Experimental setup

A network of 96 paired fenced and grazed plots that varied in age (15–68 years since establishment) and size (64–10000 m2) distributed across 14 locations were used for this research. Within each plot, a series of 1–20 quadrants with a size of 0.21–1.0 m2 were randomly distributed to quantify vegetation. Variation in the number and size of quadrants was to capture vegetation heterogeneity at different locations.
 

Vegetation

In each quadrant, vegetation composition and species abundance were determined with the Point Intercept Method (Jonasson 1988) by lowering pins at 50–100 random points and counting the number of times that the vegetation intercepted the pins. For vascular plants, all intercepts were for each separate species, but for the ground layer only one intercept per pin (the uppermost) was recorded. All data was standardized to 100 pins per quadrant and averaged per plot (fence and grazed). Data was then grouped in the following vascular vegetation groups: herb (all herbs), graminoid (rush, sedge and grass), fern (fern and club moss), fruited shrub (shrub with fleshy fruits) and shrub (shrub with non-fleshy fruit). Species richness and alpha diversity were calculated for vascular plants (Shannon 1948; Simpson 1949). IBR is defined as the number of organisms that depend on or are associated with a particular plant species, which was sourced from Tyler et al. (2021). IBR is given on a logarithmic eight-degree scale (1 = < 6 associated species, 2 = 6–12, 3 = 13–24, 4 = 25–50, 5 = 51–100, 6 = 101–200, 7 = 201–400, 8 = > 400) and does not account for biodiversity redundancy between plant species. To determine the overall IBR of each plot, we multiplied the abundance of each species with its respective biodiversity relevance value and values across all species, were summed. The sum was used instead of a community weighting because much of the contribution of herbivores to plant diversity and plant-host biodiversity is by reducing plant abundance and not by extirpating plant species. We also calculated for each of the vegetation groups a minimum (i.e., the plant species with the highest IBR) and a maximum (i.e., the addition of IBR values of all present plant species) biodiversity value to qualitatively understand how they compare to IBR (i.e., interactions) values. The first value represents the lower-end of biodiversity when assuming maximum redundancy between plant species and their associated biodiversity; whereas the second value, represents the upper-end of biodiversity when assuming no redundancy between plant species.
 

Data source

This project sourced approximately half of the data (2014 fieldwork campaign) from Sundqvist et al. 2019 and the other half (2017-2018 fieldwork campaigns) from the project’s unpublished data (Appendix A.1). Data from both studies are compatible since standardized methods were used during fieldwork.
 

Data analysis

General Linear Mixed Models (GLMM) were used for all analyses. In the first set of models, plant richness and diversity were set as responses, treatment (i.e., fence vs. grazed plots) as a fixed factor, study location and block as random factors. In a second set, the same model was tested but this time we added herbaceous cover and treatment as fixed factors and non-linearity was tested by including a quadratic term. To test the response of IBR to grazing, we set IBR grouped by different plant functional groups as a response, treatment as a fixed factor, study location and block as random factors. To test the response of IBR to different plant composition metrics, we set IBR as a response, plant metrics (i.e., abundance, richness and diversity independently) and treatment as fixed factors, study location and block as random factors. A quantitative analysis was performed to understand the effect of grazing on overall biodiversity (i.e., lower- and upper-end of biodiversity range). We tested the effect of grazing on the co-existence between herbaceous and woody plants. Herbaceous abundance was set as a response, woody abundance and treatment were set as fixed factors, study location and treatment block as random factors. Overall, we also tested non-linearity with a square term and the effect of treatment age; no significance was found and thus they were omitted from the final analysis. Akaike’s Information Criterion was used to select the best-fitting model. We used a Principal Component Analysis (PCA) to test the associations between the most common plant species (>300 hits across all plots) and treatment. All statistical analysis was done in “R studio version 1.3.1056”, in combination with the “stats version 4.0.2” package for data transformation (R Core Team 2013), the “lme4 version 1.1-23” package for GLMM (Bates et al. 2015) and the “vegan” package for the PCA (Oksanen et al. 2012).

Results

Vegetation richness and diversity

Exclusion of grazing did not affect vascular plant richness or alpha diversity (i.e., Shannon & Simpson indices) when comparing openly grazed to fenced plots (Fig. 2A-C, Appendix A.2A). However, plant richness was positively associated with herbaceous cover in both fenced and grazed plots (Fig. 2D, Appendix A.2B), and plant diversity (i.e., Shannon and Simpson) was also positively associated with herbaceous cover (Fig. 2E-F, Appendix A.2B), but only in grazed plots. In fenced plots, plant diversity peaked at medium herb abundance. At the species level, Deschampia cespitosa (R=0.63, Appendix A.3-4), Calamagrostis lapponica (r=0.55), Carex vaginata (R=0.31), Salix phylicifolia (R=0.18) and Betula nana (R=0.17) occurred more in fenced plots whereas Vaccinium myrtillus (R=-0.17) and Carex magellanica (R=-0.18) occurred more in grazed plots.