《植物学纪事》.pdf

Annals of Botany 118: 541–553, 2016 doi:10.1093/aob/mcw127, available online at www.aob.oxfordjournals.org Optimal balance of water use efficiency and leaf construction cost with a link to the drought threshold of the desert steppe ecotone in northern China Haixia Wei1,*, Tianxiang Luo1 and Bo Wu2,* 1 Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China and 2Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China *For correspondence. Email weihaixia@itpcas.ac.cn or wubo@caf.ac.cn  Background and Aims In arid environments, a high nitrogen content per leaf area (Narea) induced by drought can enhance water use efficiency (WUE) of photosynthesis, but may also lead to high leaf construction cost (CC). Our aim was to investigate how maximizing Narea could balance WUE and CC in an arid-adapted, widespread species along a rainfall gradient, and how such a process may be related to the drought threshold of the desert–steppe ecotone in northern China.  Methods Along rainfall gradients with a moisture index (MI) of 017–041 in northern China and the northern Tibetan Plateau, we measured leaf traits and stand variables including specific leaf area (SLA), nitrogen content relative to leaf mass and area (Nmass, Narea) and construction cost (CCmass, CCarea), d13C (indicator of WUE), leaf area index (LAI) and foliage N-pool across populations of Artemisia ordosica.  Key Results In samples from northern China, a continuous increase of Narea with decreasing MI was achieved by a higher Nmass and constant SLA (reduced LAI and constant N-pool) in high-rainfall areas (MI > 029), but by a lower SLA and Nmass (reduced LAI and N-pool) in low-rainfall areas (MI  029). While d13C, CCmass and CCarea continuously increased with decreasing MI, the low-rainfall group had higher Narea and d13C at a given CCarea, compared with the high-rainfall group. Similar patterns were also found in additional data for the same species in the northern Tibetan Plateau. The observed drought threshold where MI ¼ 029 corresponded well to the zonal boundary between typical and desert steppes in northern China.  Conclusions Our data indicated that below a climatic drought threshold, drought-resistant plants tend to maximize their intrinsic WUE through increased Narea at a given CCarea, which suggests a linkage between leaf functional traits and arid vegetation zonation. Key words: Carbon isotope, drought threshold, leaf area index, leaf trait relation, moisture index, sandy land, vegetation zonation, Artemisia ordosica. INTRODUCTION As aridity increases, drought-resistant plants (hereafter arid plants) tend to have a higher nitrogen content per leaf area (Narea, a ratio of mass-based nitrogen to specific leaf area, Nmass/SLA) (Cunningham et al., 1999; Wright et al., 2005; Cornwell et al., 2007), which can increase the water-use efficiency (WUE) of photosynthesis (Smith et al., 1997; Wright et al., 2001, 2003). While the positive relationship between Nmass and SLA generally exists across species and sites (Reich et al., 1997; Wright et al., 2004), higher Narea (i.e. higher Nmass at a given SLA, and vice versa) in species from low-rainfall areas could be achieved by higher Nmass or lower SLA, or both (Wright et al., 2001, 2003). Such a strategy shift in the SLA– Nmass relationship exists within the widespread species Artemisia ordosica along a rainfall gradient in northern China (Wei et al., 2011). Along a gradient of water availability, variations in leaf traits may arise from changes in leaf-level anatomical structure (Smith et al., 1997) and/or canopy foliage turnover and nitrogen allocation (Field, 1983; Farquhar et al., 2002) to maximize water- and nitrogen-use efficiencies. At the leaf level, the ultimate evolution of leaf form for arid plants tends towards a more cylindrical leaf with low SLA (i.e. high leaf thickness), which maximizes WUE by increasing the overlap area of light and CO2 inside the leaf with few changes in the mesophyll conductance (Smith et al., 1997). Increased leaf thickness and decreased SLA associated with decreasing rainfall have been observed in previous studies (Witkowski and Lamont, 1991; Cunningham et al., 1999). At the stand level, the theoretical model for the simultaneous optimization of water- and nitrogen-use efficiencies of photosynthesis suggests that at a given total amount of canopy foliage N-pool, leaf area index (LAI) generally decreases as water becomes less available, resulting in a concomitant increase in Narea (Narea ¼ N-pool/LAI) (Farquhar et al., 2002). Thus, in response to decreased rainfall, higher Narea within a widespread species may result from reduced LAI with unchanged foliage N-pool and SLA (Field, 1983; Farquhar et al., 2002; Wei et al., 2011), or from increased leaf thickness (i.e. lower SLA, Smith et al., 1997; Poorter et al., 2009) when the reduction of LAI can no longer compensate for soil water deficiency at low-rainfall sites. This suggests that maximizing Narea may be a key process C The Author 2016. Published by Oxford University Press on behalf of the Annals of Botany Company. V All rights reserved. For Permissions, please email: journals.permissions@oup.com Downloaded from https://academic.oup.com/aob/article/118/3/541/1741496 by guest on 16 October 2023 Received: 5 January 2016 Returned for revision: 17 March 2016 Accepted: 12 May 2016 Published electronically: 21 July 2016 542 Wei et al. — Linkage between leaf traits and vegetation zonation point in leaf traits and stand variables along the rainfall gradient; (2) if the positive relationship between Narea and CCarea also shifts between low- and high-rainfall groups consistent with the pattern found in the SLA–Nmass relationship, and whether the low-rainfall group has higher Narea and d13C at a given CCarea compared with the high-rainfall group; and (3) if the drought threshold identified by leaf-trait data could indicate the zonal boundary between typical temperate and desert steppes in northern China. Furthermore, we investigated the generality of the low-altitude data from northern China using the high-altitude data from the south-east Qaidam Basin of Qinghai. MATERIALS AND METHODS Study sites Artemisia ordosica is adapted to fixed and semi-fixed sandy land habitats across typical temperate steppes, desert steppes and semi-deserts in northern China, where annual rainfall ranges from 150 to 400 mm (Fig. 1) (Cui, 1991; Wang et al., 2002). Artemisia ordosica is a dominant species that forms a relatively stable community in sub-climax state in the mild and moderately degraded Mu Us Sandy Land and its neighboring areas that are characterized by arid and infertile soils. Soil textures at 0–50 cm depth are similar across different rainfall areas, with sand contents of > 96 % and clay contents of < 4 % across fixed and semi-fixed sandy lands (Duan and Liu, 1995; Chen et al., 1998; Wang, 2006; Li, 2007). We selected our study sites by overlapping the geographical distribution of A. ordosica with the map of annual rainfall isolines in northern China. Along a geographical transect from east to west in the Mu Us Sandy Land and its neighbouring areas, we selected 17 study sites (four of which are presented in Wei et al., 2011) to represent 17 different rainfall areas (Fig. 1). Locations and altitudes of the 17 study sites were recorded by a global positioning system, with latitudes of 37 270 4000 –39 430 5700 N, longitudes of 102 460 3300 –109 520 0600 E and altitudes of 1210–1783 m (Table 1). Daily meteorological data (1985–2010) for 142 meteorological stations in northern China were obtained from China’s National Meteorological Bureau. The meteorological data included atmospheric pressure (Pa), vapour pressure (Pa), mean air temperature ( C), maximum air temperature ( C), minimum air temperature ( C), mean relative humidity (%), sunshine duration (h), wind speed (m s1) and rainfall (mm). Annual potential evapotranspirations for the 142 meteorological stations were calculated with the Penman–Montieth equation (Allen et al., 1998). Moisture index (MI) was then calculated as the ratio of annual rainfall to annual potential evapotranspiration. The isoline maps of annual mean temperature, rainfall, potential evapotranspiration and MI were produced with the Krige spatial interpolation method. The climate data (averaged over 1985– 2010) of the 17 study sites were estimated according to the geographical locations. Across the 17 study sites, annual mean temperature was 75–94  C, annual rainfall 150–370 mm, annual potential evapotranspiration 867–965 mm and MI 017– 041 (Table 1). Downloaded from https://academic.oup.com/aob/article/118/3/541/1741496 by guest on 16 October 2023 in shaping arid species’ distribution and ecosystem function, and, if so, a drought threshold would exist associated with the switch change from allocating canopy leaf nitrogen to altering leaf-level anatomical structure along a rainfall gradient. However, little research has combined both theories to understand the intraspecific continuous variations in leaf traits and stand variables with rainfall. It is still unclear whether a drought threshold exists that causes a shift in controls on Narea across populations of an arid-adapted, widespread species along a large rainfall gradient. Such knowledge would help to understand the response of arid plants to climate change and to explore a simple predictor of arid vegetation zonation. Leaf construction cost (CC) is defined as the amount of glucose required for constructing a unit leaf mass or leaf area (Williams et al., 1987). A leaf with low SLA or high Nmass generally has a high content of lignin or protein to resist environmental stress (Gower et al., 1989; Groeneveld et al., 1998; Zhang et al., 2012). These compounds (lignin and protein) are expensive to produce (Williams et al., 1987; Nagel and Griffin, 2001). To maximize Narea for high WUE, the induction of a low SLA and/or high Nmass by drought may also increase CC (Penning de Vries et al., 1974; Williams et al., 1987; Griffin, 1994; Nagel and Griffin, 2001; Nagel et al., 2002; Chen et al., 2006; Zhang et al., 2012). Higher CC is usually associated with lower energy-use efficiency and growth rate (Griffin, 1994; Poorter and Villar, 1997; Baruch and Goldstein, 1999; Nagel et al., 2004; Song et al., 2007), which may hinder plant survival and competition with other species in arid environments. It has been suggested that arid plants have to balance the costs of carbon gain and water transport along a rainfall gradient by altering their leaf traits (Wright et al., 2003; Prentice et al., 2014). The regulation of Narea along a rainfall gradient should be a process to balance WUE and CC, although it is still unknown how maximizing Narea could achieve this in arid-adapted species. In arid and semi-arid sandy lands in northern China (1200– 1800 m) and in the south-east Qaidam Basin of Qinghai (3200– 3300 m), the deciduous sub-shrub A. ordosica is widely distributed in mild and moderately disturbed (fixed and semifixed, respectively) sandy lands across a broad range of annual rainfall (150–400 mm). The Mu Us Sandy Land is the distribution centre of A. ordosica, where mean air temperature and soil texture are similar across areas with differing rainfall (Wei et al., 2011). Moreover, there is no significant genetic differentiation among A. ordosica populations from divergent geographical zones (Wang et al., 2004). Such a species distribution pattern provides an ideal system for identifying the drought threshold and related mechanisms of the intraspecific shift in controls on Narea along a rainfall gradient. In this study, leaf traits (SLA, Nmass, Narea, CCmass, CCarea, d13C) and related stand variables (LAI and foliage N-pool) within populations of A. ordosica were measured across 17 study sites in the Mu Us Sandy Land and its neighbouring areas with annual rainfall ranging from 150 to 370 mm. Our aim was to test the hypothesis that below a climatic drought threshold, arid plants tend to maximize their intrinsic WUE (i.e. high leaf d13C, Farquhar et al., 1989) through increased Narea at a given CCarea. We investigated: (1) whether there is a drought threshold determining the significant shift in SLA–Nmass relationships and, if so, whether this drought threshold also determines the turning Wei et al. — Linkage between leaf traits and vegetation zonation 543 A Study site Provincial boundary Rainfall isoline Artemisia ordosica community patch 200 0 200 km Inner Mongolia Beijing Hebei Shanxi Ningxia Qinghai Shaanxi 110° 25 5 23 5 5 17 15 21 5 140 37 5 70 19 5 0 5 70 13 11 5 5 B 5 105° km 40° 39 5 35 33 5 5 31 5 29 27 5 38° FIG. 1. Location of study areas (A) and map of the geographical distribution of A. ordosica and annual rainfall isolines (B). The geographical distribution of A. ordosica was adapted from the Vegetation Map of China (Zhang, 2007). Sampling and measurement of leaf traits At each study site, leaf and soil samples were collected at both fixed and semi-fixed sandy land habitats, which were identified according to differences in vegetation cover (> 40 vs. 20–40 %, respectively) and soil crust thickness (>1 vs. 05– 1 cm, respectively) (Wu and Ci, 2002). During July and August when leaves were fully expanded, the outer-canopy leaves of nine healthy A. ordosica individuals were sampled from three 5  5-m plots per habitat. For each plant individual sampled, 50 fresh leaves were scanned with a conventional digital scanner (HP Scanjet 2400, Hewlett Packard Company, Palo Alto, CA, USA) and Downloaded from https://academic.oup.com/aob/article/118/3/541/1741496 by guest on 16 October 2023 Gansu Wei et al. — Linkage between leaf traits and vegetation zonation 544 mass (oven-dried for 48 h at 70  C). Leaf Nmass was analysed using the Kjeldahl method (Kjeldahl, 1883) and Narea was calculated as the ratio of Nmass to SLA. The d13C ratio of leaf samples, relative to a Pee Dee Belemnite (PDB) standard, was calibrated with a square of known surface area. We determined single-sided leaf area from scanned images using Image Pro Plus 6.0 software (Media Cybemetics Inc., New York, USA). SLA was calculated as the fresh leaf area divided by its dry TABLE 1. Climate and soil factors of study sites across northern China and the south-east Qaidam Basin sandy lands Site number Place name Longitude Latitude Altitude (m) MAP (mm) MAE (mm) MI Total soil N concentration (mg g1) MAT ( C) Fixed sandy land Semi-fixed sandy land 38 370 1800 39 210 1700 37 470 2700 38 090 2800 38 10 1700 39 050 5400 38 440 5300 39 030 3700 39 430 5700 38 290 4600 37 270 4000 37 330 0300 38 120 5700 38 370 2600 38 090 3800 37 420 1700 39 030 3800 1213 1350 1307 1270 1380 1295 1361 1419 1410 1431 1267 1783 1444 1210 1270 1320 1420 367 342 336 331 320 318 290 265 258 250 210 172 150 370 353 310 264 902 881 946 930 947 903 929 923 900 965 917 851 867 898 931 950 923 041 039 036 036 034 035 031 029 029 026 023 020 017 041 038 033 029 84 75 9 87 87 79 81 78 78 87 94 91 88 86 87 88 76 05560064aA 05560170aAB 03760172aBC 04060013aB 04160110aB 05160087aAB 04560142aAB 03560099aBC 03660029aBC 03260036aCD 01760051aE 02060050aE 01360033aE 02860049aD 02460012aDE 032 6 0052aCD 03760039aBC 01760043bAB 01860016bA 01060014bC 01760022bAB 00960053bC 00860014bC 01260008bBC 02160017bA 01860014bA 02160086bA 00760008bC 01060036bC 00660008bC 01860075bAB 00760016bC 019 6 0038bA 01960018bA 36 430 5300 36 150 5400 3289 3284 401 207 592 835 068 025 04 35 022 6 0049aDE 018 6 0059aE 014 6 0034aB 011 6 0012aBC Different letters within a row and a column show significant differences between each site’s sandy land habitats (lowercase) and between study sites (uppercase) at a 005 level, respectively. MAP, mean annual precipitation; MAE, mean annual potential evapotranspiration; MAT, mean annual temperature; MI ¼ MAP/MAE, moisture index. *Data from Wei et al. (2011). TABLE 2. Differences in slopes and intercepts of Nmass–SLA relationships for A. ordosica among 17 study sites and three rainfall groups in northern China sandy lands Site/group Northern China sandy lands 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Group 1 (sites 1–6, 14–16) Group 2 (site 7) Group 3 (sites 8–13, 17) MI 041 039 036 036 034 035 031 029 029 026 023 020 017 041 038 033 029 033–041 031 017–029 Slope Intercept Total FS SFS Total FS SFS 017a 022a 012a 011a 021a 021a 019a 017a 009a 025a 015a 019a 024a 016a 021a 010a 018a 017a 019a 022a 018a 020a — — 015a 020a 028a 015a 009a — 017a 014a — 028a — — — 019a 028a 018a 016a — 018a 024a 026a 021a 014a 016a 008a 020a 015a — 016a — 023a — — 022a 014a 018a 64c 001c 87c 117c 20c 24c 91b 156a 249a 95a 148a 125a 96a 61c 31c 128c 162a 60c 91b 117a 57c 40c — — 96c 43c 099b 167a 248a — 134a 174a — 29c 64c — 44c 042c 43c 18c 135b 179a 257a 154a 153a — 185a — 15c — — 16c 135b 149a — — — 50c 099b 145a Data analysis was performed by ANCOVA. Different letters within a column show significant differences between study sites or between rainfall groups at a 005 level. MI, moisture index; Total, in pooled data from FS and SFS; FS, fixed sandy land; SFS, semi-fixed sandy land. Downloaded from https://academic.oup.com/aob/article/118/3/541/1741496 by guest on 16 October 2023 Northern China sandy lands 1 Yulin 109 520 0600 2 Ejin Horo 109 460 2600 3 Uxin Henan 108 250 3600 4 Uxin 108 380 3600 5 Otog Qian 108 130 2600 6 Uxin Ju 108 590 4000 7 Otog Sumitu 108 160 3400 8 Otog 108 030 3000 9 Hangjin 108 230 4500 10 Otogqian Damiao 107 130 4900 11 Shapotou 105 000 2800 12 Gulang 103 390 2200 13 Minqin 102 460 3300 14* Yulin 109 510 5700 15* Uxin 108 380 3000 16* Otogqian Chengchuan 108 270 2200 17* Otog 108 030 3000 South-east Qaidam Basin sandy lands 18* Qinghai Lake 100 460 4800 19* Dulan 98 110 5100 Wei et al. — Linkage between leaf traits and vegetation zonation determined by combusting samples in an elemental analyser coupled to a stable isotope mass spectrometer (Flash EA þ Delta V, Thermo Fisher Scientific Inc., Waltham, MA, USA). The overall precision of the d13C analysis was 01. For each leaf sample, the heat of combustion (HC) was measured with an oxygen bomb calorimeter (PARR 1281, Parr Instrument Company, Moline, IL, USA). The HC for each sample was determined in triplicate, with the relative differences among the three measurements being <2 %. The ash concentration (AC) was determined by combustion of 1-g leaf samples in a muffle furnace at 550  C for 4 h until a white–grey residue remained. Mass-based leaf construction cost (CCmass) was calculated by the formula given by Williams et al. (1987): 545 40 A Total 30 20 CCmass ¼ ½ð0  06968HC  0  065Þ ð1  ACÞ þ 7  5062ðkN=14  0067Þ=EG B Fixed Nmass (mg g–1) where CCmass ¼ construction cost (g glucose g1), HC ¼ heat of combustion (kJ g1), AC ¼ total ash content (%), k is the oxidation state of the N source (þ5 for nitrate or –3 for ammonium), N ¼ total Kjeldah nitrogen (g g1), and EG is a constant of 089 (Williams et al., 1987). In this study, we calculated CCmass with k ¼ 5, as nitrate is the principal source of nitrogen that is available to terrestrial plants under most field conditions (Taiz and Zeiger, 1991). CCarea was calculated as the ratio of CCmass to SLA. 30 20 Measurement of stand and soil variables Within each of the 102 plots across the 17 study sites, we measured the crown diameters along the maximum and minimum axes for each A. ordosica individual clump and then calculated the projected area of a crown as the elliptical area. At each study site, 18 individual clumps of A. ordosica with different crown areas were harvested for measurements of foliage dry mass per clump. Allometric regression equations were developed between foliage mass and crown area for each study site. The foliage biomass of A. ordosica within each plot was then estimated according to the allometric equations using the clump-specific crown area measurements. LAI and foliage Npool were calculated as the foliage biomass multiplied by SLA and Nmass, respectively. For each plot, two soil samples (0–10 and 20 cm in depth) were collected and analysed for soil total nitrogen concentration (STN) using the Kjeldahl method (Kjeldahl, 1883). Additional data from the south-east Qaidam Basin Wei et al. (2011) indicated that the drought-induced shift in the SLA–Nmass relationship was also found in the data from the south-east Qaidam Basin of Qinghai. To test the generality of the CC and d13C data found in the low-altitude regions of northern China, we also measured these two traits in A. ordosica individuals across two high-altitude sandy lands in Dulan and Qinghai Lake within the south-east Qaidam Basin, using the leaf samples collected by Wei et al. (2011). The methods of leaf sampling and measurements were the same as described above. According to climate data obtained from the R 2Solid = 0·75∗∗∗ R 2grey = 0·76∗∗ R 2dashed = 0·60∗∗∗ C Semi-fixed 30 20 R 2Solid = 073∗∗∗ R 2grey = 054∗ R 2dashed = 0·64∗∗∗ 10 50 70 90 110 130 SLA (cm2 g–1) FIG. 2. Strategy shifts in SLA–Nmass relationships for A. ordosica along a rainfall gradient in northern China sandy lands. Data analyses were performed on pooled data (A) and for fixed (B) and semi-fixed (C) sandy land habitats, respectively. Empty circles and dashed trend lines are for high-rainfall areas (rainfall group 1, 310–370 mm; MI, 033–041); grey triangles and trend lines are for mid-rainfall transition (rainfall group 2, 290 mm; MI, 031); filled circles and solid trend lines are for low-rainfall areas (rainfall group 3, 150–265 mm; MI, 017–029). ANCOVA statistics are given in Table 2. Downloaded from https://academic.oup.com/aob/article/118/3/541/1741496 by guest on 16 October 2023 R 2Solid = 0·64∗∗∗ R 2grey = 0·62∗∗∗ R 2dashed = 0·48∗∗∗ Wei et al. — Linkage between leaf traits and vegetation zonation 546 14 0·6 A B 12 Total Total 0·5 10 R 2solid = 0·93∗∗∗ 0·4 8 0·3 6 0·2 R 2dashed = 0·06, ns R 2= 0·96∗∗∗ 4 D 10 0·5 Fixed 0·4 R 2solid = 0·97∗∗∗ 0·3 8 6 0·2 R 2dashed = 0·27, ns 4 R 2= 0·94∗∗∗ E 0·1 F 12 Semi- fixed 0·5 Semi- fixed 2 ∗∗∗ 10 R solid = 0·93 0·4 8 0·3 6 0·2 4 2 0·1 LAI (m2 m–2 land) N-pool (kg ha–1 land) 12 Fixed R 2dashed = 0·01, ns 0·2 0·3 0·4 0·5 Moisture index R 2= 0·96∗∗∗ 0·1 0·2 0·3 0·4 0·1 0 0·5 Moisture index FIG. 3. Variations in stand-level foliage N-pool (A, C, E) and LAI (B, D, F) for A. ordosica populations along an MI gradient in northern China sandy lands. Data analyses were performed on pooled data (A, B) and for fixed (C, D) and semi-fixed (E, F) sandy land habitats, respectively. (A, C, E) Grey dashed lines indicate a climatic drought threshold with MI ¼ 029, and the relationships between foliage N-pool and MI differ below and above the drought threshold; the solid trend lines are for the areas with MI  029 and the dashed trend lines are for the areas with MI > 029. Bars indicate mean 6 s.d. Dulan and Qinghai Lake stations, the calculated MI was 025 in Dulan and 068 in Qinghai Lake (Table 1). Data analysis One-way analysis of variance (ANOVA) was applied to assess differences in leaf traits and STN between the two sandy land habitats per site and between 17 study sites. If the results of the ANOVA were significant, Tukey’s pair-wise comparisons were made. A simple linear model (y ¼ a þ bx) was used for analysing bivariate relationships of leaf traits. Analysis of covariance (ANCOVA) in a general linear model framework was applied to test for differences in the slopes and intercepts of SLA–Nmass relationships from different rainfall areas, in which rainfall served as a grouping variable, Nmass as a dependent variable and SLA as a covariate. We tested first for the homogeneity of slopes and then for the difference in intercepts. Data from different rainfall areas were pooled as a rainfall group if there were not significant differences in slopes and intercepts. In this way, a drought threshold was determined by the significant shift in the SLA–Nmass relationship along the rainfall gradient. Accordingly, differences in slopes and intercepts for relationships of Narea-CCarea and d13C-Narea among different rainfall groups (identified by the SLA–Nmass relationship) were further tested with ANCOVA in a general linear model framework. The relationships of leaf traits (SLA, Nmass) and stand variables (LAI, foliage N-pool) with MI below and above the drought threshold were also analysed by a simple linear model (y ¼ a þ bx). Downloaded from https://academic.oup.com/aob/article/118/3/541/1741496 by guest on 16 October 2023 C 0·1 Wei et al. — Linkage between leaf traits and vegetation zonation Total A SLA (cm2 g–1) 110 –25 Total B R 2solid = 0·85∗∗ –26 90 –27 70 –28 R 2 = 0·41∗∗ R 2dashed = 0·10, ns D 1·3 R 2solid = 0·81∗∗ R 2 = 0·35∗ E F 230 3·5 190 2·5 150 R 2 = 0·82∗∗ 1·5 0·1 CCmass (g g–1) 25 20 Narea (g m–2) 1·4 R 2dashed = 0·66∗∗ 30 CCarea (g m–2) Nmass (mg g–1) 35 R 2 = 0·47∗∗ 0·2 0·3 0·4 0·5 Moisture index 0·1 0·2 0·3 0·4 110 0·5 Moisture index 13 FIG. 4. Variations in SLA (A), Nmass (C), Narea (E), d C (B), CCmass (D) and CCarea (F) for A. ordosica along an MI gradient in northern China sandy lands. Data analyses were performed on pooled data from fixed and semi-fixed sandy land habitats. (A, C) Grey dashed lines indicate a climatic drought threshold with MI ¼ 029, and the relationships of SLA (A) and Nmass (C) to MI differ below and above the drought threshold; the solid trend lines are for the areas with MI  029 and the dashed trend lines are for the areas with MI > 029. Bars indicate mean 6 s.d. At each of the 17 study sites in northern China, there were significant differences in STN between the two sandy land habitats. STN increased with increasing MI in fixed sandy lands (P < 001) but varied little in semi-fixed sandy lands (P ¼ 030) (Table 1). To examine the effect of STN on the leaf-trait relationships, data analyses were performed for fixed and semifixed sandy land habitats and as well as in pooled data. The drought threshold identified by leaf trait data was then compared with the zonal boundary between typical temperate and desert steppes by overlapping the map of vegetation zonations in Zhang (2007) with the map of the MI isolines in northern China. The statistical analysis was performed using SPSS 16.0 for Windows (SPSS Inc., Chicago, IL, USA), and all significant differences were taken at P < 005. RESULTS Drought threshold indicating a shift in the SLA–Nmass relationship and its link to stand variables along the rainfall gradient Across the two sandy land habitats and in pooled data, there were no differences in individual SLA–Nmass slopes among the 17 study sites (P ¼ 040–066) (Table 2). However, the SLA– Nmass intercepts showed significant differences between the three rainfall groups (P < 0001): high-rainfall areas (rainfall group 1, 310–370 mm; MI ¼ 033–041), mid-rainfall transition (rainfall group 2, 290 mm; MI ¼ 031) and low-rainfall areas (rainfall group 3, 150–265 mm; MI ¼ 017–029) (Fig. 2, Table 2). In general, the SLA–Nmass relationship shifted significantly Downloaded from https://academic.oup.com/aob/article/118/3/541/1741496 by guest on 16 October 2023 C δ13 C (% ) 130 547 548 Wei et al. — Linkage between leaf traits and vegetation zonation TABLE 3. Pearson correlation coefficients between leaf traits (SLA, Narea and d13C) and CC and its components (HC, AC and Nmass) in leaf samples of A. ordosica collected from 17 study sites in northern China sandy lands CC and its components SLA Total (n ¼ 281) HC 017** AC 002 037*** Nmass 002 CCmass 094*** CCarea Fixed sandy land (n ¼ 144) HC 024** AC 002 032*** Nmass CCmass 008 094*** CCarea Semi-fixed sandy land (n ¼ 137) HC 009 AC 003 041*** Nmass 010 CCmass 095*** CCarea Narea d13C 022*** 012* 065*** 041*** 053*** 025*** 015* 034*** 035*** 023*** 012 001 060*** 028** 061*** 010 005 032*** 017* 020* 031*** 025** 070*** 053*** 045*** 036*** 032*** 036*** 047*** 028** *P < 005, **P < 001, ***P < 0001. Shifts in relationships of Narea to CCarea and d13C between lowand high-rainfall areas Across the sandy land habitats and in the pooled data, the positive relationship of Narea to CCarea also shifted between low-rainfall areas and high-rainfall areas along the rainfall gradient in northern China (Fig. 5A, C, E and Table 4, test for slopes, P ¼ 012–045; test for intercepts, P < 0001). The plants in low-rainfall areas had higher Narea at a given CCarea compared with those in high-rainfall areas (Fig. 5A, C, E). Similar patterns were also found in the south-east Qaidam Basin (Fig. 6A, C, E and Table 4, test for slopes, P ¼ 071; test for intercepts, P < 005). There was a positive Narea–d13C relationship with insignificant differences of slopes and intercepts between low- and high-rainfall areas in northern China (Table 5, test for slopes, P ¼ 009–062; test for intercepts, P ¼ 028–096). In contrast to strategy shifts in relationships of SLA–Nmass and Narea–CCarea between low- and high-rainfall areas, there was a continuous positive relationship between Narea and d13C along the entire rainfall gradient (P < 0001) (Fig. 5B, D, F). Similar patterns were also found in additional data from the south-east Qaidam Basin (P < 005) (Fig. 6B, D, F). The drought threshold for the boundary between typical and desert steppes The drought threshold where MI ¼ 029 identified by leaftrait data of A. ordosica corresponded well to the zonal boundary between typical and desert steppes in northern China (Fig. 7). The sites from the high-rainfall group were distributed in the typical steppe zone in the east, while the sites from the lowrainfall group were from the desert steppe and semi-desert zones in the west (Fig. 7). DISCUSSION Maximizing Narea is a process to balance WUE and CC in arid plants along a rainfall gradient To the best of our knowledge, few studies have examined the intraspecific continuous variations in leaf traits and related stand variables along a rainfall gradient. Our data demonstrated that a continuous increase in Narea with decreasing rainfall was achieved by a reduced LAI with unchanged foliage N-pool and SLA (higher Nmass and constant SLA) in high-rainfall areas with MI > 029, but by an increased leaf thickness (lower SLA and Nmass) in low-rainfall areas with MI  029 (Figs 2–4). The results indicate a drought threshold where MI ¼ 029 determines the shift in controls on Narea associated with the switch change from allocating canopy leaf nitrogen to altering leaflevel anatomical structure along a rainfall gradient, which can be explained by the theories of Farquhar et al. (2002) and Smith et al. (1997). Such a drought threshold is close to the reported aridity threshold in controlling ecosystem nitrogen cycling of temperate grasslands in northern China (MI ¼ 032, Wang et al., 2014). Our transect data further indicated that relationships of Narea– CCarea consistently shifted between low-rainfall areas and highrainfall areas (Fig. 5), which was confirmed by additional data from the south-east Qaidam Basin (Fig. 6). Because there was a continuous positive relationship between Narea and d13C and both generally increased with decreasing MI along the entire rainfall gradient, the low-rainfall group had higher Narea and d13C at a given CCarea compared with the high-rainfall group. Our data supported the hypothesis that below a climatic drought threshold, arid plants tend to maximize their intrinsic WUE Downloaded from https://academic.oup.com/aob/article/118/3/541/1741496 by guest on 16 October 2023 between low-rainfall areas and high-rainfall areas with a transition in between (Fig. 2). Regardless of STN variability, the relationship between foliage N-pool and MI differed below and above the drought threshold. Canopy foliage N-pool increased with increasing MI in areas with MI  029 (P < 0001) but varied little in areas with MI > 029 (P ¼ 007–089) (Fig. 3A, C, E). However, LAI generally increased with increasing MI in a continuously linear model along the entire rainfall gradient (P < 0001) (Fig. 3B, D, F). In pooled data, leaf-level SLA and Nmass increased with increasing MI in areas with MI  029 (P < 001). In areas with MI > 029, Nmass decreased with increasing MI (P < 001) while SLA varied little (P ¼ 029) (Fig. 4A, C). As a result, Narea (Nmass/SLA or N-pool/LAI) decreased continuously with increasing MI along the entire rainfall gradient (P < 0001) (Fig. 4E). Because Narea and d13C were positively correlated with CC and its major components of HC and Nmass (Table 3), d13C, CCmass and CCarea generally showed a continuously decreasing trend with increasing MI (P < 005) (Fig. 4B, D, F). The same patterns were also found in fixed and semi-fixed sandy land habitats (Supplementary Data, Figs S1 and S2, respectively). Wei et al. — Linkage between leaf traits and vegetation zonation 240 A B Total Total 549 –24 –25 200 –26 –27 160 R 2solid = 0·35∗∗∗ –28 C D Fixed Fixed –25 200 –26 –27 160 R 2solid = 0·39∗∗∗ δ13C (0/100) CCarea (g m–2) R 2 = 0·26∗∗∗ –28 R dashed = 0·20∗∗∗ 2 R 2 = 0·21∗∗∗ E F Semi-fixed Semi-fixed –25 200 –26 –27 160 R 2solid = 0·32∗∗∗ –28 R dashed = 0·22∗∗∗ 2 120 1·5 2·5 3·5 4·5 5·5 Narea (g m–2) R 2 = 0·32∗∗∗ 1·5 2·5 3·5 4·5 –29 5·5 Narea (g m–2) FIG. 5. Relationships of Narea to CCarea (A, C, E) and d13C (B, D, F) for A. ordosica along a rainfall gradient in northern China sandy lands. The Narea–CCarea relationships (A, C, E) shift between high-rainfall areas (rainfall group 1, 310–370 mm; MI, 033–041) and low-rainfall areas (rainfall group 3, 150–265 mm; MI, 017– 029), contrasting with the continuous relationships of Narea–d13C (B, D, F) along the entire rainfall gradient. Data analyses were performed on pooled data (A, B) and for fixed (C, D) and semi-fixed (E, F) sandy land habitats, respectively. Symbols are as in Fig. 2. ANCOVA statistics are given in Tables 4 and 5. (d13C) through increased Narea at a given CCarea. Such ecophysiological mechanisms may explain why A. ordosica can be widely distributed in arid sandy lands and how it forms a relatively stable community in a sub-climax state. Our findings suggest that maximizing Narea for optimal balance of WUE and CC is a key process in shaping arid species distribution and ecosystem function. Variations of CC along an environmental gradient may be determined by changes in leaf biochemical composition and leaf morphology (Griffin, 1994). There are still ongoing debates about how CC changes in response to environmental stress (Chapin, 1989; Poorter and De Jong, 1999; Villar and Merino, 2001; Martınez et al., 2002). Several studies have suggested that there is an increase in CC under stress conditions (Penning de Vries et al., 1974; Amthor, 1989), while Merino (1987) found that water availability has no effects on the CC of 30 species in the Mediterranean. In this study, the d13C, CCmass and CCarea of A. ordosica continuously increased with decreasing MI along the rainfall gradient. This suggests that maximizing Narea for high WUE inevitably leads to a high CC, which is consistent with the theoretical model prediction of Prentice et al. (2014), suggesting that altering leaf-level anatomical structure might be more costly than allocating canopy leaf nitrogen. To further investigate the possible effect of soil texture on the shifts in leaf trait relationships between low- and high-rainfall areas, the literature data on soil texture across 23 sandy land sites located in our study areas were obtained from Li (2007). Downloaded from https://academic.oup.com/aob/article/118/3/541/1741496 by guest on 16 October 2023 R 2dashed = 0·18∗∗∗ Wei et al. — Linkage between leaf traits and vegetation zonation 550 TABLE 4. Differences in slopes and intercepts of CCarea–Narea relationships for A. ordosica between low- and high-rainfall groups across northern China and the south-east Qaidam Basin sandy lands Group/site MI Slope Total Intercept FS Northern China sandy lands Group 1, 033–041 337a 364a Group 3, 017–029 476a 469a South-east Qaidam Basin sandy lands – Qinghai Lake, 068 400a Dulan, 025 320a – SFS Total FS SFS 356a 499a 731a 18b 619a 74b 738a 97b 478a 383a 969a 926b – – 873a 719b 250 A B Total Total –24 –25 –26 220 –27 –28 190 R Solid = 0·49∗∗ 2 –29 R 2dashed = 0·17∗∗ C D Fixed Fixed –25 –26 220 –27 –28 190 R 2 = 0·33∗∗∗ 250 E F Semi-fixed Semi-fixed δ13C (0/00) CCarea (g m–2) 250 R 2 = 0·43∗∗∗ –29 –25 –26 220 –27 –28 190 R 2Solid = 0·65∗∗ –29 R 2dashed = 0·45∗ 160 1·5 2·5 4·5 3·5 –2 Narea (g m ) 5·5 1·5 R 2 = 0·52∗∗∗ 2·5 3·5 4·5 –2 Narea (g m ) –30 5·5 FIG. 6. The relationships of Narea–CCarea (A, C, E) and Narea–d13C (B, D, F) for A. ordosica along a rainfall gradient in the Qaidam Basin sandy lands. Empty circles and dashed trend lines are for the high-rainfall area (Qinghai Lake; rainfall, 401 mm; MI, 068); filled circles and solid trend lines are for the low-rainfall area (Dulan; rainfall 207 mm; MI, 025). Downloaded from https://academic.oup.com/aob/article/118/3/541/1741496 by guest on 16 October 2023 Data analysis was performed by ANCOVA. Different letters within a column show significant differences between rainfall groups at a 005 level. MI, moisture index; Total, in pooled data from FS and SFS; FS, fixed sandy land; SFS semi-fixed sandy land; Group 1, high-rainfall areas (sites 1–6, 14–16); Group 3, low-rainfall areas (sites 8–13, 17). The data indicated that along a rainfall gradient ranging from 210 to 350 mm, sand and clay contents in fixed and semi-fixed sandy lands varied little with rainfall (sand content: R2 ¼ 0001, P ¼ 089; clay content: R2 ¼ 009, P¼016). Given a soil water content, soil water potential calculated from the soil water retention curves (Saxton et al., 1986) based on soil texture also showed no significant variation along the rainfall gradient (R2 ¼ 004, P ¼ 038). In contrast, leaf traits in this study varied significantly along the rainfall gradient (Figs S1 and S2). Furthermore, the results of ANOVA indicated that there were no significant differences in soil texture or soil water potential between low-rainfall areas (MI  029) and high-rainfall areas (MI > 029) (P ¼ 012–059), while relationships of SLA–Nmass and Narea–CCarea shifted significantly between the two rainfall areas (Figs 2 and 5). Therefore, the variations in leaf traits were mainly driven by rainfall but not by soil texture. Wei et al. — Linkage between leaf traits and vegetation zonation A new method to link leaf functional traits with arid vegetation zonation TABLE 5. Differences in slopes and intercepts of Narea-d13C relationships for A. ordosica between rainfall groups in northern China sandy lands MI Slope Group 1 033–041 Group 3 017–029 Intercept SUPPLEMENTARY DATA Total FS SFS Total FS SFS 10a 057a 071a 051a 12a 070a 300a 288a 290a 285a 304a 294a Data analysis was performed by ANCOVA. Different letters within a column show significant differences between rainfall groups at a 005 level. MI, moisture index; Total, in pooled data from FS and SFS; FS, fixed sandy land; SFS semi-fixed sandy land; Group 1, high-rainfall areas (sites 1–6, 14–16); Group 3, low-rainfall areas (sites 8–13, 17). Supplementary data are available online at www.aob.oxfordjour nals.org and consist of the following. Figure S1: Variations in SLA, Nmass, Narea, d13C, CCmass and CCarea for A. ordosica in fixed sandy land habitats along an MI gradient in northern China. Figure S2: Variations in SLA, Nmass, Narea, d13C, CCmass and CCarea for A. ordosica in semi-fixed sandy land habitats along an MI gradient in northern China. 105° 0·2 0 4 0· 36 0·2 2 0·2 4 0·2 6 0·2 8 0·18 0·16 0·14 0·12 110° MI isoline Typical steppe Desert steppe 40° Semi-desert 60 0 60 120 km 0· 42 40 0·3 0· 2 0·3 0· 0 0·3 38 38° FIG. 7. The drought threshold (MI ¼ 029) identified by leaf-trait data of A. ordosica corresponded well to the zonal boundary between typical temperate and desert steppes in Zhang (2007). Empty circles are the sites for high-rainfall areas (rainfall group 1, 310–370 mm; MI, 033–041); the grey triangle is the site for mid-rainfall transition (rainfall group 2, 290 mm; MI, 031); and filled circles are the sites for low-rainfall areas (rainfall group 3, 150–265 mm; MI, 017–029). Downloaded from https://academic.oup.com/aob/article/118/3/541/1741496 by guest on 16 October 2023 gradient, variations in leaf traits affect plant adaptations to abiotic factors and therefore play an important role in determining plant species distribution patterns (Maharjan et al., 2011). There is evidence that leaf lifespan is a simple predictor of evergreen forest zonation in China (Zhang et al., 2010), but few studies have examined the linkage between leaf traits and arid vegetation zonation. In this study, the leaf-trait data of A. ordosica indicated that the optimal balance of WUE and CC exists below a common climatic drought threshold (MI  029). This drought threshold of MI ¼ 029 (with a transition between 030 and 032) corresponds well to the zonal boundary between typical and desert steppes in northern China (Fig. 7). As it is easy to measure leaf traits with repeatable sampling along a geographical transect, our findings suggest an operational way to link leaf functional traits with arid vegetation zonation. This is especially important to be able to detect and predict the dynamic vegetation change in arid and semi-arid regions due to climate change. According to the Vegetation Divisions of China (Editorial Committee for Vegetation of China, 1980; Zhang, 2007), the ecotone boundary between typical temperate and desert steppe zones is mainly determined by regional differences in annual rainfall, genus and species indicators, soil types, and dryland cropping systems, based on the realistic distribution map of natural and artificial vegetation. It is difficult to use such complicated indicators to predict the boundary change and to further understand the related mechanisms underlying the boundary formation. 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