DOI: 10.5061/dryad.qz612jmmk
title: Data from: Brandt’s vole (Lasiopodomys brandtii) affects the dominant position of three gramineous species by altering defense traits and interspecific competition

description: Rodents can cause considerable changes in plant community composition.
 However, relationships between shifts in species dominance and plant
 functional traits due to rodents have seldom been investigated, especially
 for belowground functional traits. In this study, a set of enclosures was
 constructed to analyze the effects of 10 years of Brandt's
 voles' activities on the defense strategies and dominant position
 changes of three gramineous plants (Leymus chinensis, Stipa krylovii, and
 Cleistogenes squarrosa) in Inner Mongolia. Results showed that long-term
 feeding by Brandt's voles resulted in a significant decrease in the
 dominance of L. chinensis and S. krylovii, whereas C. squarrosa was
 positively affected. The belowground biomass of L. chinensis and S.
 krylovii were higher in the vole treatment, which showed that they were
 increasing their escape characteristics. The leaf thickness of L.
 chinensis and the leaf C:N ratio of S. krylovii significantly increased,
 while the specific leaf area of C. squarrosa significantly decreased. All
 three gramineous showed increased resistance traits in response to
 Brandt's voles, which positively affected their dominance.
 Tolerance-related traits of S. krylovii significantly increased, with the
 increasing growth rate of root length contributing to enhancing its
 dominance. We highlight that selective feeding by rodents led to the
 selection of different defense strategies by three gramineous plants, and
 that changes in biomass allocation and functional traits in the different
 species affected plant dominance, driving changes in the plant
 communities.
description: # Data from: Brandt’s vole (Lasiopodomys brandtii) affects the dominant
 position of three gramineous species by altering defense traits and
 interspecific competition Data set of plant functional traits and
 dominance changes of three gramineous under feeding by Brandts vole,
 data-L.C.csv (data of *Leymus chinensis*); data-S.K.csv (data of *Stipa
 krylovii*); date-C.S.csv (data of *Cleistogenes squarrosa*). ##
 Description of the Data and file structure The data consists of treatment,
 sampling point, sampling time, and variables. The details of all the
 variables are as follows： PH, plant height (cm); LSFW, leaf saturated
 fresh weight (g); LDW, leaf dry weight (g); LDMC, leaf dry matter content
 (%); LT, leaf thickness (mm); SLA, specific leaf area (mm2/g); CN, leaf
 C:N ratio (/); RLD, root length density (cm/cm3); RSAD, root surface area
 density (cm2/cm3); ARD, average root diameter (mm); GRORL, growth rate of
 root length (cm/cm3·d); GRORB, growth rate of root biomass (g/cm2·d); BGB,
 belowground biomass (g/cm2); AGB, aboveground biomass (g/m2); IV,
 importance value; OIV, other species importance value. Differences in the
 importance value, biomass, and plant functional traits among different
 treatments were tested using generalized linear mixed-effect models
 (GLMMs). The differences in the importance value, biomass, and functional
 traits among different species under the same treatment were compared
 using the same method. The Tukey HSD test was used at the 5% probability
 level to determine differences among different species. The lmerTest
 package was used for the linear mixed-effects model, and Tukey's HSD
 test was implemented using the multcomp package. A structural equation
 model (SEM) was used to explore the pathways by which Brandts vole
 affected the dominant position of the three gramineous plants. The
 importance value of the three gramineous plants was taken as the dependent
 variable, and possible pathways were considered in the initial model. ##
 Sharing/access Information The data is not uploaded to other public
 databases. ## Code/Software The generalized linear mixed-effect models
 (GLMMs) used the "lmerTest" package in R, and the structural
 equation model (SEM) was completed in IBM SPSS AMOS 24 software.