Assessing the geographic specificity of pH prediction by classification and regression trees

Abstract

Soil pH effects a wide range of critical biogeochemical processes that dictate plant growth and diversity. Previous literature has established the capacity of classification and regression trees (CARTs) to predict soil pH, but limitations of CARTs in this context have not been fully explored. The current study collected soil pH, climatic, and topographic data from 100 locations across New York’s Temperate Deciduous Forests (in the United States of America) to investigate the extrapolative capacity of a previously developed CART model as compared to novel CART and random forest (RF) models. Results showed that the previously developed CART underperformed in terms of predictive accuracy (RRMSE = 14.52%) when compared to a novel tree (RRMSE = 9.33%), and that a novel random forest outperformed both models (RRMSE = 8.88%), though its predictions did not differ significantly from the novel tree (p = 0.26). The most important predictors for model construction were climatic factors. These findings confirm existing reports that CART models are constrained by the spatial autocorrelation of geographic data and encourage the restricted application of relevant machine learning models to regions from which training data was collected. They also contradict previous literature implying that random forests should meaningfully boost the predictive accuracy of CARTs in the context of soil pH.

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“…However, some studies showed that CART has better performance for pH, compared with RF [60,68]. When the interpretability of the resulting model is important for the user, logical-based machine learning models are more appropriate, as they do not function as "black boxes" [69], and the main advantage is that the former provides an estimate of the relative importance of the predictors in the model, and avoids the elimination of predictive covariates that may be relevant for soil, even if there are correlations between them [70].…”