Constructing maps of ungulate species

Transcription

1 Constructing maps of ungulate species Preliminary report for Darwin Initiative Project (Cambridge University - WWF) MsC. Lương Văn Đức - Quang Binh University 1, August Aim of research The Central Truong Son is considered the crossroads of the South Chinese and Malayan floras with a fragmented terrain, the influence of the western continental climate system and the east coast produce a high biodiversity value. Here beside the hoofed mammals found in other places there are three endemic large mammals: Sao la (Pseudoryx nghetinhensis), large-antlered muntjac (Muntiacus vuquangensis) and Truong Son muntjac (Muntiacus truongsonensis) (Hoang Ngoc Khanh, 2004). In particular, the area of Thua Thien Hue and Quang Nam is has the most records of Sao la, concentrated mainly in the districts of Nam Đông, A Lưới, Hương Thuỷ and Hương Trà in Thừa Thiên Huế province and the communes of A Nông, B Halêê, A Vương, Tà Lu and Sông Kôn in Quảng Nam. However, these species are facing extinction and are in need of urgent conservation action (Van Ngoc Thinh et al., 2006). Meanwhile, the conservation efforts, especially of endemic ungulates, face problems including the difficulty of mapping species distributions to aid protected area management plans. Because these species are rarely seen and their population densities are very low, information remains limited. Therefore, there is a need to study the current status and distribution of these species. As part of the efforts to study the distribution of ungulates, the community mapping method has been developed and applied to make use of the indigenous knowledge of local people (hunters). In particular, besides the local distribution of each species, determining the area where people go is an important source of information to assess the level of knowledge and the reliability of people's knowledge. People will have a better understanding of the areas where they regularly conduct activities than of those areas where they rarely, or never, go. Species distribution maps, once produced, will be an important source of data for the identification of priority conservation areas with the help of conservation planning tools such as Zonation (Helsinki, Finland). However, when community maps are made in each village around a study area, the results showed that the areas used for forest exploitation and the areas where the villagers know the distribution of forest animals are not the same [for each village]. Therefore, the question is how to combine and analyze the data 1 Mob: vanducwwf@gmail.com

2 (areas where people regularly go [= visit data] and distribution areas of the species) together to produce maps of the distribution of species in the study area. This study was done with the purpose of evaluating the differences between the results of the analyses when combining visit data and species distribution data. This study will have significant scientific and practical value, is the basis for the next research steps with the conservation planning tool Zonation. 2. Scope of research Thừa Thiên Huế province: communes of Thượng Long, Thượng Quảng, Thượng Nhật, Hương Hữu (Nam Đông district) and A Đớt and A Roàng (A Lưới district); data on the distribution of species in the areas of some other communes was also collected through community mapping in the above communes. Quang Nam: data on the distribution of species in the communes of A Vương, xã B Halêê, Prao, Tà Lu and Sông Kôn. Scope of the study is shown below: Figure 1. Map of the study area in Thua Thien Hue and Quang Nam.

3 Figure 2. Protected areas in the study area 3. Research Methodology Data collection Community Mapping - Interview local people and commune officials about the areas of forest exploitation and the species which are hunted. In each commune select a village (following consultation with village headmen and FPD staff) which have the most understanding of forest or which go to the forest often, to conduct interviews for a longer time than other villages, in order to better understand that commune. Community mapping: First, building build a map of streams in the following format (example from Thượng Nhật commune):

4 Figure 3. Base map for community mapping in Thuong Nhat - Community mapping is divided into two stages. In phase 1 construct the map. In phase two conduct interviews and then use the bean method to map visit and species distribution data. In the first village in the commune, phase 2 was carried out across two half-day sessions; one for interviewing and one for beaning. In the remaining villages in the commune, both these activities were combined in one half-day session. - Phase 1: Mapping with the following basic steps: + People fill the names of rivers and streams in the correct places on the printed base map. + To compare maps between interview groups, the maps are editing and then digitized in ArcGIS. + Evaluation of the results in the field with a GPS device in a few places (ground-truthing). + Finally edited with the participation of three knowledgeable local people. Figure 4. Filling in the names of rivers based on community knowledge

5 - Stage 2: Using the map (bean method). + Print the new base map with the names of all rivers and streams which have been identified. + Using the beans: ask hunters to use the beans to assess the level of: places where people from the village most go to the forest and abundance of all ungulate species (Sao la, large-antlered muntjac, Truong Son muntjac, red muntjac, sambar, wild pig, serow) and the intensity of hunting and trapping. The method works by placing more beans in areas with high encounter frequency of species or areas where people go to the forest a lot, and few beans in areas of lower intensity. The advantages of this approach are that it is rapid, easy to quantify and correct and it is clear. First, using the local names of the species, ask hunters (12 / village) to classify and describe the species ( horn shape, hair color, size, habitat where commonly encountered, food), beginning with the species most easily recognised such as wild pig, serow, etc and then moving on to the species more difficult to identify such as the different types of muntjacs. These characteristics described by the hunter are compared with the scientific literature in order to verify the accuracy of the information people give.

6 Village 5. Thượng Long Village 6. Thượng Long Figure 5. Community-based species-distribution mapping (ii). GIS methods: + Using ArcGIS to calculate the search radius: The visit area data layers need to be combined with the species distribution data layers to create the density surface for each species from the beans with a search radius (search radius: all points within the search radius will have the same value as the point where the bean is placed) r = 0.5 / n 1) A (Equation 1.1) Where: r is the search radius A is the area of the minimum convex polygon around the visit area of all the villages (convex polygon is created from the outermost boundary around surrounding the beans placed on the map to indicate the forest areas people use). n is the number of beans the local people used to indicate this forest area.

7 4. The analysis steps Step 1: For each of the villages, include all of the visit data for the village (expressed with beans) and created one convex polygon around them all. Then, based on the calculation of "nearest neighbor distance" (Expected Nearest Neighbour Distance: ENND) between the beans to create a buffer zone around the convex polygon. Those are the maps AOC1. Specifically: i) building the basic convex polygon: From paper maps with beans showing the forest area where people usually go (Visit data), create digital versions using ArcGIS software. Use the ET Geo Wizards Build Convex Hull tool to create a convex polygon. This will create a polygon around the outermost points (beans) Figure 6 Convex polygon map layer for the area where people from Huong Son village (A Roang commune) often visit. ii) Building map layer AOC1 Because each bean represents an area surrounding that bean, it is necessary to calculate the area that the bean represents. The formula for calculating the ENND can be modified as follows: r 0.5 A/( n 1) (1.1) Where: r: search radius A: area of polygon (m2) n: number of beans in the polygon area. Specifically: Count the number of polygons in the convex polygon: n = 28

8 Calculate polygon area above (Figure 7): A = m2. Therefore, the search radius (r) was calculated according to the formula 1.1 is: Then, the buffer tool in ArcGIS 9.3 is used to create a buffer around the basic convex polygon of the distance r as above. AOC1 results are shown as follows: Figure 7. AOC1 for Huong Son village (A Roang).

9 Step 2: For each of the areas, create a data class 2 including the polygon surrounding each bean representing the places where people go (visit data). Do this by creating a buffer around each bean of the ENND value 3 calculated above. The result will be the class map AOC2. Figure 8. AOC2 for Huong Son village (A Roang). Step 3: Create a map data class buffered in the same way as in step 2, but using the data on the distribution of each species. This produces the Buffered species files. The data classes are stored with names like: Saola_hh_v1, Saola_hh_v2, etc; meaning the buffered species file for Saola in village 1 (Huong Huu), for Saola in village 2 (Huong Huu), etc. Figure 9. Buffered species file data class 2 i.e. shapefile 3 i.e. r. ENND = Expected Nearest Neighbour Distance

10 Step 4: Create a raster data layer (Naïve species raster). The value of each cell (pixel) represents the number of pixels of the polygons created in step 3 which overlay on top of that cell. The size of each cell is 200x200m. This is carried out through the following steps: i) Use the Union tool to combine the polygons created in Step 3 for the species, for example: Saola_hh_v1. Call this data layer: Saola_hh_v1_union. ii) Create new columns in the attribute table of this data class ( Saola_hh_v1_union), as follows: "poly_value" (value = 1),and the coordinates of the center of each polygon: cenx, and ceny iii) Use the Dissolve tool on the above species data layer, which will create a new column: SUM_value, this column shows the number of polygon overlapping on top of each other in each grid cell. The reason is that each polygon in the same cell will have the same value of X Coordinate of the centroid X and Y Coordinate of the centroid Y are the same.

11 iv) With this done, produce a raster where the values of the cells are derived from the column SUM_value v) Use the Classify tool to assign values to NoData areas a value of 0 (assign NoData with value 0) and use the raster calculator to sum the rasters for all villages for the species studied.

12 Step 5: Create data layer of people's understanding from the polygons produced in step 1 (using the same convex polygon for all species: AOC1) (Knowledge1): Produce raster data maps with a resolution of 200x200m (each pixel cell size 200x200m), in which the value of each grid cell (pixel) represents the number of AOC1 polygons overlay on top of each other in the grid. Perform the same steps as in step 4. Step 6: Create data layer of people's understanding from the polygons produced in step 2 (which were created with the method of creating buffer polygons around each bean: AOC2) (Knowledge2 ): raster map layer with resolution 200x200m (each pixel cell size 200x200m Produce raster data maps with a resolution of 200x200m (each pixel cell size 200x200m), in which the value of each grid cell (pixel) represents the number of AOC2 polygons overlay on top of each other in the grid. As in step 5 Step 7: Create a data layer in raster format for each species Final species raster (verson 1) for each of the 7 species. This data layer was produced using the tool Raster Calculator: results of calculations according to the following formula: FLOAT (Naive species rasters/knowledge1) (the value of the grid layer Naïve species rasters (eg Saola) divided by the value of the grid layers map Knowledge1). Seven layers created will be named after the class Naïve species rasters of that species. Step 8: Final species raster (verson 2): for seven species:. Same as step 7 but use the Knowledge 2 rather than Knowledge 1 raster.

13 Research outputs Output 1: AOC1 4 : A convex polygon around all the beans in the data layer visit (Hay Đi) (for each village). Output 2: AOC2: A polygon layer formed of circles (buffers) around each individual bean in the data layer visit (Hay Đi) (for each village). Output 3: Buffered species files: species layer with buffer Output 4: Naïve species rasters: raster data layer for the species. Output 5: Knowledge1: Raster layers with resolution 200x200m (each pixel of size 200x200m), in which the value of each pixel represents the number of AOC1 polygons overlapping that pixel. Output 6: Knowledge2: Raster layers with resolution 200x200m (each pixel of size 200x200m), in which the value of each pixel represents the number of AOC1 polygons overlapping that pixel. Output 7: Final species raster (verson 1): for all 7 species: Saola, large-antlered muntjac, Truong Son muntjac, Red muntjac, Sambar, Serow, Wild Pig. Product 8: Final species raster (verson 2): for all 7 species: Saola, large-antlered muntjac, Truong Son muntjac, Red muntjac, Sambar, Serow, Wild Pig. Product 9: Report presents detailed scientific arguments, methods, interpretation methods, analytical methods. Product 10: Map shows the result of the combined analysis with the Saola protected areas and forest compartments 4 AOC stands for Area of Cognizance