Integrating Citizen Science and GIS for Wildlife Habitat Assessment

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

Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

The performance of species distribution models (SDMs) is known to be affected by analysis grain and positional error of species occurrences. Coarsening of the analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in SDMs, it is important to test this assumption. Models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate occurrences might easier end up in unsuitable environment. This can result in inappropriate conservation actions. Here, we examined the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated narrow niche virtual species using environmental variables derived from LiDAR point clouds at 5 x 5 m fine-scale. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three modelling techniques (MaxEnt, BRT and GLM) and evaluated their discrimination ability, niche overlap with virtual species and change in realized niche. We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance. We recommend fitting models with the finest possible analysis grain and as close to the response grain as possible even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrences, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences. Our findings are also applicable to coarse analysis grain, especially for fragmented habitats, and for species with narrow niche breadth

Wildlife Population Monitoring

Wildlife management is about finding the balance between conservation of endangered species and mitigating the impacts of overabundant wildlife on humans and the environment. This book deals with the monitoring of fauna, related diseases, and interactions with humans. It is intended to assist and support the professional worker in wildlife management