The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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calculated. 2) From the Global Map Japan, the main streams of 
rivers were derived in a national scale of 1:250,000. In order to 
acquire the streams in a regional scale of 1:25,000, the DEM 
was used to calculate river streams using the Hydrology tool in 
ArcMap R 9.0’s Spatial Analyst. Next, the two river streams 
were combined and the distance to streams variable was calcu 
lated by the Euclidean distance in the Spatial Analyst of 
ArcMap®. 3) All climatic variables were derived in ESRI grid 
format at a resolution of 1 km by 1 km (30 arc-seconds) from 
WorldClim database (Hijmans et al., 2005). The annual precipi 
tation was calculated from the precipitation data for 12 months. 
The minimum - and maximum temperature per month was also 
derived, and from that data the annual minimum - and maxi 
mum temperature was calculated. 4) The data of the roads were 
decoded to the XML files, and then converted to shape files in 
ESRI formats. Six different layers of roads were prepared based 
on types and width (See Table 1 for detail categorization.) The 
distance to each road was calculated by the same method as for 
river streams. 5) Vegetation cover was directly derived from the 
dataset for GIS on the Natural Environment, Japan (CD-Rom; 
the source is indicated in Table 1) and contains 57 classes at 
species level; species level is from this dataset. Four scenes of 
Landsat-7 ETM+ imagery (dated 12 Oct 1999, 13 Nov 1999, 8 
Nov 2000, and 24 Sep 2001) were geo-referenced with a maxi 
mum error of 100m and ortho-rectified. Scenes were mosaicked 
by histogram matching using overlapping areas. After creating 
a subset of a mosaicked image, the NDVI was calculated and 
resampled. 
2.3.3 Preparation for test and train data 
Plotting 1861 species records, 25 records fell outside the study 
area. These records were discarded so that the total number be 
came 1836. From all layers of environmental predictor vari 
ables, pixel values were extracted for the geographic coordi 
nates of species’ presence and absence records by ArcMap 9.0 
R ’s in-built function. 
Subsequently the dataset, containing species presence-absence 
records, geographic coordinates, and each environmental pre 
dictor’s value, was split into a train and a test dataset. The train 
dataset was used to make predictive models. Then, the test data 
set was used to assess the accuracy of these models. An inde 
pendent dataset is ideal for testing the models. Thus for Asiatic 
black bear, all records in the South Alps were used for training 
and all records in Fuji area were used for testing the models. By 
this method, also the model’s transferability can be tested 
whether models developed on one local population, South Alps, 
can be applied to another neighboring local population in Fuji. 
To maintain a balance in presence and absence data, approxi 
mately the same number of records was taken from absence 
data for training and testing. Because no independent dataset 
for the Japanese serow was available, its records were randomly 
partitioned into two subsamples. One subsample was used as 
the train dataset and another subsample was kept for testing 
models. This method is known as “split-sample approach” 
(Guisan and Zimmermann, 2000). 
2.3.4 Statistical analysis for screening predictors 
The choice of predictors is a major concern for building any 
predictive model. Therefore a set of chosen predictors was 
screened prior to creation of the predictive models using statis 
tical analysis with R-software, version 2.4.0. 
Inter-correlation among environmental predictors may cause 
bias, such as overfit and multicollinearity (Grahama, 2003). 
Because the environmental variables were not normally distrib 
uted, the Spearman's rank correlation coefficient p was adopted. 
In the preliminary data survey, we eliminated high collinearity 
within the environmental variables (exclusion of the variables 
in case of the Spearman p > 0.85). A similar approach was car 
ried out in studies of habitat-models by Bonn and Schroder 
(2001) and Fielding and Haworth (1995). 
Jackknife tests were carried out to determine the relative impor 
tance of variables by running an “experimental” model of Max- 
Ent with all environmental variables. The variables which did 
not have relative importance were eliminated. 
The screened variables were used for building the final models 
and are shown in Table 1. 
2.4 Modeling 
There are a number of modeling techniques and algorithms to 
predict the probability of species occurrences by the environ 
mental variables as limiting factors for species’ survival. Three 
modeling algorithms: GARP - Genetic Algorithm for Rule-set 
Production (Stockwell and Peters, 1999), MaxEnt -Maximum 
Entropy (Phillips et al., 2006), and GLMs - Generalized Linear 
Models (logistic regression models) (Nelder and McCullagh, 
1989) were used in this research. 
2.5 Validation and comparison of the predictive models 
The accuracy of the predictive models was measured using the 
test dataset by the Kappa statistics (Landis and Koch, 1977). 
The pixel values of the predictive maps generated by different 
modeling algorithms were extracted to the points of both train 
and test datasets for each species respectively by ArcMap 9.0*. 
A database with presence-absence data (value is either 0 or 1) 
as the ground truth, and predicted values by each modeling 
algorithm was prepared for each test dataset respectively. 
2.6 Estimation of population size within habitat patches 
Based on the comparison of the predictive models, the predic 
tive maps by the best modeling algorithm for each species were 
chosen to estimate the population size. First, the predictive 
raster maps were reclassified into predicted presence and pre 
dicted absence using optimum probability as cutoff values. The 
predicted present location was considered to represent the “core 
area” which may consist of the ecological networks if needed. 
Then, the reclassified raster maps were converted to the 
ESRI®’s shape files in order to calculate the area of core area in 
km 2 using ArcMap“ 9.0’s VBA built-in function. Finally the 
population of the target species was estimated based on known 
population density and area in km 2 , derived by a following 
equation. 
N = A*PD 
(Equation 1) 
where N is estimated population (head-count), A is area (km 2 ), 
and PD is population density (head-count/km 2 ). 
2.6.1 Estimation of population for the Asiatic black bear 
The home range of Asiatic black bear is known to vary between 
50 km 2 and 70 km 2 for adult male and approximately 30 km 2 
for adult female (Yoneda, 2001). Considering this minimum 
home range, patches smaller than 30 km 2 were eliminated from 
the core area and the other patches were grouped into potential 
suitable habitat patches. Since their home ranges are not exclu-