Eleven years' data of grassland management in Germany.

BACKGROUND: The 150 grassland plots were located in three study regions in Germany, 50 in each region. The dataset describes the yearly grassland management for each grassland plot using 116 variables.General information includes plot identifier, study region and survey year. Additionally, grassland plot characteristics describe the presence and starting year of drainage and whether arable farming had taken place 25 years before our assessment, i.e. between 1981 and 2006. In each year, the size of the management unit is given which, in some cases, changed slightly across years.Mowing, grazing and fertilisation were systematically surveyed: Mowing is characterised by mowing frequency (i.e. number of cuts per year), dates of cutting and different technical variables, such as type of machine used or usage of conditioner.For grazing , the livestock species and age (e.g. cattle, horse, sheep), the number of animals, stocking density per hectare and total duration of grazing were recorded. As a derived variable, the mean grazing intensity was then calculated by multiplying the livestock units with the duration of grazing per hectare [LSU days/ha]. Different grazing periods during a year, partly involving different herds, were summed up to an annual grazing intensity for each grassland.For fertilisation , information on the type and amount of different types of fertilisers was recorded separately for mineral and organic fertilisers, such as solid farmland manure, slurry and mash from a bioethanol factory. Our fertilisation measures neglect dung dropped by livestock during grazing. For each type of fertiliser, we calculated its total nitrogen content, derived from chemical analyses by the producer or agricultural guidelines (Table 3).All three management types, mowing, fertilisation and grazing, were used to calculate a combined land use intensity index (LUI) which is frequently used to define a measure for the land use intensity. Here, fertilisation is expressed as total nitrogen per hectare [kg N/ha], but does not consider potassium and phosphorus.Information on additional management practices in grasslands was also recorded including levelling, to tear-up matted grass covers, rolling, to remove surface irregularities, seed addition, to close gaps in the sward. NEW INFORMATION: Investigating the relationship between human land use and biodiversity is important to understand if and how humans affect it through the way they manage the land and to develop sustainable land use strategies. Quantifying land use (the 'X' in such graphs) can be difficult as humans manage land using a multitude of actions, all of which may affect biodiversity, yet most studies use rather simple measures of land use, for example, by creating land use categories such as conventional vs. organic agriculture. Here, we provide detailed data on grassland management to allow for detailed analyses and the development of land use theory. The raw data have already been used for > 100 papers on the effect of management on biodiversity (e.g. Manning et al. 2015).

Interannual variation in land-use intensity enhances grassland multidiversity.

Although temporal heterogeneity is a well-accepted driver of biodiversity, effects of interannual variation in land-use intensity (LUI) have not been addressed yet. Additionally, responses to land use can differ greatly among different organisms; therefore, overall effects of land-use on total local biodiversity are hardly known. To test for effects of LUI (quantified as the combined intensity of fertilization, grazing, and mowing) and interannual variation in LUI (SD in LUI across time), we introduce a unique measure of whole-ecosystem biodiversity, multidiversity. This synthesizes individual diversity measures across up to 49 taxonomic groups of plants, animals, fungi, and bacteria from 150 grasslands. Multidiversity declined with increasing LUI among grasslands, particularly for rarer species and aboveground organisms, whereas common species and belowground groups were less sensitive. However, a high level of interannual variation in LUI increased overall multidiversity at low LUI and was even more beneficial for rarer species because it slowed the rate at which the multidiversity of rare species declined with increasing LUI. In more intensively managed grasslands, the diversity of rarer species was, on average, 18% of the maximum diversity across all grasslands when LUI was static over time but increased to 31% of the maximum when LUI changed maximally over time. In addition to decreasing overall LUI, we suggest varying LUI across years as a complementary strategy to promote biodiversity conservation.

Environmental factors affect Acidobacterial communities below the subgroup level in grassland and forest soils.

In soil, Acidobacteria constitute on average 20% of all bacteria, are highly diverse, and are physiologically active in situ. However, their individual functions and interactions with higher taxa in soil are still unknown. Here, potential effects of land use, soil properties, plant diversity, and soil nanofauna on acidobacterial community composition were studied by cultivation-independent methods in grassland and forest soils from three different regions in Germany. The analysis of 16S rRNA gene clone libraries representing all studied soils revealed that grassland soils were dominated by subgroup Gp6 and forest soils by subgroup Gp1 Acidobacteria. The analysis of a large number of sites (n = 57) by 16S rRNA gene fingerprinting methods (terminal restriction fragment length polymorphism [T-RFLP] and denaturing gradient gel electrophoresis [DGGE]) showed that Acidobacteria diversities differed between grassland and forest soils but also among the three different regions. Edaphic properties, such as pH, organic carbon, total nitrogen, C/N ratio, phosphorus, nitrate, ammonium, soil moisture, soil temperature, and soil respiration, had an impact on community composition as assessed by fingerprinting. However, interrelations with environmental parameters among subgroup terminal restriction fragments (T-RFs) differed significantly, e.g., different Gp1 T-RFs correlated positively or negatively with nitrogen content. Novel significant correlations of Acidobacteria subpopulations (i.e., individual populations within subgroups) with soil nanofauna and vascular plant diversity were revealed only by analysis of clone sequences. Thus, for detecting novel interrelations of environmental parameters with Acidobacteria, individual populations within subgroups have to be considered.