Integrated Tree–Shrub–Grass Vegetation Restoration in Sandy Riparian Zones of the Yarlung Tsangpo River: Techniques and Ecological Implications

Integrated Tree–Shrub–Grass Vegetation Restoration in Sandy Riparian Zones of the Yarlung Tsangpo River: Techniques and Ecological Implications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrated Tree–Shrub–Grass Vegetation Restoration in Sandy Riparian Zones of the Yarlung Tsangpo River: Techniques and Ecological Implications Jiajia Zhang, Yanhui Ye, Xianlei Gao, Chuanqi Wang, Yanjun Miao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6612190/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To advance the theory and practice of vegetation restoration on desertified lands, we conducted a field-based investigation in the sand-control experimental zone of lirong Township, Milin County, situated in the middle reaches of the Yarlung Tsangpo River. Through a combination of long-term fixed-point observation and controlled field experiments, we examined the ecological impacts of integrating tree-shrub-grass species. We developed a composite planting model suited for sandy riparian zones and evaluated its performance. Results demonstrate that such vegetation assemblages effectively reduce wind speed, enhance relative humidity, and stabilize air, surface, and subsurface temperatures. Furthermore, the treatments improved soil water content and porosity while reducing bulk density and pH. Soil fertility metrics—including organic matter, total nitrogen, phosphorus, and potassium—also showed significant improvement, enhancing the land’s productivity. Among the tested configurations, the assemblage of Cupressus gigantea , Sophora moorcroftiana , Medicago sativa , Melilotus officinalis , Eragrostis nigra , and Astragalus adsurgens , when paired with rainwater-collecting trays and geocell structures, proved optimal for ecological restoration in the riparian sandy soils of the central Yarlung Tsangpo basin. Biological sciences/Ecology Biological sciences/Plant sciences Earth and environmental sciences/Environmental sciences Vegetation assemblage configuration model soil physicochemical properties Milin County Yarlung Tsangpo River Figures Figure 1 Figure 2 Introduction The Yarlung Tsangpo River, often referred to as the “Heavenly River,” is the longest plateau river in China. In recent years, the region has experienced increasing ecological stress due to global climate change and intensified anthropogenic activities, resulting in wetland shrinkage, grassland degradation, and expanding desertification1 1 , 2 . The central reaches of the Yarlung Tsangpo River represent both the cultural and economic heart of Tibet and one of the regions most severely affected by wind-driven desertification 3 , 4 , with sandy land currently covering nearly 3,000 hm 2 .The primary driver of land desertification in this area is the seasonal exposure of river sediments caused by fluctuating precipitation. These exposed sediments, under the joint influence of strong solar radiation and frequent high winds, have led to widespread vegetation degradation across the middle and lower reaches 5 , 6 . The expansion of aeolian sand activity has exacerbated ecological problems such as grassland degradation and biodiversity loss, and has also disrupted transportation infrastructure, thereby impacting the livelihoods and environmental quality of local communities. Urgent action is needed to address these issues. Considerable progress has been made in China’s battle against desertification. For example, Wang et al. 7 demonstrated that integrated treatments such as “sheep manure + fiber grid sand barriers + protected species” and “sheep manure + peat soil + protected species” significantly enhanced sand fixation and vegetation recovery in the headwaters of the Yellow River. Similarly, Jiang et al. 8 found that planting Caragana microphylla Lam. within straw grids in the Horqin Sandy Land effectively stabilized mobile dunes and improved soil and biodiversity.In the central Yarlung Tsangpo basin, researchers have also explored restoration strategies. Shen et al. 9 reported promising germination and survival rates for species such as Corethrodendron scoparium Fisch., Calligonum mongolicum , Hedysarum mongolicum and Sophora moorcroftiana in mobile sandy soils. Song and Bianba Zhuoma 10 evaluated the ecological adaptability of forages and identified Cichorium intybus , WL168HQ , Elymus nutans , and perennial ryegrass as high-biomass candidates. However, comprehensive studies on the ecological effects of mixed tree–shrub–grass vegetation assemblages for sandy land restoration in this region remain lacking. The tree–shrub–grass planting model is widely recognized for its structural complexity and multifunctionality in ecological restoration. It serves as a robust system for water and soil conservation, capable of withstanding extreme environmental conditions 11 . Both empirical knowledge and field trials confirm that biological measures—especially heterogeneous combinations of trees, shrubs, and grasses—are highly effective for stabilizing desertified land in the Yarlung Tsangpo River’s central basin 10 , 12 .Against this background, the present study focuses on the sandy land restoration trial zone in Lirong Township, Milin County. By investigating the establishment techniques and ecological outcomes of tree–shrub–grass assemblages, we aim to develop a practical and scientifically grounded restoration model. Our findings will support large-scale vegetation restoration and ecological reconstruction in the riparian sandy zones of the Yarlung Tsangpo River. Materials and Methods Study Area Overview This study was conducted in the region between Lirong Township and Wolong Town, within Milin County, located in the central reaches of the Yarlung Tsangpo River (Fig. 1 ). The study area (29°8′16″N, 93°45′26″E; elevation: 2973 m) is characterized by a semi-arid plateau temperate climate. The rainy season spans from June to September, while the dry season extends from October to May of the following year. The region receives an average annual precipitation of 698 mm, with an annual evaporation of 1964.4 mm. The mean annual temperature is 8.7°C, with the highest monthly average in July (29.5°C) and the lowest in January (–15°C). The annual effective accumulated temperature is 2791.8°C, total sunshine duration is 1669.6 hours, the frost-free period lasts 153 days, and wind-blown sand events occur approximately 52 times per year 13 , 14 . Experimental Plots Three experimental plots were established in the sand control demonstration area of Lirong Township: Treatment 1: A vegetation community composed of Cupressus gigantea, Sophora moorcroftiana, and a mix of herbaceous species, equipped with water-harvesting trays and geocell structures. Treatment 2: The same plant community configuration as Treatment 1 but without the use of water-harvesting trays and geocells. Control (CK): A bare sandy plot with no vegetation. All plots were rectangular and covered approximately 528 m² of mixed grassland or sandy soil. In Treatment 1, grass species including Medicago sativa, Melilotus officinalis, Eragrostis nigra, and Astragalus adsurgens were sown using surface broadcasting techniques with soil coverage of 1–2 cm, and overlaid with eco-friendly non-woven fabric (18 ± 2 g/m²) to preserve soil temperature and moisture. The geocells used (HQ-100-330, black, 100 mm depth, 165 mm diameter) served to support herbaceous plant rooting, while rainwater-collecting trays (Tal-Ya, Israel) were applied to support the growth of Cupressus gigantea and Sophora moorcroftiana . The density of Cupressus gigantea was 70 individuals per acre, with a survival rate of 94%. The plants were spaced at 1.2 × 2.0 m intervals, and their heights ranged from 1.37 to 2.0 m. The crown widths varied from 0.91 to 1.44 m, while the breast-height diameters were between 2.5 and 3.7 cm, and the ground diameters were 3.9–5.1 cm. For Sophora moorcroftiana ., the density was 90 individuals per acre, with a survival rate of 98%. These plants were spaced at 0.8 × 1.5 m intervals, with heights ranging from 0.81 to 1.20 m and crown widths from 0.72 to 0.80 m. The grass plant community included Medicago sativa with an average height of 22.33 cm and a density of 52 plants per square meter, Melilotus officinalis with an average height of 63.20 cm and a density of 30 plants per square meter, Eragrostis nigra with an average height of 10.83 cm and a density of 106 plants per square meter, and Astragalus adsurgens with an average height of 16.00 cm and a density of 40 plants per square meter. The vegetation cover of the forage plants was 95%. All species were sown by broadcasting, followed by a soil cover of 1–2 cm and mulching with non-woven fabric to maintain soil temperature and moisture.In Treatment 2, the cultivation of forage plants ( Medicago sativa , Melilotus officinalis , Eragrostis nigra , and Astragalus adsurgens ), shrubs ( Sophora moorcroftiana ), and trees ( Cupressus gigantea ) was conducted without the use of environmentally friendly non-woven fabric (18 ± 2 g/m²), geocell structures (HQ-100-330/black, depth 100 mm, size 165 mm), or rainwater-collecting trays (Tal-Ya, Israel). The cultivation methods, densities, and spacings of the tree–shrub–grass planting model in the grassland were identical to those in Treatment 1. The survival rate of Cupressus gigantea was 61%, with plant heights ranging from 0.87 to 1.50 m, crown widths from 0.70 to 0.94 m, breast-height diameters from 1.9 to 2.7 cm, and ground diameters from 3.2 to 4.5 cm. For Sophora moorcroftiana , the survival rate was 73%, with plant heights ranging from 0.65 to 0.82 m and crown widths from 0.63 to 0.77 m. The grass community included Medicago sativa (average height 15.46 cm, density 44 plants/m²), Melilotus officinalis (average height 50.40 cm, density 21 plants/m²), Eragrostis nigra (average height 7.65 cm, density 79 plants/m²), and Astragalus adsurgens (average height 10.30 cm, density 19 plants/m²), with a vegetation cover of 56%.The control group was an open area with no vegetation cover. Observation Period All vegetation was planted in April 2020. Five monitoring points were established within each plot. Microclimatic measurements were taken from July 25 to 30, 2022, with observations made every two hours daily. Soil sampling was conducted on July 30, 2022. Measurement Methods Microclimate Monitoring Air temperature, relative humidity, light intensity, wind speed, and subsurface temperature (at 5 cm depth) were recorded using a parallel observation method. Measurements were taken 80–100 cm above the ground using a digital thermometer-hygrometer (C20A, China), a light meter (RE-Y2061A, Zhejiang), and an anemometer (AR856, Guangdong). Soil Physical and Chemical Properties Soil water content was determined via the drying method, using soil cores extracted from 0–50 cm depths (layered into 0–10, 10–20, 20–30, 30–40, and 40–50 cm intervals).Determination of soil bulk weight and porosity: The bulk weight and porosity of the soil layers at depths of 0–10, 10–20, 20–30, 30–40 and 40–50 cm were determined by the ring knife-soaking method (ring knife volume of 100 cm 3 ) with three replications at each level. $$\:\begin{array}{c}soil\:bulk\:density\:\left(\:pd\:\right)=\frac{M}{V}\:\#\left(1\right)\end{array}$$ $$\:\begin{array}{c}Soil\:porosity\:=\:\left(\:1\:-\frac{columetric\:weight}{relative\:density}\right)\:\times\:\:100\%\:\#\left(2\right)\end{array}$$ Where, pd is the bulk density of a layer of soil (g/cm 3 ); M is the mass (g); V is the unit volume (cm 3 ) 15 , 16 , 17 . Measurement of soil chemical properties pH was measured using a soil-to-water ratio of 1:2.5 with a pH meter 18 .Total nitrogen, phosphorus, and potassium were measured following standard protocols. Organic matter content was determined using the potassium dichromate oxidation method. Data Analysis Data were processed using Excel 2022. One-way analysis of variance (ANOVA) was performed using SPSS 22.0 to assess significance (P < 0.05), and visualizations were created in Origin 9.0. Results Effects of Vegetation Assemblages on Microclimate: Air Temperature, Humidity, Light Intensity, and Wind Speed Significant differences were observed in microclimatic conditions—namely air temperature, relative humidity, light intensity, and wind speed—across the three treatments (Table 1 ). At 14:00, the highest recorded air temperatures were 31.15°C, 31.80°C, and 37.55°C for Treatments 1, 2, and the control, respectively. Compared to the control, Treatment 1 showed significantly higher air humidity at 08:00, 10:00, 14:00, 16:00, and 18:00 (P < 0.05). At all measured time points (08:00, 10:00, 12:00, 14:00, 16:00, 18:00, and 20:00), light intensity in Treatment 2 was significantly lower than in the control (P < 0.05), while at 10:00, 12:00, 14:00, and 16:00, light intensity in Treatment 1 was significantly lower than in Treatment 2 (P < 0.05), indicating enhanced shading from denser vegetation cover. Air wind speeds in Treatments 1 and 2 were significantly lower than the control at 08:00, 16:00, and 18:00 (P < 0.05). At 10:00 and 14:00, wind speed in Treatment 1 was significantly lower than the control (P < 0.05); at 12:00, wind speed in Treatment 2 was significantly reduced, with Treatment 1 showing significantly lower wind speed than Treatment 2 (P < 0.05). Surface microclimatic patterns mirrored those of the air (Table 2 ). At 14:00, the surface temperature peaked at 27.65°C, 28.90°C, and 44.50°C in Treatments 1, 2, and the control, respectively. Compared to the control, Treatment 1 showed a significant decrease in surface temperature at 12:00 (P < 0.05), and both Treatment 1 and 2 showed reductions at 14:00 (P < 0.05). Treatment 1 also had significantly higher surface humidity at multiple time points (08:00, 12:00, 16:00, and 18:00). Surface light intensity in Treatment 2 was significantly lower than in the control at most time points, while Treatment 1 showed significantly lower values than Treatment 2 (P < 0.05). Wind speed measurements followed a similar trend, with both vegetated treatments reducing wind speed significantly compared to the control. Subsurface temperature measurements at 5 cm depth revealed a similar temporal pattern to surface and air temperatures (Table 3 ), showing a general increase followed by a rise and fall during the day. Peak subsurface temperatures at 14:00 were 25.70°C, 27.15°C, and 34.10°C in Treatments 1, 2, and the control, respectively, with Treatment 1 significantly lower than the control (P < 0.05). Table 1 Plant community air temperature, humidity, light level and wind speed test results time Treatment 1 Treatment 2 control(CK) Temp-erature/℃ Humi-dity /% light level /lx wind speed m/s Temp-erature/℃ Humi-dity /% light level /lx wind speed m/s Temp-erature/℃ Humi-dity /% light level /lx wind speed m/s 06:00 14.90a 61a 9.50a 1.50a 15.55a 55a 9.50a 2.20a 16.65a 43a 14.50a 2.25a 08:00 16.20a 58a 2660b 0.95b 16.20a 50ab 3695b 0.95b 16.65a 37b 7565a 1.60a 10:00 20.95a 53a 9270c 0.85b 21.30a 41ab 19700b 1.10ab 21.90a 29b 98900a 1.40a 12:00 26.15a 39a 10000c 0.80c 26.65a 34a 28300b 1.50b 30.30a 28a 167950a 2.35a 14:00 30.90a 32a 14700c 2.00b 31.80a 28ab 73250b 2.95ab 37.55a 21b 211000a 3.65a 16:00 29.10a 34a 30200c 2.60b 29.85a 29ab 72600b 3.00b 31.85a 21b 132150a 4.70a 18:00 25.20a 40a 25100b 1.80b 26.15a 34ab 37400b 2.10b 27.90a 24b 118200a 4.05a 20:00 22.20a 48a 10800b 2.45a 21.60a 44a 15575b 2.80a 21.00a 35a 47580a 3.30a Note: Different lowercase letters in the same row indicate that the difference of the same index between different treatments is significant (P < 0.05), the same as Table 2 , Table 3 and Table 5 below. Table 2 Plant community surface temperature, humidity, illumination and wind speed test results time Treatment 1 Treatment 2 control(CK) Temp-erature/℃ Humi-dity /% light level /lx wind speed m/s Temp-erature/℃ Humi-dity /% light level /lx wind speed m/s Temp-erature/℃ Humi-dity /% light level /lx wind speed m/s 06:00 15.60a 69a 2c 0.40c 15.60a 60a 5.5b 0.85b 15.50a 48a 9a 1.30a 08:00 16.60a 64a 895c 0.40b 16.95a 55ab 1430b 0.50b 15.75a 42b 6360a 1.05a 10:00 19.55a 59a 5155c 0.40b 21.00a 46ab 10615b 0.55b 24.85a 30c 82300a 0.90a 12:00 24.60b 44a 6000c 0.40b 27.15ab 40ab 15050b 0.50b 37.20a 27b 145450a 1.40a 14:00 27.65b 38a 8650c 0.40c 28.90b 33ab 30900b 0.95b 44.50a 25b 176650a 2.85a 16:00 27.40a 40a 11200c 0.60c 28.00a 33ab 25200b 1.00b 39.25a 25b 111700a 3.15a 18:00 24.10a 46a 10300c 0.25c 25.55a 39ab 16650b 0.80b 30.95a 29b 65850a 2.95a 20:00 23.55a 54a 6002b 0.20b 22.80a 50a 7175b 0.30b 21.70a 39a 17395a 2.10a Table 3 Test results of below-ground (-5 cm) temperature (℃) of plant communities time Treatment 1 Treatment 2 control(CK) 06:00 16.00 ± 1.54a 16.00 ± 1.91a 15.50 ± 2.04a 08:00 17.00 ± 0.96a 17.00 ± 1.07a 16.00 ± 0.85a 10:00 19.00 ± 1.15a 20.00 ± 1.62a 21.00 ± 1.73a 12:00 23.00 ± 2.33a 23.65 ± 1.95a 26.15 ± 1.42a 14:00 25.70 ± 2.19b 27.15 ± 2.36ab 38.10 ± 1.90a 16:00 25.05 ± 1.84a 26.95 ± 1.58a 28.55 ± 2.03a 18:00 23.35 ± 1.25a 24.00 ± 1.77a 26.35 ± 1.00a 20:00 20.00 ± 0.92a 20.00 ± 0.81a 20.35 ± 0.64a Diurnal Variation of Air, Surface, and Subsurface Temperatures Both Treatments 1 and 2 exhibited lower air, surface, and subsurface temperatures compared to the control group (Table 4 ; Fig. 2). Temperature variations throughout the day followed an “S-shaped” curve. Maximum surface temperatures in Treatments 1 and 2 were significantly lower than in the control (P < 0.05), as were maximum subsurface temperatures in Treatment 1. Additionally, the diurnal temperature range in air, surface, and subsurface layers was significantly narrower in Treatment 1 than in the control (P < 0.05), indicating that vegetation cover effectively moderated temperature extremes. Daily minimum temperatures for all plots occurred at 04:00, while maximum temperatures were recorded at 14:00. At 04:00, the lowest air, surface, and subsurface temperatures for Treatment 1 were 14.40°C, 14.75°C, and 16.00°C, respectively; for Treatment 2, these were 14.20°C, 14.55°C, and 16.00°C; and for the control, 13.70°C, 13.30°C, and 15.00°C. Table 4 Test results of air, surface and subsurface temperatures of plant communities-Daily averages Treatm-ent Temperature indicators Maxim-um temperat-ure (℃) Minimum temperatu-re (℃) Diurnal temperature range (℃) Surface maximum temperatu-re (℃) Surface minimum temperatu-re (℃) Surface diurnal temperature range (℃) Undergr-ound maximum temperature (℃) Undergr-ound minimum temperatu-re (℃) Below-ground diurnal temperatu-re range (℃) Treatme-nt1 30.90a 14.40a 16.75b 27.65b 14.75a 12.90b 25.70b 16.00a 9.70b Treatme-nt2 31.80a 14.20a 17.50ab 28.90b 14.45a 14.35b 27.15ab 16.00a 11.50b control(CK) 37.55a 13.70a 23.25a 44.50a 13.30a 31.20a 38.10a 15.00a 19.10a Note: Different lowercase letters in the same column indicate significant differences between treatments (P < 0.05). Table 6 below is the same. Effects of Vegetation Assemblages on Soil Physical Properties As soil depth increased, soil water content and total porosity in Treatments 1 and 2 initially rose and then declined, while bulk density showed the opposite trend—decreasing and then increasing. In contrast, the control group exhibited a gradual increase in soil water content and porosity with depth and a corresponding decrease in bulk density (Table 5 ). In the 0–40 cm soil profile, Treatment 1 had significantly higher water content than the control (P < 0.05). The 20–30 cm layer recorded the highest water content (5.56% for Treatment 1, 4.14% for Treatment 2), the greatest total porosity (43.35% and 41.3%, respectively), and the lowest bulk density (1.11 g/cm³ and 1.25 g/cm³). From 20–50 cm, total porosity in Treatment 1 was significantly higher than in the control (P < 0.05), indicating enhanced soil structural development under vegetative cover. Table 5 Soil physical property test results sampling depth(cm) Treatment 1 Treatment 2 control(CK) Moistu-re content (%) capacity (g/cm 3 ) total porosity (%) Moistu-re content (%) capacity (g/cm 3 ) total porosity (%) Moistu-re content (%) capacity (g/cm 3 ) total porosity (%) 0∼10 4.04a 1.23a 32.16a 2.92ab 1.42a 38.81a 2.34b 1.52a 36.50a 10∼20 4.61a 1.22a 41.45a 3.22ab 1.38a 40.82a 2.68b 1.40a 34.99a 20∼30 5.56a 1.11a 43.35a 4.14ab 1.25a 41.30ab 3.16b 1.35a 27.51b 30∼40 5.31a 1.14a 35.20a 3.90ab 1.28a 33.14ab 3.33b 1.31a 23.32b 40∼50 4.97a 1.18a 31.82a 3.74a 1.28a 28.12ab 3.58a 1.29a 20.89b Effects of Vegetation Assemblages on Soil Chemical Properties Marked differences in soil chemical characteristics were observed across treatments (Table 6 ). Both Treatments 1 and 2 exhibited significantly higher total nitrogen and organic matter contents compared to the control (P < 0.05), with Treatment 1 outperforming Treatment 2 (P < 0.05), suggesting enhanced soil fertility under improved vegetation structure. Treatment 1 also showed significantly greater total phosphorus content than the control (P < 0.05), although no significant differences were observed in total potassium levels among treatments. Soil pH was highest in the control group (7.9), followed by Treatment 2 (7.3), and lowest in Treatment 1 (6.8). Compared to the control, soil pH in Treatments 1 and 2 decreased by 13.92% and 7.6%, respectively, indicating a substantial acidifying effect associated with vegetation recovery. Table 6 Results of soil chemical properties testing of plant communities Treatment Chemical indicators Total N(g/kg) Total P(g/kg) Total K(g/kg) Organic matter(g/kg) pH value Treatment 1 0.34a 0.62a 20.64a 5.86a 6.8a Treatment 2 0.23b 0.49ab 20.16a 3.63b 7.3a Control (CK) 0.12c 0.46b 20.05a 2.25c 7.9a Discussion Microclimatic Modulation by Vegetation Assemblages Riparian zones are recognized as ecologically sensitive interfaces where vegetation restoration presents significant challenges 19 . The selection and spatial arrangement of plant species must align with local ecological adaptability and biogeographic principles to ensure restoration success. Our previous research identified a suite of tree, shrub, and herbaceous species with high ecological suitability for the central Yarlung Tsangpo region. Building on this, the present study demonstrates that, at 14:00, surface temperatures in both Treatments 1 and 2 were significantly lower than in the control group (P < 0.05). This supports the hypothesis that vegetation, via transpiration and canopy shading, can buffer against extreme surface temperatures—findings consistent with previous studies 20 . Subsurface temperatures were similarly moderated, indicating that increased vegetation coverage can influence deeper soil thermal regimes. Significant reductions in light intensity were recorded across vegetated plots at all observation times (08:00、10:00、12:00、14:00、16:00、18:00 and 20:00), attributed to increased canopy density and shading effects. Notably, Treatment 1 consistently exhibited lower light intensities than Treatment 2 (P < 0.05), confirming denser plant coverage and a more developed vertical structure. These findings parallel earlier work on vegetation-induced light attenuation 21 .Wind is a critical driver of aeolian processes and strongly influences vegetation dynamics 22 , 23 . Our study found that vegetated treatments significantly reduced both surface and air wind speeds compared to the control, particularly in Treatment 1. This indicates the potential of woody–shrubby–herbaceous systems to mitigate wind erosion and stabilize sandy substrates—echoing prior research conducted in similar desert environments 24 .Microclimate stabilization was further reflected in increased humidity across vegetated plots. At multiple time points (08:00、10:00、14:00、16:00 and 18:00), both air and surface humidity levels were significantly higher in Treatment 1 (P < 0.05). This may result from reduced wind-driven evaporation and the ability of dense vegetation to retain transpired and soil-emitted moisture 25 , 26 . The diurnal temperature range (DTR) of air, surface, and subsurface temperatures, defined as the difference between the maximum and minimum temperatures within a day, is an important indicator of climate change 27 , 28 . Vegetation cover not only affects surface and subsurface temperatures but also significantly influences the DTR of air, surface, and subsurface temperatures. In this study, the DTR of air in Treatment 1 was significantly lower than that in the control group (P < 0.05). The maximum surface temperature in both Treatment 1 and Treatment 2 was significantly lower than that in the control group (P < 0.05), and the DTR of surface and subsurface temperatures in these treatments was also significantly lower than that in the control group (P < 0.05). These findings indicate that tree–shrub–grass plant communities have the capacity to stabilize regional temperatures and prevent the occurrence of extreme temperatures. This effect may be attributed to the ground cover provided by the vegetation, which shields the soil from direct sunlight during the day and reduces temperature drops caused by wind at night. Additionally, the reduction in wind speed is also an important factor influencing the DTR of air, surface, and subsurface temperatures. Improvement of Soil Physical and Chemical Properties Soil total porosity, bulk density, and moisture content are key indicators of soil physical properties 29 , 30 , 31 . In this study, in the 20–50 cm soil layer, the total porosity of soil in Treatment 1 was significantly higher than that in the control group (P < 0.05). This indicates that the root systems of tree–shrub–grass plants can improve soil aggregate structure, which is of great physical significance for accelerating the soil formation process of sandy soils in desertified areas. The reason may be that the dead branches and leaves of tree–shrub–grass plants mix with the sandy soil, providing necessary nutrients for microbial activity. Meanwhile, with the increase of dead branches and leaves, microbes become more active, thereby increasing soil porosity. This is similar to the findings of Yang et al. 32 on the impact of straw returning duration on the physical properties of the plow layer soil. In this study, in the 20–30 cm soil layer, the total porosity of soil in Treatment 1 and Treatment 2 reached the maximum, while the soil bulk density reached the minimum. The reason may be that the root systems of the tree–shrub–grass plant community are concentrated in this soil layer. In the 0–40 cm soil layer, the soil moisture content in Treatment 1 was significantly higher than that in the control group (P < 0.05). This indicates that the root systems of the tree–shrub–grass plant community under Treatment 1 have a strong water retention capacity. The reason may be the increase in soil total porosity and the decrease in bulk density. The improvement of soil physicochemical properties and the promotion of microbial activity accelerated the decomposition of returned materials, thereby increasing soil nutrient content and enzyme activity 33 , 34 , 35 . In this study, the total nitrogen and organic matter content of the soil in Treatment 1 and Treatment 2 were significantly higher than those in the control group (P < 0.05). This indicates that tree–shrub–grass plants have the ability to enrich soil fertility, which is of great ecological significance for reducing the amount of fertilizer applied to sandy lands and improving their productivity. This is consistent with the findings of Wei et al. 36 on the effects of mixed sowing of Medicago ruthenica and Agropyron cristatum on soil nutrient content and enzyme activity in grasslands. Moreover, compared with Treatment 2, the total nitrogen and organic matter content of the soil in Treatment 1 were significantly higher (P < 0.05), indicating that Treatment 1 has a greater capacity to enrich soil fertility than Treatment 2. The soil pH value, which serves as an indicator of soil acidity or alkalinity, can alter the distribution and transformation of soil nutrients through its effects on the physical, chemical, and biological properties of soil 37 , 38 . In this study, the control group had the highest soil pH value (7.9), while the soil pH values in Treatment 1 and Treatment 2 were reduced by 13.92% and 7.6% respectively compared with the control group. This suggests that the pH value of sandy soil is alkaline, and the tree–shrub–grass plants have reduced the pH value of sandy soil. The reason may be that the absorption by tree–shrub–grass plants and the leaching effect of precipitation have led to the consumption of base cations by plants 39 . Conclusions The vegetative assemblage comprising Cupressus gigantea , Sophora moorcroftiana , Medicago sativa , Melilotus officinalis , Eragrostis nigra , and Astragalus adsurgens , structured in a stratified the tree–shrub–grass configuration and supported by cultivation facilities (geocell structures and rainwater-collecting trays), formed a synergistic system in which species coexisted harmoniously, promoting ecological stability and functional integration.The tree–shrub–grass planting model composite ecosystem significantly improved microclimatic conditions—reducing wind speed, increasing relative humidity, and stabilizing air, surface, and subsurface temperatures. Concurrently, it enhanced soil physical and chemical properties by lowering bulk density and pH while increasing porosity, water content, and concentrations of organic matter, total nitrogen, total phosphorus, and total potassium.The integrated configuration mode of the tree–shrub–grass combination of Cupressus gigantea, Sophora moorcroftiana, Medicago sativa, Melilotus officinalis, Eragrostis nigra, and Astragalus adsurgens, combined with rainwater-collecting trays and geocell structures, can achieve comprehensive restoration of the environment, soil, and ecology. This approach is conducive to rebuilding a favorable ecological environment in the riparian sandy lands of the middle reaches of the Yarlung Tsangpo River and to realizing the sustainable use of water and soil resources. Declarations The authors declare no competing interests. Author Contribution Jiajia Zhang designed the experiment;Yanhui Ye , Xianlei Gao and Chuanqi Wang,conducted experiments, Chuanqi Wang wrote papers, Yanjun Miao revised the papers. Acknowledgements This study was funded by a project chaired by Prof. Yanjun Miao of Xizang Agricultural and Animal Husbandry University Key R&D and Transformation Projects of the Department of Science and Technology of the Tibet Autonomous Region (CGZH2024000084). Data Availability Data will be made available on request. If anyone needs data from this study, they can contact the first author, Jiajia Wang. References Ma, P.F., et al. Analysis on the sand transport wind power conditions and suggestions on the sand disaster preventions in the middle reaches of Yarlung Zangbo River, China. Journal of Desert Research 41(1):10-18 (2021). Hou, W. A preliminary study on the selection and configuration of plants for ecological restoration in the middle reaches of the Yarlung Tsangpo River. 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Identification of Sandy Land in the Midstream of the Yarlung Zangbo River. Journal of Geo-information Science 24(2):391-404 (2022). Shen, W. S., et al. Feasibility analysis of fly seeding on high cold wind sandy land in Tibet. Journal of Ecology and Rural Environment 25(1):106-111 (2009). Liu, X. Y. Soil Physics and Soil Improvement Research Method. Shanghai Scientific & Technical Publishers 1-2 (1982). Liu, M., et al. Effects of Long-term Intercropping and No-tillage on Soil Physical Properties and Crop Yield. Chinese Agricultural Science Bulletin 39(2):28-35 (2023). Xu, J. P., et al. Fractal Characteristics of Particle Composition for Soils Developed from Different Parent Materials. Acta Pedologica Sinica 57(5):1197-1205 (2020). Lu, R. K. Soil agrochemical analytical methods. China Agricultural Science and Technology Press 2000. Li, H. D., et al. Point pattern analysis of several psammophyte populations in the riparian ecotone in the middle reaches of Yarlung Zangbo River of Tibet, China. Chinese Journal of Plant Ecology 35(8):834-843 (2011). Deng, Y. C., et al. Effects of Vegetation Cover Change on Surface Temperature in Tianxingzhou of Wuhan. Journal of Green Science and Technology 25(6):53-57 (2023). Liu, H., et al. Study on Ecological Effect of Plant Communities. Journal of Shanxi Agricultural Sciences (7):81-85 (2008). Zhang, K. Q., et al. Effect of Wind Speed Fluctuation on Sand Transport Rate. Journal of Desert Research 26(3): 336-340 (2006). Sun, S. S., et al. Response of Plant Seedling Growth to the Changes in Precipitation and Wind Velocity in Horqin Sandy Land. Arid Zone Research 36(4):870-877 (2019). Wu, W. Y., et al. Variable Characteristics of Wind Profile of the Artificial Sand Dune in Sandy Land around the Qinghai Lake. Research of Soil and Water Conservation 20(6):162-167 (2013). Leck, M. A., Brock, M. A. Ecological and evolutionary trends in wetlands: Evidence from seeds and seed banks in New South Wales, Australia and New Jersey, USA. Plant Species Biology 15(2):97-112 (2010). Moran, M. S., et al. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote sensing of environment 49(3):246-263 (1994). Hansen, J., Sato. M., Ruedy, R. Long-term changes of the diurnal temperature cycle: implications about mechanisms of global climate change. Atmospheric Research 37(1):175-209 (1995). Chen, R., et al. A method for surface soil moisture estimation based on the DTR-FVC space. Journal of University of Chinese Academy of Sciences 35(6):771-781 (2018). Wang, H. L., et al. Dynamics of understory vegetation and soil physical properties in Eucalyptus plantations of different generations. Journal of Central South University of Forestry & Technology 43(2):1-9 (2023). Liu, M., et al. Effects of Long-term Intercropping and No-tillage on Soil Physical Properties and Crop Yield. Chinese Agricultural Science Bulletin 39(2):28-35 (2023). Gen, G., et al. Effects of plateau pika disturbance on plant community and soil physical property of alpine meadow in northwest Sichuan. Grassland and Turf 42(1):38-48 (2022). Yang, X. Z., et al. Effects of Straw Returning Years on Soil Physical Properties of Topsoil under Maize Shallow Buried and Drip Irrigation. Journal of Inner Mongolia Minzu University 37(4):295-300 (2022). Chen, D. D., et al. Response of Soil Microbial Biomass C and N, C Metabolism Characteristics of Microbes to Grass-Legume Mixtures of Annual Artificial Grassland in Sanjiangyuan Region. Acta Agrestia Sinica 26(5):1064-1070 (2018). Gou, W. L. Study on Productive Features of Annual Grass-legume Community in the Western Sichuan Plain. Gansu Agricultural University (2019). Zhou, J. J., et al. The effects of grass-legume mixing farming on forage nutritional quality and soil nutrient in alpine zone of Tibet. Agricultural Research in the Arid Areas 39(2):143-149 (2021). Wei, K. T., et al. Effect of Mixed Sowing Ratio on Soil Nutrient Content and Enzyme Activity of Medicago ruthenica-Bromus inermis Mixed Grassland in Longzhong Loess Plateau. Chinese Journal of Grassland 45(02):56-66 (2023). Xu, K. J., et al. Effect of the pH value on switchgrass seedling growth and development in hydroponics. Acta Ecologica Sinica 35(23):7690-7698 (2015). Mueller, K. E., et al. Tree species effects on coupled cycles of carbon, nitrogen, and acidity in mineral soils at a common garden experiment. Biogeochemistry 111(1/2/3):601-614 (2012). Zhang, J. J., et al. Effects of soil pH on soil carbon, nitrogen, and phosphorus ecological stoichiometry in three types of steppe. Acta Prataculturae Sinica 30(02):69-81 (2021). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6612190","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466542873,"identity":"6fef1d9f-54e2-41dd-a444-ec092ecb9026","order_by":0,"name":"Jiajia Zhang","email":"","orcid":"","institution":"Xizang Agricultural and Animal Husbandry University","correspondingAuthor":false,"prefix":"","firstName":"Jiajia","middleName":"","lastName":"Zhang","suffix":""},{"id":466542874,"identity":"98fed9eb-9bfd-4819-a05d-d2431454a0d3","order_by":1,"name":"Yanhui Ye","email":"","orcid":"","institution":"Xizang Agricultural and Animal Husbandry University","correspondingAuthor":false,"prefix":"","firstName":"Yanhui","middleName":"","lastName":"Ye","suffix":""},{"id":466542875,"identity":"e3f6eb9b-de1c-4e5d-b9b4-ac7b5ba59a78","order_by":2,"name":"Xianlei Gao","email":"","orcid":"","institution":"Tibet University","correspondingAuthor":false,"prefix":"","firstName":"Xianlei","middleName":"","lastName":"Gao","suffix":""},{"id":466542876,"identity":"a70bd11b-d745-4073-8555-df5f548aa0c4","order_by":3,"name":"Chuanqi Wang","email":"","orcid":"","institution":"Gansu Forestry Voctech University","correspondingAuthor":false,"prefix":"","firstName":"Chuanqi","middleName":"","lastName":"Wang","suffix":""},{"id":466542877,"identity":"aea9ea0f-dcd3-4e04-800a-c8c78b8750e0","order_by":4,"name":"Yanjun Miao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYHCCxAcfKmx4+NkbiNeSbDjjTJqMZM8B4rWwCXO2HLYxuOFApHrdGQnPmBkbzvMw3GBg/PAxhwgtZjcS0h4X7rjNwzi7gVly5jbitKQbzzxzm4dZ5gAbMy+RWtKkedvO8bBJJJCm5QAPD/FazjwABXIyjwTPwWYi/XI8BxSVdvb2x5sPfvhIjBYGgZwEKIuxgRj1QMB//ACRKkfBKBgFo2DEAgBl8Trx/28E5wAAAABJRU5ErkJggg==","orcid":"","institution":"Xizang Agricultural and Animal Husbandry University","correspondingAuthor":true,"prefix":"","firstName":"Yanjun","middleName":"","lastName":"Miao","suffix":""}],"badges":[],"createdAt":"2025-05-07 12:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6612190/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6612190/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83981002,"identity":"3c59a01e-711e-40d5-a200-60965ebfc916","added_by":"auto","created_at":"2025-06-05 10:05:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56736,"visible":true,"origin":"","legend":"\u003cp\u003eOverview map of the Study area\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6612190/v1/ca941d7f20f12f7202275a4d.jpg"},{"id":83981867,"identity":"b133c596-9de4-4457-bfba-8dc4ff69ff8b","added_by":"auto","created_at":"2025-06-05 10:13:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47806,"visible":true,"origin":"","legend":"\u003cp\u003eDaily variation of air, surface and subsurface temperatures in plant communities\u003c/p\u003e\n\u003cp\u003eA air temperature; B surface temperature; C subsurface temperature\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6612190/v1/87749b153965c718b0fbf223.jpg"},{"id":87748940,"identity":"273552eb-a25e-45ec-bb02-c5a6ca9701ad","added_by":"auto","created_at":"2025-07-28 14:39:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1399228,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6612190/v1/78be5d56-0602-4df8-b8cc-e92531d21158.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Tree–Shrub–Grass Vegetation Restoration in Sandy Riparian Zones of the Yarlung Tsangpo River: Techniques and Ecological Implications","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Yarlung Tsangpo River, often referred to as the\u0026nbsp;\u0026ldquo;Heavenly River,\u0026rdquo;\u0026nbsp;is the longest plateau river in China. In recent years, the region has experienced increasing ecological stress due to global climate change and intensified anthropogenic activities, resulting in wetland shrinkage, grassland degradation, and expanding desertification1\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003e. The central reaches of the Yarlung Tsangpo River represent both the cultural and economic heart of Tibet and one of the regions most severely affected by wind-driven desertification\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e4\u003c/sup\u003e, with sandy land currently covering nearly 3,000\u0026nbsp;hm\u003csup\u003e2\u003c/sup\u003e.The primary driver of land desertification in this area is the seasonal exposure of river sediments caused by fluctuating precipitation. These exposed sediments, under the joint influence of strong solar radiation and frequent high winds, have led to widespread vegetation degradation across the middle and lower reaches\u003csup\u003e5\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e. The expansion of aeolian sand activity has exacerbated ecological problems such as grassland degradation and biodiversity loss, and has also disrupted transportation infrastructure, thereby impacting the livelihoods and environmental quality of local communities. Urgent action is needed to address these issues.\u003c/p\u003e\n\u003cp\u003eConsiderable progress has been made in China\u0026rsquo;s battle against desertification. For example, Wang et al.\u003csup\u003e7\u003c/sup\u003edemonstrated that integrated treatments such as\u0026nbsp;\u0026ldquo;sheep manure + fiber grid sand barriers + protected species\u0026rdquo;\u0026nbsp;and\u0026nbsp;\u0026ldquo;sheep manure + peat soil + protected species\u0026rdquo;\u0026nbsp;significantly enhanced sand fixation and vegetation recovery in the headwaters of the Yellow River. Similarly, Jiang et al.\u003csup\u003e8\u003c/sup\u003efound that planting \u003cem\u003eCaragana microphylla\u0026nbsp;\u003c/em\u003eLam. within straw grids in the Horqin Sandy Land effectively stabilized mobile dunes and improved soil and biodiversity.In the central Yarlung Tsangpo basin, researchers have also explored restoration strategies. Shen et al.\u003csup\u003e9\u003c/sup\u003ereported promising germination and survival rates for species such as \u003cem\u003eCorethrodendron scoparium\u003c/em\u003e Fisch., \u003cem\u003eCalligonum mongolicum\u003c/em\u003e, \u003cem\u003eHedysarum mongolicum\u003c/em\u003e and \u003cem\u003eSophora moorcroftiana\u003c/em\u003e in mobile sandy soils. Song and Bianba Zhuoma\u003csup\u003e10\u003c/sup\u003eevaluated the ecological adaptability of forages and identified \u003cem\u003eCichorium intybus\u003c/em\u003e, \u003cem\u003eWL168HQ\u003c/em\u003e, \u003cem\u003eElymus nutans\u003c/em\u003e, and \u003cem\u003eperennial ryegrass\u003c/em\u003e as high-biomass candidates. However, comprehensive studies on the ecological effects of mixed tree\u0026ndash;shrub\u0026ndash;grass vegetation assemblages for sandy land restoration in this region remain lacking.\u003c/p\u003e\n\u003cp\u003eThe tree\u0026ndash;shrub\u0026ndash;grass planting model\u0026nbsp;is widely recognized for its structural complexity and multifunctionality in ecological restoration. It serves as a robust system for water and soil conservation, capable of withstanding extreme environmental conditions\u003csup\u003e11\u003c/sup\u003e. Both empirical knowledge and field trials confirm that biological measures\u0026mdash;especially heterogeneous combinations of trees, shrubs, and grasses\u0026mdash;are highly effective for stabilizing desertified land in the Yarlung Tsangpo River\u0026rsquo;s central basin\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e.Against this background, the present study focuses on the sandy land restoration trial zone in Lirong Township, Milin County. By investigating the establishment techniques and ecological outcomes of tree\u0026ndash;shrub\u0026ndash;grass assemblages, we aim to develop a practical and scientifically grounded restoration model. Our findings will support large-scale vegetation restoration and ecological reconstruction in the riparian sandy zones of the Yarlung Tsangpo River.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area Overview\u003c/h2\u003e \u003cp\u003eThis study was conducted in the region between Lirong Township and Wolong Town, within Milin County, located in the central reaches of the Yarlung Tsangpo River (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study area (29\u0026deg;8\u0026prime;16\u0026Prime;N, 93\u0026deg;45\u0026prime;26\u0026Prime;E; elevation: 2973 m) is characterized by a semi-arid plateau temperate climate. The rainy season spans from June to September, while the dry season extends from October to May of the following year. The region receives an average annual precipitation of 698 mm, with an annual evaporation of 1964.4 mm. The mean annual temperature is 8.7\u0026deg;C, with the highest monthly average in July (29.5\u0026deg;C) and the lowest in January (\u0026ndash;15\u0026deg;C). The annual effective accumulated temperature is 2791.8\u0026deg;C, total sunshine duration is 1669.6 hours, the frost-free period lasts 153 days, and wind-blown sand events occur approximately 52 times per year\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Plots\u003c/h2\u003e \u003cp\u003eThree experimental plots were established in the sand control demonstration area of Lirong Township: Treatment 1: A vegetation community composed of Cupressus gigantea, Sophora moorcroftiana, and a mix of herbaceous species, equipped with water-harvesting trays and geocell structures. Treatment 2: The same plant community configuration as Treatment 1 but without the use of water-harvesting trays and geocells. Control (CK): A bare sandy plot with no vegetation.\u003c/p\u003e \u003cp\u003eAll plots were rectangular and covered approximately 528 m\u0026sup2; of mixed grassland or sandy soil.\u003c/p\u003e \u003cp\u003eIn Treatment 1, grass species including Medicago sativa, Melilotus officinalis, Eragrostis nigra, and Astragalus adsurgens were sown using surface broadcasting techniques with soil coverage of 1\u0026ndash;2 cm, and overlaid with eco-friendly non-woven fabric (18\u0026thinsp;\u0026plusmn;\u0026thinsp;2 g/m\u0026sup2;) to preserve soil temperature and moisture. The geocells used (HQ-100-330, black, 100 mm depth, 165 mm diameter) served to support herbaceous plant rooting, while rainwater-collecting trays (Tal-Ya, Israel) were applied to support the growth of \u003cem\u003eCupressus gigantea\u003c/em\u003e and \u003cem\u003eSophora moorcroftiana\u003c/em\u003e. The density of \u003cem\u003eCupressus gigantea\u003c/em\u003e was 70 individuals per acre, with a survival rate of 94%. The plants were spaced at 1.2 \u0026times; 2.0 m intervals, and their heights ranged from 1.37 to 2.0 m. The crown widths varied from 0.91 to 1.44 m, while the breast-height diameters were between 2.5 and 3.7 cm, and the ground diameters were 3.9\u0026ndash;5.1 cm. For \u003cem\u003eSophora moorcroftiana\u003c/em\u003e., the density was 90 individuals per acre, with a survival rate of 98%. These plants were spaced at 0.8 \u0026times; 1.5 m intervals, with heights ranging from 0.81 to 1.20 m and crown widths from 0.72 to 0.80 m.\u003c/p\u003e \u003cp\u003eThe grass plant community included Medicago sativa with an average height of 22.33 cm and a density of 52 plants per square meter, Melilotus officinalis with an average height of 63.20 cm and a density of 30 plants per square meter, Eragrostis nigra with an average height of 10.83 cm and a density of 106 plants per square meter, and Astragalus adsurgens with an average height of 16.00 cm and a density of 40 plants per square meter. The vegetation cover of the forage plants was 95%. All species were sown by broadcasting, followed by a soil cover of 1\u0026ndash;2 cm and mulching with non-woven fabric to maintain soil temperature and moisture.In Treatment 2, the cultivation of forage plants (\u003cem\u003eMedicago sativa\u003c/em\u003e, \u003cem\u003eMelilotus officinalis\u003c/em\u003e, \u003cem\u003eEragrostis nigra\u003c/em\u003e, and \u003cem\u003eAstragalus adsurgens\u003c/em\u003e), shrubs (\u003cem\u003eSophora moorcroftiana\u003c/em\u003e), and trees (\u003cem\u003eCupressus gigantea\u003c/em\u003e) was conducted without the use of environmentally friendly non-woven fabric (18\u0026thinsp;\u0026plusmn;\u0026thinsp;2 g/m\u0026sup2;), geocell structures (HQ-100-330/black, depth 100 mm, size 165 mm), or rainwater-collecting trays (Tal-Ya, Israel). The cultivation methods, densities, and spacings of the tree\u0026ndash;shrub\u0026ndash;grass planting model in the grassland were identical to those in Treatment 1. The survival rate of \u003cem\u003eCupressus gigantea\u003c/em\u003e was 61%, with plant heights ranging from 0.87 to 1.50 m, crown widths from 0.70 to 0.94 m, breast-height diameters from 1.9 to 2.7 cm, and ground diameters from 3.2 to 4.5 cm. For \u003cem\u003eSophora moorcroftiana\u003c/em\u003e, the survival rate was 73%, with plant heights ranging from 0.65 to 0.82 m and crown widths from 0.63 to 0.77 m. The grass community included \u003cem\u003eMedicago sativa\u003c/em\u003e (average height 15.46 cm, density 44 plants/m\u0026sup2;), \u003cem\u003eMelilotus officinalis\u003c/em\u003e (average height 50.40 cm, density 21 plants/m\u0026sup2;), \u003cem\u003eEragrostis nigra\u003c/em\u003e (average height 7.65 cm, density 79 plants/m\u0026sup2;), and \u003cem\u003eAstragalus adsurgens\u003c/em\u003e (average height 10.30 cm, density 19 plants/m\u0026sup2;), with a vegetation cover of 56%.The control group was an open area with no vegetation cover.\u003c/p\u003e \u003cp\u003eObservation Period\u003c/p\u003e \u003cp\u003eAll vegetation was planted in April 2020. Five monitoring points were established within each plot. Microclimatic measurements were taken from July 25 to 30, 2022, with observations made every two hours daily. Soil sampling was conducted on July 30, 2022.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement Methods\u003c/h3\u003e\n\u003cp\u003eMicroclimate Monitoring\u003c/p\u003e \u003cp\u003eAir temperature, relative humidity, light intensity, wind speed, and subsurface temperature (at 5 cm depth) were recorded using a parallel observation method. Measurements were taken 80\u0026ndash;100 cm above the ground using a digital thermometer-hygrometer (C20A, China), a light meter (RE-Y2061A, Zhejiang), and an anemometer (AR856, Guangdong).\u003c/p\u003e \u003cp\u003eSoil Physical and Chemical Properties\u003c/p\u003e \u003cp\u003eSoil water content was determined via the drying method, using soil cores extracted from 0\u0026ndash;50 cm depths (layered into 0\u0026ndash;10, 10\u0026ndash;20, 20\u0026ndash;30, 30\u0026ndash;40, and 40\u0026ndash;50 cm intervals).Determination of soil bulk weight and porosity: The bulk weight and porosity of the soil layers at depths of 0\u0026ndash;10, 10\u0026ndash;20, 20\u0026ndash;30, 30\u0026ndash;40 and 40\u0026ndash;50 cm were determined by the ring knife-soaking method (ring knife volume of 100 cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e) with three replications at each level.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}soil\\:bulk\\:density\\:\\left(\\:pd\\:\\right)=\\frac{M}{V}\\:\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}Soil\\:porosity\\:=\\:\\left(\\:1\\:-\\frac{columetric\\:weight}{relative\\:density}\\right)\\:\\times\\:\\:100\\%\\:\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, pd is the bulk density of a layer of soil (g/cm\u003csup\u003e3\u003c/sup\u003e); M is the mass (g); V is the unit volume (cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMeasurement of soil chemical properties\u003c/p\u003e \u003cp\u003epH was measured using a soil-to-water ratio of 1:2.5 with a pH meter\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.Total nitrogen, phosphorus, and potassium were measured following standard protocols. Organic matter content was determined using the potassium dichromate oxidation method.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData were processed using Excel 2022. One-way analysis of variance (ANOVA) was performed using SPSS 22.0 to assess significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and visualizations were created in Origin 9.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEffects of Vegetation Assemblages on Microclimate: Air Temperature, Humidity, Light Intensity, and Wind Speed\u003c/h2\u003e \u003cp\u003eSignificant differences were observed in microclimatic conditions\u0026mdash;namely air temperature, relative humidity, light intensity, and wind speed\u0026mdash;across the three treatments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At 14:00, the highest recorded air temperatures were 31.15\u0026deg;C, 31.80\u0026deg;C, and 37.55\u0026deg;C for Treatments 1, 2, and the control, respectively. Compared to the control, Treatment 1 showed significantly higher air humidity at 08:00, 10:00, 14:00, 16:00, and 18:00 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). At all measured time points (08:00, 10:00, 12:00, 14:00, 16:00, 18:00, and 20:00), light intensity in Treatment 2 was significantly lower than in the control (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while at 10:00, 12:00, 14:00, and 16:00, light intensity in Treatment 1 was significantly lower than in Treatment 2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating enhanced shading from denser vegetation cover. Air wind speeds in Treatments 1 and 2 were significantly lower than the control at 08:00, 16:00, and 18:00 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). At 10:00 and 14:00, wind speed in Treatment 1 was significantly lower than the control (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); at 12:00, wind speed in Treatment 2 was significantly reduced, with Treatment 1 showing significantly lower wind speed than Treatment 2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eSurface microclimatic patterns mirrored those of the air (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At 14:00, the surface temperature peaked at 27.65\u0026deg;C, 28.90\u0026deg;C, and 44.50\u0026deg;C in Treatments 1, 2, and the control, respectively. Compared to the control, Treatment 1 showed a significant decrease in surface temperature at 12:00 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and both Treatment 1 and 2 showed reductions at 14:00 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Treatment 1 also had significantly higher surface humidity at multiple time points (08:00, 12:00, 16:00, and 18:00). Surface light intensity in Treatment 2 was significantly lower than in the control at most time points, while Treatment 1 showed significantly lower values than Treatment 2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Wind speed measurements followed a similar trend, with both vegetated treatments reducing wind speed significantly compared to the control.\u003c/p\u003e \u003cp\u003eSubsurface temperature measurements at 5 cm depth revealed a similar temporal pattern to surface and air temperatures (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), showing a general increase followed by a rise and fall during the day. Peak subsurface temperatures at 14:00 were 25.70\u0026deg;C, 27.15\u0026deg;C, and 34.10\u0026deg;C in Treatments 1, 2, and the control, respectively, with Treatment 1 significantly lower than the control (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePlant community air temperature, humidity, light level and wind speed test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eTreatment 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eTreatment 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003econtrol(CK)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemp-erature/℃\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHumi-dity /%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003elight level /lx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ewind speed m/s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTemp-erature/℃\u003c/p\u003e \u003c/th\u003e 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\u003cp\u003e14:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.90a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14700c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.80a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73250b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.95ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.55a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e211000a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.65a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.10a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30200c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.60b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.85a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72600b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.00b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.85a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e132150a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.70a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.20a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25100b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.15a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37400b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.10b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.90a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e118200a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.05a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.20a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10800b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.45a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.60a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15575b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.80a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.00a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e47580a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.30a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote: Different lowercase letters in the same row indicate that the difference of the same index between different treatments is significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the same as Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e below.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePlant community surface temperature, humidity, illumination and wind speed test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eTreatment 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eTreatment 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003econtrol(CK)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemp-erature/℃\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHumi-dity /%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003elight level /lx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ewind speed m/s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTemp-erature/℃\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHumi-dity /%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003elight level /lx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ewind speed m/s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTemp-erature/℃\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHumi-dity /%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003elight level /lx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ewind speed m/s\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e06:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.60a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.60a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.5b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.85b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.50a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e48a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.30a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e08:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.60a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e895c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.95a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1430b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.50b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.75a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e42b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6360a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.05a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.55a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5155c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.00a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10615b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.55b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.85a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e30c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e82300a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.90a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.60b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6000c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.15ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15050b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.50b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.20a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e27b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e145450a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.40a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.65b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8650c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.90b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30900b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e44.50a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e176650a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.85a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.40a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11200c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.00a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25200b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39.25a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e111700a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.15a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.10a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10300c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.55a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16650b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.80b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30.95a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e29b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e65850a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.95a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.55a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6002b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.80a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7175b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.30b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.70a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e39a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e17395a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.10a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTest results of below-ground (-5 cm) temperature (℃) of plant communities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtrol(CK)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e06:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e08:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDiurnal Variation of Air, Surface, and Subsurface Temperatures\u003c/h2\u003e \u003cp\u003eBoth Treatments 1 and 2 exhibited lower air, surface, and subsurface temperatures compared to the control group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig.\u0026nbsp;2). Temperature variations throughout the day followed an \u0026ldquo;S-shaped\u0026rdquo; curve. Maximum surface temperatures in Treatments 1 and 2 were significantly lower than in the control (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as were maximum subsurface temperatures in Treatment 1. Additionally, the diurnal temperature range in air, surface, and subsurface layers was significantly narrower in Treatment 1 than in the control (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that vegetation cover effectively moderated temperature extremes. Daily minimum temperatures for all plots occurred at 04:00, while maximum temperatures were recorded at 14:00. At 04:00, the lowest air, surface, and subsurface temperatures for Treatment 1 were 14.40\u0026deg;C, 14.75\u0026deg;C, and 16.00\u0026deg;C, respectively; for Treatment 2, these were 14.20\u0026deg;C, 14.55\u0026deg;C, and 16.00\u0026deg;C; and for the control, 13.70\u0026deg;C, 13.30\u0026deg;C, and 15.00\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTest results of air, surface and subsurface temperatures of plant communities-Daily averages\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatm-ent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003eTemperature indicators\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaxim-um temperat-ure (℃)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum temperatu-re (℃)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiurnal temperature range (℃)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurface maximum temperatu-re (℃)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSurface minimum temperatu-re (℃)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSurface diurnal temperature range (℃)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUndergr-ound maximum temperature (℃)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUndergr-ound minimum temperatu-re (℃)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBelow-ground diurnal temperatu-re range (℃)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatme-nt1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.90a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.40a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.75b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.65b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.75a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.90b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.70b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.00a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.70b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatme-nt2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.80a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.20a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.50ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.90b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.45a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.35b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.15ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.00a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.50b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrol(CK)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.55a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.70a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.25a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.50a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.30a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.20a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.10a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.00a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.10a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: Different lowercase letters in the same column indicate significant differences between treatments (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e below is the same.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffects of Vegetation Assemblages on Soil Physical Properties\u003c/h3\u003e\n\u003cp\u003eAs soil depth increased, soil water content and total porosity in Treatments 1 and 2 initially rose and then declined, while bulk density showed the opposite trend\u0026mdash;decreasing and then increasing. In contrast, the control group exhibited a gradual increase in soil water content and porosity with depth and a corresponding decrease in bulk density (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the 0\u0026ndash;40 cm soil profile, Treatment 1 had significantly higher water content than the control (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The 20\u0026ndash;30 cm layer recorded the highest water content (5.56% for Treatment 1, 4.14% for Treatment 2), the greatest total porosity (43.35% and 41.3%, respectively), and the lowest bulk density (1.11 g/cm\u0026sup3; and 1.25 g/cm\u0026sup3;). From 20\u0026ndash;50 cm, total porosity in Treatment 1 was significantly higher than in the control (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating enhanced soil structural development under vegetative cover.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSoil physical property test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003esampling\u003c/p\u003e \u003cp\u003edepth(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTreatment 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eTreatment 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003econtrol(CK)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoistu-re content (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecapacity\u003c/p\u003e \u003cp\u003e(g/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003etotal porosity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMoistu-re content (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecapacity\u003c/p\u003e \u003cp\u003e(g/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003etotal porosity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMoistu-re content (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ecapacity\u003c/p\u003e \u003cp\u003e(g/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003etotal porosity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0∼10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.04a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.16a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.92ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.81a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.34b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.52a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36.50a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10∼20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.61a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.45a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.22ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.82a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.68b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.40a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.99a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20∼30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.56a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.35a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.14ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.25a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.30ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.16b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.35a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.51b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30∼40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.31a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.20a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.90ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.14ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.33b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.31a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.32b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40∼50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.97a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.82a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.74a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.12ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.58a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.29a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.89b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eEffects of Vegetation Assemblages on Soil Chemical Properties\u003c/h3\u003e\n\u003cp\u003eMarked differences in soil chemical characteristics were observed across treatments (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Both Treatments 1 and 2 exhibited significantly higher total nitrogen and organic matter contents compared to the control (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with Treatment 1 outperforming Treatment 2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting enhanced soil fertility under improved vegetation structure. Treatment 1 also showed significantly greater total phosphorus content than the control (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), although no significant differences were observed in total potassium levels among treatments. Soil pH was highest in the control group (7.9), followed by Treatment 2 (7.3), and lowest in Treatment 1 (6.8). Compared to the control, soil pH in Treatments 1 and 2 decreased by 13.92% and 7.6%, respectively, indicating a substantial acidifying effect associated with vegetation recovery.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of soil chemical properties testing of plant communities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eChemical indicators\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal N(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal P(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal K(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrganic matter(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epH value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.64a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.86a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.8a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.16a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.63b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.3a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl (CK)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.05a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.25c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.9a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMicroclimatic Modulation by Vegetation Assemblages\u003c/h2\u003e \u003cp\u003eRiparian zones are recognized as ecologically sensitive interfaces where vegetation restoration presents significant challenges \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The selection and spatial arrangement of plant species must align with local ecological adaptability and biogeographic principles to ensure restoration success. Our previous research identified a suite of tree, shrub, and herbaceous species with high ecological suitability for the central Yarlung Tsangpo region. Building on this, the present study demonstrates that, at 14:00, surface temperatures in both Treatments 1 and 2 were significantly lower than in the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This supports the hypothesis that vegetation, via transpiration and canopy shading, can buffer against extreme surface temperatures\u0026mdash;findings consistent with previous studies \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Subsurface temperatures were similarly moderated, indicating that increased vegetation coverage can influence deeper soil thermal regimes.\u003c/p\u003e \u003cp\u003eSignificant reductions in light intensity were recorded across vegetated plots at all observation times (08:00、10:00、12:00、14:00、16:00、18:00 and 20:00), attributed to increased canopy density and shading effects. Notably, Treatment 1 consistently exhibited lower light intensities than Treatment 2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), confirming denser plant coverage and a more developed vertical structure. These findings parallel earlier work on vegetation-induced light attenuation \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.Wind is a critical driver of aeolian processes and strongly influences vegetation dynamics \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Our study found that vegetated treatments significantly reduced both surface and air wind speeds compared to the control, particularly in Treatment 1. This indicates the potential of woody\u0026ndash;shrubby\u0026ndash;herbaceous systems to mitigate wind erosion and stabilize sandy substrates\u0026mdash;echoing prior research conducted in similar desert environments \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.Microclimate stabilization was further reflected in increased humidity across vegetated plots. At multiple time points (08:00、10:00、14:00、16:00 and 18:00), both air and surface humidity levels were significantly higher in Treatment 1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This may result from reduced wind-driven evaporation and the ability of dense vegetation to retain transpired and soil-emitted moisture \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe diurnal temperature range (DTR) of air, surface, and subsurface temperatures, defined as the difference between the maximum and minimum temperatures within a day, is an important indicator of climate change \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Vegetation cover not only affects surface and subsurface temperatures but also significantly influences the DTR of air, surface, and subsurface temperatures. In this study, the DTR of air in Treatment 1 was significantly lower than that in the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The maximum surface temperature in both Treatment 1 and Treatment 2 was significantly lower than that in the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the DTR of surface and subsurface temperatures in these treatments was also significantly lower than that in the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings indicate that tree\u0026ndash;shrub\u0026ndash;grass plant communities have the capacity to stabilize regional temperatures and prevent the occurrence of extreme temperatures. This effect may be attributed to the ground cover provided by the vegetation, which shields the soil from direct sunlight during the day and reduces temperature drops caused by wind at night. Additionally, the reduction in wind speed is also an important factor influencing the DTR of air, surface, and subsurface temperatures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImprovement of Soil Physical and Chemical Properties\u003c/h2\u003e \u003cp\u003eSoil total porosity, bulk density, and moisture content are key indicators of soil physical properties \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In this study, in the 20\u0026ndash;50 cm soil layer, the total porosity of soil in Treatment 1 was significantly higher than that in the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicates that the root systems of tree\u0026ndash;shrub\u0026ndash;grass plants can improve soil aggregate structure, which is of great physical significance for accelerating the soil formation process of sandy soils in desertified areas. The reason may be that the dead branches and leaves of tree\u0026ndash;shrub\u0026ndash;grass plants mix with the sandy soil, providing necessary nutrients for microbial activity. Meanwhile, with the increase of dead branches and leaves, microbes become more active, thereby increasing soil porosity. This is similar to the findings of Yang et al.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e on the impact of straw returning duration on the physical properties of the plow layer soil. In this study, in the 20\u0026ndash;30 cm soil layer, the total porosity of soil in Treatment 1 and Treatment 2 reached the maximum, while the soil bulk density reached the minimum. The reason may be that the root systems of the tree\u0026ndash;shrub\u0026ndash;grass plant community are concentrated in this soil layer. In the 0\u0026ndash;40 cm soil layer, the soil moisture content in Treatment 1 was significantly higher than that in the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicates that the root systems of the tree\u0026ndash;shrub\u0026ndash;grass plant community under Treatment 1 have a strong water retention capacity. The reason may be the increase in soil total porosity and the decrease in bulk density.\u003c/p\u003e \u003cp\u003eThe improvement of soil physicochemical properties and the promotion of microbial activity accelerated the decomposition of returned materials, thereby increasing soil nutrient content and enzyme activity \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In this study, the total nitrogen and organic matter content of the soil in Treatment 1 and Treatment 2 were significantly higher than those in the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicates that tree\u0026ndash;shrub\u0026ndash;grass plants have the ability to enrich soil fertility, which is of great ecological significance for reducing the amount of fertilizer applied to sandy lands and improving their productivity. This is consistent with the findings of Wei et al. \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003eon the effects of mixed sowing of \u003cem\u003eMedicago ruthenica\u003c/em\u003e and \u003cem\u003eAgropyron cristatum\u003c/em\u003e on soil nutrient content and enzyme activity in grasslands. Moreover, compared with Treatment 2, the total nitrogen and organic matter content of the soil in Treatment 1 were significantly higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that Treatment 1 has a greater capacity to enrich soil fertility than Treatment 2.\u003c/p\u003e \u003cp\u003eThe soil pH value, which serves as an indicator of soil acidity or alkalinity, can alter the distribution and transformation of soil nutrients through its effects on the physical, chemical, and biological properties of soil \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In this study, the control group had the highest soil pH value (7.9), while the soil pH values in Treatment 1 and Treatment 2 were reduced by 13.92% and 7.6% respectively compared with the control group. This suggests that the pH value of sandy soil is alkaline, and the tree\u0026ndash;shrub\u0026ndash;grass plants have reduced the pH value of sandy soil. The reason may be that the absorption by tree\u0026ndash;shrub\u0026ndash;grass plants and the leaching effect of precipitation have led to the consumption of base cations by plants \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe vegetative assemblage comprising \u003cem\u003eCupressus gigantea\u003c/em\u003e, \u003cem\u003eSophora moorcroftiana\u003c/em\u003e, \u003cem\u003eMedicago sativa\u003c/em\u003e, \u003cem\u003eMelilotus officinalis\u003c/em\u003e, \u003cem\u003eEragrostis nigra\u003c/em\u003e, and \u003cem\u003eAstragalus adsurgens\u003c/em\u003e, structured in a stratified the tree\u0026ndash;shrub\u0026ndash;grass configuration and supported by cultivation facilities (geocell structures and rainwater-collecting trays), formed a synergistic system in which species coexisted harmoniously, promoting ecological stability and functional integration.The tree\u0026ndash;shrub\u0026ndash;grass planting model composite ecosystem significantly improved microclimatic conditions\u0026mdash;reducing wind speed, increasing relative humidity, and stabilizing air, surface, and subsurface temperatures. Concurrently, it enhanced soil physical and chemical properties by lowering bulk density and pH while increasing porosity, water content, and concentrations of organic matter, total nitrogen, total phosphorus, and total potassium.The integrated configuration mode of the tree\u0026ndash;shrub\u0026ndash;grass combination of Cupressus gigantea, Sophora moorcroftiana, Medicago sativa, Melilotus officinalis, Eragrostis nigra, and Astragalus adsurgens, combined with rainwater-collecting trays and geocell structures, can achieve comprehensive restoration of the environment, soil, and ecology. This approach is conducive to rebuilding a favorable ecological environment in the riparian sandy lands of the middle reaches of the Yarlung Tsangpo River and to realizing the sustainable use of water and soil resources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJiajia Zhang designed the experiment;Yanhui Ye , Xianlei Gao and Chuanqi Wang,conducted experiments, Chuanqi Wang wrote papers, Yanjun Miao revised the papers.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis study was funded by a project chaired by Prof. Yanjun Miao of Xizang Agricultural and Animal Husbandry University Key R\u0026amp;D and Transformation Projects of the Department of Science and Technology of the Tibet Autonomous Region (CGZH2024000084).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request. If anyone needs data from this study, they can contact the first author, Jiajia Wang.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMa, P.F., et al. Analysis on the sand transport wind power conditions and suggestions on the sand disaster preventions in the middle reaches of Yarlung Zangbo River, China. Journal of Desert Research 41(1):10-18 (2021).\u003c/li\u003e\n\u003cli\u003eHou, W. A preliminary study on the selection and configuration of plants for ecological restoration in the middle reaches of the Yarlung Tsangpo River. Xizang Science and Technology (9):40-44 (2021).\u003c/li\u003e\n\u003cli\u003eLiu Y, Wang Y S, Shen T. Spatial distribution and formation mechanism of aeolian sand in the middle reaches of the Yar-lung Zangbo River. Journal of Mountain Science 16(9): 1987-2000 (2019).\u003c/li\u003e\n\u003cli\u003ePan, H. X. Strengthen Desertification Combating of Yajiang Basin to Promote Ecological Barrier of Tibetan Plateau. 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Acta Prataculturae Sinica 30(02):69-81 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vegetation assemblage, configuration model, soil physicochemical properties, Milin County, Yarlung Tsangpo River","lastPublishedDoi":"10.21203/rs.3.rs-6612190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6612190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo advance the theory and practice of vegetation restoration on desertified lands, we conducted a field-based investigation in the sand-control experimental zone of lirong Township, Milin County, situated in the middle reaches of the Yarlung Tsangpo River. Through a combination of long-term fixed-point observation and controlled field experiments, we examined the ecological impacts of integrating tree-shrub-grass species. We developed a composite planting model suited for sandy riparian zones and evaluated its performance. Results demonstrate that such vegetation assemblages effectively reduce wind speed, enhance relative humidity, and stabilize air, surface, and subsurface temperatures. Furthermore, the treatments improved soil water content and porosity while reducing bulk density and pH. Soil fertility metrics\u0026mdash;including organic matter, total nitrogen, phosphorus, and potassium\u0026mdash;also showed significant improvement, enhancing the land\u0026rsquo;s productivity. Among the tested configurations, the assemblage of \u003cem\u003eCupressus gigantea\u003c/em\u003e, \u003cem\u003eSophora moorcroftiana\u003c/em\u003e, \u003cem\u003eMedicago sativa\u003c/em\u003e, \u003cem\u003eMelilotus officinalis\u003c/em\u003e, \u003cem\u003eEragrostis nigra\u003c/em\u003e, and \u003cem\u003eAstragalus adsurgens\u003c/em\u003e, when paired with rainwater-collecting trays and geocell structures, proved optimal for ecological restoration in the riparian sandy soils of the central Yarlung Tsangpo basin.\u003c/p\u003e","manuscriptTitle":"Integrated Tree–Shrub–Grass Vegetation Restoration in Sandy Riparian Zones of the Yarlung Tsangpo River: Techniques and Ecological Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-05 10:05:47","doi":"10.21203/rs.3.rs-6612190/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e45393a-7394-4a94-ba7d-8ccbdf38610e","owner":[],"postedDate":"June 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49534685,"name":"Biological sciences/Ecology"},{"id":49534686,"name":"Biological sciences/Plant sciences"},{"id":49534687,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-07-28T14:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-05 10:05:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6612190","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6612190","identity":"rs-6612190","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}