Spatial Prediction Models for Soil Stoichiometry in Complex Terrains: A Case Study of Schrenk’s Spruce Forest in the Tianshan Mountains

Abstract

Spatial patterns of soil carbon (C), nitrogen (N) and phosphorus (P) and their stoichiometric characteristics (C:N:P) play an important role in nutrient limitations, community dynamics, nutrient use efficiency and biogeochemical cycles, etc. To date, the spatial distributions of soil organic C at various spatial scales have been extensively studied, whereas little is known about the spatial patterns of N and P and C:N:P ratios in various landscapes, especially across complex terrains. To fill this gap, we estimated the spatial patterns of concentrations of soil C, N and P and C:N:P ratios in Schrenk’s spruce (Picea schrenkiana Fisch. & C. A. Mey.) forest in the Tianshan Mountains based on data from soil cores collected from 2012 to 2017, and using the following four regression models: multiple linear regression, stepwise regression, ridge regression and lasso regression. We found the following: (1) elevation and climatic variables jointly contributed to concentrations of C, N and P and C:N:P ratios, (2) soil C, N and P concentrations, and their stoichiometric ratios, demonstrated continual spatial patterns in Schrenk’s spruce forest, (3) Multiple linear regression could be reliably used to estimate the spatial patterns of soil elemental concentrations and stoichiometric ratios in mountainous terrain. We suggest that more independent variables (including biotic, abiotic and anthropogenic factors) should be considered in future works. Additionally, adjustment of multiple linear regression and other models should be used for a better delineation of spatial patterns in the concentrations of soil elements and stoichiometric ratios.