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.

Study site

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 Stipa grandis, Leymus chinensis, Agropyron cristatum, and Carex korshinskyi.  The soil is classified as dark chestnut (Calcic Chernozem according to ISSS Working Group RB, 1998).

Study design

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.

Vegetation sampling

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 S. grandis, L. chinensis, A. cristatum, and C. korshinskyi based on relative biomass.  Subsequently, ten healthy and similarly sized shoots for S. grandis, L. chinensis, and A. cristatum, 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.

Soil sampling

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.

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).

References

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