An auto-multivariate model of muntjacs habitat use for a geographic information system in southern Taiwan
Lai, Yu -Ching
Assistant Professor, Department of Environmental Design
Huafan University
1 Huafan Road, Shihting, Taipei County
China Taipei, R. O. C.
E-mail: yuching@huafan.hfu.edu.tw
Kurtis Jai-Chyi Pei
Associate Professor, Institute of Wildlife Conservation
National Pingtung University of Science and Technology
Neipu, Pingtung, China Taipei, R. O. C.
E-mail: kcjpei@mail.npust.edu.tw
Po-Jen Chiang
Institute of Wildlife Conservation
National Pingtung University of Science and Technology
Neipu, Pingtung, China Taipei, R. O. C.
Hsu Li-Ta
Associate Professor, Department of Forestry
Chinese Culture University
55 Hwa-Kang Rd. Taipei 111, China Taipei ROC.
Email: lita@faculty.pccu.edu.tw
Abstract
The ability to model habitat use, habitat use potential, and change in distribution of habitat is of considerable
importance in wildlife management as to predict patterns of biological diversity or identifying geographical
areas of conservation significance. In this study, we developed an auto-multivariate method to model
habitat- use potential using a set of Muntjac recording locations and habitat data consisting of variables such
as elevation, aspect, slope, whole light sky space, forest cover type, forest cover type diversity, distance to
river, and distance to road in the mountain area of southern Taiwan. The model is based on the multivariate
regression statistic coupled with Geographic Information System (GIS) technology to incorporate spatial
correlation of wildlife -habitat relationship. Muntjac abundance obtained from auto-triggered cameras and
habitat variables were used to explain habitat selection and to identify areas of high use potential of Muntjac
in natural forest of southern Taiwan. Calculations were made with the GIS to generate spatial habitat
variables and to produce a map containing habitat use potential that could not otherwise be identified by
independent perusal of any single map layer. Confirmation of the accuracy of predictions of Muntjac.distribution in Southern Taiwan was assessed with both survey data and expert’s evaluation. Results
shown th at this technique is a useful tool for habitat manipulation or mitigation to favor terrestrial vertebrates
that use habitats on a landscape scale.
Introduction
Chinese or Reeves’ Muntjac (Muntiacus reevesi) are native to China and Taiwan (Sheng 1992, McCullough
1974). It is one of the small ungulates that occur in temperate to tropical forests (McCullough et al. 2000).
It is of interest because of their conservation and ungulate lineage importance. Research shows that it is
predominantly forest dwelling and appear to be concentrate selectors of habitat (Hofmann 1985). However,
little is known about Reeves’ Muntjac in their native habitats because of the difficulties in long-term
observation and trace in the wild (McCullough et al. 2000).
Effective management of wildlife population largely depends upon understand and predicting their habitat
needs (Buckland and Elston 1993). Use of multivariate statistics and Geographic Information System (GIS)
to assess habitat suitability has increased in recent years because the spatial characteristics and
multi-dimensional nature of habitat limits use of simple univariate statistical techniques (Howell et al. 2000,
Brito et al. 1999, Gros and Rejmanek 1999, McKenney et al. 1998, Buckland et al. 1996, Clark et al. 1993).
However, i n regression analysis spatial autocorrelation of the information is sometimes ignores yet it has the
effect of reducing the power of the model since observations are influenced by neighboring areas that tend
to have similar conditions (Anselin 1993, Augustin et al. 1996).
In this study, Reeves’ Muntjac abundance obtained from auto-triggered cameras and habitat variables
obtained from digital terrain model (DTM) were used to explain habitat selection and to identify areas of high
use potential of Muntjac in natural forest of southern Taiwan. Spatial autocorrelations is modeled explicitly
by extending the result logistic model to include an extra covariate that is derived from the responses at
neighboring squares (Augustin et al. 1996).
Methodology
The study area located at the mountain area of southern Taiwan including Shuang -Kuel-Hu Nature Reserve,
North Ta-Wu Nature Reserve, and Ta-Wu-Shan Nature Reserve (Fig. 1). Collectively, the 3 reserves
consist of more than 234,000 acres of secondary hardwood forest. Primary vegetation includes species
such as Cyclobalanopsis morii, Adiandra lasiostyla, Cyclobalanopsis longinus, Persea japonica, Tsuga
chinensis, Chamaecyparis formosana, Castanopsis carlesii, Alnus formosana, and Acer kawakamii.
A total of 103 study sites were randomly allocated in the study area to conduct survey from 1992 to 2001 (Fig.
1). Each auto-trigger camera was installed 1.5 to 2.5 meters above ground and their locations were
recorded using GPS receiver. Depending on the a bundance of the carnivores and availability of study site,
film and battery was collected and replaced every 2 to 4 weeks.
Relative abundance for Reeves’ Muntjac was generated from the field survey. The relative abundance for
Muntjac was represented by the Occurrence Index (OI = the number of pictures taken per 1,000 camera
working hour) (Pei 1998, Pei et al. 1997). Serial pictures belong to the same individual taken during a short.period (usually within 30 minutes) will be considered only 1 picture in the calculation to prevent the
over-representative of a lingering individual, hence, to reduce thepossibility of over-estimation of the
abundance. Camera working hours for each roll of film was the time span between the starting time of a
new roll of filmand the time recorded on the last picture in the case of the film was finished before the next
checking by the researchers, or the time when the researchers arrived for collecting the film, in the case of
the film was not finished.
There were total of 8 explanatory variables used to model Muntjac distribution. The geographical variables,
i.e. elevation, slope, and aspect, were measured at study site. The landscape statistics, i.e. distance to river,
distance to major road, land-use, land-use diversity, and whole light sky space, werecalculated with 40
meter resolution using GIS database and an existing Digital Terrain Model (DTM) and land use map. Using
GIS, the number of different cover types within an 9*9 matrix of 40 -m cells (12.9ha) surrounding the center
cell was assigned to that center cell to obtain a measure of cover-type diversity. This matrix size
corresponded to a core habitat area of male Muntjac(Chapman et al. 1994, McCullough et al. 2000).
Whole-light-sky-space indicates the potential sunlight radiation denoted by percentage of open sky. Its
value was calculated from DTM data and algorithm written in C language (Cheng et al. 2002). A layer of
moist regimes was generated from Mesic to Xeric throughreclassification of aspect map according to
Whittaker (1986).
