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).