The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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2.6.2 Estimation of population for the Japanese serow 
The Japanese serow is known as a solitary ungulate (Ochiai and 
Susaki, 2002). The typical mating unit consists of a monoga 
mous pair (1 male with 1 female), but polygamous units (1 
male with 2 or 3 females) also exist (Ochiai and Susaki, 2002). 
The territory size is larger for males (10.4 ha to 22.8 ha) than 
for females (6.9 ha to 14.1 ha) (Ochiai and Susaki, 2002). Con 
sidering this minimum territory size, patches smaller than ap 
proximately 6.9 ha were eliminated from the core area and the 
other patches were grouped into potential suitable habitat 
patches. Ochiai et al. (1993a) found that a serow population 
maintained a stable density in a stable environment, in which 
food supply remained fairly constant. Contrary to this, in an un 
stable environment in which food supply fluctuated signifi 
cantly the serow density was also fluctuating (Ochiai et al., 
1993b). For instance, the density was stable from 11.7 to 16.7 / 
km 2 in a stable environment in Aomori Prefecture (Ochiai and 
Susaki, 2002), but on the other hand it did not exceed 1.0 / km 2 
in a competitive environment with Sika deer in Nikko National 
Park (Nowicki and Koganezawa, 2001). In this research, it is 
assumed that the Japanese serow occurs in a competitive envi 
ronment with Sika deer in the study area, based on the known 
national distributional maps of Japanese serow and Sika deer 
(Biodiversity center of Japan (2004) and Ohba, (2002)). From 
the maps in Aomori Prefecture, it looks like that there was no 
competition between the Japanese serow and Sika deer. Con 
trary to this, in Nikko National Park and in the study area, the 
habitat seems to be shared with Sika deer. Therefore, the popu 
lation was estimated based on the population density found in 
Nikko National Park (Nowicki and Koganezawa, 2001). 
2.7 Needs assessment for ecological networks 
Inbreeding and loss of genetic diversity is a conservation con 
cern as they increase the risk of extinction. Inbreeding increases 
the risk of extinction in captive populations, and there is now 
strong evidence that it is one of the factors causing extinctions 
of wild populations (Frankham, 2003). Loss of genetic diversity 
reduces the ability of species to evolve and cope with environ 
mental change. Inappropriate management and allocation of re 
sources is likely to result in endangering the animal populations, 
if genetic factors are ignored in management of threatened spe 
cies (Frankham, 2003). Because of lack of accurate data con 
cerning the minimum population size to be sufficient to main 
tain healthy local population for each species, the assessment 
criteria, established by the Ministry of Environment of Japan 
(Nature Conservation Bureau in Ministry of the Environment, 
2000), was followed: the habitat patches with a population 
number under 100 were labeled as patches in a serious danger 
of extinction, the habitat patches with a population size from 
100 to 400 as endangered patches, and those with a population 
size over 400 as healthy patches. 
3. RESULTS 
3.1 Asiatic black bear 
3.1.1 The best logistic regression model (GLM) 
The best logistic regression model for Asiatic black bear was 
considered to be predicted by distance to paths and stone steps 
and altitude without interaction, derived by the following pre 
dictive equation: 
log(p/(l -/>))=(- 1.486e+01)+(7.335e-04)*jc/ + (9.470e-03)*x 2 
(Equation 2) 
where x, is the distance to paths and stone steps (m), x 2 is alti 
tude (m) and p is the probability of Asiatic black bear’s occur 
rences. 
3.1.2 Accuracy assessment and comparison of models 
Table 2 shows the results of accuracy assessment by Kappa sta 
tistics. As in GARP all actual presence points were predicted 
absent wrongly in the test dataset, the Kappa showed no dis 
crimination (AM)). For the train data, at a threshold of opti 
mized probability, all modeling algorithms’ prediction was al 
most perfect in Kappa statistics (AM).98 for MaxEnt, K= 1 for 
GARP, AM).99 for GLM). Compared to a threshold of the 
probability of 0.5, all indices were much better in optimized 
probability. At a threshold of optimized probability of the test 
data, all indices except prevalence index scored better in Max 
Ent than GLM. In conclusion, the accuracy assessment of three 
modeling algorithms showed that MaxEnt was performing 
slightly better than GLM for predicting Asiatic black bear’s dis 
tribution while GARP failed to predict species’ occurrences in 
the test area. 
3.1.3 The estimated population size 
Figure 1 (a) shows the potential suitable habitat patches for 
Asiatic black bear predicted by MaxEnt, which was the most 
accurate among the three algorithms. The grouped patches were 
considered to represent the local population or sub-local popu 
lation of the Asiatic black bear. The population within each 
habitat was estimated as indicated in Table 3. These results 
showed that there are six main patches in the study area. 
In the South Alps region, the estimated population size was 
from 160 to 320. The map showed a united large habitat (1066 
km 2 ) in this region. It is known that Fuji local population of the 
Asiatic black bear consist of four sub-local populations: Mt. 
Fuji, Mt. Ashitaka, Mt. Furo, and Mt. Kenashi (Ohba and 
Mochizuki, 2001). As for the area of interest, a linear corridor 
seemed to exist to connect two local populations: the Fuji local 
population and the Tanzawa local population. The estimated 
population size was 51 to 102. From the map, it seemed that the 
connected patch contained the Tanzawa local population, sub 
local population of Mt. Fuji, and Mt. Furo. However, the 
predictive map showed that Mt.Ashitaka and Mt. Kenashi were 
isolated from other sub-local populations. The estimated 
population size in Mt. Kenashi ranged from 4 to 8; and in Mt. 
Ashitaka from 5 to 9. The predictive map also showed species’ 
suitable habitat patches in Izu Peninsula and Hakone volcano. 
Compared to the habitat in South Alps and the one in Fuji- 
Tanzawa, the habitats in this area seemed to be small and 
fragmented. Estimated population size in Izu Peninsula was 4 to 
8 and in Hakone volcano 6 to 11. A map, which overlays the 
boundaries of Fuji-Hakone-Izu National Park and Tanzawa 
Quasi-national Park, with environmental predictors used for the 
bear in MaxEnt, showed that the potential suitable habitat 
patches were geographically distant from the wide roads, paths 
and stone steps. 
The bear’s predicted distribution was almost covered by the Na 
tional Park and Quasi-national Park. However, the habitats 
around Mt. Ashitaka were not covered by the Fuji-Hakone-Izu 
National Park. Even more, the patch which consists of a linear 
corridor between Fuji and Tanzawa was not covered by the 
Fuji-Hakone-Izu National Park and Tanzawa Quasi-national 
Park. In general, the predicted distribution of the bear ranged 
from 600 to 1800 m both in South Alps and in Fuji-Tanzawa 
regions. Around Mt. Fuji the predicted habitat was located