Habitat use by the island lemurs of Nosy Be, Madagascar.

Madagascar's lemurs are threatened by forest loss, fragmentation, and degradation. Many species use flexible behaviors to survive in degraded habitat, but their ability to persist in very small areas may be limited. Insular lemurs, like those found on Nosy Be, an island off the northwestern coast of Madagascar, are at heightened risk of sudden population declines and extirpation. Nosy Be is home to two Critically Endangered species-the endemic Nosy Be sportive lemur (Lepilemur tymerlachsoni) and Claire's mouse lemur (Microcebus mamiratra)-as well as the Endangered black lemur (Eulemur macaco). Most of the remaining forest on Nosy Be is protected by the 862-ha Lokobe National Park. To document how Nosy Be lemurs use their restricted habitat, we conducted vegetation and reconnaissance surveys on 53 transects in and around Lokobe. We collected data on tree size, canopy cover, understory visibility, and elevation for 248 lemur sightings. We used a spatially explicit, multi-species occupancy model to investigate which forest-structure variables are important to lemurs. Our results represent some of the first data on habitat use by insular lemurs. Black lemurs preferred significantly larger trees and areas with less dense understory. They also occurred significantly less outside of Lokobe National Park, even when accounting for sampling effort and geography. The distributions of the sportive and mouse lemurs were not related to the forest structure variables we documented, but they did negatively predict each other-perhaps because their habitat requirements differ. These results also underscore the importance of the national park to protecting the black lemur population on Nosy Be and raise questions about what factors do influence the distribution of Nosy Be's smaller lemurs. Close monitoring is needed to prevent these populations and the ecosystem services they provide from disappearing, as have other island lemurs.

Range-wide indicators of African great ape density distribution.

Species distributions are influenced by processes occurring at multiple spatial scales. It is therefore insufficient to model species distribution at a single geographic scale, as this does not provide the necessary understanding of determining factors. Instead, multiple approaches are needed, each differing in spatial extent, grain, and research objective. Here, we present the first attempt to model continent-wide great ape density distribution. We used site-level estimates of African great ape abundance to (1) identify socioeconomic and environmental factors that drive densities at the continental scale, and (2) predict range-wide great ape density. We collated great ape abundance estimates from 156 sites and defined 134 pseudo-absence sites to represent additional absence locations. The latter were based on locations of unsuitable environmental conditions for great apes, and on existing literature. We compiled seven socioeconomic and environmental covariate layers and fitted a generalized linear model to investigate their influence on great ape abundance. We used an Akaike-weighted average of full and subset models to predict the range-wide density distribution of African great apes for the year 2015. Great ape densities were lowest where there were high Human Footprint and Gross Domestic Product values; the highest predicted densities were in Central Africa, and the lowest in West Africa. Only 10.7% of the total predicted population was found in the International Union for Conservation of Nature Category I and II protected areas. For 16 out of 20 countries, our estimated abundances were largely in line with those from previous studies. For four countries, Central African Republic, Democratic Republic of the Congo, Liberia, and South Sudan, the estimated populations were excessively high. We propose further improvements to the model to overcome survey and predictor data limitations, which would enable a temporally dynamic approach for monitoring great apes across their range based on key indicators.