DOI: 10.25829/bexis.31094-15
title: Ecosystem service values in simulated landscapes - used in Neyret et al. 2021 (Assessing the impact of grassland management on landscape multifunctionality, Ecosystem Services)

description: Land-use intensification has contrasting effects on different ecosystem services, often leading to land-use conflicts. While multiple studies have demonstrated how landscape-scale strategies can minimise the trade-off between agricultural production and biodiversity conservation, little is known about which land-use strategies maximise the landscape-level supply of multiple ecosystem services (landscape multifunctionality), a common goal of stakeholder communities.<br>Results are made accessible through an online tool that provides information on which compositions best meet any combination of user-defined priorities (https://neyret.shinyapps.io/landscape_composition_for_multifunctionality/). The tool presented can aid informed decision-making by predicting the impact of future changes in landscape composition, and by allowing for the relative roles of stakeholder priorities and biophysical trade-offs to be understood by scientists and practitioners alike.<br>This dataset contains the landscape-level ecosystem service values from simulated landscapes. Original datasets are listed in the 'Read me' file. R scripts to reproduce the results of the paper from these landscape-level data (and to reproduce the simulations from raw data) are available on GitHub (https://github.com/mneyret/Neyret_et_al_2021_Ecosystem_Services, https://doi.org/10.5281/zenodo.5521457).
description: Important notes: 1. File naming convention The files are named with the following convention, corresponding to the parameters used in the sensitivity analyses: Data_[environmental correction]_quantile_[quantile for LUI classification]_[number of plots per landscape]_[within-landscape service quantification]<br>Environmental correction: the service data was corrected (1) or not corrected (0) for environmental covariates before aggregation to the landscape level. Quantile for LUI classification: plots were classified into low, medium, high intensity plots based on the 30% (quantile_30) or 20% (quantile_20) quantile of LUI distribution. Number of plots per landscape: landscapes were composed of 7, 10, or 13 plots. When based on 20% quantiles, only 7 plots are allowed for each landscape. Within-landscape service quantification: landscape-level services were calculated as the mean or maximum of the service within the landscape.<br>The default data used in the main results of the manuscript is thus Data_1_quantile_30_10_mean.csv: data was corrected for environmental covariates, plots were classified based on 30% quantiles, 10 plots per landscapes, and landscape-level service values were calculated based on the mean of plot-level values.<br>2. File structure Each line then corresponds to one simulated landscape and corresponding ecosystem service variables.<br>Explo’: Region of the Biodiversity Exploratories. A: Swabian Alb (South), H: Hainich (Central), S: Schorfheide (North).<br>‘ls’: Landscape id<br>‘low’, ‘med’, ‘high’: proportion of low-, medium, and high-intensity plots in the landscape<br>‘variable’: ecosystem service indicator value at the landscape level (see table in ReadMe file). Includes plant gamma diversity (total species richness); bird families gamma diversity (total number of bird families); number of bird charismatic species; total protein production (plant biomass * protein content); carbon stocks in soils (2011); flower cover; butterfly abundance; presence of juniperus; cover of charismatic plants; cover of edible plants.<br>‘value’: value for the corresponding service.<br>Study Design: We combine comprehensive data collected from 150 German grassland sites with a simulation approach to identify landscape compositions, with differing proportions of low-, medium-, and high-intensity grasslands, that minimise trade-offs between the six main grassland ecosystem services prioritised by local stakeholders: biodiversity conservation, aesthetic value, productivity, carbon storage, foraging, and regional identity.<br>See paper and original datasets for data collection of individual indicators.<br>Sampling design: See paper and original datasets for data collection of individual indicators.<br>Data preparation: We estimated ecosystem service supply from several indicators, measured at each of the 150 sites. Biodiversity conservation was based on total plant species richness as plant alpha-diversity at the site level (a good proxy for diversity across multiple trophic levels at these sites, Manning et al., 2015), and gamma-diversity at the landscape level. Fodder production was calculated as total fodder protein production, a common agronomical indicator used in livestock production models, and which better represents livestock production potential than biomass, a large part of which can be indigestible (e.g. Waghorn and Clark, 2004; Herrero, 2015; Lee, 2018). We calculated this indicator from direct measures of grassland aboveground biomass production and shoot protein content. Climate change mitigation was quantified as soil organic carbon stocks in the top 10 cm, as deeper stocks are unlikely to be affected strongly by management actions. For the aesthetic value measure, we integrated direct measures of flower cover, the number of bird families observed and the abundance of butterflies. Regional identity included the species richness (resp. cover) of birds (resp. plants) associated with German identity, and the foraging service was measured based on the cover of edible plants. Details on the measurements of these different indicators and their aggregation from site to landscape level can be found in the supplementary methods of the paper.<br>Before estimating landscape-level services, we imputed missing values for individual indicators using predictive mean matching on the dataset comprising all services (98 out of 1500 values, R mice package v3.13.0). The missing values were mostly for flower cover, and some for butterfly abundance, and were equally distributed among regions and land-use intensities. We also corrected site-level indicator values for environmental covariates, thus ensuring differences were caused by management and not covarying factors. Corrected values were the residuals from linear models in which the ecosystem service indicators were the response variable and predictors were: the region, pH, soil depth, sand and clay content, mean annual temperature, mean annual rainfall (see Allan et al. (2015) for details on these measurements), and a topographic wetness index (see Le Provost et al. 2021 and supplementary methods). To account for a site’s surroundings, we also used the proportion of grassland in a 1km radius as a predictor, as surrounding grassland habitat may act as a source of colonisation for local biodiversity (e.g. Henckel et al., 2015; Le Provost et al., 2017; Tscharntke et al., 2012). Surrounding grassland cover was calculated from 2008 land-cover data that were mapped in QGIS v 3.6 and classified into five broad categories: croplands, grasslands, forests, water bodies, roads and urban areas.<br>All indicators except gamma diversities were corrected at the site level. This was done to avoid biasing landscape estimates with sites from environmental extremes. These would not be detected when averaging at the landscape level. However, plant and bird species gamma diversity can only be calculated at the landscape level, so were corrected at this scale using the landscape-level averages of the same environmental factors as described above, and also a landscape-level environmental heterogeneity variable. Landscape heterogeneity was calculated as the volume of the convex hull of the selected sites in a PCA that included all environmental variables.<br>An overview of the simulation process is presented in Fig. 1 of Neyret et al. 2021. We conducted the simulations separately for each region, as the relationships between land use and ecosystem services differed strongly between them.<br>Landscapes were simulated by randomly drawing sites from the pool of 50 sites from each region. Each artificial landscape was composed of ten sites to avoid the high similarity of landscapes composed of more sites. For instance, as there were approximately 16 sites of each intensity class in each region, the most extreme strategies (e.g. 100% low intensity) have only 1 possible landscape composition for landscapes of 16 sites, but 16 possible compositions, which share 90% of their sites for landscapes of 15 sites. For landscapes made up of 10 sites, there are 66 possible landscape compositions that differ in their proportions of low, medium and high intensity sites; ranging from 100% low intensity to 100% medium or high intensity with all possible intermediates (grey dots in the middle triangle of Fig. 1). For each of these compositions, we generated 15 unique artificial landscapes by randomly drawing sites from the regional pool, resulting in 15 x 66 = 990 landscapes. For each simulated landscape, we then calculated landscape-scale ecosystem service indicators, as described above. Simulated landscapes had a combined area of 10 x 50m x 50m sites (i.e. 2.5 ha), but we note that each site is representative of a field ranging from 3 to 148 ha, meaning the results are representative of much larger ‘farm-scale’ landscapes.