Data analysis
We assigned the recorded vascular plant species to four morphological
groups: short-lived forbs, short-lived graminoids, perennial forbs and
perennial graminoids. The full list of the recorded species and their
assignment to morphological groups are provided in Appendix 1.
We used two proxies for conservation values: Shannon diversity and
naturalness score. We calculated the Shannon diversity of the vascular
plant species in each plot. For expressing the naturalness of the
vegetation, we classified the recorded plant species into Social
Behaviour Types (SBT) according to the classification system of Borhidi
(1995). The classification system assigns a naturalness value to each
SBT category, ranging from -3 (AC – adventive competitors) to +10 (Su
– unique specialist species). We calculated cover-weighted naturalness
scores for all plots.
We used two proxies of forage quality: a Hungarian classification system
(Balázs 1949) that takes multiple palatability criteria into account and
specific leaf area (SLA), which is a proxy for hydrated and more
attractive leaves. We classified the species according to their forage
quality based on the classification system of Balázs (1949). Forage
quality scores range from -3 (toxic plants) to +8 (highly valuable
forage plants). The average of the reported SLA values was derived from
a regional database (E.-Vojtkó et al. 2020) and for the species not
represented in the regional database, the LEDA database (Kleyer et al.
2008) was used. We calculated cover-weighted forage quality and SLA
scores for all plots.
We characterised the availability of floral resources with two proxies:
flowering period and the cover of insect-pollinated species. Flowering
period was calculated as the number of months when a species is
flowering using the work of Király (2009). We calculated the
community-weighted means (CWM) of flowering period for all plots.
We used repeated measures general linear models (RM-GLMs) for testing
the effect of disturbance (two levels: disturbed plots, intact
grasslands; fixed factor), year (two levels: 2020, 2021; repeated
measures factor) and season (two levels: spring, summer; repeated
measures factor). All possible interactions between the factors were
included in the models. Dependent variables were: total vegetation
cover, cryptogam cover, perennial forb cover, perennial graminoid cover,
short-lived forb cover, short-lived graminoid cover, Shannon diversity,
naturalness score, forage quality score, specific leaf area, CWM of
flowering period and the cover of insect-pollinated plants. All
univariate statistics were calculated using the GLM repeated measures
command in IBM SPSS Statistics v. 20.0 (Armonk, NY: IBM Corp).
To assess the species composition of the vegetation in the disturbed
plots and intact grasslands across the two study years and two seasons,
non-metric multidimensional scaling (NMDS) based on percentage cover of
the species was calculated using CANOCO 5.0 program (Ter Braak &
Šmilauer 2012). For the multivariate analysis, quadrats from the same
plot, season and year were averaged.
To investigate if plant abundances differed among i) disturbance types
(disturbed plots vs. intact grasslands), ii) years (2020 vs. 2021) and
iii) seasons (spring vs. summer), we applied permutational analysis of
variance (PERMANOVA). To do so, first we calculated a continuous
distance matrix using the raw abundance records of each of the 68 plant
species, considering Bray-Curtis distance coefficients. In the following
step, we fitted six PERMANOVA tests treating types, years, and seasons
as well as their interactions as grouping factors in comparison with
abundance distances, employing the PERMANOVA function available in the
”PERMANOVA” package (Vicente-Gonzalez & Vicente-Villardon 2021) of the
R statistical programming environment (R Core Team 2021).