Adjustments of leaf traits and whole plant leaf area for balancing water supply and demand in Robinia pseudoacacia under different precipitation conditions on the Loess Plateau

Agricultural and Forest Meteorology 279 (2019) 107733

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Adjustments of leaf traits and whole plant leaf area for balancing water supply and demand in Robinia pseudoacacia under different precipitation conditions on the Loess Plateau

T

⁎

Zhang Zhongdiana,b, Huang Mingbinb,c, , Zhao Xiaofanga,b, Wu Lianhaid a

College of Natural Resources and Environment, Northwest A & F University, Yangling 712100, China State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A & F University, Yangling 712100, China c CAS Center for Excellence in Quaternary Science and Global Change, Xian, Shaanxi 710061, China d Sustainable Agriculture Sciences, Rothamsted Research, Okehampton, Devon EX20 2SB, UK b

A R T I C LE I N FO

A B S T R A C T

Keywords: Stomatal density Vein density Leaf area index Precipitation gradient Soil desiccation

The adjustments of plant traits for balancing water supply and demand are critical for keeping the survival of forests under drought stress. In this study, we aimed to determine the long-term adjustments of leaf traits and whole plant leaf area (PLA) in Robinia pseudoacacia trees under different precipitation conditions, and to provide physiological information for modelling. We characterized the temporal changes of plant traits with simulated different precipitation conditions using three levels of water supply in a controlled growth chamber. The results indicated that increasing transpiration with R. pseudoacacia growth leaded to a decline in soil moisture under each precipitation conditions. As drought progressed, leaves exhibited a coordinated increase in vein and stomatal densities, higher drought tolerance by increasing cell wall elasticity, and higher water storage capacitance. After 60 days, leaf traits were similar among the treatments while PLA decreased considerably with decreasing water supply. The field observation along a precipitation gradient indicated that PLA decreased by 64% as mean annual precipitation declined from 645.9 mm to 421.9 mm, while leaf traits did not exhibit marked differences among different sites. The variation in PLA along the precipitation gradient could be well estimated with the optimal PLA calculated by long-term simulations of soil water balance. In summary, increasing transpiration with plant growth induced similar patterns of soil desiccation under different precipitation conditions, which further resulted in the convergence in leaf traits. The adjustment of PLA achieved an optimal value to maximize plant growth and to prevent severe drought stress by balancing the water supply and demand. These results provided a way to fill the gap between experiments and modelling studies for large-scale predictions of plant traits, and should be helpful for the sustainable management of plantation forests in the Loess Plateau.

1. Introduction The Loess Plateau is a typical water-limited region with mean annual precipitation (MAP) ranging between 200 and 700 mm while mean annual pan evaporation (MAPE) varying from 1300 to 2200 mm (Zhang et al., 2015b). Water from precipitation is a sole source for plant water consumption in natural ecosystems or rain-fed agriculture as the groundwater level in the Loess Plateau generally varies from 30 to 100 m below the land surface (Jia et al., 2017). As a consequence, species distribution and plant growth on the Loess Plateau is strongly associated with the temporal and spatial variability of precipitation.

Large-scale afforestation has successfully reduced severe soil erosion and improved the environmental quality in this area. However, it has been receiving increasing attention that the extensive afforestation with exotic species like R. pseudoacacia has aggravated water scarcity and induced widespread and severe soil desiccation, which further endanger the sustainability of the fragile ecosystem (Chen et al., 2008; Jia et al., 2017; Wang et al., 2011). To compensate for the adverse effects of drought stress, perennial plant species have evolved into various response functions including short-term (within one day) physiological regulation and long-term (months to years) acclimation and adaption at different organizational

⁎ Corresponding author at: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A & F University, Yangling 712100, China. E-mail address: [email protected] (M. Huang).

https://doi.org/10.1016/j.agrformet.2019.107733 Received 23 May 2019; Accepted 25 August 2019 0168-1923/ © 2019 Elsevier B.V. All rights reserved.

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S1). The hypothesis was tested by a laboratory experiment with three levels of water supply and a field survey across the precipitation gradient on the Loess Plateau. The objectives were (1) to characterize the long-term adjustment of leaf traits and PLA under different precipitation conditions; and (2) to examine the possibility to maximize plant growth but avoid severe drought stress through adjusting PLA. The comparison in the laboratory experiment reflected the effects on development of a simple inductive cue (Carins Murphy et al., 2012, 2014), whereas the comparison among sites reflected the integration of genetic differences and plastic responses to macro-environment (Brodribb and Jordan, 2011). We further calculated the optimal PLA by simulating the long-term soil water balance with a modified Biome BioGeochemical cycle (Biome-BGC) model (Thornton et al., 2002), and compared with the observed values along the precipitation gradient.

levels (Choat et al., 2018; Zhou et al., 2016). At short time scales, the rapid response of stomatal conductance and aquaporins to atmosphere and soil drought can efficiently regulate plant water supply and loss (Hetherington and Woodward, 2003; Kaldenhoff et al., 2008). During long-term water stress, adjustments of both leaf traits and whole plant leaf area (PLA) play a critical role in optimizing water use and balancing water supply and demand (Brodribb et al., 2017; Choat et al., 2018; Martin-StPaul et al., 2013). Compared with the short-term physiological regulation, the long-term adjustments of plant traits are still poorly understood and quantified, presenting a major obstacle for anticipating the future of forests and modelling climate–vegetation–soil interactions (Yan et al., 2017a; Zhou et al., 2016, 2018). At the leaf level, the maximum conductance of liquid and vapour phases is largely determined by the arrangement and density of veins and stomata (Brodribb and Jordan, 2011). Coordination between vein density and stomata density allows leaves to maintain an efficient balance between water use and carbon acquisition, which is supported by observations within individual plants, within species, among populations of the same species, and among species (Brodribb and Jordan, 2011; Brodribb et al., 2017; Zhao et al., 2017). A high degree of plasticity in leaf drought tolerant traits in response to drought stress suggests that plants possess higher drought tolerance under low water availability (Szota et al., 2011). At the whole-plant level, the adjustment of PLA is essential for regulating plant water loss and protecting against hydraulic failure (Wolfe et al., 2016). Furthermore, the whole plant leaf area is pivotal in regulating stand water balance due to its influence on the hydrological processes including evapotranspiration, canopy interception, etc. (Zhang et al., 2015b). Previous studies suggested that, there should exist an equilibrium between climate, soil and plant leaf area in water-limited conditions (Eagleson and Tellers, 1982; Kergoat, 1998; Nemani and Running, 1989). So far, there still exists a huge gap between observations and modelling for anticipating the long-term plant responses (Zhou et al., 2018). Numerous models have been developed to investigate the role of precipitation and soil water in ecosystems, which can be divided into two types: mechanistic process-based and eco-physiological rule-based (Smith and Dukes, 2013; Sperry et al., 2017; Weltzin et al., 2003). The mechanistic process-based models explore to simulate the complex hydrologic-ecologic process interactions. These models usually contain numerous static leaf and whole-plant level parameters to describe complex eco-hydrologic processes. Constant parameters present one of the major sources of uncertainty for simulating long-term forest dynamics (Powell et al., 2013; Purves and Pacala, 2008; Zhou et al., 2016). On contrast, the eco-physiological rule-based models aim to predict the consequence of plant traits by summarizing and synthesizing system behaviours (Hoff and Rambal, 2003; Neilson, 1995; Weltzin et al., 2003), and have been proved as a useful tool to characterize the vegetation and hydrologic patterns in the water-limited area (Hoff and Rambal, 2003). Most of those models, however, assumed that plants respond to different drought conditions mainly by adjusting whole-plant or stand level traits while held the leaf level physiological parameters constant for large-scale predictions (Hoff and Rambal, 2003; Kergoat, 1998; Mackay, 2001; Neilson, 1995). This assumption remained to be tested with more observations. According to MartinStPaul et al. (2013), the temporal hierarchy (e.g. seasonal, <10 years, >10 years in their studies) of plant responses was associated with different organizational levels (e.g. leaf, branch, stand) in the tree acclimation to a drying climate. Their results indicated that the lower organizational levels could be less affected by chronic water limitation over time than at higher organizational levels. These findings provided an important basis for anticipating the adjustments of plant traits at large spatial scales using the eco-physiological rule-based models. In this study, we assessed the association between temporal hierarches and organizational levels based on the temporal changes of plant traits, and hypothesized that trees eventually exhibit similar leaf traits and differ mainly in PLA under different precipitation conditions (Fig.

2. Materials and methods 2.1. Laboratory experiment 2.1.1. Experimental design To study the developmental responses of plant traits and their interactions with soil moisture under different precipitation conditions, we simulated the natural precipitation gradient on the Loess Plateau with different levels of water supply in a laboratory experiment. In midMarch 2017, 18 two-year-old seedlings of R. pseudoacacia were transplanted from the field to acrylic columns (90 cm high, 23.5 cm in diameter) and placed in an artificial climate chamber. The plants were 65.3 ± 3.5 cm in height and 4.7 ± 0.5 mm in basal diameter at the time of the experiment. The growth conditions were 14 h days with a maximum quantum photosynthetic photon flux density of 900 ± 100 µmol m−2 s−1, 28 °C/20 °C day/night temperatures, and 60%/80% day/night relative humidity. Soil was collected from the 0 to 20 cm soil layer at the Yangling site, passed through a 2-mm sieve to remove any large objects (e.g., stones, debris), and packed into the columns at a bulk density of 1.35 g cm−3 (Wu, 2010). Before the experiment, all plants were well watered to maintain 100% field capacity (FC) for 1 month to allow natural establishment. The soil surface was covered with gravel to minimize water loss (Zhou et al., 2016). We designed three levels of water supply in the laboratory experiment based on the humidity index (HI, the ratio of MAP to MAPE): 0.45 (high, H), 0.30 (medium, M) and 0.15 (low, L). According to Jia and Wang (2013), H is within the semi-humid climatic range (0.33–1.00), M and L belong to the semi-arid climatic condition (0.14–0.33). For each treatment, plants were watered every 5 days, and the irrigation amount (I, mm) was calculated by:

I =5PE·HI

(1)

where PE is daily pan evaporation with 5.69 mm measured at the top of the column. Therefore, I was 12.8, 8.5 and 4.3 mm for H, M and L, respectively. The whole experiment lasted for 60 d. Each treatment contained six plants, and three of them were selected to monitor the dynamic of soil moisture content and PLA during the experimental period. Soil moisture profiles were monitored before each irrigation and PLA was measured every 10 d. The newest and fully expanded leaves were sampled from each plant on three occasions: 0, 30, and 60 d for measuring leaf traits including leaflet area, stomatal density, stomatal length, vein density and leaf drought tolerant traits. Any new growth was assumed to respond to the conditions experienced during its development (Carins Murphy et al., 2014). 2.1.2. Measurements Whole plant leaf area is the sum of the area of all compound leaves in a plant. A compound leaf is that consisting of several or many distinct leaflets joined to a single stem (Cavender-Bares et al., 2007). The area of each compound leaf (CLA, cm2) was estimated from the length (LL, cm) and width (LW, cm) of the compound leaf using an empirical 2

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equation developed in this study:

CLA = 0.523LL × LW + 3.0084 (R2 = 0.95, n = 46) In the first survey, all existing leaves were recorded. In the following censuses, any newly produced leaves during the interval were measured. Compound leaves that were incompletely expanded during a census were re-measured in the subsequent census (Wolfe et al., 2016). The leaflets in the middle of six compound leaves were chosen to measure the leaflet area, stomatal characteristics and vein density at each sampling time (Zhang et al., 2012b; Zhao et al., 2017). Leaflets were scanned at 300 dpi (Canon LiDE 120, Canon Inc., Tokyo, Japan) and leaflet area (LA, cm2) was measured using ImageJ (http://rsbweb. nih.gov/ij/index.html). Stomatal characteristics were examined with the impression method (Zhao et al., 2016). Clear nail varnish was applied to a 1-cm2 patch in the middle of the abaxial leaflet surface (avoiding major veins), allowed to dry, and then peeled off of the leaf and mounted on a glass slide. Stomatal prints were observed under the microscope (LEICA DM 2500, Germany) at 200× magnification, and three fields of view were photographed from each leaflet. Stomatal density (Ds, no. mm−2) was then measured as the total number of stomata per mm2 of leaf area using ImageJ. Stomatal length was averaged from five stomata per field of view (Carins Murphy et al., 2012, 2014). To measure the vein density, an approximately 1-cm2 section was excised from the central section of the same leaflet used for measurement of stomatal characteristics. The leaf samples were kept in test tubes with 5% NaOH solution. The solution was changed daily until the veins were exposed (Zhao et al., 2016, 2017). The samples were washed three times with distilled water, then placed on glass slides, dyed with 1% safranine solution, and rinsed again. For each leaf sample, three images were taken at 100× magnification using the microscope (Brodribb and Jordan, 2011). Vein density (Dv, no. mm−2) was measured as the total length of veins per mm2 of leaf area using ImageJ. Leaf drought tolerant traits were determined with parameters derived from pressure–volume curves, which were obtained with benchdrying method (Tyree and Hammel, 1972). The compound leaves were rehydrated overnight before the measurements. Then the leaves were dried on a laboratory bench for a period of 3–8 h, and repeatedly weighed and measured for water potential with a pressure chamber (PMS Instrument Co., Corvallis, OR, USA). After the final set of measurements, leaves were scanned to measure the compound leaf area with ImageJ, and then dried at 70 °C for >48 h to determine dry mass. The osmotic potential at full turgor (Ψ0, MPa), turgor loss point (Ψtlp, MPa), leaf saturated water content (the ratio of water mass to leaf dry mass in a fully saturated leaf, LSWC, g g−1), relative water content at turgor loss point (RWCtlp, %), modulus of elasticity (ε, MPa), and leaf water storage capacitance (C, mol MPa−1 m−2) were calculated using the methods developed by Sack and Pasquet-Kok (2011). Soil moisture content (SMC, cm3 cm−3) was measured using time domain reflectometry (TDR). After the columns were packed, four unbalanced three-rod probes, 15 cm in length (Campbell Scientific Inc., Logan, UT, USA), were inserted horizontally into each column at 20-cm depth intervals. Before the experiments were carried out, the TDR probes were calibrated using the gravimetric method. The profileaveraged soil moisture content and soil water storage (SWS, mm) were calculated for each column. As soil evaporation was inhibited by the covering gravel, whole-plant transpiration (T, L tree−1) during each irrigation interval could be estimated using a simple soil water balance equation:

T = (I + ΔSWS) × 0.0434

Fig. 1. The location of the four study sites and precipitation contours in the study area.

2.2. Precipitation gradient study To assess the integration of adaptive and developmental responses in R. pseudoacacia trees to the differential precipitation conditions on the Loess Plateau, we selected four sites along a precipitation gradient—Yangling (YL), Changwu (CW), Ansai (AS), and Mizhi (MZ). These sites covered semi-humid and semi-arid climate conditions and the main soil textural types of the Loess Plateau (Fig. 1; Table 1). The MAP was found to correlate highly with other climatic and soil factors, and has been widely used to examine environmental trends in this area (Wang et al., 2017; Zhang et al., 2015b, 2018). The climatic and soil variables are shown in Table 1. MAP declined from 645.9 mm at YL to 421.9 mm at MZ, while MAPE increased from 1379.2 mm to 2149.5 mm, resulting that HI decreased from 0.47 to 0.20 and the climate type transited from semi-humid to semi-arid. According to Yang et al. (1994), the vegetation zone changed from the tree zone (YL and CW with MAP > 550 mm), then the tree-shrub zone (AS with MAP in the range of 500–550 mm) to the shrub–tree zone (MZ with MAP in the range of 350–500 mm). The differential climatic conditions substantially influence the growth of R. pseudoacacia along the precipitation gradient according to the previous studies (Liu, 2008; Wang and Li, 2004). 55–78% of annual precipitation fell from June to September and mostly as high intensity rainstorms, and the inter-annual variability in rainfall and temperature was similar among sites. The stands were 15–20 years old and had similar densities (1600–1700 trees ha−1).

Table 1 Summary of climatic and soil variables of the four stands along the precipitation gradient. Site code

YL

CW

AS

MZ

Coordinates

34°18′ N 108°02′ E 344.7 13.5 645.9 1379.2

35°14′ N 107°41′ E 1186.7 9.4 575.0 1355.5

36°51′ N 109°19′ E 1155.2 9.2 506.5 1638.9

37°51′N 110°11′E 944.2 9.5 421.9 2149.5

0.47 1770.5

0.42 2051.4

0.31 2395.6

0.20 2761.2

1.40 7.28 65.61 27.11

1.29 6.30 76.78 16.93

1.28 33.90 56.97 9.13

1.24 33.66 56.58 9.76

Elevation (m a.s.l.) Mean annual temperature (°C) Mean annual precipitation (mm) Mean annual pan evaporation (mm) Humidity index Mean annual sunshine duration (h) Soil bulk density (g cm−3) Sand content (%) Silt content (%) Clay content (%)

(2)

where ΔSWS is the change in soil water storage between successive measurements (mm), and 0.0434 is the conversion factor to convert mm to L tree−1. The cumulative whole-plant transpiration (Tcum, L tree−1) over the study period was then calculated (Purcell et al., 2007).

Note: The humidity index was defined as the ratio of mean annual precipitation to pan evaporation. Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi. 3

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temperature. Snowfall is not intercepted by the vegetation canopy and is passed directly to the surface. The sublimation and snowmelt of the snowpack is estimated based on the daily air temperature and net solar radiation. Rainfall is intercepted by the canopy using a prescribed interception coefficient based on leaf area index and then evaporated from the canopy. All remaining rainfall is then routed directly to the soil. The canopy transpiration rate (Tc, mm d−1) is calculated using the Penman–Monteith equation with the two-leaf assumption, i.e. canopy leaves are divided into sun and shade leaves Jones (1992). The responses of stomatal conductance to environmental factors including photosynthetic photon flux density (PPFD, µmol m2 s−1), minimum temperature (Tmin, °C), vapour pressure deficit (VPD, kPa) and soil water potential (Ψsoil, MPa) are simulated with the Jarvis-type multiplicative model (Jarvis, 1976) for the sun and shade leaves:

There was good evidence that both leaf traits and PLA reached a steadystate by this age (Jia et al., 2017; Zheng and Shangguan, 2007), so the data collected should fully reflect the adjustments of plant traits to climatic conditions. In July 2016, fully exposed sun leaves were sampled and leaf traits including leaflet area, stomatal density, stomatal length, vein density and leaf drought tolerant traits were determined with 6 replications at each site as the methods described above. According to previous studies, this period would feature a peak in LAI (Zhang et al., 2015a), minimum soil water storage, and the most severe drought of the year (Cheng et al., 2009; Duan et al., 2017). Three 10 m × 10 m plots were established at each site. Seasonal maximum seasonal leaf area index (LAImax) in each plot was determined with an LAI-2200 plant canopy analyzer (Li-Cor, Inc., Lincoln, NE, USA). Then the seasonal maximum PLA (PLAmax) was calculated by dividing LAImax by stand density (Hammer et al., 1993; Meier and Leuschner, 2008). Three soil cores were randomly sampled in each plot for measuring soil moisture content due to small coefficient of variation of 5.46% (n = 12) according to our preliminary investigation. Soil samples were collected at 20 cm intervals in the 0–300 cm profile. Soil moisture by dry weight was determined with oven-drying method and converted to volumetric soil moisture content with soil bulk density. Soil particle analysis was conducted using a Mastersizer 2000 (Malvern Instruments, Malvern, England) at each site. Undisturbed soil samples were also collected at each sampling location with a cutting ring (5 cm diameter, 100 cm3 vol) to measure soil bulk density.

Gsun = Gmax ·f (PPFDsun)·f (Tmin )·f (VPD)·f (ψsoil )

(4)

Gshade = Gmax ·f (PPFDshade)·f (Tmin )·f (VPD)·f (ψsoil )

(5)

f (PPFD) =

2.3. The optimal PLAmax simulated using a modified Biome-BGC model 2.3.1. Model description According to the Eagleson's optimality theory of an eco-hydrological equilibrium (Eagleson and Tellers, 1982), vegetation could adjust its canopy coverage and reach an optimal plant leaf area to balance water supply and consumption in water-limited natural soil-vegetation systems. In this study, the optimal whole plant leaf area (PLAopt) for the deciduous R. pseudoacacia trees was defined as the maximum PLAmax that could be achieved until soil moisture content approached but did not reach the permanent wilting point (PWP) during a long-term growing period (Fu et al., 2012; Zhang et al., 2015b). Firstly, we calculated the stand-level optimal LAImax based on long-term simulation of soil water balance, such as 20 years, to take into account the climatic variability. Then the optimal PLAmax was obtained by dividing optimal LAImax by tree density. An initial LAImax was assumed and the soil water balance was calculated. If the soil moisture content dropped below PWP during any day of the simulation period, the LAImax was decremented and soil water balance was recalculated. Otherwise, if soil water content never approached PWP, the LAImax was incremented and the soil water balance was recalculated again. The iteration continued until an optimal LAImax was obtained under the condition that the soil moisture content approached but did not reach PWP (0.001 cm3 cm−3 above PWP was assigned as a criterion). According to the field observation of phenology (Zhang et al., 2015a), we firstly assumed that the growing season of R. pseudoacacia started on the 100th day and ended on the 300th day, and daily LAI (LAId) during the growing season was fitted with a quadratic function:

(100 ≤ d ≤ 300)

(6)

Tmin < −8 ⎧0 f (Tmin ) = 1 + 0.125Tmin − 8 ≤ Tmin ≤ 0 ⎨ 0 < Tmin ⎩1

f (VPD) =

LAId = ( −0.0001d 2+0.04d − 3)·LAImax

PPFD 75+PPFD

(7)

1 VPD< VPD1 ⎧ ⎪ VPD − VPD1 VPD1 ≤ VPD ≤ VPD2 ⎨ VPD2 − VPD1 ⎪0 VPD> VPD2 ⎩

(8)

ψsoil < ψ2 ⎧0 ⎪ ψsoil − ψ2 ψ f (ψsoil ) = ψ − ψ 2 ≤ ψsoil ≤ ψ1 ⎨ 1 2 ⎪1 ψsoil > ψ2 ⎩

(9) −1

where Gsun and Gshade are stomatal conductance (m s ) of sun and shade leaves, respectively; Gmax is the maximum stomatal conductance (m s−1); PPFDsun and PPFDshade are absorbed PPFD per leaf area of the sun and shade leaves, respectively; VPD1/VPD2 Ψ1/Ψ2 represent the start/completion of conductance reduction for VPD and Ψsoil, respectively. The soil surface infiltration rate is assumed to be equal to the rainfall rate or snow melting rate. The soil profile is divided into multiple layers and the water exchange between adjacent layers is calculated using the Darcy's equation. The change of water storage in each layer depends on the downward movement of water from its overlying layer, the transmission rate to the next lower layer, and the root uptake rate in this layer. Root uptake rate in the ith layer (S(i)) is calculated based on Tc and vertical distribution of fine root length (b(i), m−1) (Gong et al., 2006; Huang et al., 2013):

S (i) =

b (i) l

∑i = 1 b (i)

Tc (10)

where l is soil layers. The plant physiological parameters for R. pseudoacacia used in this study are shown in Table S1. In this study, the 0–300 cm soil profile was divided equally into 6 layers. The root distribution was determined from the study of Zhang et al. (2018). Soil–water retention curves were estimated using the Arya–Paris pedotransfer function (Arya et al., 1999) using the measured particle size distributions for each layer, and further fitted with van Genuchten model using RETC program. The PWP was determined at soil water potential of −1.5 MPa. The value of PWP might differ considerably among species. For R. pseudoacacia, the greenhouse and field experiments supported −1.5 MPa as PWP (Wu, 2010; Zhang et al., 2015a). Saturated hydraulic conductivity was estimated using the Kozeny–Carman equation which had previously been calibrated by Huang et al. (2011). The estimated soil parameters at each

(3)

where d is Julian day. According to previous studies, the low soil water content and high vapour pressure deficit during the growing season could lead to leaf desiccation and deciduous (Liu, 2008; Yan et al., 2017b). The daily minimum temperature normally drops below 0 °C after the 300th day of a year in this area, which could cause the deciduous at the end of growing season. The daily water balance procedure followed a logic used in the Biome-BGC model which has been modified by Huang et al. (2013). In brief, daily precipitation is routed to snowpack or soil dependent on air 4

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site are shown in Table S2. 2.3.2. Model evaluation The continuous field measurements were labour-intensive and timeconsuming, and we were not able to obtain the dynamics of soil moisture content at the four sites simultaneously. We only observed the temporal dynamics of LAI and SMC at the Changwu site in 2018 to evaluate the modified Biome-BGC model, and we further evaluated the model performance of predicting PLAmax with the measurements at four sites in 2016. According to previous studies (Jia et al., 2017; Zheng and Shangguan, 2007), the plant traits of R. pseudoacacia reached a steady state after 15 years old in this area. Based on our measurements in 2018, the leaf traits and LAI were similar to that in 2016. So the data collected in 2018 were valid for evaluating the model. The model was firstly evaluated by comparing the simulated LAI and soil moisture content with field measurements in a 10 m × 10 m plot at the CW site. During the growing season of 2018, LAI was measured monthly using a LAI-2200 plant canopy analyzer (Li-Cor, Inc., Lincoln, NE, USA) and a total of 19 soil moisture measurements were taken with soil coring method. Daily meteorological parameters including temperature, humidity, and precipitation were obtained from the nearest weather station and radiation was estimated by the MTCLIM model (Thornton et al., 2002). Initial soil water conditions were taken from the first measurements. To further quantify the spatial variation in PLAmax along the precipitation gradient, a 20-year (1998–2017) meteorological series was collected at each site to account for the adjustment of PLAmax in response to the long-term climatic conditions. The optimal PLAmax was obtained with an iteration process, and the initial soil water conditions were obtained with the ‘spin-up mode’ in the Biome-BGC (Thornton and Rosenbloom, 2005) for each iteration. 2.4. Statistical analyses Fig. 2. Time courses of (a) whole plant leaf area (PLA), (b) profile averaged soil moisture content (SMC), and (c) whole-plant cumulative transpiration (Tcum) in each watering treatment (H, 12.8 mm/5 d; M, 8.5 mm/5 d; L, 4.3 mm/5 d). Bars indicate ± SE, n = 3. An asterisk indicates significant (P < 0.05) differences among treatments.

Differences in plant traits were tested with one-way analysis of variance (ANOVA) using LSD as suggested by (Arndt et al., 2001). The model performance was evaluated with the coefficient of determination (R2) and root mean square error (RMSE). The RMSE was calculated using

RMSE =

1 n

n

∑ (Oi − Pi)2 i=1

equivalent to the amount of water supply. The progressive drought induced a significant increase in vein and stomatal densities in each treatment (Fig. 3a, b). From day 0 to day 60, vein density increased by 30% to 45% and stomatal density nearly doubled from ∼230 to ∼440 pores mm−2. On the 30th day, vein and stomatal densities in L were significantly higher than H, while both reached similar values among treatments after 60 days (Fig. 3a, b; Table 2). Vein density correlated strongly with stomatal density (r = 0.97, P < 0.01) when all data were combined. Leaflet area decreased from ∼7.7 cm2 to ∼4.5 cm2 in each watering treatment (Fig. 3c). Similarly, stomatal length also exhibited a slight decrease with time (Fig. 3d). As drought progressed, RWCtlp decreased significantly with time in each treatment, indicating that the progressive drought induced a higher drought tolerance (Fig. 4d). ε decreased by almost 50% by the 60th day while Ψ0 and Ψtlp remained relatively constant (Fig. 4a,b,e), indicating that drought tolerance was enhanced mainly by increasing cell wall elasticity rather than osmotic adjustment. C nearly doubled from around 0.33 mol MPa−1 m−2 to around 0.68 mol MPa−1 m−2 (Fig. 4f), which could enhance the ability of leaves to buffer against rapid water potential fluctuations under drought stress. No significant differences were evident with respect to leaf drought tolerant traits among treatments at each time point (Fig. 4).

(11)

where Oi and Pi are the observed and predicted values, respectively. 3. Results 3.1. Laboratory experiment Fig. 2 shows the dynamics of PLA, SMC and Tcum under different levels of water supply. PLA rapidly increased during the early days of the experiment and then growth gradually slowed (Fig. 2a). Differences in PLA among treatments were not statistically significant before day 30, after which the values of PLA diverged and showed a significant decreasing trend with decreasing water supply (P < 0.05). Soil moisture content followed a contrary trend to the changes in PLA. Plants experienced a progressive drought in each treatment because SMC decreased gradually from FC to ∼0.15 cm3 cm−3 before the 30th day and then remained relatively constant (Fig. 2b). Significant differences in SMC among treatments were mainly observed before the 30th day and then gradually exhibited a convergence. Cumulative transpiration rapidly increased during the first 30 days of treatment and then slowly increased with time (Fig. 2c). Tcum values of different treatments were comparable before the 30th day, after which the differences among treatments increased with time. After the 40th day, plant water consumption during an irrigation interval was nearly 5

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Fig. 3. Changes in (a) stomatal density, (b) vein density, (c) leaflet area, and (d) stomatal length, in each watering treatment (H, 12.8 mm/5 d; M, 8.5 mm/5 d; L, 4.3 mm/5 d). Bars indicate ± SE, n = 6. Different letters indicate significant differences at P < 0.05.

3.2. Spatial patterns of plant traits along the precipitation gradient

3.3. Simulation of optimal PLAmax using the modified Biome-BGC model

Each R. pseudoacacia stand experienced severe soil desiccation across the precipitation gradient (Fig. S2). The profile-averaged soil moisture content was about 50% of FC at YL and close to the permanent wilting point at the other sites. Leaf-level traits differed little among the four sites. Significant differences were only observed in vein density, RWCtlp and leaf capacitance, while other leaf-level traits were similar (Table 2). Along the precipitation gradient, leaf capacitance showed an increasing trend with decreasing MAP, while RWCtlp fluctuated around 90% with no significant dependency on MAP. At the whole-plant level, PLAmax decreased by 64% from YL (15.24 m2 tree−1) to MZ (5.45 m2 tree−1) and correlated strongly with MAP (r = 0.95, P < 0.05).

The seasonal changes of LAI at the CW site could be well fitted using the Eq. (3) with the LAImax equal to 2.3 (Fig. 5a, R2 = 0.96, RMSE = 0.11). On this basis, the modified Biome-BGC model reasonably captured the dynamics of soil moisture during the growing season (Fig. 5b) with an R2 value of 0.72 and an RMSE value of 0.007 cm3 cm−3, providing a useful tool for simulating the long-term soil water balance. We further estimated the optimal PLAmax at the four sites along the precipitation gradient using the modified Biome-BGC model, which also exhibited a decreasing trend from YL to MZ (Fig. 6). The observed values of PLAmax were in good agreement with the PLAopt along the precipitation gradient (Fig. 6), suggesting that the adjustments of whole plant leaf area had achieved the optimal values under

Table 2 Leaf traits and whole plant leaf area of each watering treatment (H, 12.8 mm/5 d; M, 8.5 mm/5 d; L, 4.3 mm/5 d) at the 60th day of the laboratory experiment and the four sites along the precipitation gradient. Traits

Laboratory experiment

2

LA (cm ) SL (µm) Ds (no. mm−2) Dv (mm−2) Ψ0 (MPa) Ψtlp (MPa) LSWC (g−1) RWCtlp (%) ε (MPa) C (mol MPa−1 m−2) Whole plant leaf area (m2 tree−1)

Precipitation gradient study

H

M

L

YL

CW

AS

MZ

5.24 ± 0.75 a 12.3 ± 1.0 a 447.2 ± 15.9 a 12.3 ± 1.0 a −1.04 ± 0.22 a −1.35 ± 0.17 a 2.58 ± 0.20 a 86.81 ± 1.23 a 7.55 ± 3.00 a 0.59 ± 0.23 a 0.25 ± 0.01 a

4.68 ± 1.23 a 12.1 ± 0.9 a 481.2 ± 20.6 a 12.1 ± 0.9 a −0.77 ± 0.36 −1.17 ± 0.35 a 2.37 ± 0.49 a 86.78 ± 6.55 a 5.70 ± 5.10 a 0.70 ± 0.42 a 0.20 ± 0.02 b

4.39 ± 1.04 a 11.5 ± 1.0 a 465.6 ± 33.6 a 11.5 ± 1.0 a −1.02 ± 0.20 a −1.25 ± 0.34 a 2.82 ± 1.64 a 82.09 ± 1.49 b 5.69 ± 1.32 a 0.75 ± 0.16 a 0.18 ± 0.01 c

5.74 ± 0.90 a 12.8 ± 1.0 a 435.1 ± 38.1 a 12.8 ± 0.4 b −0.89 ± 0.09 a −1.25 ± 0.05 a 1.96 ± 0.35 a 91.17 ± 0.84 ab 9.90 ± 1.14 a 0.44 ± 0.05 bc 15.24 ± 0.50 a

5.00 ± 0.68 a 12.4 ± 1.7 a 446.5 ± 48.2 a 14.4 ± 1.1 a −0.82 ± 0.11 a −1.34 ± 0.09 a 1.94 ± 0.49 a 93.62 ± 0.75 a 12.03 ± 1.30 a 0.34 ± 0.12 c 13.77 ± 0.86 b

5.31 ± 0.7 a 12.2 ± 1.6 a 443.1 ± 63.4 a 13.8 ± 0.8 ab −0.98 ± 0.13 a −1.47 ± 0.04 a 2.19 ± 0.15 a 88.88 ± 1.81 b 9.12 ± 2.05 a 0.57 ± 0.06 ab 12.44 ± 0.25 b

5.18 ± 0.64 a 12.8 ± 1.8 a 435.6 ± 28.7 a 13.7 ± 0.3 ab −0.87 ± 0.07 a −1.38 ± 0.13 a 2.32 ± 0.07 a 91.67 ± 2.09 ab 9.61 ± 3.09 a 0.62 ± 0.10 a 5.45 ± 0.44 c

Note: Data are means ± standard division. Different letters within a row in the laboratory experiment or precipitation gradient study indicate significant differences at P < 0.05. Whole plant leaf area in the precipitation gradient study refers to the seasonal maximum value. LA, leaflet area; SL, stomatal length; Ds, stomatal density; Dv, vein density; Ψ0 osmotic potential at full turgor; Ψtlp, turgor loss point; LSWC, leaf saturated water content; RWCtlp, relative water content at turgor loss point; ε, modulus of elasticity; C, leaf water storage capacitance; Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi. 6

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Fig. 4. Changes in volume-pressure curve parameters in each watering treatment (H, 12.8 mm/5 d; M, 8.5 mm/5 d; L, 4.3 mm/5 d), including (a) osmotic potential at full turgor (Ψ0, MPa), (b) turgor loss point (Ψtlp, MPa), (c) leaf saturated water content (LSWC, g g−1), (d) relative water content at turgor loss point (RWCtlp, %), (e) modulus of elasticity (ε, MPa), and (f) leaf water storage capacitance (C, mol MPa−1 m−2). Bars indicate ± SE, n = 6. Different letters indicate significant differences at P < 0.05; ns is not significant.

Ψsoil was lower than Ψ1 about 135 days and Ψsoil dropped below −1.5 MPa briefly. Gsun declined considerably due to the drought stress, and stayed around 0 m s−1 for consecutive 11 days (177th to 187th day). When PLAmax was the optimal value (13.2 m2 tree−1, LAImax = 2.2), Ψsoil was lower than Ψ1 for 68 days over the growing season, during which Gsun exhibited a much smaller decline than that at 1.5PLAopt. In this case, Ψsoil never reached −1.5 MPa.

different precipitation conditions. To illustrate the adaptive significance of optimal PLAmax, the dynamics of Gsun, Tc and Ψsoil under different values of PLAmax in a typical dry year (2012, AP = 457.9 mm) at CW was shown in Fig. 7. At the start of the growing season,Ψsoil exhibited a decreasing trend with increasing PLAmax due to the impact of earlier hydrological dynamics, while the difference was small among PLAmax. When PLAmax was 0.5PLAopt (6.6 m2 tree−1, LAImax = 1.1), water loss from canopy transpiration was low, and Ψsoil was higher than Ψ1 during the whole growing season. The stomatal conductance was not affected by soil drought stress. When PLAmax was 1.5PLAopt (19.8 m2 tree−1, LAImax = 3.3), Ψsoil dropped rapidly due to large amount of water loss from canopy transpiration during the early stage (100th to 170th day), and Ψsoil remained low values thereafter. Over the growing season,

4. Discussion The evolution of plant traits plays a critical role in helping plants adapt to different environmental conditions. To fully understand the temporal and spatial changes in plant traits along a precipitation gradient, it is essential to integrate different approaches to overcome 7

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Fig. 5. Simulated (solid lines) and observed (solid dots) leaf area index (a) and average soil moisture content (SMC, b) for 0–300 cm soil profile during the growing season in 2018 at the Changwu site.

Fig. 7. Simulated stomatal conductance of sun leaves (Gsun, a), canopy transpiration (Tc, b) and soil water potential (Ψsoil, c) under different values of seasonal maximum whole plant leaf area (PLAmax) during the growing season of 2012 at CW (Changwu) site. PLAopt is the optimal PLAmax calculated by the long-term simulation of soil water balance. Ψ1 and Ψ2 represent the start and completion of conductance reduction for Ψsoil.

were important for understanding the long-term adjustments of plant traits for adapting different climatic conditions, and could help to predict the large-scale pattern of plant traits. R. pseudoacacia is known as a typical water-intensive species (Ma et al., 2017; Wang et al., 2013). In the laboratory experiment, it showed that plant water consumption increased with plant growth, gradually exceeded water supply in each treatment, and induced the decrease in soil moisture content. Jia et al. (2017) reported a similar pattern of soil moisture content in R. pseudoacacia stands in both semi-humid and semi-arid climatic zones on the Loess Plateau. The value of Tcum in H was slightly lower than that in M and L from day 10 to 20 of the growing period. The major reason could be that the soil moisture content in H was relatively high during this period, the soil aeration was poor and root physiological activity weak (Kreuzwieser and Rennenberg, 2014; Taiz and Zeiger, 2010). Another possible reason could be that the 4 TDR probes at 20-cm interval might not well reflect the uneven distribution of soil moisture content during the early stage, and leaded to the estimation error of transpiration. The distribution of soil moisture content was relatively uniform during the later stage, and the estimated transpiration could well reflect the differences among treatments. The values for leaf traits measured in the laboratory experiment were the same order of magnitude as those determined for the natural precipitation gradient as well as previous studies of R. pseudoacacia (Xu et al., 2009; Zhang et al., 2012a, 2012b). The values of Ψtlp in the laboratory experiment ranged from −1.1 to −1.4 MPa.

Fig. 6. Observed and estimated optimal seasonal maximum whole plant leaf area (PLAmax) at the four sites along the precipitation gradient. Bars indicate ± SE, n = 3. Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi.

methodological, spatial, and temporal limitations from individual approaches (Dunne et al., 2004; Martin-StPaul et al., 2013). In this study, we examined the adjustments of leaf traits and whole plant leaf area by combining a laboratory experiment, field study along the precipitation gradient and model simulation. The laboratory experiment could well reflect the long-term interactions between plant traits and soil moisture under field conditions. We found that increasing transpiration with plant growth induced similar patterns of soil desiccation under different precipitation conditions (Fig. 2), which further resulted in the convergence in leaf traits (Table 2; Figs. 3 and 4). The results were in line with the spatial pattern of plant traits along the precipitation gradient study (Table 2), which strongly supported the association between temporal hierarchy and organizational levels. We further verified that the adjustment of whole plant leaf area to an optimal value to maximize tree growth and avoid severe drought stress by balancing water supply and demand under different precipitation conditions. These findings

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conductance, and affects stomatal response to air temperature by influencing leaf energy exchange (Baldocchi and Xu, 2007). Therefore, the long-term adjustments of leaf traits indicated that these parameters could change with time and soil water conditions. However, the mechanistic process-based models still face great difficulty in incorporating the temporal changes of these parameters, especially when simulating plant-soil-water interactions at large temporal and spatial scales (Purves and Pacala, 2008). We found that leaf traits eventually exhibited a convergence while PLA differed considerably under different precipitation conditions, which provided a basis for predicting the large-scale pattern of PLA with uniform leaf-level parameters in this area (Neilson, 1995; Weltzin et al., 2003). On this basis, we further proposed that the variation in PLAmax along the precipitation gradient could be well quantified with the optimal PLAmax, providing a simple but efficient way to quantify the spatial pattern of whole plant leaf area at a large scale. The adjustment of PLAmax to the optimal value could efficiently balance the tradeoff between maximum plant growth and preventing severe drought stress (Eagleson and Tellers, 1982; Kergoat, 1998). On short time scales, the stomatal closure in response to soil drought plays a critical role in down-regulating transpiration. On longer time scales, the decreases in both PLA and stomatal conductance contribute to reducing water loss from transpiration and preventing SMC reaching PWP (Baldocchi and Xu, 2007). There exists an interaction between the adjustments of PLA and stomatal conductance through the impact on soil water conditions (Fig. 7). Based on our analysis, the adjustment of PLA could efficiently avoid the excessive decrease of stomatal conductance and the associated negative effects on plant carbon balance by regulating water balance. The low stomatal conductance can severely inhibit the photosynthetic rate and productivity, which further increases the susceptibility to heat and light stress at the short-term scale and leads to the depletion of non-structural carbohydrate pools at the longterm scale (Choat et al., 2018; McDowell et al., 2011). Moreover, there is increasing evidence supporting that the adjustment of PLA is crucially important in maintaining plant hydraulic safety (Tyree et al., 1993; Wolfe et al., 2016). Both of these mechanisms contribute to helping plants reduce risk of mortality and improve the chance of survival under drought stress (McDowell et al., 2013).

According to the measurements of Li et al. (2012), Xu (2008) and Wang et al. (1999), Ψtlp of R. pseudoacacia ranged from −2 to −1 MPa in Loess Plateau, with higher values in seedlings than mature trees. As drought stress developed in each treatment, R. pseudoacacia seedlings gradually demonstrated adaptive leaf traits including decreasing leaf and stomatal size and increasing vein density, stomatal density, and drought tolerance. As suggested by previous studies, the smaller leaves that grow in response to drought stress are more closely coupled to the atmosphere and thus do not get too hot (Carins Murphy et al., 2012; Jones, 1992). The small, high-density stomata allow the leaf to rapidly attain high stomatal conductance under favourable conditions, but then rapidly reduce conductance when evaporative conditions are unfavourable (Hetherington and Woodward, 2003; Lawson and Blatt, 2014). The coordinated increase in vein density with stomatal density ensures an efficient balance between water supply and demand at the leaf level (Brodribb and Jordan, 2011; Schneider et al., 2017). The increase in leaf capacitance during the progressive drought suggested the leaves possessed higher capacity to stabilize the fluctuations of leaf water potential (Blackman and Brodribb, 2011). Meanwhile, the decrease of RWCtlp generally indicated a higher capacity for turgor maintenance under low leaf water content induced by drought stress. Turgor has been reported to be maintained by osmotic or elastic adjustment or both (Castro-Diez and Navarro, 2007). Our results indicated that elastic adjustment in R. pseudoacacia was not accompanied by osmotic adjustment, which was in agreement with previous studies (Saito and Terashima, 2004), and emphasized the importance of elastic adjustment. The difference of Ψtlp was not statistically significant among the treatments at each sampling time. On the 30th day, Ψtlp in L was even slightly higher than that in H and M, and Ψ0 exhibited the similar pattern. This could be ascribed to that drought stress disturbed the plant physiological activities. Drought stress can cause (1) membrane damage and ion leakage from cells; and (2) the decline of the photosynthetic CO2 assimilation rate and the depletion of available nonstructural carbohydrates (Farooq et al., 2009; Taiz and Zeiger, 2010). As a consequence, it could cause the decrease of osmotically active molecules/ions in the cells, and weaken the capacity of osmotic adjustment. Castro-Diez and Navarro (2007) also found a similar phenomenon in their study, and suggested that the responses of Ψ0 and Ψtlp to drought stress were affected by the light conditions. They found that Ψ0 and Ψtlp declined under full sunlight condition while increased under 43% of full sunlight in response to drought stress. In our study, light intensity in the artificial climate chamber was weaker than the natural sunlight, the osmotic adjustment developed slowly in plants (Saito and Terashima, 2004) and could not offset the damage of drought stress. Compared with the osmotic adjustment, the elastic adjustment developed more rapidly and functioned more effectively to maintain turgor (Saito and Terashima, 2004). Elastic adjustment is reflected by a change in ε, with a higher value indicating lower cell wall elasticity (Saito and Terashima, 2004; Tyree and Hammel, 1972). However, ε can both increase and decrease in response to drought stress, with both trends regarded as adaptive responses to water stress conditions (Saito and Terashima, 2004). Saito and Terashima (2004) suggested that leaves that developed under xeric conditions possessed greater ε than those developed under mesic conditions, while ε in mature leaves decreased under drought treatment or in a dry summer. In accordance with this idea, we found that ε decreased in response to a progressive drought in the laboratory experiment (Fig. 4e), which could facilitate tissue shrinkage and decelerate turgor decline during dehydration, enhancing drought tolerance. The eco-physiological parameters relating to plant transpiration are highly determined by the leaf traits examined in this study. Gmax is determined by the stomatal density and size (Miglietta et al., 2011). Leaf drought tolerant traits would regulate the stomatal response to atmosphere and soil drought (Bartlett et al., 2016, 2012), thus influencing f(VPD) and f(Psoil). Leaflet area relates to the boundary layer

5. Conclusions By integrating a laboratory experiment, precipitation gradient study and modelling research, we provided a comprehensive and quantitative understanding of how plant adjusts leaf traits and whole plant leaf area for balancing water supply and demand. We confirmed that soil moisture declined and induced soil desiccation with the growth of R. pseudoacacia under different precipitation conditions of the Loess Plateau. The progressive drought stress resulted in similar leaf traits while differential PLA under different precipitation conditions. The adjustment of PLA achieved an optimal value to maximize plant growth and prevent severe drought stress, acting as an efficient way to adapt to different precipitation conditions. This information was helpful for understanding and predicting the trajectories of R. pseudoacacia forests to adapt to regional climate gradient and changing precipitation regimes in the future. Acknowledgements This research was financially supported by the National Natural Science Foundation of China (No. 41571130082 and 41571213) and the Newton Fund via the Natural Environment Research Council (NE/ N007433/1). The authors thank the members of the Changwu Ecology Station, Chinese Academy of Sciences, and Ministry of Water Resources for their assistance. Special thanks are given to the editor and two anonymous reviewers for their comments and suggestions. 9

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Supplementary materials

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