Identification of the important environmental factors influencing natural vegetation succession following cropland abandonment on the Loess Plateau, China

Abstract

Identification of typical vegetation succession types and their important influencing factors is an important prerequisite to implement differential vegetation and soil management after land abandonment on the Loess Plateau, China. However, there is no reported study specifically on the identification of vegetation types and their important factors as well as the thresholds of the important factors for classification of the vegetation types, based on the medium- to long-term succession of natural vegetation after cropland abandonment. We collected vegetation and soil data on the natural vegetation with the longest 60-year-old forest communities that developed after cropland abandonment and analyzed the data using two-way indicator species analysis, detrended correspondence analysis, direct canonical correspondence analysis and classification tree model. The vegetation communities were classified into five distinct vegetation types, including Artemisia scoparia, Lespedeza davurica and Stipa bungeana, Artemisia giraldii pamp, Sophora viciifolia, Quercus liaotungensis and Biota orientalis. The years after cropland abandonment and soil C/N were further identified as important factors determining the types of vegetation. Likewise, it was observed that most of the investigated soil nutrient variables and soil texture-related variables improved with the vegetation succession while soil water in the surface layers showed a decreasing trend. These findings may provide an ecological basis for site-specific management of vegetation types after cropland abandonment in the medium-long term on the Loess Plateau. Our results encourage further exploration of vegetation succession and their important factors based on longer periods of vegetation succession after cropland abandonment under more soil and climatic conditions on the mountainous areas as the Loess Plateau.

Examining long-term natural vegetation dynamics in the Aral Sea Basin applying the linear spectral mixture model

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Stochastic Processes Shape Bacterial Community Diversity Patterns along Plant Niche Gradients

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