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Forest Ecology and Management

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Sap flow changes and climatic responses over multiple-year treatment ofrainfall exclusion in a sub-humid black locust plantation

Qiu-Yue Hea,b, Mei-Jie Yana,c, Yoshiyuki Miyazawad, Qiu-Wen Chena,b, Ran-Ran Chengc,Kyoichi Otsukie, Norikazu Yamanakaf, Sheng Dua,c,⁎

a State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, Shaanxi 712100, Chinab College of Forestry, Northwest A&F University, Yangling, Shaanxi 712100, Chinac Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, ChinadOffice for Campus Planning, Kyushu University, Motooka, Fukuoka 8190395, Japane Kasuya Research Forest, Kyushu University, Sasaguri, Fukuoka 8112415, JapanfArid Land Research Center, Tottori University, Hamasaka, Tottori 6800001, Japan

A R T I C L E I N F O

Keywords:Black locustRobinia pseudoacaciaRainfall exclusionSap flowTranspirationGranier-type sensorDrought resistance

A B S T R A C T

Black locust (Robinia pseudoacacia) plantations have been widely established in the semiarid and sub-humidareas of central China. Under the condition of global climate change, which is introducing much uncertainty ofprecipitation patterns in this region, it is of special significance to investigate their responses to precipitation.Here, we investigated sap flow response to reduced throughfall. Stem sap flow was measured from 2011 to 2017using Granier-type sensors. By placing waterproof panels within tree rows, about 47.5% precipitation was ex-cluded from treated plots since April 2015. Differences in soil water content gradually increased to 4.3% be-tween treated and control plots with continued throughfall exclusion. Decreased precipitation input significantlyreduced the average sap flux density in treated plots by 9.1%–45.3%. The extent of this reduction depended onprecipitation in the previous and current years. Transpiration and forest growth were negatively affected by thetreatment. Furthermore, sap flow response to environmental factors became insensitive, with the discrepancyincreasing with increasing drought duration, but was regained by rainfall recharge to soil water in the first twotreatment years. However, prolonged drought might damage transpiration resilience capacity, as the saturatedsap flux after soil water recharging during the wet period was still lower than that in the control plot in the latteryear. Predawn leaf water potential was significantly lower in the treated plot compared to the control plot,whereas midday leaf water potential was similar. Whole tree hydraulic conductance (GP) was also similar be-tween the two plots, except in extreme drought months when GP was much lower in the treated plot. In addition,the specific leaf area and stomatal density decreased in the treated plot. Therefore, decreases in precipitationwould cause a transpiration reduction, weakening the tree’s response to meteorological variables and loweringgrowth and productivity, potentially damaging transpiration resilience. These results suggest that black locust issensitive to water changes, its capacity of drought tolerant is restrained by the drought time scale or droughtseverity, the use of such species in reforestation in semiarid regions should be implemented with caution.

1. Introduction

Transpiration is a fundamental physiological process of plants, andis also crucial for regional hydrological cycles. It is a major part of re-newable fresh water over continents that inputs to the soil from pre-cipitation and losses to the atmosphere (Jasechko et al., 2013). Sap flowis a process of water transport in plants driven by transpiration, inwhich moisture moves through the xylem to the leaves. It constitutes a

key part of the soil-plant-atmosphere continuum flow path, driving theabsorption of soil water from massive underground root systems, whichdetermine and reflect the amount of transpiration from the entire plantcanopy.

Sap flow is influenced by internal plant factors, including species-specific ecophysiological properties and growth status (Granier et al.,1996; Oren and Pataki, 2001; Pataki and Oren, 2003; Du et al., 2007),and meteorological and edaphic factors (Granier et al., 1996; O'Grady

https://doi.org/10.1016/j.foreco.2019.117730Received 31 August 2019; Received in revised form 25 October 2019; Accepted 26 October 2019

⁎ Corresponding author at: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, 26 Xinong Rd,Yangling, Shaanxi 712100, China.

E-mail address: shengdu@ms.iswc.ac.cn (S. Du).

Forest Ecology and Management 457 (2020) 117730

Available online 09 December 20190378-1127/ © 2019 Elsevier B.V. All rights reserved.

T

et al., 2008; O'Grady et al., 2009; Du et al., 2011). As the major sourceof soil water recharge, precipitation is one of the most important factorsinfluencing transpiration and sap flow. Previous studies on transpira-tion in response to precipitation events or the amount of precipitationgenerally suggest that transpiration is positively linked with precipita-tion (Huxman et al., 2004; Scott et al., 2004; West et al., 2007;McDowell et al., 2008; Chen et al., 2014). For example, the transpira-tion of a mixed forest of Scots pine and Norway spruce was reducedduring the dry period in July and increased substantially after a rainyperiod in August (Cermak et al., 1995). The daily transpiration of aScots pine forest in the Iberian Peninsula during dry summer represents40% of average precipitation (Llorens et al., 2010). A reduction of 29%average precipitation causes a decrease of 23% in annual transpirationin Quercus ilex forests (Limousin et al., 2009). Knowledge about theresponses of trees to drought is accumulating; however, the response ishighly variable, even within a species due to acclimation to differentenvironments.

To date, studies of plant transpiration and sap flow characteristic inresponse to changes in precipitation and soil water conditions havebeen mainly carried out using three kinds of approaches. The mostfrequently applied approach is pot experiments investigating saplingsor seedlings, in which soil water conditions are easily manipulated andinfluences from other environmental factors are excluded (Peuke et al.,2002; Mantovani et al., 2014). However, substantial bias might occurwhen applying the results to mature trees or the expansion of spatialscales. The second approach uses mature trees in forests, comparingtrees or plots over different time periods, including the drought-stressedand well-watered conditions (Cermak et al., 1995; Otieno et al., 2005;Llorens et al., 2010; Du et al., 2011; Iijima et al., 2014). The thirdapproach is based on experimental rainfall manipulations, which havebeen proposed as a new way to determine how changes in precipitationcould affect ecosystem functions, while other environmental factors arenot disturbed (Cinnirella et al., 2002; Fisher et al., 2007; Pinto et al.,2012; Wightman et al., 2016). While this type of manipulation is con-sidered as a preferable new method (Limousin et al., 2009), long-termstudies suggest that changes in precipitation might not have an im-mediate effect on transpiration (MacKay et al., 2012), with higher im-pact when increasing drought time (Limousin et al., 2009; Limousinet al., 2010). The recovery of transpiration is also affected by stressseverity (Ruehr et al., 2019). Therefore, long-term experiments arerequired to determine to clarify the mechanism of transpiration andwater use by trees and forest stands in response to precipitationchanges.

The Loess Plateau of China is subject to serious land degradationresulting from both natural and anthropogenic causes (Yan et al.,2014). Rainfall is the only source of soil moisture recharge in this re-gion (Jin et al., 2011). Black locust (Robinia pseudoacacia) is a globallywidespread species which is fast-growing, nitrogen-fixing, and gen-erally drought tolerant (Gupta, 1993; De Marco et al., 2012;Campagnaro et al., 2018). This species could appear in extremely di-verse soil physicochemical conditions, from extremely acid to stronglyalkaline, and from medium to highly base saturated soils with a gra-dient to different subsurface stoniness (Vitkova et al., 2015). It hasdrawn extensive attention due to its effects on soil nitrogen cycling(Rice et al., 2004; Buzhdygan et al., 2016; Kou et al., 2016). During thepast decades, black locust had been widely used for the reforestation ofthe vegetation-degraded land in the Loess Plateau as a prominent treespecies (Fu et al., 2000). Yet, concern exists about the sustainability ofthese plantations, due to their potentially high-water consumption(Wang et al., 2004; Zhang et al., 2015). Primarily study found the sapflow of black locust decreased after intercepting parts of the rainfall (Heet al., 2018), but neither the resilience capacity nor the mechanismswas intensively discussed due to the short experimental period. Quan-titative investigations of transpiration characteristics under differentsoil water and rainfall conditions and the associated mechanisms areneeded, particularly with long-term experiments.

Mean precipitation is expected to decrease in dry areas in the mid-latitudes in the future (IPCC, 2013). Current climate research showedthat regionally averaged daily rainfall intensity have significantly de-creased in the Loess Plateau, whereas consecutive dry days have sig-nificantly increased (Sun et al., 2016). Model predictions show that thecentroid of black locust distribution will occur in warmer and drierareas in the future (Li et al., 2018). Thus, it is important to investigatethe transpiration characteristics of this species in response to changes inrainfall and other meteorological factors.

This study aimed to investigate the influence of decreased pre-cipitation and increased soil water limitation over different time per-iods in a black locust plantation. This was achieved by using athroughfall exclusion experiment and successive measurements ofgrowth and physiological parameters over six years. The specific ob-jectives were to determine the effects of precipitation decrease on (1)the growth and transpiration status of trees, (2) the response pattern ofsap flux density to environmental factors, (3) the recovery of water usecapacity, and (4) transpiration related physiological and morphologicaltraits of the trees.

2. Material and methods

2.1. Study site and throughfall exclusion treatment

This study was conducted in 2011–2017 at Yongshou forest(34°48′25′' N, 107°58′50′' E, 1430 m above sea level [a.s.l.]) of ShannxiProvince on the Loess Plateau in China. The annual mean temperatureand mean annual precipitation recorded by a meteorological station20 km away in the town (1966–2005) was 10.8 °C and 617.3 mm, re-spectively, with 70% precipitation occurring in the summer (from Julyto September). The growing season for most plant species extends fromApril to October.

The black locust plantation at the study site had been in place for31 years in 2011, with no formal management. The soil in this region istermed cinnamon soil. Undergrowth vegetation was mainly composedof Humulus scandens, Arctium lappa, Stellaria media, Rumex acetosa,Duchesnea indica, Artemisia argyi, Carpesium abrotanoides, and Litseapungens. A 20 m × 20 m plot was established in 2011, and was evenlydivided into two parts (20 m × 10 m), denoted as a control plot and atreated plot, respectively, for comparative studies on multiple subjects,e.g., long-term changes in ecosystem functioning. The density of treestands was 1175 trees ha−1, with the mean diameters at breast height(DBH) of 14.3 cm and 13.5 cm and the mean tree heights of 14.9 m and14.5 m in the control and treated plots, respectively, at the beginning ofexperiment (Appendix Table S1). The throughfall exclusion treatmentwas started in April 2015, with the previous years (2011–2014) as re-ference period for collection of long-term data. By placing 2 m highwaterproof panels within the tree rows in the treated plot, the throughrainfall on the panels was excluded and flowed out of the plot. Toprevent possible influence of the surface and soil water from outside theplots, we vertically inserted an 80-cm-wide PVC sheet around the plot,with 70 cm into the soil and 10 cm kept above ground. The ratio ofthroughfall to precipitation was measured by placing 25 rain gauges inthe plots to calculate the averaged throughfall percentage for a wholeseason in the previous year. It was estimated that approximately 47.5%precipitation was excluded from the treated plot.

2.2. Monitoring of meteorological elements

Meteorological elements were monitored in an open space of thestudy site. Sensors of solar radiation (Li-200, Li-Cor Inc., Lincoln, NE,USA), air temperature, and relative humidity (HPM 110, Vaisala,Helsinki, Finland) were set at a height of 2 m above the ground.Precipitation was measured by three tipping bucket rain gauges (Model7852, Davis Ins., Hayward, CA, USA), which were set at 0.5 m abovethe ground. The measurements were scanned at 30 s intervals and

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recorded as 30 min averages in a data logger (CR1000, CampbellScientific Ins., Logan, UT, USA). Part of the meteorological data was notrecorded due to occasional power failure, but was supplemented bydata from the POWER Data Access Viewer weather database website(https://power.larc.nasa.gov/data-access-viewer/). To reflect the sy-nergistic effect of air temperature and air humidity, vapor pressuredeficit (VPD, kPa) was calculated using the 30 min averages of airtemperature and relative humidity following the Tetens formula ofCampbell and Norman (1998) as follows:

= −+( )VPD 0.611e (1 RH)17.502Ta

Ta 240.97 (1)

where Ta (°C) is air temperature and RH is the relative humidity.Similarly, a combined variable was computed to integrate solar

radiation (RS) and VPD in this study. Because VPD is considered as thedominant environmental variable that approximately contributes morethan 2/3 driving force of transpiration (Green, 1993; Zhang et al.,1997), the variable of transpiration (VT, kPa (W m−2)1/2) was calcu-lated as (Kakubari and Hosokawa, 1992; Iida et al., 2006; Du et al.,2011):

= ×VT VPD Rs0.5 (2)

2.3. Monitoring of soil water content

Soil volumetric water content (SWC) was monitored in each plotusing S-SMC-M005 smart sensors (Onset, USA) connected to HOBOloggers (H21-002, Onset, USA), recording 1 h averages. Six sensorswere installed in a profile in each plot at depths of 6, 12, 30, 50, 70, and90 cm. The mean SWC of the whole horizon (0–100 cm; SWC0–1) wascalculated by an equation that averaged the records with differentweight according to the relative thickness represented by a sensor(Wilson et al., 2001) as:

= + + + +

+

−SWC

0.08SWC 0.07SWC 0.25SWC 0.2SWC 0.2

SWC 0.2SWC

0 1

0.06 0.12 0.3 0.5

0.7 0.9 (3)

where SWC0.06, SWC0.12, SWC0.3, SWC0.5, SWC0.7, and SWC0.9 are thedata measured by sensors at 6, 12, 30, 50, 70, and 90 cm, respectively.

2.4. Measurement of sap flow

Throughout the study period, we used Granier-type sensors tomeasure the sap flux density of 14 sample trees (seven in each plot)representing the major DBH classes. Because sapwood thickness inblack locust trees is generally around or less than 10 mm in this region(Zhang et al., 2015), 10-mm-long sensors were used in this study. Twosensors were installed on the north and south sides of each sampled treeat breast height. Each sensor consists of two probes that were 10 mmlong and 2 mm in diameter, which were installed 0.15 m verticallyapart. The lower probe was for reference, while the upper probe con-tained a heater that was continuously heated with electric power at0.15 W (James et al., 2002). The temperature difference between theheated and non-heated probes of each set were scanned every 30 s, andwere recorded as 30 min averages by a data logger (CR1000). Meanuncalibrated sap flux density (Fd) was calculated based on the empiricalequation of Granier (1987) as follows:

= × ⎛⎝

− ⎞⎠

F 119 ΔT ΔTΔTd

Max1.231

(4)

where Fd is sap flux density (mL m−2 s−1), ΔT is the difference oftemperature between heated and non-heated probes, ΔTmax is themaximum value of ΔT recorded when Fd is near zero, which often oc-curred at predawn (Rabbel et al., 2016). We used Baseliner software(Version 3.0, Duke University, USA) to determine ΔTmax, as describedby Oishi et al. (2016). As most of the sample trees had a sapwood

thickness of less than the sensor length of 10 mm, sap flux density wasrecalculated by correcting the temperature difference using the equa-tion provided by Clearwater et al. (1999) as follows:

= − −a

ΔT ΔT (1 a)ΔTsw

Max(5)

where ΔTSW is the corrected temperature difference between heatedand unheated probes; ΔT is the originally measured temperature dif-ference; a is the ratio of sapwood thickness to sensor length. Concernexists over the potential underestimation of Fd using the original coef-ficients, with species-specific calibration for the equation being re-commended (Bush et al., 2010). However, in the current study, wecompared the values within a species, with the comparisons not beingaffected by underestimates.

Plot averaged sap flux density over the total sapwood area (JS, mLm−2 s−1) was calculated to compare the control and treated plots fol-lowing Kumagai et al. (2007) as:

=∑ ×

∑=

=

As Fd

AsJ i

Ni i

iN

iS

1

1 (6)

where i is the sample tree number and N is the sample size, whichchanged over time due to the breakage of sensors. Fdi and Asi are thesap flux density and sapwood area of tree i, respectively.

The data presented in this study covered a total of six years, in-cluding three years for both reference and treatment periods, with theyear 2013 being excluded due to data missing on a substantial numberof days.

2.5. Measurement of several physiological parameters leaf water potential

Leaf water potential was measured for three representative sampletrees in each plot during the growing season of 2016, the 2nd year oftreatment. Measurements were conducted at 04:00 and 13:00 local timeon four typical sunny days from June to October to obtain the predawn(Ψpd, MPa) and midday (Ψmd, MPa) water potentials, respectively, eachday representing the average status of physiology in this month. Foreach sample tree, three canopy leafstalks with their all compoundleaves were taken from different branches and measured in a pressurechamber (Model 1000, PMS Instruments, USA).

2.6. Stable carbon isotope composition (Δ13C)

Fifteen canopy leaves were sampled from three branches (5 leavesin each branch) from each sample tree on the water potential mea-surement day. The leaves from the same tree were pooled into onecomposite sample, transported to the laboratory and dried. The stablecarbon isotope composition (Δ13C) was measured with a mass spec-trometer (MAT253, Thermo Fisher Scientific, Bremen, Germany).

2.7. Stomatal density and specific leaf area

Another fifteen canopy leaves from three branches (5 leaves in eachbranch) were sampled from each sample tree. A layer of colorless nailpolish was applied to the surface of each leaf. After a few minutes whena film was shaped, it was cut off and taken back to the laboratory. Thenumber of stomata was counted under a microscope and stomataldensity across the leaf area was calculated.

To determine the specific leaf area (SLA, cm2 g−1), another fivecanopy leaves from each of the three branches were sampled from eachsample tree. The leaves were kept in paper bags and transferred to thelaboratory. After making full-scale copy images, the leaves were oven-dried at 80 °C for 24 h and weighed. The leaf images were measured forsurface area with an area meter on Windows, and SLA was calculated asthe ratio of leaf area to dry leaf weight.

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2.8. Data analyses

The whole tree hydraulic conductance of the soil to leaf pathway(GP, mL m−2 s−1 MPa−1) was calculated according to the Darcyequation (Cohen et al., 1983; Lu et al., 1996; Sperry, 2000) as:

=−

FGΨ Ψ

dmd

pd mdp

(7)

where the Fdmd is the sap flux density at midday. This formula used Ψpd

to represent soil water potential; the sapflow driving force was thusassumed as the difference between Ψpd and Ψmd.

To analyze the response pattern of JS to VT, an exponential sa-turation function was applied following Ewers et al. (2002), Ewers et al.(2007), and Kumagai et al. (2008) as:

= − −J a e(1 )sbVT( ) (8)

where a and b are fitting parameters that represent the projectedmaximum JS and the initial slope of the regression curve, respectively.To avoid the influence of hysteresis between sap flow and the me-teorological factors, only datasets collected in the early hours of the day(06:00–14:00) were used in this analysis. In addition, VT data weremoved 1 h forward when applying the regression to reduce the influ-ence of a time lag between diurnal courses of VT and sap flow.

By using two-way repeated-measurement ANOVA, we investigatedthe physiological parameters in response to treatment, months, andtheir interactions. The Wald test was applied to check for significantdifferences in the regression parameters of JS to VT between differentfitting equations. R 3.5.0 and SPSS 23 (IBM Inc., USA) were used for thestatistical analyses.

3. Results

3.1. Seasonal and interannual variation in environmental factors, treegrowth, and sap flow

The daily values of precipitation, air temperature, solar radiation,vapor pressure deficit, soil water content, and plot-averaged xylem sapflux density at the study site from 2011 to 2017 are shown in Fig. 1. TheJS for each plot in 2013 was not included, due to the power failure.While seasonal variation of the major meteorological factors showed aregular pattern, interannual variation was not obvious. For instance,precipitation and SWC varied in different years. Annual precipitationwas highest (751.0 mm) in 2011 and lowest (516.5 mm) in 2016. SWCwas influenced by precipitation, and was clearly high in 2011, de-creasing in the latter years in the control plot. However, after the startof the throughfall exclusion treatment, SWC in the treated plot sig-nificantly decreased when compared with the control plot, and thus theannual mean difference in SWC between the two plots increased overtime. In addition to seasonal variation, JS in both plots showed inter-annual variation, with relatively higher values initially, coinciding withprecipitation and SWC trends.

Fig. 2 presents JS and SWC averaged for the growing months ofMay–September each year, and comparisons between the two plots. JSwas not significantly different for the two plots in the pre-treatmentperiod and the first year of treatment. In the latter two years (with therainfall exclusion treatment), the differences became larger and statis-tically significant. These results corresponded with SWC trends, whichalso showed a significant difference between the plots from the secondyear of treatment.

The plot-averaged annual DBH increments in both the control andtreated periods are shown in Fig. 3. In the pre-treatment period, therewas a difference of about 25% (0.1 cm) in annual DBH increment be-tween the two plots, which was not statistically significant. However,during the latter three years (with the throughfall exclusion treatment),annual DBH increment in the control plot was significantly higher(52%, p < 0.05) than that in the treated plot. Overall, the treatment

caused tree growth to decline, leading to the difference between theplots.

3.2. Variation in sap flow responses to meteorological factors undertemporary changes to soil water content separated by constant rainfall

To further elucidate how the hydraulic conductivity of black locusttrees changed with increasing soil water stress, we analyzed the re-sponse patterns of JS to atmospheric transpiration-driven factors in5 years in relation to SWC, 2 years (2012, 2014) before treatment and

Fig. 1. Daily values of plot-averaged sap flux density (Js) and environmentalvariables across the growing seasons of the whole study period. Soil watercontent (SWC) was measured in the control (C) and treated (T) plots, SWC-Cand SWC-T, respectively. Js-C and Js-T reflect plot-averaged sap flux densitiesfor trees in the control and treated plots, respectively.

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3 years (2015, 2016, 2017) during treatment. To avoid the influence ofphenology, two periods (7 clear days in each) within the vigorousgrowing season of June and July were selected before and after oneconstant rainfall event, respectively, to represent temporarily differentSWC (the data in 2011 couldn’t meet the criterion and was not in-cluded). The SWC for all comparison analysis periods is shown inTable 1, with the SWC-L and SWC-H representing the relatively dry andwet periods, respectively. SWC in the treated plot was considered to bethe same as the control plot before the treatment started, because thetwo plots were adjacent to one another. Differences in SWC betweentreated and control plots changed across days separated by constantrainfall events in all treatment years (2015–2017). In contrast to sub-stantial differences in SWC-L days, differences in SWC between treatedand control plots decreased for SWC-H days after recharge. For eachSWC period, the response of JS to VT was fitted using an exponentialsaturation function. The curves for treated versus control plots arepresented in Fig. 4.

Table 2 provides a summary of the regressions and significance testsfor the difference in each parameter between treated and control plotsin each investigated period. In each period of SWC status before thethroughfall exclusion manipulation, both parameters a and b showed nosignificant difference between the two plots. However, significant dif-ference between the plots were detected after the treatment started in2015. For each SWC-L period in 2015–2017, parameter a in the treatedplot was significantly lower than that in the control plot (p < 0.001).In comparison, parameter b showed significant differences, except forthe first treatment year, probably due to lower differences in SWC. Asexpected, differences in the regression parameters between plots dis-appeared after rainfall recharge in the SWC-H periods of 2015 and2016. Of note, during the SWC-H period of the third treatment year(2017), parameter a in the treated plot remained significantly lowerthan that in the control plot, even after the water supplement (Fig. 4I,J).

Because the recharge in SWC differed between the control andtreated plots, we also tested how parameter a and b differed betweenpaired SWC-L and SWC-H periods for each plot. Both parameter a and bsignificantly differed (p < 0.05) between different SWC conditions foreach plot in each year (data not shown).

3.3. Comparison of transpiration-related physiological parameters betweenprecipitation exclusion and control plots during the growing season

To explore the physiological changes in relation to reduced tran-spiration following the initiation of the throughfall exclusion treatment,several related physiological variables (leaf water potential, GP, Δ13C,SLA, leaf stomatal density, sapflow driving force) were measuredduring the second treatment year 2016, in addition to the SWC at eachsampling time (Fig. 5). The statistical significance for each parameter ispresented in Table 3.

Ψpd (which was assumed to be a proxy for soil water potential)trends followed that of SWC in the control plot. The treatment seemedto have an effect on Ψpd. Variations among months were highly

Fig. 2. Plot averaged sap flux density (JS), soil water content (SWC), and totalprecipitation for the growing months (May–September) during the study years.Error bars represent standard errors. Significance of differences between plotsfor JS and SWC was checked by t test (n = 5). Data for SWC in the treated plotwere not collected in the untreated period.

Fig. 3. Annual increment in DBH in the two plots before (2011–2014) andduring the treatment (2015–2017) periods. Error bars represent standard er-rors. Significance of differences between two plots was checked by t test (n = 4before treatment, n = 3 during treatment).

Table 1Soil water content during the comparison analysis periods in each year.

Year SWC-L SWC-H

Control Treated Control Treated

2012 0.248 0.2602014 0.232 0.2542015 0.224 0.212 0.233 0.2312016 0.224 0.191 0.227 0.1952017 0.193 0.174 0.259 0.258

Note: SWC-L and SWC-H are the periods when soil water contents were rela-tively low and high, respectively.

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significant for both Ψpd and Ψmd. Other variables, except SLA, werealso significantly different across months. Trends to sapflow drivingforce in control and treated plots followed that of SWC, peaking in thedry month of August. Trends in whole tree hydraulic conductance (GP)in both plots followed those of SWC from June to August, but not inOctober, because defoliation causes transpiration to decrease. GP onlysignificantly differed (p < 0.05) between the two plots during the dryperiod of August. SLA and stomatal density were significantly affectedby the treatment.

4. Discussion

4.1. Rainfall exclusion caused decreases in transpiration and the growth oftrees

The throughfall exclusion treatment in this study caused DBH in-crement and plot averaged sap flux density to decline. These resultswere generally consistent with those reported by previous studies, ex-cept that the ratio of decline varied across studies. For instance, somestudies showed the ratio of decreased transpiration followingthroughfall reduction varied across years (Wullschleger and Hanson,2006). In comparison, Limousin et al. (2009) showed a constant re-duction in transpiration in the four-year reduction of precipitationinput. Studies have also reported no transpiration changes (Wightmanet al., 2016) or changes with small decreases (Besson et al., 2014)following rainfall exclusion, due to the influence of groundwater access.There are also reports suggesting that black locust presents a moreprofligate water use strategy at increasing climate change severity be-cause its ability to mobilize water from groundwater in Mediterraneanzone (Nadal-Sala et al., 2019). In our study cite in the Loess Plateau,however, the groundwater level ranges from several tens to hundreds ofmeters. Soil water can only be replenished by rainfall and groundwateris generally out of consideration in this area.

The extent of decline in transpiration following rainfall exclusionmight depend on the dryness of a given season. Tree water use in athroughfall exclusion plot in an Amazonian rain forest was estimated tobe just 20% of that of the control plot during the dry seasons (Fisheret al., 2007). In a Mediterranean oak forest, while total annual tran-spiration only decreased by 8–13% due to the rainfall exclusion treat-ment, 20–27% reduction occurred during the summer drought period(Besson et al., 2014). In a Norway spruce forest, a strong reduction insap flux density in the treated plot was associated with the developmentof drought during the summer period (Lu et al., 1995). In the presentstudy, the small difference in JS between the plots in 2015 might berelated to large amounts of precipitation during the proximate seasonand the previous year, in addition to small differences in SWC, as thetreatment had only just started (Fig. 2). The large reduction in JS in thetreated plot in 2016 (ca 36%, p < 0.05) might be related to the re-latively low amount precipitation during the growing season and highlysignificant differences in SWC between the plots.

Furthermore, our results support the effect of treatment duration. Inthe third year of the treatment, precipitation was at a usual level, butthe difference in SWC between the plots approximated that of theprevious year, greater with differences in JS (ca 49%, p < 0.01). Theintensity of drought increases the duration required for GP to recoverafter drought (Blackman et al., 2009), and has a close relationship withcanopy conductance (Meinzer et al., 1996). Therefore, constant rainfallexclusion treatment caused extensive variables in the ecosystem tochange. Furthermore, multiple responses of various ecosystem compo-nents and processes might appear over longer timespans. This ob-servation was supported with the growth trend of DBH.

Chapin (1990) observed that stem growth is frequently identified asthe most sensitive indicator of the degree of drought stress, because it islow on the carbon allocation hierarchy. In our study, the throughfallexclusion treatment strongly influenced increments in forest DBH. Thisreduction might occur as a result of a reduction in sap flow, or a decline

Fig. 4. Sap flux density in relation to the variable of transpiration (VT) under a pair of temporary different soil water conditions in each plot across study years. Theleft panels (A, C, E, G, and I) present the response patterns during relatively dry periods, while the right panels (B, D, F, H, and J) represent the response patternsduring relatively wet periods. From top to bottom, the response patterns for 2012/2014 (before treatment, left panel group) and 2015/2016/2017 (during treatment,right panel group) are presented, respectively. Black and white dots represent the Js for control and treated trees, respectively.

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in stomatal conductance or stomatal density (Fig. 5, Table 3), whichinevitability reduce the photosynthesis. MacKay et al. (2012) recordeda 17% decrease in growth in the drought plot of a temperate pineplantation forest. Using a photosynthesis model, Fisher et al. (2007)estimated that a 13–14% drop in gross primary production (GPP) oc-curred as a result of throughfall exclusion in a 2-year experiment, with areduction of 40–45% during the driest periods. Alberti et al. (2007) alsoreported a 20% reduction to ecosystem GPP and a 35% reduction toecosystem evapotranspiration in dry treatments. Our experiment with47% precipitation exclusion for three years yielded reductions of sapflow and DBH increment of up to 49% and 52%, respectively. Similardecreased growth of stem diameter of black locust trees under droughtconditions have been observed at other sites in the Loess Plateau (Zhanget al., 2018) and central Europe (Mantovani et al., 2014; Mantovaniet al., 2015).

In comparison, species sensitivity might responsible for the resultsobtained following rainfall exclusion. In a comparative study on co-occurring species in a western subalpine forest in North America, Patakiet al. (2000) showed that Populus tremuloides is less sensitive to soilmoisture than the other conifer species (Abies lasiocarpa, Pinus contorta,and P. flexilis), with relatively high transpiration continuing late in theseason and moderate changes in the response of transpiration to me-teorological factors with decreasing soil moisture. McCarthy and Pataki(2010) also showed that the conifer species Canary Island pine (Pinuscanariensis) is more sensitive to soil–water conditions than the decid-uous species California sycamore (Platanus racemosa). In a comparativestudy with other broadleaved species in a semiarid site, black locustwas relatively sensitive to temporary soil moisture changes comparedwith native species (Du et al., 2011), while less sensitive than ripariannative species in Mediterranean zone (Nadal-Sala et al., 2017). The

present study showed that the reduction of annually averaged JS due torainfall exclusion appeared from the second year of rainfall exclusion;thus, black locust exhibits a certain degree of resilience to gradualchanges in precipitation at this sub-humid site.

4.2. Changes of soil water condition affected transpiration capacity andconductance

Leaves vary with respect to form, longevity, venation architecture,and capacity for photosynthetic gas exchange, which are probablylinked with water transport capacity. The pathways through the leafconstitutes a major part (≥30%) of the resistance to water flow throughplants, influencing transpiration and photosynthesis rates (Sack andHolbrook, 2006). Meinzer et al. (1996) demonstrated a strong stomatalresponse to humidity of leaves, which limited the increase in tran-spiration with increasing evaporative demand. The tendency for tran-spiration to decline above a critical value of VPD was previously ob-served under field conditions (Gutierrez et al., 1994; Monteith, 1995),and was characteristic of the “feedforward” stomatal response to hu-midity (Farquhar and Raschke, 1978). Oren et al. (1996) suggested that

Table 2Results of the regression analyses for the plot averaged sap flux density (JS) vs.the variable of transpiration (VT) in Fig. 3 and comparison of regressionparameters between plots.

Year SWC Control Treated p

2012 SWC-L a = 40.959 a = 37.861 nsb = 0.116 b = 0.108 nsR2 = 0.762 R2 = 0.825

SWC-H a = 93.839 a = 87.178 nsb = 0.339 b = 0.293 nsR2 = 0.907 R2 = 0.904

2014 SWC-L a = 52.503 a = 52.739 nsb = 0.112 b = 0.158 nsR2 = 0.824 R2 = 0.770

SWC-H a = 78.818 a = 71.085 nsb = 0.074 b = 0.083 nsR2 = 0.833 R2 = 0.790

2015 SWC-L a = 38.659 a = 23.841 p < 0.001b = 0.077 b = 0.086 nsR2 = 0.709 R2 = 0.686

SWC-H a = 63.956 a = 59.217 nsb = 0.114 b = 0.111 nsR2 = 0.919 R2 = 0.938

2016 SWC-L a = 86.975 a = 62.259 p < 0.001b = 0.084 b = 0.044 p < 0.001R2 = 0.871 R2 = 0.936

SWC-H a = 140.747 a = 144.457 nsb = 0.050 b = 0.042 nsR2 = 0.863 R2 = 0.905

2017 SWC-L a = 96.578 a = 44.739 p < 0.001b = 0.107 b = 0.049 p < 0.001R2 = 0.835 R2 = 0.862

SWC-H a = 110.539 a = 59.561 p < 0.001b = 0.142 b = 0.145 nsR2 = 0.841 R2 = 0.892

Note: SWC-L and SWC-H are the periods when soil water contents were rela-tively low and high, respectively. Both parameters in each plot showed sig-nificant difference between different SWC conditions each year for each plot(p < 0.05 or 0.01 or 0.001).

Fig. 5. Variation in of transpiration-related parameters during the 2016growing season. The left panels (A, C, E, and G) show predawn leaf water po-tential (Ψpd), midday leaf water potential (Ψmd), whole tree hydraulic con-ductance (GP), and stable carbon isotope composition (Δ13C). The right panels(B, D, F, and H) show sapflow driving force, special leaf area (SLA), leaf sto-matal density, and soil water content (SWC). Black and white dots representmean values of transpiration-related parameters sampled in control and treatedplots, respectively. Error bars represent standard errors.

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hydraulic conductance from the soil to the atmosphere is correlatedwith the slope of the Fd-VPD relationship, with a steep slope indicatinghigh conductance. In the current study, the datasets of half-hourly plot-averaged Fd (JS) and VT fitted well with the exponential saturationcurve (Fig. 4, Table 2). Under different soil water conditions, eitherbetween plots or between temporal periods of SWC-L and SWC-H ineach plot, transpiration and canopy conductance differed, as indicatedby the projected maximum JS (parameter a) and the initial slope(parameter b), respectively. Higher b values indicate that the treestranspired vigorously during the early mornings to reach their satura-tion level early at a lower VT when stomatal regulation might startacting.

Temporary differences in SWC caused by rainfall recharge (Table 1)created different transpiration capacities and conductance in each year,as shown by a and b parameters (Table 2 and its note). Significantdifferences in these two parameters between SWC-L and SWC-H periodsevery year in both plots indicate that the process of transpiration inblack locust is sensitive to soil moisture conditions. This result wasconsistent with previous studies comparing black locust trees with otherindigenous species (Du et al., 2011; Wang et al., 2017). The authorssuggested that during a drought period, black locust adjusts its wateruse strategy to reduce transpiration, whereas transpiration is sub-stantially increased after soil water has been well recharged by rainfall.In addition, throughfall exclusion clearly weakened the environmentalresponse of sap flow. This influence was enhanced with increasingtreatment duration. In contrast to the first two treatment years, duringwhich parameter a in treated plot could recover to that close to thecontrol plot in the SWC-H period, prolonged treatment caused a sub-stantial decrease in transpiration that did not recover in 2017. Asproposed by some researchers, decreases to stomatal conductance andleaf area might have happened, with this behavior possibly allowingtrees to avoid excessive loss of water (Liu et al., 2013; Wang et al.,2017; Miyazawa et al., 2018). Yet, it would inevitably cause photo-synthesis to decline, leading to carbon starvation, in long term.

During each SWC-H period, differences to parameter b between thetwo plots disappeared. Thus, black locust hydraulic conductance couldbe recovered after rainfall recharges the SWC. This phenomenon is alsoshown in the comparison of GP (Fig. 5E). For instance, there was nosignificant difference in GP between treated and control trees in therelatively wet months (June, July, and October); however, this differ-ence became significant (p < 0.05) during the dry month of August.This result contrasts with that of Miyazawa et al. (2018), who foundthat GP decreased in early summer and did not recover until the ex-pansion of new leaves in spring.

4.3. Black locust showed drought avoidance strategies and moderateresilience

Tree species tend to have different responses to drought dependingon their drought tolerance and drought avoidance strategies. Somespecies might reduce sap flow and canopy stomatal conductance tomaintain physiological resilience, whereas others might reduce wateruse through leaf senescence and early abscission (Pataki and Oren,2003). Species with strong stomatal control of transpiration are con-sidered isohydric and are characterized by similar Ψmd under differentSWC, which is closely related to conductance in xylem (Klein, 2014). In

the current study, Ψpd of the black locust decreased in the throughfallexclusion treatment; however, neither Ψmd nor GP significantly differedfrom control plot trees (Fig. 5, Table 3). This result was consistent withthe suggestion of Miyazawa et al. (2018) that, during severe summerdrought, GP and minimum leaf water potential (Ψmd) declines in blacklocust, while Ψmd is maintained at a constant level before and after thedrought period. This phenomenon might maintain the photosyntheticrate of this species, preventing further reductions in GP or stomatalconductance. In our study, the GP in the control and treated plots be-came significantly different (p < 0.05) in the dry month of August;however, normal status was recovered during wet periods.

The ability of trees to resist drought also includes their ability toresist embolism and recover after embolization (Awad et al., 2010;Schoonmaker et al., 2010; Choat et al., 2012). Recent studies showedthat cavitation is not an extreme event under severe water stress, butcan be recovered from (McCulloh and Meinzer, 2015). As a ring-porousspecies, black locust is considered to be at high risk of embolization(Sperry et al., 1988; Cai and Tyree, 2010). Yet, embolization was alsoregarded as a kind of strategy to control water use when soil water islimited (Cochard et al., 1992; Hoffmann et al., 2011; Garcia-Forneret al., 2016). However, prolonged drought might cause Ψmd to fallbelow its usual levels and induce xylem embolisms (Franks et al., 2007).It is difficult for trees to recover from long-term cavitation (Blackmanet al., 2009), with this phenomenon inevitably causing defoliation(Limousin et al., 2009; Poyatos et al., 2013), dieback, or the prematuredecay of forests (Tyree and Zimmermann, 2002; Tyree and Dixon,2010). In the semi-arid region of the Loess Plateau, where the blacklocust is under prolonged soil water limitation, dieback has been ob-served (Yamanaka et al., 2014). This phenomenon may be an evidenceof non-reversed cavitation in the black locust tree under long-term orsevere drought.

Furthermore, Yan et al. (2010) reported that the black locust ex-hibits higher specific leaf area and larger amounts of water use withrespect to total leaf area or biomass than local indigenous species.Mantovani et al. (2014) suggested that black locust is not a water-saving tree species, as it does not regulate transpiration under well-watered conditions, though it might adapt to prolonged drought con-ditions by reducing transpiration and leaf size. Farquhar et al. (1982)and Brugnoli et al. (1988) suggested using the stable carbon isotopecomposition to reflect the water use efficiency (WUE) and water stresscondition of plants in a certain period. In the current study, while boththe JS and DBH increment decreased in treated plot, significant differ-ence in Δ13C was not detected between plots in the second year oftreatment, probably due to the isohydric behavior of black locust undertemporary drought. This was supported by the significant lower sto-matal density in treated plot, which might help trees to reduce tran-spiration and adapt better to soil drought. It is also consistent with thesuggestion that black locust performed relatively high WUE (Nadal-Salaet al., 2017). Longer experiments with rainfall exclusion might helpelucidate how trees change dynamically to drought, using simulatedprecipitation similar to the semiarid sites.

5. Conclusions

The current study demonstrated that decreased precipitation nega-tively influences the sap flow and tree growth of black locust. This study

Table 3P values of repeated-measurement ANOVA for the parameters in Fig. 4 vs. the treatment, months, and their interactions, Ψpd and Ψmd are leaf water potentials inpredawn and midday, respectively. GP is the whole tree hydraulic conductance; Δ13C is the stable carbon isotope composition; SLA is the special leaf area.

Source of variation df Ψpd Ψmd GP Sapflow driving force Δ13C SLA Stomatal density

Treatment 1 0.023* 0.694 0.123 0.424 0.506 0.000** 0.038*Treatment × Month 3 0.510 0.772 0.615 0.724 0.495 0.187 0.284Month 3 0.002** 0.000** 0.047* 0.006** 0.003* 0.298 0.029*

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also showed that sap flow responses to the environment factors becameinsensitive compared to the non-treated period and plot. This decreasein response could recover after recharging soil moisture through rainfallfollowing short-term drought; however, prolonged soil water limitationsuppressed the capacity of transpiration by causing the recovery in-effective and changing the morphological traits of leaves. During theearly period of treatment, Ψpd decreased, while Ψmd was maintained bythe trees, which was more probably isohydric behavior. However, se-vere drought affected the water use strategy of this species, causinganisohydric behavior, as shown by the significant difference in GP be-tween treatments during the dry season. This indicates that this speciesmay not be clearly categorized into either isohydric or anisohydric type.It is suggested that the physiological responses to and recovery fromwater stress are dependent not only on the general property of a speciesbut also on the stress intensity, duration, and frequency. Decreases inprecipitation would cause transpiration reduction, weakening the tree’sresponse to meteorological variables, lowering growth and pro-ductivity, potentially damaging transpiration resilience. Although blacklocust appears as an invasive species in wet regions or in areas acces-sible to groundwater, considering its strong demand for water and theshortage of precipitation supply on the Loess Plateau, the use of suchspecies in reforestation in semiarid regions should be implemented withcaution.

Acknowledgements

This study was supported by the National Natural ScienceFoundation of China (41471440, 41411140035), the National Key R&DProgram of China (2017YFC0504601), and a joint research project fromJapan Society for the Promotion of Science.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.foreco.2019.117730.

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