3.3. Relationship between topographic heterogeneity, soil
type and fertility, human-influence factors and richness pattern
Of the total twenty-three variables, the explanatory variables of
topographic heterogeneity, specifically ELER and ELER-SD, were the
strongest predictors and showed a positive relationship to all richness
groups (Table 1). ELER explained 32-36%, whereas ELER-SD explained
30-33% of the total variance in species richness. Most of the soil
fertility and nutrient availability variables showed positive and
stronger relationship more than soil texture variables, particularly
carbon content (ORCDRC), available nitrogen (AN) and CEC were the
second-best predictors of conifer richness, and they contributed
24-31%, 14-29%, and 21-26% respectively (Table 1).
Variables of soil texture type, particularly clay (10-27%) and sand
(11-23%), contributed much higher than coarse and silt, which explained
8-12% and 2-7%, respectively. Individually, clay and silt soil types
showed a negative relationship to all richness groups; while, sand and
coarse fractions showed a positive relationship (Table 1). The
contribution of the human-influence variable was much lower for all
groups showing a negative relationship (Table 1). The proportion of
variance using extracted principal components explained by the
individual environmental and human-influence predictors showed the
significant role of topographic heterogeneity and soil fertility in
determining species richness (Fig. 2). Topographic heterogeneity was the
strongest predictor of conifer richness followed by soil fertility and
nutrient availability for all richness groups.
Hierarchical partitioning suggested that topographic heterogeneity and
soil nutrient-fertility had the highest independent effects on all
species richness, while they had the highest joint effect on
endemic-threatened species richness (Fig. 3a and d). Soil type
predictors showed a high joint effect for all species richness groups
(Fig. 3). It is worth to note the collinearity between topographic
heterogeneity and soil nutrient-fertility variables (see Table S4,
Appendix A), which can influence the result interpretation. The combined
models produced using stepwise regression (GLM) showed that ELER and
soil available nitrogen were consistently significant predictors
representing topographic-soil heterogeneity (Table 2). The variance
inflation factor (VIF) of each predictor of the four combined models of
richness groups was less than 5, which indicates insignificant
multicollinearity between the predictors in the models. It is worth to
note that when we excluded the human influence predictor (HII) from the
models, the same combination of environmental predictors was resulted
based on the lowest Akaike information criterion (AIC) indicating to the
relevant collaborative interaction between environmental factors;
meanwhile, the less importance of the human factor. The developed models
moderately predicted the conifer richness for all groups.