Dousatsu Sakata

Nathanael Lampe

Mathieu Karamitros

Ioanna Kyriakou

Oleg Belov

Mario A Bernal

David Bolst

Marie-Claude Bordage

Vincent Breton

Jeremy M C Brown

Ziad Francis

Vladimir Ivanchenko

Sylvain Meylan

Koichi Murakami

Shogo Okada

Ivan Petrovic

Aleksandra Ristic-Fira

Giovanni Santin

David Sarramia

Takashi Sasaki

Wook-Geun Shin

Nicolas Tang

Hoang N Tran

Carmen Villagrasa

Dimitris Emfietzoglou

Petteri Nieminen

Susanna Guatelli

Sebastien Incerti
email: incerti@cenbg.in2p3.fr

Evaluation of early radiation DNA damage in a fractal cell nucleus model using Geant4-DNA

Keywords: Geant4-DNA, Monte Carlo simulation, DNA damage

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

I. Introduction

The advancement of multidisciplinary research fields which study the biological damage from ionising radiation -such as: radiobiology, radiation physics, radiation protection and, in particular, medical physics -requires a clear mechanistic understanding of how cellular damage is induced by ionising radiation in cells [START_REF] Ying | An Expanded Multi-scale Monte Carlo Simulation Method for Personalized Radiobiological Effect Estimation in Radiotherapy: A feasibility study[END_REF][START_REF] Suzuki | Low-dose radiation exposure and carcinogenesis[END_REF][START_REF] Feinendegen | Responses to Low Doses of Ionizing Radiation in Biological Systems[END_REF][START_REF] Moreno-Villanueva | Interplay of space radiation and microgravity in DNA damage and DNA damage response[END_REF] . Understanding and modeling the mechanisms of such radiation induced DNA damage is still challenging due to their complexity.

Biomolecules which compose DNA can be damaged not only directly by radiation itself but also by radiolysis byproducts such as hydroxyl radicals. An increased understanding of the damage mechanisms from radiation will allow the radiation risk to be more accurately predicted in particular for radiation treatment, for radiation protection, and during space missions.

Monte Carlo (MC) simulations allow mechanistic models of radiation transport and radiation chemistry to be validated against experimental measurements of radiation damage, by simulating the entire process of early radiation damage induction in silico [START_REF] Nikjoo | Track structure in radiation biology: theory and applications[END_REF][START_REF] Nikjoo | Radiation track, DNA damage and response-a review[END_REF] . Over the last couple of decades, many research groups have developed MC track structure codes for radiobiology-related simulations such as KURBUC [START_REF] Uehara | Cross-section of water vapour for the Monte Carlo electrons track structure code from 10 eV to the MeV region[END_REF][START_REF] Nikjoo | Computational modeling of low-energy electron-induced DNA damage by early physical and chemical events[END_REF][START_REF] Nikjoo | Computational approach for determining the spectrum of DNA damage induced by ionizing radiation[END_REF] , PARTRAC [START_REF] Friedland | Monte Carlo simulation of the production of short DNA fragments by low-linear energy transfer radiation using higher-order DNA models[END_REF][11][START_REF] Friedland | Track structures, DNA targets and radiation effects in the biophysical Monte Carlo simulation code PARTRAC[END_REF][START_REF] Friedland | Comprehensive track-structure based evaluation of DNA damage by light ions from radiotherapy-relevant energies down to stopping[END_REF] and Geant4-DNA based MC code TOPAS-nBio [START_REF] Mcnamara | Validation of the radiobiology toolkit TOPAS-nBio in simple DNA geometries[END_REF] (another comprehensive review can be found in reference [START_REF] Nikjoo | Track-structure codes in radiation research[END_REF] ).

Recently, a fibroblast cell nucleus irradiated by protons was simulated using Geant4-DNA based on a step-by-step simulation of water radiolysis [START_REF] Meylan | Simulation of early DNA damage after the irradiation of a fibroblast cell nucleus using Geant4-DNA[END_REF] . A similar approach was followed in another work that used an atomistic DNA model up to the chromatin fiber level [START_REF] Rosales | Accounting for radiation-induced indirect damage on DNA with the Geant 4-DNA code[END_REF] The Geant4-DNA track structure code [START_REF] Incerti | The Geant4-DNA project[END_REF][START_REF] Incerti | Comparison of Geant4 very low energy cross section models with experimental data in water[END_REF][START_REF] Bernal | Track structure modeling in liquid water: A review of the Geant4-DNA very low energy extension of the Geant4 Monte Carlo simulation toolki[END_REF][START_REF] Incerti | Geant4-DNA example applications for track structure simulations in liquid water: a report from the Geant4-DNA Project[END_REF] , a low-energy extension of the general purpose MC simulation toolkit Geant4 was used for this work [22][START_REF] Allison | Geant4 developments and applications[END_REF][START_REF] Allison | Recent Developments in Geant4[END_REF] . These MC simulation codes are able to simulate DNA damage caused by physical processes (direct damage) such as ionisation and excitation, as well as the chemically-induced (indirect) DNA damage that arises when radiation-induced radicals created in water react with the DNA. Using chemical models based on the Independent Reaction Time (IRT) method [START_REF] Green | Stochastic modeling of fast kinetics in a radiation track[END_REF] , early DNA damage simulations using Geant4-DNA were further benchmarked in a simple geometry [START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 1: A parameter study in a simplified geometry[END_REF] , before being extended to Escherichia coli (E. coli) bacterial cell [START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 2: Electron and proton damage in a bacterial cell[END_REF][START_REF] Lampe | The Long Term Impact of Ionising Radiation on Living Systems[END_REF] . In this paper, we have extended this application to simulate more complex DNA geometries as found in human cells. Using a continuous chromatin geometry, the early DNA damage arising from proton irradiation of a cell nucleus was simulated to determine the number of single strand breaks (SSBs), double strand breaks (DSBs) and DNA fragment distributions. These simulated results are compared to experimental data and previous literature simulation results.

II. Materials and Methods

We developed a fully integrated application to simulate early DNA damage mechanistically by extending the Geant4-DNA application for a human cell nucleus as first presented by Lampe [START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 1: A parameter study in a simplified geometry[END_REF][START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 2: Electron and proton damage in a bacterial cell[END_REF][START_REF] Lampe | The Long Term Impact of Ionising Radiation on Living Systems[END_REF] . The integrated simulation calculates early SSBs and DSBs in an irradiated cell nucleus from direct and indirect damage in a single application. This application consists of a beam incident upon the detailed geometry of the cell nucleus, with both the chemistry and physics being described. These functionalities can be fully controlled through a macro file. All physical and chemical processes and damage definitions remain the same as defined in the previous work done by Lampe et al. [START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 1: A parameter study in a simplified geometry[END_REF][START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 2: Electron and proton damage in a bacterial cell[END_REF][START_REF] Lampe | The Long Term Impact of Ionising Radiation on Living Systems[END_REF] , with the exception of the addition of histones, which were defined as spheres, each with a 25 Å radius within which all chemical radicals were considered to be immediately scavenged. In this section we briefly summarise the modeling of the geometry, physics and chemistry, as well as early damage calculations.

A. Nucleus geometry

The DNA fiber can be considered as being composed of phosphate (H 3 PO 4 ) and deoxyribose (C 5 H 10 O 4 ) molecules forming a backbone that supports the nucleotide bases, guanine [START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 2: Electron and proton damage in a bacterial cell[END_REF][START_REF] Lampe | The Long Term Impact of Ionising Radiation on Living Systems[END_REF] . We approximated the phosphate and sugar molecules as spheres, whilst the bases, due to their flatter shape, were interpreted as ellipsoids [START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 2: Electron and proton damage in a bacterial cell[END_REF][START_REF] Lampe | The Long Term Impact of Ionising Radiation on Living Systems[END_REF] . In the cell nucleus, the DNA fiber is folded by histones compactly forming chromatin fiber. Our Geant4-DNA application builds a chromatin fiber out of smaller 75 nm cubes repeating straight (shown in Figure 1(a,b)) and 90 • turned (shown in Figure 1(c,d)) chromatin segments that join together to form a 14.2 µm long ellipsoidal continuous segment (shown in the left panel of Figure 3).

(C 5 H 5 N 5 O), adenine (C 5 H 5 N 5 ), cytosine (C 4 H 5 N 3 O) and thymine (C 5 H 6 N 2 O 2 )
Beginning from the same geometry for a single base pair as used in our previous works [START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 2: Electron and proton damage in a bacterial cell[END_REF][START_REF] Lampe | The Long Term Impact of Ionising Radiation on Living Systems[END_REF] , we built turned and straight chromatin segments to create the geometry. The turned segments contained 26 histones and 5024 base pairs, and the straight segments contained 38 histones and 7331 base pairs (Figure 1). Histones were linked by DNA sections interpolated between the end of one histone and the beginning of the next one using spline functions.

In order to ensure that the DNA helix joins smoothly when rotating, we also created variants of these straight and turned DNA structures, having the same number of base pairs and histones, where the chain went through an additional 90 • rotation, which we called turned-twisted segments in Figure 1(e). The difference between turned and turned-twisted is illustrated in Figure 1(g). In both the twisted and non-twisted cases, the DNA strand begins aligned with the y-axis (red line in Figure 1(f)). In the normal turned strand, the DNA is aligned when leaving the chromatin fiber along the y-axis (red line in Figure 1

(g)),
while in the twisted case, it is aligned with the z-axis (blue line in Figure 1(g)). Using The continuous chain was defined by taking a basic Hilbert curve and iterating it as a fractal 8 times, creating a fractal that could be broken into 1.68×10 7 cubic regions of straight and turned chromatin sections. An ellipsoid with dimensions (14.2 µm × 14.2 µm × 5 µm) was used to define the cell nucleus. Cubic regions from the Hilbert curve falling outside this region were excluded from the simulation, leaving 6.4 Gbp in total (bp stands for base pair). The overall bp density is ∼0.012bp/nm 3 (typical bp density of a human cell nucleus is ∼0.015bp/nm 3 29 , compared to ∼0.008bp/nm 3 in the previous Geant4-DNA simulations [START_REF] Meylan | Simulation of early DNA damage after the irradiation of a fibroblast cell nucleus using Geant4-DNA[END_REF] ). This process of using a fractal approach to seed a mostly continuous nucleus is shown in Figure 2, and the whole cell used for this work is shown in the left panel of Figure 3. When the DNA is sufficiently dense (C), a mask is applied to create a nucleus of the desired size (D). Each curved and straight section making up the curve is then replaced with chromatin in the simulation (not shown). For this work, we iterate the base Hilbert Curve eight times before applying an elliptical mask (see the left panel of Figure 3). The DNA damage to the cell was studied for various incident proton energies, with the cell nucleus being irradiated with monoenergetic beams between 0.1 -50 MeV, fired perpendicular to the 14.2 µm circular plane of the cell-nucleus's equator, as shown in the right panel of Figure 3.

B. Particle transportation and calculation of direct damage

Geant4-DNA provides track structure models for protons, our primary particle, and all secondary particles -electrons, photons and neutral hydrogen atoms. For protons, elastic scattering, excitation and ionisation processes are modelled, as well as charge-exchange, which becomes increasingly important at low energies [START_REF] Champion | Proton transport in water and DNA components: A Geant4 Monte Carlo simulation[END_REF] . Geant4-DNA provides three alternative sets of physics models for electron transportation in liquid water: (1) a model based on the partial-wave formalism for elastic scattering that also includes ionisation and exci-tation inelastic cross sections calculated from the dielectric model of Emfietzoglou 31,[START_REF] Ivanchenko | Recent improvements in Geant4 electromagnetic physics models and interfaces[END_REF] , (2) a model based on the screened Rutherford elastic and inelastic scattering processes based on an improved dielectric function [START_REF] Kyriakou | The impact of new Geant4-DNA cross section models on electron track structure simulations in liquid water[END_REF]34 and (3) the well-known CPA100 MC model for both elastic and inelastic scattering processes [START_REF] Bordage | Implementation of new physics models for low energy electrons in liquid water in Geant4-DNA[END_REF] . In this work, we use the so-called Geant4-DNA physics option 4 (corresponding to option 2), which offers a better performance for electron transport in the proton energy range under investigation [START_REF] Kyriakou | The impact of new Geant4-DNA cross section models on electron track structure simulations in liquid water[END_REF][34][START_REF] Bordage | Implementation of new physics models for low energy electrons in liquid water in Geant4-DNA[END_REF][START_REF] Kyriakou | Microdosimetry of electrons in liquid water using the low-energy models of Geant4[END_REF] . For photon transport, the Livermore low energy photon model available in Geant4 was utilised.

In order to calculate direct damage the same methodology as used in the PARTRAC simulation code was adopted 11 . For defining DNA strand breaks, all energy deposited within 4.5 Å of the centre of a DNA sugar or phosphate molecule was allocated to the closest strand molecule. After each simulation run, the total cumulative energy deposited in this region was used to determine the probability that a strand break occurred along the chain, based on a linearly increasing probability distribution, where there was a 0% probability a break occurred when less than 5 eV was deposited, and a 100% probability a break occurred when over 37.5 eV was deposited in the sugar phosphate moiety.

C. Water radiolysis and indirect damage

During irradiation, molecular species are created when particles interact in water [START_REF] Kreipl | Time-and space-resolved Monte Carlo study of water radiolysis for photon, electron and ion irradiation[END_REF][START_REF] Michalik | Computer-aided stochastic modeling of the radiolysis of liquid water[END_REF][START_REF] Davidkova | Predicting radiation damage distribution in biomolecules[END_REF] . This work uses an Independent Reaction Time (IRT) method to simulate chemistry [START_REF] Lampe | The Long Term Impact of Ionising Radiation on Living Systems[END_REF][START_REF] Karamitros | Introducing the Independent Reaction Time method in Geant4[END_REF] rather than the step-by-step (SBS) method available in the public version of Geant4-DNA [START_REF] Karamitros | Modeling radiation chemistry in the Geant4 toolkit[END_REF][START_REF] Karamitros | Diffusion-controlled reactions modeling in Geant4-DNA[END_REF] , as introduced in past works [START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 1: A parameter study in a simplified geometry[END_REF][START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 2: Electron and proton damage in a bacterial cell[END_REF] . Indirect damage depends heavily on the value chosen for the likelihood a chemical reaction between a hydroxyl radical and the sugar phosphate backbone leads to a single strand break (SSB). Following past studies [START_REF] Lampe | Mechanistic DNA damage simulations in Geant4-DNA part 1: A parameter study in a simplified geometry[END_REF] , we assign a probability of 40% to induce a strand break. Additionally, we kill all chemical species created more than 4.5 nm away from any DNA molecules, as they will likely be scavenged. This range cut is equivalent to the maximum diffusion distance of hydroxyl radical at 2.5 ns, hence all simulations are stopped at 2.5 ns after diffusion started. Chemically, we model histones as perfect scavengers. This means that any free radical species that enters a histone region (blue regions in Figure 1) will be killed 11 .

D. Damage scoring and experimental results

In our present work, all strand breaks (SBs) are classified either direct SB or indirect SB.

The number of DSBs is of particular interest when quantifying damage to the DNA. We follow the classification scheme of DSBs proposed by Nikjoo et al. [START_REF] Nikjoo | Computational modeling of low-energy electron-induced DNA damage by early physical and chemical events[END_REF] to determine DSBs which are considered to be at least two strand breaks on opposite strands within 10 bp of each other. The evaluated DNA damage is compared to experimental results [START_REF] Frankenberg | Induction of DNA Double-Strand Breaks by 1H and 4He Ions in Primary Human Skin Fibroblasts in the LET Range of 8 to 124 keV/um[END_REF][START_REF] Campa | DNA DSB induced in human cells by charged particles and gamma rays: Experimental results and theoretical approaches[END_REF][45][START_REF] Leloup | Evaluation of lesion clustering in irradiated plasmid DNA[END_REF][START_REF] Hoglund | DNA damage induced by radiation of different linear energy transfer: initial fragmentation[END_REF] and previous MC simulations (KURBUC, PARTRAC, Geant4-DNA-2017) [START_REF] Nikjoo | Computational modeling of low-energy electron-induced DNA damage by early physical and chemical events[END_REF]11,[START_REF] Meylan | Simulation of early DNA damage after the irradiation of a fibroblast cell nucleus using Geant4-DNA[END_REF] .

Experimentally, there are mainly two ways to evaluate DSB yields. The first is based on detection of foci representing the accumulation of proteins to repair DSBs 48 . The second is based on the separation of DNA fragments through agarose gel electrophoresis (AGE) [START_REF] Kryndushkin | Yeast [PSI+] Prion Aggregates Are Formed by Small Sup35 Polymers Fragmented by Hsp104[END_REF] and/or pulsed-field gel electrophoresis (PFGE) [START_REF] Iliakis | Measurement of DNA double strand breaks in CHO cells at various stages of the cell cycle using pulse field gel electrophoresis: Calibrations by means of 125I decay[END_REF] (usually scaled by the energy deposited in Gy and the length of DNA). The foci measurement is becoming a standard to evaluate radiation induced DNA damage, however, the relation between foci yield and number of DSBs is still unclear. Hence, in this paper, experimental data by AGE and PFGE have been compared to the simulated number of DSBs as benchmark. As AGE and PFGE cannot detect small DNA fragments (shorter than 5-25 kbp) because of their loss during the standard lysis procedure of cells embedded in gel plugs [START_REF] Campa | DNA DSB induced in human cells by charged particles and gamma rays: Experimental results and theoretical approaches[END_REF] , we calculate not only the total number of DSBs but also that of "distant" DSBs, those that are separated by at least 5 kbp or linear energy transfer (LET ∞ ), which is the electronic stopping power as recommended by the ICRU [START_REF] Berger | Stopping Power and Ranges for Protons and Alpha Particles (Report 49)[END_REF] . Additionally, we have calculated the fragment (gap between two DSBs) size distribution and compared it to experimental results [START_REF] Belli | DNA Fragmentation in mammalian cells exposed to various light ions[END_REF] . We evaluate the accuracy of the simulations, by calculating the average percentage error of deviation from the experimental data set as

n ∑ i=1 |(V sim i -V exp i )/V exp i |/n data × 100, where V sim i is ith simulated value, V exp i is
ith experimental value and n data is number of experimental data.

III. Results and discussion

The main goals of the application developed in this work are to create a fractal based DNA geometry having a more realistic base pair density ∼0.012bp/nm 3 (the typical bp density of a human cell nucleus is ∼0.015bp/nm 3 29 , compared to ∼0.008bp/nm 3 in the previous Geant4-DNA simulations [START_REF] Meylan | Simulation of early DNA damage after the irradiation of a fibroblast cell nucleus using Geant4-DNA[END_REF] ), and to simulate diffusion of radiolysis products based on IRT method. In addition, we introduced the linearly increasing probability model for direct damage induction which was proposed initially in the PARTRAC simulations.

The total numbers of strand breaks (SBs) are plotted in Figure 4 as a function of LET, with all strand breaks categorized into either direct or indirect damage. In this work, the number of strand breaks and their fraction of direct to indirect, obtained in this work, are similar to the results obtained by the PARTRAC code 11 . The yield of direct damage is almost flat across the LET range, indicating that the number of direct strand breaks is not sensitive to LET for proton irradiation conditions although the yield slightly increases in the very low LET region. In such cellular irradiation conditions at low LET(up to 60 keV/µm), light ions (including protons) traverse the water (and biological) medium with small scattering angle depositing their energy continuously. This is why the number of direct breaks depends largely on the density of the number of bases and the energy deposited in the track of the incident protons 11,53 and is rather insensitive to LET.

At very low LET's, however, high energy delta rays may cause direct SBs. In the previous Geant4-DNA simulations [START_REF] Meylan | Simulation of early DNA damage after the irradiation of a fibroblast cell nucleus using Geant4-DNA[END_REF] , a different direct damage model (step function probability from 17.5 eV) had been used, and the sensitive volume (nucleotide volume) for energy deposition is smaller than the one used in this work. Hence, if the step function probability is applied, the number of direct damage is increases with the increasing of LET as well as the yield of previous Geant4-DNA simulations (Geant4-DNA 2017), although the absolute value is larger than the yield of Geant4-DNA 2017.

Unlike direct damage, the indirect SB yield shows strong LET dependence with the indirect SB yield decreasing with increasing LET. At high LET, the incident protons deposit their energy locally, quickly slowing down. During this phase, protons produce many radiolytic products as well as secondary particles with LET in the range 10-20 keV/µm. In the case where the density of radiolytic products is high, the chemical species interact with each other rather than interacting with DNA components, leading to neutralization. Compared to the previous Geant4-DNA simulations (Geant4-DNA 2017) [START_REF] Meylan | Simulation of early DNA damage after the irradiation of a fibroblast cell nucleus using Geant4-DNA[END_REF] , the LET trend is similar but smaller indirect SB yield was simulated, even if very similar indirect damage model have been used. Both IRT and SBS methods are usually equivalent if fine time steps are used, although the IRT method should be significantly faster to calculate diffusion of radiolytic products. In future works, we would like to explore the impact of different geometry implementations in the exact same chemistry and physics pipeline to isolate the cause of these differences.

Figure 5 shows the simulated DSB and SSB/DSB ratio as a function of LET, and LET values corresponding to the primary proton energy, including previous simulation results [START_REF] Nikjoo | Computational approach for determining the spectrum of DNA damage induced by ionizing radiation[END_REF]11,[START_REF] Meylan | Simulation of early DNA damage after the irradiation of a fibroblast cell nucleus using Geant4-DNA[END_REF] and experimental results [START_REF] Frankenberg | Induction of DNA Double-Strand Breaks by 1H and 4He Ions in Primary Human Skin Fibroblasts in the LET Range of 8 to 124 keV/um[END_REF][START_REF] Campa | DNA DSB induced in human cells by charged particles and gamma rays: Experimental results and theoretical approaches[END_REF][45][START_REF] Leloup | Evaluation of lesion clustering in irradiated plasmid DNA[END_REF][START_REF] Hoglund | DNA damage induced by radiation of different linear energy transfer: initial fragmentation[END_REF] . As explained in Section II D, to account for experimental bias on the DSB yield, we calculate the total DSB yield (dotted red line), 5 kbp distant DSB yield (dashed red line) and 10 kbp distant DSB yield (solid red line).

For both total DSB and distant DSB yields, the simulations show similar trends to PARTRAC 11 and agree with experimental data [START_REF] Frankenberg | Induction of DNA Double-Strand Breaks by 1H and 4He Ions in Primary Human Skin Fibroblasts in the LET Range of 8 to 124 keV/um[END_REF] on DSB yields with a 13.6% average error.

At low LETs, the total DSB yield increases with the increase of LET up to 60 keV/µm, then gradually decreases. At the same time, the distant DSBs start to decrease beyond 30 keV/µm, largely due to the increase in production of small fragments. In terms of SSB/DSB ratio, the ratio decreases continuously as a function of LET, and is in close agreement with PARTRAC. We note that the uncertainties of experimental results (e.g., cell line, experimental procedure, temperature, beam properties and so on) are still large, hence underlining the need for more experimental data and associated simulations.

The fragments distribution following a 100 Gy irradiation of a cell by 1 MeV protons is shown in Figure 6. We find that the simulation produces a very similar fragment distribution from 20 kbp to 5 Mbp to PARTRAC simulation and that the results agree with experimental data 52 with a 34.8% average error, and relatively better than PARTRAC from 200 kbp to 5 Mbp. It ought to be noted that the DSB yield in this experimental measurement for a Hence if the total number of DSBs is actually lower, then the number of long fragments is expected to be larger, as simulated with Geant4-DNA.

IV. Conclusion

To help understanding the mechanisms of DNA damage induced from ionising radiation, The simulation results agree with experimental data on DSB yields within 13.6% on average and fragment distributions agree within an average of 34.8%.

FIG. 1 .

 1 FIG. 1. The DNA model in our simulation consists of joined straight (a), turned (c) and turned-

FIG. 2 .

 2 FIG.2. Fractals can be used to seed a mostly continuous DNA structure for the cell nucleus. The

FIG. 3 .

 3 FIG. 3. Left: The 3D geometry of the cell nucleus (14.2 µm × 14.2 µm × 5 µm) used in this

FIG. 4 .

 4 FIG. 4. Number of strand breaks (SBs) per Gy and per Gbp induced by protons in a cell nucleus

FIG. 5 .

 5 FIG. 5. Right top: Linear Energy Transfer (LET) as a function of incident proton energy. Left

FIG. 6 .Figure 5 ,

 65 FIG. 6. Histogram of the fragments distribution following a simulated 100 Gy irradiation of the

  we have developed an integrated Monte Carlo simulation code based on the Geant4-DNA toolkit that calculates SSBs and DSBs in a fractal chromatin based human cell nucleus model. In the presented work, a fractal structure based on a human cell nucleus model has been developed with a more realistic base pair density (∼0.012 bp/nm 3 ). The IRT method has been used to compute the diffusion of the radiolytic products, producing faster simulations. The results of this simulation are almost equivalent to past PARTRAC simulations when considering direct/indirect SB fraction, DSB yields and fragmentation distribution.

-10 14 -10 13 -10 12 -10 11 -10

Acknowledgements

We would like to acknowledge the financial support from the CNRS PICS #7340 and PICS #8235 France -Greece, from the CNRS PICS #8070 France -Serbia, the IdEx Bordeaux University -France -International Post-doctorates program in the framework of the "France-Japan Particle Physics Laboratory" International Associated Laboratory, and the IdEx Bordeaux University -France -International Doctorates program in the framework of the "France-Korea Particle Physics Laboratory" International Associated Laboratory (2017-2020) S. Guatelli, D. Sakata, S. Incerti, I. Kyriakou and D. Emfietzoglou acknowledge financial support from the Australian Research Council, ARC DP170100967. I. Kyriakou and D. Emfietzoglou acknowledge financial support from ESA (Contract No. 4000112863/14/NL/HB). M. A. Bernal thanks the CNPq for financing his research activities through the project 306775/2015-8, as well as the FAPESP foundation in Brazil, for the projects 2011/51594-2 and 2015/21873-8.


Félix Brédoire
email: bredoire.f@gmail.com

Bernhard Zeller

Zachary E Kayler

Pavel A Barsukov

Polina Nikitich

Olga Rusalimova

Mark R Bakker

Alexander Bashuk

Julien Sainte-Marie

Serge Didier

Bernd Zeller

Arnaud Legout

Delphine Derrien
email: delphine.derrien@inra.fr

The fate of leaf-litter N under contrasting pedo-climatic conditions in south-western Siberia

Keywords: 15 N-Labelled Litter, Nitrogen Cycling, Nitrogen Flow Model, Leaf-Litter Decomposition, Pedo-Climate, South-Western Siberia

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

Introduction

Siberia covers over 10 million km -2 (6.7 % of terrestrial land globally) and, given its northern proximity, it is one of the largest landmasses to be affected by climate change [START_REF] Groisman | Climate changes in siberia[END_REF]. For instance, the bioclimatic zones of south-western (SW) Siberia (steppe, forest-steppe, and southern taiga), which encompasses the transition from cold desert in the south to dense boreal forest in the north, are predicted to spread northwards at the expense of taiga zones [START_REF] Jiang | Uncertainty analysis of vegetation distribution in the northern high latitudes during the 21st century with a dynamic vegetation model[END_REF][START_REF] Lucht | Terrestrial vegetation redistribution and carbon balance under climate change[END_REF][START_REF] Soja | Climate-induced boreal forest change: Predictions versus current observations[END_REF][START_REF] Shuman | Forest forecasting with vegetation models across russia[END_REF][START_REF] Tchebakova | The effects of climate, permafrost and fire on vegetation change in siberia in a changing climate[END_REF]. With climate change, shifts in temperature and precipitation regimes directly affect microbial enzymatic activities related to nitrogen (N) transformations [START_REF] Borken | Reappraisal of drying and wetting effects on c and n mineralization and fluxes in soils[END_REF][START_REF] Rustad | A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming[END_REF] as well as indirectly by influencing transport of enzymes and N substrates through the soil profile [START_REF] Greaver | Key ecological responses to nitrogen are altered by climate change[END_REF]. How these effects manifest in SW Siberia's different bioclimatic zones is not well characterized and, given the potential of these lands as candidates for agricultural conversion [START_REF] Tchebakova | Agroclimatic potential across central siberia in an altered twenty-first century[END_REF][START_REF] Kicklighter | Potential influence of climate-induced vegetation shifts on future land use and associated land carbon fluxes in northern eurasia[END_REF], there is a pressing need to understand the soil nutrient cycling of this region.

In northern ecosystems, litter decomposition is a pivotal process for N cycling. In those regions, atmospheric N deposition is generally low [START_REF] Dentener | Nitrogen and sulfur deposition on regional and global scales: A multimodel evaluation[END_REF] and nonsymbiotic plants depend mostly on litter N recycling to fulfil their nutrient requirements [START_REF] Högberg | Tamm review: On the nature of the nitrogen limitation to plant growth in fennoscandian boreal forests[END_REF]. Furthermore, N limitation is reported to increase towards the poles [START_REF] Fay | Grassland productivity limited by multiple nutrients[END_REF][START_REF] Lebauer | Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed[END_REF][START_REF] Reich | Global patterns of plant leaf n and p in relation to temperature and latitude[END_REF][START_REF] Vitousek | Nitrogen limitation on land and in the sea: How can it occur?[END_REF]. The litter decomposition process consists of fragmentation, depolymerization and mineralization occurring across the litter-soil organic matter continuum [START_REF] Schimel | Nitrogen mineralization: Challenges of a changing paradigm[END_REF]. Soil physico-chemical properties, biological activity, and water fluxes determine the pathways of the litter-derived N transfer, mineralization and/or stabilization in the soil [START_REF] Berg | Plant Litter: Decomposition, Humus Formation, Carbon Sequestration[END_REF][START_REF] Bingham | Organic nitrogen storage in mineral soil: Implications for policy and management[END_REF][START_REF] Cotrufo | Formation of soil organic matter via biochemical and physical pathways of litter mass loss[END_REF][START_REF] Lehmann | The contentious nature of soil organic matter[END_REF][START_REF] Von Lützow | Stabilization of organic matter in temperate soils: mechanisms and their relevance under different soil conditions -a review[END_REF][START_REF] Schmidt | Persistence of soil organic matter as an ecosystem property[END_REF]. It remains to be seen how these decomposition mechanisms modulate N cycling rates with the highly variable pedo-climatic conditions that occur with the pronounced seasonality of SW Siberia.

The SW Siberia region has long cold and snowy winters with temperate summers. It is positioned on a gradient of climatic conditions with decreasing air temperature and increasing precipitation from south (forest-steppe) to north (southern taiga). During winter, the thick snow-pack in the southern taiga prevents soil from freezing while soil freezes under the shallow snow-pack in the forest-steppe. As a consequence, soil moisture availability is the most contrasting in summer and winter, when the soils of forest-steppe are particularly dry because of drought and freezing, while soil moisture remains available in the soils of southern taiga. Those pedo-climatic conditions can have a major impact on N pool sizes and process rates through the modulation of microbial processes, which generally require soil temperatures above zero and sufficient moisture availability (Brooks et al., 2011;[START_REF] Groffman | Colder soils in a warmer world: A snow manipulation study in a northern hardwood forest ecosystem[END_REF][START_REF] Matzner | Do freeze-thaw events enhance c and n losses from soils of different ecosystems? a review[END_REF][START_REF] Shibata | Impact of winter climate change on nitrogen biogeochemistry in forest ecosystems: A synthesis from japanese case studies[END_REF]. Finally, the soil hydrology is consistently different between the two regions. In the forest-steppe, water reserves are recharged by spring snow-melt, while in the southern taiga the soils are mostly refilled by autumn precipitation and the snow melt leads to raised groundwater levels and overland flow [START_REF] Brédoire | Impacts of Global Change on the Biogeochemical Cycling of Water and Nutrients in the Soil-Plant System and Consequences for Vegetation Growth in South-Western Siberia[END_REF]. Thus, in the southern taiga, snow-melt induces drainage and runoff and is followed by the retreat of the water-table from the soil surface at the beginning of summer. Such water fluxes could influence the vertical transfer of N within the soil.

Plants have adapted to these contrasting pedo-climatic conditions and exhibit specific strategies to access water and nutrients. Notably, fine roots grow deeper and at higher densities in forest-steppe than in southern taiga [START_REF] Brédoire | Distributions of fine root length and mass with soil depth in natural ecosystems of southwestern Siberia[END_REF]. As such, differ-ent uptake patterns may also participate in contrasting rates of N cycling, and vertical N transfer within the soil profile. The type of vegetation also affects the rate of N release from litter decomposition and the time during which N stays in the soil prior to plant uptake. In SW Siberia, grasslands interspersed with aspen-birch forests are typical. Grass litters generally decompose faster than forest leaf-litters given their lower lignin content [START_REF] Berg | Plant Litter: Decomposition, Humus Formation, Carbon Sequestration[END_REF]; thus they may also release N more rapidly (d'Annunzio et al., 2008).

We set out to quantify N cycling in relation to the pedo-climate, and vegetation type in remote sites of SW Siberia. Over three years, we traced the 15 N released from labelled leaf-litter during decomposition in aspen forests and grasslands located in the southern taiga and forest-steppe zones. From the collected data, we developed a N transport model that provides quantitative values of the time N resides in the different soil layers and transfer coefficient between the litter and soil layers. We checked the following hypotheses:

1. rates of aspen and grass litter decomposition at the northern southern taiga sites are faster than rates at the forest-steppe sites (based on soil temperature and moisture regimes); 2. N transfer within the soil profile is deeper in the southern taiga than in the foreststeppe (based on soil hydrology); and 3. the time between litterfall and the recycling of litter-derived N by vegetation uptake is shorter in grassland than in forest (based on litter chemistry).

Materials and methods

Site description

We selected four sites in SW Siberia that span across a transition from the foreststeppe to the southern taiga bioclimatic zones (Fig. S1). Barnaul (BAR) is located in the southern part of forest-steppe, Salair East (SAE) in the forest-steppe of the foothills of the Salair mountains range, Salair West (SAW) in the southern taiga belt of Salair mountains, and Tomsk (TOM) in southern taiga. The main geographic and climate characteristics are provided in Table 1.

As according to the updated world map of the Koeppen-Geiger climate classification [START_REF] Peel | Updated world map of the köppen-geiger climate classification[END_REF], the southern taiga is located in the very southern part of Dfc zone (cold -without dry season -cold summer) and the forest-steppe is located inside Dfb zone (cold -without dry season -warm Summer) in direction to BSk (arid -steppe -cold summer). Forest vegetation of southern taiga in West Siberia is mostly represented by the polydominant community with a prevalence of coniferous forest represented mostly by Abies sibirica as well as by Pinus sibirica and Populus tremula. This subzone is also distinguished by the absence of ground moss cover and developed herbaceous layer. All treeless areas within the subzone originated from human activities and occupied by true meadows [START_REF] Ilina | Vegetation cover of the West Siberian Plain[END_REF]. The vegetation of forest-steppe zone is characterized by the natural combination of "islands" of small-leaved deciduous forest (mostly Betula pendula and less Populus tremula) and dry meadows in its northern part and meadow steppe in the southern part. The proportion of area under meadow vegetation and the number of xerophytes in its community increases from north to south [START_REF] Shumilova | Botanical Geography of Siberia[END_REF][START_REF] Ilina | Vegetation cover of the West Siberian Plain[END_REF].

The soils in SW Siberia have undergone different development from their common origin as loess deposits. At the BAR and SAE forest-steppe sites, the main soil-forming processes are the formation and accumulation of organic matter, leaching of carbonates in the topsoil and formation of secondary carbonates in deep soil layers. Soils in the foreststeppe belong to the groups of Chernozems and Phaeozems (Table 1). At the SAW and TOM southern taiga sites, the main soil-forming processes are related to the periodic movements of the water table, clays are washed from the topsoil and accumulate in the deeper layers and carbonates have disappeared from the first meter of the soil profile. In these two southern taiga sites, soils belong to the group of Luvisols (Table 1). Physicochemical characteristics (data from Brédoire et al. (2016a)) as well as fine root densities (data from [START_REF] Brédoire | Distributions of fine root length and mass with soil depth in natural ecosystems of southwestern Siberia[END_REF]) of the topsoil are provided in Table 2.

All the study sites presented share comparable features in terms of dominant species composition and stand characteristics [START_REF] Brédoire | Distributions of fine root length and mass with soil depth in natural ecosystems of southwestern Siberia[END_REF]. The main figures are reported in Tables S1 andS2). They had almost pure aspen (Populus tremula L.) forest stands (Table S1) along with nearby grassland areas. SAW did not present grassland areas, yielding four sites with forest and three with grassland in our data set for SW Siberia. All aspen stands had a closed canopy.

Preparation and installation of 15 N-labelled litters

We used a single source of labelled material for all sites and both vegetation types, which allows the assessment of the pedo-climatic effects on its decomposition. 15 Nlabelled aspen and grass leaf-litters were produced near the village of Chebula (Novosibirsk region), located in between all our study sites. Two liters of 15 N-labelled urea (3 g urea L -1 ) were sprayed on the foliage of aspen trees and on live grasses in late June 2012. This technique allows uniform incorporation of the 15 N into the leaves [START_REF] Zeller | 15n partitioning and production of 15n-labelled litter in beech trees following [15n]urea spray[END_REF]. Approximately 2 kg (dry mass) of each labelled vegetation type were collected at the beginning of September 2012, in the late phase of senescence but before brown aspen leaves fell to the ground. The collected material was air-dried to avoid decomposition before its deposition on the experimental field sites. The δ 15 N of the labelled litters (528 % ± 103 % for the aspen litter, and 3159 % ± 474 % for the grass litter; mean ± sd for 8 and 6 replicates, respectively) were well above the natural abundances of the litters on site (1.1 % -3.9 % for aspen litter, and 0.5 % -2.3 % for the grass litter).

The elemental compositions of the labelled litters (C: 446.6 mg C g -1 ± 5.4 mg C g -1 , N:

13.2 mg N g -1 ± 0.8 mg N g -1 , and a C:N ratio of 33.9 ± 2.2 for the aspen labelled litter; and C: 424.1 mg C g -1 ± 4.0 mg C g -1 , N: 9.9 mg N g -1 ± 1.4 mg N g -1 , and a C:N ratio of 43.6 ± 6.6 for the grass labelled litter) were within the range of variation observed on sites for litterfall and grass litter (Table S1).

In late September of 2012, we deposited at each site the 15 N-labelled litter on six replicate 1.4 × 1.4 m plots. The distance between two plots ranged from 3 to 15 m. In forest plots, we carefully removed recently fallen litter (leaves and a few branches) over the area of the experimental plot and replaced it by 255 g m -2 of dry 15 N-labelled aspen litter, which is similar to the natural production estimated in a preliminary study (litter traps, Barsukov unpublished data). In grassland plots, we cut and removed dead grass and deposited a similar amount of 15 N-labelled grass litter (204, 306 and 357 g m -2 of dry 15 N-labelled litter at BAR, SAE and TOM, respectively). The labelled litter was evenly distributed over the plot area, and then fixed to the soil surface with a nylon net (mesh size of 1.5 cm) to avoid wind dispersal and contamination by non-labelled litter.

Sample collection and analyses

From May 2013 to October 2015, we sampled vegetation (herbaceous species), litter and soil twice a year, at the end of Spring and in Autumn within our plots. We sampled vegetation and litter layers within a defined area of 30 by 40 cm delimited by a rigid plastic frame. Above the netting, we collected live vegetation (understorey in forest) when available (this depended on the sampling date and plant phenology) and dead plant material, or litter that was deposited after the labelled litter was installed. We refer to the "litter layer" as the organic layers that have accumulated on the top of the mineral soil (often called "forest floor"), which contains the following sub-layers: the Ol layer that contains litter above the net used to keep the litter spatially fixed, 15 Nlabelled litter below the net (only in Spring 2013, when it still could be distinguished from older litter), litter Of layer, and litter Oh layer if present (Of and Oh layers were distinguished only at SAE forest). Four mineral soil layers were sampled within the same area: 0-2.5 cm, 2.5-5 cm, 5-7.5 cm, and 7.5-10 cm. Vegetation and litter samples were oven-dried at 60 • C and soil samples were air dried, all to a constant mass. The soil was then sieved at 2 mm to remove roots (the topsoils studied do not contain gravel). The samples were homogenized and an aliquot was finely ground before isotope analyses.

Elemental and isotopic compositions (total C, total N and δ 15 N) were determined by an IsoPrime 100 isotope ratio mass-spectrometer (IsoPrime Corporation, Cheadle, UK) and vario ISOTOPE cube elemental analyzer (Elementar Analysensysteme GmbH, Hanau, Germany) using international reference materials USGS 24 (IAEA N1 (0.4 % vN2), IAEA N2 (20.3 % vN2)) and USGS 26 (53.7 % vN2). The measurement error of δ 15 N was approximately 0.2 % .

The natural abundance of 15 N in vegetation, litter and of the five soil layers defined from 0 to 15 cm were measured at each site on material sampled in July 2013 and prepared following the same methods.

Isotopic calculations

The isotopic composition of the samples was expressed in delta units (δ 15 N, in % ):

δ 15 N = R sample -R standard R standard × 1000 (1)
where R is the mass ratio of 15 N to 14 N and R standard equals 0.0036765. Summaries of the δ 15 N values are provided in the Table S3 andS4.

The proportion of 15 N in the total amount of N measured in a sample (N tot ), the 15 N abundance (At.% 15 N), was computed as following:

At.% 15 N = 100 × 15 N 15 N + 14 N (2)
The recovery of the N derived from the litter applied to plots within a sampled layer (N LD ) was calculated, for each sampling date, as follows:

N LD = (At.% 15 N sample -At.% 15 N control ) × M N sample (At.% 15 N lab litter -At.% 15 N unlab litter ) × M N litter dep (3)
where At.% 15 N sample is the 15 N abundance in the sampled layer, At.% 15 N control is the 15 N natural abundance of the layer (without labelling), At.% 15 N lab litter is the 15 N abundance in the labelled litter, At.% 15 N unlab litter is the 15 N natural abundance in the litter, M N sample is the mass of N in the layer sampled (in g N m -2 ), and M N litter dep is the mass of N deposited with the labelled litter (in g N m -2 ).

For soil samples, M N sample was calculated with the mass, volume, and total N concentration of the soil sample after removal of roots. When the sampling volume was unknown (generally for the 7.5-10 cm layer), we used soil density [START_REF] Brédoire | Distributions of fine root length and mass with soil depth in natural ecosystems of southwestern Siberia[END_REF] corrected for the volume of large tree roots (> 1 cm diameter) that were numerous in the topsoil.

Statistical analyses

The comparison of N LD between sites (i.e. for a given combination of layer, sampling and vegetation type) and vegetation covers (i.e. for a given combination of layer, sampling and site) were tested using a one-way ANOVA (significance level of p < 0.05) followed by a post-hoc Tukey's HSD (Honestly Significant Difference) test. All data management and statistical analyses were performed with R version 3.3.3 (R Core Team, 2017).

Modelling

We developed a simple N flow model between litter and soil compartments (Fig. 1).

In this model, all the N is initially contained in the litter compartment. Along with litter decomposition, N either is transferred downwards to the adjacent compartment or lost from the system, i.e. leached, denitrified, or taken up by the vegetation. We assumed the return of the initially deposited N to the litter compartment by vegetation senescence was negligible at the timescale of modelling (three years) because the 15 N signal is greatly diluted in the growing vegetation in our experiment.

The variation in N recovery with time in each compartment was written as follows:

dN L dt = -k L N L (4) dN 1 dt = k L N L × a L→1 -k 1 N 1 (5) dN 2 dt = k 1 N 1 × a 1→2 -k 2 N 2 (6)
where: N L , N 1 and N 2 are the N recovered in the litter, the first (0-5 cm) and the second

(5-10 cm) soil compartments, respectively ; k L , k 1 and k 2 are the decomposition rates of these compartments and a L→1 and a 1→2 are allocation constants, i.e. the proportion of the output from the litter compartment and the first soil compartment that reach the first and the second soil compartment, respectively. The inverse of the decomposition rate (k) is the mean residence time (MRT ) of N in a given compartment:

MRT = 1 k (7)
The difference "1 -a x→y " is percentage of N from the compartment x that is exported out of the litter-soil system we studied.

The analytic solutions of these differential equations are:

N L (t) = N L (0) × e -k L t (8) N 1 (t) = N L (0) × a L→1 × k L k 1 -k L × (e -k L t -e -k1t ) (9) N 2 (t) = N L (0) × a L→1 × a 1→2 × k L k 1 × e -k L t (k 2 -k L )(k 1 -k L ) - e -k1t (k 2 -k 1 )(k 1 -k L ) + e -k2t (k 2 -k 1 )(k 2 -k L ) (10)
The parameters k L , k 1 , k 2 , a L→1 and a 1→2 were fitted simultaneously by minimizing the following fitness, which is the sum of the normalized mean square errors in the three compartments:

fitness = 3 i=1   1 Ōi × 1 n i × ni j=1 (S i (t) -O i,j (t)) 2   ( 11
)
where Ōi is the mean of the mean observations per date for the compartment i, n i is the number of observations in the compartment i, S i (t) is the simulated value of N recovery for the compartment i at the time t and O i,j (t) is the j th observation in the compartment i at the time t.

The optimization was performed with a genetic algorithm (population size 50, probability of crossover between pairs of chromosomes 0.8, probability of mutation in a parent chromosome 0.1, maximum number of iterations 1000, with local search by "L-BFGS-B" method) with the following constraints on the parameters: 0.00055 < k L < 0.0055, 0.00014 < k soil < 0.0055 and 0 < a < 1. In summary, the genetic algorithm helps to search the best set of model parameters giving the smallest fitness (the global minimum).

The modelling was performed with R version 3.3.3 (R Core Team, 2017) with the package "GA" version 3.0.2 [START_REF] Scrucca | GA: A package for genetic algorithms in R[END_REF] for genetic algorithms.

Results

N recovery observations

Over time, the concentration of N derived from the labelled-litter progressively decreased in the litter layer, N was partly transferred to the soil, taken up by the vegetation, or exported out of the system (Fig. 2 and3).

In the forest plots, N loss from the litter layer was faster in the southern taiga sites TOM and SAW compared to the forest-steppe sites BAR and SAE (Fig. 2). After two years, in October 2014, the N recovery in the litter layer below the netting was significantly lower in the southern taiga sites SAW (10 %) and TOM (7 %) than in the forest-steppe sites BAR (46 %) and SAE (67 %) (Tables S5 andS7).

The amount of N recovered in the soil decreased with soil depth. The N recovery in the 0-2.5 cm soil layer reached a maximum in October 2013 in the southern taiga sites SAW (51 %) and TOM (40 %) and in October 2014 in the forest-steppe site SAE (15 %).

In BAR, there was no clear peak among the observations; the highest value, 15 %, was measured in October 2015.

Live vegetation usually contained a low amount of litter-derived N (up to 3 %) but there was no clear trend in N recovery for this layer. The litter collected above the netting (i.e. naturally deposited on the top of the labelled-litter) was also enriched, even at the first sampling date, where we measured a recovery of about 3 % in BAR. Albeit the exception of BAR, the N recovered in the litter above the netting tended to increase with time.

In the grassland plots, N loss from the litter layer below the netting tended to be faster than for the forest plots at all the sites (Fig. 3); however, there were no statistically detectable differences in loss rates between the sites (Tables S6 andS7). Uncertainties

N recovery modelling

The model we developed allowed us to reproduce the average N dynamics in each compartment and provided quantitative indicators to compare the sites. It was not designed to capture the seasonal variations which partly explains the relatively high values of NRMSE together with the high variability in observations (Fig. 4 and5).

In the forest plots, the MRT of N in aspen litter was estimated to be 2.9 and 4.6 years in the forest-steppe sites BAR and SAE, respectively, and 1.5 and 0.9 years in the southern taiga sites SAW and TOM, respectively (Table 3). The MRT of N in the 0-5 cm soil compartment was shorter than its MRT in the litter layer. SAW had the shortest MRT

(1.5 years) and TOM the longest (2.1 years). In the 5-10 cm soil compartment, all the MRT but one ranged between 0.6 and 1.1 years. The allocation factor, which is the proportion of the output from one compartment that reaches the compartment below, ranged from 62 (SAE) to 94 % (SAW) in the 0-5 cm soil compartment and from 42 (SAW)

to 100 % (BAR) in the 5-10 cm soil compartment (Table 3). The parameters obtained for the 5-10 cm soil compartment must be interpreted with caution because of the short duration of the experiment and the dilution of the 15 N signal in the ecosystem.

For the grassland plots, TOM also exhibited the shortest MRT in the litter (1.2 years)

but the difference with the two other sites was less than one year (MRT of 1.6 and 1.4 years for BAR and SAE, respectively; Table 3). The MRT range (4.9 years at TOM to 9.4 years at SAE) in the 0-5 cm soil compartment was much larger than in the litter.

The MRT 's were also greater than forest estimates at the same compartment. In the 5-10 cm soil compartment, the MRT were close to 1 year in SAE and TOM, and around 2.6 years in BAR. However, the latter might be an over-estimate since the available data do not permit to constrain the curvature of the fit in the deepest layer of BAR grassland. Allocation from the litter to the 0-5 cm soil compartment and from the 0-5 to the 5-10 cm soil compartment was greater than 85 % for all sites.

We simulated the average N recoveries over 3000 days (which is about twice as long as the time frame of this study) in the three compartments of the model (Fig. 6). The simulations resulted in specific patterns related to site position and vegetation types. In forests, we distinguished 2 categories of sites. SAW and TOM presented a fast release of N from the litter and transfer to the 0-5 cm soil, but also a fast N loss out of the system.

According to the model, 50 % of litter N was lost before 1000 days in those sites. In BAR and SAE, the release of N from the litter was slower and 50 % of the N remained in the system after 1300-1700 days. In grassland, the N loss from the litter occurred rapidly on all the three sites and N was retained quite efficiently in the 0-5 cm soil compartment.

Around 50 % of the N remained in the system after 3000 days in BAR and SAE while this 50 % threshold was reached before 2000 days in TOM.

Discussion

Understanding seasonal N cycling and ecosystems transfers is critical to predicting the SW Siberian ecosystem response to changes in climate, land use, or other perturbations.

Over the three year experiment, we found higher rates of N release from leaf-litter in the southern taiga relative to the forest-steppe and in grassland relative to forest. The transfer of N down the soil profile was deeper in the southern taiga than in the foreststeppe, and in forests than in grasslands. Below, we discuss how these differences can be related to pedo-climatic conditions and vegetation type, but also how other environmental characteristics, such as soil properties and hydrology, interact to control the fate of N in these Siberian ecosystems.

N release from the litter layer: the role of climate and vegetation type

Consistent with our first hypothesis, we found aspen litter located in the forest-steppe bioclimatic zone (BAR and SAE) to release N at a slower rate than litter in the southern taiga (SAW and TOM; Table 3). These results are consistent with field observations of only a thin organic layer over the mineral soil in the aspen forests of southern taiga and a thick one in forest-steppe (up to 5 cm in summer) since litter mass and N losses generally exhibit a linear positive relationship (d'Annunzio et al., 2008). In the southern taiga, longer periods of microbial activity are likely facilitated by the combination of higher soil moisture content in the summer, and a warmer and wetter winter soil compared to the forest-steppe (Table 1 and Fig. S2), allowing for a more intense litter decomposition and N release at the annual timescale. In particular, litter degradation can persist over winter in snow-covered environments protected from frost [START_REF] Baptist | Direct and indirect control by snow cover over decomposition in alpine tundra along a snowmelt gradient[END_REF]Brooks et al., 2011;[START_REF] Saccone | The effects of snowpack properties and plant strategies on litter decomposition during winter in subalpine meadows[END_REF][START_REF] Uchida | Microbial activity and litter decomposition under snow cover in a cool-temperate broad-leaved deciduous forest[END_REF].

With about 45 % (BAR) and 65 % (SAE) of N remaining after 2 years in the litter layer (Fig. 2), the N losses from the decomposing aspen litter in the forest-steppe were comparable to those reported in various temperate and sub-tropical forests [START_REF] Zeller | Fate of nitrogen released from 15nlabeled litter in european beech forests[END_REF][START_REF] Fahey | Transport of carbon and nitrogen between litter and soil organic matter in a northern hardwood forest[END_REF][START_REF] Van Huysen | Decomposition and nitrogen dynamics of 15n-labeled leaf, root, and twig litter in temperate coniferous forests[END_REF][START_REF] Blumfield | Decomposition of nitrogen-15 labeled hoop pine harvest residues in subtropical australia[END_REF]. Surprisingly, the only result we found comparable to the southern taiga (about 10 % N remaining in litter after 2 years in SAW and TOM, Fig. 2) concerned tropical eucalyptus plantations on a sandy soil in the Congo [START_REF] Versini | The role of harvest residues to sustain tree growth and soil nitrogen stocks in a tropical eucalyptus plantation[END_REF]. In these tropical plantations, the amount of available nutrients is quite low and plant growth largely relies on the rapid recycling of plant residues [START_REF] Versini | The role of harvest residues to sustain tree growth and soil nitrogen stocks in a tropical eucalyptus plantation[END_REF]. A similar observation in the Siberian southern taiga may indicate that large soil N stocks are not available for plants, thus plants in the southern taiga may also rely on the rapid recycling of N from plant residues.

In contrast to the aspen litter, we did not find significant differences in N release from the grass litter between sites. However, partly consistent with our third hypothesis, N release from the grass litter was faster than the release from aspen litter at all foreststeppe sites while we did not detect such a difference between litter types in the southern taiga. Thus, the fast grass litter decomposition might overcome the pedo-climatic controls in the forest-steppe, while in the southern taiga the pedo-climate outweighs the influence of litter chemistry on litter N release.

The rate of N release in the forest-steppe is potentially influenced by not only the chemical traits of the different vegetation types but also the unique decomposing environment, which ranges from the organic matter milieu present in the litter layer to the micro-climate resulting from different vegetative structures. The initial aspen and grass litter used in this study had similar C (424-445 mg C g -1 ) and N (10-13 mg N g -1 )

contents, but other components, not measured here (e.g. lignin, P, K, Ca, Mg, Mn), may differ between tree leaves and grass shoots and modulate decay rates [START_REF] Berg | Decomposition patterns for foliar litter -a theory for influencing factors[END_REF][START_REF] Cornwell | Plant species traits are the predominant control on litter decomposition rates within biomes worldwide[END_REF][START_REF] Keiluweit | Long-term litter decomposition controlled by manganese redox cycling[END_REF][START_REF] Köchy | Litter decomposition and nitrogen dynamics in aspen forest and mixed-grass prairie[END_REF][START_REF] Melillo | Nitrogen and lignin control of hardwood leaf litter decomposition dynamics[END_REF][START_REF] Zhang | Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors[END_REF]. In addition to the initial quality of the labelled material we employed, the forest floor is in reality comprised of a mix of dead leaves and other materials (e.g. tree branches, understorey vegetation) that may also modulate the chemical composition of the whole organic layer and its decomposition [START_REF] Finerty | Exotic or not, leaf trait dissimilarity modulates the effect of dominant species on mixed litter decomposition[END_REF][START_REF] Gessner | Diversity meets decomposition[END_REF][START_REF] Hättenschwiler | Biodiversity and litter decomposition in terrestrial ecosystems[END_REF][START_REF] Joly | Tree species diversity affects decomposition through modified micro-environmental conditions across european forests[END_REF][START_REF] De Long | Understory plant functional groups and litter species identity are stronger drivers of litter decomposition than warming along a boreal forest post-fire successional gradient[END_REF].

Related to the pedo-climate, the micro-climate of the litter layer also slightly differs between the forest and grassland within our sites. For example, air and soil surface temperatures were generally higher in the grassland than in the forest plots (these differences reached about 2 and 2.5-4 • C at summertime, respectively; Fig. S2). Accordingly, higher temperature of the grassland litter layer may enhance its decomposition as compared to forest litter, except in summer when grassland litter might be too dry (particularly in the forest-steppe zone). Finally, since solar irradiation of the soil surface is higher under grass than tree vegetation cover, photo-degradation might further enhance litter decomposition in grasslands [START_REF] King | Shedding light on plant litter decomposition: advances, implications and new directions in understanding the role of photodegradation[END_REF].

4.2. N dynamics in the soil layers: role of climate, vegetation type and soil properties

Vertical transfer

Across all sites, there were distinct periods over the year where N transfers occurred within the soil, as revealed by the specific "build-up and release" pattern observed in the 0-2.5 cm and 2.5-5 cm soil layers: a net increase in N recovery was observed from the autumn samplings while a net decrease was observed from the spring samplings (Fig. 2 and 3).

In these Siberian ecosystems, the vertical transfer of N down the litter-soil system is probably mostly driven by water fluxes. Indeed, we did not observe a high macro-fauna activity while sampling or digging soil pits for all seasons. We suggest that the growing season (late spring-mid autumn) precipitation events contribute to the "build-up", or the accumulation of litter-derived N in the top five centimeters of the soil. During this season, the soil is drying and precipitation mostly refills the upper soil layers. Also, microbial activity is probably the highest at this period, which allows for an efficient N immobilization in the microbial biomass. In the southern taiga, the retreat of the water-table, that reached the topsoil at snow-melt, could also help in driving N downwards. In the foreststeppe soils, however, the relatively shallower transfer of N and lower concentrations corresponds with the slower rate of N release from the litter, coupled with lower intensities of both soil water fluxes and soil microbial activity due to dry summer conditions.

On the contrary, at snow-melt, the water fluxes are particularly strong and the immobilization capacity of microbes is lower, which causes the "release", or the loss of N from the top five centimeters of the soil. The larger losses recorded in forest-steppe coincide with higher infiltration rates resulting from greater winter precipitation as well as snow melting on soils which were previously saturated by autumn precipitation [START_REF] Brédoire | Impacts of Global Change on the Biogeochemical Cycling of Water and Nutrients in the Soil-Plant System and Consequences for Vegetation Growth in South-Western Siberia[END_REF]; Fig. S3). In fact, yearly drainage in forest, which occurs almost exclusively at snow-melt, has been estimated to be almost five times higher in TOM (262 ± 73 mm year -1 , 49 years mean ± standard deviation) than in BAR (60 ± 43 mm year -1 ), for example [START_REF] Brédoire | Impacts of Global Change on the Biogeochemical Cycling of Water and Nutrients in the Soil-Plant System and Consequences for Vegetation Growth in South-Western Siberia[END_REF].

Possibly contradicting our second hypothesis, in aspen forests, the proportions of litter-derived N exported out of the litter-soil system we studied (given by 1 -a L→1 )

were larger in forest-steppe than in southern taiga (Table 3). These exports are mainly attributed to leaching below 10 cm, denitrification, and/or plant uptake. It is possible that the yearly plant-uptake in forest-steppe is greater than in southern taiga due to the longer vegetation season and the generally higher net primary productivity [START_REF] Monserud | Change in phytomass and net primary productivity for siberia from the mid-holocene to the present[END_REF]. In the unfrozen soils of the southern taiga, despite stronger water fluxes relatively to the forest-steppe, the leaching of N below 10 cm could be attenuated at snow-melt by microbial immobilization due to the maintenance of a basal activity over winter (Brooks et al., 2011;[START_REF] Brooks | Snowpack controls on nitrogen cycling and export in seasonally snow-covered catchments[END_REF].

N retention in soil

Another metric provided by our model is the MRT of N in the different soil layers, providing a measure of soil N retention capacity. Our labelling approach followed by simple modelling of a single pool of N at each layer makes the implicit assumption that all N is homogeneously distributed in the soil matrix and follows the same pathway of decomposition and transfer. As our model was calibrated on a short-term experiment, our estimates of MRT are relatively short (1.5-2.1 years in aspen forests and 4.9-9.4 years in grasslands). Nevertheless, they are similar to those of fast turnover-pools of N from European beech litter [START_REF] Salleles | Étude du devenir de l'azote dérivé des litières dans le sol et dans l'arbre sur le moyen terme dans les forêts de hêtres par traçage isotopique et modélisation[END_REF]. At the timescale of our study, most of the litterderived N had not reached the slow-turnover pools [START_REF] Hatton | Transfer of litter-derived n to soil mineral-organic associations: Evidence from decadal 15n tracer experiments[END_REF][START_REF] Hicks Pries | Long term decompo-sition: the influence of litter type and soil horizon on retention of plant carbon and nitrogen in soils[END_REF], and the contribution of these pools to the N dynamics and the MRT of N in soil are then probably under-estimated here [START_REF] Derrien | Computing the mean residence time of soil carbon fractions using stable isotopes: impacts of the model framework[END_REF][START_REF] Manzoni | Analytical models of soil and litter decomposition: Solutions for mass loss and time-dependent decay rates[END_REF]. This notwithstanding, our model allows for a comparison between sites and vegetation covers at the timescale of several years.

The longer retention of N in the 0-5 cm soil layer in grassland than in forest partly contradicts our third hypothesis, but is consistent with faster soil C turnover in the topsoils of forest vegetation types compared to non-forest vegetation [START_REF] Mills | Long-term organic carbon turnover rates in natural and semi-natural topsoils[END_REF].

Soil organic matter from grasslands potentially produce more ammonium than from woody sites [START_REF] Booth | Controls on nitrogen cycling in terrestrial ecosystems: A synthetic analysis of litterature data[END_REF] and, since ammonium is usually taken up more efficiently by vegetation and micro-organisms [START_REF] Kuzyakov | Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance[END_REF], this could explain the shallow transfer of N in grasslands. In addition, grasslands in SW Siberia tend to have a greater net primary production than nearby forests [START_REF] Monserud | Change in phytomass and net primary productivity for siberia from the mid-holocene to the present[END_REF] and they have a dense root mat found in the first centimeters of the soil (Table 2 and Brédoire et al.

(2016b)), both of which facilitate N retention and cycling in the topsoils of grasslands.

Soil organic C has a faster turnover in southern taiga than in forest-steppe in these Siberian ecosystems [START_REF] Kayler | Soil evaporation and organic matter turnover in the subtaiga and forest-steppe of southwest siberia[END_REF]; however, we did not find such a pattern for the MRT of N within the first ten centimeters of the soil. Besides pedo-climatic conditions which control the transfer and immobilization of N, texture and mineralogy also control the fate of N within the soil as N is mainly associated with clay and silt fractions [START_REF] Moni | Density fractions versus size separates: does physical fractionation isolate functional soil compartments?[END_REF][START_REF] Mueller | Initial differentiation of vertical soil organic matter distribution and composition under juvenile beech (fagus sylvatica l.) trees[END_REF][START_REF] Kayler | Application of δ 13 C and δ 15 N isotopic signatures of organic matter fractions sequentially separated from adjacent arable and forest soils to identify carbon stabilization mechanisms[END_REF], and N protection increases with their increasing amounts [START_REF] Bingham | Organic nitrogen storage in mineral soil: Implications for policy and management[END_REF][START_REF] Dignac | Increasing soil carbon storage: mechanisms, effects of agricultural practices and proxies. a review[END_REF][START_REF] Kleber | Mineralorganic associations: Formation, properties, and relevance in soil environments[END_REF][START_REF] Von Lützow | Stabilization of organic matter in temperate soils: mechanisms and their relevance under different soil conditions -a review[END_REF] as well as metallic (hydr)oxides [START_REF] Bingham | Organic nitrogen storage in mineral soil: Implications for policy and management[END_REF][START_REF] Duiker | Iron (hydr)oxide crystallinity effects on soil aggregation[END_REF][START_REF] Porras | Association with pedogenic iron and aluminum: effects on soil organic carbon storage and stability in four temperate forest soils[END_REF][START_REF] Torn | Mineral control of soil organic carbon storage and turnover[END_REF]. By extending our experiment on a longer period of time, we may strengthen our estimations of MRT and seek to verify how soil texture and mineralogy control the stabilization of N in these Siberian ecosystems.

Summary and future outlook

Our approach combining isotope tracing with modelling allows for a comparison between sites and vegetation by assessing the rate of N release from the litter, the N mean residence time in soil layers, and its vertical transfer down the soil profile.

The rate of decomposition of the leaf-litter at the soil surface is a major determinant for the rate of N cycling in these ecosystems. It is faster in southern taiga (north of the region) than in forest-steppe (south). As most of the litter decomposition occurs in the vegetation growing season, lower moisture conditions in the forest-steppe might hamper decomposer activity. The faster decomposition of the grass than the aspen leaflitter might be attributable to the initial litter quality and litter and soil micro-climatic conditions.

Once in the soil, the turnover of N was comparable for all sites for a given vegetation cover. Typically, in the topsoil, we observed "build-up and release" dynamics, with N inputs during the vegetative season and outputs at snow-melt. Different processes operate in the study sites, depending on pedo-climate and soil physico-chemical properties.

Notably, winter soil freezing could be determinant for the vertical transfer of N. The retention of N in the grassland topsoils appeared to be rather efficient and substantially higher than in forest, which we interpret as an ecosystem property.

Climate change will certainly impact N cycling in these ecosystems. While increasing snowfall [START_REF] Bulygina | Changes in snow cover over northern eurasia in the last few decades[END_REF](Bulygina et al., , 2011) ) may enhance winter decomposition and microbial immobilization of N by preventing soil freezing in the south of SW Siberia, more frequent and intense summer droughts [START_REF] Groisman | Climate changes in siberia[END_REF]IPCC, 2013) would hamper decomposition processes and slow down N cycling all over the region [START_REF] Blok | Initial stages of tundra shrub litter decomposition may be accelerated by deeper winter snow but slowed down by spring warming[END_REF].

As primary production greatly depends on the synchronization of nutrient availability and plant demand in those ecosystems with a strong seasonality of climate, an increase in inter-annual climate variability may strongly modulate N cycling and occasionally hamper plant nutrition. The southernmost sites are probably the most exposed because of the primary importance of snow-fall for winter soil temperature and soil moisture availability through the year. ax→y is the proportion of the output from x (outx) that reaches y, and the difference "1 -ax→y" is the proportion of the output from x that is lost from the litter-soil system before to reach y (e.g. by plant uptake, leaching or denitrification). S7). Vegetation refers to live undertorey vegetation. The terms "above" and "below" refer to the netting which protects the 15 N-labelled litter. S7). The terms "above" and "below" refer to the netting which protects the 15 N-labelled litter. *poorly crystalline oxides extracted with an ammonium oxalate solution [START_REF] Mckeague | Dithionite and oxalate-extractable fe and al as aids in differentiating various classes of soils[END_REF] Table 3: Fitted values of the model parameters. Mean residence time (MRT = 1/k) of the deposited N in the litter layer (MRT L ), the 0-5 cm soil layer (MRT 1 ) and the 5-10 cm soil layer (MRT 2 ). Allocation of the litter-derived N from the litter layer to the 0-5 cm soil layer (a L→1 ) and from the 0-5 to the 5-10 cm soil layer (a 1→2 ). The MRT are expressed in years. The fitness is the smallest value obtained by model fitting (Equation 11).

O c t 2 0 1 3 J u n 2 0 1 3 O c t 2 0 1 4 J u n 2 0 1 4 O c t 2 0 1 5 O c t 2 0 1 2 O c t 2 0 1 3 J u n 2 0 1 3 O c t 2 0 1 4 J u n 2 0 1 4 O c t 2 0 1 5 O c t 2 0 1 2 O c t 2 0 1 3 J u n 2 0 1 3 O c t 2 0 1 4 J u n 2 0 1 4 O c t 2 0 1 5 O c t 2 0 1 2 O c t 2 0 1 3 J u n 2 0 1 3 O c t 2 0 1 4 J u n 2 0 1 4 O c t 2 0 1 5 O c t

BAR

< 1 < 1 < 1 < 1 < 1 < 1 < 1 (g kg -1 ) 15 < 1 < 1 < 1 < 1 < 1 < 1 < 1

  inherent to the sampling of such material might have altered the precision of our measurements (see the possible outliers for spring and autumn 2013 at BAR). In June 2013, almost all the labelled N was recovered in the litter layer below the netting at BAR (almost 100 %) and SAE (91 %) while only 50 % was recovered in TOM. In October 2014, only 19 and 16 % of N were recovered in this litter layer at SAE and TOM (19 and 16 %, respectively) but 40 % were still recovered at BAR. Generally, in the 0-2.5 cm soil layer, large amounts of litter-derived N were recovered (often > 40 %), which was significantly greater than in forest. Only up to 10 % of the deposited N was recovered in the soil between 2.5 and 10 cm, except in SAE where the recovery reached 20 % in the 2.5-5 cm layer in October 2014. At SAE, a peak N recovery in the 0-2.5 cm soil layer was observed in October 2014 (83 %), while at BAR and TOM the N recovery measured in October 2015 was the highest (64 and 55 %, respectively). At each sampling, live vegetation was enriched in litter-derived N. Interestingly, the highest N recovery in the vegetation was measured in June 2013 (8, 3 and 6 % in BAR, SAE and TOM, respectively). The N recovery in the litter above the netting increased with time, reaching about 2.5 % in October 2014.

Figure 1 :

 1 Figure 1: Model diagram. N L , N 1 and N 2 are the litter-derived N contents of the litter layer, the first (0-5 cm) and the second (5-10 cm) soil layers, respectively. Nx is the N content in the compartment x,

Figure 2 :

 2 Figure 2: Proportion of litter-derived N recovered in each layer in forest. Mean value ± standard deviation (number of replicates given in TableS7). Vegetation refers to live undertorey vegetation. The

Figure 3 :

 3 Figure 3: Proportion of litter-derived N recovered in each layer in grassland. Mean value ± standard deviation (number of replicates given in TableS7). The terms "above" and "below" refer to the netting

Figure 6 :

 6 Figure 6: Simulation of N recovery over time.

Table 1 :

 1 Main characteristics of the study sites. according to the name of the closest city or of the geographical area; b World Meteorological Organization (WMO) index of the closest weather station; c distance site-weather station; d due to its position in mountains and to the distance with the weather station, the values are probably over-estimated for temperature and under-estimated for precipitation and snow in SAW; e snow height at climax (usually between mid-February and mid-March); f snow was measured on the experimental sites every meter with the help of a rigid ruler on a transect of 20 m; g

	Site ID BAR SAE SAW TOM	General	Site name a Barnaul Salair East Salair West Tomsk	Bioclimatic zone Forest-steppe Forest-steppe southern taiga southern taiga	Soil in forest Haplic Phaeozem Leptic Phaeozem Haplic Luvisol Albic Luvisol	Albic Luvisol Soil in grassland Calcic Chernozem Leptic Phaeozem	56.3 Latitude N 53.41 54.39 54.18	85.43 Longitude E 83.47 85.75 85.17	232 Elevation (m) 221 305 358	Climatic features (mean annual values over the period 1981-2010)	29430 WMO index b 29838 29745 29736	38 Distance c (km) 4 18 64 d	0.9 Air temperature ( • C) 2.7 2.3 1.2 d	567 Precipitation (mm) 432 432 453 d	178 Snow-pack > 1 cm (days) 157 145 149 d	145 Snow-pack > 20 cm (days) 108 88 116 d	71 Snow height e (cm) 49 38 54 d	Field measurements of the snow height: mean (min-max) ef	64 (50-72) h Winter 2013-2014 forest 35 (20-46) g na na	58 (51-64) h Winter 2013-2014 grassland 35 (23-47) g na	88 (80-90) k Winter 2014-2015 forest 84 (65-95) i 57 (50-70) j 112 (110-120) j	78 (70-86) k Winter 2014-2015 grassland 58 (50-70) i 61 (50-70) j	a	22/02/2014; h 15/02/2014; i 24/02/2015; j 25/02/2015; k 27/02/2015

Table 2 :

 2 Soil physico-chemical properties. Composite sample from 3 soil pits that were sampled at fixed depths ± 3 cm (data fromBrédoire et al. (2016a)), except total C and total N which are means of all the samples analysed for 15 N isotopic composition (n = 3-4). Fine root (diameter < 0.8 mm) densities (data from[START_REF] Brédoire | Distributions of fine root length and mass with soil depth in natural ecosystems of southwestern Siberia[END_REF]).

		Depth		Forest			Grassland
		(cm)	BAR	SAE	SAW	TOM	BAR	SAE	TOM
	Apparent density	5	1.0	0.5	0.7	0.9	1.0	0.9	0.9
		15	1.2	0.8	1.0	1.1	1.3	1.1	1.2
	Particle size 0-2 µm (%)	5	27.7	40.7	22.2	23.2	27.5	35.3	22.0
		15	27.2	36.8	20.4	21.9	27.7	34.9	21.2
	2-20 µm (%)	5	23.2	26.5	37.9	38.8	20.7	23.8	38.9
		15	23.0	25.3	38.2	38.7	21.0	23.9	40.5
	20-50 µm (%)	5	35.3	14.9	34.4	30.2	35.5	17.0	30.8
		15	35.7	15.6	36.6	31.0	32.7	17.6	31.7
	50-200 µm (%)	5	13.0	4.4	4.6	6.9	14.0	4.7	6.7
		15	13.5	5.2	3.9	7.4	16.0	4.2	4.7
	200-2000 µm (%)	5	0.7	13.4	0.8	0.8	2.2	19.1	1.5
		15	0.5	17.0	0.8	0.9	2.5	19.3	1.8
	pH H2O	5	6.10	6.17	6.07	5.37	6.50	6.47	5.45
		15	6.12	5.97	5.45	5.25	7.20	6.58	5.54
	Total N	0-2.5	5.51	8.76	5.83	5.70	4.81	7.78	3.77
	(mg N g -1 dry soil)	2.5-5	4.25	7.41	4.41	5.02	3.07	5.47	3.01
		5-7.5	3.22	5.66	3.40	4.20	2.77	4.80	2.68
		7.5-10	3.02	4.72	2.67	3.73	2.70	4.59	2.35
		10-15	2.75	3.86	2.48	3.36	2.71	4.57	2.06
	Total C	0-2.5	73.59	115.53	78.45	76.21	56.86	95.06	49.02
	(mg C g -1 dry soil)	2.5-5	53.83	93.29	55.23	64.60	33.50	65.91	36.06
		5-7.5	39.66	72.95	39.13	50.91	30.64	57.67	29.37
		7.5-10	37.13	60.97	28.50	43.57	29.72	54.92	24.63
		10-15	33.54	53.01	26.28	38.08	29.55	54.44	21.10
	Total CaCO3	5

Acknowledgements

We gratefully acknowledge G. Bougrovskaia and A. Litvinov, in Novosibirsk, as well as C. Dubreil, C. Gehin, L. Gelhaye, M.-C. Maintenant, J. Michel, S. Moutama and A. Reichard, in Nancy, who regularly helped processing (sorting, drying, sieving, packing, grinding, weighing) piles of samples. We also thank the certified facility in Functional Ecology (PTEF OC 081) from UMR 1137 EEF and UR 1138 BEF in the research centre INRA Grand-Est-Nancy, and in particular C. Hossann, for performing/supervising isotopic analyses. The French National Research Agency supports the UR BEF and the PTEF facility through the Cluster of Excellence ARBRE (ANR-11-LABX-0002-01), and the UMR ISPA through the Cluster of Excellence COTE (ANR-10-LABX-45). Z. E Kayler was funded by the German Ministry for Education and Research (BMBF 01DJ12094). The overall project was funded by INRA Métaprogramme ACCAF and ERA.Net RUS (STProjects-226).

Author contributions BZ, PAB, DD, FB, OR and MRB conceived the ideas and designed methodology; FB, PN, PAB, OR, DD, AB, ZEK, MRB, SD, AL and BZ conducted the research; FB, DD and BZ analysed the data; FB, DD and JSM performed the modelling; FB led the writing of the manuscript with contributions of DD and ZK. All authors contributed critically to the drafts and gave final approval for publication.

Competing interests statement

The authors have no competing interests to declare.