<b>R codes from Uncover Microbial Food Webs using Machine </b><b>Learning</b>

R codes used to build the different figures in Barel et al. 2023 Soil Bill. Biochem.1_ClustOfVar_ML_comparisons_loops: codes used for machine learning model comparison to select the best model for microbial feeding links inferences.2_PVS_SA: Sensitivity analysis 1 - shuffling the predictors3_PT_SA: Sensitivity analysis 2 - test the influence of species that are new or poorly characterized at the taxonomic level4_Feeding link predictions on Case Study Data: Make feeding link inferences from the Case Study data (species lists and traits) using the best model identified following step 1.5_Calculate_Link_strength_and_FW_Collections: Calculate links strength and create a food web collection for further analyses6_FW_analyses: Plot meta webs and food webs at the plot level. Calculate food web indices.For more details, please refer to the paper: Barel, J.M., Petchey, O.L., Ghaffouli, A., Jassey, V.E.J., 2023. Uncovering microbial food webs using machine learning. Soil Biology and Biochemistry 186, 109174. doi:10.1016/j.soilbio.2023.109174</p

Soil legacies of winter cover crop mixtures

Excel data file with legacy effects of a two year rotation (2014-2015), including winter cover crop monocultures and mixtures. Contains variable description, experimental design, data on winter cover crop biomass and N, soil organic matter, mineral N, potential N mineralisation, plant feeding nematode abundance, biomass of response plants Avena sativa and Cichorium endivia