The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (B cont (0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the fieldobserved distribution of R. bieti. B cont (0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (−0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.
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Rapid global deforestation has forced many of the world's primates to live in fragmented habitats, making the understanding of their behavioral responses to degraded and fragmented habitats a key challenge for their future protection and management. The black-and-white snub-nosed monkey (Rhinopithecus bieti) is an endangered species endemic to southwest China. The forest habitat ranges from near-continuous to fragmented. In this study, we investigated the activity budget and diet of a R. bieti population that live in an isolated and degraded habitat patch at Mt. Lasha in Yunnan Province, near the current southern limit of the species. We used our data along with data from six other sites in more-continuous habitats across its range to model factors that predict stress, including feeding effort and time feeding on lichens against potential predictive parameters. Models showed feeding effort across all sites increased with increasing altitude and latitude, and with decreasing food species diversity. There was also a strong positive relationship between feeding effort and time feeding lichens. The Mt. Lasha R. bieti population exploited a total of 36 food species, spending 80.2% of feeding time feeding on lichens, Bryoria spp. and Usnea longissima. These figures are more comparable to those living in the north than those living in the mid- and southern part of the species' range. Given the models for feeding effort and time feeding on lichens, the unexpectedly high time spend feeding on lichens and feeding effort relative to latitude and elevation are suggestive of a stressed population at Mt. Lasha.
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