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Plant and Soil

, Volume 426, Issue 1–2, pp 267–286 | Cite as

Environmental factors controlling soil organic carbon stability in French forest soils

  • Laure N. Soucémarianadin
  • Lauric Cécillon
  • Bertrand Guenet
  • Claire Chenu
  • François Baudin
  • Manuel Nicolas
  • Cyril Girardin
  • Pierre Barré
Regular Article

Abstract

Aims

In temperate forests, soils contain a large part of the ecosystem carbon that can be partially lost or gained upon global change. Our aim was to identify the factors controlling soil organic carbon (SOC) stability in a wide part of French forests.

Methods

Using a set of soils from 53 French forest sites, we assessed the effects of depth (up to 1 m), soil class (dystric Cambisol; eutric Cambisol; entic Podzol), vegetation types (deciduous; coniferous) and climate (continental influence; oceanic influence; mountainous influence) on SOC stability using indicators derived from laboratory incubation, physical fractionation and thermal analysis.

Results

Labile SOC pools decreased while stable SOC pool increased with depth. Soil class also significantly influenced SOC stability. Eutric Cambisols had less labile SOC in surface layers but had more labile SOC at depth (> 40 cm) than the other soil classes. Vegetation influenced thermal indicators of SOC pools mainly in topsoils (0–10 cm). Mountainous climate forest soils had a low thermal SOC stability.

Conclusions

On top of the expected effect of depth, this study also illustrates the noticeable effect of soil class on SOC stability. It suggests that environmental variables should be included when mapping climate regulation soil service.

Keywords

Forest soils Particulate organic matter fractionation Pedology Rock-Eval 6 Soil basal respiration Soil organic carbon persistence 

Abbreviations

SOC

Soil organic carbon

RE6

Rock-Eval 6

POM

Particulate organic matter

OIRE6

Oxygen index

HI

Hydrogen index

HC

Hydrocarbons

Notes

Acknowledgements

This work was supported by the French Environment and Energy Management Agency (ADEME) [APR REACCTIF, piCaSo project] and Campus France [PRESTIGE-2015-3-0008]. We thank M. Bryant, S. Cecchini, J. Mériguet, F. Savignac, and L. Le Vagueresse for their technical support.

Supplementary material

11104_2018_3613_MOESM1_ESM.tif (2.5 mb)
Online Resource 1 Description of the Rock-Eval 6 thermal analysis (adapted from Baudin et al., 2017) and calculation of the two RE6-derived parameters (hydrogen index; T50_CO2_OX, the temperature at which 50% of the residual SOM was oxidized to CO2 during the oxidation phase). Baudin F, Tribovillard N, Trichet J (2017) Géologie De La Matière Organique. EDP Sciences, Lilles, France. (TIFF 2607 kb)
11104_2018_3613_Fig7_ESM.gif (109 kb)

High resolution image (GIF 108 kb)

11104_2018_3613_MOESM2_ESM.tif (5.4 mb)
Online Resource 2 Correlation between C content (%) of isovolumetrically pooled samples (measured in this study as detailed in Materials and Methods subsection a) and average values of the 5 replicates × 5 subplots from RENECOFOR samples (calculated with values from Jonard et al. (2017) and Ponette et al. (1997) for samples 0–40 cm and 40–100 cm, respectively) for a given soil layer (n = 242). The 1:1 line has been added in red for reference. Jonard M, Nicolas M, Coomes DA, Caignet I, Saenger A, Ponette Q (2017) Forest soils in France are sequestering substantial amounts of carbon. Sci Total Environ 574:616–628. Ponette Q, Ulrich E, Brêthes A, Bonneau M, Lanier M (1997) RENECOFOR - Chimie des sols dans les 102 peuplements du réseau: campagne de mesures 1993–95. ONF, Département des recherches techniques, Fontainebleau, France (TIFF 5566 kb)
11104_2018_3613_Fig8_ESM.gif (5 kb)

High resolution image (GIF 4 kb)

11104_2018_3613_MOESM3_ESM.xlsx (10 kb)
Online Resource 3 Details of models and their significant terms selected to explain variations in respired-C and POM-C, T50_HC_PYR, and T50_CO2_OX in the 53 study plots (analysis by profile). All models used a gls function (see details in the Calculations and statistical analyses section) (XLSX 9 kb)
11104_2018_3613_MOESM4_ESM.xlsx (13 kb)
Online Resource 4 Mean (and standard deviation) of the indicators of labile SOC (T50_HC_PYR, POM-C; respired-C) and stable SOC (T50_CO2_OX) for each soil class in the five different layers. The total SOC content was added for reference (XLSX 12 kb)
11104_2018_3613_MOESM5_ESM.xlsx (20 kb)
Online Resource 5 Table of correlations for all samples and for each layer individually between the indicators of the SOC pools and the physico-chemical properties (SOC content, C/N ratio, HI, OIRE6, texture, pH, cationic exchange capacity), the climatic data of the plots (mean annual precipitation; MAP and mean annual temperature; MAT) and the chemical properties (C/N ratio) of the inputs and humus. Significance is indicated as follows: ***: p < 0.001; **: p < 0.01; *: p < 0.05. The high (> 0.6) correlations obtained with the SOC pools indicators are marked in bold. n = 242 total; n = 53 for layers 1 to 3 and n = 50 and n = 33 for layers 4 and 5 respectively unless specified otherwise (XLSX 19 kb)
11104_2018_3613_MOESM6_ESM.tif (259 kb)
Online Resource 6 Distribution of the mean annual precipitation (MAP) and mean annual temperature (MAT) in the 53 study sites as a function of vegetation type illustrating a bias towards coniferous stands being in wetter and colder locations. n = 29 and 24 for coniferous and deciduous, respectively (TIFF 259 kb)
11104_2018_3613_Fig9_ESM.gif (19 kb)

High resolution image (GIF 18 kb)

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Laure N. Soucémarianadin
    • 1
    • 2
  • Lauric Cécillon
    • 3
  • Bertrand Guenet
    • 4
  • Claire Chenu
    • 5
  • François Baudin
    • 6
  • Manuel Nicolas
    • 7
  • Cyril Girardin
    • 5
  • Pierre Barré
    • 1
  1. 1.Laboratoire de GéologieEcole normale supérieure/CNRS UMR8538, PSL Research UniversityParisFrance
  2. 2.Laboratoire de GéologieEcole normale supérieure/CNRS UMR8538, PSL Research UniversityParisFrance
  3. 3.Université Grenoble Alpes, Irstea, UR LESSEMSt-Martin-d’HèresFrance
  4. 4.Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQUniversité Paris-SaclayGif-sur-YvetteFrance
  5. 5.AgroParisTech-INRA, UMR ECOSYSThiverval-GrignonFrance
  6. 6.Sorbonne-Université/UPMC, ISTePParisFrance
  7. 7.Office National des Forêts, R&DFontainebleauFrance

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