Data transformations cause altered edaphic-climatic controls and reduced predictability on soil carbon decomposition rates

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 performanc..., 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-1), and MBC (microbial biomass carbon, units: g C m-2), 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 t..., , # 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 ...

参考分解速率（reference decomposition rates，简称kref）常由周转时间或以其他格式推导得出，其数据变换方法被广泛用于构建生态模型以预测土壤有机质（soil organic matter，简称SOM）的持久性。然而，对kref进行倒数变换或对数变换对模型性能及土壤-气候格局的影响仍不明确。本研究将已发表的kref值转换为倒数或对数格式，并基于转换后的kref与土壤-气候预测因子构建机器学习模型。研究表明，相较于以原始kref训练得到的模型，以转换后kref训练的模型在重新转换为kref时，其模型性能下降了11.6%~68.4%。变量重要性分析结果显示，调控原始kref与其转换后形式的关键预测因子存在显著差异。这表明数据变换会改变预测因子的相对重要性，但未必能提升kref的预测性能……

本研究构建了包含859条记录的全球一级动力学参数及对应解释性预测因子在线电子表格数据集（Xiang等，2023）。该数据集内的拟合一级动力学参数取自文献，这些文献通过单库（M1）、双库（M2）或三库（M3）一级动力学模型对实验室培养数据进行拟合得到。

该数据集共包含11项解释因子，具体分为：(1) 2项气候因子：年均降水量（mean annual precipitation，简称MAP，单位：mm）与年均气温（mean annual temperature，简称MAT，单位：℃），用于表征区域气候特征；(2) 5项土壤因子：砂粒含量（sand fraction，单位：%）、黏粒含量（clay fraction，单位：%）、pH值、土壤有机碳（soil organic carbon，简称SOC，单位：g·kg⁻¹）以及微生物生物量碳（microbial biomass carbon，简称MBC，单位：g C·m⁻²），用于反映土壤属性与微生物群落的影响；(3) 2项地形因子：海拔（elevation，简称Elev，单位：m）与坡度（terrain slope，单位：°或度），用于指示……

# 数据变换会改变土壤-气候调控作用并降低土壤碳分解速率的可预测性
[https://doi.org/10.5061/dryad.5qfttdzgc](https://doi.org/10.5061/dryad.5qfttdzgc)

## 数据与文件结构说明
本数据集为包含859条记录的全球一级动力学参数及对应解释性预测因子在线电子表格（Xiang等，2023）。为全面考量土壤理化性质的影响，本研究在原有11项变量的基础上新增了5项解释变量。16项解释因子的取值部分取自对应培养实验的文献报道，对于未提供解释因子取值的研究，则通过其地理位置对应的全球地图提取相关数据。

### 文件与变量
#### 文件：Data availability.rar
**文件说明：** 该压缩归档文件包含原始数据（文件名为`compiledDataset_update.csv`）以及源代码……