DOI: 10.5061/dryad.5qfttdzgc
title: Data transformations cause altered edaphic-climatic controls and reduced predictability on soil carbon decomposition rates

description: Data transformation of the reference decomposition rates (kref), often
 derived as turnover times or in alternative formats, is commonly used to
 develop ecological models to project the persistence of soil organic
 matter (SOM). However, the effects of reciprocal or logarithmic
 transformation of kref on model performance and edaphic-climatic patterns
 remain uncertain. Here, we convert published kref values into reciprocal
 or logarithmic formats and establish machine learning models between the
 transformed kref and edaphic-climatic predictors. We show that models
 trained with the transformed kref exhibit 11.6-68.4% reductions in model
 performance upon re-conversion to kref compared to those trained with the
 original kref. The variable importance analysis identifies distinct key
 predictors governing the original kref and its transformed counterparts.
 This suggests that data transformation alters the relative significance of
 predictors without necessarily improving kref prediction performances.
 Consequently, our study underscores the importance of directly focusing on
 the original values rather than alternative representations when
 dissecting a given variable's patterns and pertinent mechanisms in
 ecological modelling.
description: A global dataset of first-order kinetics parameters and
 corresponding explanatory predictors is arranged as an online spreadsheet
 with 859 records (Xiang et al., 2023). The fitted first-order kinetics
 parameters in the arranged dataset were obtained from literatures fitting
 laboratory incubation data with one pool (M1), two pool (M2), or three
 pool (M3) first-order models. This arranged dataset
 contains eleven explanatory factors, including (i) two climatic factors:
 MAP (mean annual precipitation, units: mm) and MAT (mean annual
 temperature, units: °C), which represent the characteristics of regional
 climate conditions; (ii) five edaphic factors: Sand (sand fraction, units:
 %), Clay (clay fraction, units: %), pH, SOC (soil organic carbon, unit: g
 kg<sup>-1</sup>), and MBC (microbial biomass carbon, units: g
 C m<sup>-2</sup>), which reflect the effects of soil property
 and microbial community; (iii) two topographic factors: Elev (elevation,
 units: m) and Slope (terrain slope, units: degree or °), indicating the
 impact of terrain; (iv) one vegetation factor characterizing the effects
 of vegetation coverage: NDVI (normalized difference vegetation index); and
 (v) one factor representing incubation condition: IncT (laboratory
 incubation temperature, units: °C). To comprehensively consider the
 effects of soil physicochemical properties, we explored five explanatory
 variables in addition to the eleven variables in Xiang et al. (2023),
 including (i) one vegetation variable reflecting the effect of plant
 productivity: NPP (net primary productivity, units: g C
 m<sup>-2</sup>); (ii) one variable representing the effect of
 soil physical properties: CFVO (volumetric fraction of coarse fragment,
 units:%); and (iii) three variables representing the effect of soil
 fertility: TN (soil total nitrogen, units: g kg<sup>-1</sup>),
 CNratio (the ratio of soil organic carbon to total nitrogen), CEC (cation
 exchange capacity, units: cmol kg<sup>-1</sup>).
 The values of the sixteen explanatory factors were obtained from
 literatures corresponding to each incubation experiments. For studies not
 providing values of the explanatory factors, we extracted from global maps
 pertaining to geographic location.
description: # Data transformations cause altered edaphic-climatic controls and reduced
 predictability on soil carbon decomposition rates
 [https://doi.org/10.5061/dryad.5qfttdzgc](https://doi.org/10.5061/dryad.5qfttdzgc) ## Description of the data and file structure A global dataset of first-order kinetics parameters and corresponding explanatory predictors is arranged as an online spreadsheet with 859 records (Xiang et al., 2023). To comprehensively consider the effects of soil physicochemical properties, we explored five explanatory variables in addition to the eleven variables. The values of the sixteen explanatory factors were obtained from literatures corresponding to each incubation experiments. For studies not providing values of the explanatory factors, we extracted from global maps pertaining to geographic location. ### Files and variables #### File: Data availability.rar **Description:**  This compressed archive includes raw data (named as "compiledDataset_update.csv"), source code (named as "1st_order_2nd.R") for Random  Forest analysis and a folder "results". Files in the folder "results" contain results of the model running, where "IMP-k1.csv", "IMP-k2.csv", and "IMP-k3.csv" are relative importance of explainatory variables pertaining to the fast, slow, and passive pools,  while "ObsVsPred_k1.csv", "ObsVsPred_k2.csv", and " ObsVsPred_k3.csv" are the simulated and observed decomposition rates pertaining to the fast, slow, and passive pools. The cells containing "NA" of files in the folder "Data availability" mean that the corresponding explanatory variables were excluded after RFE (recursive feature eliminate). The cells containing "n/a" in file named "compiledDataset_update" mean missing values that were not reported from literatures. ## Code/software ``` R version 4.1.1 maybe helpful to view and run our data and code. ```