DOI: 10.25829/bexis.24867-1.1.23
title: Dataset used in Boeddinghaus et al. (2019) "Plant functional trait shifts explain concurrent changes in the structure and function of grassland soil microbial communities" Journal of Ecology

description: We investigated whether the functioning and composition of microbial communities rapidly tracks short-term changes (within three years) in abiotic soil properties, land-use intensity, plant functional traits or plant biomass properties or if microbial changes are driven by the longer-term history of the site (legacy effect). In addition, we asked whether changes in microbial soil properties and functions were best explained by direct or indirect legacy or change effects of land-use intensity or in intermediate properties, namely changes to soil pH, plant functional traits and the quantity and nutritional quality of plant biomass.
description: Theory: Land-use intensification drives changes to microbial communities and the soil functions they regulate, but the mechanisms underlying these changes are poorly understood as land use can affect soil communities both directly (e.g. via changes to soil fertility) and indirectly (e.g. via changes to plant inputs).

The speed of microbial responses is also poorly understood. For instance, whether it is long-term legacies or short-term changes in land-use intensity that drive changes to microbial communities.

To address these topics, we measured multiple microbial functions, bacterial and fungal biomass and abiotic soil properties at two time intervals three years apart. This was performed in 150 grassland sites differing greatly in management intensity across three German regions. 
|Measurements Type: Observed changes in microbial soil properties were related to both long-term means and short-term changes in: abiotic soil properties, land-use intensity, community abundance weighted means of plant functional traits and plant biomass properties in regression and structural equation models. 

To differentiate legacy effects from effects of short-term changes, we calculated separate measures for these time periods and termed them "historic" and "changes" (Δ) for the ecosystem variables. The historic land-use intensity index (LUI, see procedures) was calculated as the mean LUI of 2006–2010, historic plant functional traits as the mean from 2008–2011 (the starting year depended on the oldest available data). The changes for each microbial and ecosystem variable (y) on each site (i) in between the investigated years 2011 and 2014 were calculated as 
yiΔ=(yi2011-yi2014)×-1 
with 2011 serving as base year. 
LUIs of the years prior to sampling (2010 and 2013) were more strongly related to soil microbial properties in 2011 and 2014, than the LUI of the actual sampling years, therefore, the change in LUI was calculated between 2010 and 2013. 
|instruments: For details please see appendix 1 of Boeddinghaus et al. (2019) Journal of Ecology.|calibration: For details please see appendix 1 of Boeddinghaus et al. (2019) Journal of Ecology.|procedures: For details please see Boeddinghaus et al. (2019) Journal of Ecology.

Land-use intensity was assessed via questionnaires (Vogt et al., submitted) and calculated as index (LUI) according to Blüthgen et al. (2012).

Soil samples were collected in May 2011 and May 2014 on all 150 grassland EPs along two orthogonal transects of 20 m (14 samples from 0–10 cm depth) using core augers. Samples from each site were mixed, cooled and transported to a field lab, where soil was sieved (&lt; 2 mm), separated from roots and stones and frozen at -20 °C, all within eight hours of sampling.

Microbial biomass carbon (Cmic) and nitrogen (Nmic) were measured using the chloroform-fumigation-extraction method (CFE) of Vance et al. (1987), modified after Keil et al. (2011). Microbial phosphorous (Pmic) was measured by combining methods of Kouno et al. (1995) and McLaughlin et al. (1986).

Microbial functions were determined by measuring enzyme activities of beta-glucosidase (EC 3.2.1.21, "glucosidase"), beta-xylosidase (EC 3.2.1.37, "xylosidase"), N-acetyl-beta-glucosaminidase (EC 3.2.1.52, "chitinase"), and acid phosphatase (EC 3.1.3.2) according to Marx et al. (2001) and Berner et al. (2011), as well as urease activity (EC 3.5.1.5) after Kandeler & Gerber (1988) and denitrification enzyme activity (DEA) according to Smith and Tiedje (1979) and Keil et al. (2015). 

Bacterial and fungal biomass was determined by measuring soil phospholipid fatty acid (PLFA) composition. Extraction followed Frostegard et al. (1991) and alkaline methanolysis was performed after Dowling et al. (1986) to gain fatty acid methyl esters (FAME). FAMEs were classified as bacterial or fungal according to Ruess and Chamberlain (2010). Fungal biomass was also determined as ergosterol content according to a modified approach of Djajakirana et al. (1996). 

Soil pH was measured in 0.01 M CaCl2 (1:2.5 ratio of soil:CaCl2-solution). Each site was equipped with a weather station monitoring soil temperature (5-10 cm depth) and moisture (10 cm depth). Volumetric soil moisture was recalculated as percent of the water holding capacity of each plot (SWC). 

Every year since 2008 (May to June), the percentage cover of all vascular plant species was estimated in a 4 m × 4 m quadrat at all sites. This data was combined with data on plant traits from the TRY database (Kattge et al., 2011) to calculate community abundance weighted means (CWM) of the plant traits: specific leaf area (SLA), leaf P content (leaf P) and leaf N content (leaf N). The CWM for mycorrhizal intensity (MycInt) was calculated from the database of Akhmetzhanova et al. (2012). 

Plant biomass was sampled in May 2011 and 2014 adjacent to the vegetation record by cutting aboveground (living) plant biomass in five 0.25 m-2 squares 2-3 cm above ground. Pooled samples of each site were dried (48 h, 80 °C), milled and nutritional quality of plant biomass measured: percent neutral detergent fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL, lignin), as well as percent P and N content were analysed by near-infrared spectroscopy (NIRS) according to Klaus et al. (2016) and Kleinebecker et al. (2011). Cellulose (= ADF – ADL) and hemicellulose (= NDF – ADF) content were calculated according to Kirchgeßner (2014). |