DOI: 10.5061/dryad.wm37pvmnn
title: Tree diversity effects on soil microbial biomass and respiration are context-dependent across forest diversity experiments

description: Aim Soil microorganisms are essential for the functioning of terrestrial
 ecosystems. Although soil microbial communities and functions may be
 linked to tree species composition and diversity, there has been no
 comprehensive study of how general these potential relationships are, or
 if they are context-dependent. Here, we examine tree diversity–soil
 microbial biomass and respiration relationships across environmental
 gradients using a global network of tree diversity experiments. Location
 Global Time Period 2013 Major Taxa Studied Soil microorganisms Methods
 Soil samples collected from eleven tree diversity experiments in four
 biomes were used to measure microbial respiration, biomass, and
 respiratory quotient using the substrate-induced respiration method. All
 samples were measured using the same analytical device, method, and
 procedure to reduce measurement bias. We used linear mixed-effects models
 and PCA to examine the effects of tree diversity (taxonomic and
 phylogenetic), environmental conditions, and interactions on soil
 microbial properties. Results Abiotic drivers, mainly soil water content,
 but also soil carbon and soil pH, significantly increased soil microbial
 biomass and respiration. Optimal soil water content reduced the importance
 of other abiotic drivers. Tree diversity alone had no effect on the soil
 microbial properties, but interactions with phylogenetic diversity
 indicated that diversity effects are context-dependent and stronger in
 drier soils. Similar results were found for soil carbon and soil pH. Main
 conclusions Our results point to the importance of abiotic variables and
 especially soil water content for maintaining high levels of soil
 microbial functions and modulating the effects of other environmental
 drivers. Planting tree species with diverse water-use strategies and
 structurally complex canopies and high leaf area may crucial for
 maintaining high soil microbial biomass and respiration. Since higher
 phylogenetic distance alleviated unfavorable soil water conditions,
 reforestation efforts accounting for traits improving soil water content
 or choosing more phylogenetically distant species may assist in increasing
 soil microbial functions.
description: Soil
 samples were taken in 2013 from eleven tree diversity experiments that are
 part of the global network TreeDivNet (Verheyen et al., 2016;
 http://www.treedivnet.ugent.be/). Experiments are independent of each
 other with different experimental designs and plot configurations.
 Experiments are distributed across four continents (Asia, Europe, North,
 and South America) and four different biomes (boreal, temperate, tropical,
 subtropical, Olson et al., 2001), and differ in age, with the youngest
 experiments running for three years and the oldest for fourteen years as
 of 2013 (i.e., the year of the sampling campaign). In total, 106 tree
 species were included in this study. Experiments had a mean ± SD number of
 diversity levels of 3.7 <b>± </b>1.0, with diversity levels
 ranging from monocultures to 18 tree species in sub/tropical regions. All
 experiments had an experimental gradient in tree species richness, with
 the exception of one (BIOTREE-FD) which manipulated functional diversity
 at a constant level of tree species richness. <b>Soil
 sampling</b> Soil samples were
 taken from a depth of 0 – 10 cm, excluding the litter layer using a soil
 corer. Depending on the size of the experimental plot, different numbers
 of subsamples were taken per plot to create one composite sample. For
 plots &lt;100 m², three subsamples were taken per plot, while ten
 subsamples were taken for plots &gt;100 m2. These subsamples were
 taken to capture the spatial heterogeneity of the plot and to represent as
 many different combinations of tree species as possible. Soil samples were
 always taken in the center of surrounding trees. Immediately after
 sampling, soil samples were stored at 5°C until sieving at 2 mm and then
 were stored at -20°C until and during shipping to minimize changes in
 microbial activity, biomass, and composition. Alternatively, if shipping
 at -20°C was not possible, samples defrosted during shipping and were
 measured shortly after arrival. Altogether, 1010 plots were sampled across
 the eleven tree diversity experiments. <b>Measurement of soil
 microbial properties</b> Before the start
 of microbial measurements, samples were kept at +20°C for five days to
 unfreeze and to adapt the soil microbial community to a constant and
 standardized temperature. Three different soil microbial community
 properties were assessed using an automated O<sub>2</sub>
 micro-compensation system (Scheu, 1992). First, basal respiration (µl
 O<sub>2</sub> h<sup>-1</sup>
 g<sup>-1</sup> dry soil) was measured as the mean oxygen
 consumption per hour without the addition of any substrate. The mean
 oxygen consumption was measured for hours 15 to 20. Basal respiration
 reflects the active part of the soil microbial community at the time of
 sampling. Second, microbial biomass carbon was measured by
 substrate-induced respiration, i.e., the respiratory response of
 microorganisms to glucose and water addition. To saturate catabolic
 microbial enzymes, 8 mg glucose g<sup>-1</sup> soil dry weight
 was added as an aqueous solution to the soil samples. The lowest
 substrate-induced respiration of three contiguous hours within the first
 10 h was taken as the maximum initial respiratory response (MIRR) – a
 period when microbial growth has not started. Microbial biomass (µg C
 g<sup>-1</sup> dry soil) was calculated as 38 × MIRR (µl
 O<sub>2</sub> h<sup>-1</sup>
 g<sup>-1</sup> dry soil) following Beck et al., (1997). By
 providing water and glucose, the maximum potential of the living microbial
 biomass is activated that is able to use glucose, whereas for basal
 respiration only a fraction of the entire community is active. Third, the
 microbial-specific respiratory quotient (µl O<sub>2</sub>
 mg<sup>-1</sup> Cmic h<sup>-1</sup>) was
 calculated as the ratio of basal respiration and soil microbial biomass.
 The specific respiratory quotient is a measure of soil microbial
 carbon-use efficiency. Carbon-use efficiency is high when microbial
 biomass can be built up without high investment in basal respiration,
 which is indicated by a lower specific respiratory quotient. All
 measurements were conducted at +20°C in an air-conditioned laboratory
 using the same analytical devices (RMS Schuller, Darmstadt,
 Germany). <strong>Diversity
 metrics</strong> In addition to tree species
 richness, we aimed at testing a tree diversity metric that captures the
 functional diversity of each experimental forest plot. However, no
 comparable trait measurements were available from all experiments. We
 chose not to use data from trait databases such as TRY because some
 tropical tree species were absent from these databases and because traits
 demonstrate substantial plasticity to their abiotic and biotic environment
 (i.e., intraspecific trait variation). Instead, we used phylogenetic
 diversity as a proxy for multi-trait functional diversity (Tucker et al.,
 2018). Phylogenetic diversity indices have been shown to be powerful
 predictors of biodiversity-ecosystem functioning relationships (e.g.,
 Craven et al., 2018) and are suggested to work when key functional traits
 are not available (Paquette et al., 2015). We used the molecular phylogeny
 from previous studies (Pietsch et al., 2014; Zanne et al., 2013) as a
 backbone to build a phylogeny of all species within the tree diversity
 experiments, conservatively binding species into the backbone using dating
 information from congeners in the tree. We used the comparative.comm
 function in the R package pez to calculate a set of phylogenetic diversity
 indices, specifically MPD (mean phylogenetic diversity), MNTD (mean
 nearest taxonomic distance), and the standardized version of both to
 account for correlation with species richness (Pearse et al., 2015). MNTD
 was found to correlate less (using Pearson correlation) with log species
 richness and, therefore, was used in all following analyses. Taxonomic
 names of tree species were standardized using the website
 http://tnrs.iplantcollaborative.org/index.html. <b>Soil
 characteristics</b> We included a set
 of explanatory variables to describe the experimental sites, which were
 shown to have an effect on soil microbial properties and reflect the
 designs and local conditions of the different experiments (Fig. S1).
 Gravimetric soil water content was measured as % H2O from fresh soil
 weight by drying the whole sample at 75°C for three days. Soil pH and soil
 C (%) were measured at the block level to obtain information about soil
 quality characteristics of each experiment. Therefore, equal proportions
 of dry soil were weighed from each sample to form a composite sample. The
 whole sample was ground, and a fraction of 10 g was used for pH
 measurements by adding 0.01 m CaCl2. Soil C concentrations were analyzed
 by using the ground soil with an elemental analyzer (Vario EL Cube,
 Elementar). We further extracted clay (%), sand (%), and silt (%) content
 from the SoilGRIDS database (Hengl et al., 2014). <b>Environmental conditions</b> For each experimental site, we extracted mean annual temperature (MAT), the seasonal variability of temperature (Season Temp.), annual precipitation (MAP), and the seasonal variability of precipitation (Season Prec.) from the WorldClim database (http://www.worldclim.org/current) with 2.5 arc-minutes resolution. Potential evapotranspiration (PET) and the aridity index (MAP/PET) were extracted from CGIAR-CSI (https://srtm.csi.cgiar.org/). In addition, we obtained the age of the experiment (years) and tree density (trees m<sup>-</sup>²) from publications associated with each experiment and the TreeDivNet website (http://www.treedivnet.ugent.be/). Biomes were assigned based on Olson et al., (2001).