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Michael Lukin
Michael Lukin

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There is a rich body of literature addressing the topic of illegal hunting of wild terrestrial mammals. Studies on this topic have risen over the last decade as species are under increasing risk from anthropogenic threats. Sub-Saharan Africa contains the highest number of terrestrial mammals listed as vulnerable, endangered or critically endangered. However, the spatial distribution of illegal hunting incidences is not well documented. To address this knowledge gap, the systematic map presented here aims to answer three research questions: (1) What data are available on the spatial distribution of illegal hunting of terrestrial mammals in Sub-Saharan Africa in relation to environmental and anthropogenic correlates i.e. proximity to roads, water bodies, human settlement areas, different land tenure arrangements and anti-poaching ranger patrol bases? (2) Which research methodologies have primarily been used to collect quantitative data and how comparable are these data? (3) Is there a bias in the research body toward particular taxa and geographical areas?


Systematic searches were carried out across eight bibliographic databases; articles were screened against pre-defined criteria. Only wild terrestrial mammals listed as vulnerable, endangered or critically endangered by the International Union for Conservation of Nature (IUCN) whose geographical range falls in Sub-Saharan Africa and whose threat assessment includes hunting and trapping were included. To meet our criteria, studies were required to include quantitative, spatially explicit data. In total 14,325 articles were screened at the level of title and abstract and 206 articles were screened at full text. Forty-seven of these articles met the pre-defined inclusion criteria.


Spatially explicit data on illegal hunting are available for 29 species in 19 of the 46 countries that constitute Sub-Saharan Africa. Data collection methods include GPS and radio tracking, bushmeat household and market surveys, data from anti-poaching patrols, hunting follows and first-hand monitoring of poaching signs via line transects, audio and aerial surveys. Most studies have been conducted in a single protected area exploring spatial patterns in illegal hunting with respect to the surrounding land. Several spatial biases were detected.


There is a considerable lack of systematically collected quantitative data showing the distribution of illegal hunting incidences and few comparative studies between different tenure areas. The majority of studies have been conducted in a single protected area looking at hunting on a gradient to surrounding village land. From the studies included in the map it is evident there are spatial patterns regarding environmental and anthropogenic correlates. For example, hunting increases in proximity to transport networks (roads and railway lines), to water sources, to the border of protected areas and to village land. The influence of these spatial features could be further investigated through meta-analysis. There is a diverse range of methods in use to collect data on illicit hunting mainly drawing on pre-existing law enforcement data or researcher led surveys detecting signs of poaching. There are few longitudinal studies with most studies representing just one season of data collection and there is a geographical research bias toward Tanzania and a lack of studies in Central Africa.


Analysing spatial variation in poaching incidences is of increasing relevance as an increasing number of tools using Artificial Intelligence (AI) and Machine Learning are developed to predict where illegal hunting incidents will take place [17]. Analysing patrol data is complex because of biases in the way data are collected i.e. rangers more frequently patrol around posts and locations where carcasses were previously detected. Improved analytical insight is now possible due to improved modelling capabilities, which work to account for collection bias [18], however, outputs will always be highly dependent on the quality of the input data. One model using data from the Serengeti, Tanzania assessed the cost and benefit of different poaching hotspots in the landscape. The model made high accuracy predictions when compared with areas where animals were subsequently snared [19]. Another predictive spatiotemporal model applied in Uganda evaluated against 5 months of field data was able to successfully predict poaching locations [18].


Generating an overview of the spatial distribution of illegal hunting incidences in Sub-Saharan Africa can guide conservation efforts. The map outlines the research methodologies that have been used to collect illegal poaching data.


(1) What data are available on the spatial distribution of illegal hunting of terrestrial mammals in Sub-Saharan Africa in relation to environmental and anthropogenic correlates i.e. proximity to roads, water bodies, human settlement areas, different land tenure arrangements and anti-poaching ranger patrol bases? (2) Which research methodologies have primarily been used to collect quantitative data and how comparable are these data? (3) Is there a bias in the research body toward particular taxa and geographical areas?


The map analyses comparable static geographical information across various locations. It is recognised that there are important sociological drivers of illegal hunting i.e. education level, wildmeat value chains and alternative sources of protein, among others, however, these are not the focus of the study.


Population: Wild terrestrial mammals listed as vulnerable, endangered or critically endangered by the IUCN for whom the threat assessment includes hunting and trapping and whose geographical range falls in Sub-Saharan Africa (listed in Additional file 1).


The protocol of this review was published in November 2018 [20]. The protocol largely focuses on how tenure influences illegal hunting, however, once the review was underway the focus shifted toward the spatial distribution of illegal hunting in relation to numerous geographic variables e.g. proximity to water, transport networks, ranger patrol posts. This shift was necessary because it became apparent that the majority of studies analyse illegal hunting in one protected area looking at distribution in relation to surrounding village land. Many articles that analyse illegal hunting do not go into detail on what constitutes illegality, cross-border trafficking chains complicate legality as legal status changes across borders. It was decided that if the study referred to hunting as illegal it would be included.


The focus is on terrestrial mammals that are listed as vulnerable [21], endangered (EN) or critically endangered (CR) on the IUCN Red List. This is the global authoritative list of species in decline. Species included were further restricted to those for whom the IUCN threat assessment includes hunting and trapping as a key threat of which there are 172 species (listed in Additional file 1). The regional area of focus is Sub-Saharan Africa, as defined by the United Nations inclusive of 46 countries (Additional file 1). Many studies include multiple predator and prey species or use the catch-all expression bushmeat, if one species listed met the inclusion criteria the study was included.


The exposure of the populations outlined above to illegal hunting is the focus of this paper. While it is not necessary for a study to explicitly state the reason for the hunt, e.g. local subsistence hunting or transnational trafficking, the location of the kill must be included. The focus is on unregulated illegal hunting, hence studies on trophy hunting were excluded as this is a legal form of regulated hunting where quotas are set considering local population dynamics. Studies looking at mortality from zoonotic disease or other anthropogenic causes were also excluded.


Various kinds of study designs are included in the map as shown in the results (Fig. 3). As the search progressed it became evident that including only studies that explicitly mention property rights arrangements would yield very few eligible studies despite shifting land use and ownership being an international cause of concern for wildlife. It was decided that studies would be included so long as the status of the land was mentioned, e.g. protected area, village land, rather than the explicit ownership arrangement. Various environmental and anthropogenic correlates were assessed between studies i.e. proximity of illegal hunting incidences to roads, water bodies, human settlement areas and anti-poaching ranger patrol bases.


The evidence has to be geolocated. The locations of the kill sites were required to include primary data and not via referenced data from other studies. Data collected first hand can include records of carcass locations or signs of hunting, i.e. used shrapnel, snares, hunter arrest records or via hunter follows, interviews and/or surveys. Variation in the number of species consumed or sold is also an eligible outcome if the capture location(s) is included.


Demographic studies containing data only on species abundance and distribution were excluded. Similarly, studies that only contain data on species behaviour in response to a perceived threat, e.g. monitoring flight initiation time were excluded. Studies that infer the level of illegal