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We used data on number of carcasses of wildlife species sold in 79 bushmeat markets in a region of Nigeria and Cameroon to assess whether species composition of a market could be explained by anthropogenic pressures and environmental variables around each market. More than 45 mammal species from 9 orders were traded across all markets; mostly ungulates and rodents. For each market, we determined median body mass, species diversity (game diversity), and taxa that were principal contributors to the total number of carcasses for sale (game dominance). Human population density in surrounding areas was significantly and negatively related to the percentage ungulates and primates sold in markets and significantly and positively related to the proportion of rodents. The proportion of carnivores sold was higher in markets with high human population densities. Proportion of small-bodied mammals (<1 kg) sold in markets increased as human population density increased, but proportion of large-bodied mammals (>10 kg) decreased as human population density increased. We calculated an index of game depletion (GDI) for each market from the sum of the total number of carcasses traded per annum and species, weighted by the intrinsic rate of natural increase (rmax ) of each species, divided by individuals traded in a market. The GDI of a market increased as the proportion of fast-reproducing species (highest rmax ) increased and as the representation of species with lowest rmax (slow-reproducing) decreased. The best explanatory factor for a market's GDI was anthropogenic pressure-road density, human settlements with >3000 inhabitants, and nonforest vegetation. High and low GDI were significantly differentiated by human density and human settlements with >3000 inhabitants. Our results provided empirical evidence that human activity is correlated with more depleted bushmeat faunas and can be used as a proxy to determine areas in need of conservation action.

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 a 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.Additional co-authors: Omari Ilambu; Bila-Isia Inogwabini; Innocent Liengola; Albert Lotana Lokasola; Alain Lushimba; Joel Masselink; Valentin Mbenzo; Norbert Mbangia Mulavwa; Pascal Naky; Nicolas Mwanza Ndunda; Pele Nkumu; Valentin Omasombo; Gay Edwards Reinartz; Robert Rose; Tetsuya Sakamaki; Samantha Strindberg; Hiroyuki Takemoto; Ashley Vosper; Hjalmar S. Küh

All four chimpanzee sub-species populations are declining due to multiple factors including human-caused habitat loss. Effective conservation efforts are therefore needed to ensure their long-term survival. Habitat suitability models serve as useful tools for conservation planning by depicting relative environmental suitability in geographic space over time. Previous studies mapping chimpanzee habitat suitability have been limited to small regions or coarse spatial and temporal resolutions. Here, we used Random Forests regression to downscale a coarse resolution habitat suitability calibration dataset to estimate habitat suitability over the entire chimpanzee range at 30-m resolution. Our model predicted habitat suitability well with an r 2 of 0.82 (˘0.002) based on 50-fold cross validation where 75% of the data was used for model calibration and 25% for model testing; however, there was considerable variation in the predictive capability among the four sub-species modeled individually. We tested the influence of several variables derived from Landsat Enhanced Thematic Mapper Plus (ETM+) that included metrics of forest canopy and structure for four three-year time periods between 2000 and 2012. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. Because the models were sensitive to such temporally based predictors, our results are the first to highlight the value of integrating continuously updated variables derived from satellite remote sensing into temporally dynamic habitat suitability models to support near real-time monitoring of habitat status and decision support systems.

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