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Dryad

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

Xiang, Daifeng1; Wang, Gangsheng1; Lv, Zehao1; Li, Wanyu1; Tian, Jing1

Published Sep 05, 2025 on Dryad. https://doi.org/10.5061/dryad.5qfttdzgc

Data files

This dataset is embargoed and will be released on Sep 05, 2025 . Please contact Daifeng Xiang at nc.ude.uhw@fdgnaix with any questions.

Lists of files and downloads will become available to the public when released.

Abstract

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.