Wolfram R Grüning

Harald Bieringer

Martin Schwarzwalder

David Gajan

Aurélien Bornet

Basile Vuichoud

Jonas Milani

David Baudouin

Laurent Veyre

Anne Lesage

Martin Schwarzwälder

Sami Jannin

Geoffrey Bodenhausen

Chloé Thieuleux

Christophe Copéret

Phenylazide Hybrid-Silica -Polarization Platform for Dynamic Nuclear Polarization at Cryogenic Temperatures

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Introduction

Generating hyperpolarized samples for magnetic resonance imaging (MRI) and nuclear magnetic resonance (NMR) spectroscopy is of great interest because it enables sensitivity enhancements by several orders of magnitude. [1] In this context, Dynamic Nuclear Polarization (DNP) at cryogenic temperatures has been a very active field of research in the past few years, leading to tremendous progress in radical design and instrumentation, and opening new applications in solids under Magic Angle Spinning (MAS) as well as in solution after rapid dissolution. [1c, 2] DNP-enhanced MAS NMR and dissolution DNP both rely on the transfer of electron spin polarization to the surrounding NMR-active nuclei [3] by saturating the EPR (Electron Paramagnetic Resonance) transitions at low temperatures (1.2-105 K). For that purpose a polarizing agent (PA), e.g., a persistent organic radical, and an analyte are comixed in solution and shock-frozen, usually into a glassy matrix. The transfer of polarization from electrons to protons in solids relies on three mechanisms: the solid effect (one electron and one nucleus), the cross effect (two electrons and one nucleus) and thermal mixing (involving many electrons) [4] . Overhauser effects have been recently reported as a fourth mechanisms in insulating solids. [5] The cross effect and thermal mixing, which are the most relevant for DNP MAS NMR and dissolution DNP, respectively, require appropriate dipolar couplings between electrons to be efficient. [6] In glassy frozen solutions of monoradicals, the dipolar coupling strengths span a wide range because of the random spatial distribution of the free electrons. For mono-radicals, the concentration of the PA (usually around 10 19 spins/mL or 25 mM) is the only parameter that allows one to adjust the inter-radical distances, and hence the electronelectron dipolar couplings, which are in fine critical for the DNP efficiency in frozen glasses. To acquire a better control, we have recently introduced solid polarizing matrices made of mesostructured hybrid silica featuring TEMPO radicals distributed homogeneously on the surface of the pores and demonstrated that they could be successfully used for DNP both near 4 and 105 K. [7] . Here we use the term homogeneous in the sense of a narrow statistical distribution as it would be found in a solution of non-interacting molecules. We do not intend to imply an ordered mono-spaced arrangement of molecules. In the first generation of these materials (named HYPSO-1), the TEMPO moieties were attached to the silica surface via a propyl chain. Tailoring the distribution of radicals along the silica pore channels turned out to be key for maximizing the proton polarization and hence achieving high enhancements. The nature of the linker anchoring the TEMPO radicals to the surface of the hybrid material, flexible vs. rigid, is expected to be important parameter. In a different context, namely in catalysis, [8] we have recently reported that flexible tethers in functionalized hybrid materials enable interactions of the organic moieties with the silica surface, thus improving the catalytic performance by stabilizing reaction intermediates.

Here we describe the synthesis and characterization of a solid-state polarization matrix in which the TEMPO moiety is covalently linked to the silica surface via a rigid tether consisting of a phenyl ring (coined HYPSO-4), The performance of this material is investigated under both solidstate MAS and dissolution DNP conditions, and compared to those of HYPSO-1 where the radicals were anchored at the surface via flexible alkyl chains. [7]

Results and Discussion

The materials were prepared using apolar phenylazido silicon molecular precursors which can be readily introduced into hybrid silicas over a broad range of concentrations without compromising the structuration of the materials. The TEMPO moiety can then easily be introduced through a peptide coupling step after reduction of the azido into an amino functionality (Scheme 1B). The synthesis of the azide-precursor 1 is shown in Scheme 2; it was readily prepared in two steps by monosilylation of 1,4-diiodobenzene 2 yielding 4-iodophenyltri(isopropoxy)silane 3 [9] followed by a copper-catalyzed iodideazide exchange. [10] In a second step, materials with different concentrations of organic functionalities were synthesized by a sol-gel process under acidic conditions (pre-hydrolysis step) in the presence of EO20PO70EO20 (Pluronic ® P123) as structuredirecting agent (SDA) followed by the addition of sodium fluoride (condensation step) as depicted in Scheme 2. [11] Materials were synthesized in four decreasing concentrations from 1 with 1:Si(OEt)4 ratios of 1:34, 1:55, 1:100, and 1:200. The SDA was subsequently removed by extraction with pyridinium hydrochloride and the materials were dried under high vacuum. [12] The MatPhN3 materials exhibit type IV isotherms in N2 ad-/desorption at 77 K typical of highly mesoporous solids. Brunauer Emmett Teller [13] (BET) surface areas are consistently high (> 700 m 2 g -1 ). Pore sizes between 8-10 nm are calculated using the Barnett Joyner Halenda method at the adsorption branch of the isotherms [14] . The pore size increases with decreasing concentrations of organic precursors. TEM of the materials reveals a worm-like porous network for the most concentrated material. The network evolves towards a 2D hexagonal arrangement with decreasing concentrations, as confirmed by small angle X-ray scattering which shows a single intense broad signal at 2 ≈ 0.75° (ca. 12 nm).

Scheme 1. A) HYPSO-1 with flexible linker vs. HYPSO-4 with a rigid linker. B) Retrosynthesis of HYPSO-4.

Manipulation of the nitrogen-containing functionality leads to the final material in two steps: [7a, 11d] i) the azide materials MatPhN3 are first reacted with dimethylphenylphosphine in a Staudinger reduction [15] to yield the corresponding aniline materials MatPhNH2 and ii) the TEMPO moiety is introduced through a classical amide coupling using 4-TEMPO carboxylic acid anhydride ( 5), yielding MatPhTEMPO, hereafter referred to as HYPSO-4.0. The surface area remained high throughout all subsequent post-synthetic modifications.

DRIFT spectra of the materials were acquired at each synthetic stage. All spectra show the characteristic stretching bands corresponding to isolated and bridged silanols at 3740 and 3550 cm -1 respectively. The DRIFT spectrum of the azide material MatPhN3 (SI) shows two strong bands at 2133 and 2104 cm -1 characteristic of azide groups. These are only slightly shifted compared to 2126 and 2094 cm -1 of the molecular precursor 1. The appearance of two bands has been attributed to a Fermi resonance in the phenyl azide materials. [16] The azide groups were quantitatively converted to the corresponding amines, as evidenced by IR, in contrast to materials containing propyl linkers, for which a substantial fraction (ca. 20%) of residual azide groups were observed suggesting that the phenyl azide groups, in contrast to the propyl azide groups, are exclusively located in the mesopores rather than in the less accessible micropores of the materials. The spectra also contain C-H bands at 2955 and 2853 cm -1 characteristic for surface methoxy groups. The aniline N-Hst band overlaps with the bridging silanols, as the band would be expected at 3368 cm -1 like in the molecular analogue 4 (4triethoxysilylaniline). After introducing the TEMPO moiety, the bands associated with surface methoxy groups disappear from the spectrum of HYPSO-4 (SI). Furthermore, the introduction of the amide functionality is confirmed by the appearance of the two bands at ca. 1680 and 1502 cm -1 .

Further characterization was obtained by conventional roomtemperature solid-state NMR spectroscopy and DNP Surface Enhanced NMR Spectroscopy (DNP-SENS) near 105 K. [17] The carbon-13 CP-MAS (Cross-Polarization Magic-Angle Spinning) spectrum of MatPhN3 (Figure 1A) shows four peaks at 143, 135, 126 and 118 ppm assigned to the carbons of the phenyl ring, since these chemical shifts are almost identical to those of precursor 1. While larger line widths are observed for the DNP-SENS spectrum, due to the presence of exogenous radicals and to the low temperature of the sample, recording a 2D 1 H- 13 C HETCOR spectrum (SI) allowed corroborating the assignment. In addition, the 29 Si spectrum (SI) shows a signal at -80 ppm associated with T-substructures (T 3 ) along with more intense signals at -110 ppm corresponding to Q-substructures of bulk silica. Along similar lines, the 13 C-spectrum of MatPhNH2 (Figure 1B) reveals the characteristic shift of the resonance at 148 ppm of the quaternary carbon in position 1 of the phenyl ring, while the remaining resonances of the phenyl ring are observed at 137 and 114 ppm, in very good agreement with the analogous compound 4. Note the presence of an intense resonance of the surface methoxy groups at 55 ppm. The 1 H- 13 C HETCOR DNP-SENS spectrum (SI) also corroborates the assignment, while the 29 Si DNP-SENS spectrum (SI) remains unchanged. Finally, HYPSO-4 materials were further analyzed by EPR spectroscopy in order to evaluate the radical content by spin count experiments and the radical distribution through line width analysis (Figure 2). [7a] The radical content was found to be in the range of 0.16 -0.016 mmol g -1 (Figure 2A), which corresponds to an overall synthetic efficiency of 24-37%, slightly better than for HYPSO-1 (17-32%). Figure 2B shows a slight linear broadening of the EPR line width of HYPSO-4 with increasing radical concentration from 11.7 to 13.9 G . This broadening is less pronounced than for HYPSO-1 for which a line width of 15.3 G was found for a comparable radical concentration (0.14 mmol•g -1 ). These reduced line widths at high radical concentrations (> 0.135 mmol g -1 ) reflect a larger mean radicalradical distance for HYPSO-4 than for HYPSO-1, and hence a better radical distribution. This can be attributed, for a given PA concentration, either to a greater surface area or to a more uniform distribution of radicals on the surface. With these promising EPR data in hand, the materials were impregnated with C2H2Cl4 and their DNP performance was evaluated under MAS conditions at ca. 105 K and 9.4 T (Figure 3). The enhancements of the solvent proton, carbon and surface silicon resonances increase from ε( 1 H)/ε( 13 C)/ε( 29 Si) = 3.6/4.2/ 3.1 for HYPSO-4 1/200 (0.02 mmol (radical)•g -1 ) to 12.1/13.3/ 9.2 for the 1/55 material (0.11 mmol (radical)•g -1 ). Further increasing the radical concentration does not increase the enhancement, which reaches a plateau at ca. (10.9, 13.5, 9.5) for HYPSO-4 1/34 (0.16 mmol (radical)•g -1 ). This plateau is similar to that obtained for HYPSO-1 1/34 (0.10 mmol (radical)•g - 1 ): (13.2, 13.3, 13.3) under identical experimental conditions on the same instrument. [7a] The presence of a plateau at high radical concentrations in HYPSO-4 stands in contrast to the decrease observed for HYPSO-1 (see ref. 14) and suggests that HYPSO-4 reduces large and unfavorable electron-electron dipolar interactions at high radical coverage. When the material is wetted with either pure water or an aqueous 0.1 M solution of [ 13 C]5-proline, a very similar enhancement curve is obtained, with ( 13 C) = 2.1 for the very diluted HYPSO-4 1/200, increasing to a maximum ( 13 C) = 22 for HYPSO-4 1/55, the latter being somewhat below ( 13 C) = 32 found for HYPSO-1 1/34. 7a We also investigated the performance for dissolution-DNP experiments. The 1 H polarizations P( 1 H) and the corresponding build-up times were measured at 4.2 K and 6.7 T [START_REF] Jannin | Synthesis of a tailored hyper polarizing solid matrix towards more efficient solidstate and dissolution DNP[END_REF] for the same set of materials wetted with H2O:D2O (1:9) (Fig. 4.)The polarization P( 1 H) after 300 s and build-up times DNP are compared in Table 1. The polarization increases from 2.3% for of HYPSO-4 1/34 to 9.6% for HYPSO-4 1/55 before decreasing to 7.8% and 2.8% for HYPSO-4 1/100 and 1/200. Note that after 600 s the polarization reached P( 1 H) = 11.0% for HYPSO-4 1/100 whereas it remained nearly constant for the HYPSO-4 1/55 and reaches 10.2% for the most diluted sample, in line with its slower build up.

Conclusions

We have characterized high surface area hybrid silica materials with a worm-like arrangement of the porous network containing rigid 1,4-phenylene tethers. The phenylazide groups are uniformly incorporated and readily amenable to functionalization, allowing the homogeneous introduction of radicals with a broad range of concentrations, as demonstrated by narrow EPR linewidths even for the most concentrated materials. HYPSO-4 shows enhancements in MAS-DNP over a broader range of concentrations than the HYPSO 1 and 2 having flexible linkers. The polarization build-up at 1. 2 and 4 K is suitable for Dissolution-DNP. This study suggests that the unfavorable electron-electron dipolar interactions are reduced with HYPSO-4 at high PA concentration (0,16 mmol/g), which translates into high enhancements when C2H2Cl4 is used to wet the solid. However, this result does not translate directly into a higher proton polarization for dissolution DNP. We are currently exploring complementary molecular design to improve the polarization and understand the role of different features of the materials.

Experimental Section

General procedure. All used chemicals are commercially available und were used without further purification, except ClSi(Oi-Pr)3 that was prepared from SiCl4 and iso-propanol and purified by distillation. Dry solvents and glassware as well as argon or nitrogen as inert gases were used whenever appropriate. A blast shield was used for reactions involving molecular azides.

Synthesis of 4-(Triisopropoxysilyl)-iodobenzene

(3). p-diiodobenzene 2 (7.50 g, 22.7 mmol) was put into a 250 mL Schlenk-flask and dissolved in Et2O (100 mL). The solution was cooled to -78°C (acetone/CO2(s)) and n-BuLi (14.2 mL, 22.7 mmol, 1.6 M in hexane) was added dropwise with a syringe. After complete addition, the mixture was warmed to 0°C and stirred for 1 h. Then, the solution was cooled to -78°C and was slowly transferred via a cannula into a 250 mL two-necked flask, containing a vigorously stirred solution of ClSi(Oi-Pr)3 (17.5 mL, 68.2 mmol) in Et2O (30 mL) at -78 °C. After complete addition, the resulting green solution was warmed to 25°C and stirred for 18 h. The solution was then filtrated (celite and activated charcoal) and the solvent was removed under reduced pressure. The product was purified by Kugelrohr distillation. 4-(Triisopropoxysilyl)-iodobenzene (3) was obtained as colorless liquid (6.44 g, 69 %). 1 H NMR (300 MHz, CDCl3) /ppm = 7.71 (m, 2H), 7.39 (m, 2H), 4.24 (septet, J = 6.1 Hz, 3H), 1.20 (d, J = 6.1 Hz, 18H). 13 C NMR (75 MHz, CDCl3) /ppm = 136. 9, 136.6, 132.6, 97.5, 65.7, 25.6. HRMS (EI) calculated for C15H25IO3Si [M] + 408.0618, found 408.0613.

Synthesis of 4-(Triisopropoxysilyl)-azidobenzene (1). CuI (210 mg, 1.10 mmol) was put into a 50 mL Schlenk-flask and dissolved in DMSO (15 mL). NaL-prolinate (304 mg, 2.22 mmol), NaN3 (970 mg, 14.9 mmol) and 3 (3.06 g, 7.48 mmol) were added to the solution.,The mixture was heated to 65°C, stirred and monitored by TLC and 1 H NMR. After 6.5 h, NaN3 (310 mg, 4.77 mmol) was added. The reaction was stirred for 1 h. Then, the solution was poured into a separation funnel and Et2O (100 mL) and H2O (100 mL) were added. The phases were separated and the aqueous phase was extracted with Et2O (75 mL × 3). The combined organic phases were washed with brine (100 mL), dried over MgSO4 and the solvent was removed under reduced pressure. The product was purified by column chromatography with gradient elution (cyclohexane : CH2Cl2 = 9 : 1 (2 L), cyclohexane : CH2Cl2 = 4 : 1 (1 L), cyclohexane : CH2Cl2 = 1 : 1 (1 L), with 1 % Et3N in each eluent). 4-(Triisopropoxysilyl)-azidobenzene (1) was obtained as yellow oil (826 mg, 34 %). 1 H NMR (300 MHz, CDCl3) /ppm = 7.66 (m, 2H), 7.02 (m, 2H), 4.25 (septet, J = 6.1 Hz, 3H), 1.21 (d, J = 6.1 Hz, 18H). 13 C NMR (75 MHz, CDCl3) /ppm = 141. 8, 136.6, 129.7, 118.5, 65.6, 25.6 General procedure for Phenylazide materials (MatPhN3 1/34). A 250 mL polyethylene flask was filled with Pluronic ® P123 (SDA) (2.64 g, 0.45 mmol) and aqueous HCl (106 mL, pH 1.5) was added. A mixture of Si(OEt)4 (5.49 g, 26.3 mmol) and 1 (251 mg, 0.77 mmol) was added. The resulting solution was stirred for 2 h at 25°C. The solution was heated to 40 °C and NaF (50.3 mg, 1.20 mmol) was added under stirring (400 rpm). The turbid mixture was stirred for 72 h at 40 °C. The solid was filtered off and washed with H2O (125 mL × 3). The resulting yellowish solid was suspended in a mixture of pyridine (17.5 mL), H2O (17.5 mL) and aqueous HCl (5.8 mL, 2 M). The slurry was stirred for 12 h at 70 °C. The solid was filtered off, washed with H2O (125 mL × 3), acetone (125 mL × 3), and Et2O (125 mL × 3) and dried under high vacuum (10 - 5 mbar, 25 °C, 16 h) Note the increased thermal sensitivity of this material compared to the previously reported 3-n-propylazide system. MatPhN3 1/34 was obtained as a pale yellow powder (1.54 g). 1 H MAS-SSNMR (700 MHz) /ppm = 8.20 (s), 7.44 (s), 2.37 (broad, as.) . 13 C MAS-SSNMR (100 MHz) /ppm = 143. 9, 135.2, 127.0, 117.4. 29 Si MAS-SSNMR (139 MHz) /ppm = -90.1, -101.1, -110.5. IR (DRIFT) /cm -1 = 3740, 3547, 2134, 2104, 1598, 1503. Surface area (BET, N2, 77 K) 982 m 2 /g, pore diameter (BJH N2, 77 K) 9.3 nm.

General procedure for Aniline materials (MatPhNH2 1/34). MatPhN3 1/34 (1.04 g) was suspended in THF (35 mL) and cooled to 0 °C. Dimethylphenylphosphine (0.65 mL, 4.53 mmol) was added. After complete addition, the yellow suspension was warmed to 25°C and stirred for 18 h. MeOH (30 mL) was added, and after 40 min H2O (20 mL) was added. After 20 min the solid was filtered off and washed with H2O (125 mL × 3) and MeOH (125 mL × 3). The material was washed by Soxleth extraction (MeOH) for 48 h. Then, the solid was filtered off, washed with MeOH (125 mL × 3), acetone (125 mL × 3), and Et2O (125 mL × 3), and dried under high vacuum (10 -5 mbar, 140 °C, 5 h). MatPhNH2 1/34 (6), was obtained as white powder (850 mg). 13 C MAS-SSNMR (176 MHz) /ppm = 149.0 135. 5, 113.2, 49.0. 29 Si MAS-SSNMR (139 MHz) /ppm = -94. 5, -102.4, -110.7. IR (DRIFT) /cm -1 = 3731, 3470, 2955, 2853, 1625, 1603, 1512, 1464. Surface area (BET, N2, 77 K) 848 m 2 /g; pore diameter (BJH N2, 77 K) 8.1 nm.

General procedure for HYPSO-4 (MatPhTEMPO 1/34). MatPhNH2 1/34 (327 mg) was put into a 50 mL Schlenk-flask and suspended in CH2Cl2 (9 mL). A mixture of 4-(dimethylamino)-pyridine (DMAP) (10.3 mg, 0.08 mmol) and 5 (288 mg, 0.75 mmol) was added and the resulting orange suspension was stirred for 48 h at room temperature. The solid was filtered off, washed with CH2Cl2 (75 mL × 3), THF (75 mL × 3), H2O (75 mL × 3), acetone (75 mL × 3) and Et2O (75 mL × 3). The resulting pale orange solid was transferred into a 20 mL Schlenk-flask and H2O (7 mL) was added. The suspension was stirred at 50 °C for 20 h. The solid was filtered off, washed with acetone (10 mL × 3) and Et2O (10 mL × 3) and dried under high vacuum (10 -5 mbar, 140 °C, 12 h). HYPSO-4 1/34 was obtained as pale orange powder (200 mg). IR (DRIFT) /cm -1 = 3735, 3522, 2958, 2922, 1625, 1603, 1512, 1462. Surface area (BET, N2, 77 K) 841 m 2 /g; pore diameter (BJH N2, 77 K) 8.1 nm. Radical concentration (CW EPR): 0.16 mmol/g.

Scheme 2 .

 2 Scheme 2. Synthesis of 4-tri-i-propoxysilylphenylazide, materials and post-synthetic functionalization 1. a) 1) n-BuLi, Et2O -78°C to 0°C, 1 h; 2) ClSi(Oi-Pr)3, Et2O, -78°C to 25°C 12 h. b) NaN3, CuI (5 -10 mol%), Na-L-prolinate, DMSO, 65°C, 7 h. 22% over 2 steps c) 1) Si(OEt)4 (n equiv.) NaF, P123, aq. HCl pH 1.5, 40 °C, 72 h. 2) py, aq. HCl, 70 °C, 16 h. d) 1) PMe2Ph, THF, 0 °C to 25 °C, 22 h. 2) H2O/MeOH. 3) MeOH Soxhlet extraction, 48 h. e) 4-TEMPO-Anhydride 5, DMAP, CH2Cl2, 25 °C, 48 h.

Figure 1 A) 13 C

 13 Figure 1 A) 13 C NMR spectra at 100 MHz of MatPhN3: DNP-SENS CP-MAS spectrum at 105 K (,top), room temperature CP-MAS spectrum (176 MHz middle) and 13 C solution-state NMR spectrum of the precursor 1 (in CDCl3, 75 MHz; bottom) B) 13 C-NMR spectra of MatPhNH2: DNP-SENS CP-MAS spectrum 100 MHz (,top), room temperature CP-MAS spectrum (176 MHz, middle) and

Figure 2 A

 2 Figure 2 A) radical concentration vs. precursor dilution ratio. B) X-band EPR line width vs. radical concentration.

Figure 3

 3 Figure 3 DNP enhancements for materials with different radical concentrations (mmol•g -1 ) of HYPSO-4 wetted with C2H2Cl4. The spectra were acquired at ca. 105 K on a Bruker 9.4 T DNP solid-state NMR spectrometer equipped with an Avance III console and a low-temperature 3.2 mm triple resonance probe. The sample spinning frequency was 12 kHz for 1 H and 13 C, and 8 kHz for 29 Si.Two regimes of polarization kinetics are observed: the more concentrated HYPSO-4 1/34 and 1/55 show a rapid, almost perfectly mono-exponential build-up of the polarization whereas the more diluted HYPSO-4 1/100 and 1/200 show a slower biexponential build-up. We attribute the fast component to the direct polarization of protons by the radicals and the slow component to the propagation of the polarization by nuclear spin diffusion to sample areas where DNP is less effective. The material HYPSO-4 1/55, which shows the most favorable polarization kinetics, was investigated at 1.2 K. The 1 H polarization build-up (SI) can be characterized by two build-up times DNP fast = 20 s and DNP slow = 104 s.

Figure 4 .

 4 Figure 4. Polarization build-up at 4.2 K and 6.7 T for HYPSO-4 with different radical concentrations expressed in terms of dilution ratios.

  . HRMS (EI) calculated for C15H25N3O3Si [M] + 323.1665, found 323.1660. IR (ATR) /cm -1 = 2971 (m), 2126 (m), 2094 (m), 1592 (w), 1381 (m), 1369 (m), 1290 (m), 1173 (m), 1112 (s), 1026 (s), 887 (m), 870 (m), 817 (m), 756 (m), 719 (m) cm -1 .

C solution-state NMR spectrum of the precursor 4 (in CDCl3, 75 MHz; bottom). The DNP-SENS and CPMAS spectra were acquired with 32 and 10240 scans respectively and processed with an exponential line broadening of 100 Hz.

Acknowledgment

We acknowledge financial support from the Swiss National Science Foundation, the ETH Zürich, EPFL, and Bruker BioSpin AG. We also want to thank Prof. L. Emsley (EPFL) for helpful discussions.

Abbreviations


D R Mathieu Santonja
email: mathieu.santonja@univ-rennes1.fr

Lou Barbe
email: lou.barbe@univ-rennes1.fr

Functionally or phylogenetically distinct neighbors turn antagonism among decomposing litter species into synergy

Keywords: Dissimilar neighbors favor synergistic decomposition ecosystem functioning, functional traits, grassland communities, litter mixing, nonadditive effects, phylogenetic distance, plant litter decomposition, plant neighborhood

1. Plant species coexisting in direct contact produce patches of mixed litters. Mixing litter sometimes synergistically accelerates and sometimes antagonistically decelerates litter decomposition, but we insufficiently understand why.

2.

Here we hypothesize that antagonism or synergy within a mixed-litter patch depends on the neighboring litter matrix. Specifically, phylogenetical or functional dissimilarity within neighboring litter, or among patch and neighboring litter, may favor complementarity and thereby within-patch synergy.

3.

From a pool of 20 grassland species, we created 120 mixed-litter patches of two species, and exposed these patches to neighborhoods in long-term grassland mesocosms of different functional and phylogenetic compositions.

4.

We found 60% less (antagonism) to 80% more (synergy) decomposition than expected from single-species litters. Functionally similar, and grass-dominated, mixed-litter patches decomposed most synergistically. Synergy was most strongly favored by phylogenetic distance among neighbors and functional dissimilarity between neighbors and patch.

Synthesis.

Our results show that the relationship between biodiversity and ecosystem functioning was context-dependent. We suggest that the coexistence of grasses and the formation of phylogenetically diverse, functionally distinct, patchy vegetation may be reinforced by synergistic nutrient recycling.

INTRODUCTION

The relationship between species richness and ecosystem functioning has been strongly debated in ecology [START_REF] Grime | Benefits of plant diversity to ecosystems; immediate, filter and founder effects[END_REF][START_REF] Cameron | the year of the "diversity-ecosystem function" debate[END_REF][START_REF] Reiss | Emerging horizons in biodiversity and ecosystem functioning research[END_REF][START_REF] Gessner | Diversity meets decomposition[END_REF][START_REF] Loreau | Biodiversity and ecosystem stability: a synthesis of underlying mechanisms[END_REF]. In terrestrial ecosystems, an important aspect of ecosystem functioning is plant litter decomposition, driving nutrient cycling as well as carbon storage [START_REF] Swift | Decomposition in terrestrial ecosystems[END_REF].

Many studies investigated whether multi-specific litter mixes decomposed faster or slower than the

Accepted Article

. corresponding mono-specific litters alone, i.e. whether coexisting species in mixed litters exerted respectively synergistic or antagonistic effects during decomposition. These studies found opposite results [START_REF] Gartner | Decomposition dynamics in mixed-species leaf litter[END_REF][START_REF] Hättenschwiler | Biodiversity and Litter Decomposition in Terrestrial Ecosystems[END_REF][START_REF] Gessner | Diversity meets decomposition[END_REF], depending on abiotic conditions [START_REF] Jonsson | Context dependency of litter-mixing effects on decomposition and nutrient release across a long-term chronosequence[END_REF][START_REF] Santonja | Climate change effects on litter decomposition: intensive drought leads to a strong decrease of litter mixture interactions[END_REF], composition and functional properties of mixed litter [START_REF] Wardle | The influence of plant litter diversity on decomposer abundance and diversity[END_REF][START_REF] Chapman | What type of diversity yields synergy during mixed litter decomposition in a natural forest ecosystem[END_REF][START_REF] Pérez Harguindeguy | More than the sum of its parts? Assessing litter heterogeneity effects on the decomposition of litter mixtures through leaf chemistry[END_REF][START_REF] Barantal | C, N and P fertilization in an Amazonian rainforest supports stoichiometric dissimilarity as a driver of litter diversity effects on decomposition[END_REF][START_REF] Bílá | Disentangling community functional components in a litter-macrodetritivore model system reveals the predominance of the mass ratio hypothesis[END_REF], and identity or activity of decomposers [START_REF] Schädler | Do invertebrate decomposers affect the disappearance rate of litter mixtures?[END_REF][START_REF] Vos | Macro-detritivore identity drives leaf litter diversity effects[END_REF][START_REF] Vos | Plant removals in perennial grassland: vegetation dyanimcs, decomposers, soil biodiversity, and ecosystem properties[END_REF][START_REF] Handa | Consequences of biodiversity loss for litter decomposition across biomes[END_REF]. Many of these studies showed that litter mixtures that are functionally dissimilar decompose synergistically, probably by providing complementary resources needed by generalist decomposers or by multiple specialist decomposers, which have complementary effects on decomposition [START_REF] Meier | Links between plant litter chemistry, species diversity, and below-ground ecosystem function[END_REF][START_REF] Gessner | Diversity meets decomposition[END_REF][START_REF] Vos | Plant removals in perennial grassland: vegetation dyanimcs, decomposers, soil biodiversity, and ecosystem properties[END_REF][START_REF] Bílá | Disentangling community functional components in a litter-macrodetritivore model system reveals the predominance of the mass ratio hypothesis[END_REF][START_REF] Tardif | The relationship between functional dispersion of mixed-species leaf litter mixtures and species' interactions during decomposition[END_REF]. But, other studies showed that trait dissimilarity might also render decomposition antagonistic as a given decomposer or detritivore might find its preferred resources diluted in unpreferred ones [START_REF] Pan | Larger phylogenetic distances in litter mixtures: lower microbial biomass and higher C/N ratios but equal mass loss[END_REF]. However, the majority of studies were done in natural conditions, in which the ambient plant communities were not held constant or even taken into account to explain litter-mixture decomposition [START_REF] Jonsson | Context dependency of litter-mixing effects on decomposition and nutrient release across a long-term chronosequence[END_REF][START_REF] Makkonen | Do physical plant litter traits explain non-additivity in litter mixtures? A test of the improved microenvironmental conditions theory[END_REF][START_REF] Santonja | Climate change effects on litter decomposition: intensive drought leads to a strong decrease of litter mixture interactions[END_REF]. This community context may control abiotic and biotic conditions surrounding litter mixtures, and might ultimately determine synergies or antagonisms during litter-mixture decomposition.

Under natural conditions, decomposition of a patch of litter mixture may depend on the ambient community matrix. Synergy or antagonism among co-decomposing litter species is induced by small, mostly microscopic organisms interacting with litters across very short distances of some centimeters. But these minute patches of mixed litter are surrounded by larger patches formed by the neighboring vegetation, reflecting for instance the clonal growth of plant species (which often promotes an aggregated vegetative growth, rendering plants different from their larger neighboring plant matrix) or short-distance dispersal of plant species [START_REF] Kershaw | Pattern in vegetation and its causality[END_REF][START_REF] Herben | Spatial pattern formation in plant communities[END_REF][START_REF] Semchenko | Plants are least suppressed by their frequent neighbours: the relationship between competitive ability and spatial aggregation patterns[END_REF]. Due to this plant-community heterogeneity, a patch of litter mixture may be

Accepted Article

. embedded in a plant matrix ranging from identical to very different, both in traits and phylogenetic positions. Moreover, neighborhoods as such can be homogenous or composed of functionally or phylogenetically distinct plant species. We will outline below how this variation in distinctness of neighborhoods might strongly affect the synergy or antagonism during decomposition of a litter mixture, i.e. how the effect of diversity on ecosystem functioning might be dependent on the community context.

First, synergy among co-decomposing litter species might be favored by a plant neighborhood composed of functionally dissimilar species. A functionally dissimilar neighborhood might provide dissimilar resources and constraints. For instance, dissimilar specific leaf areas (SLA), leaf C:N ratios or water-holding capacities (WHC), might correspond to dissimilar nutritional compositions or microclimates. Thereby, functionally dissimilar plant neighborhoods may favor resource complementarity and harbor diverse and generalist decomposers and detritivores (Wardle et al. 1999;[START_REF] Spehn | Plant diversity effects on soil heterotrophic activity in experimental grassland ecosystems[END_REF][START_REF] Hättenschwiler | Biodiversity and Litter Decomposition in Terrestrial Ecosystems[END_REF][START_REF] Meier | Links between plant litter chemistry, species diversity, and below-ground ecosystem function[END_REF][START_REF] Gessner | Diversity meets decomposition[END_REF].

Diverse and generalist decomposers and detritivores might not only tolerate but require the combination of multiple litter resources to be fully performant and active, which might trigger a synergy during litter-mixture decomposition [START_REF] Hedde | Patterns and mechanisms responsible for the relationship between the diversity of litter macro-invertebrates and leaf degradation[END_REF][START_REF] Vos | Macro-detritivore identity drives leaf litter diversity effects[END_REF][START_REF] Handa | Consequences of biodiversity loss for litter decomposition across biomes[END_REF][START_REF] Coulis | Functional dissimilarity across trophic levels as a driver of soil processes in a Mediterranean decomposer system exposed to two moisture levels[END_REF]. Therefore, we hypothesize that functional dissimilarity within the plant neighborhood triggers synergy during decomposition of a litter mixture (Fig. 1).

Second, decomposition of a litter mixture might be synergistic if the plant neighborhood is functionally similar to the litter mixture. In such a case, ambient detritivores and decomposers might show particular adaptations for using the mixed-litter type in the local litter-mixture patch. For instance, ambient decomposers and detritivores that have been exposed to leaf dry matter contents (LDMC) or WHC similar to those in the litter mixture likely show the adaptations needed for decomposing the litters in the patch. Availability of decomposers adapted to a locally dominant litter explains why litters often decompose faster below the plants that have produced them, an effect

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. called "Home-Field Advantage" [START_REF] Ayres | Soil biota accelerate decomposition in highelevation forests by specializing in the breakdown of litter produced by the plant species above them[END_REF][START_REF] Austin | There's no place like home? An exploration of the mechanisms behind plant litter-decomposer affinity in terrestrial ecosystems[END_REF][START_REF] Freschet | Multiple mechanisms for trait effects on litter decomposition: moving beyond home-field advantage with a new hypothesis: Substrate-matrix quality interactions in decay[END_REF][START_REF] Veen | Litter quality and environmental controls of home-field advantage effects on litter decomposition[END_REF]. Such adapted decomposer species might immigrate into the litter mixture and might there be capable of using both litters and profiting from their complementarity, thereby triggering synergistic decomposition. In contrast, decomposers that might colonize a litter mixture from a dissimilar neighborhood might be capable of profiting at most from a single of the litter species, and might suffer from dilution of that species among a second entirely unusable species, thereby possibly triggering antagonistic decomposition. Whether Home-Field Advantage may also foster synergy among litter mixtures is still unknown. We hypothesize that high functional similarity between plant neighborhood and litter mixture triggers Home-Field Advantage also in terms of synergy during decomposition (Fig. 1).

Third, such neighborhood dependency of synergistic effects also implies that the relationship between litter mixing and litter decomposition may change with the phylogenetic diversity of the litter mixture as well as that of the plant neighborhood. Phylogenetically distant species may have diverged in traits related to litter decomposition [START_REF] Crisp | Phylogenetic niche conservatism: what are the underlying evolutionary and ecological causes?[END_REF][START_REF] Pan | Larger phylogenetic distances in litter mixtures: lower microbial biomass and higher C/N ratios but equal mass loss[END_REF]; but see [START_REF] Prinzing | Less lineages more trait variation: phylogenetically clustered plant communities are functionally more diverse[END_REF]. Overall, phylogenetic dissimilarity between two species drawn at random from a pool can be used as a proxy for their functional dissimilarity, which may be useful if traits of interest are numerous and hard to measure (for example, concentrations in possible micronutrients for decomposers and detritivores). Phylogenetic dissimilarity within plant neighborhood and similarity between plant neighborhood and litter mixture may hence trigger synergy for the same reasons as previously listed for functional dissimilarity. Phylogenetic dissimilarity within a litter mixture may also reflect divergence of litter traits and hence complementarity in litter resources, triggering synergy as explained above. Overall, we hypothesize that decomposition of a litter mixture might be synergistic if phylogenetic dissimilarity within litter mixture and within plant neighborhood is large, or if phylogenetic dissimilarity between litter mixture and plant neighborhood is small (Fig. 1).

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Fourth, and independently of the neighborhood, synergy may especially arise in poorly decomposable litter mixtures. Litter decomposition rate of any species may have a maximum, determined by the local environmental conditions, e.g. available decomposers. A highly decomposable litter species, which decomposes at this maximal rate, might not decompose even better due to synergistic effect resulting from litter mixing. Such high litter decomposability results from particular litter traits, for instance high WHC (ensuring water availability for decomposers and detritivores: [START_REF] Makkonen | Do physical plant litter traits explain non-additivity in litter mixtures? A test of the improved microenvironmental conditions theory[END_REF], high SLA or low LDMC (representing respectively thin leaves and leaves with few recalcitrant compounds, facilitating colonization by decomposers and consumption by detritivores: [START_REF] Pérez-Harguindeguy | Chemistry and toughness predict leaf litter decomposition rates over wide spectrum of functional types and taxa in central Argentina[END_REF][START_REF] Garnier | Plant functional markers capture ecosystem properties during secondary succession[END_REF][START_REF] Santiago | Extending the leaf economics spectrum to decomposition: evidence from a tropical forest[END_REF], or low C:N ratio (ensuring nitrogen inputs for decomposers: [START_REF] Kazakou | Co-variations in litter decomposition, leaf traits and plant growth in species from a Mediterranean old-field succession[END_REF]Fortunel et al. 2009). Litter mixtures having such high quality traits will decompose rapidly but close to the maximal rate, so they might decompose less synergistically. Moreover, some of these traits may be established in some lineages more or less than in others. Grasses for instance, tend to be less decomposable than eudicots [START_REF] Cornwell | Plant species traits are the predominant control on litter decomposition rates within biomes worldwide[END_REF], which might give much space for synergistic effects due to litter mixing.

Overall, we hypothesize that litter mixtures composed of poorly decomposable species (for instance, grasses) decompose most synergistically (Fig. 1), and that some traits decreasing decomposition per se increase synergy.

Here we tested whether and how the functional and phylogenetic neighborhood of litter mixtures drive synergistic or antagonistic decomposition of litter mixtures (see Fig. 1). We used a long-term mesocosm experiment with functionally or phylogenetically different neighborhoods, in which we exposed 2-species litter mixtures. We quantified phylogenetic and functional dissimilarities at all scales of the experiment (litter mixture, plant neighborhood, and between both), and we quantified functional dissimilarities using four traits: SLA, LDMC, WHC and C:N ratio. We measured litter decomposition and non-additive effects (synergy to antagonism) for each litter mixture. We tested the following prediction of our above hypotheses: synergy of litter decomposition within a litter mixture is favored by (i) high functional and phylogenetic dissimilarities within plant neighborhood;

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(ii) low functional and phylogenetic dissimilarities between plant neighborhood and litter mixture;

(iii) high functional and phylogenetic dissimilarities within litter mixture; (iv) low decomposability of litter species.

MATERIAL & METHODS

Selection and combination of litter-mixture species

Based on existing floristic regional datasets, we selected 20 species commonly present in grassland ecosystems of Britanny (Western France) to conceive litter mixtures: Achillea millefolium (L.), Angelica sylvestris (L.), Brachypodium pinnatum (L.), Centaurea nigra (L.), Cirsium arvense (L.), Dactylis glomerata (L.), Deschampsia cespitosa (L.), Elytrigia repens (L.), Festuca rubra (L.), Filipendula ulmaria (L.), Holcus lanatus (L.), Holcus mollis (L.), Molinia caerulea (L.), Plantago lanceolata (L.), Potentilla erecta (L.), Potentilla reptans (L.), Ranunculus repens (L.), Rumex obtusifolius (L.), Succisa pratensis (L.), and Symphytum officinale (L.). These species were selected to encompass various functional properties and phylogenetic positions (8 grasses, 12 eudicots, and various families within eudicots). From these 20 species, we conceived 120 distinct and contrasted combinations of 2-species litter mixtures in order to obtain a wide, continuous range of functional and phylogenetical dissimilarities (most combinations had only one replicate). 37 litter mixtures included two grass species, 48 mixtures included only one grass species and 35 mixtures included no grass species -see Tab. S1 for the occurrence of each species in litter mixtures.

Selection and floristic characterization of plant neighborhoods

We selected the plant neighborhoods in the mesocosm experiment of the University of Rennes 1 (Western France, 48°06'58.6"N 1°38'15.5"W; see [START_REF] Benot | Fine-scale spatial patterns in grassland communities depend on species clonal dispersal ability and interactions with neighbours[END_REF]. In this experiment, set up in 2009, we selected 60 mesocosms, involving 6 different mixtures of grassland plant species replicated 10 times. These 6 mixtures corresponded to four levels of initial species richness (1,4,8,12), with two different specific compositions for the level of 4 and 8 species richness. The different species

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. richness and the different specific compositions of mixtures were selected in order to mimic different plant communities of grassland ecosystems. The mesocosms were 1.30 x 1.30 m in surface and 0.25 m height. Soil was composed of 20% sand and 80% garden soil, and was homogenized before construction of mesocosms. At the beginning of the experiment in 2009, we applied a mineral fertilizer with slow release (N/P/K: 20/5/10) to fully homogenize the soil nutrient quantity among mesocosms. Initial mean soil nutrient properties were: [NO 3 -] = 34.4 µg.g -1 ; [P] = 0.37 µg.g -1 ;

[PO 4 2-] = 40.3 µg.g -1; C:N ratio = 7.21. Mesocosms were located outside in a microclimatically homogeneous common garden, and were watered by rain during most of the year, with an additional punctual artificial watering during dry summer weeks. The mesocosms harbored natural decomposer communities that were initially present in the soil, and decomposer communities that colonized the soil since the setting up of the experiment in 2009 (fauna as well as bacteria and fungi). Mesocosms were yearly mowed and plant material was exported to mimic semi-natural grasslands. The 12 sown species are very common and abundant in Western France and were:

Agrostis stolonifera (L.), Agrostis tenuis (L.), Brachypodium pinnatum, Centaurea nigra, Chamaemelum nobile (L.), Dactylis glomerata, Elytrigia repens, Festuca rubra, Holcus lanatus, Holcus mollis, Lolium perenne (L.) and Ranunculus repens. We mapped plant species distribution in each mesocosm in early spring 2015, with an 80 x 80 cm square grid of 10-cm cell size (64 cells per grid, following [START_REF] Benot | Fine-scale spatial patterns in grassland communities depend on species clonal dispersal ability and interactions with neighbours[END_REF]. The presence of each rooted species was noted in each cell. Based on this mapping, we selected in each mesocosm two distinct neighborhoods, separated by at least 30 cm. These neighborhoods were the experimental units of our design. Importantly, the realized range of species compositions in neighborhoods was very large and continuous due to major variation in plant species distribution within mesocosms. Plant neighborhood composition was quantified by calculating the number of cells occupied per each neighboring species in a radius of 15 cm around the center of the neighborhood (i.e. comprising the cell of the center of the neighborhood and the eight surrounding cells). Finally, we randomly assigned one litter mixture to each plant

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neighborhood. A given neighborhood covered 707 cm², i.e. a distinctly larger surface than the 64 cm² of a given litterbag (see below).

Characterization of functional dissimilarities

Functional dissimilarities were calculated measuring four functional traits indicative of litter quality:

Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Water Holding Capacity (WHC) and

Carbon:Nitrogen ratio (C:N). These functional traits mainly determine the physical properties of litter (thinness of leaves, and their physical capacity to store water) and the litter microclimate [START_REF] Santiago | Extending the leaf economics spectrum to decomposition: evidence from a tropical forest[END_REF][START_REF] Makkonen | Do physical plant litter traits explain non-additivity in litter mixtures? A test of the improved microenvironmental conditions theory[END_REF] as well as the litter nutrient content [START_REF] Pérez-Harguindeguy | Chemistry and toughness predict leaf litter decomposition rates over wide spectrum of functional types and taxa in central Argentina[END_REF][START_REF] Quested | Plant traits relate to whole-community litter quality and decomposition following land use change[END_REF]). We measured these traits on all species present in the pool of litter mixtures and plant neighborhoods. We measured on two green leaves collected on five different individuals the SLA and the LDMC, following the protocols of [START_REF] Pérez-Harguindeguy | New handbook for standardised measurement of plant functional traits worldwide[END_REF]. The five individuals were collected in different local environments, to obtain a representative mean value for each trait and each species. For each of the 5 individuals, we also measured the C:N ratio on naturally senesced leaves, using an elemental analyzer (FLASH EA 1112 Thermo Finnigan, Waltham, Massachusetts, USA). We also measured the WHC, on dead leaves that were immersed in distilled water during 24h, drained and weighed, then dried at 65°C during 48h and weighed again. WHC was calculated following the formula: water-saturated weight / dry weight. A Principal Component Analysis (PCA) ordination of the four litter traits of the 20 litter species is shown in Fig. S1. The mean and variance of trait values for each species and each trait are described in Tab. S1.

For each pair of species present in the pool of litter mixtures and plant neighborhoods, we extracted the Euclidian distance between both species in the 4-dimensional space with an axis for each trait we measured [START_REF] Villéger | New multidimensional functional diversity indices for a multifaceted framework in functional ecology[END_REF]. Trait data were standardized to give similar weight to each trait in the multi-dimensional space. This approach has the advantage of not reducing information by an initial ordination, which was unnecessary since traits were not overly correlated (all r<0.14, except for LDMC vs WHC: r=-0.52). We calculated three types of functional dissimilarity:

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(i) functional dissimilarity within litter mixture, as the Euclidian distance between both species composing the litter mixture; (ii) functional dissimilarity within plant neighborhood, as the abundance-weighted mean Euclidian distance between all pairs of species composing the neighborhood; (iii) functional dissimilarity between litter mixture and plant neighborhood, as the abundance-weighted mean Euclidian distance between each species of the litter mixture and each species of the neighborhood. The distance of a litter species to a conspecific neighbor was zero. Such zero distances did not impact further calculations as no multiplications were involved, only averaging. Moreover, averaging always involved some non-zero distances, which was inevitable given the study design. The variation range of functional dissimilarities is described in Tab. S2.

Characterization of phylogenetic dissimilarities

We calculated phylogenetic dissimilarities using a recent angiosperm phylogeny corresponding to a dated tree of the Dutch flora based on rbcL DNA sequences (see [START_REF] Hermant | Disparate relatives: Life histories vary more in genera occupying intermediate environments[END_REF]). The tree is highly congruent with, but often more resolved than that of [START_REF] Durka | Leaf traits capture the effects of land use changes and climate on litter decomposability of grasslands across Europe[END_REF] for the larger region of Europe. The tree is also congruent with that of [START_REF] Zanne | Three keys to the radiation of angiosperms into freezing environments[END_REF]. Zanne et al.'s tree covers some 10-15% of the global Angiosperm flora, which renders it inevitably less complete and representative of the Western France grassland genera than the complete tree we used. We note that estimation of age of the crown node of Angiospermae from dating molecular phylogenies is still an issue of considerable uncertainty [START_REF] Bell | The age and diversification of the angiosperms re-revisited[END_REF][START_REF] Smith | An uncorrelated relaxed-clock analysis suggests an earlier origin for flowering plants[END_REF][START_REF] Magallon | Land plant evolutionary timeline: Gene effects are secondary to fossil constraints in relaxed clock estimation of age and substitution rates[END_REF].

However, the congruence among several dated molecular reconstructions of the earliest evolution in Eudicotyledoneae and paleobotanic records [START_REF] Doyle | Diversification of early angiosperm pollen in a cladistic context[END_REF] suggest that age estimate provides a robust constraint for calibrating phylogenies in this large taxon (see [START_REF] Magallon | Land plant evolutionary timeline: Gene effects are secondary to fossil constraints in relaxed clock estimation of age and substitution rates[END_REF].

We therefore used this estimate as a fixed age constraint for the crown node of Eudicotyledoneae in our dating analyses of Dutch angiosperms. Hermant et al. (2012, Appendix E) provide details on other node calibrations. For each pair of species present in the pool of litter mixtures and plant neighborhoods, we calculated the patristic distance, i.e. distance among both species as path-length

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distance along branches of tree from one species to the other one. These distances represent twice the age of the species' last common ancestor. Calculations were made using Mesquite software [START_REF] Maddison | Mesquite: a modular system for evolutionary analysis[END_REF].

We calculated phylogenetic dissimilarities within litter mixture, within plant neighborhood and between both, as we did for functional dissimilarities. We calculated the phylogenetic dissimilarity within litter mixture as the patristic distance between both species composing the litter mixture, and we calculated the phylogenetic dissimilarity within plant neighborhood as the abundance-weighted mean patristic distance between all pairs of species composing the neighborhood. Finally, we calculated the phylogenetic dissimilarity between litter mixture and plant neighborhood as the abundance-weighted mean patristic distance between each species of the litter mixture and each species of the neighborhood. The variation range of phylogenetic dissimilarities is described in Tab.

S2, and the correlations between functional and phylogenetic dissimilarities are given in Tab. S3,

showing that both variables did not correlate across litter mixtures, but correlated positively across plant neighborhoods and partly across litter mixture-plant neighborhood comparisons. Correlations between phylogenetic and trait distances across the 20 litter-mixture species were at most weak, and only that involving LDMC was significant (R²=0.04, Tab. S3). The PCA ordination in Fig. S1 shows that along the first, LDMC-related axis Poaceae are distinctly separated from the other (dicotyledons) families, while these remaining families are little separated among each other.

Position along the second axis and the three other traits clearly does not correspond to family membership.

Decomposition of litter mixtures and comparisons to monospecific litters

In autumn 2014, we collected naturally senesced litter for the 20 litter-mixture species. Species present both in mesocosms and litter mixtures (8 species) were collected in mesocosms and species only present in litter mixtures (12 species) were collected nearby grasslands in the Rennes region.

For a given field-sampled species, litter was collected in the same grassland and was homogenized

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after. Also, litter of species sampled in mesocosms was homogenized. Litter was air-dried, and the 120 litter mixtures were assembled and placed into 8 x 8 cm litterbags. We tried to have identical masses for both species composing each litter mixture and the precise mass of each species were noted. Litterbags had 2 mm mesh on their upper side to avoid contamination by allochthonous litter -which is important in grassland ecosystems since grass species produce litter throughout the year -and 5 mm mesh on their lower side to facilitate access for the numerous detritivores that shuttle between litter and soil [START_REF] Santonja | Plant litter mixture partly mitigates the negative effects of extended drought on soil biota and litter decomposition in a Mediterranean oak forest[END_REF]. During the period of exposure, litter always remained moist and was never brittle, preventing the loss of small fragments of litter. Each litterbag contained 1g of litter, oven-dried equivalent (air-dry/oven-dry ratio calculated from subsamples that were oven-dried but not exposed to decomposition).

We exposed each litter mixture in its respective plant neighborhood, positioning litter mixture on bare ground by gently pushing aside plant neighbors (i.e. without removing any neighbor), and we started the decomposition experiment in February, 2015. To calculate synergistic or antagonistic effects during litter-mixture decomposition, i.e. non-additive effects (NAE), we also exposed in each neighborhood two litterbags containing the corresponding mono-specific litters alone (the three litterbags occupied around 27% of the soil surface of the neighborhood, leaving enough space for the neighboring vegetation to recover). These mono-specific litterbags were exposed very close (one to two centimeters) to the litter mixture, contained 1g of litter (oven-dried equivalent) and had mesh identical to mesh of litter-mixture litterbags. All litterbags were collected 6 weeks later: a phase that is of particular interest as litter-mixture interactions mainly occur in the initial stages of litter decomposition [START_REF] Hoorens | Litter quality and interactive effects in litter mixtures: more negative interactions under elevated CO2?[END_REF][START_REF] Hättenschwiler | Biodiversity and Litter Decomposition in Terrestrial Ecosystems[END_REF][START_REF] Srivastava | Diversity has stronger top-down than bottom-up effects on decomposition[END_REF]. At that moment, litter mixtures had reached between 30-60% mass loss, reflecting the rapid decomposition during the humid and relatively warm, frost-free winter in the study region which increases decomposer activity. High decomposition also results from the high decomposability of herbaceous eudicots [START_REF] Cornelissen | An experimental comparison of leaf decomposition in a wide range of temperate plant species and types[END_REF][START_REF] Cornwell | Plant species traits are the predominant control on litter decomposition rates within biomes worldwide[END_REF]. We cleaned the litter by hand, ovendried it at 65°C for three days and calculated mass loss (%) as (1-(m 1 /m 0 )) x 100, where m 1 is the

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oven-dry weight at collection and m 0 the initial oven-dry equivalent dry weight. We calculated the expected decomposition of litter mixtures, as the mean of the decomposition of the two species in mono-specific treatment, weighted by the precise proportion of the two species in the litter mixture.

Then, we calculated non-additive effects (NAE) of litter-mixture decomposition as:

Where O is the observed decomposition of litter mixture and E is the expected decomposition of litter mixture. Division by E ensures a similar scale of non-additive effects for highly and poorly decomposable litter mixtures. Litter-mixture decomposition could be additive (NAE=0, i.e. no significant difference between observed and expected decomposition), synergistic (NAE>0, i.e.

observed decomposition higher than expected), or antagonistic (NAE<0, i.e. observed decomposition lower than expected; [START_REF] Wardle | Biodiversity and Plant Litter: Experimental Evidence Which Does Not Support the View That Enhanced Species Richness Improves Ecosystem Function[END_REF]. Note that alternative quantifications of non-additive effects (i.e. log-ratios) led to the same results thereafter.

Statistical analyses

Prior to analyses, we center-reduced all independent and dependent variables (i.e. for each variable, subtracting from each value the mean of the variable and then dividing this difference by the standard deviation of the variable), permitting comparisons between regression coefficients of different variables within a given model and among models. We used multiple ordinary least squares regression models to test the effects of functional and phylogenetic dissimilarities on (a) nonadditive effects of litter-mixture decomposition and (b) litter-mixture decomposition per se. As independent variables, we also included in these models the mean functional characteristics of litter mixtures (SLA, LDMC, WHC and C:N) as they may also influence decomposition; we also included the percentage of grasses within litter mixture, to account for phylogenetic position of litter species and not only for phylogenetic distances between them. As non-additive effects can by definition only be quantified for litter mixtures, we also restricted the analysis of decomposition per se to litter

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. mixtures. To understand the effect of overall functional dissimilarities on non-additive effects tested in (a), we tested (c) how non-additive effects depended on dissimilarities of each of the individual traits (SLA, LDMC, WHC, C:N ratio) within litter mixture, within plant neighborhood and between both (see Tab. S4). The identity and abundance of species (in both neighborhood and litter-mixture species) might also affect non-additive effects [START_REF] Wardle | Determinants of litter mixing effects in a Swedish boreal forest[END_REF][START_REF] Wardle | The influence of plant litter diversity on decomposer abundance and diversity[END_REF][START_REF] Ball | Consequences of nonrandom species loss for decomposition dynamics: experimental evidence for additive and nonadditive effects[END_REF][START_REF] Nielsen | Soil biodiversity and carbon cycling: a review and synthesis of studies examining diversity-function relationships[END_REF], we hence tested (d) the effect of abundance of species composing the litter mixtures and the plant neighborhoods on non-additive effects (see Tab. S5). Note that we also included the abundances of all litter species and the neighborhood species richness in the initial set of variables tested in (a), as well as mesocosm as a random factor (because per mesocosm there were two data points, which could result in statistical non-independence), but all these variables were excluded during model selection. Finally, we tested (e) the effect of decomposability of litter mixtures (averaged decomposition from the two monospecific litterbags) on non-additive effects, through a simple ordinary least squares regression model. For analyses in (a), (b), (c) and (d) we built all possible models, i.e. all possible combinations of independent variables, including in (a), (b) and (c) the interaction terms between dissimilarity within litter mixture and dissimilarity within plant neighborhood -testing whether dissimilar litter mixtures decompose better if the plant neighborhood is dissimilar too. Then, we performed a best subset search and selected the 10 best models based on AIC and BIC (Burnham & Anderson 2003;[START_REF] Chen | Extended Bayesian information criteria for model selection with large model spaces[END_REF]). This procedure is more robust than a backward stepwise selection procedure when independent variables are numerous compared to replicates and differences between models are likely to be small [START_REF] Miller | Subset selection in regression[END_REF]. We graphically explored residuals using probability plots and predicted vs residual plots, and we excluded five major residual outliers (out of 120 data points) to fulfill the assumption of normality and homogeneity of residuals. Not excluding outliers reduced explained variance, but changed only one minor result of the analyses: the effect of mean C:N of litter mixtures on nonadditive effects became slightly non-significant. The 10 best models explaining non-additive effects by functional and phylogenetic dissimilarities were highly significant and were very close in terms of

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. likelihood and explained variance, with AIC and BIC always varying from a given model to its closest one by less than 2, giving high confidence in the variables selected in all models (Tab. S6, and Tab. S7

for models for decomposition per se). Results were robust despite the somewhat large number of variables (relative to 120 replicates), with best models of a given analysis all identifying the same set of variables (Tab. S6 and Tab. S7). As AIC and BIC gave priority to the best model, we hence only present that model in Results. All statistical analyses were performed with R 3.0.3 software (R Development Core Team, 2014), with the packages car and leaps.

RESULTS

In general, litter mixtures decomposed quickly if composed of species that each decomposed quickly (Fig. S2), but 24% of the variance in litter-mixture decomposition remained unexplained by the decomposition of single litters, which may at least in part reflect the effect of synergies or antagonisms. Litter-mixture decomposition triggered strong non-additive effects, from antagonistic effects with 60% less decomposition than expected to synergistic effects with 80% more decomposition than expected (Fig. S3). Around 40% of litter mixtures showed only minor nonadditive effects, i.e. comprised between -10% and +10%. Overall, the mean of non-additive effects was around 8% synergy and was significantly larger than 0 (t=3.0, P=0.004, df=114).

Phylogenetic dissimilarity within plant neighborhood and functional dissimilarity between litter mixture and plant neighborhood render decomposition synergistic

Non-additive effects were explained by several variables concerning trait dissimilarity and trait means of litter mixtures, plant neighborhoods and both (Tab. 1). The model with these retained variables explained more variance than models using only species identities (see Tab. S5): retained variables explained 19% of the variance of non-additive effects, in a highly significant model (P=5.10 -

5

). We note that variance inflation factors were distinctly smaller than what is usually considered indicative of overly multicollinearity (Tab. 1). Species identities and interaction terms among dissimilarities were excluded from the best model, therefore we did not present them in Tab. 1.

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Overall, we observed that phylogenetic dissimilarity within plant neighborhood triggered synergy during litter-mixture decomposition, and functional dissimilarity between litter mixture and plant neighborhood turned antagonism into synergy (Tab. 1, Fig. 2,A,B). For instance, when the plant neighborhood was composed of grasses as well as eudicotyledons, and of various families within eudicotyledons, litters decomposed more rapidly compared to alone; when the litter mixture had functional traits different from that of the plant neighborhood, litters also decomposed more rapidly compared to alone. These results are not biased by the correlation between functional and phylogenetic dissimilarities within plant neighborhoods (Tab. S3): even after exclusion of phylogenetic dissimilarity within plant neighborhood, functional dissimilarity within plant neighborhood remained non-significant (albeit showed a tendency: P=0.08). Contrary to the neighborhood effect, functional dissimilarity within litter mixture turned synergy into antagonism, but with a weaker effect than the previously-mentioned effects related to plant neighborhood (Tab.

1, Fig. 2 C). The effect of functional dissimilarity within litter mixture and between litter mixture and plant neighborhood was especially due to one functional trait, LDMC (see Tab. S4). For example, when the litter mixture was different from the plant neighborhood in terms of LDMC, litters decomposed much more quickly together compared to alone; in contrast, when litters composing the litter mixture had different LDMC, litters decomposed more slowly compared to alone.

Poorly decomposable litters decompose more synergistically

Synergy among litter mixtures was high if species composing mixtures, in monospecific treatments, were poorly decomposable: mean monospecific decomposability of litter-mixture species decreased synergy among species (P=0.001, r²=0.08, F=11.4, df=114, Fig. 3,A). Consistently, several variables that decreased litter-mixture decomposition per se increased synergy (see Tab. 1). Specifically, low WHC and high percentage of grasses within litter mixture decreased litter-mixture decomposition per se, but increased synergy (Fig. 3, B, C -note that high C:N ratio, in contrast, increased both

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. decomposition and synergy). We note that decomposition per se was very well explained (adj-R²=0.55) in comparison to non-additive effects (adj-R²=0.19).

DISCUSSION

We demonstrated that litter mixing in temperate grassland communities triggered both synergy and antagonism during decomposition, and that synergy and antagonism were driven by the characteristics of litter-mixture species as well as those of species of the plant neighborhood. We observed that, within litter mixtures, functional dissimilarity tended to turn synergy into antagonism.

However, plant neighborhoods had stronger and opposing effects: phylogenetic dissimilarity within neighborhood and functional dissimilarity between litter mixture and neighborhood tended to turn antagonism into synergy. We also showed that poorly decomposable litter mixtures decomposed more synergistically.

A neighborhood of phylogenetically distant species might harbor diverse decomposers and detritivores thereby favoring synergistic decomposition of litter mixtures

We observed that phylogenetic dissimilarity within plant neighborhood triggered synergy during decomposition of litter mixtures. The synergistic decomposition rate in dissimilar neighborhoods might be due to a larger range of biotic and abiotic conditions, caused by more distantly related species ultimately harboring a more diverse pool of detritivores and decomposers. From such a diverse pool, the litter-mixture patch might recruit the decomposers and detritivores capable of benefiting from litter mixing, which might be more efficient in decomposing litter mixtures than mono-specific litters (Wardle et al. 1999;[START_REF] Spehn | Plant diversity effects on soil heterotrophic activity in experimental grassland ecosystems[END_REF][START_REF] Milcu | The response of decomposers (earthworms, springtails and microorganisms) to variations in species and functional group diversity of plants[END_REF]. As phylogenetic dissimilarity within neighborhood increased synergy and functional dissimilarity did not, the neighbors' functional traits involved in the effect of phylogenetic dissimilarity were not those we measured. Moreover, as phylogenetic dissimilarity influenced synergistic effects only within plant neighborhood, these unmeasured traits acted only at the scale of the plant neighborhood, i.e. acted rather on the pool of detritivores and decomposers than on the properties of litter mixture.

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Candidate traits that differ among distantly related species and likely to drive the composition of the pool of detritivores and decomposers may be root traits such as root density, root microbiota or root exudates [START_REF] Kuzyakov | Root exudate components change litter decomposition in a simulated rhizosphere depending on temperature[END_REF][START_REF] Lindahl | Ectomycorrhizal fungi -potential organic matter decomposers, yet not saprotrophs[END_REF][START_REF] Soudzilovskaia | Quantitative assessment of the differential impacts of arbuscular and ectomycorrhiza on soil carbon cycling[END_REF]. We stress, however, that we did not sample detritivores and decomposers nor measure their activity, so this decomposer-pool mechanistic hypothesis remains to be tested: do neighborhoods composed of distantly related plant species harbor pools of decomposers that are more efficient in decomposing mixed than mono-specific litters, and does this reflect the diversity of niche conditions in the neighborhood?

Functional dissimilarity between neighborhood and litter mixture might favor synergy through Away-

Field Advantage

We found that ambient plant neighborhoods that are functionally dissimilar to the litter mixture tended to turn antagonism into synergy. This is contrary to our hypothesis that similarity between litter mixture and neighborhood should favor synergy due to a Home-Field Advantage, i.e. the availability of decomposers and detritivores being particularly adapted to litter-mixture quality [START_REF] Ayres | Soil biota accelerate decomposition in highelevation forests by specializing in the breakdown of litter produced by the plant species above them[END_REF][START_REF] Freschet | Multiple mechanisms for trait effects on litter decomposition: moving beyond home-field advantage with a new hypothesis: Substrate-matrix quality interactions in decay[END_REF][START_REF] Austin | There's no place like home? An exploration of the mechanisms behind plant litter-decomposer affinity in terrestrial ecosystems[END_REF][START_REF] Veen | Litter quality and environmental controls of home-field advantage effects on litter decomposition[END_REF]. Instead, our result suggests that synergy within a litter mixture might be favored by the uniqueness of this patch within its neighborhood: a functionally unique litter patch is one that attracts across a large scale the decomposers preferring the characteristics of this patch, possibly leading to an over-proportional decomposer activity facilitating synergy. Specifically, litter patches that have high or low LDMC compared to their neighborhood decomposed most synergistically (Tab. S4). These patches possibly attracted across a large neighborhood decomposers whose activity benefited from extreme LDMC:

either decomposers whose colonization and activity was facilitated by low LDMC, or detritivores performing compensatory feeding on high LDMC (i.e. enhanced consumption rate on poor-quality litter to ensure sufficient resource assimilation to meet metabolic needs; [START_REF] Gessner | Diversity meets decomposition[END_REF]. We may also speculate that this synergy might compensate for a possible negative effect of dissimilarity

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between litter mixture and neighborhood on decomposition per se (as predicted by Home-Field Advantage), and might explain why we and other authors [START_REF] Gießelmann | Lack of home-field advantage in the decomposition of leaf litter in the Atlantic Rainforest of Brazil[END_REF][START_REF] Veen | Litter quality and environmental controls of home-field advantage effects on litter decomposition[END_REF] did not found this negative effect on decomposition per se. Future research should test whether litter mixtures that are functionally unique within their neighborhood do indeed attract specialist decomposers across large surfaces. Also, the consequences of decomposer assembly for litter decomposition and synergistic effects require further investigation.

Functional similarity within litter mixture might favor synergy through increased resource concentration

We showed that functional similarity within litter mixtures tended to turn antagonism into synergy during decomposition. These results contradict numerous previous studies that mainly reported that functional trait similarity decreased synergistic effects [START_REF] Pérez Harguindeguy | More than the sum of its parts? Assessing litter heterogeneity effects on the decomposition of litter mixtures through leaf chemistry[END_REF][START_REF] Berglund | When will litter mixtures decompose faster or slower than individual litters? A model for two litters[END_REF][START_REF] Makkonen | Do physical plant litter traits explain non-additivity in litter mixtures? A test of the improved microenvironmental conditions theory[END_REF]. We suggest that functional similarity among co-decomposing litter species increased concentration of litter resources for specialist decomposers and detritivores, in comparison to litter mixtures with high dissimilarity. Colonization of specialist decomposers or consumption by specialist detritivores might be favored, and these multiple specialists might have complementary effects on decomposition, leading to synergistic effects [START_REF] Pan | Larger phylogenetic distances in litter mixtures: lower microbial biomass and higher C/N ratios but equal mass loss[END_REF].

Specifically, we showed that this effect of functional dissimilarity was especially due to one particular trait, LDMC (see Tab. S4). In litter mixtures with such high LDMC dissimilarity, the litter species with high LDMC might have inhibited colonization of the other litter species, due to the dilution of easily decomposable compounds by recalcitrant compounds such as lignin and cellulose [START_REF] Hedde | Patterns and mechanisms responsible for the relationship between the diversity of litter macro-invertebrates and leaf degradation[END_REF][START_REF] Vos | Macro-detritivore identity drives leaf litter diversity effects[END_REF]). In addition, synergy or antagonism during litter-mixture decomposition depended on functional dissimilarities within litter mixture or among litter mixture and plant neighborhood, but did not depend on phylogenetic dissimilarities. The traits we measured were hence likely important, albeit we remind that unmeasured traits like [P], [K], [Ca], lignin content are also likely important (Makkonen et al. 2012), and that some traits like litter acidity may

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have little phylogenetic signal [START_REF] Pan | Larger phylogenetic distances in litter mixtures: lower microbial biomass and higher C/N ratios but equal mass loss[END_REF]. Future studies would need to test whether abundance and activity of a given decomposer indeed declines overproportionally if traits of involved litter species become more dissimilar.

Poorly decomposable litters might decompose more synergistically due to a large margin between realized and maximal possible decomposition rate

Finally, and independently of the neighborhood effects, our results showed that high decomposability of litter species statistically decreased the synergy we observed when mixing these species. Consistently, some traits that increased litter-mixture decomposition per se, such as high mean WHC or low percentage of grasses, decreased synergy. These results validated our hypothesis that litter decomposition might have a maximum, basically the physiological optimum for fragmentation of leaves and respiration of carbohydrates. Synergy will not permit going beyond this maximal decomposition. For instance, in litter mixtures with high mean WHC, the moisture level was already favorably high and constant. Therefore, the litter species with the relatively lowest quality probably did not benefit much from the presence of the other litter species. In contrast, in litter mixtures with low mean WHC, moisture conditions were unfavorable for decomposers and the litter species with the lowermost WHC probably benefited more from the presence of another litter species with somewhat higher WHC. We note however that other studies have found a positive relationship between decomposability of litter mixtures and synergistic effects [START_REF] Cuchietti | Leaf litter mixtures and neighbour effects: Low-nitrogen and high-lignin species increase decomposition rate of high-nitrogen and low-lignin neighbours[END_REF]Setiawan et al. 2016), the issue hence requires further investigation.

Possible feedbacks on community assembly processes

Synergistic or antagonistic nutrient recycling within a litter mixture correlated with the functional and phylogenetic characteristics of the mixture but also with those of the neighborhood. We hence demonstrated the combined effects of plant assemblages across multiple scales on litter species interactions. At the local scale of a litter mixture, synergistic decomposition was favored by similarity among litters suggesting that decomposers profited from high resource concentration. At the larger

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scale of neighborhoods, in contrast, synergistic decomposition within a local litter mixture was favored by dissimilarities among neighbors (or between mixture and neighbors), suggesting that such neighborhoods provided complementary resources and conditions which maintained detritivores and decomposers capable of profiting from a highly concentrated local patch. Overall, the results suggest that nutrient recycling is most synergistic in a homogenous litter mixture within a heterogeneous and distinct matrix. Such spatial vegetation structures occur for instance through clonal growth or low-dispersal distances [START_REF] Herben | Spatial pattern formation in plant communities[END_REF][START_REF] Semchenko | Plants are least suppressed by their frequent neighbours: the relationship between competitive ability and spatial aggregation patterns[END_REF]. We may hypothesize that synergistic decomposition among litters of two coexisting species might possibly contribute to the maintenance of such vegetation structures, through an increased availability of soil nutrient benefiting to the growth of both coexisting species (i.e. facilitation). Increased soil nutrient availability could also mainly benefit the most resource-acquisitive species, providing the most decomposable litter in the mixture, so that increased synergy during decomposition might represent an indirect competitive strategy. However, our study was too short duration (6 weeks) to verify this hypothesis and more long-term studies [START_REF] Mao | Non-additive effects of species mixing on litter mass loss and chemical properties in a Mongolian pine plantation of Northeast China[END_REF] are needed.

Mixed litters also decomposed more synergistically when composed of species of low WHC or of grasses. Low WHC may be representative of a resource-conservative strategy as we find it in stressful environments [START_REF] Wright | The worldwide leaf economics spectrum[END_REF]). It has been suggested that facilitation is particularly important in such environments [START_REF] Maestre | Refining the stress-gradient hypothesis for competition and facilitation in plant communities[END_REF][START_REF] López | Enhanced facilitation at the extreme end of the aridity gradient in the Atacama Desert: a community-level approach[END_REF]. The observed synergy in decomposition might be another form of facilitation, which has not been recognized so far: synergistic decomposition might indeed increase nutrient availability and benefit to both species providing the mixed litter. Similarly, facilitation through synergistic decomposition might also favor coexistence of grasses. Surprisingly, grasses very often coexist despite being very similar [START_REF] Cahill | Does phylogenetic relatedness influence the strength of competition among vascular plants? Perspectives in Plant Ecology[END_REF][START_REF] Linder | Global grass (Poaceae) success underpinned by traits facilitating colonization, persistence and habitat transformation[END_REF]. Coexistence among grass species increases the number of patches that can be occupied per species and might thereby reduce extinction probability [START_REF] Prinzing | High-co-occurrence genera": weak but consistent relationships with global richness, niche partitioning, hybridization and decline: Local co-occurrence versus global richness[END_REF]. We may speculate that ultimately such facilitation in nutrient recycling has contributed to the impressive

Accepted Article

. success of grasses [START_REF] Linder | Global grass (Poaceae) success underpinned by traits facilitating colonization, persistence and habitat transformation[END_REF], through feedback between ecological coexistence, ecosystem functioning and evolutionary diversification of lineages [START_REF] Prinzing | Benefits from living together? Clades whose species use similar habitats may persist as a result of eco-evolutionary feedbacks[END_REF].

CONCLUSION

Our results suggest that decomposition of litter mixtures was synergistic if (i) mixed species provided similar resources, (ii) the ambient plant neighborhood provided complementary resources, and (iii)

individually, mixed litters were poorly decomposable. Thus, the relationship between increasing diversity due to mixing litters and the ecosystem function of decomposition was highly dependent on the plant community context. We hope these results open new insights and contribute to a more pluralistic view on diversity-ecosystem functioning relationships.

Accepted Article

. High decomposability of species composing litter mixtures (i.e. high average across their respective monospecific decomposition rates) was related to low synergy (A, P=0.001, r²=0.08, F=11.4, df=114).

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Grasses decomposed slower but the more synergistically (A, see Tab. 1; note that interaction term between grass percentage and decomposability was not significant). Also, high mean WHC of litter mixture increased litter-mixture decomposition per se (B, standardized regression coefficient = 0.19, P < 0.01, see Tab. 1) while it decreased synergy (C, standardized regression coefficient = -0.22, P < 0.05, see Tab. 1). Data points in B and C are partial residuals of the best model showed in Tab. 1, so account for other independent variables. Black dotted lines around slopes represent the 95% confidence interval.

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FIGURE CAPTIONS Figure 1 .

 CAPTIONS1 FIGURE CAPTIONS

Figure 2 .

 2 Figure 2. Non-additive effects of litter-mixture decomposition (positive values indicating synergy)

Figure 3 .

 3 Figure 3. Litter-mixture decomposability decreased synergy. Decomposability of litter species

Figure 1 .

 1 Figure 1.

Table 1 .

 1 Summary of the best model explaining (i) non-additive effects of litter-mixture decomposition and (ii) decomposition per se of litter mixtures. Non-additive effects of litter-mixture decomposition as well as litter-mixture decomposition per se depended on phylogenetic and functional dissimilarities of litter-mixture (LM) and plant neighborhood (PN), on litter-mixture trait means (Specific Leaf Area, SLA; Leaf Dry Matter Content, LDMC; Water Holding Capacity, WHC; Carbon:Nitrogen ratio, C:N) and on percentage of grasses within litter mixture. Variance inflation factors of independent variables ranged from 1.06 to 1.82 in the best model of non-additive effects and from 1.03 to 2.39 in the best model of decomposition per se. Moreover, variable selection was very stable (see Tab. S6 and Tab. S7). Multiple regression analysis was hence not biaised by the partial correlation between LDMC and WHC. Given model parameters are the selected variables with their coefficient and significance (*: P<0.05; **: P<0.01; ***: P<0.001), the degree of freedom (Df) the adjusted-R² (adj-R²), the F statistic (F) and the probability of the model (P).

	TABLES

ACKOWLEDGMENTS

We thank Marie-Lise Benot and Anne-Kristel Bittebiere for their participation in the plant mapping campaigns. We also acknowledge Valérie Gouesbet, Thierry Fontaine and Fouad Nassur for the maintenance of the experimental design.

AUTHOR'S CONTRIBUTION

L. Barbe, V. Jung, C. Mony and A. Prinzing conceived the experiments. L. Barbe performed the experiments, with help of V. Jung, C. Mony and A. Prinzing. L. Barbe analyzed the data, with help of A. Prinzing. L. Barbe wrote the manuscript, with help of V. Jung, C. Mony, A. Prinzing, M. Santonja and I. Bartish, who also provided the phylogenetic tree.

DATA ACCESSIBILITY

Data is available at: https://doi.org/10.5281/zenodo.1165521 [START_REF] Barbe | Data from: Functionally or phylogenetically distinct neighbors turn antagonism among decomposing litter species into synergy[END_REF]

Accepted Article

. 100% grasses 50% grasses 0% grasses

Accepted Article