Climate change effects on the stability and chemistry of soil organic carbon pools in a subalpine grassland

Mountain soils stock large quantities of carbon as particulate organic matter that may be highly vulnerable to climate change. To explore potential shifts in soil organic matter (SOM) form and stability under climate change (warming and reduced precipitations), we studied the dynamics of SOM pools of a mountain grassland in the Swiss Jura as part of a climate manipulation experiment. The climate manipulation (elevational soil transplantation) was set up in October 2009 and simulated two realistic climate change scenarios. After 4 years of manipulation, we performed SOM physical fractionation to extract SOM fractions corresponding to specific turnover rates, in winter and in summer. Soil organic matter fraction chemistry was studied with ultraviolet, 3D fluorescence, and mid-infrared spectroscopies. The most labile SOM fractions showed high intra-annual dynamics (amounts and chemistry) mediated via the seasonal changes of fresh plant debris inputs and confirming their high contribution to the microbial loop. Our climate change manipulation modified the chemical differences between free and intra-aggregate organic matter, suggesting a modification of soil macro-aggregates dynamics. Interestingly, the 4-year climate manipulation affected directly the SOM dynamics, with a decrease in organic C bulk soil content, resulting from significant C-losses in the mineral-associated SOM fraction (MAOM), the most stable form of SOM. This SOC decrease was associated with a decrease in clay content, above- and belowground plants biomass, soil microbial biomass and activity. The combination of these climate changes effects on the plant–soil system could have led to increase C-losses from the MAOM fraction through clay-SOM washing out and DOC leaching in this subalpine grassland

Comparison of infrared spectroscopy and laser granulometry as alternative methods to estimate soil aggregate stability in Mediterranean badlands

Soil aggregate stability is a key indicator of soil resistance to erosion, but its measurement remains fastidious for large scale uses. Alternative time and cost-effective methods are thus needed. Our objective was to assess and compare the efficiency of laser granulometry (LG) and soil mid- and near-infrared spectroscopy (MIR/NIR) as alternative methods to assess soil aggregate stability in Mediterranean badland soils. A collection of 75 badland soil samples was used, showing wide variations in soil aggregate stability. Three different categories of measurements were performed: (i) the aggregate breakdown kinetics of the [< 1 mm] size fraction under stirring and sonication, tracked by repeated particle size distribution measurements, using LG, (ii) mid-(diffuse-MIR-DR and attenuate transmitted reflectance — MIR-ATR) and near-(NIR-DR) infrared spectra of the fine soil fraction [< 2 mm] and (iii) the soil aggregate [3–5 mm] stability, using the standardized method (ISO/FDIS 10930, 2012). Partial least squares regression models were used to predict soil aggregate stability using LG data and infrared spectra. Results showed that NIR-DR and MIR-ATR data provided the best prediction model for soil aggregate stability values (RPD = 2.61 & 2.74; R2 = 0.85 & 0.87), followed by MIR-DR data (RPD = 2.24; R2 = 0.89) and finally LG data (RPD = 2.12; R2 = 0.80). For a quantitative use of the models to assign soil samples to standardized soil aggregate stability classes (ISO/FDIS 10930, 2012), infrared spectra also provided the best accuracy, with a misclassification rate below 30% for NIR-DR and MIR-ATR models, while it reached 43% with the LG-based model. The combination of IR and LG data did not yield a better prediction model for soil aggregate stability values and classes. Infrared-based method also provided best results in terms of time-saving strategy, reducing the measurement time to 8 min only. To conclude, infrared spectra (NIR-DR and MIR-ATR) outperformed LG-data to predict soil aggregate stability. Further development of this technique would require calibrating a set of soil-type specific prediction models for a wide range of soil types

Evaluer l’intérêt agro-écologique de l’utilisation de semences locales pour la revégétalisation des prairies d’altitude

International audienc

MEC-11-1215_filtered_data_de

Text file showing the filtered data produced by primers ewD/ewE

MEC-11-1215_sequence_data_bc

Fasta file containing all unique sequences produced with the ewB/ewC primer

MEC-11-1215_filtered_data_bc

Text file showing the filtered data produced by primers ewB/ewC

MEC-11-1215_sequence_data_de

Fasta file containing all unique sequences produced with the ewD/ewe primer

Pansu_et_al_SBB_ewDE_sequence_data_subplots_sampling

This fasta file contains merged reads assigned to their original sample obtained with the subplots soil sampling scheme. Amplicons were amplified using ewDE primers (ewD: 5’- ATTCGGTTGGGGCGACC-3’ and ewE: 5’- CTGTTATCCCTAAGGTAGCTT-3’) (Bienert et al., 2012). Sequences were obtained by a 2 x 100 bp paired-end sequencing on Illumina HiSeq platform. First filtering steps were performed using the OBITOOLS software (http://metabarcoding.org/obitools) following the data filtering description in supplementary material (Pansu et al., 2015 Soil Biology and Biochemistry). Direct and reverse reads corresponding to the same sequence were aligned and merged thanks to the IlluminaPairEnd program. Only merged sequences with a high alignment quality score were retained (>=40). Then, the ngsfilter program assigned each merged sequence to its original sample using the tags information previously added to primers. Only sequences containing both primers (with a maximum of 3 mismatches per primer) and exact tag sequences were selected. Sequences containing ambiguous nucleotides or shorter than 55 bp were discarded. Strictly identical sequences were merged together while keeping information about the origin of sequences. Strict singletons (i.e. sequences occurring only once in the dataset) were removed

Pansu_et_al_SBB_ewDE_sequence_data_alternative_sampling

This fasta file contains merged reads assigned to their original sample obtained with the alternative soil sampling scheme covering the entire plot surface. Amplicons were amplified using ewDE primers (ewD: 5’- ATTCGGTTGGGGCGACC-3’ and ewE: 5’- CTGTTATCCCTAAGGTAGCTT-3’) (Bienert et al., 2012). Sequences were obtained by a 2 x 100 bp paired-end sequencing on Illumina HiSeq platform. First filtering steps were performed using the OBITOOLS software (http://metabarcoding.org/obitools) following the data filtering description in supplementary material (Pansu et al., 2015 Soil Biology and Biochemistry). Direct and reverse reads corresponding to the same sequence were aligned and merged thanks to the IlluminaPairEnd program. Only merged sequences with a high alignment quality score were retained (>=40). Then, the ngsfilter program assigned each merged sequence to its original sample using the tags information previously added to primers. Only sequences containing both primers (with a maximum of 3 mismatches per primer) and exact tag sequences were selected. Sequences containing ambiguous nucleotides or shorter than 55 bp were discarded. Strictly identical sequences were merged together while keeping information about the origin of sequences. Strict singletons (i.e. sequences occurring only once in the dataset) were removed