**1. Introduction**

Habitats provide resources such as food, shelter, potential nesting sites, and mates for wildlife to achieve survival and reproduction [1]. Understanding the requirements or preferences of wildlife on their habitats and assessing the quality of wildlife habitat is of great importance for conservation biologists and conservation managers [2]. For example, wildlife habitat assessment supports conservation practices such as ex situ or reintroduction and restoration conservation, predicting risk of invasive species, systematic conservation planning, assessing threats, and setting conservation priorities [3–6].

One approach to assessing wildlife habitat quality is to predict wildlife habitat suitability maps indicating the spatial variation of habitat suitability [7]. Habitat suitability mapping is often carried out in a geographic information system (GIS) [8]. Conceptually, spatial prediction of wildlife habitat suitability requires GIS data layers characterizing the environmental conditions (environmental data) and knowledge on the relationship between wildlife habitat suitability and environmental conditions. Based on the relationship and the environmental conditions at a location (e.g., a pixel), the in situ habitat suitability can be inferred. Inferring habitat suitability at every location in the study area of interest results in a suitability map [7]. Such a habitat suitability map can then be used to assess the spatial variation of wildlife habitat quality and to support conservation. With the rapid development of geospatial technologies, environmental data for characterizing environmental conditions are becoming abundant and increasingly available [9, 10]. The key for wildlife habitat assessment through habitat suitability mapping therefore lies in obtaining knowledge on the relationship between wildlife habitat suitability and environmental conditions (environmental niche).

Data-driven approaches are most commonly adopted in deriving the relationship between wildlife habitat suitability and environmental conditions (environmental niche modeling) [7]. Data-driven approaches for environmental niche modeling require wildlife data indicating habitat use, for example, abundance data, presence and/or absence data. Wildlife data are overlaid with environmental data layers to extract the environmental conditions at locations where habitat use occurs. The relationship between wildlife habitat suitability and environmental conditions can then be derived through statistical analysis, machine learning, data mining, or other modeling techniques [7]. Thus, wildlife data become the key to deriving the relationship between habitat suitability and environmental conditions for mapping wildlife habitat suitability for wildlife habitat assessment.

Traditionally, wildlife data are collected using various techniques such as field observation, radio telemetry, infrared trapping cameras, and global positioning system (GPS) collars [11, 12]. Accurate wildlife data can be collected through these techniques, but admittedly these techniques are also somewhat expensive to deploy [13]. The high cost may prevent these techniques from being used in wildlife data collection, particularly for areas and projects with limited budget support. Besides, some of these techniques (e.g., field observation and GPS collars) are logistically difficult for areas with rugged terrains and limited accessibility [13]. Low-cost techniques such as trailing wildlife markings and interviewing local people about wildlife sightings through questionnaires are also used for wildlife data collection, but wildlife data collected with these techniques can be of low quality (e.g., inaccurate spatial location and/or time) [14, 15]. Cost-effective methods for collecting wildlife data of satisfactory quality are ideal for wildlife habitat assessment and sustainable conservation given that much of the world's biodiversity occurs in the world's poorest and remote countries [16].

Local residents were proven to be a cost-effective source of obtaining wildlife data [17, 18]. Many local residents, such as those living in remote rural areas and particularly those whose livelihoods are closely linked to ecosystem services (e.g., subsistence farmers, shepherds, and hunters), spend a great deal of time in the field. They encounter wildlife in its natural environment and, as a result, accumulate a rich knowledge about the wildlife habitat use. Wildlife data elicited from local residents at relatively low cost, although may be subject to data quality issues (e.g., data credibility, positional accuracy, spatial bias, etc.), could be used to support and sustain conservation programs with limited budget.

From a broader perspective, the increasing availability of citizen-contributed data accompanied by the advancements in GIS has created the opportunity to make full use of citizen science to address many real-world problems. On the one hand, citizen-contributed data have become increasingly available with the resurrected popularity of citizen science [19] and the emerging phenomenon of volunteered

**21**

*Integrating Citizen Science and GIS for Wildlife Habitat Assessment*

**2. Citizen science for wildlife data collection**

geographic information (VGI) [20]. One prominent example is the eBird citizen science project that is driven by bird watchers and documenting bird species across the globe [21]. On the other hand, the advancements in GIS capabilities (e.g., geovisualization and spatial analysis) have made it possible to accommodate the data quality issues associated with citizen-contributed data to make use of such

This chapter offers an overview of citizen science for wildlife data collection and its integration with GIS for wildlife habitat assessment. A case study of habitat assessment for the black-and-white snub-nosed monkey (*Rhinopithecus bieti*) using data contributed by local residents in Yunnan, China, is presented as an illustration.

The term *citizen science* was formally added to the Oxford English Dictionary only recently in 2014 [24], referring to "Scientific work undertaken by members of the general public, often in collaboration with or under the direction of professional scientists and scientific institutions" [25]. Nonetheless, citizen science has been practiced for centuries, long before *scientist* slowly became a profession throughout the seventeenth to nineteenth centuries [24]. For example [26], Benjamin Franklin (1706–1790), as a physicist, was famous for his discoveries and theories regarding electricity while he was also a printer, diplomat, and politician; Charles Darwin (1809–1888) as a biologist was best known for his contributions to the theories of evolution, but on the Beagle voyage, he was sailing as an unpaid companion, not as a professional scientist. Even after the *scientist-as-profession* paradigm has been well established, average citizens continue to engage in scientific work at various levels of involvement [27]: *contributory* where citizens mostly contribute to data collection, *collaborative* where citizens also participate in data analysis, and *co-created* where citizens get involved at all stages of the project including conceiving and designing the research. Exemplary long-running citizen science projects related to wildlife population monitoring are the Christmas Bird Count (CBC) established in 1900 [28] and the Breeding Bird Survey (BBS) established in 1965 [29] for monitoring bird species in North America. Data contributed by participants in such citizen projects are now supporting wildlife population trends monitoring [30] and

The rapid advancements of geospatial information technologies in the last decade have greatly prompted the flourish of citizen science. Location-aware portable devices constantly connected to the Internet (e.g., GPS-enabled smart phones) are now commonplace. Average citizens thus can conveniently contribute georeferenced wildlife observations using such devices via social media, mobile map, citizen science project mobile apps, etc. [26, 32, 33]. From a geographic and GIS perspective, citizen science involving geospatial data generation (e.g., wildlife sightings with location information) is called "geographic citizen science" [34] and the georeferenced wildlife observations are a form of VGI [20, 34]. Due to the increasing availability of enabling technologies, millions of citizens across the world are participating in citizen science projects and many of them are contributing large volumes of wildlife observations on a daily basis. Interested readers can check out a wide range of ongoing citizen science projects (not limited to wildlife-related projects) at scistarter.com and search for specific projects by topic and/or location. As of the time of writing, searching projects at scistarter.com by the topic "Animals," "Birds," and "Insects & Pollinators" returned 382, 162, and 190 projects,

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

data for scientific inquires [22, 23].

decision-making in conservation [31].

**2.1 Citizen science**

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

geographic information (VGI) [20]. One prominent example is the eBird citizen science project that is driven by bird watchers and documenting bird species across the globe [21]. On the other hand, the advancements in GIS capabilities (e.g., geovisualization and spatial analysis) have made it possible to accommodate the data quality issues associated with citizen-contributed data to make use of such data for scientific inquires [22, 23].

This chapter offers an overview of citizen science for wildlife data collection and its integration with GIS for wildlife habitat assessment. A case study of habitat assessment for the black-and-white snub-nosed monkey (*Rhinopithecus bieti*) using data contributed by local residents in Yunnan, China, is presented as an illustration.
