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Data Processing, Algorithm and Modelling
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An auto-multivariate model of muntjacs habitat use for a geographic information system in southern Taiwan
However, since the logistic predicts the OI values which gives the distribution of suitability of habitat instead
of the absent/present of Muntjac, if the distance to observed OI value was to be used, the matching
coefficient increased to 85% (Table 3).
Table 3. Matching counts of differences between predicted sample and observed sample for logistic model
Simple matching coefficient=86/103=0.84
*: 5 samples were matched exactly
Overall, the auto-multivariate model of Reeves’ Muntjac agrees with field survey and field experience,
therefore, provides good estimation of distribution. In this study, landscape variables are proofed to be
useful explanatory variable for Muntjac distribution model. Part of the reason for the high prediction rate of
using landscape variables only may because of Muntjac is a more common species that is not very selective
for their habitat. If habitat use of other species were to be assessed, other explanatory variables such as
vegetation species and vegetation structure may have to be included.
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