DOI: 10.5061/dryad.xsj3tx9pb
title: Data from: Grassland management regimes alter the coordination of plant functional traits and nutrient resorption in semiarid grasslands

description: Grazing and enclosure (grazing exclusion) are the main grassland
 management regimes that affect nutrient cycling and ecosystem function by
 altering plant traits.  However, the coordination among plant
 functional traits and nutrient resorption under different grassland
 management regimes remains unclear. We examined the coordination of eight
 root and four leaf traits along with the nitrogen (N) and phosphorus (P)
 resorption efficiencies under four grazing intensities and two enclosure
 chronosequences in a semiarid steppe ecosystem in China. The
 principal components analysis (PCA) of root traits and multiple factors
 analysis of root and leaf traits showed two-dimensional economic spaces at
 the individual level.  Increasing grazing intensity shifts
 species and community trait composition from conservative to acquisitive
 with increases in specific root length, specific root area, root alkaline
 phosphatase activity, and specific leaf area, accelerating nutrient
 cycling and coordination among traits.  Increasing grazing
 intensity promoted nutrient resorption efficiency.  Such
 adaptations optimize plant nutrient acquisition and nutrient conservation
 under nutrient-poor conditions. Conversely, long-term enclosure increases
 plant nutrient and light acquisition by promoting root tissue density,
 mycorrhizal colonization, and specific leaf area.  The
 conservative (e.g., Stipa grandis) and acquisitive species (e.g., Carex
 korshinskyi) dominated under grazing, whereas the mid-acquisitive species,
 including Leymus chinensis and Agropyron cristatum, dominated under
 enclosure.  The observed N and P resorption efficiencies decrease
 with increasing PC1 score (increase in nutrient acquisition by itself) in
 root PCA, indicating the trade-off between nutrient-conservative and
 nutrient-acquisitive strategies.  Community-weighted mean traits
 were primarily driven by intraspecific trait variation, enhancing the
 adaptability of plants and communities to environmental changes and
 external stressors. Our study highlights the coordination among above- and
 below-ground traits, as well as the trade-off between root nutrient
 acquisition and leaf nutrient resorption under different grassland
 management regimes in semi-arid grassland ecosystems.  From these
 findings, we conclude that enhancing soil nutrient availability is the
 most effective approach to the solution to grazing-induced grassland
 degradation.  This knowledge is crucial for devising more
 effective strategies for sustainable land use and biodiversity
 conservation in grassland ecosystems.
description: <strong>Study site</strong> The
 study was conducted in the Xilin River basin in China’s Inner Mongolia
 Autonomous Region, in the Sino-German grazing experiment sites and
 enclosure (grazing exclusion) sites of the Inner Mongolia Grassland
 Ecosystem Research Station (IMGERS, 43º38'N, 116º42'E, 1260m
 a.s.l.), affiliated with the Chinese Academy of Sciences.  Here, the mean
 annual temperature is 0.3°C and the mean annual precipitation is 346.1mm,
 with precipitation primarily occurring during the main growing season of
 June to August (Bai et al., 2010).  The dominant species are
 <em>Stipa grandis</em>, <em>Leymus chinensis,</em>
 <em>Agropyron cristatum, and</em> Carex korshinskyi.  The soil
 is classified as dark chestnut (Calcic Chernozem according to ISSS Working
 Group RB, 1998). <strong>Study
 design</strong> The grazing experiment site was
 started in 2005 and has been maintained continuously for 15 years by 2020
 (Wan et al., 2011).  The grazing experiment employed a split-split plot
 design within a randomized complete block encompassing a total area of 128
 hectares.  First, two blocks were established, one on flat and one on
 sloped land.  Within each block, two paddocks were established as the
 management system, distinguishing between traditional and mixed systems.
  Within each paddock, different grazing intensities (GI) were applied to
 seven nested plots (0, 1.5, 3.0, 4.5, 6.0, 7.5, and 9.0 sheep/ha).  Within
 each plot, subplots had one of two land uses applied (grazing or
 haymaking).  This design resulted in the establishment of 56 experimental
 units.  The plot size of grazing intensity was 2 ha in all treatments
 except under the lowest grazing intensity (1.5 sheep ha-1) which was
 extended to 4 ha to accommodate a minimum of six sheep.  The grazing
 experiments were conducted continuously throughout the growing season,
 spanning from June to September (Wan et al., 2011; Li et al., 2017).  The
 two enclosure (grazing exclusion) sites were located near grazing
 experiment sites, which have been fenced since 1999 (21 years, covering 28
 ha) and 2013 (7 years, covering 2 ha). Our experiments
 were conducted on the grazing and enclosure experiment sites in 2020.  We
 selected four grazing intensities (GI=0, 3, 6, 9 sheep ha-1, representing
 no, light, moderate, and heavy grazing, respectively) and enclosure
 chronosequences (grazing exclusion) sites began in 1999 (E99) and 2013
 (E13).  Five 3 × 3m fenced subplots (enclosure cages) were established in
 each selected plot for sampling before grazing in 2020 to ensure healthy
 and intact plant leaves. <strong>Vegetation
 sampling</strong> In mid-August 2020, a randomly
 placed 0.5×1.0 m quadrat was employed within every fenced subplot in both
 grazing and enclosure sites, and all plants were clipped to ground level
 to determine the relative abundance of <em>S. grandis</em>,
 <em>L. chinensis,</em> <em>A. cristatum,</em> and
 <em>C. korshinskyi</em> based on relative biomass.
  Subsequently, ten healthy and similarly sized shoots for <em>S.
 grandis</em>, <em>L. chinensis,</em> and <em>A.
 cristatum</em>, while thirty healthy and similarly sized shoots for*
 C. korshinskyi* were chosen to ensure adequate samples for subsequent
 assays in each subplot.  We also tagged equal numbers of shoots with red
 string for harvesting flush to the ground after complete senescence in
 late October. In August, we carefully excavated the
 entirety of each focal plant from the ground (at about 30 cm depth) at 20
 cm from the base of each plant.  This process ensured the retrieval of an
 intact root system (Zhou et al., 2018).  We selected absorptive fine roots
 (first or second root order) and removed soil attached to the roots,
 obtaining rhizosphere soil using the adhering soil method to determine
 root alkaline phosphatase activity (Guo et al., 2008; Han et al., 2023).
  We separated the above-ground and below-ground parts of the plant, pooled
 the samples from each species in each subplot, and placed them in
 envelopes, storing them at 4 °C for subsequent measurement of plant
 functional traits. <strong>Soil
 sampling</strong> Additionally, we used a soil
 auger (6 cm diameter) to extract four soil cores and mix them into a
 composite sample for each subplot in both grazing and enclosure sites.
  Each sample underwent sieving with a 2mm sieve to eliminate visible roots
 and stones and was briefly stored at 4 °C for subsequent
 analysis. <strong>Plant functional trait
 measurements</strong> We measured twelve plant
 traits that are related to plant resource economic strategies, following
 standardized protocols (Pérez-Harguindeguy et al., 2016).  The four leaf
 traits were measured on the third or fourth leaf, as these leaves are the
 greatest biomass in the leaf cohorts.  We measured leaf area (LA, cm2)
 using a Li-3100 (Li-COR, Lincoln, NE, USA). We measured saturated water
 content after the leaves were immersed in deionized water for 24 hours.
  Leaf dry mass was obtained by oven-drying each leaf at 65 °C for 48 hours
 and then weighing it.  The specific leaf area (SLA, cm2 g-1) was
 calculated as leaf area divided by its dry biomass.  The leaf dry matter
 content (LDMC, mg g-1) was water-saturated fresh biomass per unit of dry
 mass. We measured leaf N (N, mg g-1) and P (P, mg g-1) concentrations with
 a Kjeltec 2300 Analyzer Unit (Foss, Sweden) and the molybdenum blue
 colorimetric method followed by colorimetric analysis (Murphy &
 Riley, 1962). To determine the eight root chemical and
 morphological traits, we selected the first two root orders, which are
 typical absorptive segments among fine roots (Guo et al., 2008). The roots
 were scanned at a resolution of 300 dpi using the Epson Perfection V750
 Pro (Seiko Epson Corp.).  Then, the roots were oven-dried at 65 °C for
 48 hours and weighted after scanning.  The scanned images were analyzed
 using the software WinRHIZO Pro 2019 (Regent Instruments Inc.) to measure
 root length, average root diameter, root volume and root surface area for
 each root sample.  Specific root length (SRL, cm g-1) was calculated as
 total root length divided by root dry mass.  Root tissue density (RTD, g
 m-3) was calculated as root dry mass divided by root volume.  Specific
 root area (SRA, cm2 g-1) was calculated as root surface area divided by
 root dry mass.  Root carbon (RC, mg g-1) and nitrogen (RN, mg g-1) were
 determined by the element analyzer (vario MACRO cube, Elementar, Germany).
 Root alkaline phosphatase activity (alkaline phosphatase activity, ug PNP
 g-1 soil h-1) was measured using S-AKP protocol kits (Beijing Solarbio
 Science & Technology Co., Ltd., Beijing, China) (Li et al., 2023).
  Root mycorrhizal colonization (RMC, %) and soil inorganic N concentration
 (mg g-1) and available P concentration (mg g-1) were measured using
 standardized protocols (Details in the Supplementary Material).
 <strong>Calculations</strong> The
 nutrient resorption efficiency (NuRE), defined as the proportion of
 nutrient resorbed from the green (mature) leaves by the plant during the
 senesced season, was calculated as Lü et al. (2013).
 The contributions of intraspecific traits variability and species
 turnover to community-weighted mean traits were assessed based on the sum
 of squares decomposition following Leps et al. (2011).
 First, we calculated the community-weighted mean for each trait,
 using biomass-weighted sample from each subplot to represent the total
 variability of the traits (Niu et al., 2016).
 References Bai, Y.F., Wu, J.G., Clark, C.M.,
 Naeem, S., Pan, Q.M., Huang, J.H., Zhang, L.X. & Han, X.G. (2010).
 Tradeoffs and thresholds in the effects of nitrogen addition on
 biodiversity and ecosystem functioning: evidence from inner Mongolia
 Grasslands. <em>Global Change Biology,</em>
 <strong>16,</strong> 358-372. Guo, D.L.,
 Xia, M.X., Wei, X., Chang, W.J., Liu, Y. & Wang, Z.Q. (2008).
 Anatomical traits associated with absorption and mycorrhizal colonization
 are linked to root branch order in twenty-three Chinese temperate tree
 species. <em>New Phytologist,</em>
 <strong>180,</strong> 673-683. Han, M.G.,
 Chen, Y., Sun, L.J., Yu, M., Li, R., Li, S.F., Su, J.R. & Zhu, B.
 (2023). Linking rhizosphere soil microbial activity and plant resource
 acquisition strategy. <em>Journal of Ecology,</em>
 <strong>111,</strong> 875-888. Leps, J., de
 Bello, F., Smilauer, P. & Dolezal, J. (2011). Community trait
 response to environment: disentangling species turnover vs intraspecific
 trait variability effects. <em>Ecography,</em>
 <strong>34,</strong> 856-863. Li, W.H., Xu,
 F.W., Zheng, S.X., Taube, F. & Bai, Y.F. (2017). Patterns and
 thresholds of grazing-induced changes in community structure and ecosystem
 functioning: species-level responses and the critical role of species
 traits. <em>Journal of Applied Ecology,</em>
 <strong>54,</strong> 963-975. Li, Y.W., Lu,
 X.M., Su, J.S. & Bai, Y.F. (2023). Phosphorus availability and
 planting patterns regulate soil microbial effects on plant performance in
 a semiarid steppe. <em>Annals of Botany</em>.
 Lü, X.T., Reed, S., Yu, Q., He, N.P., Wang, Z.W. & Han,
 X.G. (2013). Convergent responses of nitrogen and phosphorus resorption to
 nitrogen inputs in a semiarid grassland. <em>Global Change
 Biology,</em> <strong>19,</strong> 2775-2784.
 Murphy, J. & Riley, J.P. (1962). A modified single
 solution method for the determination of phosphate in natural waters.
 <em>Analytica Chimica Acta,</em>
 <strong>27,</strong> 31-36. Niu, K.C., He,
 J.S. & Lechowicz, M.J. (2016). Grazing-induced shifts in community
 functional composition and soil nutrient availability in Tibetan alpine
 meadows. <em>Journal of Applied Ecology,</em>
 <strong>53,</strong> 1554-1564.
 Pérez-Harguindeguy, N., Díaz, S., Garnier, E., Lavorel, S.,
 Poorter, H., Jaureguiberry, P., Bret-Harte, M.S., Cornwell, W.K., Craine,
 J.M., Gurvich, D.E., Urcelay, C., Veneklaas, E.J., Reich, P.B., Poorter,
 L., Wright, I.J., Ray, P., Enrico, L., Pausas, J.G., de Vos, A.C.,
 Buchmann, N., Funes, G., Quétier, F., Hodgson, J.G., Thompson, K., Morgan,
 H.D., ter Steege, H., van der Heijden, M.G.A., Sack, L., Blonder, B.,
 Poschlod, P., Vaieretti, M.V., Conti, G., Staver, A.C., Aquino, S.
 & Cornelissen, J.H.C. (2016). New handbook for standardized
 measurement of plant functional traits worldwide (vol 61, pg 167, 2013).
 <em>Australian Journal of Botany,</em>
 <strong>64,</strong> 715-716. Wan, H.W.,
 Bai, Y.F., Schonbach, P., Gierus, M. & Taube, F. (2011). Effects
 of grazing management system on plant community structure and functioning
 in a semiarid steppe: scaling from species to community. <em>Plant
 and Soil,</em> <strong>340,</strong> 215-226.
 Zhou, M., Bai, W.M., Zhang, Y.S. & Zhang, W.H. (2018).
 Multi-dimensional patterns of variation in root traits among coexisting
 herbaceous species in temperate steppes. <em>Journal of
 Ecology,</em> <strong>106,</strong> 2320-2331.
description: # Grassland management regimes alter the coordination of plant functional
 traits and nutrient resorption in semiarid grasslands **Plant functional
 trait measurements** We measured twelve plant traits that are related to
 plant resource economic strategies, following standardized protocols
 (Pérez-Harguindeguy et al., 2016).  The four leaf traits were measured on
 the third or fourth leaf, as these leaves are the greatest biomass in the
 leaf cohorts.  We measured leaf area (LA, cm2) using a Li-3100 (Li-COR,
 Lincoln, NE, USA). We measured saturated water content after the leaves
 were immersed in deionized water for 24 hours.  Leaf dry mass was obtained
 by oven-drying each leaf at 65 °C for 48 hours and then weighing it.  The
 specific leaf area (SLA, cm2 g-1) was calculated as leaf area divided by
 its dry biomass.  The leaf dry matter content (LDMC, mg g-1) was
 water-saturated fresh biomass per unit of dry mass. We measured leaf N (N,
 mg g-1) and P (P, mg g-1) concentrations with a Kjeltec 2300 Analyzer Unit
 (Foss, Sweden) and the molybdenum blue colorimetric method followed by
 colorimetric analysis (Murphy & Riley, 1962). To determine the eight
 root chemical and morphological traits, we selected the first two root
 orders, which are typical absorptive segments among fine roots (Guo et
 al., 2008). The roots were scanned at a resolution of 300 dpi using the
 Epson Perfection V750 Pro (Seiko Epson Corp.).  Then, the roots were
 oven-dried at 65 °C for 48 hours and weighted after scanning.  The scanned
 images were analyzed using the software WinRHIZO Pro 2019 (Regent
 Instruments Inc.) to measure root length, average root diameter, root
 volume and root surface area for each root sample.  Specific root length
 (SRL, cm g-1) was calculated as total root length divided by root dry
 mass.  Root tissue density (RTD, g m-3) was calculated as root dry mass
 divided by root volume.  Specific root area (SRA, cm2 g-1) was calculated
 as root surface area divided by root dry mass.  Root carbon (RC, mg g-1)
 and nitrogen (RN, mg g-1) were determined by the element analyzer (vario
 MACRO cube, Elementar, Germany). Root alkaline phosphatase activity
 (alkaline phosphatase activity, ug PNP g-1 soil h-1) was measured using
 S-AKP protocol kits (Beijing Solarbio Science & Technology Co., Ltd.,
 Beijing, China) (Li et al., 2023).  Root mycorrhizal colonization (RMC, %)
 and soil inorganic N concentration (mg g-1) and available P concentration
 (mg g-1) were measured using standardized protocols (Details in the
 Supplementary Material). **Calculations** The nutrient resorption
 efficiency (NuRE), defined as the proportion of nutrient resorbed from the
 green (mature) leaves by the plant during the senesced season, was
 calculated as Lü et al. (2013). The contributions of intraspecific traits
 variability and species turnover to community-weighted mean traits were
 assessed based on the sum of squares decomposition following Leps et al.
 (2011). First, we calculated the community-weighted mean for each trait,
 using biomass-weighted sample from each subplot to represent the total
 variability of the traits (Niu et al., 2016).