III. RESEARCH METHODOLOGY 3.1. Time and Research Area The field work was taken place in primary forest around Toro village in Lore Lindu National Park, Indonesia. The study area located in 120 o 2 53 120 o 7 44 E and 1 o 27 3 1 o 30 51 S. This area administratively is part of Toro Village, Sub District of Kulawi Central Sulawesi Province. Study area was approximately taken 2,300 Ha of Lore Lindu national park area with 1,151 Ha sampling area and 1,149 Ha accuracy assessment area. From data observation, study area has altitude range from 814 meter above sea level in Toro Village settlement area up to 2173 meter above sea level in top of mountain with various slopes from 0 up to 62 percent. Lore Lindu is tropical rainforest with high humidity with temperatures vary from 26-32 o C. High altitude areas are significantly cooler with temperature drops 6 o C every 1100 m. The annual rainfall in Lore Lindu National Park varies between 2000 4000 mm per year with heaviest rain periods between November to April. The fieldwork data collection was conducted from April to July 2008 including the process of vegetation identification which conducted in Herbarium Celebenses, Tadulako University, Central Sulawesi. While the data processing and thesis writing conducted in Laboratory of Remote Sensing and GIS, MIT-BIOTROP Bogor, from July to August 2008 and University of Tsukuba from August 2008 to August 2009. This research was funded by BMZ and STORMA program. STORMA (Stability of Rainforest Margin in Indonesia) is an Indonesian- German collaborative Research Centre funded by the German Research Foundation (DFG).
Figure 3.1 Research Study Area 3.2. Data Collection Data collected for this study was taken from many sources with different data acquisition methods, direct and undirect data acquisition. Direct data acquired in collecting presence and absence sign while environmental parameter layer was taken from spatial database. Anoa presence data was collected from field survey by identifying the sign of Anoa presence. The sign of Anoa presence can be identify from its footprint, feces, horn and other sign that can identify as sign of Anoa presence. This data collection was conducted with assistance from local people who formerly working as Anoa hunter. In this field work we use GPS Garmin 76CS, GPS Garmin Vista and Nikon D70s camera. We marking the anoa sign geographically and documenting sign and landscape condition using camera. In addition, we not only locating the Anoa sign of presence but also try to marking Anoa track by following its footprint. Detailed information, such as Anoa shelter location, was also collected regarding Anoa core area identification.
All the data were used for generating the model that were derived into spatial format and considered as the input parameter within the grid analysis unit. In this study we generated grid analysis unit that considered as observation unit for each parameter variable. The 100 m grid analysis unit was generated using ArcMap Create Fishnet Feature Class Toolbox. 100 meter grid resolution were chosen base on approximate detail can be obtain from parameter layer. The parameter grid inputted using Hawth Tools Extension in ArcGIS software. Each parameter will be derived first into 100 meter grid then inputted using polygon to polygon analysis in Hawth Tools using mean weighted area method. Each cell in simulation grid will be consists of value that represent current condition in grid area. The result for each parameter will be joined into one feature using Add Join Table features in ArcMap. The result of this features are continuous value that represent grid area condition. The absence or presence data conducted using select by location features. The grid that intersected with point of presence Anoa location were considered as suitable habitat for Anoa and will be given value 1 while other grid which not intersected were considered as pseudo absence and were given value 0 or classified as non-suitable area. Table 3.1. Environmental Variables Abbreviation Description Unit ALT SLO DTR DTS DTFE Altitude Slope Distance to River Distance to Settlement Distance to Forest Edge Meter Above Sea Level Percent Meter Meter Meter The Anoa presence data collections were conducted by observation of species activities in field survey. Anoa presence data was collected from field survey by identifying the sign of Anoa presence. The sign of Anoa presence were identified from footprint, feces, horn and
other sign that considered as sign of Anoa presence. Another information like Anoa shelter location also collected regarding to find core area of Anoa. The first parameter use in this study is topographic condition. Mountain Anoa inhabits primary rainforest, usually in hilly landscapes, at elevations up to 2,300 meters (National Research Council, 1983; Sugiharta, 1994). In topographic parameter were divided into altitude and slope. These topographic parameters are important factor in determining habitat suitability for Anoa. Mountain Anoa is dwarf buffalo families that live in mountainous area with certain topographical condition for their habitat. In this research we also find the suitable topographical factor for Anoa by considering the distribution of Anoa in topographic parameter. The topographical data were derived from Digital Elevation Model from Satellite Radar Topographic Mission imagery which has 90 meter spatial resolution. Digital number in SRTM image represents the altitude value in meter unit. This imagery then processed in Topographic Analysis in ERDAS Imagine which resulted altitude and slope raster. This raster then converted into vector features using raster to vector conversion in Arc Toolbox. The slope features consists slope in percent value while the altitude was used meter above sea level unit as representation of area level altitude. The next parameter is distance from human activity area. Anoa was known as solitaire animal that avoid direct contact with human. This animal tends to keep a distance with human activity area like settlement, road and plantation area and other human activities area. Mustari (2003) explain that Anoa showed a trend towards being seen more than one kilometer from settlement, road, and other human activities point in Tanjung Amalengo. This fact indicating distance from human activities will affect the Anoa presence and suitable habitat. In this research, the distance from human activities parameters are distance from settlement and distance from forest edge. Distance from forest edge is chosen because most of plantations were located in forest edge around Lore Lindu National Park. Many human activities take place in this area. Distance from settlement and distance from forest edge factor are developed using Euclidean distance in ArcTool Box. The
settlement features are taken from Indonesia National Coordination Agency for Surveys and Mapping with 1: 50.000 of scale. While the forest edge features are taken from land cover map derived from Landsat ETM 7+ acquisition date October 6 th, 2005. Mustari (2003) also explain that like other species in the genus Bubalus, Anoa is water dependent animal. They need water for drinking everyday and they were frequently observed wallowing. Distribution of Anoa is significantly associated with water source during the dry season. This statement clearly explains that Anoa have high dependencies in hydrological resource. Based on this fact the hydrological condition will be the one of the parameters in estimating Anoa s habitat suitability and presence. Hydrological factor was taken from river feature in Peta Rupa Bumi Bakosurtanal with 1: 50.000 of scale. The stream line feature was processed using Euclidian distance to develop distance from river value in each grid observation. Hydrological data were consists of river stream line, both for all year round rivers and seasonal river. 3.3. Habitat Suitability Model Habitat model is not a definitive attempt to predict presence or absence, but it is more an attempt to identify areas where conservation and forest habitat enhancement could be prioritized. The deductive modeling approach allows the utilization of statistically significant quantitative data to build a habitat model that describes the similar areas to those used by species. The absence of Anoa in predicted suitable area will not indicate unsuitable Anoa habitat. By identifying similarities, in habitat features across the Lore Lindu National Park, the improvement in Anoa s preservation could be increased suitable habitat patch size and connect neighboring patches. The habitat suitability map was constructed by coupling field data with geospatial information derived from satellite imagery and other parameter spatial resources. This study is compiling field data indicating the presence or absence of Anoa from suitable habitat parameter. The logistic regression approach uses the environmental parameter layers to
characterize the habitat of known Anoa locations as well as those areas with no sign of Anoa. Areas throughout the Lore Lindu National Park exhibiting land cover, vegetation, and biophysics characteristics similar to locations where Anoa was observed in the field are associated with a higher metric in the derived map. Areas exhibiting characteristics similar to locations where field data indicated the absence of Anoa are associated with a lower metric on the map. Lore Lindu National Park Field Survey Presence Absence Data Logistic Regression Environment Variable Fit Threshold Altitude Slope Distance to Settlement Distance to River Fit Logistic Regression Model Distance to Forest Edge Best Logistic Regression Model Habitat Suitability Map Figure 3.2 Habitat Suitability Model Workflow 3.4. Statistical Test Spatial distribution model will build the model based on ecological parameter and testing it against current Anoa locations. To assess fit and relative strength of the selected model, we used the Hosmer and Lemeshow goodness of fit test and Nagelkerke s rescaled R 2 respectively. Probabilities from the logistic regression are used to derive a predictive habitat map from the significant habitat characteristics across the
landscape at threshold values. The results of the regression produced variable coefficients that were then applied to the area and the entire study area ranging from zero to one, where values greater than 0.5 were considered to be presence of Anoa. 3.5. Vegetation Analysis In order to get detailed information about structure and composition of vegetation in Anoa presence location, we conducted Vegetation Analysis method using Purposive Random Sampling. Vegetation analysis was conducted in place where Anoa sign were found. By knowing the structure and composition, it will describe the vegetation condition which might have specific characteristic. Figure 3.3 Line Transect Method In vegetation analysis, we use line transect method to distribute nested plots for vegetation analysis. In line transect method the plants are classified into tree (woody plants which has diameter at breast high more than 20 cm), poles (woody plants which has diameter at breast high from 10 to 20 cm), sapling (woody plants which has diameter at breast less than 10 cm and height more than 1.5 meter), seedling (woody plants with height up to 1.5 meter), and undergrowth plant like herbs, ferns, liana, and grasses.
The plot sizes in vegetation analysis are 20 x 20 m for trees, 10 x 10 m for poles, 5 x 5 m for sapling and 2 x 2 m for seedling and undergrowth plant. Data collected from the field was calculated to determine information about Density, Frequency, Dominance and Importance Value. The formula used in calculating these values are a. Density Species Density = Individu Plot Sample Area Relative Density = Species Density x 100% Total Species Density b. Frequency Species Frequency = Relative Frequency = c. Dominance Plot where species found Total Plot Plot where species found Total Plot x 100% Dominance = Species Basal Area Plot Sample Area Relative Dominance = d. Importance Value For tree and poles Species Dominance Species Dominance x 100 % Importance Value = Relative Density + Relative Frequency + Relative Dominance For sapling and seedling Importance Value = Relative Density + Relative Frequency