Responses of Tree Growth and Intrinsic Water Use Efficiency to Climate Factors and Human Activities in Upper Reaches of Tarim River in Alaer, Xinjiang, China

Abstract

With global warming and increasing human activities, exploring the impact of the rising atmospheric carbon dioxide concentration and climate change on forest ecosystems is crucial. In this study, we focus on Euphrates poplar (Populus euphratica Oliv.) in the upper reaches of the Tarim River in the Alaer region of Xinjiang. We use dendrochronological methods, tree-ring width, and stable carbon isotope series to explain basal area increment (BAI) and intrinsic water use efficiency (iWUE) changes. We further explore the influence of past climate change and human activities on the radial growth and iWUE of P. euphratica through stable oxygen isotope analysis combined with historical literature records. The results showed that relative humidity had an essential effect on Δ¹³C and δ¹⁸O fractionation in P. euphratica tree rings, whereas the vapor pressure deficit (VPD) was considered the main factor influencing the inter-annual variability of the iWUE and BAI. Since 1850, long-term variations in iWUE have exhibited an upward trajectory correlated with rising atmospheric CO₂ levels. Approximately 13% of this iWUE increase can be attributed to changes in carbon-concentration-induced water use efficiency (^cciWUE). Although Δ¹³C and δ¹⁸O were generally uncorrelated between 1850 and 2018, around 1918, their relationship changed from being weakly correlated to being significantly negatively correlated, which may record changes related to the upstream Tarim River diversion. During the period from 1850 to 2018, both the BAI and iWUE showed an increasing trend for P. euphratica growth; however, the relationship between them was not stable: during 1850–1958, both variables were mainly influenced by climatic factors, while during 1959–2018, the most important influence was due to human activities, specifically agricultural development and irrigation diversions. An abrupt surge in the BAI was observed from 1959 to 1982, reaching its peak around 1982. Surprisingly, post-1983, the escalating iWUE did not correspond with a continuation of this upward trajectory in the BAI, highlighting a divergence from the previous trend where the enhanced iWUE no longer facilitated the growth of P. euphratica. Despite P. euphratica having adapted to the continuously rising C_a, improving its iWUE and growth capacity, this adaptive ability is unstable and may easily be affected by human activities. Overall, the increase in C_a has increased the iWUE of P. euphratica and promoted its growth at a low frequency, while human activities have promoted its development at a high frequency.

1. Introduction

Forests play a crucial role in terrestrial ecosystems’ carbon and water cycles, making it essential to understand how they respond to global change [1,2]. Human-induced CO₂ emissions over the past century have led to global warming and directly impacted land plants, significantly affecting their physiological and ecological processes [3,4,5]. Tree rings offer reliable information on age and environmental conditions due to their strong continuity and high resolution [6], as well as unequivocal traces of natural and anthropogenic disturbances [7]. By examining changes in tree rings, variations in environmental conditions can be inferred, allowing for an assessment of their impact on ecosystems [8]. Climate change affects tree rings’ width and isotopic composition during growth. Carbon discrimination provides valuable information reflecting the ratio of internal leaf CO₂ (C_i) to ambient atmospheric CO₂ (C_a), along with intrinsic water use efficiency (iWUE) [9].

Meanwhile, the δ¹⁸O value of tree rings is mainly influenced by the δ¹⁸O source water and the enrichment of leaf water through evaporation [10], and, combined with δ¹³C, can reveal dynamic changes in the stomatal conductance and photosynthetic rate over time [11,12]. Changes in the iWUE may affect tree growth. When the iWUE increases, trees can assimilate more carbon per unit of water lost through transpiration, influenced by factors such as stomatal behavior and the vapor pressure deficit. However, the relationship between an increased iWUE and tree growth is complex, and influenced by various factors such as climate variability and plant functional types [13]. Some studies suggest that increasing iWUE does indeed promote tree growth [14,15,16], while others find that it does not necessarily lead to increased growth rates [17,18,19] because other limiting factors also affect tree growth, such as fertilization effects [20] or negative impacts from droughts that cannot be offset by an increased iWUE alone [21]. Sudden increases in iWUE may even serve as an early alert signal of an upcoming drought-induced mortality event [22].

Forests are under threat from various human activities. Over the past few decades, there has been a significant impact on the change in vegetation cover due to human activity [23]. Climate change caused by human activities can positively or negatively affect trees. While it may enhance hydrological cycles, it can also reduce rainfall during dry seasons [24]. Human actions at forest edges have altered microclimates, affecting tree growth in those areas [25]. Air pollution is another primary concern that significantly changes plant nutrition and soil chemical properties by deposing pollutants such as sulfur and nitrogen [26,27,28,29,30], leading to a potential forest decline. Unsustainable practices like land reclamation, road expansion, runoff regulation, and water diversion irrigation can negatively affect tree growth [31,32].

The Tarim River Basin is important in northwest China, where P. euphratica is a typical tree species. Desert riparian forests are major biomass generators in the inland river systems of arid regions and are well-adapted to long-term environmental conditions within these basins, making them an essential component of arid ecosystems. These forests have unique water-use strategies for survival, making them a hot topic among domestic and international ecologists. Researchers have studied stable isotopes tracing sources of P. euphratica’s water supply since the 1950s [33]. They have also studied ecological effects after artificial watering [34,35,36], and the relationship between the radial increment rate and groundwater level changes [34,37]. However, there has been little research on the comprehensive impacts of climate change or increases in atmospheric CO₂ concentrations, iWUE, or δ¹³C for P. euphratica [38].

The Alaer region upstream from the Tarim River Basin holds excellent potential for using tree rings to study responses to paleoclimates and human activities’ environmental impact since agricultural development began in the 1950s, creating new oasis farmland amidst Gobi Desert wildernesses [39]. There has been no report regarding iWUE changes in the study area and its potential impact on tree growth. Therefore, this study focuses on P. euphratica in the Alaer area upstream from the Tarim River drainage to establish tree-ring width sequences, δ¹³C sequences, δ¹⁸O sequences, and iWUE sequences. The aim was to analyze how the increasing atmospheric carbon dioxide concentration (C_a) and human activities affect the BAI and iWUE of P. euphratica by comparing historical BAIs with those of δ¹³C, δ¹⁸O, and the iWUE of P. euphratica. Examining the intrinsic water use efficiency of P. euphratica can help us to understand the water-use strategy of desert riparian forests, as well as provide data and theoretical support for their conservation.

2. Materials and Methods

2.1. Study Area and Sampling Location

Located on the south bank of the upper alluvial plain of the Tarim River (longitude 80°30′–81°58′ E; latitude 40°22′–40°57′ N), the sampling site is situated at the northwest edge of the Taklamakan Desert and the eastern end of the Tarim Reclamation Area of the First Agricultural Division. The altitude at this location is 1012 m (Figure 1). This area belongs to a typical temperate continental arid climate zone with little rainfall. The Tarim River is this region’s primary irrigation water source, mainly supplied by glaciers and snow accumulation. The groundwater flows from west to east, consistent with the Tarim River’s direction. The vegetation primarily consists of shrubs, grassland, and alpine forests dominated by P. euphratica, while the soil types are predominantly desert brown soils and saline soils. According to the International Tree Ring Data Bank’s sampling criteria, samples were collected from 25 living trees. Each tree was cored twice in different directions using a 5 mm internal diameter borer. This group of samples is referred to as the ALE.

2.2. Tree Ring Analysis

During sampling, healthy and older trees were selected. The collected cores were returned to the laboratory, where they were dried and sanded until the annual rings became visible. LINTAB 6 was used to measure the core widths with an approximate precision of 0.01 mm, followed by cross-dating using TSAPwin software version 4.81 (Rinntech, Heidelberg, Germany) and verification with the COFECHA program for accuracy [40]. The resulting ring-width series were detrended and normalized into a chronology with Mod Neg Exp in the “dplR” package in R language [41].

For analysis purposes, we converted the ring width into the basal area increment (BAI) while minimizing errors by selecting fifteen trees with piths over one hundred years old for BAI calculation; individual BAI values were calculated before averaging them into a regional BAI sequence. Based on our research objectives and the length of stable carbon isotope sequences in tree rings, we chose the period between 1850 and 2018 as our study period. The BAI was calculated by assuming that the cross-section of a tree trunk is circular and converting the width values of each annual ring into the corresponding BAI values. The formula for the basal area increment is as follows: BAI = π(r²_t − r²_t−1), where r_t represents the growth radius of the basal section in year t, r_t−1 represents the growth radius of the basal section in year t − 1, and BAI represents the basal area increment in year t. The BAI was calculated using the “R” language [42], utilizing the “bai. out” command for computation.

2.3. The δ¹³C and δ¹⁸O Analyses

Based on accurate dating, four samples (ALE-03A, ALE-15A, ALE-26B, and ALE-54B) with clear tree rings, no fractures, and no contamination were selected for stable isotope measurement. Each annual ring was separated with a fine and sharp knife under a microscope. To reduce variation in the isotope signatures, we used the extracted alfa-cellulose in this study [43]. The oxygen isotope ratios were measured using a Delta V Advantage mass spectrometer coupled with a high-temperature pyrolysis elemental analyzer (TC/EA). Fibrous cellulose samples weighing 0.12–0.16 mg were placed in silver cups, with every eight samples inserted into one standard sample. An autosampler automatically pushed each packaged sample into the high-temperature pyrolysis elemental analyzer. We selected MERCK cellulose microcrystalline (27.7‰) as our laboratory standard [44]. The δ¹⁸O value represents the oxygen isotope ratio of the measured sample relative to the Vienna Standard Mean Ocean Water (VSMOW) standard sample, with an analytical precision of less than ±0.2‰ after correction against Merck sample δ¹⁸O values. The FLASH2000 elemental analyzer combined with the Delta V Advantage isotope mass spectrometer was used to measure the δ¹³C value of the samples by burning them in an element analyzer and transferring the CO₂ produced during combustion to a mass spectrometer through a CONFLO IV continuous flow interface. Carbon isotope ratios were presented as deviations relative to Pee Dee Belemnite (PDB), and according to repeated measurements based on working standards, their accuracy was less than ±0.2‰. Finally, the calibrated tree-ring δ¹³C actual values were obtained by correcting the measured tree-ring δ¹³C values against IAEA-CH-3 [45] values.

2.4. Calculation of Physiological Parameters

We assumed the Farquhar et al. [9] theory to correlate the carbon discrimination and the internal and atmospheric CO₂ partial pressure. The carbon isotope discrimination in C₃ plants can be represented as follows.

Δ¹³C = (δ¹³C_atm − δ¹³C_tree)/(1 + δ¹³C_tree/1000)

(1)

where δ¹³C_atm is the δ¹³C value of atmospheric CO₂ and δ¹³C_tree is the δ¹³C value of tree-ring cellulose.

∆¹³C values can also be calculated as the following equation [10]:

Δ¹³C = a + (b − a) (C_i/C_a)

(2)

where a (∼4.4‰) represents the isotopic discrimination that results from the diffusion of CO₂ from the atmosphere into the intercellular space of cells, b (∼27‰) represents the isotopic discrimination caused by the discrimination of RuBP carboxylase against ¹³CO₂ [10], and C_i and C_a represent the concentration of CO₂ in the leaf intercellular spaces and the atmosphere, respectively.

The intrinsic water use efficiency is specified as the ratio of the carbon assimilation rate (A) to the stomatal conductance (g_s) [46]. The calculation formula is as follows:

iWUE = A/g_s = Ca × [(1 − C_i/C_a)/1.6]

(3)

To remove the impact of climate signals on water use efficiency, it is necessary to first separate the climate signal from the atmospheric CO₂ concentrations inside the leaves [47]. Specifically, the initial step involves correlating C_i with monthly climate variables to identify the month with the most significant correlation. Subsequently, principal component analysis is performed on climate factors such as temperature, precipitation, and vapor pressure deficit (VPD) to obtain the optimum climatic target (PCT). Then, by using the empirical formula C_i + τ × (C_a − 280) and changing the τ values (τ ∈ [−1,2], step length 0.004), a series of simulated intra-leaf CO₂ concentration sequences (C_i-climate) can be obtained [47]. The pre-industrial atmospheric CO₂ concentration, used as a baseline in this study, was approximately 280 ppm, adopted by the Intergovernmental Panel on Climate Change (IPCC) as the pre-industrial baseline for CO₂ [48]. When the variance explained via the simulated C_i-climate sequence is highest for the PCT and their residual sequence has the slightest slope, this simulation sequence is considered optimal; it represents changes in intra-leaf CO₂ concentrations caused by climatic factors. The difference between them represents changes in water use efficiency caused by an increased CO₂ concentration. Finally, ^cciWUE can be obtained through the formula C_i + τ × (C_a − 280).

2.5. Climate Data

The sampling point is located near the Alaer Meteorological Station (40°33′ N, 81°16′ N) at 1012 m. The annual total precipitation at the Alaer Station during 1959–2018 (Figure 2) was 505.67 mm, with rainfall mainly concentrated in the months of June to August. The region experiences high evaporation rates, averaging 2110.50 mm per year. The hottest month is July, with an average temperature of 24.80 °C, while January is the coldest month, with an average temperature of −8.28 °C and an annual average temperature of 10.79 °C. The relative humidity averages 53.29% annually, with April being the driest month, recording a humidity level of 36.48%, and December being the most humid month, recording a humidity level of 69.04%. Climate data were downloaded from the Meteorological Information Center of China (http://data.cma.cn/, accessed on 10 April 2021).

The vapor pressure deficit (VPD) reflects the air water potential, influencing plant stomatal closure, thereby controlling physiological processes such as transpiration and photosynthesis that significantly affect crop water loss through transpiration and evaporation. The value can be calculated using the relative humidity (RH) and temperature (T) using this formula:

VPD = (1 − RH) × 0.6108 × e ^{(17.27T/(T + 273.3))}

(4)

VPD represents the monthly mean vapor pressure deficit, RH is the monthly mean relative humidity, and T is the monthly mean temperature.

2.6. Data Analysis

Traditional mathematical and statistical methods were utilized in dendroclimatology to analyze the data. The Pearson correlation coefficient (r) was calculated to investigate the relationship between the measured climate data and various tree-ring width indices, including the BAI, and stable isotope series such as Δ¹³C and δ¹⁸O, and C_i and iWUE. Furthermore, a 31-year sliding correlation was conducted to investigate temporal trends in the relationships between tree-ring Δ¹³C and δ¹⁸O, BAI, and iWUE. The relevance, linear regression, and time series analysis were performed using the SPSS 25.0 statistical software package (SPSS, Chicago, IL, USA).

3. Results

3.1. The Trends of Tree-Ring Width, Tree-Ring Width Index, and BAI

Starting in 1958, humans began cultivating the Alaer area [39], dividing its history into 1850–1958 and 1959–2018. Our findings indicate that from 1850 to 2018, tree-ring width exhibited an upward trend. After a rapid increase post-1958, tree-ring width showed a downward trend after 1977 (Figure 3a). Figure 3b illustrates that the index of tree-ring width reached its maximum value in 1982, with relatively high growth periods between 1865–1894 and 1960–1994, when the tree-ring width steadily increased above average values. From 1904 to 1957 was a period of relatively low growth, while fluctuations decreased after 1994. The BAI continued to grow from 1850 to 2018, with an average value of 19.78 cm²/year, reaching its peak in 1982. From 1962 to 2018, the BAI and tree-ring width displayed similar changes (Figure 3c), indicating that human influence led to rapid growth followed by slower increases.

3.2. Long-Term Changes in Δ¹³C and δ¹⁸O, and iWUE and δ¹⁸O

Between 1850 and 2018, the correlation between the Δ¹³C and δ¹⁸O in tree rings from P. euphratica was weak overall. However, around 1918, this relationship changed in intensity and direction, becoming significantly negatively correlated (r = −0.52, p > 0.01 in Figure 4b). The moving correlation analysis over 31 years suggests that factors controlling the Δ¹³C and δ¹⁸O have changed. From 1918 to 2018, there was a significant correlation between δ¹⁸O and iWUE (r = 0.25, p < 0.01 in Figure 4d), with changes in the δ¹⁸O explaining up to 6% of the variation in the iWUE.

3.3. Climate Response of Δ¹³C, δ¹⁸O, C_i, BAI, and iWUE

The climate monitoring data from 1958 to 2018 indicate that the average annual temperature in the Alaer region remained relatively stable, while the average yearly precipitation presented a remarkable increasing tendency, with a growth rate of 0.35 mm/a. Additionally, the relative humidity exhibited a notable increasing trend, with a growth rate of 0.15 mm/a (Figure 2). The Pearson correlation analysis determined the relationship between the Alaer tree-ring Δ¹³C chronology and various climatic factors. The results indicated (Figure 5a) a significant correlation between stable carbon isotope series of P. euphratica tree rings in Alaer and the mean temperature and relative humidity during the growing season (April–September). Negative correlations were observed with the monthly average temperature from July to September, while positive correlations were found with the monthly average relative humidity from April to September. The tree-ring δ¹³C was positively correlated with growing season precipitation and relative humidity, suggesting that water vapor pressure changes were the main factors determining δ¹³C fractionation during this period. In contrast, the tree-ring δ¹⁸O had an opposite response direction under the influence of temperature and moisture conditions (precipitation and relative humidity) (Figure 5b). April through September are the months exerting the most significant impact on tree-ring δ¹⁸O. VPD and relative humidity displayed the most robust correlations during this period, with r = 0.61, p < 0.01 and r = −0.66, p < 0.01, respectively.

The correlation analysis also revealed that the C_i was positively correlated with the early stages of the growing season as well as temperatures in April–May (r = 0.37, p < 0.05); however, its relationship with precipitation was weak overall but stronger when considering May through September’s mean relative humidity (r = 0.81, p < 0.05). The C_i had negative correlations, mainly concentrated on May through September (r = −0.76, p < 0.01), with a VPD throughout most months. The response between the mean relative humidity from May to September and the C_i had the best correlation coefficient (r = 0.81, p < 0.05 Figure 5c). In the early growing season, the iWUE is more sensitive to the temperature and VPD in March and April, with correlation coefficients of 0.50 and 0.59, respectively (Figure 5d), while its relationship with precipitation was weak overall but most vital when considering the VPD from March to April in a year. Finally, the BAI was negatively correlated with temperature from May to September, during which the BAI responded significantly positively to the precipitation (r = 0.35, p < 0.01) and relative humidity in June (r = 0.4, p < 0.01). The highest correlation between the VPD from May through July and the BAI reached −0.43 (p < 0.05, Figure 5e).

3.4. Long-Term Changes of C_i and iWUE

Using meteorological data from Alaer between 1959 and 2018, we conducted a principal component analysis on the temperature, precipitation, relative humidity, and VPD. The first principal component explained 78.85% of the total variance and was used as the target climate factor (PCT) to calculate the C_i-climate. We found that the correlation between the C_i-climate at τ = 0.175 and the PCT was the highest (r = 0.89, p < 0.01, in Figure 6c), with their residual slope reaching a minimum.

Between 1850 and 2018, the C_a increased by approximately 122 ppmv, while the C_i gradually rose from 1850, with a maximum value exceeding 273.36 ppmv (Figure 6a). Compared to the mean Ci value from 1850 to 1859, the last decade (2009–2018) saw an increase of only 111.44 ppmv in C_a, while a rise of 87.27 ppm occurred in C_i, thus indicating that the magnitude of the increase in C_i is lower than that of C_a.

The sequence of the changes in C_i influenced by climate factors (C_i-climate at τ = 0.175) had the highest explanatory variance for C_i at 78.85% (Figure 6c). Looking at their rate of change, the C_i and C_i-climate had average values of 12.72% and 14.53%, respectively. The difference between them mainly occurred after 1950 (Figure 7a).

There is an increasing trend in iWUE in the Alaer region, especially after 2000, with an average value of 77.60 μmol/mol compared to 69.50 μmol/mol from 1850 to 1859, indicating an increase of 8.10 μmol/mol or approximately 10% (Figure 7b). As shown in Figure 7c, the water use efficiency caused by the carbon dioxide concentration (^cciWUE) also exhibits an upward trend, with a growth rate of 13%. Further, the change in iWUE from 1850 to 2018 was closer to the predicted value under a constant C_i/C_a scenario [49], explaining 10.30% of the variation in the iWUE and indicating that trees have become more responsive to CO₂ increase. Additionally, we calculated the rate of change in iWUE relative to C_a, denoted as (iWUE/C_a). From 1850 to 2018, the average growth rate of the iWUE relative to the C_a was 0.22, and all growth rates were positive (Figure 7d). When we divided the data into before and after 1965 (commonly used as a reference point for the rapid increase in C_a [50]), the average iWUE/C_a from 1901 to 1965 was 0.23, while it was 0.20 from 1966 to 2018, a decrease of about 13%.

3.5. Relationship between BAI and iWUE of Populus Euphratica

The iWUE of tree rings is negatively associated with the Δ¹³C of tree rings (r = −0.32, p < 0.01), indicating that the photosynthetic rate contributes to the variation in iWUE. However, the iWUE positively correlates with the δ¹⁸O of tree rings (r = 0.24, p < 0.01), indicating that stomatal conductance also contributes to the variation in iWUE. During 1959–2018, there was no significant relationship between the iWUE and the δ¹⁸O, suggesting that other factors may have influenced the changes in iWUE over this period.

Over the past 160 years, the iWUE and BAI of P. euphratica in the upper Tarim River have generally increased, correlating positively (r = 0.32, p < 0.01, n = 169). This correlation fluctuates, with negative phases after 2012, 1867–1872, and 1914–1929, and positive phases during 1878–1898, 1941–1957, and 1969–1974. No significance was found in other periods. Since the late 1980s, the correlation between the iWUE and BAI has attenuated. The first-order difference analysis indicated no significant correlation between the high-frequency BAI and iWUE. Thus, iWUE has a direct relationship with radial growth over long-term changes, but their correlation is not strong Figure 8.

4. Discussion

4.1. The Impact of Climate and Human Activities on the Growth Process

The desert riparian forest is the primary vegetation type in arid inland river basins. Its growth and development are mainly influenced by soil moisture and groundwater depth. However, due to the dry climate, limited precipitation, intense evaporation, and extremely scarce water resources in these basins, desert riparian forests’ distribution, survival, and succession are severely restricted [51]. The core issue of plant drought resistance and water-saving mechanisms is the regulation of water relations. Stomata are channels for leaf transpiration and for photosynthetic CO₂ to enter cells. The ability of plants to regulate stomatal opening enables them to adjust their transpiration rate. A high gradient or differential in the VPD between inside and outside leaves causes a decrease in stomatal conductance for most plants, resulting in reduced transpiration consumption [52]. Most plants decrease their stomatal conductance under elevated VPD conditions, which typically leads to reduced transpiration, photosynthesis, and, potentially, radial growth, as an adaptive strategy to meet their water balance requirements [53]. The BAI is markedly negatively related to the temperature and VPD from May to September. The correlation coefficient between the BAI and the VPD from May to July reaches −0.43 (p < 0.01, n = 51; Figure 5). Meanwhile, the BAI has a significant forward correlation to precipitation and relative humidity in the growing season. These correlations suggest that water stress is the leading environmental factor affecting tree radial growth, especially during the growing season. The high summer temperatures exceeded the suitable levels for photosynthesis. At the same time, the shallow groundwater depth did not limit stomatal conductance(g_s), thus increasing the internal carbon dioxide concentration (C_i) within leaves, leading to the negative correlation between the BAI and the VPD under these two factors’ combined effect.

There is a negative correlation between the carbon isotopes (Δ¹³C) and oxygen isotope ratios (δ¹⁸O) in tree rings [38]. Tree-ring δ¹³C reflects changes in the C_i/C_a, while tree-ring δ¹⁸O mainly reflects changes in the relative humidity. The relationship between the δ¹³C and δ¹⁸O can be used to deduce changes in plants’ intrinsic water use efficiency. Changes in both the photosynthetic rate (A) and stomatal conductance (g_s) can cause variations in tree-ring δ¹³C [11], whereas only stomatal conductance affects tree-ring δ¹⁸O [54]. If the change in the photosynthesis rate is more significant than that of the stomatal conductance, there will be no correlation between the values of the δ¹⁸O and the δ¹³C; if A increases but g_s remains unchanged, then there will be a negative correlation between the values of the δ¹³C and the δ¹⁸O [11,12]. From 1850 to 2018, the Δ¹³C and δ¹⁸O were generally unrelated; however, around 1918, this relationship changed from being irrelevant to being significantly negatively correlated. This result suggests that factors controlling the Δ¹³C and δ¹⁸O may have changed during this period. Based on field investigations, historical documents, and maps, Han Chunxian [55] researched the Shaya section of the mainstream Tarim River above and below Alaer (located upstream of Shaya). The analysis showed that in the 1910s, the Shaya section shifted northward along its upstream river course, following a position along the northern river. According to historical records and deductions based on Δ¹³C-δ¹⁸O relationships, there may have been a channel shift in the upstream reaches of the Tarim River around 1918.

Since large-scale agricultural development began in the 1950s, human activities have increasingly altered natural water–sediment processes within the Tarim River Basin [56], leading to significant changes in the river’s geomorphology. In addition, groundwater has been extensively used for irrigation along the Tarim River basin, leading to increased groundwater levels [57]. According to previous studies on land reclamation, before its development, the underground water depth was 5–7 m [39]; after the construction of irrigation systems, it rose by about 1–4 m [58]. Peng et al. [59] showed that desert poplars rely heavily on groundwater for growth, with the groundwater level being the foremost environmental element influencing radial growth. Groundwater depth directly affects soil moisture dynamics [60] and nutrient dynamics [61], which are the main factors determining P. euphratica’s distribution, development, and population succession [34,62,63]. In arid environments with strong evaporation rates, high groundwater levels can lead to ineffective dissipation due to evaporation and cause soil salinization that stresses plants; low groundwater levels can inhibit plant growth due to water stress leading to the degradation or even death of vegetation, resulting in desertification [64]. Wang Jing [65] employed a geographic detector to conduct a simulation analysis exploring the relationships between artificial and natural oasis vegetation in Alaer, as well as various soil and groundwater elements. The study revealed that mineralization was the primary determinant affecting vegetation distribution and growth among the factors influenced by groundwater. Hou et al. [66] believed that soil salinization is a major factor inhibiting the growth of P. euphratica forests, with reducing salt content in soils being a priority measure for ecological restoration.

The above analysis shows that the incremental growth of P. euphratica was influenced by various climate and environmental factors. Although the impact of climate factors on its radial growth is relatively low, human activities such as agricultural development and irrigation have led to a reduction in the Tarim River’s flow, changes in groundwater depth, soil salinization, and other problems, which have had a profound impact on the radial growth of Alaer P. euphratica.

4.2. Analysis of the Reasons for Changes in Intrinsic Water Use Efficiency of Populus Euphratica and Its Relationship with BAI

Under elevated levels of CO₂, plant stomata tend to close, reducing water loss and potentially improving intrinsic water-use efficiency (iWUE). The effect on photosynthesis and intercellular CO₂ concentration can vary depending on other environmental factors [67]. This study found that C_a significantly increased, resulting in a 13% change in water use efficiency (^cciWUE) (Figure 7), indicating that the increase in atmospheric carbon dioxide has a particular promoting effect on the iWUE. In addition, the iWUE changes from 1850 to 2018 are closer to the predicted values under the constant C_i/C_a scenarios [49], explaining 10.3% of the iWUE changes and suggesting that trees are becoming more responsive to increases in CO₂. The observed 13% increase in iWUE is comparatively modest when set against non-riparian species [68]. Climate factors such as temperature, precipitation, relative humidity, and vapor pressure deficit can also affect plants’ stomatal conductance and photosynthetic rates, and further influence iWUE. Research has demonstrated that desert plants resist drought stress by increasing their water use efficiency. When P. euphratica experiences moderate drought stress, the stomatal aperture decreases to reduce water loss while increasing intrinsic water use efficiency [69]. The core issue of plants’ drought resistance mechanisms is the regulation of water relations through stomata channels, where leaf transpiration occurs and photosynthetic raw materials enter cells; therefore, regulating stomatal opening allows plants to adjust their transpiration rates. An increase in the VPD causes most plant leaf stomatal conductance to decrease while reducing transpiration consumption [52] to adapt to moisture balance and survival needs. Accordingly, our correlation analysis results show that iWUE significantly correlates with VPD during most months. A larger VPD indicates less atmospheric moisture because when VPD values are high, plants have more substantial pressure to lose water, causing them to close their stomata, thereby reducing intercellular CO₂ concentration and plant photosynthesis. Zhang et al. [70] suggested that the increase in VPDs caused by global warming may be a reason for the annual growth of 0.13% in ecosystems’ iWUEs. Therefore, rising greenhouse temperatures and increasing values of VPD could result in more intense droughts, potentially affecting plant and forest ecosystems [71]. They indicated that VPD has an essential impact on iWUE in the Alaer region.

Over the past 160 years, iWUE has shown a clear growth trend in the P. euphratica growing upstream of the Tarim River. However, since the late 1990s, with increased human activities, this correlation has weakened due to the increased relative humidity (Figure 2d), which reduces stomatal conductance and thus decreases iWUE. The dependence on climate δ¹⁸O signals can explain whether changes in the iWUE were caused by differences in stomatal conductance or changes in the photosynthetic rate [38]. Tree-ring δ¹⁸O is positively correlated with iWUE (r = 0.24, p < 0.01), indicating that stomatal conductance also contributes to the variation of iWUE. In contrast, the transition from no correlation between tree-ring Δ¹³C and δ¹⁸O to a significant negative correlation (Figure 4c) after 1918 critically suggests the increase in stomatal conductance in determining tree-ring Δ¹³C, thus highlighting its essential contribution to regulating iWUE. The changing impact of stomatal conductance on variations in iWUE is revealed through a moving correlation analysis over 31 years (Figure 4d). From 1872 to 1917, there was a weak correlation between the iWUE and the δ¹⁸O, indicating that drought-induced effects were not necessary during this period, while from 1959 to 2018, there was no significant relationship between them, suggesting that other factors may have influenced the variations instead.

Water conditions, especially groundwater depth, influence P. euphratica growth, and soil organic matter content is one of its main determinants [66]. Zhang et al. [72] used stable isotope technology to explore water sources and utilization efficiency among different diameter classes of P. euphratica downstream of the Tarim River, concluding that water utilization efficiency would decrease with root growth at different stages. From 1958 to 2018, the BAI showed a “first increase then decrease” trend, while the iWUE continued to increase. This secondary relationship indicates that from 1958, when it was first cultivated, until 1982, when all indicators such as groundwater level and salinity, soil moisture content, and salt content were suitable for tree growth, trees utilized favorable conditions, resulting in a higher radial growth of P. euphratica. After 1983, under warm–humid climate conditions [73] and intensified background of human activities, over-matured P. euphratica entered a decline stage with an increased proportion of dead branches and roots [74], thus requiring an appropriate improvement in the iWUE to maintain their survival rather than promoting their growth during this period. Liu et al. [38] found that elevated atmospheric CO₂ concentrations significantly stimulated tree-ring width downstream of the Heihe River, Inner Mongolia, compensating for the negative impacts caused by the reduced river flow rate and drought during the study period. However, from 1850 to 1958, the BAI and iWUE may have been mainly affected by natural climatic factors, while from 1959 to 2018, human activities profoundly influenced them. The P. euphratica in the riparian forest has adapted to the increasing atmospheric CO₂ concentration, and has improved its water use efficiency and enhanced its growth ability. However, this adaptive capacity is unstable and easily affected by human activities.

The increase in atmospheric CO₂ concentrations promotes P. euphratica growth at a low frequency, but other factors (such as groundwater mineralization) may offset the potential benefits of rising carbon dioxide concentration [75]. Human activities have also promoted P. euphratica growth at a high frequency compared to historical periods.

5. Conclusions

This study conducted a comprehensive analysis using dendrochronology and isotope data (δ¹³C and δ¹⁸O) to study the response of P. euphratica in the upstream region of Alaer, Tarim Basin, to the increased atmospheric CO₂ concentration and human activities. From 1850 to 2018, we observed an increasing trend in the BAI and iWUE of P. euphratica, mainly due to the rising atmospheric CO₂ concentrations and favorable groundwater conditions. We found that 13% of this increase was attributed to changes in the water use efficiency (^cciWUE) caused by elevated atmospheric CO₂ concentrations, a notably low value compared to non-riparian species. This limited response is likely due to the groundwater dependency of riparian species, which inherently limits their iWUE values and subsequent increases, resulting in less efficient water use. Around 1918, there was a shift from the weak correlation between the Δ¹³C and δ¹⁸O in tree rings, to a significant negative correlation, possibly related to the river channel migration upstream of the Tarim River. The BAI of P. euphratica growing along the Tarim River was positively correlated with the iWUE, indicating that an increased iWUE promoted tree growth somewhat. From 1958 to 1981, under human influence, P. euphratica grew rapidly. However, the iWUE continued to increase after 1983, and it did not promote further growth, as indicated by the secondary relationship between the BAI and iWUE, suggesting other factors, such as groundwater depth, were limiting growth. Overall, low-frequency increases in the atmospheric CO₂ concentrations promoted P. euphratica growth, while high-frequency increases were associated with human activities promoting its growth. Due to the lack of long-term monitoring data on groundwater level fluctuations at this site, our interpretation needs further strengthening through future research.