DOI: 10.5061/dryad.f4qrfj76n
title: Sustainable land use enhances soil microbial respiration responses to experimental heat stress

description: Soil microbial communities provide numerous ecosystem functions, such as
 nutrient cycling, decomposition, and carbon storage. However, global
 change, including land-use and climate changes, affects soil microbial
 communities and activity. As extreme weather events (e.g., heatwaves) tend
 to increase in magnitude and frequency, we investigated the effects of
 heat stress on the activity (e.g., respiration) of soil microbial
 communities that had experienced four different long-term land-use
 intensity treatments (ranging from extensive grassland, intensive
 grassland to organic and conventional croplands) and two climate
 conditions (ambient vs. predicted future climate). Here, using soils from
 a long-term field experiment and laboratory heat stress, we investigate
 the combined history effects of climate change and land-use intensity on
 soil microbial respiration and its respiration response to heat stress
 (Fig. 1). Soil samples were collected from the Global Change Experimental
 Facility (GCEF, Fig. 1A), where soils had been subjected to a future
 climate treatment and varying levels of land-use intensity for ten years.
 To simulate heat stress, soils were incubated at either 20 °C, 25 °C,
 30 °C, or 35 °C under laboratory conditions, and we assessed the soil
 microbial respiration response (Fig. 1C).
description: To investigate how soil microbial communities respond to
 short-term climatic extremes under realistic background conditions, we
 applied a controlled heat stress treatment on top of an existing long-term
 global change experiment. The experiment has been running for over a
 decade and includes a future climate manipulation (+0.5°C warming, altered
 precipitation patterns with a summer drought) combined with different
 land-use regimes. Importantly, these treatments are superimposed on
 ambient environmental conditions, meaning that natural climate variability
 and extreme events such as heatwaves have occurred throughout the duration
 of the experiment (Bei et al., 2023). As a result, the microbial
 communities have been shaped by both the experimental treatments and
 naturally occurring events, creating a realistic baseline of chronic
 global change exposure. The additional heat stress treatment was applied
 to simulate an acute short-term stressor, enabling us to assess microbial
 responses to extreme temperature events in the context of pre-existing
 long-term stress. This approach reflects future climate scenarios, where
 ecosystems will be increasingly exposed to compound stressors (IPCC, 2021;
 Pascual et al., 2022). <strong>Study
 site</strong> The Global Change Experimental
 Facility (GCEF) is located in Bad Lauchstädt, Central Germany
 (51°23’30"N, 11°52’49"E, 116 m a.s.l.) and is part of the
 Helmholtz Centre for Environmental Research - UFZ (Schädler et al., 2019).
 The GCEF was established in 2013 on a former arable field to study the
 influence of climate change on terrestrial ecosystems within different
 land-use intensities (Fig. 1A). The region is characterized by a
 sub-continental climate with an average precipitation of 442 mm and a mean
 temperature of 10.9°C (2014-2024). The soil type is Haplic Chernozem,
 which contains 70% silt and 20% clay. Therefore, it holds high levels of
 organic carbon and has a high water-holding capacity (Altermann et al.,
 2005; Korell et al., 2024). <strong>Experimental
 setup and land-use treatments</strong> The GCEF
 consists of ten main plots (80 m × 24 m), each divided into five plots (16
 m × 24 m), resulting in a total of 50 plots. In each main plot, five
 land-use treatments were randomly assigned to experimental plots:
 extensively used grassland, intensively used grassland, organic cropland,
 conventional cropland, and an extensively used pasture treatment which was
 excluded from this study to balance the cropland<em> vs.
 </em>grassland treatments. The treatments vary in management
 intensity, including differences in fertilization, pesticide use, plant
 species richness, and management practices. Extensive grasslands,
 representing the lowest management intensity, consist of 56 plant species,
 receive no fertilization, and are mown twice per year. Intensive
 grasslands, by contrast, are composed of five grass cultivars, are
 fertilized with nitrogen (N) and phosphorus (P), and are mown up to four
 times per year. Organic cropland is managed without any pesticides and
 includes a six-year crop rotation, with legumes sown every three years,
 along with potassium-magnesium-sulfur (K-Mg-S) fertilization. Conventional
 cropland, the most intensive management type, follows a three-year crop
 rotation of winter rape, winter wheat, and winter barley, and relies on
 the use of N-P-K fertilizers and pesticides (Schädler et al.,
 2019). <strong>Future climate
 treatment</strong> Half of the plots are exposed
 to a future climate scenario, whereas the other half is exposed to ambient
 climate conditions. The future climate scenario was designed based on
 regional climate models for 2070–2100: It features a general temperature
 increase of 0.55°C, a 10% increase in precipitation during spring and
 fall, and a 20% decrease in precipitation during summer (Schädler et al.,
 2019). The climate manipulation was achieved by steel roof structures
 above each main plot, entailing tarpaulins which are closed from sunset to
 sunrise to achieve passive nighttime warming (Suppl. Figure S1).
 Additionally, the tarpaulins also reduce rainfall in summer, while an
 irrigation system is used to increase precipitation. Control plots had
 similar roof constructions to account for potential side effects (Kreyling
 et al., 2017). <strong>Soil
 sampling</strong> Soil samples were collected on
 October 10, 2024, using a steel core sampler with a diameter of 1.5 cm and
 a depth of 15 cm. To account for potential heterogeneity, five subsamples
 were taken per plot, pooled, and sieved through a 2 mm mesh. The resulting
 soil samples were then used to measure soil microbial respiration at four
 different soil temperatures, with two separate measurements conducted
 (i.e., technical replicates) to ensure reliable results. For the first
 measurement, the samples were stored at 4°C. For the second measurement,
 the samples were frozen at -20°C to preserve them for later analysis.
 Prior to soil microbial respiration and community analysis, unfrozen
 samples were acclimated at 20°C for three days, while frozen samples were
 acclimated for seven days at 20°C to ensure complete defrosting.
 <strong>Soil microbial analyses and heat
 treatment</strong> Soil microbial respiration was
 measured using an O<sub>2</sub>-micro-compensation system
 (Scheu, 1992). For each soil sample, four subsamples (approx. 7g) were
 collected. Each subsample was subjected to one out of the four temperature
 conditions—20°C, 25°C, 30°C, or 35°C—for a duration of 20 h, while
 measuring soil microbial respiration as the oxygen consumption per hour
 per dry weight of soil in µl O<sub>2</sub> per gram dry weight
 per hour for a 24-hour interval (Suppl. Table S1). To assess total soil
 microbial biomass (μg C<sub>mic</sub>
 g<sup>−1</sup> soil dry weight), we measured the maximum
 respiratory response by adding glucose (4 mg g<sup>−1</sup>
 dry weight soil, dissolved in 1.25 ml distilled water) to soil samples
 measured at 20°C (following Scheu, 1992). This approach provides a robust
 proxy for the size of the active microbial biomass. We did not correct
 samples for soil moisture content across treatments, as soils were
 supposed to reflect natural field conditions under different long-term
 treatments. We acknowledge that differences in initial moisture content
 may influence microbial respiration, but these were considered an integral
 part of the treatment effect and not experimentally
 standardized. We used phospholipid fatty acid (PLFA)
 analysis to assess microbial community structure, as it provides
 quantitative measurements of living microbial biomass and major functional
 groups (e.g., Gram-positive/Gram-negative bacteria, fungi). This method
 captures functionally relevant shifts that are closely linked to microbial
 respiration, offering a more direct connection to ecosystem processes such
 as respiration than DNA-based approaches, which may include relic DNA and
 inactive community members (Carini et al., 2016). We followed the
 methodology described by Frostegård et al. (1991), using 5 g of fresh soil
 per sample. Fatty acid methyl esters were analysed on a gas chromatograph
 (as described in Cesarz et al., 2023). We used FA 19:0 as an internal
 standard to quantify bacterial and fungal PLFAs and neutral lipid fatty
 acids (NLFAs) in ng fatty acid methyl ester (FAME) g⁻¹ soil and to assign
 them to microbial groups. Bacteria were grouped in Gram-positive bacteria
 (PLFAs a15:0, i15:0, i16:0, and i17:0), Gram-negative (cy17:0, cy19:0) and
 widespread bacteria (16:1ω7), while fungi were grouped in arbuscular
 mycorrhizal fungi (NLFA 16:1ω5), and saprotrophic and ectomycorrhizal
 fungi (PLFA 18:2ω6,9, Ruess & Chamberlain, 2010). Finally, the
 fungal-to-bacterial ratio was calculated by dividing the sum of all
 fungi-specific PLFAs and NLFAs by the sum of all bacteria-specific
 PLFAs. <strong>Statistical
 analyses</strong> All data handling and
 statistical analyses were performed using the R software (version 4.4.2.).
 R scripts used for this project can be found in Supplementary Material
 S1-S4. All of the following linear mixed-effect models were tested using
 the lmer function from the ‘lme4’ package (Bates et al., 2015), and
 statistical hypotheses (i.e., residuals normality, homoscedasticity) of
 the following linear models were tested using the model_check function
 from the ‘performance’ package (Lüdecke et al., 2020).
 <em>Land use and climate effects on soil
 respiration</em> We used linear mixed models and
 normal distribution assumptions to test the effects of land use (four
 levels) and climate (two levels) treatments on soil microbial respiration
 at 20°C. In addition, the experimental sampling plot nested within the
 main plot (Schädler et al., 2019) and the respiration measurement time
 (unfrozen <em>v.s</em>. frozen samples) were set as random
 factors. Soil microbial respiration was log-transformed to fulfill
 statistical assumptions. Our experimental data were completely orthogonal
 and free of missing values. The significance of the explanatory variables
 was tested using an ANOVA type I. In addition, a post hoc test was
 performed to test the differences between land-use levels using the glht
 function from the ‘multcomp’ package (Hothorn et al., 2002).
 <em>Land use and climate effects on soil respiration
 response to heat treatment</em> Similarly, we
 used linear mixed models and normal distribution assumptions to test the
 effects of heat treatment (as a linear temperature variable), land use,
 climate, and their interactions on soil microbial respiration. The same
 random terms (sampling plot nested within the main plot and the
 respiration measurement time) were applied and the soil microbial
 respiration was log-transformed to fulfill statistical assumptions. The
 significance of the explanatory variables was tested using an ANOVA type
 I. <em>Land use and climate effects on soil
 microbial biomass and community composition</em>
 We used linear mixed models and normal distribution assumptions
 to test the effects of land use, climate, and their interaction on soil
 microbial community composition; namely microbial biomass (based on
 substrate-induced respiration), fungal biomass, bacterial biomass, and
 fungal-to-bacterial ratio. Fungal biomass, bacterial biomass, and
 fungal-to-bacterial ratio were log-transformed to fulfill statistical
 assumptions. The sampling main plot was set as a random factor. The
 significance of the explanatory variables was tested using an ANOVA type
 I. In addition, a post hoc test was performed to test the differences
 between land-use levels using the glht function from the ‘multcomp’
 package.
description: # Sustainable land use enhances soil microbial respiration responses to
 experimental heat stress Dataset DOI: 10.5061/dryad.f4qrfj76n ##
 Description of the data and file structure ### File: data-for-analyses.csv
 **Description**: Soil microbial respiration dataset used in the analyses
 **Variables**: * mainplot: GCEF experimental block [no unit] * plot: GCEF
 plot (within the "mainplot") [no unit] * climate: climate
 treatment (Ambient vs. Future) [no unit] * landuse_type: Land use type in
 the plot (Grassland vs. Cropland) [no unit] * intensity: Land use
 intensity (Low vs High) for a given type [no unit] * landuse: Land use on
 the plot (Extensive grassland "EM" vs. Intensive grassland
 "IG" vs. Organic cropland "OF" vs. Conventional
 cropland "CF") [no unit] * climate_landuse: Combination of land
 use and climate treatment [no unit] * Device: measurement device in the
 lab [no unit] * rep: lab measurement replication (1 or 2) [no unit] *
 temperature: temperature treatment in the lab [20°C, 25°C, 30°C, or 35°C]
 * resp: soil microbial respiration [µl O2 per gram dry weight per hour] *
 qo2: soil microbial respiratory quotient [µl O2 per μg Cmic] ## File:
 GCEF\_PLFA.xlsx **Description**: Soil microbial community composition
 measurement using PLFA analyses **Variables**: * plot: GCEF plot (within
 the "mainplot") [no unit] * mainplot: GCEF experimental block
 [no unit] * all_bacteria: soil bacterial biomass [ng fatty acid methyl
 ester (FAME) g⁻¹ soil] * all_fungi: soil fungal biomass [ng fatty acid
 methyl ester (FAME) g⁻¹ soil] * f_b: fungi-to-bacterial ratio [no unit] ##
 File: Microbial\_respiration.xlsx **Description**: Soil microbial carbon
 biomass (Cmic) measured using the maximum respiratory response glucose
 addition. **Variables**: * Device: measurement machine [no unit] *
 mainplot: GCEF experimental block [no unit] * plot: GCEF plot (within the
 "mainplot") [no unit] * Cmic: soil microbial biomass [μg Cmic
 g−1 soil dry weight] # Code/software **R software version** --- R version
 4.4.2 (2024-10-31) Platform: aarch64-apple-darwin20 Running under: macOS
 Sequoia 15.4.1 --- Hypothesis-1&2.R: code testing the hypotheses and
 generating the figure 2 (input: data-for-analyses.csv &
 Microbial_respitation.xlsx) Hypothesis-3.R: code testing the hypotheses
 and generating the figure 3 (input: data-for-analyses.csv &
 Microbial_respitation.xlsx) Hypothesis-4.R: code testing the hypotheses
 and generating the figure 4 (input: data-for-analyses.csv, GCEF_PLFA.xlsx
 & Microbial_respitation.xlsx)