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

Data transformation of the reference soil organic matter (SOM) decomposition rates (kref), often derived as turnover times or in alternative formats, is commonly used to develop ecological models for projecting the persistence of 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 a 11.6%−68.4% reduction in 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 performance. 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 mechanisms in ecological modeling. Core Ideas Data transformation caused reduced predictive performance upon re‐conversion to kref compared to the original kref. Models trained with data transformation exhibited divergent edaphic‐climatic controls for different formatted kref. This study underscores the importance of directly focusing on original values rather than alternative representations. Plain Language Summary The first‐order model is commonly used to simulate the dynamics of soil organic matter decomposition. The reference decomposition rates (kref) in the first‐order model are usually transformed into reciprocal or logarithmic formats, which may affect model performance as well as the regulatory patterns of edaphic‐climatic factors. In this study, we investigated the effects of data transformation on predictive performance and variable importance. We found that models trained with transformed kref exhibited reduced performance upon re‐conversion to kref, compared to those trained with the original kref. In addition, data transformation altered the distribution and range of the dependent variable, leading to divergent controlling roles of explanatory variables pertaining to models trained with different formatted kref. Our study underscores the need to apply models directly to the