build predictive models. (5) The multi-modeling assessment 
approach was performed in this study. This included the 
application of a single model to data describing patterns at 
different spatial scales and the comparison of several models 
using a common dataset. 
2. STUDYAREA 
We chose a rectangular study area, encompassing the Huisun 
Forest Station, and it has a total area of 17,136 ha. The 
Huisun Forest Station is in central Taiwan, situated within 
242'-24 5' N latitude and 1213-121 7' E longitude (Figure 
1) This station is the property of National Chun-Hsing 
University. The entire study area ranges in elevation from 454 
m to 3,418 m, and its climate is temperate and humid. In 
addition, the study area has nourished many different plant 
species more than 1,100 and is a representative forest in Taiwan. 
It comprises five watersheds, including two larger watersheds, 
Kuan-Dau at west and Tong-Feng at east. So far, all of the 
Chinese guger-tree samples (in situ data) were collected from 
the Tong-Feng, Yo-Shan, and Kuan-Dau sites in the Huisun 
study area by using a GPS. 
  
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Figure 1. Location map of the study area 
3. MATERIALS AND METHOD 
3.1 Species occurrence data 
We collected in situ CGTs data by using a GPS linked with a 
laser range, and then performed a post-processed differential 
correction that makes them have an accuracy of sub-meters. 
The dataset was eventually converted into ArcView shapefile 
format for later use. So far, CGT samples were collected from 
Tong-Feng (122), Yo-Shan (8), and Kuan-Dau (64) sites in the 
Huisun study area, respectively.  Pseudo-absences were 
generated for those models that required them (all except 
DOMAIN) by taking 500 samples randomly in study area. 
Three sampling designs (SD) were created for model 
development and validation through different combinations of 
CGT samples from aforementioned three sites (see figure 1). 
SD-1: we randomly selected two-thirds of Tong-Feng dataset 
for building “Tong-Feng base model" and the remaining 
one-third of that dataset for model validation. 
SD-2: we used the same base model built in SD-1 and only 
used samples taken from Yo-Shan about 0.5 km away from the 
Tong-Feng site to test the base model. 
    
    
   
  
  
    
    
   
    
    
   
   
   
   
   
   
   
   
    
   
  
  
  
  
     
   
   
    
   
  
  
   
   
    
  
    
   
   
      
   
   
    
   
    
   
    
     
    
     
  
SD-3: we still used the same base model in SD-1 and only used 
samples taken from the Kuan-Dau site about 5 km away from 
Tong-Feng site to test the base model. Then we evaluated the 
spatial extrapolation ability of the four models. 
3.0 Environmental data 
We collected digital elevation model (DEM) of 5 m resolution, 
orthophoto base maps (1:10,000), and two-date SPOT images. 
DEM was acquired from the Aerial Survey Office, Forestry 
Bureau of the Council of Agriculture, Taiwan. To meet the 
requirements of the study, the DEM was interpolated into 5 x 5 
m grid size, geo-referenced to the coordinate system, TWD67 
(Taiwan Datum, spheroid: GRS67) and Transverse Mercator 
map projection over two-degree zone with the central meridian 
121°E. The two-date SPOT-5 images were acquired from 
Center for Space and Remote Sensing Research, National 
Central University (CSRSR, NCU), Taiwan (O SPOT Image 
Copyright 2004 and 2005, CSRSR, NCU). System calibration 
and geometric correction with level 2B were performed on the 
images, and then they were rectified to the TWD67 Transverse 
Mercator map projection and resampled to 5 m resolution to be 
consistent with the layers from DEM. We chose the two-date 
SPOT-5 images (07/10/2004 and 11/11/2005) because they 
have the best quality with the amount of clouds less than 10%. 
Elevation, slope, and aspect were generated from DEM by 
ERDAS Imagine software module, and hill-shade data layer by 
ArcGIS spatial analyst module. The ridges and valleys in the 
study area were used together with DEM to generate terrain 
position layer. The main ridges and valleys over the study 
area were directly interpreted from the orthophoto base maps; 
these lines were then digitized to establish the data layer by 
using ARC/INFO software for later use. The data layer in a 
vector format was then converted into a new data layer in a 
raster format by ERDAS Imagine software module, and then 
combined with DEM to generate terrain position layer 
(Skidmore, 1990). Vegetation indices were derived from the 
two-date SPOT images, one in autumn (11/11/2005), the other 
in summer (07/10/2004), based on the concepts stated in Hoffer 
(1978), and is expressed in equation (1): 
NIR rom E MIR um (1) 
NIR — MIR 
summer summer 
3.3 Model development 
Predictive distribution models were formulated using the four 
different modeling algorithms. The modeling algorithms are 
briefly described below. 
We implemented maxent entropy using version 3.3.3 of the free 
software developed by S. Phillips and colleagues 
(http://www.cs.princeton.edu/-schapire/maxent/). And other 
methods implemented ModEco by using version 1.0 of the free 
software (http://gis.ucmerced.edu/ModEco/). 
1) MAXENT can make predictions or inferences from 
incomplete information (Phillips e£ a/., 2006), and may remam 
effective from small sample sizes (Kumar and Stohlgren, 2009). 
The principle of MAXENT is based on the concepts of 
thermodynamic entropy, and then is used to describe the 
probability distribution in several domains, and Bayesian 
statistics is for exploring the probability distribution of each 
  
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