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AbstractIn many traditional societies, beliefs and taboos influence human behaviour towards the natural environment. Such beliefs and taboos are informal institutions where norms rather than official laws determine land use and nature protection in general and wildlife in particular. The present study on beliefs and taboos of the people of the northern periphery of the Korup National Park is an attempt to reveal how norms influence their relation to the environment. A total of 195 households were sampled through a household survey conducted in four villages. The results revealed that before the application of "modern" approaches for wildlife protection and management, the people relied on norms to establish a relationship with wildlife and nature. The enactment of the 1994 legislation on forest, wildlife and fisheries resulted in stiff resistance as it contradicts traditional norms. It was found that 57.4 % of the respondents still perceive wildlife as a resource that can never get extinct. Traditional norms had a differentiated impact especially on game protection. The study recommends that a wildlife benefiting code of beliefs and taboos is developed to provide a basis for establishing a synergy between park management laws and traditional belief/taboo systems that drive the management of wildlife.

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.