Spatial plant resource acquisition traits explain plant community effects on soil microbial properties

Accepted Manuscript Title: Spatial plant resource acquisition traits explain plant community effects on soil microbial properties Authors: Katja Steinauer, Fel´ıcia M. Fischer, Christiane Roscher, Stefan Scheu, Nico Eisenhauer PII: DOI: Reference:

S0031-4056(17)30048-3 http://dx.doi.org/doi:10.1016/j.pedobi.2017.07.005 PEDOBI 50508

To appear in: Received date: Revised date: Accepted date:

27-2-2017 24-7-2017 24-7-2017

Please cite this article as: Steinauer, Katja, Fischer, Fel´ıcia M., Roscher, Christiane, Scheu, Stefan, Eisenhauer, Nico, Spatial plant resource acquisition traits explain plant community effects on soil microbial properties.Pedobiologia - International Journal of Soil Biology http://dx.doi.org/10.1016/j.pedobi.2017.07.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Steinauer et al.

Spatial plant resource acquisition traits explain plant community effects on soil microbial properties Katja Steinauer

1,2*,

Felícia M. Fischer ³, Christiane Roscher

4,1,

Stefan Scheu 5, Nico

Eisenhauer 1,2 1

German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz

5e, 04103 Leipzig, Germany 2

Institute of Biology, Leipzig University, Johannisallee 21, 04103 Leipzig, Germany

³ Department of Ecology, Universidade Federal do Rio Grande do Sul -UFRGS, Porto Alegre, RS, Brazil 4

UFZ, Helmholtz Centre for Environmental Research, Physiological Diversity, Permoserstrasse

15, 04318 Leipzig, Germany 5

J. F. Blumenbach Institute of Zoology and Anthropology, Georg August University Göttingen,

Berliner Straße 28, 37073 Göttingen, Germany. * Corresponding author Phone: +49-341-973 33173; Fax: ++ 49 341 9739350; E-mail: [email protected] Running title: Plant trait effects on soil microbes Summary Trait-based approaches have recently been employed to develop a more mechanistic understanding of plant community effects on the assembly and functioning of terrestrial ecosystems. Despite the broad consensus that soils provide essential ecosystem services, plant community effects on soil communities and functions have rarely been linked to aboveground and belowground plant traits. Here, we studied the effects of plant species richness, plant trait diversity, and single plant 1

Steinauer et al. functional traits related to spatial and temporal resource acquisition on soil microbial properties over five years in a grassland biodiversity experiment. The main response variables were soil basal respiration and microbial biomass. Above- and belowground plant traits associated with spatial (plant height, leaf area, rooting depth, and root length density) and temporal resource acquisition (growth start, flowering start) were selected to design communities with different levels of functional diversity as well as to calculate realized community means weighted by plant species cover. Plant species richness and trait diversity effects on soil microbial properties were nonsignificant over the course of the five-year experiment. After four years, however, we found significantly higher soil basal respiration in plant communities with smaller leaves and both denser and shallower root systems than in plant communities with taller plants and sparse root systems. One year later, these effects were significant for both soil basal respiration and soil microbial biomass. Structural equation modeling revealed that plant community effects on soil microbial properties were mostly due to differences in rooting depth, although the explanatory power of our models was low. Our findings highlight the importance of incorporating plant traits, particularly root traits, in analyses of plant community effects on soil biota and functions. Selecting for particular plant traits in communities and considering interactive effects of specific plant traits may facilitate the targeted management of grasslands to maintain essential ecosystem services. Highlights:   

Soil microbial properties were higher in plant communities with taller plants. Plant communities with dense root systems showed higher soil microbial properties. Differences in rooting depth explained plant community effects on soil microbes.

Keywords: aboveground-belowground interactions; basal respiration; biodiversity–ecosystem functioning; Jena Experiment; soil microbial biomass Introduction

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Steinauer et al. Global change processes such as variations in climate, land use, and nitrogen input are among the most important drivers altering plant species richness and the composition of plant communities (Sala et al., 2000). These alterations in community composition and diversity are also an important cause of changes in grassland ecosystem functions and services themselves (Cardinale et al., 2011; Hooper et al., 2012). Aside from effects on aboveground ecosystem functions (Li et al., 2014; Tilman et al., 2001), changes in plant diversity also impact soil microbial communities and belowground processes that they mediate (Eisenhauer et al., 2010; Hooper et al., 2000; Tilman et al., 2006; Zak et al., 2003). Previous studies have shown that the biomass and activity of soil microorganisms increases with increasing plant diversity (Eisenhauer et al., 2010; Steinauer et al., 2015). Species-rich plant communities show greater net primary productivity, soil carbon inputs via exudation and turnover of plant biomass, and soil microbial biomass (Zak et al., 2003). However, the mechanisms underlying plant diversity effects are still not fully understood. The study of plant functional traits has emerged as a powerful approach to identify the mechanisms by which plants affect ecosystem properties (De Deyn et al., 2008; Diaz and Cabido, 2001; Kardol et al., 2015; Lavorel and Garnier, 2002). An increasing number of studies indicates that plant traits reflecting resource acquisition and plant size are significant predictors of ecosystem functions (Laliberte and Tylianakis, 2012; Lavorel and Grigulis, 2012). Although, it is widely accepted that soils provide essential ecosystem services (Diaz et al., 2016; Laliberte and Tylianakis, 2012; Wall and Six, 2015), plant community effects on soil microbial properties have rarely been linked to above- and belowground plant traits. Notable exceptions are some studies reporting links between aboveground plant traits and litter decomposition and net nitrogen mineralization (Legay et al., 2016; Scherer-Lorenzen, 2008; Tilman and Wedin, 1991). On the level of individual plant species, plants with a high relative growth rate, high specific leaf area, high leaf N concentration, and low

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Steinauer et al. leaf dry matter content (i.e., plants with more acquisitive strategy) have been reported to favor bacterial-dominated soil microbial communities (Orwin et al., 2010). When studying entire plant communities, ecosystem processes should be largely determined by the functional traits of particular species dominating the biomass of the community (communityweighted means), as described by the biomass ratio hypothesis (Grime, 1998). Following this approach, variations in total soil C and N and decomposition rates in grasslands could be explained by community-weighted means of leaf N concentrations, relative growth rate, and leaf dry matter content (Garnier et al., 2004). At the landscape scale, variations in soil microbial communities could be explained by community-weighted means of aboveground plant traits like specific leaf area and leaf N concentration, along with abiotic factors (de Vries et al., 2012). While relationships between aboveground plant traits and soil microbial processes have been documented (de Vries et al., 2012; Garnier et al., 2004), few studies have comprehensively examined whether root traits are related to belowground functional processes (Bardgett et al., 2014). Birouste et al. (2012) found root decomposition rates of Mediterranean herbaceous species to be closely related to the chemical composition of the roots, and Makita et al. (2012) reported a significant relationship between tree root tissue density as well as specific root length and coarse root respiration. However, there are still knowledge gaps of how specific above- and belowground plant traits influence both soil microbial communities and their specific functions under field conditions. In particular, belowground plant traits may have strong impacts on soil microbial communities via rhizodeposition (Bardgett et al., 2014). Here, we aimed to bridge this gap by studying the effects of above- and belowground plant traits on soil basal respiration and biomass over five years within the framework of the Jena Experiment (Roscher et al., 2004). The “Trait-Based Biodiversity Experiment” varies plant species composition using different levels of species richness. Furthermore, three different species pools of eight plant 4

Steinauer et al. species were defined using their relative position along functional axes of trait dissimilarity in above- and belowground spatial and temporal resource acquisition (Ebeling et al., 2014). We expected (1) higher plant species richness and functional diversity to increase soil basal respiration and microbial biomass, particularly so after an establishment phase of about four years (Eisenhauer et al., 2010). Furthermore, we expected those effects to be most pronounced in the species pool spanning a gradient in spatial resource acquisition. Additionally, we hypothesized (2) soil basal respiration and microbial biomass would be higher in plant communities dominated by smaller plants with dense root systems than those dominated by plants with sparse roots due to differences in rhizodeposition. Since previous studies reported the strength of plant diversity effects on soil microbial properties to increase over time (Eisenhauer et al., 2010; Strecker et al., 2015), we assumed that effects of plant traits on soil microbial properties would also get stronger with time. Material and Methods Experimental design In 2010, the Trait-Based Biodiversity Experiment (TBE) (Ebeling et al., 2014) was established within the framework of a long-term grassland biodiversity experiment (The Jena Experiment, Thuringia, Germany; Roscher et al. 2004). For about 8 years, the site of the TBE was an unfertilized managed grassland, mown twice a year. On 138 experimental plots (3.5 x 3.5 m) plant species richness (1, 2, 3, 4, and 8 species) and plant functional diversity (1, 2, 3, and 4) were manipulated using different levels of complementarity in resource acquisition of 20 different grass and nonlegume forb species. The resulting plant species richness and plant functional diversity combinations and replicates are shown in detail in supplementary material (Table S1). To form the functional diversity gradient (FD), above- and belowground plant traits reflecting spatial [maximum plant height (MH), leaf area (LA), rooting depth (RD) and root length density (RLD)] and temporal resource acquisition [growth start (GS), flowering start (FS)] were selected, 5

Steinauer et al. representing the levels from low (1) to high (4) trait complementarity in the plant community. In order to define the trait-based species mixtures, all six traits were analyzed by a standardized principal components analysis (PCA) (see Supplementary Figure S1 for an visual representation of how pools and sectors were defined and Figure 1 in Ebeling et al., 2014, for information as to how individual species fit into these pools). PCA axis 1 spans species according to their resource acquisition along a spatial gradient. The second PCA axis positions species according to differences in temporal resource use. Based on this information, three different species pools of eight plant species were defined: species pool 1 covers the entire PCA axis 1 (choosing species with intermediate positions on PCA axis 2), and pool 2 covers PCA axis 2 (choosing species with intermediate positions on PCA axis 1). Pool 3 covers species at the outer edges of the ordination space representing PCA axes 1 and 2 combining the extremes in both spatial and temporal resource acquisition (Ebeling et al., 2014). Furthermore, species pools were divided into 4 sectors. The range and distance of sectors covered by a plant community defined its FD (1, 2, 3, and 4). Plots were arranged in three blocks to account for differences in soil conditions at the field site. To maintain the diversity gradient, all non-target species were removed by manual weeding three times per year. The experimental communities were mown twice per year and did not receive any fertilizer as is common for extensively managed hay meadows in the study region. Soil sampling Soil samples were taken in October 2011, September 2013, August 2014 and August 2015 for soil microbial measurements. Of noteworthy importance, in June 2013 a natural flood event occurred on the experimental field site. From each plot, five soil samples were taken to a depth of 10 cm (2011, 2013, and 2015) or 15 cm (2014) using a metal corer (diameter 2 cm) and pooled per plot in a plastic bag and stored in a cooler for transport. The samples were sieved (2 mm) to remove stones, roots, and invertebrates >2 mm, and then stored at -20°C until soil microbial properties 6

Steinauer et al. were measured. Prior to the measurements of soil microbial properties, soil samples were thawed at room temperature for one week for soil microorganisms to adapt to analysis temperature. Soil microbial measurements Approximately 4.5 g soil (fresh weight) was used to determine soil microbial respiration and soil microbial biomass. Using an automated respirometer based on electrolytic O2 microcompensation (Scheu, 1992), microbial respiration (µl O2 h-1 g-1 soil dry mass) was measured continuously as the mean of the O2 consumption rates between 14 to 24 h after the start of the measurements. Soil microbial biomass was calculated from the maximum initial respiratory response (MIRR) after addition of D-glucose-monohydrate using the substrate-induced respiration method (SIR) (Anderson and Domsch, 1978; Beck et al., 1997). Catabolic enzymes of soil microorganisms were saturated by adding 40 mg glucose g-1 soil dry mass as an aqueous solution. Plant community measurements Cover (%) of all plant species sown into a particular mixture was estimated using a decimal scale (modified after Londo 1976) on the whole plot area (3.5 x 3.5 m size) for all years (end of July 2011, mid-September 2013, mid-August 2014 and 2015). Statistical analysis Plant diversity effects on soil microbial properties To test our first hypothesis of whether higher plant species richness and functional diversity increase soil basal respiration and microbial biomass, we used linear mixed-effects models. Here, we tested the effects of plant species richness (SR, log-transformed) and plant functional diversity (FD), species pool (1, 2, and 3; as a factor), year (2011, 2013, 2014, and 2015; as a factor), and all possible interactions on both response variables, soil basal respiration and microbial biomass (logtransformed). Block and plot nested in block were used as random effects to account for repeated measurements on the same plots and the block design of the experiment. 7

Steinauer et al. Resource acquisition plant trait effects on soil microbial properties To test our second hypothesis, whether soil basal respiration and microbial biomass are higher in plant communities dominated by smaller plants with dense root systems than those dominated by plants with sparse roots, we used the original species scores on the PCA ordination to calculate the community mean scores (CMS) for the first two ordination axes (PCA1 and PCA2; see Fischer et al. 2016). This way, we tested the effects of specific aboveground and belowground plant trait combinations on soil basal respiration and microbial biomass. We fitted linear mixed-effects models using CMS_PCA1, CMS_PCA2 and year and all possible interactions for all response variables. Block and plot nested in block were used as random effects. Community-weighted plant trait effects on soil microbial properties In the next step, we investigated effects of each trait considered in designing the TBE on all response variables using community-weighted means (CWM) of trait values based on their relative species abundances (species-specific cover) calculated for each plant community (Garnier et al., 2004; Roscher et al., 2012). The calculations were done using the R package FD (Laliberté et al., 2011; Laliberté and Legendre, 2010). Afterwards, we performed linear mixed-effects models testing the effects of CWM of each plant trait (MH, LA, RD, RLD, GS, FS) and year and the interaction between plant traits and year on both response variables. Block and plot nested in block were used as random effects. This analysis was done to identify potentially important communityweighted means (CWM) of trait values for subsequent structural equation models. P- and F-values and degrees of freedom were estimated with Type III Satterthwaite approximation for all linear mixed-effects models. Linear mixed effects models were performed using lme4 (Bates et al., 2015) and lmerTest (Kuznetsova et al., 2016) within the R statistical environment (Version 3.02, http:// www.r-project.org). Structural equation models 8

Steinauer et al. As we found significant plant trait effects on soil microbial properties in 2014 and 2015, we explored the indirect and direct effects of particular plant traits on the soil microbial communities via their interactions with each other using structural equation models (SEM). Here, we hypothesized indirect and direct relationships between above- and belowground plant traits LA, RD, and RLD in 2014 as well as LA and RD in 2015 (Fig. 3a, b), and soil basal respiration and microbial biomass. In the initial models, we used all variables that had significant effects in the linear mixed-effects models, but models could be improved by removing non-significant variables and paths. The adequacy of models was based on chi-square tests and Akaike information criteria (AIC). Path model analysis was carried out in the lavaan package (Rosseel, 2012) built for R statistical software. Results Plant diversity effects on soil microbial properties We found no significant main or interactive effects of plant diversity and functional diversity (FD) on soil basal respiration and soil microbial biomass (Table S2, S3). Additionally, species pool and year showed no significant effects on soil microbial properties. Resource acquisition plant trait effects on soil microbial properties Overall, community mean scores on PCA axis 1 of spatial resource acquisition strategies (CMS_PCA1) affected soil basal respiration and soil microbial biomass in later years of the experiment but not in early years (Table 1, Fig. 1), whereas community mean scores on PCA axis 2 of temporal resource acquisition strategies (CMS_PCA2) showed no significant effect on any response variable (Table 1). Plant communities with higher values of CMS_PCA1 represent tallerstatured species with larger leaves and deeper, sparser roots, whereas lower values contain plant communities with smaller-statured species with smaller leaves and denser, shallower root systems. Four years after the establishment of the experiment (in 2014), we found a significant decrease in 9

Steinauer et al. soil basal respiration with increasing CMS_PCA1 (F1,

135

= 4.10; p = 0.045; Fig. 1c). This

relationship was even more pronounced in 2015 (F1, 134 = 7.16; p = 0.008; Fig. 1d). In 2015, soil microbial biomass also was significantly higher in plant communities with smaller leaves and denser shallower root systems compared to taller-statured plant communities (F1, 134 = 8.62; p = 0.004; Fig. 1h). Community-weighted plant trait effects on soil microbial properties We found spatial resource acquisition traits like community-weighted means of rooting depth and leaf area were significantly related with soil microbial biomass (Table 2), whereas communityweighted means of root length density and maximum height showed no clear relationships with soil microbial properties over the course of the experiment (Table 2). However, soil microbial biomass increased with increasing CWM of maximum plant height in 2011 and was constant in the following years (data not shown), resulting in a significant interaction effect (Table 2). Moreover, community-weighted means in temporal resource acquisition traits like growth start and flowering start were not significantly related with soil microbial properties within the duration of the study. Generally, CWM of rooting depth and CWM of root length density were significantly negatively correlated (Fig. S2). CWM of leaf area and CWM of root length density were significantly positively correlated (Fig. S3), whereas CWM of leaf area and CWM of root length density were negatively correlated (Fig. S4). Year-specific responses to variations in these variables provided more detailed insights into driving factors of microbial properties. In 2014, soil basal respiration (F1, 135 = 3.56; p = 0.061; Fig. 2c) and soil microbial biomass increased with increasing CWM of rooting depth (F1, 133 = 2.75; p = 0.099; Fig. 2g). One year later, this relationship was significant for both soil basal respiration (F1, 134 = 4.35; p = 0.039; Fig. 2d) and soil microbial biomass (F1, 134 = 5.83; p = 0.017; Fig. 2h). Additionally, soil basal respiration increased significantly with increasing CWM of root length density in 2014 (F1, 134 = 4.32; p = 0.039; Fig. 2k) but not in 2015 10

Steinauer et al. (Fig. 2l). Soil microbial biomass was not significantly affected by root length density over the course of the experiment. Moreover, soil basal respiration tended to decrease with increasing CWM of leaf area in 2014 (F1, 134 = 3.01; p = 0.085; Fig. 2s), and decreased significantly in 2015 (F1, 133 = 5.13; p = 0.025; Fig. 2t). Further, soil microbial biomass tended to decrease with increasing CWM of leaf area in 2015 (F1, 134 = 3.54; p = 0.062; Fig. 2x). Structural equation models Based on plant trait effects on soil microbial properties, two conceptual models were specified for SEM (Fig. 3a, b). The final models for both years were identified after reducing the conceptual models to improve the fit of each model. The models in 2014 were improved by removing CWM of root length density and corresponding correlations with CWM of leaf area, rooting depth, and soil microbial properties. In addition, in both years the models were improved by removing the direct association between CWM of leaf area and soil microbial properties. In 2014, the final SEM (Fig. 3c; 2 = 0.43, df = 1, p = 0.510, AIC = 532.23) showed that CWM of leaf area was strongly positively correlated with CWM of rooting depth (p-value < 0.001), and CWM of rooting depth was negatively associated with basal respiration (p-value = 0.047). Furthermore, there was a weak negative relationship between CWM of leaf area and basal respiration (p-value = 0.797). Similarly, in 2015, the final SEM for basal respiration showed that (Fig. 3d; 2 = 2.17, df = 1, p = 0.140, AIC 529.83) CWM of leaf area was positively associated with CWM of rooting depth (p-value < 0.001), and CWM of rooting depth was negatively related to basal respiration (p-value = 0.091). Moreover, the negative relationship between CWM of leaf area and basal respiration was not significant (p-value = 0.206). In 2014, the final SEM (Fig. 3e; 2 = 0.22, df = 1, p = 0.640, AIC = 533.76) reported that CWM of leaf area was positively correlated with CWM of rooting depth (p-value < 0.001), and CWM of rooting depth tended to be negatively associated with basal respiration (p-value = 0.123). 11

Steinauer et al. Furthermore, there was a weak positive relationship between CWM of leaf area and soil microbial biomass (p-value = 0.487). In 2015, the final SEM (Fig. 3f; 2 = 0.84, df = 1, p = 0.360, AIC 528.12) displayed that CWM of leaf area was positively associated with CWM of rooting depth (pvalue < 0.001), and CWM of rooting depth was negatively associated with soil microbial biomass (p-value = 0.033). Further, the negative relationship between CWM of leaf area and basal respiration was not significant (p-value = 0.375). Discussion In this study, we found that plant community effects on soil microbial properties were mediated by root traits four to five years after the set-up of the Trait-Based Biodiversity Experiment. In contrast to our first hypothesis, we could not find any significant effects of plant species richness, functional diversity (FD), and species pool on soil microbial properties. Notably, a former study in the adjacent main Jena Experiment (Roscher et al., 2004) also found weak plant species richness effects on soil microbial biomass within the range of plant monocultures to 8-species mixtures within the first five years after establishment. However, within the subsequent 5 years, significant plant diversity effects on soil microbial biomass were reported (Eisenhauer et al., 2010). This indicates that time plays a crucial role in the establishment of plant species richness and functional diversity effects on soil microbial properties (Eisenhauer et al., 2010; Thakur et al., 2015), and several years are required to allow for significant plant diversity effects on soil microbial properties due to the slow accumulation of plant-derived resources in the soil (Eisenhauer and Reich, 2012; Kuzyakov and Xu, 2013). Supporting this conclusion, four years after the establishment of the experiment, we found higher soil basal respiration in plant communities with smaller-statured plants and a denser root system than in plant communities with taller plants and sparser root systems. One year later, this was true for both soil basal respiration and soil microbial biomass, confirming our second hypothesis. SEM 12

Steinauer et al. revealed, however, that CWM of rooting depth was especially important for soil microbial properties in plant communities with shallower roots supporting more active and abundant soil microbial biomass. This might further indicate that a trait-based approach might describe plant community effects on soil microorganisms in more detail and after a shorter time than when focusing only on plant species richness. However, future analysis of shifts in soil microbial community structure could more precisely capture the mechanisms of carbon enhancement or utilization and would be of great importance to fully understand plant trait effects on soil microorganisms. Interestingly, we found no significant effects of both plant traits (growth start and flowering start) related to temporal resource acquisition on soil microbial properties. However, we only sampled once per year and temporal resource acquisition traits are closely related to seasonality and plant growth, and therefore we cannot rule out possible effects on soil microbial properties. Multiple measurements across the growing season and additional temporal plant traits (i.e. temporal patterns of rhizodeposition, tissue turnover and resource allocation into different functions) might be necessary to uncover effects of temporal resource acquisition traits on soil microorganisms. Previous studies already pointed out the high importance of considering root traits in addition to aboveground plant traits to advance the explanatory power for variation in soil microbial functioning in grassland soils (Legay et al., 2014). Legay et al. (2014) found root diameter, root dry matter content, and root C/N ratio to be the most important factors predicting dissimilarities in soil microbial community structure across sites in montane grasslands. Furthermore, root functional traits, such as root surface area and root length, are important drivers of soil carbon storage, plant resource acquisition, and plant productivity (DuPont et al., 2014). In our study, increased soil basal respiration and soil microbial biomass in plant communities with denser and shallower root systems may have been due to higher root exudation and therefore enhanced C input 13

Steinauer et al. into the rhizosphere, which is a key factor controlling soil microbial communities (Farrar et al., 2003; Sørensen et al., 2009). Notably, our measurements focused on the upper 10 – 15 cm of the soil where the biomass of fine roots (Mueller et al., 2013; Ravenek et al., 2014) and the activity of soil organisms is highest. Therefore, an increased turnover of fine-roots in plant communities with a denser root system (composed of mainly grasses) might have a positive effect on soil microbial properties due to an increased availability of resources (Zak et al., 2003). This highlights the importance of incorporating root traits into trait-based approaches in studies investigating plantsoil interactions. Moreover, we found reduced CWM of leaf area to be associated with higher soil basal respiration and soil microbial biomass. However, SEM revealed that the supposed leaf area effect was explained mostly by covariation with rooting depth. Within the experimental design, plant species with lower leaf area were mainly grass species with shallower and denser root systems in the topsoil (Ebeling et al., 2014; Fischer et al., 2016). As we found no direct effect of leaf area on soil microbial properties, we assume that effects of aboveground plant traits like leaf area on soil microbial properties might be due to their covariation with root traits. It should be noted that, overall, the explanatory power of our models was low; possible explanations could be that plant trait effects need even longer to manifest below ground. Furthermore, our calculation of CWM was based on trait values obtained from monocultures of the plant species established on the experimental field and used for designing the Trait-Based Biodiversity Experiment (Ebeling et al., 2014). Previous studies in the main Jena Experiment have shown that the expression of aboveground plant traits changes plastically in plant communities of increasing diversity and is seasonally variable (Gubsch et al., 2011; Lipowsky et al., 2015), potentially limiting the informative value of static trait values. Unfortunately, such information is not available for root traits due to logistical challenges of deriving species-specific belowground information in plant mixtures, and therefore we cannot rule 14

Steinauer et al. out that community-specific trait values would have improved plant trait-based predictions of soil microbial properties. In summary, we found plant traits related to spatial resource acquisition to be significant but weak predictors of soil microbial properties. Particularly plants with smaller leaves and both denser and shallower root systems enhanced soil basal respiration and microbial biomass. Additionally, we found that specific plant traits, rather than plant species richness, drive early plant community effects on soil microbial properties. Although plant trait-based approaches are promising, caution has to be taken when trying to derive general rules across habitats and ecosystems as plant trait expression may vary between different environments. Given that microbial functions depend on both hotspots and hot moments in soil (Kuzyakov and Blagodatskaya, 2015), future studies should extend the present approach to higher spatial (e.g., different soil depths) and temporal resolutions (e.g., repeated measurements within years) to study plant community effects on soil microbial community structure and their functions. Finally, our results point toward the important role of root functional traits (Bardgett et al., 2014; Laliberté, 2016), underlining the growing notion that those are key determinants of aboveground-belowground linkages and of the functioning of terrestrial ecosystems. Conflict of Interest The authors declare no conflict of interest. Acknowledgements We would like to thank Madhav Prakash Thakur for his suggestions on data analysis. The Jena Experiment was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation; FOR 1451). NE acknowledges funding by the German Research Foundation (Ei 862/3-2). Further support came from the German Centre for Integrative Biodiversity Research (iDiv) Halle-JenaLeipzig funded by the German Research Foundation (FZT 118). We would like to thank two 15

Steinauer et al. anonymous reviewers for their helpful comments, which considerably improved the manuscript. We thank the gardeners, technicians, and managers for their work in maintaining the field site and also many student helpers for weeding of the experimental plots. Authors’ contributions KS, SS and NE designed the study, KS and CR collected the data, KS and FF analyzed the data, KS wrote the first draft of the manuscript, and all authors contributed to the subsequent versions of the manuscript. All authors read and approved the final manuscript. References Anderson, J., Domsch, K., 1978. A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10, 215–221. Bardgett, R.D., Mommer, L., De Vries, F.T., 2014. Going underground: Root traits as drivers of ecosystem processes. Trends Ecol. Evol. 29, 692–699. doi:10.1016/j.tree.2014.10.006 Bates, D., Maechler, M., Bolker, B., 2015. lme4: Linear mixed-effects models using Eigen and S4 R package. Beck, T., Joergensen, R.G., Kandeler, E., Makeschin, F., Nuss, E., Oberholzer, H.R., Scheu, S., 1997. An inter-laboratory comparison of ten different ways of measuring soil microbial biomass C. Soil Biol. Biochem. 29, 1023–1032. Cardinale, B.J., Matulich, K.L., Hooper, D.U., Byrnes, J.E., Duffy, E., Gamfeldt, L., Balvanera, P., O’Connor, M.I., Gonzalez, A., 2011. The functional role of producer diversity in ecosystems. Am. J. Bot. 98, 572–592. doi:10.3732/ajb.1000364 De Deyn, G.B., Cornelissen, J.H.C., Bardgett, R.D., 2008. Plant functional traits and soil carbon sequestration in contrasting biomes. Ecol. Lett. 11, 516–531. doi:10.1111/j.14610248.2008.01164.x

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Steinauer et al. Figure 1: The effects of community mean scores of spatial resource acquisition traits (PCA1) on basal respiration and soil microbial biomass. Basal respiration [µg O2 g−1 soil dry mass h−1] in (a) 2011 , (b) 2013, (c) 2014, and (d) 2015; soil microbial biomass [µg C g−1 soil dry mass] in (e) 2011, (f) 2013, (g) 2014 and (h) 2015. **: P < 0.01, *: P < 0.05, ns: P > 0.1. Equations for each panel are given in Table S4. Figure 2: The effects of three spatial resource acquisition traits on basal respiration and soil microbial biomass. Relationships between basal respiration [µg O2 g−1 soil dry mass h−1] in (a) 2011, (b) 2013, (c) 2014, and (d) 2015 and rooting depth [m], and relationships between soil microbial biomass [µg C g−1 soil dry mass] and rooting depth in (e) 2011, (f) 2013, (g) 2014, and (h) 2015. Relationships between basal respiration [µg O2 g−1 soil dry mass h−1] in (i) 2011, (j) 2013, (k) 2014, and (l) 2015 and rooting length density [cm/cm³], and relationships between soil microbial biomass [µg C g−1 soil dry mass] in (m) 2011, (n) 2013, (o) 2014, and (p) 2015 and rooting length density [cm/cm³]. Relationships between basal respiration [µg O2 g−1 soil dry mass h−1] in (q) 2011, (r) 2013, (s) 2014, and (t) 2015 and leaf area [cm²], and relationships between soil microbial biomass [µg C g−1 soil dry mass] in (u) 2011, (v) 2013, (w) 2014, and (x) 2015 and leaf area [cm²]. *: P < 0.05, (*): P < 0.1, ns: P > 0.1. Equations for each panel are given in Table S4. Figure 3: Structural equation models of plant trait effects on soil microbial properties. Conceptual models for (a) plant trait effects (leaf area, rooting depth, and rooting length density) on soil microbial properties in 2014 and (b) plant trait effects (leaf area and rooting depth) on soil microbial properties in 2015. Final models for relationships among plant trait effects (leaf area and rooting depth) and (c) basal respiration in 2014 (2 = 0.43, df = 1, P = 0.51), (d) basal respiration in 2015 (2 = 2.17, df = 1, P = 0.14), (e) soil microbial biomass in 2014 (2 = 0.22, df = 1, P = 0.64), and (e) soil microbial biomass in 2015 (2 = 0.84, df = 1, P = 0.36). Numbers on arrows are

23

Steinauer et al. standardized path coefficients. Single-headed arrows indicate directed relationships, and doubleheaded arrows indicate correlations. Black lines and positive coefficient indicate positive relationships, whereas negative relationships are indicated in grey and negative coefficients. Bold path coefficients (with asterisks) indicate significant (P < 0.05) relationships.

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Steinauer et al.

Table 2: Linear mixedeffects model: table of Fand P-values for the effects of community weighted means of plant trait values (RD: rooting depth, RLD: root length density, MH: maximum height, LA: leaf area, GS: growth start, FS: flowering start) and year (2011, 2013, 2014, and 2015) on soil basal respiration and soil microbial biomass. Soil basal Soil respiratio microbial n biomass F- PF- Pval val val valu df ue ue df ue e 1, 1, 4 53 0.4 0.4 4 8.2 0.0 RD 3 2 04 9 87 2 1, 1, 4 53 33. <0. 2 39. <0. year 3 11 001 4 97 001 1, 1, 4 RD x 53 0.6 0.4 2 10. 0.0 year 3 0 41 5 35 01 1, 1, 4 53 0.7 0.3 5 3.0 0.0 RLD 3 1 80 2 96 9 1, 1, 4 53 77. <0. 4 170 <0. year 3 04 001 6 .93 001 1, RLD 1, 4 x 53 0.7 0.3 5 3.7 0.0 year 3 7 53 9 75 4

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Steinauer et al. 1, 1, 4 53 0.0 0.8 4 3.7 0.0 MH 3 8 53 4 37 0 1, 1, 4 54. <0. 53 5 68. <0. year 3 28 001 2 30 001 1, MH 1, 4 x 53 0.0 0.8 5 4.5 0.0 year 3 8 33 2 79 1

LA

1, 53 0.6 0.4 3 2 31

1, 53 231 <0. year 3 .27 001 1, LA x 53 0.6 0.4 year 3 6 16

1, 4 5 5 1, 4 2 3 1, 4 3 6

4.5 0.0 2 34

435 <0. .72 001

5.4 0.0 7 20

1, 1, 4 53 0.0 0.8 6 0.1 0.6 GS 3 5 27 1 6 87 1, 1, 4 53 9.4 0.0 5 17. <0. year 3 9 02 7 23 001 1, 1, 4 GS x 53 0.0 0.8 5 0.1 0.6 year 3 2 86 7 6 89 1, 1, 4 53 0.7 0.3 4 0.8 0.3 FS 3 4 89 8 6 44 1, 1, 4 53 83. <0. 3 167 <0. year 3 39 001 3 .35 001 1, 1, 4 FS x 53 0.7 0.3 3 1.1 0.2 year 3 5 88 7 4 86

26

Steinauer et al. Signi fican t res results (P<0.10) are given in italics.

27

2011 [µg O2 g−1 soil dry mass h−1]

Basal respiration

8

a)

soil dry mass]

[μg C

b)

n.s.

c)

8

2015

6

6

4

4

4

4

2

2

2

2

0

0

0

0

0

0.2

0.4

n.s.

e)

-0.4 -0.2 1600

0

0.2

0.4 n.s.

f)

-0.4 -0.2 1600

0

0.2

0.4

n.s.

g)

d)

8



6

1600

g-1

8

n.s.

2014

6

-0.4 -0.2

Soil microbial biomass

2013

-0.4 -0.2 1600

1200

1200

1200

1200

800

800

800

800

400

400

400

400

0

0

0

0

-0.4 -0.2

0

0.2

0.4

-0.4 -0.2

0

0.2

0.4

-0.4 -0.2

0

0.2

0.4

0

0.2

0.4 

h)

-0.4 -0.2

community mean scores of spatial resource acquisition traits (PCA1)



0

0.2

0.4

2014

2015

a)

b)

Conceptual model

Leaf area

Leaf area

Rooting Length Density

Rooting Depth

Rooting Depth

Soil microbial properties

c)

Soil microbial properties

d)

Basal respiration

Leaf area

Leaf area

0.62

0.62

- 0.03

Rooting Depth 39%

- 0.15

Rooting Depth 38%

- 0.17

- 0.14

Basal respiration

Basal respiration

3%

2%

e)

f) Leaf area

Soil microbial biomass

Leaf area

0.62

0.62

0.08

Rooting Depth

- 0.10

Rooting Depth 38%

39%

- 0.18

- 0.13

Soil microbial biomass

Soil microbial biomass 2%

3%