Integrating Citizen Science and GIS for Wildlife Habitat Assessment

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.

Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping

“…This volunteered effort is prone to biases and limitations related to variability in species, time, locality, and volunteer skills [84,85]. However, spatial bias can be degraded to a certain degree using existing geospatial analytical methods [86] and consensus of the multiple automated point counts can be a natural validation method for the individual counts. Availability of photo-based counts increases the number of volunteer investigators (i.e., observation units) which increases statistical representation power.…”

Uniting citizen science and natural history in China

“…Some biodiversity CS projects focus on species monitoring and habitat protection. The former projects range from general plants (China Biodiversity Observing Network [6], Kunming Botanical Garden project [7]), birds [8] (birdwatching from Shan Shui's China Nature Watch program, the China Coastal Waterbird Count [9]) to specific species such as snow leopard [10] and black-and-white snub-nosed monkey [11]. The projects in the latter category include seagrass assessment for coastal habitats in Hainan [12] and "Ant Forest" afforestation programs in Gansu and Inner Mongolia [13].…”

Are social media reports useful for assessing small ape occurrence? A pilot study from Peninsular Malaysia

“…Gray literature reports from citizen scientists have been effectively deployed to assess the distribution of territorial hornbills in Malaysia (Yeap & Perumal, 2017), but the utility of this method for arboreal mammals, including primates, has yet to be demonstrated. There is reason for caution in applying this method to other taxa, as the quality of data may differ across taxa (Zhang, 2019). “Birding” is a popular pastime in many countries (Callaghan et al, 2018; Steven, Morrison, & Castley, 2014), including Malaysia (Orenstein et al, 2010), and there is a robust birding culture that promotes the sharing of information among birders (and competition among birders may take the form of posting of photographs of rare species).…”

“…There is reason for caution in applying this method to other taxa, as the quality of data may differ across taxa (Zhang, 2019). "Birding" is a popular pastime in many countries (Callaghan et al, 2018;Steven, Morrison, & Castley, 2014), including Malaysia (Orenstein et al, 2010), and there is a robust birding culture that promotes the sharing of information among birders (and competition among birders may take the form of posting of photographs of rare species).…”