An auto-multivariate model of muntjacs habitat use for a geographic information system in southern Taiwan
Multivariate analyses were used to develop wildlife- habitat relationships. All variables were then tested for
normality and correlation. The Pearson Correlation Coefficient matrix was generated to test the correlation
of covariates. A stepwise multiple regression models was used to determine which of the 8 landscape
variables accounted for the greatest amount of variation in Muntjacs abundance (OI value). K-S
goodness-of- fit test and stepwise elimination process with P<0.05 was per formed to understand habitat use
by Muntjac by individual map layers (forest type, elevation, slope, aspect, and diversity). The multivariate
model was expanded to generate Reeves’ Muntjac distribution map of the whole study area. Adding the
term for autocorrelation, Ai, leads to the auto-multivariate model
is a weighted average of the number of occupied squares amongst a set of ki neighbors of squarei. The
weight given to square j, wij, is the reversed Euclidean distance between squares i and j. The same male
core range, i.e.12.9ha was used for the clique size in the study. The auto -multivariate model was fit to every
pixel of expanded distribution map. Model accuracy was examined by comparing study site observations
with model predictions.
Result and Discussion
Among the 103 camera sites, 80 sites have recorded Muntjac activities, which include about 77.67% of total
study sites. Muntjacs were absent from other 23 camera sites. With all covariates and their
transformations, 5 variables were selected for the multiple regression model with R
2
=0.39. They were
elevation, slope, moist regime, land use diversity, and whole-light-sky-space was selected for the model.
For th e 103 camera sites, frequencies of elevation and slope were equally distributed between the rages of
400 meter to 2,900 meter and 0 to 50 degrees, respectively. Whole light sky space ranges from 32% to 82%,
however, extreme poor or abundant of sunlight radiation potential were less. Aspect was classified into
moisture regime in a descending manner with class 1 as mesic and class 5 xeric. Land use diversity showed
most camera sites were located in continuous land use patches instead of fragmented land use patch.
Pearson correlation coefficient showed all continuous variables were independent to each other, which
indicated the selection of each variable did not affect the contributions of other variables. The multivariate
regression equations obtained for the study area showed that Muntjacs were more abundant in lower
elevation, flatter slope, with higher land use diversity and medium WLS. Subxeric to submesic appears to
be most suitable moisture class to Muntjacs (Table 1).
Table 1. Rsults of stepwise multiple regression of landscape and vegetation effects on Reeves' Muntjac distribution in Shuang-kuel-Nature Reserve, North Ta Wu Shan Nature Reserve in Taiwan
The stepwise multiple regression model was applied to the whole landscape with existing DTM data and GIS
technology (Figure1 ). The estimated Muntjac distribution showed a relatively higher Muntjac abundance in
lower elevation and flatter slope area. In higher elevation, the Munjac abundance was usually lower except
for area of more Mesic environment.
Figure 1. Estimated Reeves’ Muntjac distribution and camera sites in Shuang-Kuel-Hu Nature Reserve,
North Ta-Wu Nature Reserve, and Ta-Wu-Shan Nature Reserve in Taiwan
The simple matching coefficient showed that the predicted and observed values agreed for 78%
((75+5)/103=0.78) in the model (Table 2).
Table 2. Matching counts of logistic model : a method to assess the matching of predicted sample and
observed sample
Simple matching coefficient = (75+5)/103=0.78
