhaoZ. J.KangD. W.LiJ. Q. (2016). Age-dependent radial growth responses of Picea purpurea to climatic factors in the subalpine region of Western Sichuan Province, China.Acta Ecol. Sin.36173–179. 10.5846/stxb201409121815ZhaoZ. J.TanL. Y.KangD. W.LiuQ. J.LiJ. Q. (2012). Responses of Picea likiangensis radial growth to climate change in the small zhongdian area of Yunnan province, southwest China.Chin. J. Appl. Ecol.23603–609. 10.13287/j.1001-9332.2012.0081
Edited by: Jinping Liu, North China University of Water Resources and Electric Power, China
Reviewed by: Zhao Peng, Gansu Desert Control Research Institute, China