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

### **2.1 Citizen science**

*Wildlife Population Monitoring*

(environmental niche).

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

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

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

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

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

to support and sustain conservation programs with limited budget.

wildlife habitat suitability for wildlife habitat assessment.

world's poorest and remote countries [16].

**20**

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 decision-making in conservation [31].

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,

respectively. As a prominent example, the eBird project [21, 35], launched in 2002 by the Ornithology Lab at Cornell University and the National Audubon Society, as of November 2016, has engaged over 330,000 bird watchers from more than 250 countries who have reported observations of over 10,300 bird species. As of June 2018, eBird has accumulated over 500 million bird observations in its global database; in recent years, there have been more than 100 million bird observations added to the database each year.

Wildlife data contributed by participants in such citizen science projects are a form of geospatial big data [36, 37]. Complex patterns can be discovered from such intensive data through visualizations, simulations, data mining, and various modeling techniques to provide valuable insight for forming concrete hypotheses about the underlying ecological, biological, and geographical processes that generated the observed data [37]. Thus, the abundance of citizen-contributed wildlife data has the potential of shifting research paradigm in biological, ecological, and geographical studies from the traditional hypothesis-driven approach to the emerging datadriven approach; for instance, scholars are promoting the idea of "data-intensive science" for biodiversity studies and "data-driven geography" [36–38].

#### **2.2 The (dis)advantages of citizen science for collecting wildlife data**

Citizen science has several advantages as an alternative mechanism for collecting wildlife data. Citizen-contributed data contain rich local information that spans a wide temporal spectrum because citizens, as local experts and sensors [20], have long been sensing and accumulating knowledge of their respective areas. Citizen science also has the potential to provide wildlife data over large areas, given that billions of networked human sensors are distributed across the globe. In addition, citizen science can provide timely updated wildlife data that are difficult to obtain and maintain through other techniques but can be easily elicited from citizens living in the local areas. Moreover, citizen-contributed data are much less expensive than traditional scientific data collection protocols (e.g., biological survey). In many cases, citizens contribute data purely voluntarily [20]. This low cost is of great practical significance in many real-world programs falling short of funding support.

Due to the above advantages of citizen science, it is possible to obtain timely updated wildlife data using citizen science over large areas. Citizen science thus has a great potential to support and sustain long-time wildlife population monitoring at large spatial scale (e.g., eBird) and provide wildlife data for wildlife habitat assessment.

In spite of the strengths, one should be aware of the shortcomings of the "citizen science" approach to wildlife data collection. For example, this approach cannot be used in areas with low population where sufficient local citizen observers/ informants are lacking. It is also not good for collecting data on evasive animals with little contact with humans. Most importantly, there can be data quality issues associated with wildlife data contributed by volunteer citizens (i.e., non-professionals) which make the data challenging to standardize and analyze [17, 18]. The following sections detail some of the data quality issues, their implications for wildlife habitat assessment, and how GIS techniques (geovisualization, geospatial analysis, geocomputation, etc.) can be adopted to tackle the issues toward reducing the impact of such issues on wildlife habitat assessment.

#### **2.3 The data quality issues of citizen-contributed wildlife data**

The quality of citizen-contributed wildlife data is the major concern when using such data for wildlife habitat assessment. The average citizens engaged in citizen science projects are not well-trained professionals; their voluntary data collection

**23**

*2.3.3 Spatial bias*

*Integrating Citizen Science and GIS for Wildlife Habitat Assessment*

actions are mostly constrained by internal commitment. Thus, citizen-contributed wildlife data may or may not be accurate [20, 39]. Three aspects of data quality are particularly relevant to the use of citizen-contributed wildlife data for wildlife

In order to be useful for wildlife habitat assessment, wildlife data (e.g., sightings) reported by citizen participants need to be credible, that is, provide ground truth wildlife observations. Data credibility is affected by the characteristics of both the wildlife and the citizen observers (e.g., local residents). On the one hand, local residents often only observe wildlife that is active in the daytime. The target wildlife should be easily recognizable to reduce misidentification given that local residents usually have no training on species identification [17, 40]. On the other hand, local resident knowledge of the target wildlife, age, length of residence, and formal education also influence data credibility [41]. For instance, performance in georeferencing tasks differs between novice and expert citizen participants [42]; there exists both between-observer differences [43] and within-observer differ-

Various methods have been developed for increasing the credibility of citizencontributed wildlife data. Ref. [45] identified a total of 12 strategies that have been adopted by citizen science programs to increase their data credibility across different program stages including training and planning, data collection, and data analysis and program evaluation. As an example, eBird uses a two-part approach to assure data credibility during data entry [39]: automated data quality filters flag records for review based on observation date and geographic location; a flagged entry, once confirmed as legitimate by the observer, is then reviewed by a regional

Position of the wildlife data used for habitat suitability mapping needs to be accurate so that the locations can be used to accurately obtain the corresponding environmental conditions at these locations from environmental data layers. Insufficient positional accuracy of wildlife data leads to mismatch between the locations of wildlife habitat use and the corresponding environmental conditions, and thus degrades the accuracy of environmental niche modeling and habitat suitability mapping [46].

Nonetheless, it is also important to note that the impact of positional accuracy of wildlife data on habitat suitability mapping depends on the spatial resolution at which suitability mapping is conducted. Mapping at high spatial resolution (e.g., using environmental data of 30 m × 30 m grids) definitely requires wildlife data of high positional accuracy that is comparable to the spatial resolution of the environmental data so that values of the environmental conditions at these locations can be accurately extracted from environmental data layers. In contrast, for mapping at coarse spatial resolution (e.g., 1000 m × 1000 m grids), the absolute positional accuracy of wildlife data does not have to be very high as long as it is accurate enough relative to

Wildlife observations contributed by citizens are often concentrated more in some geographic areas than others (i.e., spatial bias) because observations made

habitat assessment: data creditability, positional accuracy, and spatial bias.

ences (over time) [44] in BBS participant bird-counting skills.

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

*2.3.1 Data credibility*

expert reviewer again.

*2.3.2 Positional accuracy*

the spatial resolution of environmental data in use.

actions are mostly constrained by internal commitment. Thus, citizen-contributed wildlife data may or may not be accurate [20, 39]. Three aspects of data quality are particularly relevant to the use of citizen-contributed wildlife data for wildlife habitat assessment: data creditability, positional accuracy, and spatial bias.
