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split-sample evaluation. We used 2/3 of all as the training 
dataset for modeling, and used the remaining 1/3 as test dataset 
for model evaluation. To build the predictive models for each 
SD, we used five methods, DA, DT, MAXENT, ML, 
DOMAIN. 
3.5.1 Discriminant Analysis (DA): DA is an algorithm that 
tries to find the most robust boundary within variables for 
group participation. A grouping variable and few discriminant 
variables are implemented in DA to establish the discriminant 
function to participate the original samples into few categories 
(Lowell, 1991). The following equation is the typical structure 
of discriminant function. 
Y=bp tb Xt bXy ti. ob Xt... HAN 
Where Y = the grouping variable 
Xi = discriminant virables 
3.5.2 Decision Tree (DT): DT (also called Classification and 
Regression Trees, CART) is a non-parametric classification 
algorithm for data mining with both classifying and predicting 
capability. DT could build classified rules from observations 
or some experiences (Guisan and Zimmermann, 2000). 
Decision tree algorithm sequentially partitions the dataset with 
some important predictors in order to maximize differences on 
a dependent variable. The decision pathways originate from a 
starting node (root) that contains all observations, then classify 
step by step into binary subsets based on the important 
predictors, and so on. Finally, it will end at multiple nodes 
containing unique subsets of observations. Terminal nodes 
are assigned a final outcome based on group membership of the 
majority of observations (De’ath and Fabricius, 2000; Bourg ef 
al., 2005; O’Brien et al., 2005). 
3.5.3 Maximum Entropy (MAXENT): One of novel methods 
used in ecology field is MAXENT. It can build robust and 
stable prediction models by applying incomplete information 
and small sample size (Kumar and Stohlgren, 2009; Phillips ef 
al, 2006). Entropy means the uniform condition in 
thermodynamic. The axiom of MAXENT is to searching the 
maximum entropy of species distribution under limited 
conditions. When reaching the maximum entropy, the species 
distribution is similar to the natural condition. 
~~ Max, = Min, 
> ES 
P(x)= | À, duisi mm, - linear PredietorNormalizer‘ / Z 
Where f, (x)— min, : hinge feature; 
max, —min, 
^, : weight coefficient 
Linear Predictor Normalizer: a constant for numerical stability 
Z: a scaling constant that ensures that P sums to 1 over all grid 
cells 
The MAXENT software is free and online available 
(http;/www.cs.princeton.edu/-schapire /[MAXENT). 
3.54 Maximum Likelihood (ML): ML is a widely used 
method in classification algorithm (Wu and Shao, 2002; Mclver 
and Fridel, 2002). ML algorithm is based on the probability 
to assign the pixel to one of the predefined Kk class with 
   
   
  
     
    
   
    
   
   
   
  
  
   
    
    
   
    
   
  
  
    
   
    
   
  
     
  
   
   
    
    
    
   
     
  
   
    
   
   
      
maximum likelihood (Atkinson and Lewis, 2000; Lo and 
Yeung, 2002). 
3.5.5 DOMAIN: This method assigns a classification value to 
the candidate area according to a point-to-point similarity 
metric, and also base on this criterion to find the area where 
environment is similar with the sample data. Sum of the 
standardized distance between two points of each environment 
variable is used to quantify the similarity. And equalization of 
variable contribution is achieved by standardizing the 
environment variables. The classification value of each pixel 
in the study area is decided by the maximum similarity between 
each pixel a set of data points. It is necessary to set a 
similarity threshold to converge the predicted distribution 
pattern (Carpebter et al, 1993; Hernandez et al, 2006). In 
this study, the similarity threshold was set in 0.97, in which the 
kappa coefficient was reasonable. 
3.6 Model Evaluation and Assessment 
The test and training data sets are used to evaluate the model 
performance and reliability. In each data set, the evaluation 
indices contain producer's accuracy (PA), user's accuracy (UA) 
and overall accuracy (OA). Kappa agreement coefficient is 
extremely important to assess the agreement between predicted 
map and reference test dataset. The kappa coefficient 
compares the marginal and diagonal value in matrix fairly due 
to the calculation containing not only PA and UA but also OA 
(Referred and Fitzpatrick-Lins, 1986; Congalton, 1991; Paine 
and Kiser 2003). Furthermore, in the model evaluation of 
“Tong-Feng (SD1)” model, the test dataset of “Kuan-Dau” JET 
samples were used as independent samples to evaluate the 
ability of extrapolating predicting model through space. 
Again, we treat the same process evaluating “Kuan-Dau (SD2)” 
model with the test dataset of “Tong-Feng” samples. In the 
evaluation in “merged samples of two watersheds (SD3)” 
model, we split the test set into two subsets according to the 
watersheds’ boundary. The two subsets of SD3's test sample 
were used as two independent sample sets to demonstrate that 
the model performance was still reasonable when using these 
two subsets solely. 
4. RESULTS AND DISCUSSION 
We calculated the statistics of five environmental factors 
corresponding to the entire study area and all of the JET 
samples in two watersheds and compared the difference in 
statistics between them, as shown in Table 1. The elevation 
range of the “Tong-Feng” and “Kuan-Dau” JET samples 
(1,122-2,027 m and 1,076—1,559, respectively) were within the 
nature distribution range, from low elevation to 2,200 m above 
sea level. The means of slope statistics in "Tong-Feng" and 
“Kuan-Dou” samples were 22° and 27°, respectively. The 
mean slope of all JET samples is obviously lower than that of 
the entire study area; consequently, this result is due to the 
nature behavior of JET. JETs prefer to grow on the flat areas 
beside ridges with unclosing canopy structure, where they are 
illuminated by abundant solar radiation. This behavior could 
be demonstrated by the mean of terrain position statistics. 
The predicted distribution maps of SD3 used to represent 
overall prediction showed in Figure 2. At the earlier stage of 
this result, each method eliminated vegetation index from the 
effective variables because the contribution of vegetation index 
in model performance is less than 1 percent. The most 
important effective variables were slope, aspect and terrain