Point-intercept vegetation recording was done using a quadratic frame with a regular 5 × 5 cm point grid within a sampling area of 25 × 25 cm, i.e. 25 regularly spaced sampling points, at each of which a 0.8 cm thick wooden stick was inserted vertically. The frame was elevated above the herbaceous canopy on 50 cm legs. All leaves and stems of plants intercepted by the stick were recorded with species’ identity. Dead plant parts were recorded as litter. If the main part of a plant was alive, all its parts was recorded as live. If the main part of the plant was withered, it was recorded as litter. Contact points with bryophytes were also recorded, but were not used in the regression models.
Plant diversity metrics
Species richness per quadrat (alpha diversity) was assessed as one of the simplest metrics of biodiversity.
An index of community unicity was calculated based on species’ occupancy within Denmark, i.e. Atlas Flora Danica, AFD (Hartvig & Vestergaard 2015). These data consist of presence and absence records of all vascular plants in 1300 grid cells, each 5 × 5 km, dispersed across the country. Each species i recorded in a quadrat was given a value equivalent to the inverse of its range size and the resulting values summed per quadrat, thus
\(\sum_{i=1}^S\frac{1}{n\ AFD\ grid\ cells\ with\ species\ i\ present}\), in which S is the number of species in a given quadrat.
The ratio of forbs to graminoids was included as an indicator of floral resources available to anthophilous insects, thus \(\ \frac{n\ intercepts\left(\text{forbs}\right)}{n\ intercepts\left(\text{graminoids}\right)}\), or the equivalent for biomass estimates.
Finally, the amount of leaf litter was used as an indication of grazing naturalness.
Statistical analysis of point intercept data
First, an overall model of intercepts per vascular plant species as a function of above-ground biomass was built as a generalized linear model. Next, the residual variation of this model was investigated using analysis of variance with dry mass per point-intercept as the dependent variable and species identity, block and treatment as factors, using data from 24 quadrats recorded i 2020. This was done to investigate the assumption that species identity would explain variation in this ratio. Subsequently, one linear regression model per plant species was built. This was done for plant species with three or more data points, and additionally for genera with more than one species present and for functional groups meeting the same criterion. The functional groups were: Broad-leaved graminoids (leaf blades > 2 mm), narrow-leaved graminoids (leaf blades < 2 mm), Juncus effusus-type rushes (for which intercepts with stems were recorded, as they bear no leaf blades), forbs, woody plants (trees and shrubs, incl Calluna vulgaris and Cytisus scoparius). Initially, separate models were built for forbs with basal leaves, e.g. rosettes, vs. post-and-flag type forbs, but model estimates were very similar, so the two groups were combined to produced a single model with comparable R2 and lower standard error. A separate model was built for standing dead litter. No data transformations were used. All regressions models were forced through the origin, because otherwise - when predicted biomass was accrued per plot over species - total biomass per plot would gain a spurious strong positive relationship with plot species richness.
Second, for the 45 quadrats sampled in 2021, calibrated biomass values per species per plot were calculated from point-intercept data and summed over species present in plots (total biomass, plus forb and graminoid biomass separately). For each species present in a plot, the best available model was used, i.e. first choice was a species-specific model, second choice a genus-specific model, third choice a model for functional group and, in case none of these were available, a general model based on all point-intercepts was used.
Statistical analysis of treatment data