Exploring the determinants and distribution patterns of soil mattic horizon thickness in a typical alpine environment using boosted regression trees

Abstract

Mattic horizon, grassroots intertwined surface horizon for soils in alpine meadow, is of great importance for alpine ecosystems with its thickness serving as a critical indicator for the extent of alpine meadow degradation. However, the distribution patterns and key environmental factors controlling mattic horizon thickness (MHT) remain unknown. We evaluated the potential of a boosted regression trees (BRT) based variable selection approach to identify the determinants of MHT, to reveal the predictor-response relationship, and to map the spatial distributions of MHT. Results showed that the selected six key environmental variables can reliably reflect the pedogenic processes of mattic horizon in such alpine environment. Among which, the most important environmental variables controlling MHT were modified soil-adjusted vegetation index 2 (relative importance of 31.9%), elevation (22.7%), followed by slope (13.2%), topographic wetness index (11.4%), relative slope position (10.4%), and mean annual precipitation (10.4%). Additionally, the BRT model using those six key variables for mapping MHT performed an acceptable prediction with R2 value of 0.447 (standard deviation of 0.019). The variable selection procedure dramatically reduced the number of environmental variables by 77% (from 26 to 6), which could not only improve computational efficiency but also significantly improve model performance with a 30.3% increase in R2. Our results demonstrate that the proposed BRT algorithm-based variable selection approach can reliably identify the determinants of MHT both statistically and pedologically. It can be used as a promising approach for revealing the predictor-response relationship, mapping the distribution patterns of MHT, and may provide a solution to map soil depth over large areas.