position, followed by aspect. So, we used these four effective 
variables to build and evaluate each model in three SDs (Table 
2). Overall, in three SDs, the best method for model building 
was DOMAIN (kappa = 0.83-0.87), followed by DT 
(0.72—0.80), MAXENT (0.63—0.68), ML (0.51—0.54) and DA 
(0.23—0.47) in order. SDs didn’t affect the model performance. 
It is clear to realize that DOMAIN, DT and MAXENT is 
efficient in converging the predicted distribution patter. 
Convergence of prediction helps ecologist to reduce the 
consuming of field survey. 
We used the independent samples aforementioned to evaluate 
the extrapolating ability, and the result is shown in Table 1. The 
result reveals that when evaluating model performance by 
independent samples, the kappa value of each model decreased 
sharply in both SD1 and SD2. In contrast, the kappa value of 
each model in SD3 declined slightly. The result in Table 3 
indicated that the model prediction of SD1 and SD2 could not 
extend through watershed. 
5. CONCLUSIONS 
The methods we adopted papered in different ratability, but the 
performance efficiency of these methods should be at the same 
grade. DOMAIN had the highest prediction accuracy, and the 
agreement of model performance between training dataset and 
test dataset in DOMAIN was also the best. Although the 
  
  
  
  
    
    
     
     
    
   
    
   
    
    
   
   
   
    
    
    
   
    
   
    
performance of DT and MAXENT were slightly lower than 
DOMAIN, but this three method had the same level of kappa 
coefficient in this study. Hence, we suggest that DOMAIN, 
DT and MAXENT a high potential in similar research and 
ecological application. 
The evaluation of SDs showed that it is hard to extend the 
distribution pattern through spatial (e.g. watersheds and 
mountain). The phenomenon is strongly established by the 
result of two-way extrapolation we designed. The result 
indicated that it is hard to extend the spatial patterns of JETs 
from one watershed to another and vice versa. By comparing 
the microclimate and micro-terrain of the two watersheds, the 
humidity and sunlight affected by micro-terrain of these two 
watersheds are remarkably different. ^ Consequently, the 
models merely based on topographic variables performed 
poorly on two-way spatial extrapolation between these two 
watersheds. Not surprisingly, the kappa values of predictive 
models developed from the merged samples of the two 
watersheds in SD3 just declined slightly. ^ The results 
suggested that the vegetation indices derived from SPOT-5 
images could not improve model accuracy for a widely 
distributed tree species due to the limitations of spectral 
resolution and spatial resolution with SPOT-S imagery. 
Follow-up studies will attempt to extract spectral information 
associated with species from hyperspectral data and LIDAR 
DEM and use it as variable for model development so that 
models are applicable on a broader spatial scale. 
  
  
Figure 2. Predicted distribution maps of SD3 included (a) DA, (b) DT, (c) MAXENT, (d) ML, and (e) DOMAIN. 
  
      
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