Habitat loss and hunting threaten bonobos (Pan paniscus), Endangered (IUCN) great apes endemic to lowland rainforests of the Democratic Republic of Congo. Conservation planning requires a current, data-driven, rangewide map of probable bonobo distribution and an understanding of key attributes of areas used by bonobos. We present a rangewide suitability model for bonobos based on a maximum entropy algorithm in which data associated with locations of bonobo nests helped predict suitable conditions across the species’ entire range. We systematically evaluated available biotic and abiotic factors, including a bonobo-specific forest fragmentation layer (forest edge density), and produced final model revealing the importance of simple threat-based factors in a data poor environment. We confronted the issue of survey bias in presence-only models and devised a novel evaluation approach applicable to other taxa by comparing models built with data from geographically distinct sub-regions that had higher survey effort. The model’s classification accuracy was high (AUC = 0.82). Distance from agriculture and forest edge density best predicted bonobo occurrence with bonobo nests more likely to occur farther from agriculture and in areas of lower edge density. These results suggest that bonobos either avoid areas of higher human activity, fragmented forests, or both, and that humans reduce the effective habitat of bonobos. The model results contribute to an increased understanding of threats to bonobo populations, as well as help identify priority areas for future surveys and determine core bonobo protection areas.