Biodiversity–ecosystem functioning (BEF) experiments have shown that local species richness promotes ecosystem functioning and stability. Whether this also applies under real-world conditions is still debated. Here, we focus on larger scales of space, time and ecological organization. We develop a quasi-experimental design in which we relate land-cover type richness as measure of landscape richness to 17-year time series of satellite-sensed functioning in 4,974 landscape plots 6.25 or 25 ha in size. We choose plots so that landscape richness is orthogonal to land cover-type composition and environmental conditions across climatic gradients. Landscape-scale productivity and temporal stability increase with landscape richness, irrespective of landscape plot size. Peak season near-infrared surface albedo, which is relevant for surface energy budgets, is higher in mixed than in single land-cover type landscapes. Effect sizes are as large as those reported from BEF-experiments, suggesting that landscape richness promotes landscape functioning at spatial scales relevant for management.

In these datasets (data_250m.csv, data_500m.csv) we combine a variety of geographic information data to elucidate the relationship between land-cover type richness and landscape-level functioning (satellite-sensed proxies of productivity, phenology and albedo) in the years 2000-2016 across large environmental gradients in Central Europe (Switzerland). Data were extracted for landscapes with an area of either 250m x 250m (in data_250m.csv) or 500m x 500m (in data_500m.csv) across Switzerland, averaged for the 17 year time period and then aggregated according to land-cover type compositions and blocks of biogeographic region - altitude range combinations.

Satellite-sensed proxies of productivity, phenology and albedo were derived from MODIS MOD13Q1, MOD13A1 and MCD43A3 products (Didan 2015, Schaaf & Wan 2015). Information on land-cover was derived from data provided by the Swiss Federal Statistical Office (GEOSTAT, product name: NOAS04). Topographic data was obtained from the Swiss Federal Office of Topography (swisstopo, product name: DHM25). Climatic variables were derived from interpolated gridded monthly datasets provided by the Swiss Office of Meteorology and Climatology (MeteoSwiss, product names: TabsM, RhiresM, msg.SIS.M). Data processing was done using the R 3.5 software (http://r-project.org).

References:

Didan, K. (2015). MOD13Q(A)1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250(500)m SIN GridV006. doi:10.5067/MODIS/MOD13Q(A)1.006. Accessed on January,16,2016 (November,20,2017) at https://e4ftl01.cr.usgs.gov/MOLT. NASA EOSDIS Land Processes DAAC.

Schaaf, C. & Wan, Z. (2015). MCD43A3 MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global - 500m V006. NASA EOSDIS Land Processes DAAC. Accessed on January, 12, 2017 at https://doi.org/10.5067/modis/mcd43a3.006.

We provide a description of the dataset column names in the data_description.txt file. For further details on data characteristics and processing, please have a look at the Methods section and the reporting summary file of the related main manuscript (DOI: 10.1038/s41467-019-14002-7).