**4. Integrating citizen science and GIS for wildlife habitat assessment: a** *Rhinopithecus bieti* **case study**

A case study of mapping black-and-white snub-nosed monkeys' (*Rhinopithecus bieti*) habitat suitability using *R. bieti* sighting data elicited from local villagers at Mt. Lasha in Yunnan, China, was presented to demonstrate the integration of citizen science and GIS for wildlife habitat assessment.

#### **4.1 Species and study site**

*R. bieti* is an "endangered" species on the IUCN (International Union for Conservation of Nature) Red List of Threatened Species [62]. *R. bieti* is endemic to the eastern Himalayas in northwest Yunnan and southeast Tibet, China, between the upper Mekong and Yangtze Rivers with 19 relatively isolated groups [63]. The monkeys use lichens (e.g., arboreal fruticose *Bryoria* and *Usnea* spp.) as their main food [64]. They prefer fir-larch forest at higher elevations in the

**27**

**Figure 1.**

*John Wiley and Sons).*

*Integrating Citizen Science and GIS for Wildlife Habitat Assessment*

northern part of the range but also stay in mixed coniferous and broad-leaf forest at lower elevations (above 2600 m) in the southern range [65]. Across its geographic distribution areas, the habitat of *R. bieti* has undergone degradation (e.g., habitat reduction and fragmentation) in the past decades due to the growth of human population that mostly employed traditional modes of production in its distribution area (e.g., clear-cutting forests for farming, grazing and firewood

The study site is Mt. Lasha area located in northwest Yunnan Province, southwest China (**Figure 1**). Mt. Lasha is near the southern-most part of its geographic

Sightings of *R. bieti* were elicited from local villagers for assessing the habitat of the monkeys in the study area across historical periods. Local villagers whose livelihoods are closely dependent on ecosystem services have long been living in the local area and accumulated information about *R. bieti* habitat use. Sightings of *R. bieti*

*Location of the study area: (a) Mt. Lasha in northwest Yunnan, China; (b) a 3D perspective image of the Mt. Lasha area; and (c) a family of R. bieti in their natural habitat (extracted from [40] with permission from* 

100 *R. bieti* individuals in 11 one-male multi-female units and two all-male units [66]. *R. bieti* is a significant species with a strong historic dimension in local communities [63]. On the one hand, hunting poses the greatest threat to the monkeys [63]. Local residents had long been hunting the monkeys for various purposes. Even after the Chinese government has designated the species in the first class of protected animals since 1977, illegal hunting had not been stopped completely. On the other hand, *R. bieti* habitat use is closely related to forest-provisioned resources including food and shelter [63, 65]. In the study area, two historical events had significant impacts on these forest-provisioned resources: in 1979 the China Environmental Protection Act was enacted; in 2006, the Mt. Lasha area became a protected area as part of the Yunling Nature Reserve. The forestry policy implementations associated with these events in the study area directly affected local

study area is an important habitat for a group of about

*DOI: http://dx.doi.org/10.5772/intechopen.83681*

consumption; hunting) [65].

range [63, 65]. The 20.31 km2

residents' exploitation of the forests.

*4.2.1 Wildlife data elicited from local villagers*

**4.2 Data collection**

*Integrating Citizen Science and GIS for Wildlife Habitat Assessment DOI: http://dx.doi.org/10.5772/intechopen.83681*

northern part of the range but also stay in mixed coniferous and broad-leaf forest at lower elevations (above 2600 m) in the southern range [65]. Across its geographic distribution areas, the habitat of *R. bieti* has undergone degradation (e.g., habitat reduction and fragmentation) in the past decades due to the growth of human population that mostly employed traditional modes of production in its distribution area (e.g., clear-cutting forests for farming, grazing and firewood consumption; hunting) [65].

The study site is Mt. Lasha area located in northwest Yunnan Province, southwest China (**Figure 1**). Mt. Lasha is near the southern-most part of its geographic range [63, 65]. The 20.31 km2 study area is an important habitat for a group of about 100 *R. bieti* individuals in 11 one-male multi-female units and two all-male units [66]. *R. bieti* is a significant species with a strong historic dimension in local communities [63]. On the one hand, hunting poses the greatest threat to the monkeys [63]. Local residents had long been hunting the monkeys for various purposes. Even after the Chinese government has designated the species in the first class of protected animals since 1977, illegal hunting had not been stopped completely. On the other hand, *R. bieti* habitat use is closely related to forest-provisioned resources including food and shelter [63, 65]. In the study area, two historical events had significant impacts on these forest-provisioned resources: in 1979 the China Environmental Protection Act was enacted; in 2006, the Mt. Lasha area became a protected area as part of the Yunling Nature Reserve. The forestry policy implementations associated with these events in the study area directly affected local residents' exploitation of the forests.

### **4.2 Data collection**

*Wildlife Population Monitoring*

as the background data.

**3.3 Geocomputation to enable big data analysis**

**a** *Rhinopithecus bieti* **case study**

**4.1 Species and study site**

citizen science and GIS for wildlife habitat assessment.

the spatial bias in presence-only data. This is achieved by feeding MAXENT with background data that have the same spatial bias as the presence data. For example, occurrence data of a target group of species that are observed by similar methods can be taken as the estimate of the effort information and thus are used

Recently, a general representativeness-directed approach was proposed to spatial bias mitigation in citizen-contributed wildlife observations (i.e., samples) for habitat suitability mapping [59]. The key idea is to define and quantify the representativeness of samples and then properly weigh the samples to improve representativeness. Sample representativeness is defined as the "goodness-of-coverage" of the samples in the environmental covariate space, which in turn is measured by the similarity between the probability distribution of the samples in the covariate space and the probability distribution of all mapping units (e.g., pixels) within the study area. Spatial bias is then mitigated by weighting the samples toward increasing sample representativeness. The optimal weights that maximize sample representativeness are determined through an optimization procedure using a genetic algorithm.

Citizen-contributed wildlife data are an important source of geospatial big data. In spatial analysis or modeling of such large volume of data (e.g., point pattern analysis, wildlife habitat assessment, and species distribution modeling), it is urgent to address the associated computational challenges. Geocomputation

**4. Integrating citizen science and GIS for wildlife habitat assessment:** 

*bieti*) habitat suitability using *R. bieti* sighting data elicited from local villagers at Mt. Lasha in Yunnan, China, was presented to demonstrate the integration of

*R. bieti* is an "endangered" species on the IUCN (International Union for Conservation of Nature) Red List of Threatened Species [62]. *R. bieti* is endemic to the eastern Himalayas in northwest Yunnan and southeast Tibet, China, between the upper Mekong and Yangtze Rivers with 19 relatively isolated groups [63]. The monkeys use lichens (e.g., arboreal fruticose *Bryoria* and *Usnea* spp.) as their main food [64]. They prefer fir-larch forest at higher elevations in the

A case study of mapping black-and-white snub-nosed monkeys' (*Rhinopithecus* 

For example, over 100 million bird observations were added to the eBird database each year. Point pattern analysis is commonly used to discover patterns from such data. Existing point pattern analysis software tools are not able to handle geospatial big data efficiently. Cutting-edge geocomputation technologies such as cloud computing and GPU (graphics processing units) computing can be leveraged to accelerate point pattern analysis algorithms. The massively parallel computing powers of cloud computing and GPU computing effectively sped up point pattern analysis tasks on big data by a factor of hundreds [60, 61]. Given the significant acceleration brought by the geocomputation technologies, geospatial big data analysis tasks that once were computationally prohibitive can now be conducted in a timely manner.

technologies could be utilized to address such computational challenges.

**26**

#### *4.2.1 Wildlife data elicited from local villagers*

Sightings of *R. bieti* were elicited from local villagers for assessing the habitat of the monkeys in the study area across historical periods. Local villagers whose livelihoods are closely dependent on ecosystem services have long been living in the local area and accumulated information about *R. bieti* habitat use. Sightings of *R. bieti*

#### **Figure 1.**

*Location of the study area: (a) Mt. Lasha in northwest Yunnan, China; (b) a 3D perspective image of the Mt. Lasha area; and (c) a family of R. bieti in their natural habitat (extracted from [40] with permission from John Wiley and Sons).*

were elicited from local villagers through interviews. A 3D geovisualization tool was adopted to aid the interviews by using it to help the local villagers recall and locate where they sighted the monkeys more accurately.

*R. bieti* sightings were collected through structured interviews with local villagers (**Figure 2**). Sightings of the monkeys and activity routes of the villagers were elicited. The interviews were conducted using 3dMapper, a 3D geovisualization GIS tool that uses high-resolution DEM and satellite imagery to produce an intuitive 3D view of the study area [67] (freely available from solim.geography.wisc.edu). The user can zoom, pan, and easily draw points, lines, or polygons over the 3D scene. We introduced this geovisualization tool to the villagers to help them identify locations where they had sighted the monkeys and the daily routes they took in the area. The villagers also recalled the year and month when they sighted the monkeys or took the routes. The year of *R. bieti* sightings recalled by the interviewees was crosschecked with and refined with reference to timing of major events such as national policy implementations, date of marriage and child born, etc. and the month to seasonal activity patterns in the area such as farming and grazing. Information on where and when they sighted the monkeys was recorded as polygons. Information on the routes they took and the frequency with which they took each route was recorded as lines.

Geovisualization interview sessions were carried out by one biologist and one field assistant who were very familiar with the study area during July and August 2010. Sixty-eight local residents including herdsmen, hunters, and farmers who had extensive experience in the mountains from all five nearby villages were interviewed. The majority of them are aged between 30 and 60 (**Table 1**). The elicited *R. bieti* sightings and activity routes of the villagers cover a temporal span from the 1950s through 2010. Constrained by the availability of environmental data needed for habitat assessment, only *R. bieti* sightings in three historical periods (1973–1981, 1987–2005, and 2006–2010) were used for habitat assessment (see [40] for details) (**Figure 3**).

### *4.2.2 Environmental data*

Environmental factors impacting *R. bieti* habitat use include terrain, water source, shelter and food, and human-posed disturbance [65, 68, 69]. Accordingly, the following environmental data layers were used in habitat assessment (habitat suitability mapping) for *R. bieti* in the study area [23, 40]: elevation, slope gradient, slope

#### **Figure 2.**

*Geovisualization interview sessions with the local residents using 3dMapper: (a) the local residents locating monkey sightings and activity routes and (b) a 3D scene of a small portion of the study area on which the local residents outlined monkey sightings and routes (extracted from [40] with permission from John Wiley and Sons).*

**29**

**Figure 3.**

*John Wiley and Sons).*

*Integrating Citizen Science and GIS for Wildlife Habitat Assessment*

**4.3 Accounting for positional uncertainty and spatial bias**

aspect, distance to river, distance to village or road, and vegetation type. Interested readers can refer to [40] for details on how to obtain these environmental data layers.

Age 19–30 31–40 41–50 51–60 61–70 71–78 Count 7 12 16 18 10 5

Data elicited from local villagers impose two challenges, namely positional uncertainty and spatial bias. First, local villagers often recall *R. bieti* sightings in the form of "I saw the monkeys over this area." Clearly, "over this area" can be depicted using a polygon, but this does not mean that the monkeys showed up at every location in the polygon area and certainly not at an equal probability within the polygon. Thus, taking all locations in the polygon as sightings is not appropriate. The question then is how to obtain locations that are representative of the actual presence of wildlife in the area outlined by the local villagers. The second challenge is the spatial bias in the elicited *R. bieti* sightings due to local villagers' opportunistic observation effort. For example, multiple sightings of monkeys at one location by many villagers do not necessarily mean that the location is highly preferred by the monkeys; it might be that the location is easily visible from multiple activity routes. Thus, every time a monkey shows up at this location, it is spotted by some villager(s). On the other hand, a monkey that shows up at locations that are preferred by monkeys but less visible to the villagers will have a lesser chance of being spotted. This spatial bias must be compensated for when using the elicited *R. bieti*

Geospatial analysis methods provisioned by GIS were adopted to address the two challenges. First, a frequency sampling strategy [23, 70] was applied to reduce the

*Sightings of R. bieti and activity routes elicited from the local residents through geovisualization interviews: (a) 1973–1981 period; (b) 1987–2005 period; (c) 2006–2010 period (extracted from [40] with permission from* 

*DOI: http://dx.doi.org/10.5772/intechopen.83681*

*Age composition of the interviewed local villagers.*

**Table 1.**

sightings for wildlife habitat assessment.

*Integrating Citizen Science and GIS for Wildlife Habitat Assessment DOI: http://dx.doi.org/10.5772/intechopen.83681*


**Table 1.**

*Wildlife Population Monitoring*

recorded as lines.

*4.2.2 Environmental data*

where they sighted the monkeys more accurately.

were elicited from local villagers through interviews. A 3D geovisualization tool was adopted to aid the interviews by using it to help the local villagers recall and locate

*R. bieti* sightings were collected through structured interviews with local villagers (**Figure 2**). Sightings of the monkeys and activity routes of the villagers were elicited. The interviews were conducted using 3dMapper, a 3D geovisualization GIS tool that uses high-resolution DEM and satellite imagery to produce an intuitive 3D view of the study area [67] (freely available from solim.geography.wisc.edu). The user can zoom, pan, and easily draw points, lines, or polygons over the 3D scene. We introduced this geovisualization tool to the villagers to help them identify locations where they had sighted the monkeys and the daily routes they took in the area. The villagers also recalled the year and month when they sighted the monkeys or took the routes. The year of *R. bieti* sightings recalled by the interviewees was crosschecked with and refined with reference to timing of major events such as national policy implementations, date of marriage and child born, etc. and the month to seasonal activity patterns in the area such as farming and grazing. Information on where and when they sighted the monkeys was recorded as polygons. Information on the routes they took and the frequency with which they took each route was

Geovisualization interview sessions were carried out by one biologist and one field

Environmental factors impacting *R. bieti* habitat use include terrain, water source,

shelter and food, and human-posed disturbance [65, 68, 69]. Accordingly, the following environmental data layers were used in habitat assessment (habitat suitability mapping) for *R. bieti* in the study area [23, 40]: elevation, slope gradient, slope

*Geovisualization interview sessions with the local residents using 3dMapper: (a) the local residents locating monkey sightings and activity routes and (b) a 3D scene of a small portion of the study area on which the local residents outlined monkey sightings and routes (extracted from [40] with permission from John Wiley and* 

assistant who were very familiar with the study area during July and August 2010. Sixty-eight local residents including herdsmen, hunters, and farmers who had extensive experience in the mountains from all five nearby villages were interviewed. The majority of them are aged between 30 and 60 (**Table 1**). The elicited *R. bieti* sightings and activity routes of the villagers cover a temporal span from the 1950s through 2010. Constrained by the availability of environmental data needed for habitat assessment, only *R. bieti* sightings in three historical periods (1973–1981, 1987–2005, and 2006–2010) were used for habitat assessment (see [40] for details) (**Figure 3**).

**28**

*Sons).*

**Figure 2.**

*Age composition of the interviewed local villagers.*

aspect, distance to river, distance to village or road, and vegetation type. Interested readers can refer to [40] for details on how to obtain these environmental data layers.
