The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
Serow feeds on six plant groups: the deciduous broad-leaved 
tree, the evergreen broad-leaved tree, the conifer, the forb, the 
graminoid and the fern (Deguchi et al., 2002). According to 
Matsumoto et al. (1984), the Japanese serow ate 37 plant spe 
cies in 28 families including grass and tree species, whereas ac 
cording to Yamaguchi et al. (1998) bamboo species are part of 
their diets. According to a study of habitat selection between 
the Sika deer and the Japanese serow (Nowicki and Ko- 
ganezawa, 2001), serow selects steep slopes and areas close to 
roads, seemingly in order to avoid Sika deer. The Japanese se 
row is known as a species that prefers habitats of greenery steep 
slopes in a hillside, from 300 to 800 m (Yamaguchi et ah, 1998). 
2.3 Data management 
2.3.1 Species’ records extraction 
Species’ presence data consist of localities of 1) 14 tracked Asi 
atic black bears in the South Alps region (715 points) and 56 
observed field signs of Asiatic black bear in the Fuji region 
(Mochizuki et ah, 2005) and 2) 160 observed Japanese serow in 
Tanzawa region (Yamaguchi et ah, 1998). In total 49 paper 
maps of the above mentioned sources were scanned; four maps 
for bears of the Fuji local population, 44 maps for bears of the 
South Alps local population, and one map for Japanese serow in 
Tanzawa Mountains. Next, the scanned maps were geo- 
referenced respectively. The 1 st order polynominal affine trans 
formation was conducted to rectify the scanned images within 
50 m of the total RMS error for the bear's maps. The maximum 
total RMS error was 251 m for the distribution maps of the 
Japanese serow. Thereafter, the rectified images were used to 
extract species observation points. 931 points were digitized 
manually. Geographic coordinates were calculated from point 
features by the ArcMap’s VBA built-in function. 
The national distributional map of Japanese mammals 
(Biodiversity center of Japan, 2004) was used to create points 
representing species’ absence data. It was based on interviews 
and questionnaires in 1978 and in 2003 for grids of 5 by 5 km. 
Following Corsi et ah (1999), any area where no evidence of 
stable target species’ presence had been gathered in the last 26 
years has been defined as species’ absent area. Each image was 
geo-referenced using the intersections of administrative 
boundaries, whereafter the species’ known presence and ab 
sence areas was digitized as a polygon. Random points distrib 
uted within the absent range for each species were considered 
to represent each species’ absence data. To ensure a balance in 
the number of species’ presence and absence records, the same 
number of records was plotted for each species: 770 random 
points for the Asiatic black bear and 160 random points for the 
Japanese serow. 
2.3.2 Geo-database for predictors 
Based on known Asiatic black bear’s and Japanese serow’s 
ecology, the selected environmental predictors can be catego 
rized in five groups: 1) topographical variables, 2) water-related 
variables, 3) climatic variables (Guisan and Zimmermann, 
2000), 4) variables related to roads (Okumura et ah, 2003), 5) 
variables related to vegetation (Flashimoto et ah, 2003; Huy 
gens et ah, 2003). All variables were obtained digitally from 
various sources (Table 1) and stored in a GIS environment. 
Considering the relatively small size of important landscape 
elements in the Japanese landscape and the high precision of 
the species’ records, all predictor variables were compiled at a 
resolution of 30 by 30 m. For calculating NDVI value, Erdas 
Imagine“ 8.7 was used, and for other variables, ArcGIS* 9.0 
was used. 
1) From SRTM, altitude data were resampled at a target resolu- tion by the nearest neighboring. From this data, slope angle was 
Category (Source) 
Environmental predictor 
Unit 
Asiatic black bear 
Japanese serow 
Garp 
Maxent 
Glm 
Garp 
Maxent 
Glm 
Topography (*') 
Altitude 
m 
X 
X 
X 
X 
X 
X 
Slope 
O 
X 
X 
X 
Water resources 
Distance to river streams 
m 
X 
X 
Climate (* 3 ) 
Annual mean precipitation 
mm 
Annual minimum temperature 
°C 
Annual maximum temperature 
°C 
Roads (* 4 ) 
All 
Distance to all roads 
m 
Type 
Distance to highways 
m 
X 
X 
Distance to general roads 
m 
X 
X 
Distance to paths and stone steps 
m 
X 
X 
X 
X 
X 
Width 
Distance to wide roads (more than 13 m) 
m 
X 
X 
Distance to narrow roads (less than 13 m) 
m 
Vegetation 
(* 5 for NDVI and * 6 
for vegetation cover) 
NDVI 
- 
X 
X 
Vegetation cover types (at species level) 
— 
X 
*' The CGIAR Consortium for Spatial Information. "SRTM (Shuttle Rader Topography Mission)" 
http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp (accessed April 2008). 
* 2 Geographical Survey Institute of Japan, 2006. “Download of the Global Map Japan Version 1” 
http://wwwl.gsi.go.jp/geowww/globalmap-gsi/download/index.html (accessed April 2008). 
* 3 WorldClim, “WorldClim Version 1.4”, http://www.worldclim.org/ (accessed April 2008). 
* 4 The National Land Agency, 2003. Digital Map 25000 (Spatial Data Framework) KANAGAWA/ SHIZUOKA (CD-Rom). 
* 5 Earth Science Data Interface, “GLCF(Global Land Cover Facility): Earth Science Data Interface”, 
http://glcf.umiacs.umd.edu/data/ (accessed April 2008). 
* 6 Nature Conservation Bureau in Ministry of the Environment, 1997. 14 KANAGAWA/22 SHIZUOKA The dataset for GIS on the Natural Envi 
ronment, Japan. Nature Conservation Bureau in Ministry of the Environment (CD-Rom). 
Table 1 Potential spatial predictors and selected predictors for final models