Site description
Our study was conducted at Yehe National Forestry Center in the Qishui watershed, which had a 12-year-old R. pseudoacacia plantation located in Fufeng County, Shaanxi Province, which is situated on the southern Loess Plateau in China (34.55°N, 107.90°E; 1080 m a.s.l.). The forest covers an area of 2980 ha. The soil in the area is classified as Gleyic Phaeozems, and according to the World Reference Base for Soil Resources, these soils are 11% sand, 20% clay, and 69% silt (Zhang et al. 2018). Stand age is ten, stand density is ∼1300 trees ha− 1, mean tree height is 9.23 ± 1.22 m, and mean diameter at breast height (DBH) is 8.65 ± 0.86 cm, with an average leaf area index (LAI) of ~ 2.05 (based on digital hemispherical photographs). Throughout the region, artificial afforestation communities, which included R. pseudoacacia and Pinus tabuliformis, were established during the end of the 20th century. R. pseudoacacia accounted for more than 90% of the forest coverage. The dominant understory vegetation was Stipa bungeana and Artemisia argyi, which had a coverage of 80% − 90%.
The area has a semi-humid, temperate, continental climate. Mean annual precipitation was 592 mm, 80 % of which falls between June and October based on 45 years of meteorological data (1971–2016). Mean annual temperature was 11.5°C, and mean monthly temperature ranged from − 2°C in January to 26°C in July. From Fig. 1, 2015 can be considered a wet year, while 2016 was a normal year.
Experimental design
This study focused on the first 2 years of a large precipitation manipulation experiment that was established at the site in summer 2015 (Zhang et al. 2018). The experiment consisted of two treatments: drought and ambient control. Treatments were applied to 20 × 20 m plots (400 m2) that contained 50 target trees each. The two treatments were replicated in three randomly selected blocks for a total of six experimental treatment plots. The blocks were at least 500 m apart, and each had the same soil type and similar topography. In the drought treatment, rainfall was reduced by approximately 50% relative to ambient using a system of plastic panels and plastic-lined gutters that were fixed to rails approximately 1 m high. Drainage tunnels (30 cm deep) were trenched to remove the effect of through-flow of soil water. To control for any temporary damage to roots from the trenching, the control plot was also trenched to the same depth.
In 2014, an instrumented flux tower was installed near the tower base which continuously measured major environmental variables (Ma et al. 2017). Soil volumetric water content (VWC) was measured continuously using eight electrically conductive sensors (EC-5, Decagon, USA). The sensors were installed around the trees at depths of 10, 20, 40, 60, 80, and 100 cm in one drought plot and one control plot, and measured from June 2015 to December 2016 (Zhang et al. 2018). The VWC were powered and controlled a data logger (CR1000 Campbell Scientific, Logan UT, USA) which scanned every 30 s, and this was recorded the value of averaging with 10 min.
Sap-flow and leaf area index
Six R. pseudoacacia trees were selected randomly for sap flow measurements from the drought and control plots. The thermal dissipation probe (TDP) method (Granier 1987) was used to measure the sap-flow density, and it was calibrated with the empirical equation established by Ma et al. (2017). We used 10-mm-long sensors, and the detailed information about the sensors is described in Peng et al. (2015). All of the TDP sensors were connected to a CR1000 data logger with an AM16/32 multiplexer (Campbell Scientific, Logan UT, USA). The temperature difference between the upper heated probe and the lower reference probe was measured every 30 s, and this was recorded the value of averaging with 10 min. The stand transpiration (Q, mm) was calculated as the product of sap-flow density and sapwood area of the plot. The sapwood area was determined by cutting neighboring trees and discriminating between the sapwood and the heartwood based on color differences.
The LAI for the two treatments was estimated indirectly twice a month at the center of each plot. The LAI was calculated in June–November 2015 and April–November 2016 by processing digital hemispherical photographs, which were collected under uniform sky conditions near dawn and sunset on clear days. Each photograph was analyzed using CAN-EYE 4.0 software (Demarez et al. 2008) to calculate an index of integrated light availability, expressed as percent open sky, at each measurement location (Ambrose et al. 2016). The camera and lens were Nikon D90 and Nikon AF DX 10.5mm f/2.8G ED, respectively.
Leaf water potential
Pre-dawn and midday leaf water potentials (Ψpd and Ψmd, respectively) were measured once a month during the study. Five randomly-selected trees per plot were measured from May-October 2015 and May-October 2016. Two south-facing twigs with healthy foliage were collected from each selected tree before sunrise and immediately measured with a portable water potential pressure chamber (615D, PMS Instrument Company, Albany, OR, USA).
Native embolism and the vulnerability curves
In all cases, current year extension growth from the uppermost branches exposed to full sun was sampled. We limited the measured material to current year growth to avoid embolism that originated from conditions prior to the growing season (i.e. no freeze-thaw embolism) and to facilitate the cleanest cuts possible, thus reducing the possibility that damage to the xylem during harvest would affect our results. Zhang and Wang (2016) showed that the mean vessel length for R. pseudoacacia is 19.6 cm. In our study, therefore, branches longer than 50 cm were cut from the trees in the early morning and placed immediately in black plastic bags to prevent dehydration. We also collected coarse root (diameter about 3 cm) segments longer than 40 cm which were immediately placed in black plastic bags. Branch and root samples were collected using the same protocol as above ‘Leaf water potential measurements’ occurred during October 2015 and October 2016, and for these measurements, we used the same sample trees as we used for the analysis of leaf water potential. Excised samples were immersed in water immediately and transported to the laboratory within 1 hour.
For branches and roots, we measured native and vacuum-infiltrated hydraulic conductivity. The exposed segments were cut underwater into 30-cm-long segments. The sections were attached to a tube that was filled with degassed and 0.2 µm-filtered 20 mmol/L KCl solution, and a gravitational pressure of 0.01 MPa was used to force the solution through the segments. Hydraulic conductivity was measured with a 0.1 mg precision balance. Then, we vacuum-infiltrated the samples for 1 h to refill any embolized conduits, and we re-measured conductivity (Kolb et al. 1996). We calculated percent loss of conductivity (PLC, %) as the percent difference between native and vacuum-infiltrated conductivity. Note that in all cases, measurement segments excised under water may prevent artifacts (Sperry 2013).
Branch and root vulnerability curves (VCs) can be used to quantify the percentage loss of conductivity by air-injection method (Cai et al. 2010; Venturas et al. 2015). These partially debarked segments slightly longer than 30 cm were mounted in the double ended pressure collar. Each segment was flushed at 200 kPa water pressure for 30 min to remove embolism. The basal cut end was connected to a 1 m- long tube of approximately 25 mm in internal diameter, which is large enough to allow the escape of air bubbles coming out of the stem. The pressure was increased from 0 to 4 MPa, with each step followed by 10 min of relaxation, and flow was then measured at a constant background of 100 kPa air pressure until 88% of conductivity was lost. From the sigmoid shape of a VCs, the three variables were acquired:(i) the xylem pressure (Ψx) that corresponded to 50% loss of hydraulic conductivity (Ψ50), the most commonly used index of embolism resistance; (ii) the air entry threshold (Ψe) that corresponded to about 12% loss of conductivity, where PLC begins to rise sharply with declining ΨX; and (iii) Ψe-Ψ50, which was defined as the hydraulic safety margin, where a larger value of Ψe-Ψ50 indicated a more gradual rise of PLC after Ψx had fallen below Ψe (Meinzer et al. 2009).
NSC analysis
Using samples cut by a tree climber from fully sunlit twigs, five leaves were taken from the same trees as for PLC analysis. Samples of tree stem tissue were taken using a 4.3 mm diameter increment borer (Häglof Company Group, Långsele, Sweden) at breast height; this sampling was collected from five different trees to avoid excessive damage that would occur by repeated boring the same tree. The coarse roots (diameter > 5 mm) were also collected from different trees. All these samples were collected late in the morning and were immediately labeled and frozen by immersion in liquid nitrogen. Samples were collected in June and November 2015, and April to October 2016.
Here, NSCs are defined as starch plus soluble sugars, which included sucrose, glucose, and fructose. All samples were oven-dried at 70°C to a constant mass and then fine ground to a powder with a ball mill (FOSS CT410, Sweden). The analyses for determining NSC followed Andrew et al. (2013). About 0.5 g of each plant organ powder was vacuum-infiltrated with 80% ethanol for 15 min, and then it was centrifuged at 7000 rpm for 10 min. A further two extractions were carried out with 80% ethanol. Supernatants were combined, filtered through a 0.45-µm syringe filter, and analyzed for soluble sugars. The ethanol-insoluble pellets were used to determine starch content.
The ethanol-soluble fractions were analyzed using a Waters Alliance high-pressure liquid chromatographic (HPLC) system (Milford, MA, USA) (Andrew et al. 2013). The separated soluble sugars were identified and quantified with known standards and expressed as per cent of dry matter.
Starch was extracted from the remaining dry matter in a boiling solution of 0.02 NaOH for 1 h, followed by hydrolysis to glucose with α-amyloglucosidase (EC 3.2.1.3, Boehringer Mannheim Biochemicals, Mannheim, Germany). Glucose that was formed by hydrolysis was measured colorimetrically at 340 nm (spectrophotometer model 2550, Shimadzu, Japan). Starch concentrations were calculated from standard curves and expressed as per cent of dry matter.
Statistical analyses
A one-way analysis of variance (ANOVA) was used to test the effect of treatment on DBH, LAI, leaf water potential, PLC, and plant water use at each sampling date (P < 0.05). A one-way ANOVA was used to test the effect of treatment on the sugar and starch concentrations for each plant organ (leaf, stem, and root) at each sampling date (P < 0.05). Relationships between Ψx (negative value) and PLC in stems and roots were fitted using a three-parameter, sigmoidal regression function (Wang et al. 2015; Zuo et al. 2012):
All statistical analyses were performed using SPSS v.18.0 (SPSS, Inc., Chicago, USA).