Environmental Factors’ Effects on Stem Radial Variations of Populus euphratica in the Lower Reaches of the Tarim River in Northwestern China

Abstract

Microscopic understanding of tree xylogenesis processes and their relationships with environmental factors is important for tree conservation. To assess the relationship between Euphrates poplar (Populus euphratica) radial growth and environmental factors on short time scales, micro-core samples were collected in the lower Tarim River in northwest China in 2020. We analyzed the intra-annual radial variation in P. euphratica and its relationship with the environment. Our results showed that P. euphratica started to manifest stem radial variations in late April (around DOY (day of year) 114) and stopped displaying stem radial variations in early to mid-September (around DOY255), with an annual stem radial increment of 2620.89 μm and a maximum stem radial variation rate of 22.42 μm/d. The stem radial variation rate was positively correlated with the mean temperature (R² = 0.55, p < 0.01), the maximum temperature (R² = 0.45, p < 0.01), the minimum temperature (R² = 0.49, p < 0.01), the surface temperature (R² = 0.64, p < 0.01), and the vapor pressure deficit (R² = 0.49, p < 0.01), and negatively correlated with the mean atmospheric pressure (R² = 0.41, p < 0.01) and the groundwater depth (R² = 0.45, p < 0.01). The stem radial variations of P. euphratica are mainly influenced by groundwater during the main growing period, and its stem radial variation rate was positive when the fluctuation range of the groundwater depth was 4.17~5.38 m. Therefore, the stem radial variations of P. euphratica in the lower reaches of the Tarim River are mainly influenced by groundwater, which has a reasonable range of 4.17~5.38 m.

1. Introduction

Forests contain important terrestrial ecosystems that play key roles in the global carbon cycle [1]. In recent years, continued temperature increases and increased drought have led to changes in environmental factors that have accelerated widespread forest mortality around the world [2], directly affecting the role of trees in the carbon cycle [3]. Therefore, it is important to clarify the relationship between environmental factors and forest ecosystems for the protection of those ecosystems. Micro-coring technology, developed in the 21st century, can accurately determine the process of tree layer cell formation from dormancy to division as well as xylem production [4] and also finely reflect the physiological process of stem radial variation as well as responses to trees’ external environments within one year [5]. Thus, the stem radial variations dynamics of xylogenesis during the growing season, measured by continuous monitoring, using micro-coring technology and wood anatomy, and analyzing the relationships between the stem radial variation dynamics of the xylogenesis and environmental factors can better reflect the growth process of trees and their responses to those factors.

The xylogenesis of trees is closely related to the environment, with temperature being an important factor that influences the activity of cambium at the onset of the growing season [6]. Numerous observations and experiments have shown that cambium activity in trees in Norway does not start until temperatures have exceeded 12 °C [7] and that low temperatures lead to shortened periods of cambium activity. High temperatures promote a longer growth period for the xylem [8], as well as stem radial variation [4]. Spatial variations in temperature also affect cambium activity, which starts earlier and ends later at lower elevations than at higher elevations [9], making temperature a limiting factor for higher-elevation tree cambium activity. The minimum temperature threshold for tree cambium activity in colder regions is 4~5 °C [10], and the temperature threshold for tree cambium activity in humid subtropical regions is much higher than those in colder and higher-elevation areas [11]. Temperature can also affect cambium activity by influencing other factors, such as the melting of snow and ice at high latitudes and the timing of soil thawing [12]. Moisture conditions have also been shown to be an important influence on phloem activity [13], affecting the cell division rate by influencing the xylem vessel size [14], with larger vessel diameters resulting in more efficient water transport and faster xylem cell growth [15]. Precipitation has been found to be closely related to cell expansion and lignification [7], and a study of Australian eucalypts (Eucalyptus spp.) found that the growth response to precipitation decreased only when the precipitation increased to 1400 mm [16]. Additionally, the growth of the Canadian birch (Betula papyrifera Marsh.) was positively correlated with precipitation [17]. Moisture conditions can also have effects on the growth cycle of trees; early in the growing season, they have a dramatic effect on the timing of the onset of cambium activity [18], with poor moisture conditions during the growing season leading to shorter cycles of xylem activity [19], and the cell activity in trees under drought conditions ceasing five weeks earlier than in trees under irrigated conditions [20].

The Tarim River has 54% of the world’s P. euphratica forest area [21], and the constructed desert riparian P. euphratica forest ecosystem plays an irreplaceable role in maintaining the regional ecological balance and ensuring the agricultural security of the oasis. However, in recent decades, P. euphratica forests have declined and died on a large scale, and their ecological functions have been drastically degraded [11,22]. Clarifying the relationship between P. euphratica growth and environmental factors has become the primary problem to be solved in order to protect and save degraded P. euphratica forests, and it is also important to respond to the vision of carbon peaking and carbon neutrality. Studies of the stem radial variations of P. euphratica have been limited to annual tree ring width responses to environmental factors and reconstruction [21,23], while a more microscopic and precise scale is limited to the study of its intra-annual stem radial growth dynamics. Therefore, this study was conducted to investigate the intra-annual responses of dynamic changes in the cambium of P. euphratica to environmental factors based on experimental data from the lower Tarim River and using a wood anatomical approach and the micro-coring technique. This was carried out by (1) assessing the intra-annual stem radial variation dynamics of P. euphratica; (2) determining the relationship between P. euphratica’s stem radial variation and environmental factors (i.e., mean temperature, maximum temperature, minimum temperature, vapor pressure deficit, relative humidity, wind speed, mean atmospheric pressure, vapor pressure deficit, and groundwater) during the growing season; (3) identifying the main environmental factors (groundwater) that influence the stem radial variations of P. euphratica; (4) determining a groundwater table interval that is conducive to P. euphratica growth. This study contributes to a better understanding of the growth pattern and driving environmental factors of P. euphratica, which can provide a reference for the restoration and conservation of mullein in the lower Tarim River basin.

2. Materials and Methods

2.1. Study Area

Our study was conducted in an area adjacent to the Daxihaizi reservoir (Figure 1a) in the lower Tarim River (40°26′15.88″–40°26′16.41″ N; 87°55′34.12″–87°55′36.41″ E) in China. The study area has low precipitation, with mean annual precipitation of 17.4 to 42.0 mm, mean annual temperature of 10.5 °C, and average annual evaporation of over 2500 mm, making it a typical warm, temperate arid desert zone [24,25]. The dominant trees and shrubs are P. euphratica and tamarisk (Tamarix ramosissima Ledeb), and the dominant herbs are reeds (Phragmites australias Trin) and camel thorn (Alhagi spaysifolia Shap) [26]. The soils are mainly meadow, saline, and windy–sandy soil [27]. The locations of our sampling points are shown in Figure 1a. The habitat and growth of P. euphratica are shown in Figure 1b,c.

2.2. Data Collection

2.2.1. Micro-Core Sampling

Eight healthy P. euphratica trees were selected during the 2020 growing season (April-October), and micro-core samples were taken from them using a micro-growth cone (Trephor) at breast height (about 1.3 m) [28] once a week, 27 times in total, for a total of 216 samples. To avoid errors caused by differences in growth rate due to different sampling directions, the sampling site and direction needed to be kept consistent and “Z” required to be maintained from top to bottom [29]. The samples were fixed rapidly in an FAA (Formalin/Glacial Acetic Acid/Ethanol/DI Water) solution.

2.2.2. Micro-Core Sample Processing

The samples were removed from the FAA solution, marked with a pencil in the vertical vascular direction of the vascular, placed in embedding boxes, and clearly marked with sample numbers. They were then treated with ethanol stepwise dehydration, limonene transparency, paraffin soaking, and embedding. The samples were cut into 8 μm slices using a rotary slicer (LEICA RM2255, Wetzlar, Germany), the paraffin slices were fixed on slides by the process of spreading and fishing, and the samples were dried by baking at 50 ℃ for 2 h in an oven. The dried samples were taken out, and the wax was removed and made transparent using limonene and anhydrous ethanol in turn, then stained with safranin (1 g safranin + 100 mL distilled water) and star blue (0.5 g star blue + 2 mL acetic acid + 100 mL distilled water) for 15 min and washed in 70% and 100% concentrations of alcohol and xylene, respectively, in turn. After the samples were stained and sealed with neutral gum and photographed under a 10× microscope, we monitored the cambium activity of P. euphratica throughout the growing season. As fixed-point continuous sampling was used, the xylem stem radial variations within the year were measured using cumulative values; i.e., each measurement was cumulated from the previous annual round. The total xylem stems radial incrementation was measured using LAS V 4.12 software, and the process was randomly repeated three times for each sample along the radial growth direction of the xylem and averaged to reduce measurement errors [30].

2.3. Environmental Data Collection

The meteorological data for the lower reaches of the Tarim River were obtained from the Tieganlike meteorological station in that area. The main meteorological data used in this study include the mean temperature (T_amean), the maximum temperature (T_amax), the minimum temperature (T_amin), the land surface temperature (LST), the relative humidity (RH), the mean wind speed (WS_mean), the mean atmospheric pressure (P_mean), the vapor pressure deficit (VPD), etc. The data used are daily averages for the period of January to December 2020. Groundwater depth (GD) variation data were obtained from the Tarim River Basin Authority and measured for the duration of each sampling period. VPD is calculated from temperature and relative humidity as follows:

$$\mathrm{V}\mathrm{P}\mathrm{D}=\mathrm{A}\times \mathrm{e}\mathrm{x}\mathrm{p}\left[\frac{\mathrm{b}\times {\mathrm{T}}_{\mathrm{i}}}{{\mathrm{T}}_{\mathrm{i}}+\mathrm{c}}\right]+\left(1-\mathrm{R}\mathrm{H}\right),$$

(1)

where T_i is the air temperature; RH is the relative humidity; A, b, and c are constants of 0.611 kPa, 17.052 and 240.97 °C, respectively.

2.4. Data Analysis

In our study, three columns of cells were selected along the stem radial variations of P. euphratica based on weekly micro-coring samples to calculate the mean cell number that represented the growth of each sample tree, and the counted cell number had to be normalized [29,31]:

$$\mathrm{n}{\mathrm{c}}_{\mathrm{i}}=\mathrm{n}\mathrm{c}{\mathrm{m}}_{\mathrm{i}}\times \frac{\mathrm{r}{\mathrm{w}}_{\mathrm{m}}}{\mathrm{r}{\mathrm{w}}_{\mathrm{s}}},$$

(2)

where nc_i is the normalized cell number, ncm_i is the actual measured cell number, rw_m is the average wheel width or cell number of all samples in the previous year, and rw_s is the wheel width or cell number of the current sample in the previous year.

The Gompertz equation is an S-model that describes the growth process and is often used to fit growth trends [17,32]. We used the Gompertz function module in Origin 2018 software to fit the stem radial variations of P. euphratica and determine the onset of the growth and length of the growing season:

$$\mathrm{y}=\mathrm{A}\mathrm{e}\mathrm{x}\mathrm{p}{\left[-\mathrm{e}\right]}^{\mathsf{\beta }-\mathrm{k}\mathrm{t}},$$

(3)

where y denotes the accumulated growth; t denotes the annual cumulative days; A denotes the asymptote; β and k are the x-axis intercept and rate of change constants, respectively; e = 2.718. The start and end of growth were defined as the annual cumulative days in which 5% and 95% of the total growth, respectively, were reached.

The maximum stem radial variation rate (r_max), the time thereof (t_p), and the mean stem radial variation rate (r_mean) could be obtained from the Gompertz function, as shown in Equations (4)–(6), respectively:

$${\mathrm{t}}_{\mathrm{p}}=\mathsf{\beta }/\mathrm{k},$$

(4)

$${\mathrm{r}}_{\mathrm{m}\mathrm{a}\mathrm{x}}=\mathrm{k}\mathrm{A}/\mathrm{e},$$

(5)

$${\mathrm{r}}_{\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{n}}\approx 9/40\mathrm{e}{\mathrm{r}}_{\mathrm{m}\mathrm{a}\mathrm{x}}.$$

(6)

We used linear regression analysis to model and quantify the relationship between the stem radial variation rate and environmental factors; Pearson correlation was used to verify this. Simultaneously, considering the lag of tree growth in response to environmental factors, we used the average of the week before each sampling time in our environmental factor date. We also used Pearson correlation analysis to analyze the relationships between the environmental factors that affect radial variation in P. euphratica.

To clarify the magnitude of the effects of hydrothermal factors on the radial growth of the xylem, we used the relative contribution calculation method [33], in which groundwater and air temperature jointly affect the stem radial variation rate of the xylem within a year, i.e., we established a regression model between the groundwater (X₁), the air temperature (X₂), and the stem radial variation rate of the xylem (Y) and then used the relative contribution calculation method to determine the relative contributions of X₁ and X₂, respectively, to Y. In the relative contribution calculation method, one variable was fixed at its mean value, and the other variable was allowed to vary within plus or minus one standard deviation of its mean value in order to record the range of variation in Y. Then, the variables were swapped, and the variation in the other variable on Y was recorded.

To analyze the effect of groundwater depth on the radial growth of P. euphratica, we selected seven equations (linear, quadratic, cubic, quartic, logarithmic, exponential, and power functions), both linear and non-linear, to fit the relationship between those factors. We selected the functional equation with the highest degree of fit between the groundwater depth and the radial growth change in P. euphratica, and the derivative of the function was calculated to obtain the relationship between the major environmental factors and the radial change rate of P. euphratica. We considered the range of change in the major environmental factors when the stem radial variation rate was positive and in the range suitable for P. euphratica growth.

All data were processed using Excel 2010. Pearson’s correlation analysis was performed with SPSS 19.0 software (SPSS Inc., Chicago, IL, USA). All function-fitting and mapping work was performed in Origin 2022 software (Origin Lab, Corporation, Northampton, MA, USA).

3. Results

3.1. Meteorological Factors and Groundwater Change Characteristics

The T_amean of the study area in 2020 was 12.18 °C (Figure 2a), with the coldest month (December) averaging −7.24 °C and the hottest month (July) averaging 27.09 °C. The T_amax occurred on 5 August at 40.5 °C (Figure 2b), and the T_amin occurred on 29 December at −19.2 °C (Figure 2c). The T_amean at the maximum stem radial variation rate (DOY 169) in P. euphratica was 26.5 °C (Figure 2a). The LST reached its maximum on 3 August at 39 °C (Figure 2d). The RH and the VPD reached their extremes for the year on 21 December, with the highest RH at 73% and the lowest VPD at 0.39 kPa (Figure 2e,h); the RH at the maximum stem radial variation rate (DOY 169) in P. euphratica was 30%, and the VPD was 55.272 kPa (Figure 2e,h). The average annual WS was 1.67 m/s (Figure 2f), and the P_mean had a minimum of 90.210 kPa on August 9 and a maximum of 93.770 kPa on 27 December (Figure 2g). During the growing season, the GD varied from 3.82 to 6.64 m (Figure 2i).

3.2. Stem Radial Variations Dynamics of P. euphratica during the Year

The stem radial variation curves of all eight sample trees showed an overall synchronous response (Figure 3a–h). The mean conditions of the eight sample trees (Figure 3i) showed that cambium layer cells started to grow in 2020 on DOY102 (mid-April), with an overall trend of faster growth from DOY102 (mid-April) to DOY137 (mid-May), rapid growth from DOY137 (mid-May) to DOY249 (early September), and a plateau from DOY249 (early September) to the end of the growing season.

We used the Gompertz function model (please refer to Section 2) to fit the stem radial variations of P. euphratica (Figure 4), and this fit explained 98% (p < 0.001). The xylem of P. euphratica started changing at around DOY114 and stopped changing at around DOY255; the length of the changing season was 141 days, and the annual stem radial increment was 2620. 89 μm. The average stem radial variation rate was 13.71 μm/d, and the maximum stem radial variation rate was 22.42 μm/d, occurring on DOY169 (

Table 1. Relevant indices of stem radial variations of P. euphratica.

DBH	rmax	rmean	tp	Start Growth	Stop Growth	Length of Time	Annual Growth Volume
cm	μm/d	μm/d	DOY	DOY	DOY	d	μm
26.57 ± 7.43	22.42	13.71	169.43	114.34	255.06	140.72	2620.89

). The stem radial variation trend during the growing season showed a rapid increase before DOY169 and a slow decrease after DOY169.

3.3. The Relationship between the Stem Radial Variation Rate of P. euphratica and Environmental Factors

Based on the above analysis, we extracted the stem radial variation rate (Figure 4) of P. euphratica throughout the growing season and analyzed whether there were relationships with environmental factors (please refer to Figure 5). This showed that the stem radial variation rate (SR rate) was positively correlated (Pearson’s correlation, please refer to Section 2) with the T_amean (R² = 0.55, p < 0.01), T_amax (R² = 0.45, p < 0.01), T_amin (R² = 0.49, p < 0.01), LST (R² = 0.64, p < 0.01), and VPD (R² = 0.49, p < 0.01) values. The SR rate was also positively correlated with the P_mean (R² = 0.41, p < 0.01) and negatively with the GD (R² = 0.45, p < 0.01) but not correlated with the LST (R² = −0.03, p = 0.729) or the VPD (R² = −0.04, p = 0.936).

3.4. Relationships between Environmental Factors That Affect Stem Radial Variation in P. euphratica

From the results of the above study (Figure 5a–d,g–i), we found that the T_amean, T_amax, T_amin, LST, P_mean, VPD, and GD values were all significantly correlated with the stem radial variation rate. To further determine the key factors that affect P. euphratica growth, we analyzed the relationships between these environmental factors (Figure 6). The results showed that the T_amean is significantly positively correlated with the T_amax, T_amin, LST, and VPD values, significantly negatively correlated with the P_mean, and not correlated with the GD, so the mean air temperature can be used as a key factor to analyze the radial variation in P. euphratica instead of the maximum air temperature, minimum air temperature, surface temperature, mean air pressure, or saturated water pressure deficit.

3.5. Contribution of Hydrothermal Factors to Stem Radial Variation

In order to identify the key environmental factors that influence the growth of P. euphratica, we applied the relative contribution calculation method (please refer to Section 2) to first establish a regression model (R² = 0.832, p < 0.001) between the radial change rate, the T_amean, and the GD and then found that the relative contributions of the T_amean and the GD to the radial change rate of P. euphratica were different. When the T_amean was fixed at the mean and the GD was allowed to fluctuate within one standard deviation, the radial change rate of P. euphratica was found to vary up to 84% under the influence of the GD, and when the GD was fixed at the mean and the T_amean was allowed to fluctuate within one standard deviation, the radial change rate of P. euphratica was found to vary up to 16% under the influence of the T_amean; therefore, the radial change rate of P. euphratica was mainly influenced by the GD during the growing season.

3.6. Range of Groundwater Variability Suitable for P. euphratica Growth

In order to quantify the relationship between the intra-annual stem radial variations of P. euphratica and the groundwater depth, we selected seven models, both linear and non-linear, to simulate the relationship between the two, and the results thereof are shown in

Table 2. Regression model of stem radial variations of P. euphratica and groundwater depth.

Model	Regression Equation	R2	p
Linear Function	Y = 68.918X + 1110.999	−0.04	0.792
Quadratic Function	Y = −931.447X2 + 9661.474X − 23,111.022	0.37	<0.01
Cubic Function	Y = −536.574X3 + 7586.062X2 − 34,667.570X + 52,382.228	0.41	<0.01
Quartic Function	Y = 596.563X4 − 13,009.471X3 + 104,294.950X2 − 364,155.857X + 468,471.878	0.43	<0.01
Logarithmic Function	Y = 884.683 × In(X + 0.033)	−0.03	<0.01
Exponential Function	Y = 1253.566 × 1.031X	−0.04	<0.01
Power Function	Y = 861.356 × X0.324	−0.03	<0.01

. There is a high regression relationship between the intra-annual stem radial variations of P. euphratica and the groundwater depth, and the quadratic (R² = 0.37), cubic (R² = 0.41), and quartic (R² = 0.43) functions simulated the intra-annual stem radial variations of P. euphratica with the groundwater depth very well. The regression model of the intra-annual stem radial variations of P. euphratica and the groundwater level, simulated with the above functions, was synthesized, and we found that for these factors, the quartic function had the highest degree of fit, with an R² value of 0.43.

To further analyze the appropriate groundwater level for P. euphratica stem radial variations, we used the best-fitting quartic function to fit the trend relationship between the groundwater depth and the P. euphratica stem radial variations (Figure 7a), and the derivative of the function was calculated to obtain the rate curve of the intra-annual stem radial variation with the groundwater depth (Figure 7b). In this curve, the growth rate function is non-monotonic, with positive stem radial variation rates for groundwater depths in the range of 4.17–5.38 m and negative values for the rest of the range.

4. Discussion

4.1. Stem Radial Variations of P. euphratica

In this study, intra-annual stem radial variations were demonstrated using the microcore technique (Figure 3 and Figure 4). The length of the radial growth cycle of trees tends to be related to latitude, and it has been reported [34] that there is no dormant period for tree growth in the tropics, while the stem radial variation cycle of the trees in the temperate and northern frigid belt is from April to September and the stem radial increment of the xylem has an “S”-shaped curve during the year. Our study area, the lower Tarim River, is a temperate region, and the stem radial incrementation of P. euphratica in that area mainly occurs from April to September (DOY114 to DOY255), with an “S” curve during the year (Figure 4), which is consistent with the results of Deslauriers et al. [35]. Tixier et al. [36] found that deciduous trees tend to use the non-structural carbohydrates (NSCs) accumulated in the previous year when they start to grow, and our study found (Figure 4) that xylem stem radial variations showed more rapid growth until mid-May (Doy137), which may have been a result of more use of those NSCs in the early stage of the P. euphratica cambium activity. The trend of the stem radial variation rate (Figure 4) within the growing season showed a rapid rise before mid-June (DOY169) and a slight decrease afterward due to the increases in temperature and the melting of snow and ice in the early main growing period, which provided abundant water to the trees and ensured the growth of P. euphratica in the early part of the growing season. The slow decline in the growth rate after mid-June may have been due to an increase in evaporation caused by the rise in temperature and the low rainfall in the Tarim River dry zone, as well as the fact that June is the growing season for most plants and plant growth leads to increased water consumption, which is the cause of water stress. Therefore, to restore and protect P. euphratica forests, priority should be given to considering ecological water transfer after mid-June. The stem radial variations of trees are closely linked to the dynamics of the nitrogen cycle; around the summer solstice, trees regulate the efficiencies of nutrients in their leaves so that the efficiencies of nitrogen use and photosynthesis are at their maximums, meaning tree growth will be less restricted or even eliminated by temperature and plants will adapt their internal resource cycles to the external environment through their own physiological regulation [37]. The maximized stem radial variation rate of P. euphratica around mid-June (DOY169) was probably because P. euphratica gradually adapts its internal resource cycle thusly and achieves growth optimization (Figure 4).

4.2. The Relationship between the Stem Radial Variation Rate of P. euphratica and Environmental Factors

Trees can adapt and respond to environmental changes [38,39], and xylem cambium activity has been shown to start when the ambient temperature is higher than the minimum threshold temperature. Changes in temperature also affect the length of the main growing period [40], and the first spring precipitation also stimulates xylem cell division and accelerates xylem cambium activity [41]. Our study area is located in a temperate, arid zone with a late temperature rise and low spring precipitation. The temperature rise in spring not only provides a suitable temperature environment for the xylem cambium activity of P. euphratica but also accelerates the melting of snow and ice to provide an abundant water supply for xylem growth. We found that the stem radial variation rate of P. euphratica was positively correlated with the mean, maximum, minimum and land surface temperatures (Figure 5a–d), indicating that temperature has a promoting effect on the growth of P. euphratica xylem, probably because the increased temperature not only promotes faster cell growth rates but also increases respiration to promote higher tree growth rates [42]. Liu et al. [43] showed that vapor pressure deficit is the main environmental factor that affects stem sap flow rates and transpiration of trees and growth rates, and we found a significant positive correlation between the vapor pressure deficit and the stem radial variation rate (Figure 5h). In our study, the P. euphratica stem radial variation rate was mainly influenced by groundwater during the growing season, and the stem radial variation rate was negatively correlated with the groundwater depth (Figure 5i), indicating that P. euphratica xylem growth is mainly limited by moisture conditions. Drought leads to xylem vessel embolism, resulting in blocked water transport, stomatal closure, and reduced photosynthetic capacity, which ultimately lead to slow growth; tree growth also remains sensitive to water efficiency, even under perennially wet conditions [44,45], which is consistent with our findings (Figure 5i) that the P. euphratica stem radial rate is significantly negatively correlated with groundwater depth and that P. euphratica growth during the growing season is mainly affected by water conditions. Moisture conditions affect the stem radial variation rates of trees, and better water supply conditions promote faster cell division [46], which promotes increases in cell area and cell numbers and facilitates xylem formation.

4.3. Groundwater Levels Suitable for Growing P. euphratica

During the main growing period, the rate curve of the intra-annual radial growth of P. euphratica showed (Figure 7b) that the growth rate function was non-monotonic and the intra-annual stem radial variation rate of P. euphratica varied greatly, in the range of 3.82–4.17 m, with the groundwater depth, indicating that the intra-annual stem radial variations of P. euphratica vary greatly with the ups and downs of groundwater depth in this range. When the fluctuation range of groundwater depth was 4.17–5.38 m, the stem radial variations of P. euphratica were positive, indicating that they were not under stress, and when the groundwater depth was greater than 5.38 m, these variations were negative, indicating that the radial growth of the P. euphratica was under stress. Therefore, the suitable groundwater range for P. euphratica growth is 4.17–5.38 m, and when the restoration and protection of P. euphratica forests are carried out, a water level range of 4.17–5.38 m should be ensured through water transfer from the basin. It has also been observed that the photosynthetic rate (PN) of P. euphratica is not sensitive to changes in groundwater level when the groundwater depth is 4.2–6.8 m [47,48]. In this study, the appropriate groundwater level for the radial growth of P. euphratica in the lower Tarim River was found to be 4.17–5.38 m, which is not significantly different from the appropriate ecological water levels derived from previous studies.

5. Conclusions

In this study, we used wood anatomical samples and environmental factor data to analyze the relationship between radial variation characteristics (onset, end, and duration of growth, cumulative annual radial growth, and intra-annual radial variation rate) and environmental factors. The results thereof showed that the main growing period of P. euphratica is from April to September, with the fastest radial variation rate in mid-June. Groundwater depth is a key factor that affects P. euphratica growth, and the range of suitable groundwater depths suitable for this is 4.17–5.38 m. In the restoration and protection of P. euphratica forests, priority should be considered to ecological water transfer after mid-June to ensure a groundwater depth range of 4.17–5.38 m. Therefore, in order to have a more comprehensive understanding of the relationship between P. euphratica growth and the environment, longer years of monitoring are required.