Statistical analyses
Relative interaction intensity (RII) was used to assess the effect of shrubs on under-canopy vegetation (Armas et al., 2004) and was calculated based on the cover, richness, and diversity (expressed as Shannon index) of under-canopy vegetation: RII = (value under shrub – value in the open)/(value under shrub + value open). Samples were paired between each A. kopetdaghensis shrub and its neighbouring open plot. RII was used as an indicator of the facilitation by the target shrub, based on the performance of under-canopy plants. The interaction index has defined limits [-1,+1], with positive values indicating facilitation and negative values indicating competition.
The differences in RII indices for species richness, cover, and diversity between the HG and LG sites and between the arid and semi-arid regions were tested using the linear mixed-effect models, with “sampling areas” as a random effect, “climatic region” and “grazing” as fixed effects and RII based richness (RII-Richness), cover (RII-Cover), and Shannon H (RII-Shannon diversity) as response variables. All univariate analyses were performed in the R software, using the NLME package. The script for the model testing the interaction between “climate” and “grazing” were “lme(Relative interaction intensity~climatic region*grazing, random=~1|sampling area)”. The normality of the input data was assessed based on Shapiro-Wilk tests, and the normality of residuals was checked visually, by plotting the observed values against the fitted values.
Further, we used the method of indicator species analysis to reveal the preference of individual species for the HG versus LG sites in both the arid and semi-arid climatic regions. With this approach, we could determine the indicator species sensitive or resistant to high grazing intensity in two different climatic regions. Indicator species analysis has two main components: (i) recorded on either HG or LG sites only (exclusivity); (ii) recorded on all samples of either the HG or LG group (fidelity). The indicator value index was assigned to all species, identifying species with the highest association values. The permutation tests (999 permutations) were used to estimate the statistical significance of individual species’ indicator values (Dufrêne & Legendre, 1997). The indicator species analyses were performed using the “indicspecies” package of the R software (R Development Core Team, 2013).
We also calculated the values for CSR plant strategies for all indicator species as well as for A. kopetdaghensis , following Pierce et al. (2017), based on the following traits: specific leaf area (SLA), leaf dry matter content (LDMC) and leaf area (LA). We collected the leaves from robust and well-grown plants. Leaf material was collected from 10 individuals of each species, packed in moist paper bags, sealed in plastic bags, and stored in a thermal box until storage at 4 °C for 12–24 h. Depending on the size of leaves, 2–10 undamaged, fully expanded young leaves (including the petiole) were measured per individual. We determined the leaf area using a digital scanner and Leaf Area Measurement v1.3 software (Andrew Askew, University of Sheffield, UK). Turgid leaf fresh weight (LFW) was obtained from saturated leaves, and leaf dry weight was determined after drying for 72 h in an oven at 70 °C. For CSR strategy analysis, values of LA, SLA, and LDMC were inserted into the ‘StrateFy’ spreadsheet 3 to calculate C, S, and R percentages for each species (Pierce et al., 2017).