Etienne Gourc
email: etienne.gourc@insa-toulouse.fr

Sébastien Seguy
email: bsebastien.seguy@insa-toulouse.fr

Guilhem Michon
email: cguilhem.michon@isae.fr

Alain Berlioz
email: dalain.berlioz@univ-tlse3.fr

Chatter control in turning process with a nonlinear energy sink

Keywords: Dynamical, Turning, Nonlinear Energy Sink, Chatter

This paper presents the interest of an original absorber of vibration in order to reduce chatter vibration in turning process. The device is composed of a linear oscillator corresponding to a flexible cutting tool -subject to chatter -strongly coupled to a Nonlinear Energy Sink (NES), with purely cubic stiffness. The novelty of this work is the use of a nonlinear cutting law, more accurate for modeling the cutting process. The delayed equations of motion are analyzed using a combination of the method of multiple scales and harmonic balance. Different types of responses regimes are revealed such as periodic response and also Strongly Modulated Response (SMR). Analytic results are then compared with numerical simulations. Finally, the potential of the NES is demonstrated to control chatter in turning process.

Introduction

Surface quality of parts produced by machining operation is strongly affected by the well-known regenerative chatter. The chatter instability is induced by the time delay between two consecutive workpiece revolutions. By the effect of some external disturbance, the tool start damped oscillations relative to the workpiece, and the surface roughness is undulated. For two consecutive workpiece revolutions, the chip thickness is modulated. This regenerative mechanism is well known and was presented first by Tobias [START_REF] Tobias | Theory of regenerative machine tool chatter[END_REF]. Since this work, many researchers have improved the knowledge by the stability lobe representation, see e.g. [START_REF] Mann | Stability of up-milling and down-milling, part 2: experimental verification[END_REF][START_REF] Gourc | Dynamical modeling of spindle with active magnetic bearing for milling process[END_REF][START_REF] Gourc | Chatter milling modeling of active magnetic bearing spindle in high-speed domain[END_REF][START_REF] Mousseigne | Predicting the dynamic behaviour of torus milling tools when climb milling using the stability lobes theory[END_REF]. The behavior of a cutting tool on a lathe has also been studied using the method of multiple scales [START_REF] Nayfeh | Analysis of the cutting tool on a lathe[END_REF].

Various techniques for chatter suppression have been investigated. In [START_REF] Seguy | Suppression of period doubling chatter in high-speed milling by spindle speed variation[END_REF], a variable spindle speed in milling was used to disturb the time delay. Another approach to reduce chatter is the use of linear tuned vibration absorbers. Recently, an analytical optimized method was presented for linear absorbers in the context of chatter [START_REF] Sims | Vibration absorbers for chatter suppression: a new analytical tuning methodology[END_REF]. These linear absorbers are successfully applied on boring process [START_REF] Moradi | Tuneable vibration absorber design to suppress vibrations: an application in boring manufacturing process[END_REF]. Active absorbers have been also proposed with piezoelectric tool [START_REF] Harms | Tool adaptator for active vibration control in turning operations[END_REF]. However all these linear absorbers are limited by the small frequency bandwidth, and in practice their efficiency is not interesting for the machinist.

The idea of attaching a nonlinear oscillator to a lathe is relatively new [START_REF] Wang | Feasibility study of nonlinear tuned mass damper for machining chatter suppression[END_REF][START_REF] Gourc | Delayed dynamical system strongly coupled to a nonlinear energy sink: application to machining chatter[END_REF]. In recent studies, it has been demonstrated that addition of a small mass attachment with a strong nonlinear coupling (i.e. a nonlinear energy sink) to a linear oscillator can be benefit for vibration mitigation [START_REF] Gendelman | Quasiperiodic energy pumping in coupled oscillators under periodic forcing[END_REF][START_REF] Vakakis | Non-linear dynamics of a system of coupled oscillators with essential stiffness non-linearities[END_REF], even in presence of gravity [START_REF] Savadkoohi | Vibratory energy exchange between a linear and a nonsmooth system in the presence of the gravity[END_REF]. In [START_REF] Gendelman | Bifurcations of nonlinear normal modes of linear oscillator with strongly nonlinear damped attachment[END_REF], a general analytical procedure was presented. The possibility to control self-excitation regimes in a Van der Pol oscillator with a NES has been demonstrated in [START_REF] Nankali | Suppression of machine tool chatter using non-linear energy sink[END_REF]. Systems with NES exhibit regimes that are not related to fixed points, and cannot be explained using local analysis [START_REF] Starosvetsky | Strongly modulated response in forced 2dof oscillatory system with essential mass and potential asymmetry[END_REF]. These regimes are related to relaxation oscillations of the slow flow and are also interesting for passive control [START_REF] Gourc | Delayed dynamical system strongly coupled to a nonlinear energy sink: application to machining chatter[END_REF].

In this paper, the possibility of controlling regenerative chatter using a nonlinear energy sink is analyzed for turning process, including a nonlinear cutting law. Theoretical predictions are compared with numerical integration. In the next section, the model considered is described. In the third section, the asymptotic analysis of the equation of motion is performed. Then, the different response regimes accompanied with numerical simulation are presented.

Mechanical model

The model developed herein consists of a cutting tool on a lathe with an embedded NES. The cutting tool is assumed to vibrate only on its first flexible mode and the workpiece is considered to be rigid. A schematic of the model is given in Fig. 1. (1a) m c r k y rx 0 (1b) where x and y represent the tool tip and NES displacement respectively. m # , c # and k # (i 1, 2) are the mass, damping and stiffness of the tool and NES. r is the influence coefficient, depending on the position of the NES on the tool. Finally, F is the cutting force taking into account the regenerative effect. The cubic polynomial form due to Shi and Tobias [START_REF] Shi | Theory of finite amplitude machine tool instability[END_REF] is expressed as follow:

F )h t , p ρ h t ρ h t ρ h t (2) where p is the chip width (depth of cut), h t is the chip thickness and ρ # (i 1, 3) are specific cutting coefficient obtained by fitting the experimental cutting force measurement with a third order polynomial. The chip thickness h t is expressed as follow:

h t h 0 ∆h t (3) h 0 represent the nominal chip thickness in the absence of vibration and ∆h t is the chip thickness variation gives by: ∆h t x t x t τ (4) where x t τ is the delayed position of the tool and τ is the time delay between two workpiece revolutions τ 2π/Ω, with Ω the spindle speed. In the steady state, x t x 0 , y t y 0 .

Substituting Eq. 3 and Eq. 4 into Eq. 1a, Eq.1b gives:

k x 0 p)ρ h 0 ρ h 0 ρ h 0 , (5)
In the unsteady state, the displacement would be the sum of the steady state component and an unsteady component:

x t = x 0 + u t , y t = y 0 + v t (6) Substituting Eq. 3, Eq. 5, Eq. 6 into Eq. 1a, Eq. 1b gives: m + c + k x + c r -r + k rx -y r = -p α ∆h t + α ∆h t + α ∆h t

(7a) m + c -r + k y -rx = 0 (7b)
where α = ρ + 2ρ h 0 + 3ρ h 0 , α = ρ + 3ρ h 0 , α = ρ . Changes of variables are introduced as follow:

9ω , ω , μ , μ , ε, η, κ, β , β @ = A B C D C , B D , E C D C F C , E D F C , D D C , GH C D C F C , F F C , H H C , H I H C J ( 8
)
The time is rescaled t ̃= ω t, τ L = ω τ. The nonlinear terms of the cutting law are also rescaled β = εβ M , β = εβ M . Dropping the tilde, the compact form of equation of motion are as follow:

uN + 2μ uO + u + 2εμ ruO -vO r + εκ ru -v r + η ∆h t + β ∆h t + β ∆h t = 0 (9a) εvN + 2εμ rvO -uO + εκ rv -u = 0 (9b) where the dots denotes differentiation with respect to time.

Analysis of the linear system

The linear uncoupled system is obtained by setting ε = 0 in Eq. 9a:

uN + 2μ uO + u + η)u t -u t -τ , = 0 (10) Eq. 10 admits solution of the form:

u t = u 0 e QR#F
(11) where ω is the frequency of the oscillations, γ is the grow or decay rate and u 0 depends on initial conditions. Substituting Eq. 11 into Eq. 10 gives:

γ + iω + 2μ γ + iω + 1 + η)1 -e TU QR#F , = 0 (12)
At the stability boundary, γ = 0. It is possible to prove that this bifurcation γ, η, ω = 0, η E , ω E is a Hopf bifurcation because it results from two complex conjugate eigenvalues. Splitting Eq. 12 into real and imaginary parts and setting γ = 0 yields:

1 -ω E + η E 1 -cos ω E τ = 0 (13a) 2ω E μ + η E sin ω E τ = 0 (
13b) Solving Eq. 13a, Eq. 13b for cos ω E τ and sin ω E τ and using trigonometric identity, the frequency of the bifurcated periodic orbit is obtained as:

ω E = 1 + η E -2μ ± Z η E -2μ -4μ (14)
The stability boundary is often plot in the space of parameters Ω, η and is called stability lobes.

Analysis of the coupled system

In this section the system with the NES is studied. First, a new coordinate representing the internal displacement of the NES is introduced:

w t = ru t -v t (15)
Substituting Eq. 15 into Eq. 9a, Eq. 9b gives: uN 2μ uO + u + 2εμ wO r + εκw r + η ∆h t + β ∆h t + β ∆h t = 0 (16a) ε ruN -wN -2εμ wO + εκw = 0 (16b) System Eq. 16a, Eq. 16b cannot be analyzed using standard approach such as the method of multiple scales or the method of averaging due to the lack of linear stiffness in the NES equation. A mixed multiple scale/harmonic balance method proposed by Luongo [START_REF] Luongo | Dynamic analysis of externally excited NES-controlled systems via a mixed Multiple Scale/Harmonic Balance algorithm[END_REF] is used. A detuning parameter representing the nearness of η to the critical value η E is introduced as:

η η E εσ (17) A first order uniform approximation in the vicinity of the Hopf bifurcation has the form: u t; ε = u 0 T 0 , T + εu T 0 , T + ⋯ (18a) w t; ε = w 0 T 0 , T + εw T 0 , T + ⋯ (18b) where T a ε a t (n 0, 1, …). The time delay is considered of O 1 which is rather natural, since the most interesting zone for the machinist is close to the first Hopf lobe. The delayed position of the tool is expressed as:

u t -τ; ε = u 0 T 0 -τ, T -ετ + εu T 0 -τ, T -ετ + ⋯ (19)
which upon expansion for small ε becomes: u t -τ; ε = u 0 T 0 -τ, T + εu T 0 -τ, T + ⋯ (20) Substituting Eq. 18a, Eq. 18b, Eq. 20 into Eq. 16a, Eq. 16b and equating coefficients of like power of ε yields: order ε 0 : D 0 u 0 2μ D 0 u 0 + u 0 + η E u 0 -u 0U = 0 (21) order ε :

D 0 u 2μ D 0 u + u + η E u -u U = -2D 0 D u 0 -2μ D u 0 -2μ rD 0 w 0 -η E τD u 0U - σ u 0 -u 0U -η E eβ u 0 -u 0U + β u 0 -u 0U f -κrw 0 (22a) D 0 w 0 rD 0 u 0 + 2μ D 0 w 0 + κw 0 = 0 (22b) where D a D ∂ D / ∂T a
D and u aU = u a T 0 -τ, T . The general solution of Eq. 21 can be expressed as:

u 0 = A T e #F i j k + ∑ mA a T e Q n T#F n j k o + cc p aq
(23) where cc stand for the complex conjugate of the preceding terms, ω E is the critical frequency of the oscillation at the boundary of Hopf bifurcation given in Eq. 14. γ a -iω a are the remaining roots of Eq. 21. Close to the Hopf bifurcation, all the roots have negative real parts except one which change sign at the stability boundary as explained in [START_REF] Als | Analysis of a system of linear delay differential equations[END_REF]. After transient, all the roots decay with time and the long time behavior at O 1 is given by:

u 0 = A T e #F i j k + cc
(24) To study the effect of the NES and nonlinear terms, Eq. 22a, Eq. 22b at O ε are now analysed. Eq. 22b is first considered. Since this equation does not admit exact solution and since we are interested in the behavior of the system in the vicinity of the 1: 1 resonance, we seek a solution in the form of a one term Harmonic Balance expansion:

w 0 = B T e #F i j k + cc (25)
Substituting Eq. 24, Eq. 25 into Eq. 22b and balancing term of ω E frequency gives:

2iμ Bω E + 3κB B t -Bω E + rAω E = 0 (26) Expressing A and B in polar form:

A Re #v , B Ne #x (27) Substituting Eq. 27 into Eq. 26, splitting into real and imaginary part and rearanging gives:

cos Δ = z)F i T {z , |}F i (28a) sin Δ = - ~ z F i |} (28b) R z | F i • e 3κN -ω E + 4μ ω E f (28c)
where Δ δ θ. Setting Y R and Z N , Eq. 28c is rewritten as follow:

Y " | F i • e 3κZ -ω E + 4μ ω E f (29)
Eq. 29 defines the Slow Invariant Manifold (SIM) of the problem. Depending on the value of μ , the SIM can consist either in one monotonous branch or admit extremums. To determine the critical value of μ , the derivative of the right hand side of Eq. 29 is equated to zero and solved for Z:

Z # C I … I F i ± C I †F i T ~ ‡F i { (30)
Consequently, the SIM will admit extremums if μ < √

. By conducting a standard stability analysis, it can be shown that if μ > √ , the SIM will be constituted of only one stable branch, but if μ < √ , the SIM will be constituted of two stable divided by one unstable branch. This scenario is typical with system that can perform relaxation-type oscillation. To investigate this possibility, Eq. 22a at 0 ε will be analysed. Substituting Eq. 24, Eq. 25 into Eq. 22a and eliminating terms that produce secular terms with respect to e #F i j k gives:

-D A)2iω E + 2μ + η E τe T#F i U , -σA)1 -e T#F i U , -3κrB B t -2iμ rω E B + 3η E β A A t )e #F i U -1, e T #F i U = 0
(31) Expressing A and B in polar form, splitting into real and imaginary parts and rearranging gives:

ΨD R Nre3κN 2ω E -sin ω E τ η E τ -2μ ω E cos ω E τ η E τ + 2μ f sin Δ + Nre-3κN 2μ + cos ω E τ η E τ + 2μ ω E sin ω E τ η E τ -2ω E f cos Δ -R -σ + 6R η E β cos ω E τ - 6R η E β τη E cos ω E τ -cos ω E τ τη E + 2 cos ω E τ μ + τ sin ω E τ η E -2ω E sin ω E τ - 2μ (32) where Ψ 4η E τ sin ω E τ ω E + 4η E τ cos ω E τ μ + η E τ sin ω E τ + η E τ cos ω E τ + 4ω E + 4μ
(33) Substituting Eq. 28a, Eq. 28b into Eq. 32 and multiplying by R the following equation governing the evolution of Y is obtained:

• Ž Ψ• • 2'• Ž •ecos • Ž ' '" Ž -2 cos • Ž ' " -'" Ž + 2• Ž sin • Ž ' + 2" f + 12" Ž • • Ž • cos • Ž ' -1 ecos • Ž ' '" Ž -2 cos • Ž ' " -'" Ž + 2• Ž sin • Ž ' + 2" f + 4" • Ž -e2" " Ž ' cos • Ž ' 4" " 2• Ž " Ž ' sin • Ž ' • Ž f 6-• Ž -e2μ • Ž 4" • Ž 4" • Ž sin • Ž ' " Ž ' cos • Ž ' " Ž '• Ž 2" sin • Ž ' " Ž 'f 18--e2" cos • Ž ' '" Ž f (34)
In order to understand the dynamics of the system qualitatively, it is only necessary to know how the fixed points are located on the SIM. This can be understood graphically as the intersection of the SIM and the two parabola obtained by setting the right hand side of Eq. 34 to zero and solving for Y.

Analysis of some response regimes

The behavior of an embedded NES on a lathe cutting tool has been previously studied in [START_REF] Gourc | Delayed dynamical system strongly coupled to a nonlinear energy sink: application to machining chatter[END_REF], it was shown that passive control of chatter by using a NES is possible and good correspondence between analytical prediction and numerical integration was observed. However, only a linear cutting law was considered. The aim of this section is to highlight the changes in the behavior due to the nonlinear terms in the cutting law. The integration scheme used for numerical simulation is the Matlab dde23 algorithm. First, the three passive control mechanisms for a linear cutting law are presented, and next, the behavior of the system comprising a nonlinear cutting law will be presented.

Linear cutting law.

The linear cutting law is obtained by setting β β 0. The parameters used for the numerical simulations are: ε 0.01, μ 0.05, μ 0.1, κ 1, η E 0.12, τ 3.94, r 1

When a linear cutting law is considered, three control mechanisms are possible, depending on the value of the detuning parameter: complete suppression of chatter, stabilization of chatter and passive control of chatter through relaxation oscillation, also called Strongly Modulated Response (SMR).

The SIM of a small value of the detuning parameter (σ 0.1) is presented in Fig. 2(A). Two fixed points exists, one is stable and is located on the origin (denoted with a red circle), and the other one is unstable (red cross) and is located on the second stable branch of the SIM (not showed on the figure for visibility reason). The red circle and cross represents the stable and unstable fixed points, and the green line represents the projection of the temporal integration of the equations of motion on the SIM. In this case, the slow flow is repelled to the origin and chatter is fully suppressed. This scenario is confirmed by numerical integration presented in Fig. 2(B). 'o' and '+' denotes stable and unstable fixed points. Green line represents the projection of numerical integration of the coupled system on the SIM. (B) Numerical verification; blue line : coupled system, green line: uncoupled system.

(A) (B)
For a higher value of the detuning parameter (σ 0.3 in Fig. 3(A)), the fixed points located at the origin becomes unstable, and a stable fixed points is now located on the first stable branch of the SIM. This corresponds to the stabilization of chatter, which induce small amplitude periodic oscillations of the tool. The numerical integration corresponding to this case is presented in Fig. 3(B). Increasing the value of the detuning parameter to ' 0.9, the previously stable fixed point cross the folded point -, and the only way for the slow flow is to perform relaxation oscillation, that is passive control of chatter through SMR. This response regime is also verified numerically in Fig. 4(B). It is observed that four fixed points are now located on the SIM. The stable fixed point is located at the origin, two unstable fixed points are located on the two stable branch of the SIM and another unstable fixed point is on the unstable branch of the SIM. In this case, if the initial conditions are below first unstable fixed points, the slow flow will be attracted directly by the stable fixed points and if the initial conditions are above the first unstable fixed points, the flow will perform a relaxation cycle before being attracted by the stable fixed point. This corresponds to complete suppression of chatter.

Increasing σ to σ 0.2 again for the same set of parameters, the SIM is presented in Fig. 6(A). The unstable fixed points on the first stable branch of the SIM is now located below the landing point Z . In this case, with initial conditions above the first unstable fixed points, since the landing point is higher than the unstable fixed points, the flow will be repelled to the unstable branch of the SIM, and the perform relaxation oscillation. This is confirmed by numerical integration in Fig. 6(B). Another different configuration can be obtained with the following set of parameters (all the others parameters remain unchanged): κ 20, β 10, β 100. The SIM in this case is displayed in Fig. 7(A). In this case, two stable fixed points are located on the first stable branch of the SIM. Depending on the initial conditions, the flow will be either attracted by the fixed points located at the origin, which correspond to a complete suppression of chatter, or by the other fixed points, which correspond to the stabilization of chatter. These scenarios are confirmed by numerical integration in Fig. 7(B).

Conclusion

In this paper, the interest of a nonlinear absorber to reduce chatter vibration in turning process was studied.

The original device was composed of a linear oscillator corresponding to a flexible cutting tool, strongly coupled to a Nonlinear Energy Sink (NES). The NES was composed of a small mass attached with a purely nonlinear cubic stiffness. The originality of this work was the use of a nonlinear cutting law, in order to model the turning process. The model takes into account the delay term of the regenerative effect, the nonlinear term of the cutting law and the nonlinear stiffness of the NES. The whole system has been studied using a combination of the method of multiple scales and harmonic balance method. Different responses regimes were also discussed by studying the location of the fixed points on the Slow Invariant Manifold (SIM). Regimes are characterized by a complete suppression of chatter, a stabilization of chatter and a chatter control through relaxation cycle oscillations called SMR response. This study show that taking into account a nonlinear cutting law induces a more complex behavior compare to a linear cutting law, however passive control of chatter is still possible. Analytical prediction and numerical simulation are in good qualitative agreement.

The control of regenerative vibration with a nonlinear energy sink variation was clearly presented by analytical and numerical simulation; machining chatter was suppressed by a proper NES.

Figure 1 .

 1 Figure 1. Scheme of the model.

Figure 2 .

 2 Figure 2. Case of complete suppression of chatter for the set of parameters (Eq. 35) and σ = 0.1 (A) Structure of the SIM. Solids and dashed blue lines represents the stable and unstable branch of the SIM respectively.'o' and '+' denotes stable and unstable fixed points. Green line represents the projection of numerical integration of the coupled system on the SIM. (B) Numerical verification; blue line : coupled system, green line: uncoupled system.

Figure 3 .

 3 Figure 3. Case of stabilization of chatter for the set of parameters (Eq. 35) and σ = 0.3. (A) Structure of the SIM. Solids and dashed blue lines represents the stable and unstable branch of the SIM respectively. 'o' and '+' denotes stable and unstable fixed points. Green line represents the projection of numerical integration on the SIM. (B) Numerical verification.

Figure 4 .Figure 5 .

 45 Figure 4. Case of passive control of chatter via SMR for the set of parameters (Eq. 35) and σ = 0.9 (A) Structure of the SIM. Solids and dashed blue lines represents the stable and unstable branch of the SIM respectively. 'o' and '+' denotes stable and unstable fixed points. Green line represents the projection of numerical integration on the SIM. (B) Numerical verification.

Figure 6 .

 6 Figure 6. Case of passive control of chatter via SMR for the set of parameters (35) and σ = 0.2 (A) Structure of the SIM. Solids and dashed blue lines represents the stable and unstable branch of the SIM respectively.'o' and '+' denotes stable and unstable fixed points. Green line represents the projection of numerical integration on the SIM. (B) Numerical verification.

  Structure of the SIM. Solids and dashed blue lines represents the stable and unstable branch of the SIM respectively. 'o' and '+' denotes stable and unstable fixed points. Green and purple line represents the projection of numerical integration on the SIM. (B) Numerical verification.


Jean-Yves Chauleau

William Legrand

Nicolas Reyren

Davide Maccariello

Sophie Collin

Horia Popescu

Karim Bouzehouane

Vincent Cros

Nicolas Jaouen

Albert Fert

Albert Fert²

Chirality in magnetic multilayers probed by the symmetry and the amplitude of dichroism in X-ray resonant magnetic scattering

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Chirality is central to understand many fundamental mechanisms in various domains of physics and chemistry. In condensed matter, a large variety of physical phenomena hinge on the emergence of these complex chiral windings of order parameters, their observation and subsequently their control especially in magnetism and spintransport at the nanoscale. The ability to probe the nature of these chiral magnetic textures has now become a crucial element of modern magnetism and is therefore essential to gain a deeper understanding of these mechanisms. Spin-polarized scanning tunneling microscopy (SP-STM) revealed that magnetic textures with a cycloidal configuration of the magnetization and Néel domain walls are stabilized in ultra-thin magnetic films (one or a few atomic layers) on heavy metal substrates [1] . It was realized that these magnetic textures are stabilized by Dzyaloshinskii-Moriya (DM) interaction [2,3], (∑ 𝑫 𝑖𝑗 〈𝑖𝑗〉 • 𝑺 𝑖 × 𝑺 𝑗 , with Si and Sj two neighboring spins) which is the anti-symmetric analog of the Heisenberg interaction favoring a curling magnetization textures around the DM vector 𝑫. The DM interaction requires spin-orbit coupling (SOC) and broken inversion symmetry, found either in specific crystalline structures, such as B20 materials [4], or at film interfaces [5,6].

In this letter, we demonstrate that XRMS experiments (Figure 1a) allow us to reveal the actual magnetic textures existing in ultrathin magnetic multilayers with perpendicular anisotropy and large interfacial chiral interaction. Importantly, this determination is straightforward and does not require any assumptions or careful comparison to parameter-dependent micromagnetic simulations. Moreover, the circular dichroism in XMRS enables to identify directly not only the direction but also the sense of the magnetic winding and therefore of the actual sign of DM interaction as we proved by a thorough analysis. This information has a far-reaching impact in particular for spin-orbitronics and spin-orbit torque (SOT) studies. Indeed, SOT is a recent very promising approach to move efficiently domain walls [7] and magnetic skyrmions along magnetic race-tracks and shows therefore a great potential for future spintronic devices. Yet, the detailed texture of the domain walls plays a major role in their motion [8]: the DW high speed is related to its nature (Néel or Bloch) while its direction of motion depends on its chirality [7]. Besides, DM interaction can play an important role for existing technological applications in spintronics already using ultrathin magnetic multilayers with perpendicular magnetic anisotropy such as MRAM or detectors, in which the presence and the impact of non-negligible DM interaction has been largely overlooked (see e.g. [9,10]), emphasizing thus the need to probe its consequences on magnetic textures. XRMS presents several advantages that might soon turn it to a standard way to characterize and quantify the DM interaction. First, XMRS is a non-perturbative technique contrary to the approach based on magnetic domain analysis though magnetic force microscopy (MFM) imaging or domain wall motion. Second, such study is applicable not only to metallic chiral magnets but can be extended to the substantial number of insulating magnetic systems. Third, this experiment is based on the scattering on magnetic domains so sample patterning is not required. Then, this technique is sensitive to thicknesses of only a few nanometers of total magnetic materials (either in a single film or in multilayers) that conveniently can be buried below a few nanometers of materials (e.g. capping). Finally, XMRS experiments can be extended to time resolved experiments using synchrotron [11] or free electron laser [12], which should open new horizons to the exploration of the dynamics of magnetic chirality.

Here, we perform XRMS experiments using circularly polarized x-rays on prototypical Co/Pt based multilayers which have proven to be systems of choice for the emergence of homochiral magnetic distributions and are consequently also prototypical for SOT applications [13,14]. Our magnetic multilayers have been prepared using magnetron sputtering on thermally oxidized silicon substrates (see [15] for details]. Two types of samples composed of five-fold repetition of a trilayer are considered: //[Pt(1nm)/Co(0.8nm)/Ir(1nm)]x5 and its reversedstacking counterpart, //[Ir(1nm)/Co(t)/Pt(1nm)]x5. The symbol "//" stands for the substrate and the 10-nm-thick Pt buffer. To avoid oxidation, a 3-nm-thick Pt capping is deposited on top of the multilayers. In these samples, we measure a saturation magnetization MS ≈ 1 MA/m and a large effective out-of-plane anisotropy Keff ≈ 0.1 MJ/m 3 . According to our previous studies on these systems [16], the mean DM magnitude (arising mainly from the Pt/Co interfaces but also from Co/Ir interfaces) is about 2 mJ/m² . Such DM magnitude is large enough to impose pure Néel DW texture [8], with counterclockwise (CCW) Néel DW structure in case of a thin Co layer deposited on top of Pt, and conversely a clockwise (CW) Néel DW in case of Pt is deposited on Co. Before running the XRMS experiments, magnetic domain configurations have been imaged at room temperature using (MFM) after a demagnetization process using an out-of-plane field. In Figure 1b, we display a characteristic randomly disordered magnetic stripe patterns with a mean period of about 180 ± 30 nm (see the FFT of the MFM image in the inset of Figure 1b).

XRMS experiments have been performed on the two types of multilayer stackings at the SEXTANTS beamline [17] of the SOLEIL synchrotron. They have been performed in reflectivity conditions for circularly left (CL) and right (CR) incident X-ray beam polarizations at the Co L3 edge (photon energy = 778.2 eV) using the RESOXS diffractometer [18]. The diffracted X-rays are collected on a Peltier-cooled square CCD detector covering 6.1° at the working distance of this study. Typical diffracted patterns of the domain structure are displayed in Figures 1c-e. All the images have been geometrically corrected along the Qx-direction in order to account for the projection related to the photon incidence angle θ (of 18.5° in this case). As shown in Fig. 1e, the sum of the images obtained with CR (Fig. 1c) and CL (Fig. 1d) polarized light gives rise to a clear ring-shaped diffraction pattern around the specular beam (blocked by a beamstop to avoid saturation of the CCD) in the reciprocal plane (Qx,Qy). The ring radius, labelled as qp indicates the in-plane isotropy of the domain sizes with a typical periodicity in real space 𝑝 = 2𝜋/𝑞 𝑝 . The domain size p/2 of 90 ± 9 nm agrees with the one determined by MFM (see Fig. 1b) as the Fourier transform of the MFM image can be directly compared to the XRMS sum image. The circular dichroism (CD) of the scattering signal is defined as (ICL-ICR)/( ICL+ICR), where ICL and ICR are the intensities collected by the camera for CL and CR polarizations respectively. The colored map of the diffracted CD in Fig. 1f displays two lobes, one blue and one red, indicating an opposite dichroism sign. The amplitude of the dichroism is rather large, typically 10% of the sum signal, and for sure sufficient to univocally reveal the nature of the DW texture as explained hereafter. Indeed, XRMS has been recently used to characterize magnetic bulk compound hosting skyrmions. In this case, it unambiguously determined the winding number of skyrmions lattices in Cu2OSeO3 [19,20] through the analysis of the symmetry of the scattering signal. In our work, by leveraging the dichroism in XRMS scattering signal observed with circularly polarized light, we demonstrate how it provides a simple and straightforward novel approach to identify the nature of DW texture, i.e. its character (Néel or Bloch) and the magnetic chirality. We believe that these results, that take inspiration in the study by Dürr et al. [21] of the chiral behavior of closure magnetic domains at the surface of 40 nm thick FePd layer, is of high importance given the huge recent interest for this type of magnetic multilayers with large interfacial DM interaction leading to promising applications for a new generation of spin-orbitronic devices [22].

In order to precisely analyze the dichroism in XRMS, we use the following expression of the diffracted intensity I(Q) for a scattering vector Q = kf-ki in the kinematical approximation:

𝐼(𝑸) ∝ |∑ 𝑓 𝑛 •exp(𝑖𝑸 • 𝒓 𝑛 ) 𝑛 | 2
where fn is the resonant scattering amplitude of a single ion at the rn position in the electron-dipole approximation. In case this ion carries a magnetic moment mn, different contributions to this scattering amplitude can be distinguished at resonance [23][24][25] 𝑓 𝑛 = 𝑓 0 + 𝑓 𝑚 1 + 𝑓 𝑚 2 where f0 is the anomalous charge scattering amplitude and fm 1 and fm 2 are the magnetic resonant scattering amplitudes:

𝑓 𝑚 1 ∝ -𝑖 (𝜖̂× 𝜖̂′) • 𝒎 𝑛 𝑓 𝑚 2 ∝ (𝜖̂′ • 𝒎 𝑛 )(𝜖̂• 𝒎 𝑛 )
with 𝜖̂ and 𝜖̂′ the polarization state of the incident and diffracted X-ray beam respectively. Note that while fm 1 scales with m, fm 2 scales with m 2 . Considering the extracted characteristic domain period, the magnetic diffracted intensity is mainly related to fm 1 . The diffracted intensity for a given incident beam polarization is expressed as follows [26]: 𝐼(𝑸) = Tr[𝑓 ̃𝑛 𝜌 𝑓 ̃𝑛 † ] where 𝑓 ̃𝑛 and 𝑓 ̃𝑛 † are the Fourier transform of the scattering amplitude 𝑓 𝑛 and its complex conjugate and 𝜌 is the density matrix of the incident X-ray beam. In the Stoke-Poincaré representation [25] this density matrix for a circularly left or right incident beam is expressed as follows:

𝜌 CL = ( 1 -𝔦 +𝔦 1 ) 𝜌 CR = ( 1 +𝔦 -𝔦 1 )
In the following, we consider two types of possible magnetic windings, i.e. helicoidal (or Bloch-like) and cycloidal (or Néel-like) depicted in Fig. 2a and 2b respectively, that are the ones expected in the multilayer systems considered in this study. From both these winding configurations, the circular dichroism of their diffraction patterns can be calculated using (𝐼 CL -𝐼 CR ) (𝐼 CL + 𝐼 CR ) ⁄ . In Fig. 2c, we present the calculated orthoradial profiles of the normalized circular dichroism for the two possible windings and for each of them for the two possible chiralities, i.e. clockwise (CW) or counterclockwise (CCW). For the case of a helicoidal winding (Blochlike), the circular dichroism is respectively maximum (for CW) and minimum (for CCW) in positions corresponding to incident beam plane of 𝜓 = 0 or 180° and vanishes for 𝜓 = 90° or 270°. On the contrary, for a cycloidal winding (Néel-like), the maximum (for CW) and minimum (for CCW) are obtained for 𝜓 = 90° and 270°, resulting in the rotation of the dichroic diffraction pattern by 90° around the specular beam. Thus, it provides a simple way to determine unambiguously the actual texture of the domain wall from the orientation of the experimentally observed dichroism. In Fig. 2d, we display the orthoradial experimental profile of the circular dichroism we obtained for the //[Ir(1)/Co(0.8)/Pt(1)]x5 multilayer measured at an incidence angle 𝜃 = 17.5°. We emphasize that this angle corresponds to the first multilayer peak where the dichroic signal is enhanced. From the comparison with the predicted profile (yellow curve in Fig. 2d), and notably the positions of the maxima and minima, we can directly assert that the magnetic winding in our Ir/Co/Pt multilayers is of Néel type. Hence, it confirms by a direct measurement and without assumptions, that the DW texture is Néel (CW or CCW) as already known for this multilayer system with additive interfacial DM interaction either indirectly through analysis of the skyrmion size and comparison with micromagnetic simulations [15] or directly through Lorentz transmission electron microscopy [27]. Other methods have already been utilized to access to the texture of the DW or skyrmions such as spin-polarized STM [1,28], scanning nitrogen-vacancies-magnetometry (NV) [29,30] or spinpolarized low-energy electron microscopy (SPLEEM) [31]. On the other hand, DM interaction has also been probed by spin wave spectroscopy techniques such as Brillouin light scattering (BLS) [32] or time-resolved Kerr microscopy [33]. Once the type of winding is determined, the chirality (CW or CCW) of the Néel texture is usually indirectly deduced, for example, from DW dynamics and micromagnetic comparison [START_REF] Vansteenkiste | [END_REF], is straight-forwardly assessed by XRMS. Indeed, from Fig. 2d, we conclude that the magnetic texture for sample //[Ir(1)/Co(0.8)/Pt(1)]x5 corresponds to a fixed CW Néel DW chirality. Finally, the global circular dichroism pattern (changing sign only twice) allows to corroborate that the overall topological winding number of the entire magnetic distribution is N=1 [19]. We notice that beyond this qualitative comparison, we observe a deviation between the measurement and the calculated curves in Fig. 2d outside the angles corresponding to maxima and minima. We attribute it to the use of the kinematical approximation in our simulation that is known not to be strictly valid in the soft x-ray range [START_REF] Authier | Int. Tables Crystallogr. Vol. B Reciprocal Space[END_REF] but this does not affect the main conclusion. In order to experimentally confirm the origin of this dichroic contrast in the diffraction maps, we compare directly in Fig. 3, two samples having the same multilayer constitution but opposite stacking: //[Ir(1)/Co(0.8)/Pt(1)]x5 (top of Fig. 3) or //[Pt(1)/Co(0.8)/Ir(1)]x5 (bottom of Fig. 3). In these two multilayers, we expect a reversed direction of DM vector as predicted from the shape of the interfacial origin of the DM interaction [5,16] in bilayers or multilayers with magnetic material in contact with heavy materials. In Fig. 3b and3e, we display the MFM images obtained after demagnetization under perpendicular field on each sample prior to the XRMS experiments. For both samples, we find a maze domain configuration, with a difference in the mean domain width (see FFT in the insets of Fig. 3b and Fig. 3e) due to some (quasi-unavoidable) non-negligible difference in the magnetic parameters of the two multilayers, such as the perpendicular magnetic anisotropy, saturation magnetization and DM interaction. However, despite these differences, it can be directly and unambiguously concluded from the dichroism XRMS diagram (Fig. 3c and3f) that both the //Ir/Co/Pt and //Pt/Co/Ir multilayers have the same pure Néel domain wall configuration but with opposite winding signs. Indeed, they both present a complete disappearance of the dichroism for 𝜓 = 0 or 180° but with a clear reversal of the sign of the dichroism. Thus, a simple XRMS map taking a few minutes (the dichroism is so large that even a single circular polarity might be enough to conclude) directly reveals that a system with Co on top of Pt has a CCW Néel DW configuration, while for Co below Pt, a CW Néel DW is found, as expected from theoretical predictions for bilayers. The result of energy minimization using micromagnetic simulations using MuMax3 code [START_REF] Vansteenkiste | [END_REF], using our film parameters, confirms opposite Néel DW spin textures in the two stackings (Fig. 3a and3d). In conclusion, we investigated the circular dichroism in the X-ray resonant magnetic scattering to determine chiral magnetic distribution, i.e. its type (Neel or Bloch) and its magnetic chirality in thin films presenting a strong DMI. In this work, we apply XRMS to the prototypical Pt/Co system, evidencing Néel type domain walls as well as their chirality depending on the stacking order. However, this method can be applied to any type of magnetic materials, either in static or in time resolved mode, and will be useful to investigate, for instance, metallic mutlilayers based on Co or Fe. Besides, further analysis associated to modelling might be performed to get some quantitative information such as DM magnitude and even anisotropies. The power of this method is its unambiguous conclusion regarding the DMI sign independently of other magnetic parameters. In a broader perspective, Circular Dichroism in x-ray scattering appears to be a unique tool for studying the type and the chirality of the magnetic phase of any magnetic material. Similar investigations could be profitably extended to different types of systems showing a non collinear magnetic ordering such as skyrmion lattices or conical/helical phases stabilized by the Dzyaloshinskii-Moriya interaction as well as more complex systems such as the recently observed antiskyrmion lattices [37].

Figure 1 :

 1 Figure 1 : a) XRMS measurement principle and experimental configuration. b) 6x6 µm 2 MFM phase image showing the out-of-plane magnetic contrast of [Ir(1)/Co(0.6)/Pt(1)]x5 multilayer with its corresponding FFT pattern (inset) evidencing a 180 ± 30nm period disordered magnetic stripe pattern . Corresponding diffracted patterns for circularly left, CL (c) and right, CR (d) polarized incident X-ray beam. e) Resulting sum image of the diffracted pattern (CL+CR), confirming that the diffraction corresponds to the magnetic domains observed by MFM. f) Normalized difference image (CL-CR/CL+CR) evidencing a pronounced circular dichroism. Note that the straight black area crossing the diffraction ring is the shadow of a beam-stop used to block the specular reflection.

Figure 2 :

 2 Figure 2 : (a) diagram of a Bloch (helicoidal) winding and of a (b) Néel (cycloidal) winding. (c) Calculated orthoradial profile of the circular dirchroism of different magnetic textures: clockwise (CW, orange) and counter-clockwise (CCW, green) Bloch (dashed) and Néel (solid) windings for an incident angle θ=17.5°. (d) Comparison with the experimental circular dichroism orthoradial profile of a [Ir(1)/Co(0.8)/Pt(1)]x5 multilayer for the same incident angle θ=17.5° (dots). Note that the red rectangles indicate the position of the beamstop.

Figure 3 :

 3 Figure 3 : Comparison of two samples with inverted stacking //[Ir(1)/Co(0.8)/Pt(1)]x5 (a-c) and //[Pt(1)/Co(0.8)/Ir(1)]x5 (d-e). The corresponding magnetization textures are obtained by micromagnetic simulations based on material parameters (a & d). (b & e) MFM images with labyrinth-shape domains and their Fourier transform in inset. (c & f) Normalized circular dichroism signal (ICL-ICR)/(ICL+ICR) measured on sample a (e) and sample b (f).The diffraction sum images are shown in inset, confirming again that the diffraction corresponds to the magnetic domains.

Ackownledgments:

SoGraph (ANR-15-GRFL-0005) and MAGicSky (European Union Grant, No. FET-634 Open-665095) are acknowledged for financial support.


Florent Rossi
email: florent.rossi@univ-bpclermont.fr

Joan Artigas

Clarisse Mallet

Structural and functional responses of leaf-associated fungal communities to chemical pollution in streams

Keywords: chemical stress, translocation, ligninolytic enzymes, aquatic fungi

SUMMARY

1. In forested lotic ecosystems, organic matter decomposition processes, involving aquatic fungi and their ligninolytic activities play a key role. In this study, the relationship between realistic water chemical pollution (including nutrients, metals and pesticides) and the biomass, structure and enzyme activities of leaf-associated fungal communities was evaluated.

2. The experiment was performed through a microcosm approach by comparing fungal communities from a less-polluted upstream site to a more-polluted downstream site in the agricultural basin of the Auzon stream (Puy-de-Dôme region, Centre France). The resistance as well as the ability of these fungal communities to recover from pollution was also assessed through a translocation experiment.

3. Results showed a lower laccase activity and higher band richness in fungal communities from the downstream site comparing to the upstream site, that can partially be explained by the greater pesticide toxicity (r = -0.50) and nutrient concentration (r = -0.45, dissolved organic carbon) of downstream waters.

4. Specifically, a negative relationship between laccase activity and nitrate concentration was observed, irrespectively of the studied site, whereas the relationship between nitrate and phenol oxidase/peroxidase appeared much weaker.

5. The translocation experiment evidenced i) a fast decrease in laccase activity concomitant with a shift in fungal structure after stress exposure, and ii) a recovery ability in terms of laccase activity and fungal community structure two weeks after stress removal.

6. The present study underlines the sensitivity of leaf-associated fungal communities in terms of laccase activity and community structure to variations in chemical stress, as well as their ability to recover once the stress is removed. This study highlights the potential use of laccase as indicators of stream water chemical pollution.

INTRODUCTION

Low-order forested streams, representing more than 70% of the total stream network in most of European and US basins [START_REF] Benda | The network dynamics hypothesis: how channel networks structure riverine habitats[END_REF][START_REF] Kristensen | European small water bodies[END_REF], are seasonally fueled with allochthonous organic matter coming from the riparian forest [START_REF] Mninshall | Role of allochthonous detritus in the trophic structure of a woodland springbrook community[END_REF].

These organic matter inputs, mostly in the form of leaf litter, can represent up to 90% of the total organic matter in streams [START_REF] Mninshall | Role of allochthonous detritus in the trophic structure of a woodland springbrook community[END_REF][START_REF] Triska | Seasonal distribution of aquatic hyphomycetes in relation to the disappearance of leaf litter from a woodland stream[END_REF][START_REF] Cummins | Structure and function of stream ecosystems[END_REF][START_REF] Wallace | The role of macroinvertebrates in stream ecosystem function[END_REF]. Consequently, the decomposition of leaf litter becomes a central process in these ecosystems since it i) intervenes in various biogeochemical cycles (C, P, N and S; [START_REF] Webster | Organic matter budgets for streams: a synthesis[END_REF], ii) provides mineral nutrients to the entire stream food web [START_REF] Gessner | Importance of stream microfungi in controlling breakdown rates of leaf litter[END_REF], and iii) returns CO 2 to the atmosphere [START_REF] Gholz | Long-term dynamics of pine and hardwood litter in contrasting environments: toward a global model of decomposition[END_REF]. Leaf litter decomposition process in streams is performed by the action of both physical (abrasion, leaching and/or fragmentation) and biological factors; the latter including the action of microorganisms and macroinvertebrates [START_REF] Mann | Detritus formation from eelgrass (Zostera marina L.): The relative effects of fragmentation, leaching, and decay[END_REF][START_REF] Gessner | A perspective on leaf litter breakdown in streams[END_REF][START_REF] Gaudes | Contribution of microbial and invertebrate communities to leaf litter colonization in a Mediterranean stream[END_REF]. While macroinvertebrates shredder taxa account for the major portion of the leaf mass loss in streams [START_REF] Suberkropp | Fungi and bacteria associated with leaves during processing in a woodland stream[END_REF], it has been estimated that microorganisms (including fungi and bacteria) can contribute up to 25-45% of the leaf mass loss, even if these estimations vary depending on leaf species and sites studied [START_REF] Hieber | Contribution of stream detrivores, fungi, and bacteria to leaf breakdown based on biomass estimates[END_REF][START_REF] Baldy | Leaf litter breakdown budgets in streams of various trophic status: effects of dissolved inorganic nutrients on microorganisms and invertebrates[END_REF]. Although the contribution of each microbial group on leaf litter decomposition remains hard to evaluate, especially because of the difficulty to separate them from the leaf matrix, it is widely accepted that microbial decomposition is mostly attributed to the action of aquatic fungi [START_REF] Krauss | Fungi in freshwaters: ecology, physiology and biochemical potential[END_REF][START_REF] Canhoto | Biology and ecological functions of aquatic hyphomycetes in a warming climate[END_REF]. Indeed, aquatic fungi (mostly the group of aquatic hyphomycetes) represent the major microbial biomass colonizing the leaf substratum (> 95% of the total microbial biomass;

Hieber & Gessner 2002), and possess a large panel of extracellular hydrolytic and oxidative enzymes such as cellulases, hemicellulases and/or laccases [START_REF] Abdullah | Extracellular enzymatic activity of aquatic and aero-aquatic conidial fungi[END_REF][START_REF] Chandrashekar | Production of extracellular cellulase byLunulospora curvula andFlagellospora penicillioides[END_REF][START_REF] Abdel-Raheem | Lignocellulolytic enzyme production by aquatic hyphomycetes species isolated from the Nile's delta region[END_REF] capable to breakdown leaf structural compounds (e.g. cellulose, pectin, lignin).

In the actual scenario of streams pollution, mostly resulting from the anthropogenic activity occurring in the watersheds (i. e. agriculture, industry, urbanization), it has been proved that chemical pollution (by heavy metals, nutrients and xenobiotics) can threaten aquatic fungal communities and their decomposition abilities [START_REF] Artigas | Effects of the fungicide tebuconazole on microbial capacities for litter breakdown in streams[END_REF], and by extension the entire ecosystem functioning [START_REF] Duarte | Copper and zinc mixtures induce shifts in microbial communities and reduce leaf litter decomposition in streams[END_REF][START_REF] Maltby | Fungicide risk assessment for aquatic ecosystems: importance of interspecific variation, toxic mode of action, and exposure regime[END_REF]. Amongst those pollutions, the effects of nutrients on aquatic fungal communities is certainly one of the most studied [START_REF] Gessner | A perspective on leaf litter breakdown in streams[END_REF]. Although nutrient enrichment (by phosphorus and nitrogen) is commonly associated with increases in fungal biomass, diversity, and activity [START_REF] Artigas | Organic matter decomposition by fungi in a Mediterranean forested stream: contribution of streambed substrata[END_REF][START_REF] Hagen | Are leaf breakdown rates a useful measure of stream integrity along an agricultural landuse gradient?[END_REF][START_REF] Magbanua | Responses of stream macroinvertebrates and ecosystem function to conventional, integrated and organic farming[END_REF], some studies also observed a negative effect of the nutrients, depending on the concentration and nature of the amendments [START_REF] Baldy | Microbial dynamics associated with leaves decomposing in the mainstem and floodplain pond of a large river[END_REF]Duarte et al. 2009b). For instance, high nitrate and ammonium concentrations in eutrophic streams have been shown to decrease fungal diversity and biomass, as well as their potential for decomposing leaf litter [START_REF] Baldy | Microbial dynamics associated with leaves decomposing in the mainstem and floodplain pond of a large river[END_REF]Duarte et al. 2009b). Effects of metals (cadmium, copper, and zinc) are commonly associated with reductions in fungal species richness, biomass, sporulation rates, and leaf decay rates [START_REF] Krauss | Aquatic hyphomycetes occur in hyperpolluted waters in Central Germany[END_REF]Duarte, Pascoal & Cassio 2009a;[START_REF] Fernández | Effects of fungicides on decomposer communities and litter decomposition in vineyard streams[END_REF].

Recently, field experiments have attempted to assess the effects of pesticides on microbial litter decomposition in agricultural streams [START_REF] Krauss | Aquatic hyphomycetes in polluted groundwater habitats of Central Germany[END_REF][START_REF] Rasmussen | Impacts of pesticides and natural stressors on leaf litter decomposition in agricultural streams[END_REF][START_REF] Fernández | Effects of fungicides on decomposer communities and litter decomposition in vineyard streams[END_REF]. In most of the cases, the exposure to pesticides lead to declines in fungal species richness, concomitant with decreases in litter decay rates [START_REF] Krauss | Aquatic hyphomycetes in polluted groundwater habitats of Central Germany[END_REF][START_REF] Rasmussen | Impacts of pesticides and natural stressors on leaf litter decomposition in agricultural streams[END_REF]. More precisely, the type of the pesticide molecule as well as its mode of action would determine its potential impact on target and non-target leaf microorganisms [START_REF] Maltby | Fungicide risk assessment for aquatic ecosystems: importance of interspecific variation, toxic mode of action, and exposure regime[END_REF]. This is the case of fungicides, being more toxic to aquatic fungi than other pesticides, due to their direct action on fungal physiology [START_REF] Artigas | Effects of the fungicide tebuconazole on microbial capacities for litter breakdown in streams[END_REF][START_REF] Fernández | Effects of fungicides on decomposer communities and litter decomposition in vineyard streams[END_REF][START_REF] Fernández | Effects of fungicides on decomposer communities and litter decomposition in vineyard streams[END_REF] but also indirectly affecting bacteria co-existing with fungi (Artigas et al. 2014). Up to now, however, the effects of multi-stressors, including pesticides and environmental stressors (e.g. temperature, droughts, nutrients…) in leafassociated fungal communities are weakly studied in the literature [START_REF] Fernández | Effects of fungicides on decomposer communities and litter decomposition in vineyard streams[END_REF].

Besides, [START_REF] Piscart | Leaf litter recycling in benthic and hyporheic layers in agricultural streams with different types of land use[END_REF] did not find any correlation between pesticide pollution and microbial decay rates of leaves in a stream draining vineyard area in France. The absence of correlations was partially explained by the low temperatures recorded in their experiment which may have masked pesticides impact on microbial litter decomposition.

Translocation experiments are suitable approaches to assess such multi-stress impacts on stream microbial communities (including aquatic fungal communities). Indeed, this approach, which consist on transferring biological communities from polluted sites to pristine sites and vice versa (i. e. [START_REF] Ivorra | Translocation of microbenthic algal assemblages used for in situ analysis of metal pollution in rivers[END_REF][START_REF] Proia | Effects of pesticides and pharmaceuticals on biofilms in a highly impacted river[END_REF], permits to identify resistance/resilience mechanisms set up by microbial communities to cope stress [START_REF] Sridhar | Response of aquatic hyphomycete communities to changes in heavy metal exposure[END_REF]Duarte et al. 2009a). At present, various studies have performed translocation experiments on periphytic biofilms (mostly targeting diatom communities dynamics; [START_REF] Ivorra | Translocation of microbenthic algal assemblages used for in situ analysis of metal pollution in rivers[END_REF][START_REF] Morin | Diatom immigration drives biofilm recovery after chronic copper exposure: Role of diatom immigration in recovery[END_REF]). To our knowledge, two studies have performed translocation experiments to assess metal pollution on leaf-associated fungal communities [START_REF] Sridhar | Response of aquatic hyphomycete communities to changes in heavy metal exposure[END_REF]Duarte et al. 2009a) but none regarding the overall influence of nutrients, metals and pesticides. Studies on metals showed that copper, zinc, and manganese exposure stopped litter decomposition and declined sporulation rates. However, these studies also showed that fungal communities were able to recover when stress by metals was removed after translocation from a polluted site to a cleaner one.

The present study investigates the sensitivity of leaf-associated fungal communities to stream water pollution (including nutrients, metals, and pesticides) by comparing communities from upstream (less polluted) and downstream (more polluted) sites of an agricultural stream (the Auzon, Puy-de-Dôme region, Centre France). Our main objective is to assess the resistance and recovery abilities of leaf-associated fungal communities to stream water pollution using a translocation experiment in laboratory microcosms. As base hypothesis we presume that stream water pollution observed downstream (higher nutrients, metals, and pesticides concentrations) would promote shifts in the structure and activity of leaf-associated fungal communities comparing to communities from the upstream site (less polluted). As second hypothesis, we expected that chemical stress exposure due to translocation from upstream to downstream would lead to fungal communities similar to that of downstream in terms of both structure and activity. In the other hand, the proved recovery potential of leaf-associated fungal communities from single stressors suggest enough abilities in such communities to recover after stress removal during the translocation from downstream to upstream.

MATERIALS AND METHODS

Study sites and preparation of fungal inoculums

Leaf-associated fungal communities were obtained from the Auzon, a third-order forested stream draining a basin surface area of 6074 ha in the Puy-de-Dôme region (Centre France).

Two study sites were selected for this work: a less-polluted upstream site (first order section) close to the river source (45°41'30.0"N 3°04'58.5"E; Channonat) and a more-polluted downstream site (fourth order section) before the confluence of the Auzon with the Allier River (45°43'30.3"N 3°12'27.4"E; Cournon d'Auvergne). The riparian forest, mainly composed of Alnus glutinosa (L.) Gaertn and Corylus Avellana (L.) species, was well preserved at both upstream and downstream sites. However, streambeds were composed by gravel and cobbles in the upstream section and by sand in the downstream section. Land use variations throughout the watershed lead to an upstream section mostly occupied by forests and prairies and a downstream section devoted to agriculture (38% of the total watershed surface area, mainly cereal crops) and some dispersed urbanizations (10%). According to the report on phytosanitary molecules detection in surface waters from the Puy-de-Dôme region, the Auzon is one of the most herbicide-polluted streams, especially in its downstream section reaching concentration peaks of aminomethylphosphonic acid (AMPA), glyphosate, and Smetolachlor above 1 µg L -1 (Phyt'eauvergne 2014).

Fungal communities were obtained from the upstream and downstream sites of the Auzon in October 2014 using Alnus glutinosa (Alnus) leaf species as substratum. Freshly fallen Alnus leaves were harvested from the upstream site, dried 72h at room temperature, and placed in a total of six bags (1 mm of mesh size), each containing 10 g of the pre-dried leaves.

Three of these litter bags were placed upstream and the three others downstream in order to obtain fungal communities representative of the two sites. After three weeks of colonization in-stream, litter bags were retrieved and transported to the laboratory where leaves were cleaned with filtered (0.2 µm) stream water, cut into small circles (2 cm in diameter) and placed in sterile 250 mL flasks (1 flask per bag). Each flask contained 10 leaf disks filled with 100 mL of filtered (0.2 µm) stream water from each respective site [START_REF] Gessner | Ergosterol-to-biomass conversion factors for aquatic hyphomycetes[END_REF]. Fungal mycelia growth and sporulation was achieved by incubations at 19 °C under agitation at 180 rpm [START_REF] Artigas | Effect of nutrients on the sporulation and diversity of aquatic hyphomycetes on submerged substrata in a Mediterranean stream[END_REF]. After 48 h, water suspension and leaf disks that were crushed and well mixed, were transferred into sterilized flasks and used as inoculum for the subsequent microcosm experiment.

Microcosm experiment and sampling

In order to assess only the effect of stream water chemistry on microbial communities colonizing leaf litter, the experiment was performed in microcosm under controlled condition of temperature (room temperature, 19 ± 0.1 °C), light (dark/light 13 h : 11 h, 846 ± 50 and 0 µmol photons cm -2 s -1 , respectively) and flow (0.1 L s -1 ). In total, six glass indoor stream channels (l × w × d = 63 cm × 11 cm × 4 cm), representing the upstream (3 replicate channels) and downstream (3 replicate channels) sites of the Auzon were set up in a thermoregulated room. Each channel was independent and connected to a separate 50 L tank through an aquarium pump (Newjet 1200, Newa, Italy) allowing water recirculation. Each tank was filled with river water taken directly from the corresponding study sites (upstream and downstream) of the Auzon. After one-day stabilization of microcosm devices with stream water, seven packs of pre-dried Alnus leaves (5 g dry weight per pack) were deployed in each channel, supplemented with 100 mL of the corresponding fungal inoculum described above.

The first sampling was performed shortly after having released the fungal inoculum into the channel (week 0). The other samplings were performed weekly for a total of four weeks (weeks 1, 2, 3 and 4). The river water was replaced each seven days to avoid nutrient limitation and samplings were performed just before water replacement.

Translocation was performed in the middle of the experiment (after the sampling at week 2, the same day), 14 days after the beginning of the experiment) by transferring two randomly chosen leaf packs from i) upstream to downstream channels (Ups → Dws) and ii) downstream to upstream channels (Dws → Ups). One week after (7 days, week 3) the first translocated samples were sampled. Translocated samples were used to assess the impact (resistance) and the recovery (resilience) of leaf-associated fungal communities to stream water pollution. Fungal communities having remained permanently in upstream and downstream channels were monitored in parallel during translocation.

At each sampling time (weeks 0, 1, 2, 3, and 4), one randomly selected leaf pack was sacrificed from each channel and leaf disks (1 cm in diameter) were obtained for further analyses on the fungal community structure (DGGE), ergosterol concentration, extracellular enzyme activities (laccase, peroxidase and phenol oxidase), leaf dry weight, and lignin content. While extracellular enzyme activity measurements were performed the same sampling date, samples for fungal community structure, ergosterol concentration and lignin content were stored frozen at -20 °C until analyses. Additionally, water samples from each channel were taken at each sampling time to determine nutrients (nitrate, phosphate, and dissolved carbon), multi-residues (including pesticides) and metals (cadmium, copper, manganese, zinc) concentrations.

Water physical and chemical parameters

In each stream channel, water temperature, dissolved oxygen, conductivity and pH were measured at each sampling time using portable probes (ProODO YSI, Cond340i WTW, pHMeter CG818, respectively). Soluble reactive phosphorus [START_REF] Murphy | A modified single solution method for the determination of phosphate in natural waters[END_REF] and nitrate concentrations (Nitrat-Test kit, Merck, Darmstadt, Germany) in water were measured spectrophotometrically. Total dissolved carbon (TDC), dissolved organic carbon (DOC) and inorganic carbon (DIC) as well as nitrogen concentrations were measured using a total organic carbon analyzer (TOC-VCPN , Shimadzu, Kyoto, Japan). In parallel, water samples taken directly from the river were sent to Laboratoire de Touraine (COFRAC agreement number 1-0677) for pesticide multi-residues and heavy metals analyses. Predicted toxicity of pesticides in water samples was estimated according to the toxic units (TU) concept [START_REF] Sprague | Measurement of pollutant toxicity to fish-III: Sublethal effects and "safe" concentrations[END_REF]. TU for each pesticide and metabolite compound was calculated according to the following equation:

Where C i is the actual concentration of the pesticide in the water sample and EC 50i is the median acute effect of this pesticide inhibiting 50% of the growth rate of standard aquatic microbial species. Predicted toxicity of the pesticide cocktail, noted as ƩTUs, was also calculated at both upstream and downstream sites for each week as the sum of all TU for the pesticides detected in a single water sample. Since toxicity data (EC 50 ) for pesticides on aquatic fungi are not available in the literature [START_REF] Maltby | Heterotrophic Microbes[END_REF][START_REF] Maltby | Fungicide risk assessment for aquatic ecosystems: importance of interspecific variation, toxic mode of action, and exposure regime[END_REF], EC 50 values of microalgae (Scenedemus subspicatus, Scenedesmus abundans, Pseudokirchneriella subcapitata and Chlamydomonas angulosa) were used as surrogates (see [START_REF] Rasmussen | Impacts of pesticides and natural stressors on leaf litter decomposition in agricultural streams[END_REF].

Biomass and structure of fungal communities

Ergosterol concentration determination was used as proxy for fungal biomass in leaves [START_REF] Gessner | Use of solid-phase extraction to determine ergosterol concentrations in plant tissue colonized by fungi[END_REF]. Lipids were extracted from leaves (fifteen leaf disks per sample) after incubation in 0.14 M KOH methanol at 80 °C during 30 min. Extracts were purified and concentrated using solid-phase extraction (tC18 cartridges, Sep-Pak Vac RC, 500 mg, Waters) and then analyzed by a high pressure liquid chromatography system (Lachrom L-7400, Merck-Hitachi, Tokyo, Japan) equipped with an ODS-2 Hypersil column (250 × 4.6 mm, 5 µm particle diameter; Thermo Scientific, San Jose, CA, U.S.A.) and using methanol as the mobile phase at a flow rate of 1.4 mL min -1 . Ergosterol was detected at 282 nm and quantified according to an ergosterol standard (ergosterol purity ≥ 95%, Sigma-Aldrich, St. Louis, Missouri, USA) ranging from 0 to 200 μg mL -1 . Results were expressed in μg ergosterol per g of leaf dry mass (DM).

Total DNA extraction was performed from 5 leaf disks per sample using the FastDNA SPIN Kit for soil (MP Biomedicals, Santa Ana, California, USA) following the manufacturer's instructions with some minor modifications. Briefly, two homogenization steps (FastPrep, speed 6.0 for 40 seconds) were applied instead of one in order to improve fungal mycelium extraction from the leaf matrix. Extracted DNA was quantified using a Nanodrop (Nanodrop TM 2000, Wilmington, Delaware, USA), amplified by polymerase chain reaction (PCR) targeting the Internal Transcribed Spacer (ITS) region 2 of the fungal rRNA and then analyzed using denaturing gradient gel electrophoresis (DGGE) method. PCR mix was composed of 2.5 μl of MgCl (50 mM), 0.5 μl of BSA (50 mg mL -1 ), 1 μL dNTPs (10 mM), 0.3 μL of Taq polymerase (5 U μL -1 ), 2 μL of each primers (10 pmol μL -1 ), 5 μL of 10X buffer and 4 μL of DNA (10 ng L -1 ), completed with H 2 O to a final volume of 50 μL. Primers, purchased from MWG Biotech, were the forward ITS 3, with a GC tail attached in 5' (5'-CGC CCG CCG CGC CCC GCG CCC GGC CCG CCG CCC CCG CCC CGC ATC GAT GAA GAA -3') and the reverse ITS 4 (5'-TCC TCC GCT TAT TGA TAT GC 3') [START_REF] White | Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics[END_REF]). PCR conditions were set at 95 °C for 5 minutes, followed by 35 steps (30 s at 95 °C, 45 s at 55 °C and 1 minute at 72 °C) and a final elongation phase of 7 minutes at 72 °C.

PCR products were then checked on 1% agarose gel (size expected ranging 200-450 bp) and quantified using the DNA Quantification Kit, Fluorescence Assay (Sigma-Aldrich, Saint-Louis, USA) before loading into DGGE. DGGE was run with 500 ng of PCR product per sample, using the TIGEK 2401-220 device (CBS Scientific, San Diego, North Carolina, USA) in 1X TAE buffer for 16 h, with a temperature of 59 °C and a voltage of 90 V. Gels contained 7.5% (w/v) of polyacrylamide and a linear urea-formamide denaturing gradient ranging of 25%-48% (100% = 7M urea and 40% (v/v) formamide). At the end of the electrophoresis, gels were stained in 1X TAE buffer containing 1/20000 dilution of Gel star (Lonza, Rockland, USA) and digitized using a BioSpectrumAC Imaging System (UVP, Upland, California, USA). DGGE bands profiles were then analyzed using the Gelcompare2 software (Applied Maths, Belgium) in order to obtain the fungal band matrix, based on presence/absence of bands from each sample. Using this matrix, a cluster analysis was performed to assess homologies among experimental conditions tested.

Mass loss and lignin determination in leaves

Decomposition rates of Alnus leaves (k) were evaluated from leaf mass loss. Leaf mass loss was surveyed between weeks 0 to 4 in the different experimental conditions. The percent loss in dry mass (DM, oven-weight after 48 h at 60 °C) from ten leaf disks (pooled in one replicate sample) permitted to calculate decay rates using an exponential decay model:

M t = M 0 e -kt ,
where M 0 is the initial DM (g), M t is the final DM (g) at time t, and k is the breakdown coefficient [START_REF] Petersen | Leaf processing in a woodland stream[END_REF].

Lignin content in Alnus leaves was measured using the acetyl bromide procedure described by [START_REF] Iiyama | An improved acetyl bromide procedure for determining lignin in woods and wood pulps[END_REF]. Five leaf disks were digested with 2.5 mL of acetyl bromide solution in acetic acid (25%, w/w) at 70 °C for 30 minutes. Extracts were then transferred into 50 mL flasks and rinsed with a 15 mL acetic acid up to a final volume of 50 mL. Results were obtained spectrophotometrically and expressed as lignin percentage per leaf DM, using the following equation:

Where A is the absorbance of the sample at 280 nm, ε is the absorption coefficient for lignin (ε lignin = 20 g -1 L cm -1 ), l is the path length of the beam in cm and C is the concentration of the sample in g of DM L -1 .

Extracellular enzyme activities

Phenol oxidase (EC 1.14.18.1), peroxidase (EC 1.11.1.7), and laccase (EC 1.10.3.2) activities were measured in leaf samples (three leaf disks per sample) using 3,4-Dihydroxy-Lphenylalanine (L-DOPA) and 2,2′-Azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) commercial substrates analogues (Sigma-Aldrich, St. Louis, Missouri, USA). All enzyme activities were conducted under saturating conditions of substrate (previously determined from substrate saturating curves) and according to the protocols of Sinsabaugh, Osgood & Findlay (1994a) and [START_REF] Johannes | Laccase activity tests and laccase inhibitors[END_REF]. Phenol oxidase and peroxidase activities assays consisted in dark incubations of 3 leaf disks per replicate at 19 °C under agitation in glass test tubes containing 2 mL of water from the corresponding channel with 2 mL of 1.5 mM L-DOPA (final concentration) in acetate buffer (pH 4.5). Peroxidase activity assay received an addition of 0.2 mL of 0.3% H 2 O 2 . After 1 hour incubation, spectrophotometric readings at 460 nm were performed for phenol oxidase and peroxidase using the Ultrospec 2000 (Pharmacia Biotech, Trowbridge, UK). Net peroxidase activity was obtained after subtraction of phenol oxidase activity. Laccase activity was determined from 3 leaf disks per replicate, following the oxidation of 2 mL ABTS (3 mM, final concentration) in 0.1 M of citrate-phosphate buffer (pH 4.5) with 2 mL of water from the corresponding channel at 19 °C, in the dark and under agitation (150 rpm) for 1 h. Absorbance was then determined at 420 nm. All enzyme activities were corrected by control, performed with water from the corresponding site (no leaf disks), and expressed in µmol substrate h -1 g leaf DM -1 .

Data analyses

Differences between upstream and downstream sites for water physical and chemical parameters (oxygen, conductivity, pH, nutrients, metals, and pesticides) were evaluated by a two-way ANOVA assessing the sources of variation for the factors time (weeks 0 to 4) and site (upstream, downstream) as well as their interactions. Differences between fungal community descriptors (ergosterol concentration, fungal band richness and extracellular enzyme activities) and litter characteristics (lignin content) were assessed using a first twoway ANOVA on upstream and downstream samples for the whole experiment (factors time : weeks 0 to 4 ; factors condition : upstream and downstream) and a second two-way ANOVA on all samples at week 3 and 4 corresponding to the translocation experiment (factors time: week 3 and 4 ; factors condition : upstream, downstream, translocation upstream to downstream, and translocation downstream to upstream). All data were transformed using the Box Cox function prior to the ANOVA tests to meet assumptions of normality and homoscedasticity of data residuals (Box and Cox, 1964), and were complemented with post hoc multiple comparison tests (Tukey's test, P < 0.05).

From the fungal fingerprints obtained after DGGE analyses, the fungal band presence/absence matrix was transformed to Euclidean distance, and fungal communities were classified by a joining tree analysis using the Ward's hierarchical clustering method (Ward 2 criterion). Moreover, two redundancy analyses (RDA) were performed to determine relationships between water chemical and fungal community parameters. As described for ANOVA tests, a first RDA (RDA UpDw ) was performed on upstream and downstream samples for the whole experiment, whereas the second RDA (RDA Transloc ) was performed on translocated and non-translocated samples at weeks 3 and 4 only. Briefly, fungal community descriptors data matrices were composed of 5 parameters: ergosterol concentration, fungal band richness and extracellular enzyme activities (phenol oxidase, peroxidase, and laccase).

Water physico-chemical data matrices included 15 parameters: dissolved oxygen, pH, conductivity, and concentrations of metals (Cu, Cd, Mn, Zn), phosphate, nitrate, dissolved carbon and nitrogen concentrations and ƩTUs. Lignin percentage in leaves was also included in the above data matrix. All data from the matrices were transformed prior to analysis using the Hellinger transformation according to [START_REF] Legendre | Ecologically meaningful transformations for ordination of species data[END_REF], and a forward selection was performed to select water chemical parameters significantly correlated with fungal community descriptors using a Monte Carlo permutation test (anova.cca function, 999 unrestricted permutations, P = 0.05).

All statistical analyses were computed using the R software, and RDA as well as related methods were performed using the Vegan package (http://cran.r-project.org/).

RESULTS

Physical and chemical characteristics of stream water

A total of 17 pesticide compounds (14 herbicides, 2 insecticides, and 1 fungicide) were detected in water samples from the Auzon stream (Table 1). Overall, pesticides displayed relatively low concentrations (below 0.05 µg L -1 ) except for the herbicide glyphosate and its metabolite the aminomethylphosphonic acid (AMPA) (ranging 0.10-1.40 μg L -1 , irrespectively of the studied site), the aminotriazole and the diuron. While only three molecules were detected at the upstream site (glyphosate, AMPA, and lindane), the whole 17 pesticide compounds were detected downstream. Potential pesticide toxicity calculations, represented as the ƩTUs, were on average (weeks 1 to 4) thirty times higher downstream (35.96 x 10 -3 ± 4.78 x 10 -3 ) compared to upstream (1.14 x 10 -3 ± 0.39 x 10 -3 ) (ANOVA, Site effect, P < 0.0001). This higher pesticide toxicity observed downstream can be attributed to both the number of pesticide compounds and the presence of diuron (TU = 2.06 x 10 -2 ), oxadiazon (TU = 1.81 x 10 -3 ) and terbutryne (TU = 3.13 x 10 -3 ), contributing to the highest TU observed downstream (Table 1). In addition, heavy metals (Cu, Zn, Mn, Cd) displayed very low concentrations at both sites (Table 1) though their concentrations were significantly higher downstream compared to upstream (ANOVA site effect, P < 0.05). Conductivity, pH, dissolved carbon (total and organic), and phosphate concentrations were also greater at the downstream site (ANOVA, site effect, P < 0.001 for each parameter; Table 1 andFigure 1.1 and1.2). Conversely, no differences were observed for total nitrogen and nitrate concentrations between sites (ANOVA, site effect, P = 0.1210 and P = 0.1197 respectively).

More precisely, average nitrate concentrations were 0.51 ± 0.04 mg L -1 and 0.53 ± 0.04 mg L - 1 in upstream and downstream channels, respectively (Figure 1.2). At week 2, N-NO 3 concentration peaked up to 0.70 ± 0.10 mg L -1 upstream and 0.78 ± 0.09 mg L -1 downstream (ANOVA time effect, P < 0.0001).

Litter decomposition rates and lignin content

Decay rates of Alnus leaves calculated between weeks 0 and 4 were similar between downstream (0.0162 day -1 ) and upstream (0.0101 day -1 ) sites, as well as after translocation experiments, although no statistical analysis were performed. Besides, lignin content in Alnus leaves (data not shown) decreased from 10.45 ± 1.39 %DW (week 0) to 4.30 ± 0.52 %DW (week 4) upstream, representing a loss of 58%. Downstream, lignin loss from leaves appeared lower than upstream (29%) with values ranging from 8.20 ± 0.79 %DW (week 0) to 5.82 ± 1.63 %DW (week 4). Specifically, the major decrease in lignin content from leaves was observed at week 2 in upstream and downstream samples (ANOVA, time effect, P < 0.0001).

However, no effects of translocation were observed in lignin loss from leaves.

Biomass and structure of fungal communities

Ergosterol concentration in Alnus leaves showed values ranging from 80.25 ± 11.45 μg gDM -1 to 417.37 ± 24.56 μg gDM -1 upstream and 61.84 ± 2.79 μg gDM -1 to 321.87 ± 84.24 μg gDM -1 downstream (Figure 2.1). The major shifts in ergosterol concentrations were between week 0 and week 1 (Tukey's test, P < 0.0001), evidencing the colonization phase of Alnus leaves by fungi. No significant differences were observed in ergosterol concentration between upstream and downstream sites during the four weeks of study (Tukey's test, P > 0.05), but concentrations tended to be slightly higher upstream. Translocation did not have any effects on ergosterol concentration (Figure 2.2).

Fungal band richness tended to increase from weeks 0 to week 4 (ANOVA, P < 0.05) in both upstream (from 21 ± 1 to 44 ± 1 band) and downstream (from 36 ± 1 to 49 ± 1 band) samples, evidencing a fungal species succession on leaves. However, fungal band richness was on average (weeks 0 to 4) higher in downstream samples (S = 41 ± 1) compared to upstream samples (S = 34 ± 3, Tukey test, Site effect, P < 0.05). Moreover, fungal band richness responded to both translocations (Dws → Ups and Ups → Dws), displaying samples with intermediary values (Tukey's test, P > 0.05; Table 2).

Cluster analysis, performed on fungal community presence/absence matrix, showed a sample ordination, first by time and second by site (Figure 3). Effects of translocation on fungal community structure were perceptible at week 3, and characterized by the splitting of translocated samples from their origin, despite still being in the same cluster. However, such effects became more evident at week 4. After stress removal (Dws → Ups translocation, week 4) translocated samples tended to form a separate cluster with those of upstream (less polluted site) and to separate from those of downstream (site of origin). Conversely, after stress exposure (Ups → Dws translocation, week 4) translocated samples tended to form a separate cluster with those of downstream (more polluted site) and to separate from those of upstream (site of origin, Figure 3).

Enzymatic activities

Peroxidase and phenol oxidase activities measured in Alnus leaves were not different between upstream and downstream samples (Tukey test, P > 0.05) (Figure 4A1, B1), though a clear time effect was observed in both activities (ANOVA, time effect, P < 0.0001). Time effects were characterized by peaks of activity at week 2, multiplying by two-to-four the basal activity values recorded at the other sampling times in both upstream and downstream samples (Figure 4A1,B1). Translocation of leaves had no effect on the above mentioned enzyme activities (figure 4A2,B2).

In contrast, laccase activity showed differences between sites (Tukey's test, site effect, P < 0.0001), with higher average values (weeks 0 to 4) in upstream fungal communities (1.84 ± 0.29 U gDW -1 h -1 ) comparing to those from downstream (0.39 ± 0.12 U gDW -1 h -1 , Figure 4C1). Laccase activity also showed significant differences over time, especially at week 2, where a 3 to 5 times drop of activity in upstream samples was observed (ANOVA, time effect, P < 0.0001). Concerning translocation experiments, after stress exposure (Ups → Dws translocation, week 4) samples displayed an intermediate state, characterized by a decrease in laccase activity compared to upstream samples (samples of origin) and approached to the activity recorded in downstream samples at the same date (Tukey's test, P < 0.05, Figure 4C2). Similarly, after stress removal (Dws → Ups translocation, week 4) translocated samples displayed an increase in laccase activity, reaching values similar to those from the upstream samples (Tukey's test, P < 0.05).

Relationship between water chemistry and fungal community parameters

RDA UpDw , performed with upstream and downstream samples between weeks 0 to 4 (Figure 5.1), displayed two axes representing 85.97 % of the total variance explained by biological and environmental descriptors. The first Axis (RDA1 explaining 70.38% of the variance) was mainly correlated with nitrate concentration (r = 0.76) characteristic of samples at week 2 located in the right up corner of the RDA UpDw (peak of nitrate concentration), and coincided with high phenol oxidase and peroxidase activities, but low laccase activity. The second axis (RDA 2 explaining 15.59% of the variance) appeared mostly linked with heavy metals concentration (Zn, r = -0.45) and pesticides toxicity (TU, r = -0.46), characteristic of samples from the downstream site showing higher values of fungal band richness and lower ergosterol concentrations. In contrast to the RDA UpDw, the RDA Transloc displayed two axes representing a much lower percent of the total variance (46.46%). Here, only nitrate concentration (NO3) was retained by the permutation test as explicative of the variance in biological descriptors (Figure 5.2). Again, the first axis (RDA1 explaining 38.88% of the variance) was mainly correlated with nitrate concentration (r = -0.59), but here coinciding with low rates of the three ligninolytic activities (laccase, phenol oxidase and peroxidase). Specifically, samples translocated from the downstream site to the upstream site at week 4 appeared in the right up corner of the RDA, and displayed low nitrate concentrations but high ligningolytic activities.

In addition, fungal band richness appeared also negatively related to the nitrate concentration in water.

DISCUSSION

Despite confinement effects due to our microcosm approach performed under controlled conditions (temperature, light and flow), the obtained decay rates of Alnus glutinosa leaves appeared similar between sites (0.0101-0.0162 day -1 ) and were in the same range of that found in field experiments ((k=0.0054 day -1 ) Chauvet 1987; (k=0.0161 day -1 ) Canhoto & Graça 1996; (k=0.035 day -1 ) [START_REF] Hieber | Contribution of stream detrivores, fungi, and bacteria to leaf breakdown based on biomass estimates[END_REF]. Only water, supplied to upstream and downstream leaf-associated fungal communities, was different, coming from the two different sites of the Auzon stream. Therefore, differences observed in terms of ligninolytic activity and structure of fungal communities responsible of Alnus leaves decomposition can be essentially attributed to variations in stream water chemistry. Among all chemical parameters measured in the stream water, nitrate concentration was tightly related to ligninolytic activities expression in fungal communities.

As expected, the Auzon appeared as a contaminated stream (Phyt'eauvergne 2014, Water agency, 2003). While DOC concentrations categorized its upstream and downstream sites in two different class of quality according to the water agency classification system (respectively poor and bad), nutrients (NO 3 , PO 4 ) and pesticides categorized the two sites into the same "good" class of quality (Table 1). Despite such small differences in terms of water quality between sites, upstream water led to microbial communities with higher laccase activity and a lower fungal band richness comparing to those grown in downstream water. Besides, no differences in fungal biomass (ergosterol concentration) between communities were observed.

The influence of stream water chemistry on the microbial community descriptors was also supported by the translocation of leaves from upstream to downstream waters (stress exposure) which induced a sharp decrease in laccase activity concomitant with changes in the structure of the fungal community, but again without influence on fungal biomass. These results seems to support the "stress gradient" hypothesis proposed by [START_REF] Niyogi | Effects of stress from mine drainage on diversity, biomass, and function of primary producers in mountain streams[END_REF], stating that biodiversity has a low threshold of response to a stress, whereas biomass and function remains stable but decrease only under high stress. However, laccase activity appeared to be very sensitive to water chemical stress.

Ligninolytic activities from fungal communities colonizing Alnus leaves were the most sensitive parameters to nitrate variations in our study. Indeed, nitrogen effect (including nitrate), have already been observed in other studies working on microbial communities from forest soils [START_REF] Gallo | Microbial community structure and oxidative enzyme activity in nitrogen-amended north temperate forest soils[END_REF][START_REF] Baldrian | Fungal laccases-occurrence and properties[END_REF][START_REF] Sinsabaugh | Phenol oxidase, peroxidase and organic matter dynamics of soil[END_REF]) and streams [START_REF] Artigas | Organic matter decomposition by fungi in a Mediterranean forested stream: contribution of streambed substrata[END_REF]. Despite the fact that ligninolytic activity regulation is still unclear, several studies reported negative correlations between nitrogen and both laccase, phenol oxidase and peroxidase activities, as observed in the second RDA (RDA Transloc ) of our study. However, our study showed also that after a two-fold increase of nitrate concentrations probably resulting from the rainfall event occurring at week 2, laccase activity tended to be inhibited (Pearson, r = -0.497, P < 0.05), whereas peroxidase and phenol oxidase activities tended to be stimulated (Pearson, r = 0.807 and r = 0.549 respectively, P < 0.05, RDA UpsDws , Figure 5.2). The fact that ligninolytic activities responded differently to the nitrate increase is surprising. Usually, they evolve in the same manner, since laccases represent one of the largest class of phenol oxidases [START_REF] Baldrian | Fungal laccases-occurrence and properties[END_REF][START_REF] Sinsabaugh | Phenol oxidase, peroxidase and organic matter dynamics of soil[END_REF]. Generally, phenol oxidases and peroxidase activities are assayed using L-DOPA as substrate, the latter activity being calculated as the incremental response relative to phenol oxidases (Sinsabaugh, Osgood & Findlay 1994b;[START_REF] Sinsabaugh | Phenol oxidase, peroxidase and organic matter dynamics of soil[END_REF], whereas laccase activities are assayed using a more specific substrate: the ABTS ( [START_REF] Baldrian | Fungal laccases-occurrence and properties[END_REF][START_REF] Junghanns | Biochemical and molecular genetic characterisation of a novel laccase produced by the aquatic ascomycete Phoma sp. UHH 5-1-03[END_REF]). Consequently, the differential relationships obtained between ligninolytic activities and nitrate concentration can be due to the choice of two different substrates for activity assays [START_REF] Bach | Measuring phenol oxidase and peroxidase activities with pyrogallol, L-DOPA, and ABTS: effect of assay conditions and soil type[END_REF]. Besides, the antagonistic results between laccase and the other two ligninolytic activities can also reflect the effect of translocation in microbial communities. Indeed, RDA UpsDws was performed on upstream and downstream samples over the four weeks of the experiment, whereas RDA Transloc was performed on week 3 and 4 only. Consequently, the greater ligninolytic activities of translocated samples at week 4 (right up corner of the RDA Transloc ) may reflect a transient response of communities to the translocation from the more polluted to less polluted site.

Altogether, our results suggest that laccase activity is more sensitive to nitrate than phenol oxidase and peroxidase activities.

Site effects were also observed on laccase activity, with higher values upstream compared to downstream. Here, differences were neither explained by nitrate concentrations nor by differences in fungal biomass (ergosterol concentration) since no differences were observed between sites. These observations suggest that other factors may be involved in laccase activity regulation. We observed a negative correlation between dissolved organic carbon (DOC) and laccase activity (Pearson, r = -0.45, P < 0.05) as well as between Toxic Units (TU) and laccase activity (Pearson, r = -0.50, P < 0.05). As suggested in others studies, we hypothesized that both DOC quantity and quality [START_REF] Elisashvili | Use of Pleurotus dryinus for lignocellulolytic enzymes production in submerged fermentation of mandarin peels and tree leaves[END_REF][START_REF] Mikiashvili | Effects of carbon and nitrogen sources on Pleurotus ostreatus ligninolytic enzyme activity[END_REF][START_REF] Romaní | Extracellular enzymes in aquatic biofilms: microbial interactions versus water quality effects in the use of organic matter[END_REF] or the presence of some pesticide compounds could be important in laccase activity regulation [START_REF] Zapp | Biomonitoring of methomyl pesticide by laccase inhibition on sensor containing[END_REF][START_REF] Oliveira | Biosensor based on multi-walled carbon nanotubes paste electrode modified with laccase for pirimicarb pesticide quantification[END_REF]. Alternatively, shifts in fungal communities structure, highlighted by changes in fungal band richness, can partially explain the differences in laccase activity observed between sites and after translocation [START_REF] Treton | Competitive interaction between two aquatic hyphomycete species and increase in leaf litter breakdown[END_REF][START_REF] Frey | Chronic nitrogen enrichment affects the structure and function of the soil microbial community in temperate hardwood and pine forests[END_REF][START_REF] Osono | Ecology of ligninolytic fungi associated with leaf litter decomposition[END_REF]. When looking at the fungal band absence/presence matrix, fungal species specific of the upstream fungal communities were not found in the downstream communities. Selection and/or shifts in dominance between species can be due to changes in water chemistry. As reported by [START_REF] Abdel-Raheem | Extracellular enzyme production byfreshwater ascomycetes[END_REF] and [START_REF] Abdel-Raheem | Lignocellulolytic enzyme production by aquatic hyphomycetes species isolated from the Nile's delta region[END_REF] in culture experiments, not all aquatic fungal species have shown the ability to produce all types of ligninolytic enzymes.

Besides, since laccases have an indirect role in carbon uptake by fungi from leaf litter [START_REF] Baldrian | Fungal laccases-occurrence and properties[END_REF], the greater DOC concentrations (and perhaps different DOC quality) in water downstream may have led to the establishment of fungal species low-laccase producers, more competitive than some high-laccase procuders upstream. Our study reveals that stress by chemical pollution can induce shifts in the structure of fungal communities accompanied by decreases in laccase activity. However, no effect on the overall leaf mass loss was observed, suggesting a certain functional redundancy in these communities (Pascoal, Cássio & Marvanová 2005) probably explained by compensation mechanisms in the organic matter degradation by extracellular enzymes.

When stress by pollution was removed through the translocation from the polluted site (downstream) to the less polluted site (upstream), leaf-associated fungal communities displayed the ability to recover as stated in our base hypothesis. Both laccase activity and fungal community structure tended to get closer to that of fungal communities from upstream within 2 weeks. Such observations are consistent with other translocation studies, which showed that aquatic hyphomycetes communities were able to recover in terms of structure, decomposing activity (decay rates), and sporulation rates when translocated from highly metal-exposed sites to cleaner sites in about two to three weeks [START_REF] Sridhar | Response of aquatic hyphomycete communities to changes in heavy metal exposure[END_REF] in the field; Duarte et al. 2009a in microcosms). Regarding at the fungal band presence/absence matrix, this fast recovery in the structure of leaf fungal communities can actually mostly be explained by the disappearance of species in the translocated samples. Since fungal communities are very sensitive to nutrient variations in streams [START_REF] Suberkropp | Regulation of leaf breakdown by fungi in streams: influences of water chemistry[END_REF][START_REF] Bärlocher | Nutrient enrichment overwhelms diversity effects in leaf decomposition by stream fungi[END_REF], the combined effects of low nutrient concentrations in the upstream channels and the osmotic stress resulting from a sudden change of water chemistry (conductivity values were 2.7 times higher downstream than upstream) may have affected the dominance of certain fungal species from downstream communities.

Overall our study indicates that variations in stream water chemistry, resulting from anthropogenic activities in the watershed, induce changes in the structure and enzymatic pathway of leaf-associated fungal communities. However, recovery assessment allowed us to evidence an almost full recovery in these communities which encourages river restoration programs. But linking fungal community responses to stream water chemistry remains difficult considering the amount of stress compounds as well as their potential interactions (antagonistic, synergistic) and few EC 50 data for pesticides on aquatic fungi are available in the literature, which decrease the reliability of pesticide risk assessment in these communities [START_REF] Maltby | Fungicide risk assessment for aquatic ecosystems: importance of interspecific variation, toxic mode of action, and exposure regime[END_REF]. Thus, future researches should take into account aquatic fungi as species sensitive to the chemical quality of stream waters. platinum nanoparticles in ionic liquid phase supported in montmorillonite. Sensors and Actuators B: Chemical 155, 331-339. Zubrod J.P., Feckler A., Englert D., Koksharova N., Rosenfeldt R.R., Seitz F., et al. (2015) Inorganic fungicides as routinely applied in organic and conventional agriculture can increase palatability but reduce microbial decomposition of leaf litter. Journal of Applied Ecology 52, 310-322.

  (H), Insecticides (I) and Fungicides (F)) in the upstream and downstream waters of the Auzon stream during the laboratory experiment. Values are means and (±) standard errors of the five sampling times for nutrients (n = 15) and four sampling times for pesticides and heavy metals (n = 12). Significant differences between sites (ANOVA, P < 0.05) are shown by an asterisk. TU calculation were based on EC 50 of Scenedemus subspicatus a , Pseudokirchneriella subcapitata b , Scenedesmus quadricauda c , Scenedesmus abundans d and Chlamydomonas angulosa e .

Figure 1 :

 1 Figure 1: Dissolved inorganic (DIC) and organic (DOC) concentrations (1) as well as nitrate

Figure 2 :

 2 Figure 2: Ergosterol concentration on Alnus leaves in upstream and downstream samples for

Figure 3 :

 3 Figure 3: Clustering analysis on the structure of the Alnus-associated fungal communities,

Figure 4 :

 4 Figure 4: Values of the peroxidase (A), phenol oxidase (B) and laccase (C) activities

Figure 5 :

 5 Figure 5: Redundancy analysis (RDA) performed on upstream and downstream samples for

Figure 1 .Figure 4 .

 14 Figure 1. Rossi et al.

  Figure 5.2. Rossi et al.

Table 1 :

 1 Physical and chemical characteristics and pesticide compounds detected (Herbicides

Table 2 :

 2 Fungal band richness in leaf-associated fungal communities obtained during the translocation experiment (translocated samples are highlighted in italics). Values are means (weeks 3 and 4) ± standard errors for each experimental condition. Significant differences between conditions for each week are shown (a ≠ b, Tukey's test, P < 0.05).

	Experimental
		Time (weeks)
	conditions
		3	4
	Upstream	20.67 ± 0.33 a	37.67 ± 2.19 a
	Ups ->Dws	25.33 ± 0.67 a	41 ± 0.58	ab
	Downstream	36.33 ± 1.20 b	49.33 ± 1.20 b
	Dws -> Ups	34.33 ± 3.48 b	42.33 ± 2.03 ab

ACKNOWLEDGEMENTS

This study was funded by the Fédération des Recherches en Environnement (UBP/CNRS FR 3467/INRA). We also thank Fanny Perriere for her help in the water chemical analyses. Florent Rossi was funded by a PhD grant from the French Ministry of Higher Education and Research.