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Provisioning can significantly affect the ranging patterns, foraging strategies, and time budget of wild primates. In this study, we document for the first time, the effects of provisioning on the activity budget and foraging effort in an Asian colobine. Over 3‐years, we used an instantaneous scanning method at 10‐min intervals to collect data on the activity budget of a semiprovisioned breeding band (SPB) of black‐and‐white snub‐nosed monkeys (Rhinopithecus bieti) (42–70 individuals) at Xiangguqing (Tacheng), Yunnan, China. We then compared the effects of provisioning in our study band with published data on a sympatric wild nonprovisioned breeding band (NPB) of R. bieti (ca. 360 monkeys) at the same field site. The SPB spent 25.6% of their daytime feeding, 17.1% traveling, 46.9% resting, and 10.3% socializing. In comparison, the NPB devoted more time to feeding (34.9%) and socializing (14.1%), less time to resting (31.3%), and was characterized by a greater foraging effort (1.74 versus 0.96, foraging effort = (feeding + traveling)/resting; see Methods). There was no difference between bands in the proportion of their activity budget devoted to traveling (15.7% vs. 17.1%). In addition, the SPB exhibited a more consistent activity budget and foraging effort across all seasons of the year compared to the NPB. These findings suggest that the distribution, availability, and productivity of naturally occurring feeding sites is a major determinant of the behavioral strategies and activity budget of R. bieti. Finally, a comparison of our results with data on six nonprovisioned R. bieti bands indicates that caution must be raised in meta‐analyses or intraspecific comparisons of primate behavioral ecology that contain data generated from both provisioned and nonprovisioned groups. Research Highlights: We studied the activity budget of a semiprovisioned band of black‐and‐white snub‐nosed monkeys and compared the results with six wild nonprovisioned bands.The semiprovisioned band spent less time feeding, more time resting, and had a lower foraging effort than the nonprovisioned bands.Caution must be exercised in conducting meta‐analyses when using data from both provisioned and nonprovisioned primate bands. [ABSTRACT FROM AUTHOR]

• A framework for integrating multi-source wildlife data for habitat mapping. • A case study of Rhinopithecus bieti habitat mapping was conducted in Yunnan, China. • Sightings from volunteer villagers and official patrol records were integrated. • Data integration improved habitat mapping accuracy. • The integration framework can support biodiversity monitoring and conservation. 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. [ABSTRACT FROM AUTHOR]