High microbial diversity promotes soil ecosystem ...

AEM Accepted Manuscript Posted Online 16 February 2018 Appl. Environ. Microbiol. doi:10.1128/AEM.02738-17 Copyright © 2018 American Society for Microbiology. All Rights Reserved.

High microbial diversity promotes soil ecosystem functioning

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Pierre-Alain Maron1#, Amadou Sarr1, Aurore Kaisermann1, Jean Lévêque2, Olivier Mathieu2,

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Julien Guigue2, Battle Karimi1, Laetitia Bernard3, Samuel Dequiedt1, Sébastien Terrat1, Abad

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Chabbi4, Lionel Ranjard1

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UMR 1347 Agroécologie, AgroSup Dijon, INRA, Univ. Bourgogne Franche-Comté, Dijon,

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France1, UMR 6282 CNRS/uB Biogéosciences, Univ. de Bourgogne Franche-Comté, Dijon,

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France2, Inst Rech Dev, UMR Eco&Sols, Montpellier, France3, Univ Paris 06, CNRS, INRA,

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UMR, Lab Biogeochim & Ecol Milieux Continentaux, Thiverval Grignon, France4

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Running title: Microbial diversity affects carbon cycling

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Number of words: Abstract (187), Importance (117), Text (5 406)

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#

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[email protected]

Corresponding author: Phone 33 (0) 3 80 69 34 46. Fax 33 (0) 3 80 69 32 24. e-mail: pierre-

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Statement of authorship: PAM and LR designed the study, AS, AK, JL, OM, JG performed

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the research, ST, LB and SD performed bioinformatic and statistical analysis, PAM wrote the

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first draft of the manuscript and all authors contributed substantially to revision.

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Conflict of interest statement: The authors declare no conflict of interest.

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Downloaded from http://aem.asm.org/ on February 20, 2018 by INRA - Institut National de la Recherche Agronomique

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ABSTRACT In soil, the link between microbial diversity and carbon transformations is

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challenged by the concept of functional redundancy. Here, we hypothesized that functional

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redundancy may decrease with increasing carbon source recalcitrance, and that coupling of

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diversity with C-cycling may change accordingly. We manipulated microbial diversity to

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examine how diversity decrease affects the decomposition of easily degradable (i.e.

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allochthonous plant residues) vs. recalcitrant (i.e. autochthonous organic matter) C-sources.

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We found that a decrease in microbial diversity (i) affected the decomposition of both

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autochthonous and allochthonous carbon sources hence reducing global CO2 emission by up

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to 40%, and (ii) shaped the source of CO2 emission towards preferential decomposition of

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most degradable C-sources. Our results also revealed that the significance of the “diversity

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effect” increases with nutrient availability. Altogether, these findings show that C-cycling in

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soil may be more vulnerable to microbial diversity changes than expected from previous

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studies, particularly in ecosystems exposed to nutrient inputs. Thus concern about the

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preservation microbial diversity may be highly relevant in the current ‘global changes’

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context assumed to impact soil biodiversity and the pulse inputs of plant residues and

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rhizodeposits into the soil.

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IMPORTANCE With hundreds of thousands of taxa per gram of soil, microbial diversity

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dominates soil biodiversity. While numerous studies have established that microbial

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communities respond rapidly to environmental changes, the relationship between microbial

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diversity and soil functioning remains controversial. Using a well-controlled laboratory

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approach, we provide empirical evidence that microbial diversity may be of high significance

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for organic matter decomposition, a major process on which rely many of the ecosystem

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services provided by the soil ecosystem. These new findings should be taken into account in

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future studies aimed to understand and predict the functional consequences of changes in

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microbial diversity on soil ecosystem services and carbon storage in soil.


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Keywords: Microbial diversity, soil organic matter, priming effect, functional redundancy,

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carbon mineralization

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INTRODUCTION

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Biodiversity enhances ecosystem stability and productivity. This assumption has been broadly

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verified for plant communities thanks to the vast body of evidence from more than two

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hundred years of studies, most of which were based on the manipulation of taxonomic

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diversity and/or diversity of functional groups (1-4). Compared to plant ecology, microbial

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ecology is still lacking demonstrations of the relationship between biodiversity and function

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(2, 5). Consequently, while it is widely recognized that microorganisms perform a crucial role

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in many key ecosystem functions involved in soil fertility and environmental and water

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quality (6), the importance of microbial diversity is still debated (7-8). However, this question

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is critical when considering of the impact of climatic changes (9) and land management (10-

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12) on microbial diversity in soil ecosystems.

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With its hundreds of thousands of taxa per gram of soil, microbial diversity dominates soil

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biodiversity (8). Due to this enormous diversity, functional redundancy within the soil

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microbial component is assumed to be very high; so high, in fact, that it is still widely

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assumed that community diversity and composition are decoupled from functioning (8, 13).

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Nevertheless, functional redundancy may vary between the numerous processes driven by soil

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microorganisms (14). More precisely, processes carried out by small pools of microbial

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species might be more affected by changes in diversity than processes involving large number

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of microbial taxa (15-16). Within the soil microbial community, the decomposition of organic

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matter is one of the most redundant functions since most soil microorganisms can be broadly

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grouped as heterotrophs. Such high redundancy was evidenced by several studies which

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reported that microbial diversity had no influence on a global estimation of organic matter

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decomposition based on total CO2 release (17-18). However, total CO2 release integrates a

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large range of emission sources, ranging from labile to recalcitrant C-organic compounds, and

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the enzymatic capacities required to degrade recalcitrant compounds, as compared to more

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labile C-compounds, are provided by only a small pool of microbial species (16, 19-20). This

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suggests that functional redundancy may decrease with increasing carbon source

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recalcitrance, and that coupling of diversity with C-cycling may change accordingly. As a

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result, modifications in microbial diversity may not necessarily affect the intensity of the

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broad CO2 flux itself (17-18), but rather the composition of this flux in terms of the relative

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contributions of CO2 emitted from labile vs. recalcitrant C-sources. Studies providing such

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evidence should be highly relevant to predict how much the current impact of anthropogenic

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activities and climate changes on soil microbial diversity is likely to affect the carbon balance

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in soil.

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The aim of this study was to investigate the impact of changes in microbial diversity

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(bacteria and fungi) on the decomposition of different C-substrates in soil. We hypothesized

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that a decrease in microbial diversity would have less effect on the decomposition of

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allochthonous easily degradable C-sources than on more recalcitrant autochthonous C-

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sources. To test this hypothesis, we took advantage of the particularities of the soil microbial

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component, i.e. microscopic size and rapid growth rates which make it a highly suitable

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model easy to manipulate for testing a "diversity-function" hypothesis under controlled

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conditions. We simulated an erosion of soil microbial diversity by using a dilution to

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extinction approach in a microcosm experiment (16, 21). This approach has advantages over

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other possible ways of manipulating diversity, such as communities construction (22-23), in

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that (i) manipulation is possible irrespective of the cultivability of the microbial species, and

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(ii) it leads to the establishment of highly complex communities composed of several

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hundreds of different species, i.e. is realistic with regard to the huge genetic diversity that

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characterizes the soil microbial world. 13C-labelled wheat residues were used as allochtonous

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carbon input to distinguish the relative contributions of mineralization from allochthonous

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(13C-CO2) and autochthonous (12C-CO2) carbon sources according to microbial diversity.

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RESULTS

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Microbial biomass. Microbial molecular biomass at T0 was similar at all the three levels of

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diversity (Fig. 1). In the control microcosms, microbial biomass did not evolve with time and

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remained similar between D1, D2 and D3 at each sampling date throughout the 60 days of

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incubation (p>0.05). Likewise, similar bacterial and fungal densities were observed in the

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three diversity levels (qPCR, Fig. S1). Incorporation of wheat residues increased microbial

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biomass in the amended microcosms, with significantly higher values occurring up to 14 days

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incubation compared to controls (p D2 > D3. More

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precisely, at the end of incubation the total C-CO2 emitted from the amended microcosms (Rt)

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ranged from 1.63±0.109 (D1) to 1.34±0.08 (D2) and 0.88±0.07 (D3) mg.g-1 soil,

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corresponding to a decrease in C-CO2 release of 46% and 18% respectively in D3 and D2,

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compared to D1. Analysis of the difference between the respiration in control and wheat

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amended microcosms for each of the three levels of diversity (Fig. 3B) evidenced three

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distinct periods with (i) an initial early phase represented by the first week following wheat

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incorporation and characterized by similar increases in respiration for each of the three

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diversity levels, (ii) a second phase between 10 and 21 days during which the respiration

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increase was significantly higher in D1 than in D2 and D3, and (iii) a third and later phase

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from 21 to 60 days characterized by a gradient in the respiration increase which followed the

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gradient of diversity.

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The increased C-CO2 release in amended microcosms was explained in part by wheat

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residues decomposition (Rr), which occurred in all three diversity levels (Fig. 4A). However,

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wheat mineralization increased significantly with increasing soil microbial diversity (pD3 (Table 1). It was clearly

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apparent that the decreased diversity could be ascribed to a loss of the least abundant (i.e.

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“rare”) species at the moment of dilution, as evidenced by the richness index which was

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decreased by 34% and 52% for bacteria and by 23% and 58% for fungi in D2 and D3

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respectively, compared to D1. However, the ecological attributes of the inoculated

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populations may also have determined the fitness of these populations during soil colonization

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and hence determined the diversity of the finally established community (30). This may

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explain why the differences between the three diversity levels did not simply result from the

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arithmetic loss of microbial populations due to dilution (31). More precisely, the decrease of

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evenness observed for bacterial and fungal communities along the diversity gradient

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highlighted that dilution led to the increased dominance of a particular subset of microbial

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populations at the end of colonization, in agreement with previous studies (30). However, the

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composition of the established communities suggested that the mechanisms that determined

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the success of the microbial populations in colonizing the soil differed between bacteria and

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fungi. For bacteria, evenness was decreased along the gradient due to the increased

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dominance of the most opportunistic fast-growing r-strategists over the slower-growing

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populations (interestingly, the same phyla were involved as the phyla, discussed above, that

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differed between the native and D1 communities). This suggests that the growth rate may

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have been the main determinant of the success of bacterial populations during soil

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colonization. In contrast, for fungi the decrease of evenness was mainly associated with an

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increased dominance of Basidiomycota over Ascomycota. Both phyla are reported to be major

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contributors to C-cycling in soil. However, Basidiomycota are assumed to possess improved

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metabolic capacities over Ascomycota for decomposing more recalcitrant soil C-compounds

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(19, 32). Accordingly, our results suggest that the success of the fungal populations during the

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colonization phase may have been determined more by their metabolic abilities than by their

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growth rate. It is noticeable that this difference in colonization strategy between bacteria and

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fungi led to an increase in the relative importance of fungi in determining the properties of the

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microbial network in the inoculated soils, as evidenced by the decrease in the ratio between

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the numbers of bacterial to fungal links in D1 (1.88), D2 (2.89) and D3 (2.71) compared to the

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native community (4.86). However in our study this did not impact labile carbon availability

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in the three diversity levels, as estimated from the size of the dissolved organic carbon pool by

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applying the method of Guigue et al. (33). At T0 this pool was similar for D1 (737±103g g-1

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soil), D2 (532±207g g-1 soil) and D3 (724±28g g-1 soil).

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In spite of large differences in terms of bacterial and fungal diversity, the microbial

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biomass and density at T0 were similar for all three diversity levels (Fig. 1). This indicated

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that after inoculation, the microbial communities developed until the soil carrying capacity

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was attained (16, 34), which was not dependent on the diversity of the soil microbial

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community. The microbial biomass in the controls remained stable throughout the experiment

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but was similarly increased by the addition of wheat residues in all three diversity levels. The

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possibility that any further observed shifts in respiration rates with dilution would be due to a

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community “biomass effect” could therefore be excluded.

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Decrease in microbial diversity affects the decomposition of allochthonous and

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autochthonous carbon sources. High microbial diversity generally stimulated the

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decomposition of both autochthonous and allochthonous carbon sources (Fig. 3 and 4). In the

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controls, basal respiration (i.e. autochtonous organic matter decomposition) was not affected

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by the decrease of microbial diversity at the two highest diversity levels D1 and D2 (Fig. 3A),

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in agreement with the functional redundancy hypothesis (18). However, respiration decreased

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by 33% in the most species-poor community D3 compared to D1, highlighting the limits of

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functional redundancy to buffer the effect of diversity loss on ecosystem processes (30, 35).

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This was unexpected, however, since previous studies had not shown any impact of an even

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greater decrease in microbial diversity on basal respiration (18, 36). This discrepancy with

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previous published data may be attributed to our experimental design, which included

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mechanical mixing of the soil in the controls to mimic the disturbance associated with the

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incorporation of wheat residues in amended microcosms. Mechanical disturbance is known to

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release additional C-substrates due to the disruption of soil aggregates and hence to increase

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nutrient availability for microbes, particularly during the first days following the perturbation

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(37). Such a transitory increase of nutrient availability may have contributed to the functional

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dissimilarity observed in the lowest diversity level (D3) during the first week of incubation, in

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agreement with van der Heijden et al. (6) who proposed that the relationship between

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microbial diversity and ecosystem functioning may differ between nutrient poor and nutrient

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rich ecosystems. The importance of nutrient availability was verified in recent studies that

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evidenced an increase of the diversity effect with increasing nutrient availability for

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denitrification, or microbial stability following disturbance (30, 34).

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In our study, an increase of the functional significance of microbial diversity with

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increasing nutrient availability was further evidenced by the results obtained in wheat-

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amended microcosms. As expected, C-CO2 release was increased following wheat residues

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input, corresponding to the input of an easily degradable C-substrate (38), which stimulated

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microbial biomass during the first two weeks of incubation (Fig. 1). However, whereas the

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increase in microbial biomass was similar at all three diversity levels, decreasing microbial

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diversity had a strong and highly significant effect on decreasing C-CO2 emission. In contrast

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to the above-mentioned results obtained for basal respiration, no saturation effect was

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observed in amended microcosms since the total C-CO2 released after 60 days was decreased

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respectively by 18% and 44 % in D2 and D3 compared to D1 (Fig. 3A). Altogether, our

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results indicate that functional redundancy may have been overestimated and that C-

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transformations in soil may be more sensitive to microbial diversity changes than previously

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expected, particularly in ecosystems exposed to nutrient inputs.

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Interestingly, the delta of C-CO2 release between the controls and their respective amended

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treatments for each of the three diversity levels increased with time (Fig. 3B). We suggest that

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this increase of functional dissimilarity between the three diversity levels with time may in

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part be explained by (i) the chemical and structural complexity of the allochthonous C-source,

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and (ii) the sequential decomposition of the different components of wheat residues that

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occurred during the degradation process, from labile (i.e. sugars) to more recalcitrant (i.e.

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lignin) compounds (39). While the labile fraction of wheat residues provides an easily

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available C- and energy-source for many microorganisms (i.e. highly redundant function), the

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co-metabolic nature of lignin breakdown depends on energy providing processes and requires

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a specific set of enzymes and microbial populations (i.e. weakly redundant function) (19-20).

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Accordingly, during the first week of decomposition similar values of delta C-CO2 were

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observed at all three diversity levels, reflecting the availability and decomposition of the most

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degradable wheat compounds. After 7 days, the decrease of delta C-CO2 observed in D3 and

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D2 compared to D1 may reflect the increased recalcitrance of residues due to prior

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decomposition of the most degradable compounds, hence requiring more specialized

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functions carried out by a less redundant community and more likely to be represented in the

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most species-rich community. Finally, the gradient observed between D1>D2>D3 after 28

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days may be explained by the subsequent increased recalcitrance of the remaining residues.

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Altogether, these results show that functional redundancy decreases with increasing carbon

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source recalcitrance, leading to an increased effect of biodiversity with time following

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allochthonous C-source inputs. To our knowledge, the dynamic of the diversity effect

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following an allochthonous C-source input has not been reported previously. However, it may

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be highly relevant in the current ‘global changes’ context assumed to impact the pulse inputs

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of plant residues and rhizodeposits into the soil (40-41).

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In addition to CO2 emitted from wheat decomposition, the stimulation of autochthonous

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carbon mineralization after wheat residues addition to the soil, i.e. priming effect, also

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contributed significantly to the increased CO2 release in amended microcosms (Fig. 4B). This

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was in agreement with numerous studies that evidenced a PE following the incorporation of

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plant residues into the soil (26, 42-44). However, here we provide the proof of concept that

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PE is positively dependent on microbial diversity since it decreased along the diversity

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gradient (Fig. 4B). This diversity effect was highly significant since autochthonous C

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mineralization was 30% higher in D1 than in D3. In addition to the amount of C-CO2 emitted

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through PE, the “efficiency” of wheat-C to produce C-CO2 through PE increased with

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increasing microbial diversity (0.86; 0.73; and 0.53 C-units were emitted per unit of C-wheat

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mineralized in D1, D2 and D3, respectively). Different microbial mechanisms have been

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proposed to explain the PE (27, 45-46). However, in our study involving an erosion of

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microbial diversity, the differences in PE and better efficiency observed according to diversity

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may be mainly explained by an increased co-metabolism process in species-rich communities:

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with autochthonous-C decomposers (i.e. k-strategist populations carrying a specific set of

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enzymes) more likely to be present in species-rich communities and degrading recalcitrant

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SOM compounds by using the allochthonous C-source as energy source (47).

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The dynamics of the ratio between C-CO2 released from allochthonous/autochthonous C-

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sources provided additional interesting information in this sense (Fig. 5). According to our

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hypothesis, the relative contribution of C-CO2 emitted from the allochthonous C-source was

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increased in the most-depauperate communities (D2 and D3) up to 3 weeks after wheat

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residues incorporation into the soil, indicating that species-poor communities preferentially

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decomposed the more degradable allochthonous C-sources rather than the autochthonous C-

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source. However, after 3 weeks, similar values of this ratio were observed at all three

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diversity levels, probably resulting from the establishment of limiting conditions due to

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nutrient depletion and a higher recalcitrance of the remaining residues (39), becoming close to

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the recalcitrance of the autochthonous C-sources.

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In our study based on the use of the dilution to extinction approach to manipulate the

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bacterial and fungal diversity, it must be kept in mind that higher trophic-level organisms, i.e.

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specialized predators like protozoa are likely to have been influenced most by dilution,

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because these organisms are less abundant in soil food webs (48). More precisely, considering

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that protozoa are present at an average density of 106.g-1 soil in most soils, they may have

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been weakly present in D3 (dilution 10-5) compared to D1 and D3. Given the importance

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these organisms for ecosystem functions (for review, see 49), it may have contributed, in

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addition to the erosion of the fungal and bacterial diversity discussed above, to the changes in

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C-CO2 emissions observed between the three diversity levels. The loss of protozoa may

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indeed possibly have decreased the availability of macronutrients in the D3 through the

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decrease of the release of N by bacterivors and/or the immobilisation of N in the microbial

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biomass due to lower stimulation of the microbial activity and turnover, hence limiting the

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mineralization of organic carbon (49).

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This “food web hypothesis” may explain the slight decrease of mineralisation observed in

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D3 compared to the two other diversity levels in controls. However, it cannot explain the

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gradient of mineralisation observed following wheat addition between D1, D2 and D3, despite

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the presence of protozoa in D1 and D2. In addition, it was globally estimated in a recent meta-

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analysis that grazing decreases the soil microbial biomass and bacterial abundance by 16 and

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17 % (49). In our study, neither the microbial biomass nor the bacterial density was decreased

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between the three diversity levels, suggesting that the grazing pressure was similar in the three

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diversity levels. In other respect, our results show that the priming effect decreased with the

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decrease of the microbial diversity. Again, this is not in agreement with the “food-web

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hypothesis” since a decrease of macronutrient availability would on the contrary have

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increased the intensity of the PE do to stimulation of the nutrient mining mechanism (44, 50,

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51) that has been demonstrated to take place in case of nutrient depletion. Finally, the

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hypothesis of a decrease of nutrient availability is not in agreement with the decrease of the

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decomposition of lignin following the erosion of diversity previously reported in the context

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of the same experiment (16). Limiting nitrogen conditions were on the contrary shown to

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induce ligninolytic activity (52). Altogether, these elements highlight that the decrease of

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mineralization observed between D1, D2 and D3 was ascribed to the decrease of microbial

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diversity rather than the loss of higher trophic levels.

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In conclusion, our results demonstrate that C-cycling in soil may be more vulnerable to

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microbial diversity changes than expected from previous studies. They indicate mainly that a

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decrease of soil microbial diversity (i) affected the decomposition of both autochthonous and

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allochthonous carbon sources hence reducing global CO2 emission (i.e. emission from

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allochthonous + autochthonous sources) by up to 40% (ii) modified the source of CO2

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emission toward preferential decomposition of allochthonous C-substrates, and that (ii) the

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significance of the “diversity effect” increases with nutrient availability. Altogether, these

381

highly relevant new findings should be taken into account in future studies aimed to

382

understand and predict the functional consequences of decreased microbial diversity on soil

383

ecosystem services and carbon storage in soil, particularly in the current context of ‘global

384

changes’ assumed to impact both microbial diversity and the pulse inputs of plant residues

385

and rhizodeposits into the soil (i.e. the functional significance of the “microbial diversity”

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variable in predictive models of C-turnover in soil will be increased with the increase of C-

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substrates availability). The coupling of community diversity with ecosystem functioning also

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implies that concerns about the need to preserve biodiversity are also relevant for soil

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microbial communities.

390 391

MATERIALS AND METHODS

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Plant culture and 13C-labelling. Seeds of wheat (Triticum aestivum cv Caphorn) were

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germinated at 4°C in darkness. Plantlets were grown in a mix of sand (1/4) and perlite (3/4) in

394

an air-tight chamber which allowed accurate regulation of atmospheric gas composition and

395

environmental parameters (GRAP, CEA Cadarache, France). The plants were watered with

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half-diluted Hoagland’s nutrient solution and CO2 concentration was maintained at 380µl.l-1.

397

The partial pressures of both 13CO2 and 12CO2 in the chamber were continuously monitored

398

by Near Infrared Spectroscopy to determine 13C enrichment of the CO2. Regulation was

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achieved by automatic injection of pure (>99% atom% 13C) 13CO2 (Purchased from

400

CortecNet, Paris, France). The 13C isotope excess in the chamber was fixed at >80% atom%

401

during the first 10 days and at > 90% atom% thereafter. Plants were grown for 196 days (i.e.

402

to maturity). The roots and shoots were then separated and dried before further use. The wheat

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was thus labeled with 13C at 96 atom% and characterized by a C/N ratio of 78.2. Only the

404

shoots (aerial parts) were used for the experiment, the roots were discarded.

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Soil. Soil samples were taken from the top 10 cm of a Cambisol on a temporary grassland

406

site part of a long-term observatory for environmental research (Lusignan LTO-ACBB,

407

INRA), located in the south-west of France (46°25′12.91″ N; 0°07′29.35″ E). The soil pHH2O

408

was 6.6 and contained 17.5% clay; 36.9% fine silt; 30.4% coarse silt; 15.2% sand; CaCO3< 1

409

g.kg-1; 2.29% organic C and 0,13% total N. The C/N ratio was 9.9. The soil was sieved at 4

410

mm and homogenized prior to gamma-ray sterilization (35 kGy; Conservatome, Dagneux,

411

France). The sterility of the irradiated soil was verified by spreading serial dilutions of the soil

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onto nutrient agar plates.

413

Experimental design. Microcosms were set up by placing 40 g of dry sterile soil in 150-

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ml plasma flasks. Sterile soil microcosms were inoculated with suspensions of the same non-

415

sterile soil (16, 30, 34). Briefly, an initial soil suspension was prepared by mixing 100 g of

416

native soil (equivalent dry mass) with 300 ml sterile distilled water using a Waring blender.

417

After blending for 5 min at maximum speed, the soil suspension was serial diluted. Three

418

levels of dilution of the soil suspension were used as inocula to create a gradient of diversity,

419

i.e. undiluted (100; D1), 1/103 dilution (D2) and 1/105 dilution (D3), and the soil moisture was

420

fixed at 60% water-holding capacity. After inoculation, the microcosms were closed to avoid

421

air contamination and pre-incubated for 6 weeks at 20°C (i.e. Average autumn temperature at

422

the sampling site) with weekly aeration and verification of soil water content to allow

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colonization and stabilization of the inoculated communities in terms of density and structure.

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On day 0 (T0, after 6 weeks of pre-incubation), for each diversity level, half of the

425

microcosms were amended with 13C-labelled wheat residues (dried, ground 99.999%, N48, Air Liquide, France),

445

previously calibrated against a certified isotopic standard (δ13C = -25.5 ± 0.2‰ vs. PDB, ISO-

446

TOP, Air Liquide), was selected as internal standard. Gas samples were manually injected

447

into the Trace Gas with a gas tight syringe.

448 449 450 451

By using 13C-labelled plant residues, the autochthonous soil C (Rs) and plant residue C (Rr) respiration (mg C-CO2 kg-1 soil) could be calculated from mass balance equations: Rs + Rr = Rt Rs × As13 + Rr × Ar13 = Rt × At13

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428

where As13 is the 13C abundance (dimensionless) of soil carbon, Ar13 the 13C abundance of

453

plant residue, Rt the total CO2 emitted by soil with plant residue and At13 its 13C abundance.

454

The priming effect (PE, mg C-CO2 kg-1 soil) induced by the addition of plant residue was

455

calculated as PE = (Rs soil with plant residue) – (Rs control soil) where (Rs control soil) was

456

the CO2 emitted by control soil.

457

DNA extraction from soil and molecular microbial biomass determination. At T0,

458

T7, T14, T28, and T60 days of incubation, DNA was extracted from 2 g soil from all triplicate

459

microcosms of each treatment and diversity level, and quantified as previously described (11).

460

DNA was also extracted from triplicates of native soil samples. DNA concentrations of crude

461

extracts were determined by electrophoresis in a 1% agarose gel using a calf thymus DNA

462

standard curve, and used as estimates of microbial molecular biomass (53). After

463

quantification, DNA was purified using a MinElute gel extraction kit (Qiagen, Courtaboeuf,

464

France).

465

Pyrosequencing of 16S and 18S rRNA genes. Bacterial and fungal diversity was

466

determined by 454 pyrosequencing of ribosomal genes in the three replicates of each of the

467

three diversity levels at T0, i.e. at the end of the six weeks pre-incubation and in three

468

replicates of the native soil. For bacterial communities, a 16S rRNA gene fragment with

469

sequence variability and the appropriate size (about 450 bases) was amplified using the

470

primers F479 and R888. For fungal communities a 18S rRNA gene fragment of about 350

471

bases was amplified using the primers FR1 and FF390 (54). Primers and PCR conditions were

472

as described previously (11). Finally, the PCR products were purified and quantified, and

473

pyrosequencing was carried out on a GS FLX Titanium (Roche 454 Sequencing System).

474

Taxonomic assignments and clustering of 16S and 18S rRNA gene fragments.

475

Bioinformatics analyses were done with GnS-PIPE developed by the GenoSol platform

476

(INRA, Dijon, France) and described previously (11). First, all reads were sorted according to

20


452

the chosen multiplex identifiers. Then, raw reads were filtered and deleted: (a) if the exact

478

primers were not found both at the beginning and end of the sequence, (b) if the sequences

479

contained any ambiguity (Ns), or (c) if the sequence length was below 350 and 300 bases for

480

16S and 18S reads respectively. A PERL program was then applied to obtain strict

481

dereplication. The dereplicated reads were then aligned using INFERNAL alignments (Cole

482

et al. 2009) and clustered into operational taxonomic units (OTU) at 95% sequence similarity

483

using a PERL program that clusters rare reads to abundant ones, and does not count

484

differences in homopolymer lengths (main bias of pyrosequencing technologies). Another

485

homemade filtering step was then applied to eliminate potential sources of error (e.g., PCR

486

chimeras, sequencing errors, OTU overestimation), based on taxonomic results. To efficiently

487

compare the datasets and avoid biased community comparisons, high-quality reads were

488

rarefied by random selection closed to the lowest datasets (5.630 and 4.763 reads for 16S and

489

18S rRNA gene sequences respectively). Kept reads were then compared for taxonomy-based

490

analysis against the Silva database (version r111) using similarity approaches (USEARCH).

491

Finally, diversity indexes were determined using the detected taxonomic groups at the genus

492

level. The numbers of OTU, and the Shannon (H’) and Evenness (J) indexes were used as

493

indicators of soil microbial richness, diversity and structure, respectively.

494

Statistical analysis. Molecular biomass and diversity indexes were compared between

495

the three levels of diversity by applying the nonparametric Kruskal-Wallis test. For gaseous

496

data, statistical analyses were conducted using two-way ANOVA and differences between

497

means were tested with the Fischer test (P ≤ 0.05). These statistical calculations were carried

498

out using XLSTAT software (Addinsoft®, Paris, France). Microbial co-occurrence networks

499

were built using the method described previously (55). For each dilution level and for the

500

native soil, the core community of OTUs in the 3 replicates was used to identify the

501

significant Spearman correlations (corrected with the False Discovery Rate method; p < 0.05)

21


477

that were interpreted as non-random co-occurrences. The bacterial, fungal and combined

503

networks were computed to estimate the proportion of links involving bacteria only, fungi

504

only or both bacteria and fungi. The calculation and definition of the network metrics have

505

been detailed in Karimi et al. (56).

506 507

Accession number. The nucleotide sequences determined in this study have been submitted to the EBI database (PRJEB19513).

508 509

ACKNOWLEDGEMENTS

510

This study was financially supported by the French National Research Agency (ANR) under

511

the framework of the ANR Systerra project DIMIMOS (ANR-08-STRA-06). We thank D.

512

Warwick for correction and improvement of English in the manuscript.

513

22


502

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700

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701

Tables and figures legends

703

TABLE 1

704

communities (bacteria and fungi) calculated from the sequencing data for the native

705

community and for each level of diversity D1 (high), D2 (medium) and D3 (low) at T0 (i.e.

706

end of the 6 weeks of pre-incubation).

707

Richness refers to the number of OTU defined at the genus level.

708

Nb nodes refers to the number of taxa connected in the network; Bact links and Fungal links

709

refer to the number of links involving respectively bacteria only and fungi only ; B/F refers to

710

the ratio between bacterial and fungal links ; P/N refers to the ratio between positive and

711

negative links in the co-occurrence network.

712

For each level of diversity, values with different letters differ significantly (P < 0.05). Values

713

are expressed as means ± standard deviation; n=3 biological replicates.

714

FIG 1 Soil microbial biomass (μg DNA g−1 of dry soil) in control and wheat amended

715

microcosms for each of the three levels of diversity D1 (black), D2 (Grey) and D3 (light

716

grey)) during the incubation. Letters in bracket indicate a significantly different biomass (P
1% are shown. Abundance of each phylum was

721

determined from n=3 biological replicates.

722

FIG 3 (A) Cumulative total soil respiration (mg C-CO2 g-1 dry soil) over 60 days in control

723

(dashed lines) and wheat amended (full lines) microcosms for the three levels of diversity

724

with high diversity (D1, black), medium diversity (D2, Grey) and low diversity (D3, light

Diversity and microbial co-occurrence network indices of the microbial

31


702

grey). (B) Difference between respiration of control and wheat amended microcosms (∆ mg

726

CO2 g-1 dry soil) for the three levels of diversity (D1; black, D2; grey and D3; light grey). For

727

each incubation time and level of diversity, values with different letters differ significantly (P

728

< 0.05). Inset represents a focus on the first 10 days to visualize the early shifts. Error bars

729

denote standard deviation of biological replicates (n = 3).

730

FIG 4 (A) Cumulative respiration of wheat residues (mg C-CO2 g-1 dry soil) and (B)

731

Cumulative priming effect (mg C-CO2 g-1 dry soil) measured during the 60 days incubation

732

for the three levels of diversity with high diversity (D1, black), medium diversity (D2, Grey)

733

and low diversity (D3, light grey). For each incubation time and level of diversity, values with

734

different letters differ significantly (P < 0.05). Insets represent a focus on the first 10 days to

735

visualize the early shifts. Error bars denote standard deviation of biological replicates (n = 3).

736

FIG 5 Relative contribution of allochtonous vs. autochthonous carbon sources to total CO2

737

emission (Bars) and cumulative total soil respiration in wheat-amended microcosms (curves)

738

during the 60 days of incubation for the three levels of diversity, with D1 (high divers, black),

739

D2 (medium diversity, grey) and D3 (low diversity, light grey). For each incubation time and

740

level of diversity, values with different letters differ significantly (P < 0.05). Error bars denote

741

standard deviation of biological replicates (n = 3).

32


725

Indices of diversity Bacterial community

Native D1 D2 D3

Richness 1004 ± 98.8 a 659 ± 19.4 b 435 ± 110.7 c 313 ± 56.6 c

Shannon 5.48 ± 0.13 a 4.20 ± 0.09 b 3.62 ± 0.54 b 2.97 ± 0.40 c

Evenness 0.79 ± 0,01 a 0.65 ± 0.02 b 0.59 ± 0.06 b 0.51 ± 0.05 c

Indices of microbial networks Fungal community

Richness 449 ± 54.8 a 462 ± 51.8 a 356 ± 51.5 a 194 ± 91.0 b

Shannon 3.87 ± 0.21 ab 3.95 ± 0.31 a 3.32 ± 0.29 ab 2.51 ± 1.42 b

Evenness 0.63 ± 0,07 a 0.64 ± 0.04 a 0.57 ± 0.04 a 0.47 ± 0.22 a

Microbial community Nb nodes 446 331 186 107

Tot links 16822 9524 3718 1367

Bact links 8099 3222 1499 593

Fungal links 1668 1718 532 219

B/F 4.86 1.88 2.89 2.71

P/N 1.09 1.12 1.07 1.09


Level of diversity

Microbial molecular biomass (mg DNA g-1 dry soil)

18 16 14 12

D1

10

D2 D3

8 6 4 2 0 T0

T3

T7

T14

T28

T60

T3

T7

T14

T28

T60

Incubation time (days)

FIG 1 Soil microbial biomass (μg DNA g−1 of dry soil) in control and wheat amended microcosms for each of the three levels of diversity D1 (black), D2 (Grey) and D3 (light grey)) during the incubation. Letters in bracket indicate a significantly different biomass (P < 0.05). Error bars denote standard deviation of biological replicates (n = 3)


20

B

100

100 Unclassified Verrucomicrobia Gammaproteobacteria Deltaproteobacteria Betaproteobacteria Alphaproteobacteria Planctomycetes Nitrospirae Gemmatimonadetes Firmicutes Chloroflexi Chlorobi Bacteroidetes Armatimonadetes Actinobacteria Acidobacteria

90 80 70 60 50 40 30 20 10 0

90 80

Environmental

70

Unclassified

60

Unknown Glomeromycota

50

Cryptomycota

40

Chytridiomycota

30

Blastocladiomycota Basidiomycota

20

Ascomycota

10 0

Native

D1

D2

D3

native

D1

D2

D3

FIG 2 Relative abundances of bacterial (A) and fungal (B) phyla for the native community and each level of diversity D1 (high), D2 (medium) and D3 (low) at T0. Only phylogenetic groups (phyla) with a relative abundance > 1% are shown. Abundance of each phylum was determined from n=3 biological replicates.


A

1.6

1.2

Δ Respiration (mg C-CO2 g-1 dry soil)

Respiration (mg C-CO2 g-1 dry soil)

Respiration (mg C-CO2 g-1 dry soil)

B 0,4

0 0

2

4 6 Incubation time (days)

8

10

0.8

0.4

0 0

10

20

30

40


50

60

1.2

c

D1 D2 D3

1

c b

b

0.8

b b b

0.6

a

a a

b

a

0.4 b a a

a

aa

0.2 0 3

7

10

14

21

28

44

60


FIG 3 (A) Cumulative total soil respiration (mg C-CO2 g-1 dry soil) over 60 days in control (dashed lines) and wheat amended (full lines) microcosms for the three levels of diversity with high diversity (D1, black), medium diversity (D2, Grey) and low diversity (D3, light grey). (B) Difference between respiration of control and wheat amended microcosms (∆ mg CO2 g-1 dry soil) for the three levels of diversity (D1; black, D2; grey and D3; light grey). For each incubation time and level of diversity, values with different letters differ significantly (P < 0.05). Inset represents a focus on the first 10 days to visualize the early shifts. Error bars denote standard deviation of biological replicates (n = 3)


A

0,1

c

b

c

a

b a

b

0 0

2

0.4

4

6

8

10


b

c b b

b a

b

0.2

b bb a

b b

a

ab a

a

D1 D2 D3

a a

a

Priming effect (mg C-CO2 g-1 dry soil)

a

c

b

b

0.6

b b b

0

10

20

30 40 50 Incubation time (days)

60

b b

c

b b

a

c

b a a

0 0

2

4

0.4

6

8

10


c b b

b b

0.2 b

0

10

a

b

a

a

a

b b b b b a a a a

0

0

D1 D2 D3

Priming effect (mg C-CO2 g-1 dry soil)

D1 D2 D3

Wheat C-mineralization (mg C-CO2 g-1 dry soil)

Wheat C-mineralization (mg C-CO2 g-1 dry soil)

B

0,2

a

20

30 40 50 Incubation time (days)

D1 a D2 D3

60

FIG 4 (A) Cumulative respiration of wheat residues (mg C-CO2 g-1 dry soil) and (B) Cumulative priming effect (mg C-CO2 g-1 dry soil) measured during the 60 days incubation for the three levels of diversity with high diversity (D1, black), medium diversity (D2, Grey) and low diversity (D3, light grey). Insets represent a focus on the first 10 days to visualize the early shifts. For each incubation time and level of diversity, values with different letters differ significantly (P < 0.05). Error bars denote standard deviation of biological replicates (n = 3)


0.6

A

1.8

b

Respiration allochthonous/autochthonous carbon source

b

b

1.6

b

0.6

b

c b

ab

b

1.4

a

0.5

1.2

a

a

a

a

0.4

1

0.8

0.3

0.6 0.2 0.4

Total respiration in wheat-amended microcosms (mg C-CO2 g-1 dry soil)

b

0.1 0.2

0

0 3

7

10

14

21

28

44

60

FIG 5 Relative contribution of allochtonous vs. autochthonous carbon sources to total CO2 emission (Bars) and cumulative total soil respiration in wheat-amended microcosms (curves) during the 60 days of incubation for the three levels of diversity, with D1 (high divers, black), D2 (medium diversity, grey) and D3 (low diversity, light grey). For each incubation time and level of diversity, values with different letters differ significantly (P < 0.05). Error bars denote standard deviation of biological replicates (n = 3)

D1 D2 D3 Tot-D1 Tot-D2 Tot-D3


0.7