Data from "Ecological clusters of soil taxa within bipartite networks are highly sensitive to climatic conditions in global drylands"

Databases used in the paper "Ecological clusters of soil taxa within bipartite networks are highly sensitive to climatic conditions in global drylands" by David S. Pescador, Manuel Delgado-Baquerizo, Anna Maria Fiore-Donno, Brajesh K. Singh, Michael Bonkowski, Fernando T. Maestre <br>There are five spreadsheets with data relative to a) Operational Taxonomic Unit (OTUs) tables for bacteria, fungi and cercazoa, b) taxonomic assignments for each OTU and c) environmental variables from field sites sampling.<br>OTUs spreadsheet for bacterial (Bact_OTUs), fungal (Fung_OTUs) and cercazoa (Cerco_OTUs) sequences that forms the basis for most analyses in Pescador et al. 2022. The first column of each OTU table corresponds to OTU ID (link to "OTU_ID" column in "Tax" spreadsheet), the other columns are field site names (correspond to "Idb" column in "Env_Var" sheet). Bacterial and fungi OTUs were picked based on 97% similarity threshold for 16S rRNA gene and ITS sequences respectively and using the UCLUST algorithm implemented in VSEARCH. <br>"Tax" spreadsheet corresponds to the taxonomic assignment of OTU tables. Taxonomic assignment for 16S rRNA gene sequences was performed querying the OTUs identified against the Greengenes database v. 13_850 (DeSantis et al. 2006, McDonald et al. 2012). For fungal ITS sequences, taxonomy was assigned by using BLAST against the UNITE database v. 6.9.7 (Kõljalg et al. 2013) and an e-value &lt; 1·10−5. Finally, BLAST+ with an e-value &lt; 1·10−5 and keeping only the best hit was used to identified cercozoan sequences against the PR2 database (Guillou et al. 2012). The resulting OTU abundance tables for bacterial and fungal sequences were rarefied to an even number of sequences per sample to ensure an equal sampling depth (i.e. 13,225 and 13,433 for 16 rRNA gene and ITS, respectively). In the cercozoa database we used very stringent parameters when filtering the reads to avoid overestimation of the diversity due to amplification and sequencing errors. That means that, in addition to filtering out the reads at every step, we also deleted the OTUs, that, according to a mock community of 10 known species, sequenced as a sample along the soil samples were noise. So, in this dataset, we deleted all OTUs containing less than 0.0017% of the total sequences, that is, 100 sequences and then we rarefied OTU abundance table to 7122 sequences.<br>"Env_Var" spreadsheet represents coordinates, climate, soil and vegetation characteristic from field sites used in this survey and includes the following fields: • Lat_decimal: latitude in decimal format. • Long_decimal: longitude in decimal format. • ARIDITY: Mean annual temperature (MAT) and aridity (1 – AI; mean annual precipitation/potential evapotranspiration) determined using the database provided by Zomer et al. 2008. • AMT: Annual Mean Temperature Climatic data were obtained from Worldclim (http://www.worldclim.org/; Hijmans et al. 2005). • ORCb: total soil organic carbon for bare areas of field sites estimated by colorimetry after oxidation with K2Cr2O2 and H2SO4 (Anderson &amp; Ingram, 1993). • TPb: total P for bare areas of field sites determined on a SKALAR++ San Analyzer (Skalar, Breda, The Netherlands) after digestion with H2SO4 • pHb: soil pH of field sites measured using a pH-meter, in a 1:5 soil:water solution reached after extract soil in de-ionised water for 1 hour • SAC_b: soil sand content (particules of 2.0-0.05 mm) measured in air-dried soil samples according to Kettler et al. 2001. • TCT: percentage of total cover estimated as the average of 4 linear 30 m transects in each field site. • SR: species richness representing the number of species present in the 80 1.5 x 1.5 m quadrats sampled per field site. • NDVI_c: average Normalized Difference Vegetation Index of the images before, during and after the soil survey obtained from MODIS (Moderate Resolution Imaging Spectroradiometer) instrument aboard the NASA’s Terra satellites (https://modis.gsfc.nasa.gov).<br>ReferencesAnderson J, Ingram J. 1993 Tropical soil biology and fertility. A handbook of methods. UK: CAB International.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, et al. 2006 Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069-5072.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, Boutte C, Burgaud G, De Vargas C, et al. 2012 The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597-D604. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. International journal of climatology 25, 1965-1978 (doi:10.1002/joc.1276).Kettler T, Doran JW, Gilbert T. 2001 Simplified method for soil particle-size determination to accompany soil-quality analyses. Soil Sci. Soc. Am. J. 65, 849-852.Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AF, Bahram M, Bates ST, Bruns TD, Bengtsson‐Palme J, et al. 2013 Towards a unified paradigm for sequence‐based identification of fungi. Mol. Ecol. 22, 5271-5277.McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P. 2012 An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. The ISME journal 6, 610–618. Zomer RJ, Trabucco A, Bossio DA, Verchot LV. 2008. Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric., Ecosyst. Environ. 126, 67-80 (doi:10.1016/j.agee.2008.01.014). <br>