Abstract

With the rapid advancement and popularity of geospatial technologies such as location-aware smartphones, mobile maps, etc., average citizens nowadays can easily contribute georeferenced wildlife data (e.g., wildlife sightings). Due to the wide spread of human settlements and lengthy living histories of citizens in their local areas, citizen-contributed wildlife data could cover large geographic areas over long time spans. Citizen science thus provides great opportunities for collecting wildlife data of extensive spatiotemporal coverage for wildlife habitat assessment. However, citizen-contributed wildlife data may be subject to data quality issues, for example, imprecise spatial position and biased spatial coverage. These issues need to be accounted for when using citizen-contributed data for wildlife habitat assessment. Geovisualization and geospatial analysis capabilities provisioned by geographic information systems (GISs) can be adopted to tackle such data quality issues. This chapter offers an overview of citizen science as a means of collecting wildlife data, the roles of GIS to tackle the data quality issues, and the integration of citizen science and GIS for wildlife habitat assessment. A case study of habitat assessment for the black-and-white snub-nosed monkey (Rhinopithecus bieti) using R. bieti sightings elicited from local villagers in Yunnan, China, is presented as a demonstration.

Highlights

  • IntroductionHabitats 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]

  • From a geographic and geographic information systems (GISs) perspective, citizen science involving geospatial data generation is called “geographic citizen science” [34] and the georeferenced wildlife observations are a form of VGI [20, 34]

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Summary

Introduction

Habitats provide resources such as food, shelter, potential nesting sites, and mates for wildlife to achieve survival and reproduction [1]. The key for wildlife habitat assessment through habitat suitability mapping lies in obtaining knowledge on the relationship between wildlife habitat suitability and environmental conditions (environmental niche). 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 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. 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

Citizen science
The (dis)advantages of citizen science for collecting wildlife data
The data quality issues of citizen-contributed wildlife data
Data credibility
Positional accuracy
Spatial bias
The roles of GIS
Geovisualization to improve positional accuracy
Geospatial analysis to tackle spatial bias
Geocomputation to enable big data analysis
Species and study site
Wildlife data elicited from local villagers
Environmental data
Accounting for positional uncertainty and spatial bias
Habitat assessment
Conclusions
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