Range‐wide indicators of African great ape density distribution

Abstract

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.

All-You-Can-Eat: Influence of Proximity to Maize Gardens on the Wild Diet and the Forest Activities of the Sebitoli Chimpanzee Community in Kibale National Park

“…Since the forest appears to provide sufficient resources and maize is not an essential fallback food for the survival of Sebitoli chimpanzees, we recommend the installation of buffer zones with unpalatable crops between the fields and the forest. Although the majority of great apes live outside of protected areas where human activities are expanding [ 130 ], improving human–ape cohabitation at the border of preserved habitats is essential for the conservation of these threatened species.…”

Threat of mining to African great apes

“…Countries with the largest overall overlaps in ape population abundance and mining areas (in terms of numbers of apes potentially affected) included Gabon, Congo, and Cameroon in Central Africa and Guinea in West Africa (table S2). Although our dataset included fewer mining areas in Central (12% of total mining areas) than in East (27%) and West African range countries (61%), more individual apes would potentially be threatened by mining in this region, because of higher overall ape densities in this region ( 42 ). Countries that had the largest proportional overlaps between ape population abundance and mining areas (in terms of proportion of population potentially affected) included Liberia, Sierra Leone, Mali, and Guinea, all of which are located in West Africa ( Fig.…”

Threat of mining to African great apes

“…Countries with the largest overall overlaps in ape population abundance and mining areas (in terms of numbers of apes potentially affected) included Gabon, Congo and Cameroon in Central Africa, and Guinea in West Africa (Table S2). Although our dataset included fewer mining areas in Central-(12% of total mining areas) than in East-(27%) and West African range countries (61%), more individual apes would potentially be threatened by mining in this region, because of higher overall ape densities in this region ( 41 ). Countries that had the largest proportional overlaps between ape population abundance and mining areas (in terms of proportion of population potentially affected), included Liberia, Sierra Leone, Mali, and Guinea, are all located in West Africa (Fig.…”

“…We used Mollweide equal area projection to analyze all data listed in Table 1 and matched all spatial layers at a 1*1 km pixel resolution. We combined great ape density distributions modeled by ( 40 ) and ( 7, 41 ), and mapped this for each ape range country in Africa (referred to as “range country” throughout the text). We excluded the DRC and the Central African Republic from analysis due to a lack of ape density information ( 41 ).…”

“…We combined great ape density distributions modeled by ( 40 ) and ( 7, 41 ), and mapped this for each ape range country in Africa (referred to as “range country” throughout the text). We excluded the DRC and the Central African Republic from analysis due to a lack of ape density information ( 41 ). However, in DRC there is extensive mining occurring within the Eastern chimpanzee and Grauer gorilla’s range, inside and outside protected areas, and thus the impact on their population is likely significant ( 44 ).The metric for ape density distribution is the number of apes per pixel.…”