DOI: 10.71615/bexis.31924
title: Spatial pair-wise plant data including species-specific metrics, land-use and environmental drivers (all experimental plots 2008-2018)

description: Land-use intensification is considered a major driver of biodiversity loss. However, most evidence for land use effects come from space-for-time substitutions, comparing biodiversity in areas differing in current land use intensity. Despite their frequent use, the robustness of space-for-time substitutions in capturing temporal changes in biodiversity remains unclear. We compared spatial and temporal responses of plant and arthropod communities to changes in land-use intensity, using 150, ten-year time series from grasslands in three German regions (Schwäbische Alb, Hainich-Dün, Schorfheide-Chorin) (this dataset, 31921, 31925, 31926). This data set comprises the data used to calculate spatial diversity responses of plant-communities to land-use changes (all experimental plots 2008-2018).
description: Study design: Our analysis aimed to compare diversity responses of plants and arthropods to land-use changes over time and space. For this we created four datasets (this dataset, 31921, 31924, 31925). With this data set we calculated plant diversity responses to land-use change across space, for each of the sampled years and used all experimental plots in grasslands with annual records of plant community data. Land-use intensity was measured as land use intensity index (LUI, Blüthgen et al. 2012) with annual data per experimental plot manually standardized across all years and regions (see method section of the publication and the source code in Zenodo: https://doi.org/10.5281/zenodo.14899738). To account for environmental covariates over-shading land-use effects we included beyond data on land use intensity (LUI) and its components form the respective years, also the following variables in our analysis (see supplementary dataset description for details, which can be found as attachment to this dataset):&lt;br&gt;&lt;br&gt;1) plant communities  (dataset ID: 24247)&lt;br&gt;2) land-use intensity (dataset ID: 25086)&lt;br&gt;3) geographic position (dataset ID: 20826)&lt;br&gt;4) year (dataset ID: 25086)&lt;br&gt;5) grassland permanency (dataset ID: 31018)&lt;br&gt;6) grassland isolation (dataset ID: 18148)&lt;br&gt;7) soil conditions (pH, soil nutrients) (dataset ID: 14446, 14447, &lt;br&gt;    17086)&lt;br&gt;8) climatic conditions (temp, precipitation) (dataset ID: 24766) | Sampling design: This data set was created to analyse the diversity responses of plants to spatial land use changes by building all possible combinations of plot pairs across space (11175 unique pairs of 150 plots x 11 years) within time. For these plot-pairs we calculated a series of alpha- and beta-diversity indices, their mean and difference per plot pair, as well as plot means and differences in land-use intensity (LUI and it´s components) and environmental factors (see attached supplementary dataset description for details about selected factors and the Method section, as well as the provided R code of the publication for data handling).&lt;br&gt;&lt;br&gt;In short: &lt;br&gt;&lt;br&gt;Vascular plants were sampled in 4 m × 4 m subplots in each plot once every year in early summer. Data on relative abundance of occurring species was used to calculate indices of α and β diversity.&lt;br&gt;&lt;br&gt;Land-use intensity (LUI) was calculated following (Blüthgen et al. 2012): sqrt((Fertilisation / Global mean Fertilisation) + (Mowing / Global mean Mowing) + (Grazing / Global mean Grazing)) and subsequently calculating the mean for each year between 2008-2018. In the analysis we used mean LUI and difference in LUI (deltaLUI) between any two plot pairs (different experimental plots) within the same year. To account for temporal variation between platial plot pairs and its effect on mean LUI, LUI values were scaled to year averages by using the residuals of the linear model correlating LUI or its components to year as explanatory factor (LUIres). mean LUI was measured as average between LUIres of experimental plot 1 (LUI1res) and plot 2 (LUI2res). As delta LUI should not be influence by the overall LUI level per year accross plots, delta LUI was based on the difference of "raw" LUI values as obtained from the LUI tool and after standardization (rawLUI1, rawLUI2).&lt;br&gt;&lt;br&gt;Soil conditions were summarised using prinicpal component analysis (PCA), reducing the data variation in few super variables (PC axis) containing the majority of information in soil conditions. the first PC axis (soilPC1) from the input data from 2011 on soil pH, nitrogen stock, total soil nitrogen, organic carbon stock, soil organic carbon, soil C:N ratio) according to Birkhofer et al.(2012).&lt;br&gt;&lt;br&gt;Grassland permanency was calculated based on historical land-use maps quantifying the stability of grassland covers between 1820/50 (1820: Schwaebische Alb; 1850: Hainich, Schorfheide-Corin) and 2008 within 500 m radius of each plot &lt;br&gt;&lt;br&gt;Grassland isolation was calculated as described in Gossner et al. (2016). Based on arial photographs with 40 cm resolution from 2008 we measured the cover of all potential grassland habitats within a 500 m radius of each plot; one minus this grassland proportion was used as an estimate of grassland isolation.&lt;br&gt;&lt;br&gt;Climatic conditions were included through the annual sum of mean day air temperature at 2 m above surface and the number of rainy days in each plot. Temperature data were measured with data loggers on each plot. Information on rainy days is based on precipitation data from the German Weather Service (Deutscher Wetterdienst, https://www.dwd.de/). | Data analysis: We used linear models and generalised dissimilarity models to compare effects of temporal and spatial land-use intensity variation on differences in plant and arthropod alpha diversity and beta-diversity, respectively. For details see dataset 31939. | Literature references: 1) Birkhofer, Klaus, Ingo Schöning, Fabian Alt, Nadine Herold, Bernhard Klarner, Mark Maraun, Sven Marhan, et al. 2012. “General Relationships between Abiotic Soil Properties and Soil Biota across Spatial Scales and Different Land-Use Types.” PloS One 7 (8): e43292. https://doi.org/10.1371/journal.pone.0043292.&lt;br&gt;&lt;br&gt;2) Blüthgen, Nico, Carsten F. Dormann, Daniel Prati, Valentin H. Klaus, Till Kleinebecker, Norbert Hölzel, Fabian Alt, et al. 2012. “A Quantitative Index of Land-Use Intensity in Grasslands: Integrating Mowing, Grazing and Fertilization.” Basic and Applied Ecology 13 (3): 207–20. https://doi.org/10.1016/j.baae.2012.04.001.&lt;br&gt;&lt;br&gt;3) Gossner, Martin M., Thomas M. Lewinsohn, Tiemo Kahl, Fabrice Grassein, Steffen Boch, Daniel Prati, Klaus Birkhofer, et al. 2016. “Land-Use Intensification Causes Multitrophic Homogenization of Grassland Communities.” Nature 540 (7632). https://doi.org/10.1038/nature20575. | Data references: LUI Tool: https://www.bexis.uni-jena.de/lui/LUICalculation/index&lt;br&gt;&lt;br&gt;Ostrowski, Andreas; Lorenzen, Katrin; Petzold, Eleonora; Sirko Schindler (2025): Land-use intensity index (LUI) calculation tool of the Biodiversity Exploratories project for grassland survey data from three different regions in Germany since 2006. Version 5. Biodiversity Exploratories Information System. Dataset. https://www.bexis.uni-jena.de/ddm/data/Showdata/31514&lt;br&gt;&lt;br&gt;Hänsel, Falk; Forteva, Spaska; Wöllauer, Stephan; Nauss, Thomas (2025): Öffentlich verfügbare Klimadaten der Exploratorien / Open Climate Data of the Exploratories Project. Version 11. Biodiversity Exploratories Information System. Dataset. https://www.bexis.uni-jena.de/ddm/data/Showdata/24766