Gradients analyses of forests ground vegetation and its relationships to environmental variables in five subtropical forest areas, S and SW China
Authors: Liu H. , Økland T. , Halvorsen R. , Gao J. , Liu Q. , Eilertsen O. , Bratli H. 
Published: October 2011

Gradients analyses of forests ground vegetation and its relationships to environmental variables in five subtropical forest areas, S and SW China

Monitoring of ground vegetation and environmental variables in subtropical forests in China was initiated in 1999 as part of the "Integrated Monitoring Programme of Acidification of Chinese Terrestrial Systems". The study areas were selected to span regional gradients, in deposition of airborne pollutants and climatic conditions. All five study areas are located in the southern and south-western parts of China and consist of subtropical forests. In each study area 50 1-m2 plots were randomly chosen within each of ten 10×10 m macro plots, each in turn positioned in the centre of 30×30 m extended macro plot. All 250 1-m2 plots were subjected to vegetation analysis, using frequency in subplots as measure of species abundance. A total of 33 environmental variables were recorded for 1-m2 plots as well as 10×10 m macro plots. A major objective of this study is to identify the environmental variables that are most strongly related to the species composition of ground vegetation in S and SW Chinese subtropical forests, as a basis for future monitoring.

Comparison among DCA, LNMDS and GNMDS ordination methods, an additional objective of the study, was achieved by using a set of different techniques: calculation of pair-wise correlation coefficients between corresponding ordination axes, Procrustes comparison, assessment of outlier influence, and split-plot GLM analysis between environmental variables and ordination axes. LNMDS and GNMDS consistently produce very similar ordinations. GNMDS ordinations are generally more similar to DCA than LNMDS to DCA. In most cases DCA, LNMDS and GNMDS extract the same main ground vegetation compositional gradients and the choice of LNMDS or GNMDS is therefore hardly decisive for the results. GNMDS was chosen for interpretation and presentation of vegetation-environment relationships. The dimensionality of GNMDS (number of reliable axes) was decided by demanding high correspondence of all axes with DCA and LNMDS axes. Three dimensions were needed to describe the variation in vegetation in two of the areas (TSP and LXH), two dimensions in the other three areas (LCG, LGS and CJT).

Environmental interpretation of ordinations (identification of ecoclines; gradients in species composition and the environment) was made by split-plot GLM analysis and non-parametric correlation analysis. Plexus diagrams and PCA ordination were used to visualize correlations between environmental variables. Several graphical means were used to aid interpretation.

Complex gradients in litter-layer depth, topography, soil pH/soil nutrient, and tree density/crown cover were found to be most strongly related to vegetation gradients. However, the five study areas differed somewhat with respect to which of the environmental variables that were most strongly related to the vegetation gradients (ordination axes). Litter-layer depth was related to vegetation gradients in four areas (TSP, LCG, CJT and LXH); topography in four study areas (TSP, LGS, CJT and LXH); soil pH in three areas (LCG, LGS and CJT); soil nutrients in one area (LGS); and tree density/crown cover in one area (LCG).

The ecological processes involved in relationships between vegetation and main complex-gradi-ents in litter-layer depth, topography, soil pH/soil nutrient, and tree density/crown cover, in subtropical forests, are discussed. We find that gradient relationships of subtropical forests are complex, and that heavy pollution may increase this complexity. Furthermore, our results suggest that better knowledge of vegetation-environment relationships has potential for enhancing our understanding of subtropical forests that occupy vast areas of the S and SW China.