Integrating multi-source data for wildlife habitat mapping: A case study of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China

Abstract

Wildlife habitat mapping is a widely used tool for supporting decision making in conservation. It requires data indicating wildlife habitat use to model and map habitat suitability. Collecting wildlife data, however, requires much effort, especially for species in remote mountainous regions of limited accessibility. Such circumstances often necessitate the integration of limited amounts of data available from multiple sources for habitat mapping. To that end, this study presents a framework for integrating multi-source wildlife data for habitat mapping. For evaluating the integration framework, a case study of mapping habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) by integrating sightings elicited from local volunteer villagers and obtained from official patrol records was conducted in Yunnan, China. The integration was explored at three levels: data-, knowledge- and model-level following disparate principles. The predicted habitat suitability maps were validated against monkey occurrence data independently collected though field-tracking. Results show the suitability maps predicted based on data integration were more accurate compared to maps predicted based on individual data sources. Data- and model-level integration achieved higher accuracy compared to knowledge-level integration. Further, data- and model-level integration following a conservative principle, i.e., the ‘minimum’ operator, led to higher mapping accuracy. The integration framework is generally applicable for integrating data from multiple sources for habitat mapping. It is also easy to implement and thus can be conveniently adopted by practitioners. Habitat suitability maps generated based on integrated data from multiple sources could better supporting decision making in biodiversity monitoring and conservation.