DOI: 10.5061/dryad.1zcrjdg0w
title: Below-ground root nutrient-acquisition strategies are more sensitive to long-term grazing than above-ground leaf traits across a soil nutrient gradient

description: Understanding how plant nutrient acquisition strategies respond to grazing
 at the community level is critical to understanding ecosystem structure
 and functioning in grasslands. However, few studies have simultaneously
 compared the difference in aboveground (leaf) and belowground (root)
 nutrient-acquisition strategies in response to long-term grazing,
 especially at the regional scale. Here, we measured a set of leaf and
 fine-root traits that correspond to the fast-slow economic spectrum at the
 community level in 10 experimental sites from paired grazed and ungrazed
 grasslands across a soil nutrient gradient covering three major types of
 grasslands in northern China. We found that patterns of variations of leaf
 and fine-root traits were consistent with both a leaf and root economic
 spectrum at the community level for both grazed and non-grazed plots.
 Grazing had a minor effect on community-level leaf nutrient-acquisition
 strategies but strongly influenced community-level root
 nutrient-acquisition strategies. Specifically, root nutrient-acquisition
 strategies were shifted to more exploitative resource use in grazed
 communities. Moreover, soil nutrients contributed to the changes in both
 leaf and root nutrient-acquisition strategies, which tended towards a more
 resource-acquisition strategy with increasing soil nutrient levels.
 Grazing significantly interacted with soil nutrients to affect root
 nutrient-acquisition strategies, and grazing contributed more to root
 nutrient-acquisition strategies than soil nutrients. Our results
 demonstrated completely inconsistent responses of community-level above-
 and below-ground resource acquisition strategies to long-term grazing, and
 below-ground acquisition strategies were more sensitive to long-term
 grazing. Our findings also suggest that high-intensity anthropogenic
 activities such as grazing may strongly modify below-ground resource
 acquisition strategies.
description: <strong>Study area and experimental
 design</strong> The study area was located in
 temperate grasslands of the Inner Mongolian Plateau in Northern China
 (111.23 E–120.03 E, 41.25 N–49.52 N), where the climate is predominantly
 arid and semi-arid continental; Mean annual precipitation ranged from 225
 to 433 mm and mean annual air temperature ranged from −2.3°C to 5.4°C. We
 selected three different types of grasslands along this transect from east
 to west: meadow steppe, typical steppe, and desert steppe. A total of ten
 sites with livestock long-term grazing and with various dominating plant
 species were selected along this transect, including five meadow steppes,
 three typical steppes, and two desert steppes (Zhang et al., 2023; See
 Table S1; Figure S1, S2 for details of the study sites, their locations,
 and their climatic conditions). Within each site, a long-term grazing
 exclosure was established in an area with a history of long-term heavy
 grazing. Livestock were excluded via the exclosure for over 10 years at
 each of the ten sites, and we observed many important changes in
 vegetation structure (Zhang et al., 2023; Figure S1).
 At each site, a 50 m × 50 m plot was selected randomly, and five
 1 m × 1 m quadrats were set at the four corners and the centre of the
 plot. The adjacent quadrats were separated by about 20 m. In total, there
 were 100 quadrats (10 sites × 2 grazing treatments × 5 quadrats per
 plot). <strong>Field sampling and
 measurements </strong> We measured leaf and root
 traits on several species of plants that were among the more common
 species at each site (those comprising at least 90% of the total plant
 abundance in each site) in late July 2020. In each quadrat, we randomly
 selected at least 5 mature plants from each species. For each sampled
 individual, we carefully excavated the plant and surface soil (0–30 cm),
 and removed all soils that adhered to the roots. We then placed samples in
 vacuum bags and put them in a car refrigerator for transport to the
 laboratory, where they were frozen at −20°C for subsequent morphological
 and chemical analyses. For leaves, we determined the
 leaf projected area using an area meter (YMJ-CH, Top, China). We then
 weighed leaves for wet weight and dry weight after oven-drying at 65 °C
 for 48 h. We estimated specific leaf area (SLA) as the leaf area per unit
 of dry leaf mass and leaf dry matter content (LDMC) as dry mass per unit
 of water-saturated fresh mass. We then ground leaves to fine powder and
 measured leaf N concentration (LNC) using a Discrete Auto Analyzer
 (Smartchem 600, AMS Alliance, Italy). For roots, we
 conducted measurements on fine roots (root diameter &lt; 2mm). Prior
 to measurements, we washed fine roots in deionized water and then arranged
 and scanned roots from each quadrat on an Epson scanner (Epson Expression
 12000XL, Epson, Japan). Thereafter, we oven-dried roots from each sample
 at 65°C for 48 hr and weighed them. From these, we estimated root diameter
 (RD) as the total root length and volume using the scanned images with the
 WinRHIZO software, specific root length (SRL) as the total root length
 divided by its dry mass, and root tissue density (RTD) as the ratio of
 root dry mass to its volume. After grinding root tissues to a powder, we
 measured root N concentration (RNC) using a Discrete Auto Analyzer
 (Smartchem 600, AMS Alliance, Italy). To measure soil
 nutrient availability, we collected three soil cores (2.5 cm diameter) at
 10 cm depth in each of the five quadrats per plot. We then mixed these
 soil cores to form one composite sample representing each quadrat. After
 removing the rocks and roots, we passed the soil through a 2-mm-mesh sieve
 and separated it into two parts. We kept the first part at -20 °C until
 analyses could be made and then dried samples at 105 °C for 24 h to
 measure soil water content (SWC). We air-dried the second part at room
 temperature and further sieved it to 0.15 mm to determine soil total
 nitrogen (STN) and total phosphorus (STP) with a Discrete Auto Analyzer
 (Smartchem 600, AMS Alliance, Italy).
 <strong>Statistical analyses</strong>
 For each species in the non-grazed and grazed plots at each site,
 we calculated the mean values of leaf traits (SLA, LDMC and LNC) and root
 traits (SRL, RD, RTD and RNC) and then calculated the community-weighted
 means (CWM) of each trait for each quadrant. To evaluate the existence of
 a fast-slow economic spectrum of leaf and root (LES and RES), we performed
 principal component analysis (PCA) on the standardized CWM of leaf and
 root traits from non-grazed and grazed grasslands. The first PCA axis
 captured a high proportion of the CWM variation (&gt;50%).
 Consequently, its scores can be used as a proxy of the economics spectrum
 given that they represent gradients of trait variation across sites (PC1).
 For the PCA analysis, we used the rda function within the vegan package
 (Oksanen, 2023). We used linear mixed-effects models to analyze the
 interactive effects of grazing and soil nutrients on the leaf and root
 economic spectrum (LES and RES; PC1) and leaf and root traits. To do so,
 grazing, soil nutrients and their interaction were fitted as fixed
 factors, and quadrat and site (plot IDs) nested within grassland types
 were fitted as random factors to control for pseudo-replication. To reduce
 the number of variables characterizing the soil nutrient and their
 collinearity, the first PCA axis represented the majority of the variation
 used in the above analysis. The function lmer within the lme4 package was
 used to fit linear mixed effects models (Bates, Maechler, Bolker,
 & Walker, 2014). The percentage of variance explained by each
 variable and its interaction (soil nutrient and grazing and their
 interaction) was determined using the pamer.fnc function in the
 LMERConvenienceFunCtions package (Tremblay & Ransijn, 2011). We
 chose the lower.p.val. Also, we used FDR method to adjust the p-value. To
 distinguish the relative contribution of ITV and species turnover on
 changes in the CWM for each leaf and root trait, we used a sum of squares
 decomposition following Lepˇs et al. (2011). We computed three parameters
 for each functional trait at the community level: (1) CWMspeciﬁc were
 calculated by the relative organ abundance and trait values of each
 species in a given soil core, where the total variation included both
 species turnover and ITV effects. (2) CWMﬁxed were calculated by the
 relative organ abundance of each species in a given soil core and the
 trait values were averaged over the total number of plots, which only
 included the effects of species turnover. (3) ITV was calculated as the
 difference between the CWMspeciﬁc and CWMﬁxed. General linear models for
 specific average, fixed average, and intraspecific variability were
 performed separately with grazing as the predictor. We extracted the sum
 of squares (SS) for each trait value that was explained by the grazing
 factor, so that SStotal, SSturnover, and SSintra, represented the
 grazing-induced total variation, effect of species turnover, and effect of
 ITV, respectively. The effects of covariation (SScov) were then calculated
 by subtracting SSturnover and SSintra from SStotal.
description: # Below-ground root nutrient-acquisition strategies are more sensitive to
 long-term grazing than above-ground leaf traits across a soil nutrient
 gradient [https://doi.org/10.5061/dryad.1zcrjdg0w
 ](https://doi.org/10.5061/dryad.1zcrjdg0w) ## Description of the data and
 file structure Description: We have submitted our raw data (Below-ground
 root nutrient-acquisition strategies are more sensitive to long-term
 grazing than above-ground leaf traits across a soil nutrient
 gradient.xlsx). The data came from ten sites across three grassland types.
 The values of plant functional traits were community-weighted means. * G:
 It means grazing or exclosure. NG means exclosure. G means grazing. *
 Location: It means the grassland type. * P: It means site. * Q: It means
 quadrat. * SLA: Specific leaf area (mm^2/mg) * LDMC: Leaf dry matter
 content (mg/g) * LNC: Leaf nitrogen content (mg/g) * SRL: Specific root
 length (cm/mg) * RD: Root diameter (mm) * RTD: Root tissue density
 (g/cm^3) * RNC: Root nitrogen content (mg/g) * STN: Soil total nitrogen
 (g/kg) * STP: Soil total phosphorus (g/kg) * SWC: Soil water content (%)