Supporting Information for
Plant diversity in hedgerows and road verges across Europe This document contains SI Materials and Methods
In this document, we provide additional information on the selection criteria for the study sites as well as the calculation of the different explanatory variables. This file accompanies the Materials and Methods section of the main text.
Details on the site selection criteria
In each region, four hedgerows and/or four graminoid-dominated road verges were selected (Supporting Information Table S1) according to a predefined set of criteria.
All selected corridors and corresponding core habitats were located on a similar soil type (Luvisol or Cambisol; IUSS Working Group WRB 2015) to maximize comparability, and situated at least 1 km apart to avoid the effects of spatial autocorrelation.
Each selected hedgerow was connected to an ancient forest (i.e. always forest and no land-cover change according to the oldest available sources), whereof the canopy was mainly composed of broadleaf species. Furthermore, the hedgerows were at least 50 years old, which is considered as a critical age for colonization by typical forest plant species (Brunet & Von Oheimb 1998). They had a diverse, preferably multi-storied, structure (e.g. trees, pollards and shrubs), and they were surrounded by open and treeless habitats (e.g. farmland or road). We strictly excluded all linear forest remnants (i.e. former forests that had been largely cut and reduced to a linear structure resembling a hedgerow) as this would affect the position and dynamics of plant species within the corridor. The selected road verges were connected to a species-rich, semi-natural grassland of the class Molinio-Arrhenatheretea (Mucina et al. 2016), they were at least 10 years old (since the last road construction) and they were mowed once or twice a year.
Details on the calculation of explanatory variables
Nitrogen deposition and macroclimatic conditions
Standardized N deposition data were obtained from the European Monitoring and Evaluation Program (EMEP) (http://www.emep.int). We used data from the year 2000 and calculated the total atmospheric deposition rates for each site by summing the modelled rates of wet and dry oxidized and reduced N, using a dry deposition factor equal to 1 (Erisman & Draaijers 2003). Following the assumptions of Dupré et al.
(2009), we used values from the year 2000 as a proxy for cumulative amounts of N deposition (see Verheyen et al. 2012). Cumulative values of N deposition reflect the long-term effects of atmospheric pollution, which have been widely recognized as a major driver of plant diversity changes across temperate Europe (Bobbink et al.
2010).
Macroclimatic conditions were extracted from WorldClim 2.0 (http://worldclim.org/version2). We focused on MAT and MAP, which are generally considered as standard variables for describing global patterns of species diversity (Fick & Hijmans 2017). Furthermore, we also evaluated the effect of climate extremes (Supporting Information Table S3–S4). All bioclimatic variables are long- term averages for the years 1970–2000 with 30 arc-second spatial resolution (approx.
1 km at the equator).
Canopy structure and composition
For each plot inside the hedgerows and forests, the height of the shrub layer and tree layers was measured with a vertex instrument (Haglöfs Vertex IV). The total cover of the shrub and tree layers was computed as the sum of the cover percentages of species in these respective layers (Supporting Information Fig. S1). To characterize the composition of the canopy, we focused on two variables, i.e. the shade casting ability (SCA) and litter quality (LQ) of canopy species (including both shrub and tree species). These variables were calculated per plot as the cover-weighted average of
species-specific SCA and LQ scores, respectively (sensu Verheyen et al. 2012; Maes et al. 2019). The scores range between ‘1’ (very low SCA or LQ) and ‘5’ (very high SCA or LQ), and are listed for all canopy species in Supporting Information Table S5.
We also assessed the effect of total canopy cover (measured with a convex spherical crown densiometer; Forestry Suppliers, Model A) and overstorey species diversity (determined for each plot as the sum of overstorey shrub and tree species) (Supporting Information Table S3–S4).
Soil properties
In each plot, three soil samples were collected to a depth of 10 cm and merged into one soil sample. The mixed samples were subsequently dried to constant weight at 40
°C for 48h, ground and sieved through a 2 mm mesh. Next, the samples were analysed for pH-H2O by shaking a 1:5 ratio soil/H2O mixture for 5 min at 300 rpm and measuring with a pH meter (Orion 920A with pH electrode model Ross sure-flow 8172 BNWP, Thermo Scientific Orion, USA). To determine the total carbon (C) and N content of the soil, the samples were combusted at 1200°C and the gases were measured by a thermal conductivity detector in a CNS elemental analyser (vario Macro Cube, Elementar, Germany). Bioavailable phosphorous (P) (i.e. available for plants within one growing season) was determined by extraction in NaHCO3 [POlsen; according to ISO 11263:1994(E)] and colorimetric measurement according to the malachite green procedure (Lajtha et al. 1999). Finally, exchangeable K+, Ca2+ and Mg2+ concentrations were measured by atomic absorption spectrophotometry (AA240FS, Fast Sequential AAS) after extraction in 0.1 M BaCl2 (NEN 5738:1996).
Microclimate
To quantify the microclimate in each site, the air temperature was recorded at a two- hourly interval between September 1, 2017 and September 1, 2018 using miniature temperature sensors (type HOBO 8K Pendant Temperature/Alarm Data Logger – UA- 001-08; accuracy at 0–50 °C: ±0.53 °C; resolution at 25 °C: 0.14 °C). In each forest-
hedgerow transect, we deployed one temperature sensors in every vegetation plot. In the grassland-road verge sites, we installed a single sensor in the middle of the grassland patch. All sensors were mounted in a radiation shield at 1 m above the soil surface and oriented towards the north to avoid direct solar radiation. For each sensor, we computed daily mean, minimum and maximum temperature values.
Corresponding ‘free-air’ (macroclimate) temperature data were obtained for each study site from nearby weather stations (using the sources listed in Supporting Information Table S6). For each station, we extracted the daily mean, minimum and maximum statistics for the same period of September 1, 2017 to September 1, 2018.
The magnitude of the temperature offset for daily mean, maximum and minimum temperature values was then calculated as microclimate temperatures minus macroclimate temperatures. We mainly focussed on the effect of minimum and maximum temperature offsets during summer (June, July and August) and winter (December, January and February). We also assessed the effect of mean summer and winter temperature offsets as well as minimum, mean and maximum annual temperature offsets (Supporting Information Table S3–S4).
Additional references
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Dupré, C., Stevens, C.J., Ranke, T., Bleeker, A., Peppler-Lisbach, C., Gowing,
D.J.G., . . . Diekmann, M. (2009) Changes in species richness and composition in European acidic grasslands over the past 70 years: the contribution of cumulative atmospheric nitrogen deposition. Global Change Biology, 16, 344- 357. doi: 10.1111/j.1365-2486.2009.01982.x.
Erisman, J.W. & Draaijers, G. (2003) Deposition to forests in Europe: most important factors influencing dry deposition and models used for generalisation.
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Fick, S.E. & Hijmans, R.J. (2017) Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology. doi.
IUSS Working Group WRB (2015) World Reference Base for Soil Resources 2014, update 2015. International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports FAO, Rome.
Lajtha, K., Driscoll, C., Jarrell, W. & Elliott, E. (1999) Soil Phosphorus:
Characterization and Total Element Analysis. Standard Soil Methods for Long- Term Ecological Research (eds R. GP, C. DC, B. CS & S. P), pp. 115–142. Oxford University Press, New York.
Maes, S.L., Blondeel, H., Perring, M.P., Depauw, L., Brūmelis, G., Brunet, J., . . . Verheyen, K. (2019) Litter quality, land-use history, and nitrogen deposition effects on topsoil conditions across European temperate deciduous forests.
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Mucina, L., Bültmann, H., Dierßen, K., Theurillat, J.-P., Raus, T., Čarni, A., . . . Tichý, L.
(2016) Vegetation of Europe: hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities. Applied Vegetation Science, 19, 3-264. doi: 10.1111/avsc.12257.
Verheyen, K., Baeten, L., De Frenne, P., Bernhardt-Römermann, M., Brunet, J., Cornelis, J., . . . Verstraeten, G. (2012) Driving factors behind the
eutrophication signal in understorey plant communities of deciduous temperate forests. Journal of Ecology, 100, 352-365. doi: 10.1111/j.1365- 2745.2011.01928.x.
