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Véquaud, Pierre; Thibault, Alexandre; Derenne, Sylvie; Anquetil, Christelle; Collin, Sylvie; Contreras, Sergio; Nottingham, Andrew T; Sabatier, Pierre; Werne, Josef P; Huguet, Arnaud (2022): MAAT, MAP and pH in soils and peats [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.947062

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Abstract:
Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are a family of bacterial lipids which have emerged over time as robust temperature and pH paleoproxies in continental settings. Despite improvements in brGDGT analytical methods and development of refined models, the root-mean-square error (RMSE) associated with global calibrations between brGDGT distribution and MAAT in soils and peats remains high (~ 5 °C). Here we proposed to extend the global brGDGT terrestrial dataset previously proposed (n = 663; Dearing Crampton-Flood et al., 2019) with 112 soil samples from 6 altitudinal transects located in France (n = 49), Italy (n = 24), Tibet (n = 17), Chile (n = 8) and Peru (n =14). The transects were selected to take into account as much climatic and environmental variability as possible. All of these surficial soil samples (0 -10 cm depth) cover a wide range of temperatures (0°C to 26°C) and pH (3 to 8) and are representative of a wide diversity of environmental variables, vegetation and soil type. These new data were combined with previously published ones. This allowed the development of a new global terrestrial brGDGT temperature calibration from a worldwide extended dataset (i.e. 775 soil and peat samples) using a machine learning algorithm. This new model, called random Forest Regression for PaleOMAAT using brGDGTs (FROG), represents a refined brGDGT temperature calibration (R² = 0.8; RMSE = 4.01°C) for soils and peats, more robust and accurate than previous global soil calibrations while being proposed on an extended dataset.
Keyword(s):
branched GDGT; Branched GDGTs; GDGT; GDGTs; global temperature calibration; MAAT; machine learning; peat; Soil
Related to:
Bonanomi, Giuliano; Zotti, Maurizio; Mogavero, Valentina; Cesarano, Gaspare; Saulino, Luigi; Rita, Angelo; Tesei, Giulio; Allegrezza, Marina; Saracino, Antonio; Allevato, Emilia (2020): Climatic and anthropogenic factors explain the variability of Fagus sylvatica treeline elevation in fifteen mountain groups across the Apennines. Forest Ecosystems, 7(1), 5, https://doi.org/10.1186/s40663-020-0217-8
Dearing Crampton-Flood, Emily; Tierney, Jessica E; Peterse, Francien; Kirkels, Frédérique M S A; Sinninghe Damsté, Jaap S (2020): BayMBT: A Bayesian calibration model for branched glycerol dialkyl glycerol tetraethers in soils and peats. Geochimica et Cosmochimica Acta, 268, 142-159, https://doi.org/10.1016/j.gca.2019.09.043
Huguet, Arnaud; Coffinet, Sarah; Roussel, Anthony; Gayraud, Félix; Anquetil, Christelle; Bergonzini, Laurent; Bonanomi, Giuliano; Williamson, David; Majule, Amos; Derenne, Sylvie (2019): Evaluation of 3-hydroxy fatty acids as a pH and temperature proxy in soils from temperate and tropical altitudinal gradients. Organic Geochemistry, 129, 1-13, https://doi.org/10.1016/j.orggeochem.2019.01.002
Véquaud, Pierre; Derenne, Sylvie; Anquetil, Christelle; Collin, Sylvie; Poulenard, Jérôme; Sabatier, Pierre; Huguet, Arnaud (2021): Influence of environmental parameters on the distribution of bacterial lipids in soils from the French Alps: Implications for paleo-reconstructions. Organic Geochemistry, 153, 104194, https://doi.org/10.1016/j.orggeochem.2021.104194 (Véquaud et al., 2021b)
Véquaud, Pierre; Derenne, Sylvie; Thibault, Alexandre; Anquetil, Christelle; Bonanomi, Giuliano; Collin, Sylvie; Contreras, Sergio; Nottingham, Andrew T; Sabatier, Pierre; Salinas, Norma; Scott, Wesley P; Werne, Josef P; Huguet, Arnaud (2021): Development of global temperature and pH calibrations based on bacterial 3-hydroxy fatty acids in soils. Biogeosciences, 18(12), 3937-3959, https://doi.org/10.5194/bg-18-3937-2021 (Véquaud et al., 2021a)
Véquaud, Pierre; Thibault, Alexandre; Derenne, Sylvie; Anquetil, Christelle; Collin, Sylvie; Contreras, Sergio; Nottingham, Andrew T; Sabatier, Pierre; Werne, Josef P; Huguet, Arnaud (2022): FROG: A global machine-learning temperature calibration for branched GDGTs in soils and peats. Geochimica et Cosmochimica Acta, 318, 468-494, https://doi.org/10.1016/j.gca.2021.12.007
Wang, Jun-Tao; Cao, Peng; Hu, Hang-Wei; Li, Jing; Han, Li-Li; Zhang, Li-Mei; Zheng, Yuan-Ming; He, Ji-Zheng (2015): Altitudinal Distribution Patterns of Soil Bacterial and Archaeal Communities Along Mt. Shegyla on the Tibetan Plateau. Microbial Ecology, 69(1), 135-145, https://doi.org/10.1007/s00248-014-0465-7
Wang, M; Zheng, Z; Zong, Yongqiang; Man, Meiling; Tian, L (2019): Distributions of soil branched glycerol dialkyl glycerol tetraethers from different climate regions of China. Scientific Reports, 9(1), https://doi.org/10.1038/s41598-019-39147-9
Whitaker, Jeanette; Ostle, Nicholas; Nottingham, Andrew T; Ccahuana, Adan; Salinas, Norma; Bardgett, Richard D; Meir, Patrick; McNamara, N P (2014): Microbial community composition explains soil respiration responses to changing carbon inputs along an A ndes‐to‐ A mazon elevation gradient. Journal of Ecology, 102(4), 1058-1071, https://doi.org/10.1111/1365-2745.12247
Funding:
Sorbonne Université, Paris, France, grant/award no. EC2CO (CNRS/INSU – BIO- HEFECT/MICROBIEN): SHAPE
Sorbonne Université, Paris, France, grant/award no. ECOS SUD/ECOS ANID #C19U01/190011
Coverage:
Median Latitude: 29.083811 * Median Longitude: 33.972118 * South-bound Latitude: -38.708000 * West-bound Longitude: 5.866233 * North-bound Latitude: 45.681339 * East-bound Longitude: 94.416667
Date/Time Start: 2010-12-01T00:00:00 * Date/Time End: 2017-10-01T00:00:00
Minimum ELEVATION: m a.s.l. * Maximum ELEVATION: 4479 m a.s.l.
Event(s):
Chile * Latitude: -38.500000 * Longitude: -71.500000 * Location: Chile * Method/Device: Multiple investigations (MULT)
France * Latitude: 46.000000 * Longitude: 2.000000 * Method/Device: Multiple investigations (MULT)
Italy * Latitude: 43.000000 * Longitude: 12.000000 * Location: Italy * Method/Device: Multiple investigations (MULT)
Parameter(s):
#NameShort NameUnitPrincipal InvestigatorMethod/DeviceComment
1Event labelEventVéquaud, Pierre
2Sample IDSample IDVéquaud, Pierre
3Reference/sourceReferenceVéquaud, Pierre
4Sample typeSamp typeVéquaud, Pierre
5Area/localityAreaVéquaud, Pierre
6ELEVATIONElevationm a.s.l.Véquaud, PierreGeocode
7LATITUDELatitudeVéquaud, PierreGeocode
8Latitude 2Latitude 2Véquaud, Pierre
9LONGITUDELongitudeVéquaud, PierreGeocode
10Longitude 2Longitude 2Véquaud, Pierre
11DATE/TIMEDate/TimeVéquaud, PierreGeocode
12Temperature, air, annual meanMAAT°CVéquaud, Pierre
13Precipitation, annual, meanMAPmm/aVéquaud, Pierre
14pHpHVéquaud, Pierre
Status:
Curation Level: Enhanced curation (CurationLevelC)
Size:
725 data points

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