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Article

Inter- and Intra-Annual Variations in Oak Tree Ring δ13C Values across Different Elevations and Their Climatic Responses in Qinling Mountains

by
Chao Li
1,
Rong Fan
1,*,
Weilong Jiang
2,
Hang Zhang
1,
Xin Li
3,
Feiyu Chen
1 and
Huan Zhang
1,4
1
Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Science, Northwest University, Xi’an 710127, China
2
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
3
Northwest Research Institute of Engineering Investigations and Design, Xi’an 710003, China
4
State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(10), 1228; https://doi.org/10.3390/atmos15101228
Submission received: 28 August 2024 / Revised: 23 September 2024 / Accepted: 9 October 2024 / Published: 15 October 2024
(This article belongs to the Section Biometeorology and Bioclimatology)
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Abstract

:
The Qinling Mountains, serving as a natural geographical and climatic boundary in China, require comprehensive climatic records to elucidate the trends in climate changes across the country. While stable isotopes in tree rings are widely employed to indicate historical environmental changes, investigations into tree ring isotopes in the Qinling Mountains, particularly within the widespread broad-leaf oaks, remain limited. In this study, we investigated both intra- and inter-annual variations in the δ13C values of tree rings and their correlations with climatic signals over the past two decades for Quercus aliena var. acuteserrata, a dominant species among oak trees on the main peak of the Qinling Mountains. Our results reveal that responses to climate differ among altitudes and individual trees, with trees at higher altitudes exhibiting higher sensitivity to extreme climate, which is low temperatures and rainfall fluctuations during the growth period in intra-annual δ13C variations. Furthermore, the positive correlations are observed between temperature during growing season and both tree growth and the inter-annual δ13C variations. However, the climate signal appears to be hampered by oak-specific factors, such as intense competition among individuals and the age of trees. Therefore, we suggest a more rigorous selection of sampling and propose further investigations into isotopic fractionation processes in oaks for future studies.

1. Introduction

The impacts of global climate change are increasingly exacerbating [1,2]. For instance, the occurrence of extreme weather events is increasing, leading to a rise in meteorological disaster risks. Moreover, drought is intensifying globally, especially in certain areas [3,4]. Climatic changes are evident across various regions of China, with the Qinling Mountains and the Huaihe River serving as the geographical boundary between the northern and southern parts of the country. In the past twenty years, there has been a noticeable upward trend in temperatures, with greater fluctuations in the north compared to the south [5]. Regarding precipitation, in some regions, especially in the northwest region, the variation in annual precipitation is quite substantial with a notable increase. Moreover, the variation in seasonal precipitation can be found especially in winter, with a gradual decrease in precipitation in the south and an increase in rainfall in the north. In addition, there have been changes in precipitation patterns, mainly characterized by an increase in average precipitation intensity and a decrease in rainy days [6]. Moreover, the Qinling Mountains act not only as a geographical divide but also as a natural boundary between the subtropical and warm temperate zones, as well as between humid and semi-humid climates. This region is, therefore, a key area for studying climate change in China. Additionally, the diverse and complex ecosystem of the Qinling Mountains makes it an ideal site for investigating the response of land vegetation to climate change.
In recent years, due to the impact of global climate change, the climate in the Qinling Mountains area has undergone drastic changes, especially in the 1990s; during this decade, the changes on the south slope and the north slope were not consistent [7]. After 1993, the temperature transitioned from fluctuating decline to rapid increase [8], with a more noticeable rise on the northern slope compared to the southern slope [9]. The rise in temperature leads to the growth season of plants becoming longer [8]. Alongside the temperature rise, there is a downward trend in precipitation. Consequently, the number of consecutive rainy days, heavy precipitation days, and maximum 5-day rainfall decreased [10]. The decline in precipitation is more gradual on the southern slope compared to the northern slope [11], with a lower precipitation amount recorded on the southern slope [12], accentuating a more apparent drought trend on the northern slope. This reduction in precipitation has led to an increase in extreme drought events and the expansion of drought-prone areas, elevating the drought rate from 17.64% to 38.19% [13]. Influenced by changes in temperature and precipitation in the Qinling Mountains, the water retention capacity has been decreasing year by year, but the decline varies between the northern and southern slopes. Although some watersheds on the southern slope show a slight increase in water retention, the overall trend remains downward [14]. Water retention capacity on the northern slope is declining faster than on the southern slope, and central watersheds are experiencing a more rapid decline compared to those on the outskirts [14]. These pronounced climate changes in the Qinling Mountains have already impacted the ecotope. The potential distribution warm temperate vegetation is continuously expanding, while the distribution areas of temperate, sub-frigid, and alpine vegetation are shrinking [15]. This indicates an upward trend in the altitude of each vegetation type’s distribution area [15]. Therefore, gaining a deeper understanding of the climatic information of the Qinling Mountains region and predicting possible future climate change patterns is essential for forecasting future water environments and biological changes.
The traditional method for acquiring climatic information involves collecting meteorological data through in situ measurements. However, the availability of measured meteorological datasets in the Qinling mountainous areas is limited, given the sparse distribution of stations in the mountainous landscapes, and the relatively short establishment period of these stations. A comparison between climate data from the Climatic Research Unit gridded time series (CRU TS 4.05) [16] and meteorological data from Meixian station reveals obvious differences. For instance, the records of temperature and precipitation from Meixian station are approximately 4 °C higher and 100 mm lower compared to CRU TS data (Figure S1). Consequently, to more accurately assess climate changes in the Qinling regions, it is crucial to identify additional proxies for reconstruction of long-term climate records in situ.
The use of tree rings for climate reconstruction possesses the characteristic of a long time scale and high time resolution, which can record both inter-annual and intra-annual environmental changes. For example, tree-ring-based temperature reconstructions over millennia have aided researchers in understanding climatic variations at local and regional scales [17,18,19,20,21]. Moreover, even if the wood samples are from less extreme sites, the tree ring stable isotope can also exhibit strong climate signals [22,23,24,25]. Consequently, stable isotope dendroclimatology has become a powerful method for reconstructing past climate variations worldwide [26,27,28,29,30]. The growth of trees is influenced by numerous factors, including solar radiation intensity, temperature, precipitation, air humidity, soil and groundwater, and nutrient utilization, as well as melting permafrost [24]. Both tree ring width and tree ring isotope composition serve as indicators for changes in temperature and moisture regimes [31], with fractionation processes during CO2 uptake being crucial for the variation in the δ13C values in plants [32,33]. The carbon isotopic composition in tree rings reflects water availability and the signal of air humidity influenced by climate on photosynthesis. During photosynthesis, several fractionation steps occur. Firstly, atmospheric carbon dioxide diffuses into the cell interstitial space. Secondly, the Rubisco enzyme fixes carbon dioxide. The opening and closing of stomata determine the control of water, with stomata closing under dry conditions to conserve water and opening under moist conditions, also resulting in optimizing carbon assimilation. Changes in leaves’ assimilation rates influence the intercellular CO2 concentration (ci), and alterations in stomatal conductance affect the rate at which internal CO2 (ci) is replenished [33]. Plants discriminate more strongly against 13C under conditions of low ci, when stomata are relatively closed or photosynthesis is high. As trees respond to water stress, particularly under low precipitation and relatively warm and dry conditions by reducing the stomatal conductance and photosynthetic rate, this results in diminishing ci [31,34,35]. While there have been numerous studies on tree rings in the Qinling Mountains area, most have focused on the tree ring widths, with little research on tree ring isotopes. Additionally, when studying tree ring isotopes, there is a lack of attention paid to intra-annual isotopic variations, with a stronger emphasis on inter-annual scale variations.
In this study, we focus on the following questions: (1) compare the differences and similarities in climate response between tree ring width and stable carbon isotope compositions using species of broad-leaf trees at different elevations; (2) explore the implications of stable carbon isotopic compositions in tree rings for climate changes; (3) assessing whether the oak species, Quercus aliena var. acuteserrata, as one of the dominant broad-leave species in the Qinling Mountains, is suitable for local climate reconstruction. To address these questions, we present a combined study of tree ring width and stable carbon isotopic compositions (δ13C values) for latewood from oaks growing on Mount Taibai in the center of Qinling Mountains, China, for the period from 1984 to 2021.

2. Materials and Methods

2.1. Study Area, Meteorological, and Hydrological Conditions

The samples were collected from Mount Taibai (33°49′ N–34°08′ N, 107°41′ E–107°51′ E, at elevations ranging from 1286 to 1538 m a.s.l), the main peak of the Qinling Mountains central China (Figure 1). The Qinling Mountain Range (32°30′ N–34°45′ N, 104°30′ E–112°45′ E) spans 1500 km from east to west, serving as a crucial geographical boundary. Due to its east–west orientation, this mountain range represents a climatic and vegetation distribution boundary in the central mainland of China, effectively separating semi-arid areas from humid regions. Mount Taibai is situated on the northern slope of the Qinling Mountains.
We obtained monthly meteorological data from both the nearest meteorological station, Meixian station (34°18′ N, 107°44′ E), and the gridded dataset (Climate Research Unit, CRU TS 4.05), covering the period from 1970 to 2022 for the research area. The climate variables include mean monthly air temperature (i.e., Tmean), monthly precipitation (i.e., PRE), relative humidity (i.e., RH), and sunshine duration hours (i.e., SSD). Additionally, we estimated monthly vapor pressure deficit (i.e., VPD) based on RH and Tmean as follows [36]
es = 0.611 exp [(17.27 ∗ Tmean)/(Tmean + 237.3)]
ea = RH ∗ es/100
VPD = es − ea
where Tmean represents the mean monthly air temperature (°C), RH (%) represents the mean monthly relative humidity, es represents the water vapor pressure, and ea represents the actual water vapor pressure. The highest temperature typically occurs in July, while the lowest temperature is observed in December. Rainfall is primarily concentrated in the summer and early autumn, comprising approximately 60% of the total precipitation in a year (Figure 2a). This period is also the highest RH (Figure 2b), while late spring and summer experience the most sunlight.
The oak trees, Quercus aliena var. acuteserrata, are one of the predominant broad-leaf tree species growing in the mid-altitude regions of the Qinling Mountains, widely distributed across elevations ranging from 1150 to 2000 m, with the growing season extending from April to October; the soil in the distribution area is primarily Alfisols [37]. Considering the potential variations in the growth environment due to future climate change, it becomes crucial to establish the dendrochronological parameters of Quercus aliena var. acuteserrata, such as tree ring width and stable isotopes, in response to climatic variables.

2.2. Sampling and TRW Chronology

We collected tree ring cores with two cores per tree from 9 Quercus aliena var. acuteserrata oaks, totally 18 cores, at two different altitudes (34°05′ N, 107°42′ E, 1286 m a.s.l, and 34°06′ N, 107°42′ E, 1538 m a.s.l) using a 12 mm increment borer (Haglof, Sweden). Sampling was conducted exclusively on isolated, mature, and healthy individuals. Following standard dendrochronological techniques [38], these trees grew naturally without human interference, the sampled tree cores underwent the following experimentally pre-treated and measured process: (i) natural air-dried cores, (ii) mounting on grooved wooden boards, and (iii) extensive polishing for measuring tree ring widths. Subsequently, each core was marked under a microscope. The tree ring widths (TRW) were measured using an LINTAB 6.0 platform (Rinntech, Heidelberg, Germany) and for cross-dating, the TRW measurements standard dendrochronological methods were applied [39,40]. Final cross-dating of samples was performed both visually and using the TSAP-Win™ dendrochronological software (version 4.69). Quality controlling of dating measurements was ensured using COFECHA [41], a computer program developed by Richard Holmes to detect irregularities in TRW dendrochronology. The results were converted to standardized ring width indices using the ARSTAN_41d program [42].

2.3. Tree Ring Stable Carbon Isotope Analysis

The signal-to-noise ratio of stable isotopes in tree rings is high, allowing relatively few trees to provide a representative average value [43]. To measure carbon isotope values, we selected 4 sample cores (i.e., S1-5 for the lower sampling site, S2-1, S2-2, and S2-3 for the higher sampling site) that exhibited indistinctive width differences, normal growth, fewer missing rings, and clear ring boundaries for isotope analysis [44,45]. Our criteria for sample selection included choosing cores with rings not close to the pith, thereby avoiding potential juvenile effects on the tree ring δ13C values [45]. Due to significant climate changes over the past 20 years, we conducted a study on the inter-annual response of oak trees to climate change for each core, and further analysis of intra-annual variations was also performed within this timespan. Considering that tree rings are typically very narrow (about 1~3 mm), the proportion of earlywood is small, which has already formed in just one month, we focused our measurements solely on the δ13C values of latewood with a forming period of over five months. Studies have indicated that there is no age trend in the stable isotopes of oak trees [46]. Moreover, to observe the intra-annual changes of the δ13C values, the samples were separated into three to six sections (approximately 0.2–0.8 mm thickness, depending on the annual ring width) using a scalpel under a binocular microscope with an internal scale. Subsequently, 100–150 µg sample material was weighed and wrapped in tin capsules. The stable carbon isotope compositions of the samples (whole wood) were determined using an Elemental Analyzer (EA Isolink, Thermo Fisher Scientific) coupled to an Isotope Ratio Mass Spectrometer (MAT253 plus, Thermo Fisher Scientific) at Northwest University, Xi’an, China. Every tenth sample was measured twice for comparison. To ensure analytical accuracy, laboratory standard, which was measured against, and international standard (δ13Ck-Tyr = −26.25‰, δ13Ck-Ala = −23.47‰, and δ13CUSGS40 = −26.39‰) were measured at intervals of 10 samples, verifying that the analytical accuracy was better than 0.1‰.
To identify the primary climatic drivers of tree ring δ13C, we utilized monthly local climate data obtained from Meixian meteorological station and the CRU database (TS 4.05). Pearson’s correlation coefficients (r) between the inter-annual variation of tree ring δ13C values and climate factors were determined using the R (R Core Team, 2022, v. 4.2.1).

3. Results

3.1. Inter-Annual Variability of Tree Ring δ13C Values

The δ13C year series for each selected individual tree ring core were as follows: 10 years for core S1-5, 36 years for core S2-1, 26 years for core S2-2, and 10 years for core S2-3. A total of 296 intra-annual samples were measured for the tree ring δ13C values. The δ13C values ranged from −28.78‰ to −25.02‰, with average δ13C values for the analyzed cores falling between −27.31‰ and −26.07‰ (Table 1).
The curves of δ13C values obtained for the four cores exhibited notable short term differences. The S1-5 curve shows a sharp negative slope after 2012 and displays low values until 2016. The S2-1 curve shows a gentle rise between 1985 and 2000, followed by a negative slope until 2011, then exhibits fluctuations until 2021. The S2-2 curve shows a sharp rise between 1995 and 2005, followed by a negative slope until 2012, with oscillations observed until 2021. After 2012, the S2-1 and S2-2 curves show the same change pattern. The S2-3 curve increases initially and then decreases, with high values observed in 2014 and 2017 (Figure 3).

3.2. Intra-Annual Variability of Tree-Ring δ13C

Variations in δ13C values were observed in intra-annual tree rings of all four oak individuals (Figure 4). The range of intra-annual δ13C values was less pronounced, spanning from −28.67 ‰ to −25.05 ‰ in S1-5, −31.70 ‰ to −25.29 ‰ in S2-1, −28.63 ‰ to −25.90 ‰ in S2-2, and −27.97 ‰ to −24.65 ‰ in S2-3 (Figure 4). An unexpected low δ13C value was found in mid-growing season in 2012 on core S2-1 (−31.70‰, Figure 4b), possibly attributed to competition between trees, as indicated by crown area, and microclimatological variations within the canopy [47]. In all cores, the highest values of δ13C tend to occur between the earlywood and latewood boundary (i.e., approximately in June) or the second half of the latewood, starting to decrease until reaching the late-growing season, approximately in September and October.

3.3. Correlations between Isotopic Composition and Environmental Variables

We found significant (p < 0.05) correlations between inter-annual variations in δ13C values and monthly environmental variables (Tmean, PRE, SSD, RH and VPD) (Figure 5 and Figure 6). The inter-annual variations in δ13C values in core S2-2 were correlated with Tmean (apr, r = 0.47; Mar, r = 0.44; May, r = 0.5; Mar–May, r = 0.56), PRE (aug, r = 0.39), SSD (mar, r = 0.47; aug, r = 0.4; Apr, r = 0.4; May, r = 0.49; Mar–May, r = 0.51), RH (apr, r = −0.4; May, r = −0.45; Mar–May, r = −0.39), and VPD (apr, r = 0.46; May, r = 0.45; Mar–May, r = 0.49) (Figure 5).
The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted. The inter-annual variations in δ13C values in core S2-3 were influenced by temperature of the previous year (Figure 6). The δ13C values in core S2-3 were significantly (p < 0.05) positively correlated with Tmean (mar, r = 0.7; sep, r = 0.71; oct, r = 0.83). In addition to temperature, other climatic variables of the previous year, including SSD (aug, r = 0.72), RH (aug, r = −0.75), and VPD (aug, r = 0.8), also showed correlations with the δ13C values in core S2-3. The primary drivers for the variations in δ13C values in core S2-3 were the climatic conditions during the growing season, with stronger influences in the current year (Figure 6). Similar to the core S2-2, the inter-annual variations in δ13C values in core S2-3 were significantly correlated with Tmean (Feb, r = 0.76; Jul, r = 0.78), PRE (Mar, r = 0.78; Jun, r = 0.75; Jul, r = 0.84; Jun–Aug, r = 0.7), SSD (Jul, r = 0.79; Sep, r = −0.68; Jun–Aug, r = −0.81), RH (Apr, r = 0.68; Jul, r = −0.76; Jun–Aug, r = 0.71), and VPD (Apr, r = −0.73; Jul, r = −0.71; Jun–Aug, r = −0.68).
In this study, we utilized meteorological data from two different sources, the Meixian Meteorological Station and Climate Research Unit (CRU TS 4.05). Tree ring width shows a positive correlation (r = 0.58, p < 0.05) with temperature data from the Meixian Station, but no significant correlation was found with the CRU data. Moreover, a stronger correlation is observed between the δ13C values of oaks and climate data from the Meixian Station compared to the data from CRU (Figure 5 and Figure 6). In contrast to the climatic variables obtained from the Meixian station, the correlation between the climatic data recorded in CRU and Qinling oak trees was not significant, particularly in the case of core S2-2 (Figure 5). These findings suggest that, in climatically complex regions, the use of additional proxies remains necessary for climate reconstruction.

4. Discussion

4.1. Climatic Signals Recorded by the Growth of Oaks in the Qinling Mountains

Numerous studies on the radial growth of several species of oak trees, such as Quercus aliena var. acuteserrata, and Quercus variabilis, suggest that the tree ring widths of oak trees exhibit a robust response to climatic conditions in the Qinling Mountains. For instance, it was found that the tree ring widths of these oak species exhibit strong correlations with various climatic parameters, including explained variance, mean sensitivity, and signal-to-noise ratio at different elevations ranging from 1322 to 1991 m on Mount Taibai of the Qinling Mountains [48]. In our study, the standard chronology of Quercus aliena var. acuteserrata, which consists of six trees, demonstrated a stronger correlation with locally measured temperature data from the Meixian station (r = 0.58, p < 0.05), while no significant correlation was observed with temperature data from CRU. Additionally, an obvious increase in temperature and corresponding tree ring width was observed after 1995 (Figure S2). These findings indicate that the growth of oaks, Quercus aliena var. acuteserrata, can also serve as an indicator of the climatic changes in the Qinling Mountains. However, the expressed population signal (EPS) in our standard chronology did not meet the threshold of 0.85, and the mean sensitivity (MS) was also not sufficiently high. We speculate that the suboptimal EPS and MS could be attributed to intense competition between Quercus aliena var. acuteserrata trees [48], leading to slow growth of suppressed trees and the accumulation of annual rings. This competition is an important limiting factor for oak growth in the Qinling Mountains region. It may also be the case that not all sampled oaks are dominant individuals in sampling sites, or that the sampling cores are generally short. Therefore, for future research on Quercus aliena var. acuteserrata in the Qinling Mountains region, it is advisable to select dominant individuals with longer cores.

4.2. The Relationship between Inter-Annual Tree Ring δ13C Values and Climatic Signals

We reported on the inter-annual and intra-annual stable carbon isotopic compositions in tree rings from oaks growing in the Qinling Mountains. In previous studies [49,50], research on stable isotopes of tree rings in the Qinling Mountains region predominantly focused on coniferous trees, with a primary emphasis on ring width for broad-leaf trees. In our study, we found that the stable carbon isotopic compositions in tree rings of Quercus acutissima (broad-leaf trees) exhibit a positive response to climate variations.
Due to previous studies indicating that trees in high-altitude regions are typically more sensitive to climatic changes [48], and considering the better correlations observed in our study between high-altitude cores and climate factors, particularly temperature, the focus of this study was primarily on cores sampled in high-altitude sites (Figure 5, Figure 6, Figures S3 and S4). For core S2-2 (Figure 5) and core S2-3 (Figure 6), climate factors such as Tmean, SSD, RH, and VPD significantly impact the growth of tree S2-2 during spring (March to May) and summer (July), with temperature showing the strongest influence (r = 0.44–0.71, p < 0.05). However, the two trees exhibit slightly different responses to climate, with S2-2 primarily responding to spring conditions, while S2-3 is more sensitive to summer climate.
These two trees exhibit different responses to the climate, which can be attributed to intense competition between species. According to Qiu’s classification of tree age based on diameter at breast height (DBH) [51], tree S2-3 falls into the middle-aged trees category (25 cm ≤ DBH < 35 cm), while tree S2-2 is categorized as a young tree (15 cm ≤ DBH < 25 cm). Previous studies in the Qinling Mountains region suggest that middle-aged trees possess the strongest adaptation capability to environment [51]. Their crowns are located in the middle and upper layers of the forest canopy, thus they are exposed to receiving more sunlight, thereby enhancing their competitiveness [51]. Consequently, the better response to the climate observed in tree S2-3 compared to tree S2-2 in our study. Compared to tree S1-5 (Figure S3) and tree S2-1 (Figure S4), the correlation between their inter-annual isotopes and monthly climate is more remarkable, which may be attributed to the difference in tree age. We also observed a clear correlation between climate and the oak of tree ring δ13C from the previous year (Figure 6), with a more pronounced effect in dominant individual S2-3. For oaks, a species forming ring porous wood, they utilize stored carbohydrates to form new tissues, transmitting potential climate signals from one year to the next [52,53]. Variations in the amount of reserves used under different climate conditions may affect changes in isotope trends and correlations [52,53].
Our results show a positive correlation between the inter-annual variation of tree ring δ13C values in oak and the temperature during the growing season in the Qinling Mountains. The production and activity of photosynthetic enzymes are primarily influenced by temperature and light intensity, which subsequently affect the rate and efficiency of photosynthesis, resulting in a positive correlation between temperature and plant δ13C values [51,52]. In addition to temperature, we have observed that RH, VPD, and PRE also impact tree ring δ13C values. Changes in precipitation and relative humidity affect the humidity gradient and vapor pressure, which, in turn, influence stomatal conductance and CO2 concentration within intercellular spaces [54,55]. Therefore, it is evident that the stable carbon isotope composition in tree rings reflects the balance between photosynthetic rate and stomatal conductance, which are influenced by temperature and precipitation, respectively [51,52]. Therefore, the stable carbon isotope composition in tree rings reflects the balance between photosynthetic rate and stomatal conductance [24,33]. However, the stronger correlations between tree ring δ13C values and temperature and sunlight, compared to those with precipitation, suggest that the rate of photosynthesis is the main controlling factor for oaks in the Qinling region, rather than stomatal conductance. This is because, in humid regions with minimal water stress, variations in stable carbon isotope signals are primarily driven by summer irradiance and temperature, rather than by precipitation, relative humidity, or soil moisture conditions [24]. As this region is exposed to a temperate monsoon climate with hot and rainy summers, this results in little drought stress for trees. These findings are in agreement with previous studies indicating that in a humid environment where the trees are rarely exposed to moisture stress, the relationship between δ13C variations and the growing season temperature is more closely related than the relationship with air humidity and precipitation [56]. Moreover, in the case of water stress, morphological changes in oak leaves can mitigate the impact of water stress on the plants. The Quercus aliena var. acuteserrata leaves can develop the smaller and higher stomatal density, facilitating a quicker response to changes in environmental factors such as light intensity and water availability [57]. A previous study showed that Quercus aliena var. acuteserrata does not suffer from hydraulic imbalance or carbon starvation, and remains unaffected when rainfall decreases [58]. This could explain the absence of a clear trend in the δ13C values of tree rings in the year 2016 with the highest precipitation amount, or in contrast the years 2014 and 2017 with the lowest precipitation. Temperature and light emerge as the primary influencing factors for the growth of Quercus aliena var. acuteserrata, which explains its stronger correlation with temperature, while precipitation signals may not be clearly reflected in tree ring isotopes.

4.3. The Implications from Intra-Annual Tree Ring δ13C Values of Oaks in Qinling Mountains

For the intra-annual variations in tree ring δ13C values, exceptionally low δ13C values were observed in 2012 at the high-altitude sampling site (1530 m) for all cores, with δ13C values of −31.70 ‰, −28.63 ‰, and −27.97 ‰ (Figure 4). These anomalies in δ13C values during 2012 at the high-altitude sampling site can be attributed to abnormal fluctuations in climate factors. For instance, the low temperature and fluctuating precipitation can be found during that year. In detail, the monthly average temperature in 2012 was lower than that recorded between 2012 and 2020 except for June. Furthermore, there were drastic fluctuations in rainfall during 2012. The precipitation from March to August in 2012 was 8.2, 10.8, 90.9, 31.4, 113.7, and 135.8 mm, with changes of −66%, −80%, +42%, −63%, +35%, +58% compared to the average precipitation of each month from 2012 to 2020. The abnormal fluctuations in temperature and precipitation throughout the year accordingly changed the physiological processes of oak trees, reducing their photosynthetic activities and resulting in a decrease in δ13C values. Additionally, we observed a consistent trend of decreasing δ13C values during the late growth season (i.e., September and October), which may be related to reduced photosynthetic rates in this period because of the increased precipitation and reduced sunshine duration hours [59].

5. Conclusions

In this study, we observed that inter-annual variation in δ13C values is positively influenced by temperature changes, particularly during the growing season (March, May, and July), with this effect being more pronounced at higher altitudes, consistent with previous findings. Additionally, during our analysis of intra-annual δ13C values, we found that extreme fluctuations in rainfall—both increases and decreases—had a more significant impact on these variations than changes in overall precipitation amounts. In 2012, due to the combined effects of low temperatures and fluctuating rainfall, intra-annual δ13C values across all cores were lower than in other years. This suggests that the intra-annual variations in oak tree ring δ13C values are sensitive to extreme climate events in the study area. It is important to note, however, that while correlations between oaks and climate were identified, strong interspecies competition may limit the expression of clear climate signals in individual species and, in some cases, obscure them. Therefore, while oaks can be used to study the effects of extreme climate events on tree growth, a more rigorous sample selection is required when using oaks to reconstruct past climates. It is essential to select middle-aged, well-developed trees and avoid those growing under shaded conditions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos15101228/s1, Figure S1: Comparison of climate data from Meixian Meteorological Station and gridded dataset (Climate Research Unit, CRU TS 4.05); Figure S2: The contrasting trends of tree ring width and temperature; Figure S3: The Pearson’s correlation coefficients of the δ13C values of core S1-5 versus monthly environmental variables; Figure S4: The Pearson’s correlation coefficients of the δ13C values of core S2-1 versus monthly environmental variables.

Author Contributions

C.L. and R.F. conceived the idea; C.L., R.F., W.J., H.Z. (Hang Zhang) and X.L. completed the sampling; C.L., R.F., W.J., H.Z. (Huan Zhang) and F.C. performed the experiment; C.L. and W.J. analyzed the data; C.L. and R.F. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by National Natural Science Foundation of China (Program No. 42307559), Natural Science Basic Research Program of Shaanxi (Program No. 2021JQ-445), the Education Department of Shaanxi Provincial Government (Program No. 22JK0576), and State Key Laboratory of Loess and Quaternary Geology (Program No. SKLLQG2037).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Acknowledgments

We kindly thank Ninglian Wang for his support in all of our lab works. We also thank Shuheng Li, Yili Guo, and Yijie Han for their help in tree ring analysis.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Wheeler, T.; von Braun, J. Climate Change Impacts on Global Food Security. Science 2013, 341, 508–513. [Google Scholar] [CrossRef] [PubMed]
  2. Revesz, R.L.; Howard, P.H.; Arrow, K.; Goulder, L.H.; Kopp, R.E.; Livermore, M.A.; Oppenheimer, M.; Sterner, T. Improve economic models of climate change. Nature 2014, 508, 173–175. [Google Scholar] [CrossRef] [PubMed]
  3. Huang, J.; Ji, M.; Xie, Y.; Wang, S.; He, Y.; Ran, J. Global semi-arid climate change over last 60 years. Clim. Dyn. 2015, 46, 1131–1150. [Google Scholar] [CrossRef]
  4. Piao, S.; Ciais, P.; Huang, Y.; Shen, Z.; Peng, S.; Li, J.; Zhou, L.; Liu, H.; Ma, Y.; Ding, Y.; et al. The impacts of climate change on water resources and agriculture in China. Nature 2010, 467, 43–51. [Google Scholar] [CrossRef]
  5. Nian, W. Spatio-Temporal Distribution of Near Surface Temperature Andits Influencing Forces of China in Recent 20 Year. Master’s Thesis, Shanxi University, China, 2023. [Google Scholar]
  6. Song, W.Q.; Wang, Z.H. Trends and Changes in Temperature, Precipitation, and Water Surplus and Deficit in China over the Last 30 Years. Clim. Environ. Res. 2023, 28, 1–16. [Google Scholar]
  7. Ma, X.; Bai, H.; Fe, H.; LI, P.; Mi, Y. Runoff variation and its influencing factors compared between south and north of Qinling Mountains during 50 years. Arid. Land Geogr. 2013, 36, 1032–1040. [Google Scholar] [CrossRef]
  8. Zhang, Y.; Bai, H.; Huang, X.; Su, K. Variation of Extreme Temperature and Its Impact on Regional Warming in Qinling Mountains During Recent 55 a. Mt. Res. 2018, 36, 23–33. [Google Scholar] [CrossRef]
  9. Zhang, C.; Wang, J.; Li, S.; Hou, L. Multiscale characteristics of the early spring temperature and response to climate indices over the past 179 years in the Qinling Mountains. Iforest—Biogeosciences For. 2022, 15, 491–499. [Google Scholar] [CrossRef]
  10. Meng, Q.; Gao, X.; Bai, H.; Zhang, Y.; Wang, H. Temporal and Spatial Variations and Trends of Extreme Precipitation in Qinling Mountains During the Period 1960–2015. Res. Soil Water Conserv. 2019, 26, 171–178+183. [Google Scholar] [CrossRef]
  11. Liu, H.; Deng, C.; Shao, J.; Guo, Y. Spatiotemporal Variations of Precipitation and the North-South Differences in the Qinling Mountains from 1964 to 2017. Res. Soil Water Conserv. 2021, 28, 210–216+223. [Google Scholar] [CrossRef]
  12. Jiang, C.; Wang, F.; Mu, Y.; Li, Y. Effects of climate change on net primary productivity of vegetation in the northern and southern regions of the Qinling Mountains(I):Temporal and spatial characteristics of climate change in recent 52a. Sci. Soil Water Conserv. 2012, 10, 56–63. [Google Scholar] [CrossRef]
  13. Zhang, S.; Qi, G.; Su, K.; Zhou, L.; Meng, Q.; Bai, H. Changes of drought and flood in the Qinling Mountains in the last 60 years. Acta Ecol. Sin. 2022, 42, 4758–4769. [Google Scholar]
  14. Wang, H.; Song, J.; Wu, Q. Temporal and Spatial Variation Characteristics of Water Conservation Function of Qinling Mountains Under the Background of Climate Change. J. Soil Water Conserv. 2022, 36, 212–219. [Google Scholar] [CrossRef]
  15. Zhao, T.; Bai, H.; Li, J.; Ma, Q.; Wang, p. Changes of vegetation potential distribution pattern in the Qinling Mountains in Shaanxi Province in the context of climate warming. Acta Ecol. Sin. 2023, 43, 1843–1852. [Google Scholar]
  16. Harris, I.; Osborn, T.J.; Jones, P.; Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 2020, 7, 109. [Google Scholar] [CrossRef]
  17. Moberg, A.; Sonechkin, D.M.; Holmgren, K.; Datsenko, N.M.; Karlén, W.; Lauritzen, S.-E. Erratum: Corrigendum: Highly variable Northern Hemisphere temperatures reconstructed from low- and high-resolution proxy data. Nature 2006, 439, 1014. [Google Scholar] [CrossRef]
  18. Cook, E.R.; Anchukaitis, K.J.; Buckley, B.M.; D’Arrigo, R.D.; Jacoby, G.C.; Wright, W.E. Asian Monsoon Failure and Megadrought During the Last Millennium. Science 2010, 328, 486–489. [Google Scholar] [CrossRef]
  19. Briffa, K.R.; Shishov, V.V.; Melvin, T.M.; Vaganov, E.A.; Grudd, H.; Hantemirov, R.M.; Eronen, M.; Naurzbaev, M.M. Trends in recent temperature and radial tree growth spanning 2000 years across northwest Eurasia. Philos. Trans. R. Soc. B Biol. Sci. 2007, 363, 2269–2282. [Google Scholar] [CrossRef]
  20. Esper, J.; Cook, E.R.; Schweingruber, F.H. Low-frequency signals in long tree-ring chronologies for reconstructing past temperature variability. Science 2002, 295, 2250–2253. [Google Scholar] [CrossRef]
  21. Liu, Y.; An, Z.; Linderholm, H.W.; Chen, D.; Song, H.; Cai, Q.; Sun, J.; Tian, H. Annual temperatures during the last 2485 years in the mid-eastern Tibetan Plateau inferred from tree rings. Sci. China Ser. D Earth Sci. 2009, 52, 348–359. [Google Scholar] [CrossRef]
  22. Cernusak, L.A.; English, N.B. Beyond tree-ring widths: Stable isotopes sharpen the focus on climate responses of temperate forest trees. Tree Physiol. 2015, 35, 1–3. [Google Scholar] [CrossRef] [PubMed]
  23. Hartl-Meier, C.; Zang, C.; Buentgen, U.; Esper, J.; Rothe, A.; Goettlein, A.; Dirnboeck, T.; Treydte, K. Uniform climate sensitivity in tree-ring stable isotopes across species and sites in a mid-latitude temperate forest. Tree Physiol. 2015, 35, 4–15. [Google Scholar] [CrossRef] [PubMed]
  24. McCarroll, D.; Loader, N.J. Stable isotopes in tree rings. Quat. Sci. Rev. 2004, 23, 771–801. [Google Scholar] [CrossRef]
  25. Treydte, K.; Frank, D.; Esper, J.; Andreu, L.; Bednarz, Z.; Berninger, F.; Boettger, T.; D’Alessandro, C.M.; Etien, N.; Filot, M.; et al. Signal strength and climate calibration of a European tree-ring isotope network. Geophys. Res. Lett. 2007, 34, L24302. [Google Scholar] [CrossRef]
  26. Szymczak, S.; Joachimski, M.M.; Bräuning, A.; Hetzer, T.; Kuhlemann, J. Are pooled tree ring δ13C and δ18O series reliable climate archives?—A case study of Pinus nigra spp. laricio (Corsica/France). Chem. Geol. 2012, 308–309, 40–49. [Google Scholar] [CrossRef]
  27. Loader, N.J.; Santillo, P.M.; Woodman-Ralph, J.P.; Rolfe, J.E.; Hall, M.A.; Gagen, M.; Robertson, I.; Wilson, R.; Froyd, C.A.; McCarroll, D. Multiple stable isotopes from oak trees in southwestern Scotland and the potential for stable isotope dendroclimatology in maritime climatic regions. Chem. Geol. 2008, 252, 62–71. [Google Scholar] [CrossRef]
  28. Woodley, E.J.; Loader, N.J.; McCarroll, D.; Young, G.H.F.; Robertson, I.; Heaton, T.H.E.; Gagen, M.H. Estimating uncertainty in pooled stable isotope time-series from tree-rings. Chem. Geol. 2012, 294–295, 243–248. [Google Scholar] [CrossRef]
  29. Liu, Y.; Ma, L.M.; Leavitt, S.W.; Cai, Q.F.; Liu, W.G. A preliminary seasonal precipitation reconstruction from tree-ring stable carbon isotopes at Mt. Helan, China, since AD 1804. Glob. Planet. Change 2004, 41, 229–239. [Google Scholar] [CrossRef]
  30. Liu, X.; Shao, X.; Wang, L.; Liang, E.; Qin, D.; Ren, J. Response and dendroclimatic implications of δ13C in tree rings to increasing drought on the northeastern Tibetan Plateau. J. Geophys. Res. Biogeosci. 2008, 113. [Google Scholar] [CrossRef]
  31. Liu, X.; Shao, X.; Wang, L.; Zhao, L.; Wu, P.; Chen, T.; Qin, D.; Ren, J. Climatic significance of the stable carbon isotope composition of tree-ring cellulose: Comparison of Chinese hemlock (Tsuga chinensis Pritz) and alpine pine (Pinus densata Mast) in a temperate-moist region of China. Sci. China Ser. D Earth Sci. 2007, 50, 1076–1085. [Google Scholar] [CrossRef]
  32. Leavitt, S.W.; Chase, T.N.; Rajagopalan, B.; Lee, E.; Lawrence, P.J. Southwestern U.S. tree-ring carbon isotope indices as a possible proxy for reconstruction of greenness of vegetation. Geophys. Res. Lett. 2008, 35. [Google Scholar] [CrossRef]
  33. Farquhar, G.D.; Ehleringer, J.R.; Hubick, K.T. Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Biol. 1989, 40, 503–537. [Google Scholar] [CrossRef]
  34. Scheidegger, Y.; Saurer, M.; Bahn, M.; Siegwolf, R. Linking stable oxygen and carbon isotopes with stomatal conductance and photosynthetic capacity: A conceptual model. Oecologia 2000, 125, 350–357. [Google Scholar] [CrossRef] [PubMed]
  35. Siegwolf, R.T.W.; Lehmann, M.M.M.; Goldsmith, G.R.R.; Churakova, O.V.V.; Mirande-Ney, C.; Timoveeva, G.; Weigt, R.B.B.; Saurer, M. Updating the dual C and O isotope-Gas-exchange model: A concept to understand plant responses to the environment and its implications for tree rings. Plant Cell Environ. 2023, 46, 2606–2627. [Google Scholar] [CrossRef]
  36. Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao Rome 1998, 300, D05109. [Google Scholar]
  37. Chang, Q.; Lei, M.; Feng, L.; Ming, X. Genetic characteristics and taxonomy of soils on the northern slope of the qinling mountain. Acta Pedol. Sin. 2002, 39, 227–235. [Google Scholar]
  38. Fritts, H.C. Dendroclimatology and dendroecology. Quat. Res. 1971, 1, 419–449. [Google Scholar] [CrossRef]
  39. Fan, Y.; Shang, H.; Wu, Y.; Li, Q. Tree-Ring Width and Carbon Isotope Chronologies Track Temperature, Humidity, and Baseflow in the Tianshan Mountains, Central Asia. Forests 2020, 11, 1308. [Google Scholar] [CrossRef]
  40. Cook, E.R.; Kairiukstis, L.A. Methods of Dendrochronology: Applications in the Environmental Sciences; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2013. [Google Scholar]
  41. Nakatsuka, T.; Ohnishi, K.; Hara, T.; Sumida, A.; Mitsuishi, D.; Kurita, N.; Uemura, S. Oxygen and carbon isotopic ratios of tree-ring cellulose in a conifer-hardwood mixed forest in northern Japan. Geochem. J. 2004, 38, 77–88. [Google Scholar] [CrossRef]
  42. Finaev, A.F. The model of dust aerosol accumulation in Tajikistan. Geogr. Environ. Sustain. 2014, 7, 97–107. [Google Scholar] [CrossRef]
  43. McCarroll, D.; Pawellek, F. Stable carbon isotope ratios of latewood cellulose in Pinus sylvestris from northern Finland: Variability and signal-strength. Holocene 1998, 8, 675–684. [Google Scholar] [CrossRef]
  44. Foroozan, Z.; Grießinger, J.; Pourtahmasi, K.; Bräuning, A. Evaluation of Different Pooling Methods to Establish a Multi-Century δ18O Chronology for Paleoclimate Reconstruction. Geosciences 2019, 9, 270. [Google Scholar] [CrossRef]
  45. Leavitt, S.W. Tree-ring isotopic pooling without regard to mass: No difference from averaging δ13C values of each tree. Chem. Geol. 2008, 252, 52–55. [Google Scholar] [CrossRef]
  46. Büntgen, U.; Kolář, T.; Rybníček, M.; Koňasová, E.; Trnka, M.; Ač, A.; Krusic, P.J.; Esper, J.; Treydte, K.; Reinig, F.; et al. No Age Trends in Oak Stable Isotopes. Paleoceanogr. Paleoclimatology 2020, 35, e2019PA003831. [Google Scholar] [CrossRef]
  47. Skomarkova, M.V.; Vaganov, E.A.; Mund, M.; Knohl, A.; Linke, P.; Boerner, A.; Schulze, E.D. Inter-annual and seasonal variability of radial growth, wood density and carbon isotope ratios in tree rings of beech (Fagus sylvatica) growing in Germany and Italy. Trees-Struct. Funct. 2006, 20, 571–586. [Google Scholar] [CrossRef]
  48. Huang, Y. Environmental Responses Analysis of Radial Growth of Quercus aliena var. Acutiserrata and Quercus variabilis in Qinling Mountains. Master’s Thesis, Chinese Academy of Forestry, China, 2020. [Google Scholar]
  49. Zhu, N.; Chen, H.; Zhang, Q.; Lin, Q.; Liu, S. Responses of δ13C in Pinus tabuliformis Tree Rings to the Changes of Climatic Factors in the Qinling Mountains. J. Northwest For. Univ. 2019, 34, 21–27. [Google Scholar]
  50. Liu, Y.; Wang, Y.; Li, Q.; Song, H.; Linderhlom, H.W.; Leavitt, S.W.; Wang, R.; An, Z. Tree-ring stable carbon isotope-based May-July temperature reconstruction over Nanwutai, China, for the past century and its record of 20th century warming. Quat. Sci. Rev. 2014, 93, 67–76. [Google Scholar] [CrossRef]
  51. Qiu, J.; Han, A.; He, C.; Yi, Q.; Jia, S.; Luo, Y.; Li, C.; Hao, Z. Spatial distribution pattern and intraspecific association of dominant species Quercus aliena var. acutiserrata in Qinling Mountains, China.serrata. Chin. J. Appl. Ecol. 2022, 33, 2035–2042. [Google Scholar] [CrossRef]
  52. Reynolds-Henne, C.E.; Siegwolf, R.T.W.; Treydte, K.S.; Esper, J.; Henne, S.; Saurer, M. Temporal stability of climate-isotope relationships in tree rings of oak and pine (Ticino, Switzerland). Glob. Biogeochem. Cycles 2007, 21, GB4009. [Google Scholar] [CrossRef]
  53. Pilcher, J.R. Biological considerations in the interpretation of stable isotope ratios in oak tree-rings. Paläoklimaforschung 1995, 15, 157–161. [Google Scholar]
  54. Jia, Y.; Lv, G.; Guligena, H.; Qin, L.; Peng, Z.; Abudureheman, R.; Zhang, R. Differences in the Responses of Tree-Ring Stable Carbon Isotopes of L. sibirica and P. schrenkiana to Climate in the Eastern Tianshan Mountains. Forests 2023, 14, 1032. [Google Scholar] [CrossRef]
  55. McCarroll, D.; Pawellek, F. Stable carbon isotope ratios of Pinus sylvestris from northern Finland and the potential for extracting a climate signal from long Fennoscandian chronologies. Holocene 2001, 11, 517–526. [Google Scholar] [CrossRef]
  56. Seftigen, K.; Linderholm, H.W.; Loader, N.J.; Liu, Y.; Young, G.H.F. The influence of climate on 13C/12C and 18O/16O ratios in tree ring cellulose of Pinus sylvestris L. growing in the central Scandinavian Mountains. Chem. Geol. 2011, 286, 84–93. [Google Scholar] [CrossRef]
  57. Lawson, T.; Blatt, M.R. Stomatal Size, Speed, and Responsiveness Impact on Photosynthesis and Water Use Efficiency. Plant Physiol. 2014, 164, 1556–1570. [Google Scholar] [CrossRef] [PubMed]
  58. Cheng, Z.; Lu, H.; Liu, S.; Liu, X.; Liu, C.; Wang, X. The responses of hydraulic architecture and growth of Quercus aliena to rainfall reduction. Acta Ecol. Sin. 2018, 38, 2405–2413. [Google Scholar]
  59. Eglin, T.; Francois, C.; Michelot, A.; Delpierre, N.; Damesin, C. Linking intra-seasonal variations in climate and tree-ring δ13C: A functional modelling approach. Ecol. Model. 2010, 221, 1779–1797. [Google Scholar] [CrossRef]
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Figure 1. Location of the sampling site and Meixian meteorological station. The red dot indicates the specific location of the sampling area.
Figure 1. Location of the sampling site and Meixian meteorological station. The red dot indicates the specific location of the sampling area.
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Figure 2. Average monthly meteorological data of the Meixian station from 1970 to 2022. (a) Precipitation (mm) and temperature (°C); (b) relative humidity (%) and sunshine duration hours (h).
Figure 2. Average monthly meteorological data of the Meixian station from 1970 to 2022. (a) Precipitation (mm) and temperature (°C); (b) relative humidity (%) and sunshine duration hours (h).
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Figure 3. The inter-annual series of δ13C values for four tree ring cores, S1-5 (blue line), S2-1 (red line), S2-2 (brown line), and S2-3 (purple line).
Figure 3. The inter-annual series of δ13C values for four tree ring cores, S1-5 (blue line), S2-1 (red line), S2-2 (brown line), and S2-3 (purple line).
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Figure 4. The intra-annual variations of δ13C values in tree ring cores from 2012 to 2020; (a) core S1-5; (b) core S2-1; (c) core S2-2; (d) core S2-3.
Figure 4. The intra-annual variations of δ13C values in tree ring cores from 2012 to 2020; (a) core S1-5; (b) core S2-1; (c) core S2-2; (d) core S2-3.
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Figure 5. The Pearson’s correlation coefficients of the δ13C values of core S2-2 versus monthly environmental variables. Only significant correlations (p < 0.05) are shown. On the x-axis, lowercase and uppercase letters indicate the month of the previous and current year, respectively. The climate variables include monthly mean temperature, precipitation, sunshine duration hours, relative humidity, and vapor pressure deficit.
Figure 5. The Pearson’s correlation coefficients of the δ13C values of core S2-2 versus monthly environmental variables. Only significant correlations (p < 0.05) are shown. On the x-axis, lowercase and uppercase letters indicate the month of the previous and current year, respectively. The climate variables include monthly mean temperature, precipitation, sunshine duration hours, relative humidity, and vapor pressure deficit.
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Figure 6. The Pearson’s correlation coefficients of the δ13C values of core S2-3 versus monthly environmental variables. Only significant correlations (p < 0.05) are shown. On the x-axis, lowercase and uppercase letters indicate the month of the previous and current year, respectively. The climate variables include monthly mean temperature, precipitation, sunshine duration hours, relative humidity, and vapor pressure deficit.
Figure 6. The Pearson’s correlation coefficients of the δ13C values of core S2-3 versus monthly environmental variables. Only significant correlations (p < 0.05) are shown. On the x-axis, lowercase and uppercase letters indicate the month of the previous and current year, respectively. The climate variables include monthly mean temperature, precipitation, sunshine duration hours, relative humidity, and vapor pressure deficit.
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Table 1. Characteristics of the δ13C time series.
Table 1. Characteristics of the δ13C time series.
Codeδ13C Series Time Span (A.D.)δ13C Range (‰)Average (‰)
S1-52011–2020−28.36~−25.38−26.50
S2-11985–2021−27.30~−25.04−26.07
S2-21994–2020−28.78~−25.96−27.31
S2-32012–2021−27.17~−25.02−26.12
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Li, C.; Fan, R.; Jiang, W.; Zhang, H.; Li, X.; Chen, F.; Zhang, H. Inter- and Intra-Annual Variations in Oak Tree Ring δ13C Values across Different Elevations and Their Climatic Responses in Qinling Mountains. Atmosphere 2024, 15, 1228. https://doi.org/10.3390/atmos15101228

AMA Style

Li C, Fan R, Jiang W, Zhang H, Li X, Chen F, Zhang H. Inter- and Intra-Annual Variations in Oak Tree Ring δ13C Values across Different Elevations and Their Climatic Responses in Qinling Mountains. Atmosphere. 2024; 15(10):1228. https://doi.org/10.3390/atmos15101228

Chicago/Turabian Style

Li, Chao, Rong Fan, Weilong Jiang, Hang Zhang, Xin Li, Feiyu Chen, and Huan Zhang. 2024. "Inter- and Intra-Annual Variations in Oak Tree Ring δ13C Values across Different Elevations and Their Climatic Responses in Qinling Mountains" Atmosphere 15, no. 10: 1228. https://doi.org/10.3390/atmos15101228

APA Style

Li, C., Fan, R., Jiang, W., Zhang, H., Li, X., Chen, F., & Zhang, H. (2024). Inter- and Intra-Annual Variations in Oak Tree Ring δ13C Values across Different Elevations and Their Climatic Responses in Qinling Mountains. Atmosphere, 15(10), 1228. https://doi.org/10.3390/atmos15101228

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