Long-term overharvesting of wild animals for their meat threatens wildlife and the people dependent on wild animal meat for their diets and incomes. Interventions to reduce wild meat consumption must be built upon a complete understanding of the roles of wild meat and its alternatives within food systems. Here, we conduct a national-scale analysis of how urbanization, market access and price impact the use of wild and alternative meat and fish in Gabon. We obtained data on the acquisition and consumption of wild and alternative meat and fish for >6900 households from the WILDMEAT database, the largest dataset for Gabon to date. We then analysed associations between settlement size, market access, and price with the probability of consuming wild meat, alternatives, or no meat, and how these foodstuffs were acquired by households. We found the probability of consuming wild meat and no meat to be negatively associated with settlement population size, whereas consumption of alternative meats was more likely in larger settlements. In villages, consumption of both wild and alternative meats became more likely as market access increased. Consumption of all meat types was then negatively associated with price, except traded fish products, which were consumed more in villages at higher prices. Acquiring meat through hunting and fishing was more likely in the most isolated and smallest villages and, as population sizes and market access increased, buying meat became more likely. Our results suggest that more isolated, rural households depend on harvesting wild meat and fish from the environment, alongside a narrow range of traded, tinned fish products, as alternatives to hunting and fishing. Conversely, households in larger settlements and high-market-access villages can purchase and consume alternative meats and traded wild meat. Policy Implications: In Gabon, settlements >3500 people, where most wild meat is bought and alternatives are usually available, may suit market-based and behaviour change interventions. Settlements of 900-3500 people may be effective targets of livelihood support projects. Nutritional analyses should be conducted in settlements <900 people, to understand the conditions under which wild meat is essential to nutritional security.

Camera-trap monitoring in African tropical forests increasingly extends beyond closed-canopy interiors to riverbanks, clearings, and park edges. Among available open tools for African forest camera-trap classification, DeepForestVision is the only one providing a matched offline workflow for both photographs and videos, and previous work showed that it outperformed other available baselines on a comparable benchmark. However, it was designed for closed-canopy, ground-level forest interiors and uses a 35-class prediction space that becomes too coarse when deployments encounter arboreal primates, birds, semi-aquatic taxa, or human-associated confounders such as livestock. We present DeepForestVisionV2, an ecology-driven expansion from 35 to 64 prediction classes (61 animal classes plus human, vehicle, and blank) designed to address three recurrent deployment gradients: vertical stratification, scene openness, and anthropogenic interfaces. DeepForestVisionV2 retains the same offline workflow and is trained on 1,535,010 photographs and 243,354 videos from multi-country African tropical-forest projects. Evaluation combines a cross-country cropped-photo validation set, used to assess robustness across sites and camera-trap settings, with three held-out Uganda video benchmarks spanning the targeted gradients. On the validation set, DeepForestVisionV2 reaches 0.86 accuracy, 0.82 macro-F1, and 0.81 balanced accuracy. On the deployment benchmarks, it preserves or improves baseline accuracy despite its harder classification task, while increasing the number of identified taxa from 22 to 29 in forest-interior videos and from 4 to 9 at riverbanks. In the park-edge use case, it raises accuracy from 0.62 to 0.86 and reduces false alarms from 11 to 0. These results show that DeepForestVisionV2 materially improves field utility while preserving robustness across sites, habitats, and camera-trap settings.

To decide where and when to move, animals combine memorised information with environmental cues. Wind speed and direction can affect the way animals perceive the environment by reducing the detection and shaping the spatial distribution of sensory cues. Although these cues are expected to be used for forage localisation and predation avoidance in large herbivores, for instance, we still do not know to what extent wind can ultimately influence their large-scale movement decisions. To tackle this knowledge gap, we used GPS data from four species of large African herbivores experiencing contrasted predation risks (blue wildebeest, Connochaetes taurinus, plain zebras, Equus quagga, African buffalo, Syncerus caffer, African elephant, Loxodonta africana), in multiple protected areas. We first investigated whether individuals reduce predation risk by avoiding long-distance movements under windy conditions. We then analysed whether they favour moving upwind to maximise their information gain as they travel. We found no clear decrease in the largest step length as wind speed increases and suggest that local habitat could buffer the strength of wind speed (i.e. topography or vegetation). We, however, found that large herbivores tend to move upwind rather than cross- or downwind, although the effect was generally small. We point out that individuals might be more constrained in their use of cues carried by the wind than initially thought, due to the existence of dominant wind directions at all sites. Altogether, our study suggests that wind has little general, consistent, effects on large herbivore movement decisions. Some sites or species-specific results, however, call for delving deeper into the context-specificity of wind effects.

While human activities are driving widespread declines in wildlife populations1,2, in Central Africa, the meat of wild animals, or wild meat, represents a major component of the diets of millions of people3. To halt faunal degradation while ensuring sustainable use of wildlife, it is crucial to understand the scale and drivers of wild meat consumption. Here, using data from over 12,000 households from 252 locations in Central Africa, we show that wild meat is a fundamental component of the diets of rural populations, accounting for 20% of the recommended daily protein intake, compared with 13% and 6% for those living in towns and cities. We estimate that the total annual biomass of wild meat consumed in Central Africa increased from 0.73 million to 1.10 million tonnes between 2000 and 2022, with increasing demand from towns and cities. To ensure that wild meat is available to rural communities, in accordance with the Sustainable Development Goals4 and the Kunming-Montreal Global Biodiversity Framework5, reducing wild meat consumption in urban metropolises is key. While our results are based on the most comprehensive dataset available, the geographical coverage is incomplete and the dataset represents a minimal fraction of the entire population of Central Africa. Targeted studies are needed to validate our model and assess critical areas of intervention.

Abstract Background: Approximately 60% of recently emerging infectious diseases are zoonotic, with over 70% originating from wildlife. These diseases have significant public health and economic impacts globally, as exemplified by the Ebola epidemics. Community-based surveillance is increasingly recognized as a corner-stone of early warning and prevention systems of zoonotic spillover. In sub-Saharan Africa, wildlife hunting is a vital livelihood activity for rural communities, positioning hunters as frontline observers of wildlife health and potential early indicators of zoonotic threats. Method: We designed and piloted a community-based zoonoses surveillance system in Ogooué-Lolo, eastern Gabon. The study utilized a participatory co-construction approach aimed at engaging all relevant stakeholders. It proceeded in three steps, namely (1) the assessment of existing actors, resources, and barriers relevant to zoonotic disease surveillance; (2) the co-design process of the pilot surveillance system involving community members and institutional stakeholders; and (3) the implementation of the pilot surveillance system. Results: The assessment phase revealed specific challenges related to zoonoses surveillance among involved actors, including unavailability of local animal health services, limited communication between communities and the administration, and limited knowledge of zoonotic risks and capacity to recognize disease suspicions among communities. A One Health approach relying on the collaboration of different sectors (public health, agriculture, and environmental services) was adopted in response, a research institute being responsible for training local actors in disease suspicion recognition and in conducting investigations and laboratory analyses. The pilot surveillance system successfully detected and managed 10 disease suspicions, demonstrating its operational feasibility. Discussion: We demonstrate the feasibility of early warning zoonotic disease surveillance in the challenging context of wild meat systems of Central Africa, using a community-based One Health approach. Further investment in community training, animal health expertise, and financial and material resources is needed to ensure sustainability and scalability of the system. Continued engagement of stakeholders at all levels will be critical to the long-term success of this One Health surveillance model. One Health impact statement Early detection of zoonotic diseases at the wildlife-domestic animal-human interface presents a significant global health challenge. Our study demonstrates the feasibility of establishing a community-based, cross-sectoral surveillance system tailored to the wild meat value chain, where hunters in remote areas serve as primary observers of unusual animal morbidity and mortality. Through the application of participatory methods, we identified key actors, available resources, and barriers to effective surveillance and co-designed a system that integrates hunter communities, government services in the three key One Health sectors, and research institutions. This inclusive process enhanced the acceptance and operationalization of the system. The approach offers a scalable model for One Health surveillance in similar high-risk contexts across Central Africa and beyond.

Tropical forests hold most of Earth's biodiversity and a higher concentration of threatened mammals than other biomes. As a result, some mammal species persist almost exclusively in protected areas, often within extensively transformed and heavily populated landscapes. Other species depend on remaining remote forested areas with sparse human populations. However, it remains unclear how mammalian communities in tropical forests respond to anthropogenic pressures in the broader landscape in which they are embedded. As governments commit to increasing the extent of global protected areas to prevent further biodiversity loss, identifying the landscape-level conditions supporting wildlife has become essential. Here, we assessed the relationship between mammal communities and anthropogenic threats in the broader landscape. We simultaneously modeled species richness and community occupancy as complementary metrics of community structure, using a state-of-the-art community model parameterized with a standardized pan-tropical data set of 239 mammal species from 37 forests across 3 continents. Forest loss and fragmentation within a 50-km buffer were associated with reduced occupancy in monitored communities, while species richness was unaffected by them. In contrast, landscape-scale human density was associated with reduced mammal richness but not occupancy, suggesting that sensitive species have been extirpated, while remaining taxa are relatively unaffected. Taken together, these results provide evidence of extinction filtering within tropical forests triggered by anthropogenic pressure occurring in the broader landscape. Therefore, existing and new reserves may not achieve the desired biodiversity outcomes without concurrent investment in addressing landscape-scale threats.

In the face of the escalating frequency of diseases emergences originating from wildlife, the development of reliable strategies for controlling zoonotic diseases transmission at the interface between wildlife and human is becoming a global priority. Rural communities whose subsistence is based on hunting for wild meat extraction are natural targets of such interventions, because of their regular contacts with wildlife. To date there have been few attempts at building preventive sanitary strategies taking into account the socioeconomic and institutional constraints in which wild meat systems operate. The study presented here, conducted in eastern Gabon, aimed at conceiving risk-reduction strategies of zoonotic diseases transmitted from wildlife in a two-phase approach, namely (1) an assessment phase, based on a survey on risk knowledge and practices conducted with members of communities living on wild meat, and (2) a co-design phase based on focus group discussions to identify acceptable prevention strategies aimed at limiting the contacts creating the major risks of exposure to zoonoses infections. The use of participatory methods aiming at eliciting issues and solutions from the participants, enabled the conception of strategies that were adapted to the context and well accepted by stakeholders at different stages, namely the track, capture, transport of wild animals, the butchering of carcasses, cooking and consumption process. However, some limitations to the effective application of the strategies can be anticipated notably because of (1) the current low and biased perception of zoonotic risks by wild meat actors, and (2) the economic incentives for maintaining risky behaviors like the capture and trade of live animals and the consumption or sale of animals found dead or displaying signs of disease infection.

Tropical forests are rich in biodiversity but face the rapid loss of their wildlife due to increasing anthropogenic pressure, underscoring the urgent need for effective monitoring. Remote-sensing tools such as camera traps offer faster, less invasive alternatives to human observations. These technologies can provide complementary insights into elusive species, especially in habitats that are difficult to access through direct observation. However, analysing the large volumes of data they produce is labour-intensive, often leaving datasets underutilised due to limited human resources. Deep learning algorithms can automate aspects of data analysis, but their value to research and conservation efforts depends on their ability to reliably identify target species and be easily deployed in field conditions. To improve wildlife monitoring in African forests using camera trap data, we develop DeepForestVision, the first deep learning algorithm tailored to these challenging habitats that can be used in the field to process both photographs and videos. DeepForestVision was trained on an unprecedented dataset of 2,775,671 photographs and 221,982 videos gathered from camera traps from more than 63 research sites across 11 African countries. It identifies 33 non-human vertebrate taxa, including 31 mammal taxa, from the most common to the most threatened ones observed on ground-level camera traps, as well as humans, vehicles and blank photographs or videos. Classification tests demonstrate that DeepForestVision achieves an accuracy of 87.7% on the video test set with 23 taxa. It outperforms the three existing species identification algorithms applicable to these environments: Zamba by 13.1%, Mbaza by 45.0% and SpeciesNet by 37.7%. We provide the model weights for researchers and developers and offer DeepForestVision through a free offline interface. The interface is designed to function in the field in a low-resource setting and requires no programming expertise. Solution: DeepForestVision is a reliable field tool for monitoring species observed on camera trap photos and videos in African tropical forests. Used by research, conservation, private or political actors, it can guide conservation strategies inside and outside of protected areas, and thus contribute to reducing the loss of biodiversity in African tropical forests. Les for & ecirc;ts tropicales africaines sont les habitats terrestres les plus riches en biodiversit & eacute;, mais elles subissent une perte rapide de leur faune en raison de la pression anthropique croissante. Afin de r & eacute;pondre & agrave; l'urgence d'un suivi efficace de cette biodiversit & eacute;, les outils de t & eacute;l & eacute;d & eacute;tection comme les cam & eacute;ras & agrave; d & eacute;tection automatique offrent des alternatives plus rapides et moins invasives que les observations humaines, en particulier pour des esp & egrave;ces cryptiques et dans des habitats difficiles d'acc & egrave;s. N & eacute;anmoins, l'analyse des grands volumes de donn & eacute;es issues de ces & eacute;quipements est chronophage, et les donn & eacute;es acquises sont fr & eacute;quemment sous-utilis & eacute;es en raison de ressources humaines limit & eacute;es. Des algorithmes d'intelligence artificielle permettent d'automatiser certains aspects de l'analyse, mais leur utilit & eacute; pour la recherche et la conservation d & eacute;pend de leur fiabilit & eacute; pour identifier les esp & egrave;ces cibles et de leur capacit & eacute; & agrave; & ecirc;tre facilement d & eacute;ploy & eacute;s sur le terrain. Dans le cadre de l'initiative One Forest Vision (https://www.oneforestvision.org/), nous avons d & eacute;velopp & eacute; DeepForestVision, le premier algorithme d'apprentissage profond adapt & eacute; & agrave; ces habitats exigeants, capable de traiter sur le terrain & agrave; la fois des photos et des vid & eacute;os. DeepForestVision a & eacute;t & eacute; entra & icirc;n & eacute; sur un jeu de donn & eacute;es sans pr & eacute;c & eacute;dent comprenant 2,775,671 photos et 221,982 vid & eacute;os issues de cam & eacute;ras automatiques collect & eacute;es sur 63 sites de recherche r & eacute;partis dans 11 pays africains. Il identifie 33 taxons de vert & eacute;br & eacute;s non humains, dont 31 taxons de mammif & egrave;res, allant des plus communs aux plus menac & eacute;s observ & eacute;s au niveau du sol. Test & eacute; sur un jeu de donn & eacute;es vid & eacute;os du Sebitoli Chimpanzee Project dans le parc national de Kibale en Ouganda, DeepForestVision r & eacute;alise 87.7% de pr & eacute;dictions correctes pour les 23 taxons pr & eacute;sents, surpassant les trois algorithmes existants applicables & agrave; ces environnements: Zamba (+13.1%), Mbaza (+45.0%) et SpeciesNet (+37.7%). DeepForestVision est accessible gratuitement dans AddaxAI, une interface hors ligne con & ccedil;ue pour fonctionner sur le terrain dans un contexte & agrave; faibles ressources et ne n & eacute;cessitant aucune comp & eacute;tence informatique. Nous mettons & eacute;galement & agrave; disposition les poids du mod & egrave;le pour la communaut & eacute; scientifique. Solution: DeepForestVision est un outil fiable pour le suivi des esp & egrave;ces observ & eacute;es sur les photos et vid & eacute;os issues de cam & eacute;ras automatiques dans les for & ecirc;ts tropicales africaines. Utilis & eacute; par des acteurs de la recherche, de la conservation, du secteur priv & eacute; ou des politiques publiques, il pourra orienter les strat & eacute;gies de conservation & agrave; l'int & eacute;rieur et & agrave; l'ext & eacute;rieur des aires prot & eacute;g & eacute;es, et ainsi contribuer & agrave; la pr & eacute;servation de la biodiversit & eacute; animale.

In agrarian systems where animal mobility is crucial for feed management, nutrient cycles and household economy, there is a notable lack of precise data on livestock mobility and herding practices. We introduce a methodology leveraging GPS-based behavioural models to analyse and document pastoral mobility in the Sahel. Over 2.5 years, we conducted a continuous collection of GPS data from transhumant and resident cattle herds in the Senegalese agropastoral semiarid rangelands. We developed a Hidden Markov Model robustly fitted to these data to classify recordings into three states of activity: resting (47% overall), foraging (37%) and travelling (16%). We detail our process for selecting the states and testing data subsets to guide future similar endeavours. The model describes state changes and how temperature affects them. By combining the resulting dataset with satellite-based land-use data, we show the distribution of activities across landscapes and seasons and within a day. We accurately reproduced key aspects of cattle mobility and characterised rarely documented features of Sahel agropastoral practices, such as transhumance phases, nocturnal grazing and in-field rainy season paddocking. These results suggest that our methodology, which we make available, could be valuable in addressing issues related to the future of Sahelian pastoralism.

Background Strengthening the surveillance of zoonotic diseases emergence in the wild meat value chains is a critical component of the prevention of future health crises. Community hunters could act as first-line observers in zoonotic pathogens surveillance systems in wildlife, by reporting early signs of the possible presence of a disease in the game animals they observe and manipulate on a regular basis. Methods An experimental game was developed and implemented in a forested area of Gabon, in central Africa. Our objective was to improve our understanding of community hunters' decision-making when finding signs of zoonotic diseases in game animals: would they report or dissimulate these findings to a health agency? 88 hunters, divided into 9 groups of 5 to 13 participants, participated in the game, which was run over 21 rounds. In each round the players participated in a simulated hunting trip during which they had a chance of capturing a wild animal displaying clinical signs of a zoonotic disease. When signs were visible, players had to decide whether to sell/consume the animal or to report it. The last option implied a lowered revenue from the hunt but an increased probability of early detection of zoonotic diseases with benefits for the entire group of hunters. Results The results showed that false alerts—i.e. a suspect case not caused by a zoonotic disease—led to a decrease in the number of reports in the next round (Odds Ratio [OR]: 0.46, 95% Confidence Interval [CI]: 0.36–0.8, p < 0.01). Hunters who had an agricultural activity in addition to hunting reported suspect cases more often than others (OR: 2.05, 95% CI: 1.09–3.88, p < 0.03). The number of suspect case reports increased with the rank of the game round (Incremental OR: 1.11, CI: 1.06–1.17, p < 0.01) suggesting an increase in participants’ inclination to report throughout the game. Conclusion Using experimental games presents an added value for improving the understanding of people’s decisions to participate in health surveillance systems.

Engaging local communities is pivotal for wildlife conservation beyond protected areas, aligning with the 30 × 30 target of the Kunming-Montreal Global Biodiversity Framework. We assessed the effectiveness of 33 offtake indicators, derived from hunter declarations, in monitoring the status and extent of degradation of hunted wildlife sourced from camera trap surveys and faunal composition analysis. The rodents:ungulates ratio in offtake and the mean body mass of total offtake emerged as practical and robust indicators of faunal degradation within hunting systems, with significant potential for broader application in similar tropical forest environments. Our findings provide a blueprint for managing and conserving natural resources in tropical regions through community-based initiatives. Involving local stakeholders ensures sustainable wildlife use and fosters ownership and responsibility. This study advances conservation efforts, bridging scientific rigor with community engagement for effective biodiversity preservation.

The African buffalo (Syncerus caffer) is a wild bovid with a historical distribution across much of sub-Saharan Africa. Genomic analysis can provide insights into the evolutionary history of the species, and the key selective pressures shaping populations, including assessment of population level differentiation, population fragmentation, and population genetic structure. In this study we generated the highest quality de novo genome assembly (2.65 Gb, scaffold N50 69.17 Mb) of African buffalo to date, and sequenced a further 195 genomes from across the species distribution. Principal component and admixture analyses provided little support for the currently described four subspecies. Estimating Effective Migration Surfaces analysis suggested that geographical barriers have played a significant role in shaping gene flow and the population structure. Estimated effective population sizes indicated a substantial drop occurring in all populations 5-10,000 years ago, coinciding with the increase in human populations. Finally, signatures of selection were enriched for key genes associated with the immune response, suggesting infectious disease exert a substantial selective pressure upon the African buffalo. These findings have important implications for understanding bovid evolution, buffalo conservation and population management.

In this chapter we envision the possible futures of the African buffalo populations in Africa by reflecting on the regional and international factors and their relationships that could positively or negatively impact the healthiness of the buffalo species in the next 30 years. Using the expertise of the authors of this book, we drafted and validated a list of factors of change that could impact the futures of African buffalo populations on the continent and use a set of prospective methods, i.e. structural analysis, critical uncertainty matrix and morphological analysis to develop seven synopses which provided caricatural African contexts within which the consequences for African buffalo populations could be imagined. In 2050, the futures of the African buffalo will vary according to each country specific social, technical, economic, environmental, political and value contexts. In a context of climate change that will impact increasingly the environmental contexts in Africa, good futures for buffalo were often associated with political stability and good governance. The proportion of African living in cities will also be important. The ratio of urban versus rural African will not only determine the intensity of the agricultural pressure on land but also the African worldviews towards nature and its conservation. The influence of non-African states will also be determinant, especially in extractive industries and their request for land. A pivotal factor is the conservation models that will prevail in 2050: to what extent they are still influenced and constrained by part of the Western opinion; to what extent they are funded by them; and to what extent African worldviews push for the design of new conservation models based on different relationship between people and nature. Probably, landscapes associating land-sparing (e.g. national parks) and land-sharing management options, based on the sustainable use of natural resources will provide the best futures for buffalo to thrive on the continent.

The African buffalo is one of the best-researched of all ungulate species even though it must give way to some North American deer species, an elephant-seal species and the red deer. The African buffalo had some monographs dedicated to it, but much new research has been carried out on the species since that time, which is brought up to date in the present volume. This allowed us to make an inventory of what we do not know yet about this important species. For that purpose, we made an inventory of research topics, or questions tabulated under three different knowledge domains, (i) ‘known unknowns’, (ii) ‘unknown unknowns’ and (iii) ‘unknown knowns’. The ‘known unknowns’ we categorized as those research questions sitting as it were in the backs of the minds of the current suite of African buffalo specialist; our inventory yielded 37 research issues. The ‘unknown knowns’, we portrayed as evidence-based scientific knowledge on buffalo that a current generation of scientists appear to have forgotten. This proved difficult, but three topics were identified. Here we also draw attention to the fact that modern scientists appear to ignore francophone literature, which is rather unfortunate as West and Central Africa are to a large extent francophone. Not using this repository of information may lead to knowledge decay. Finally, we share thoughts on the ‘unknown unknowns’, which we described as ‘knowledge once we have it will upset our present thinking, perhaps about African buffalo, perhaps on ecology evolution, or on aspects of the veterinary sciences’. Under this category, we touched on 15 issues, but perhaps our imagination was too limited. So, we share in total some 60-odd questions and ideas, and we hope that at least some of these questions or ideas will kindle someone’s imagination and drive to bring knowledge on this great species further.

Some aspects of the well-described planet- and satellite-like framework of, respectively, mixed herds and bachelor groups of buffalo recently were challenged. Associations of female buffalo are now considered more fluid than the initial idea of a stable mixed herd defined by a home range. Within mixed herds, adult female associations are unstable and transient despite using largely overlapping home ranges. Mixed herds in most instances do not seem to overlap much in space. Between mixed herds exchanges exist, apparently dominated by juvenile females (but almost no information exists on juvenile males). The dynamics of fusion–fission events within mixed herds are largely driven by habitat heterogeneity, the quality and quantity of grazing and surface water, and the influence of predation, parasitism and fires. The influence of the human/buffalo interface on these dynamics is not yet well understood. Future studies will benefit from more advances in telemetry and new technologies, new information sources (e.g. sound recorders) and non-invasive genetic studies to enhance our knowledge of buffalo social dynamics. Knowledge of buffalo social dynamics would also benefit from studies more representative of the African distribution of the species and across its subspecies.

Whether practiced legally or illegally, formally or informally, hunting buffalo for meat occurs broadly across African cultures. Nearly all buffalo parts are prized in addition to the meat. Buffalo are also hunted for traditional medicine, social positioning, mystical reasons and in retaliation for causing damage to people and crops. The buffalo is a major game for the hunting industry in every country, but the reasons vary from place to place. In South Africa, buffalo is the first income-generating game despite being the least hunted of all important game. In Tanzania, despite a trophy fee that is lower than that of other species, buffalo is the top tax-earning game because it is the most hunted among the important game. As duly gazetted protected areas, hunting areas are contributing internationally to the global network of conservation areas. They more than double the land area that is used for wildlife conservation in sub-Saharan Africa. Acting as buffer zones of national parks and as corridors between national parks, hunting areas are the last frontier of the African buffalo outside national parks. In South Africa, where all buffalo are fenced and buffalo hunting occurs behind fences, the buffalo is subject to genetic manipulation to enlarge trophy horns and produce disease-free herds. While ‘clean buffalo’ widely contributed to expanding the land dedicated to wildlife conservation in a beef-exporting country, ‘augmented buffalo’ remain a matter of concern for the long-term conservation of the taxon. Several non-African countries imposed bans on importing hunting trophies of CITES-listed species from Africa, leading to a drop in the hunting market. The bans are having two impacts on buffalo: (i) although not CITES-listed, the buffalo is a collateral victim of the bans because many abandoned hunting areas are exposed to poaching and habitat conversion; and (ii) unintentionally, the bans are lifting the value of buffalo as a leading flagship game in an attempt to compensate for the loss of CITES-listed game. Hence, once a commodity game, the buffalo is turning into a high-value game.

This chapter presents the distribution, abundance patterns and trends of African buffalo in the 38 countries of its distribution area based on recent aerial and ground census data and feedback from field experts. For the period 2001–2021, we collected abundance data from 163 protected areas or complexes of protected areas and presence data from 711 localities. The savanna buffalo population is estimated in 2022 at over 564,000 individuals, after deduction of the 75,000 buffalo under intensive private management in South Africa. Its abundance is roughly equivalent to that estimated 25 years ago (625,000). The subspecies conservation status is highly unbalanced. The Cape buffalo is by far the most abundant, representing 90 per cent of the total estimated population (510,000 individuals). The West and Central subspecies respectively represent 4 and 6 per cent (>20,000 individuals and >34,000 individuals). The conservation status of the Central African savanna buffalo, whose abundance has been nearly halved over the last 25 years, is worrisome, with exception of the steadily increasing populations of Zakouma NP (Chad) and Garamba NP (DRC). Estimating the abundance of forest buffalo is challenging, as is establishing a trend. Our investigations showed that the forest buffalo is still well represented in Central Africa in areas with low human density. The forest buffalo’s most important stronghold in Central Africa is probably the Greater TRIDOM/TNS (Tri-National Dja-Odzala-Minkébé / Trinational Sangha), a vast contiguous block of mainly pristine moist forest covering 250,000 km2 and straddling Cameroon, Congo, Gabon and Central African Republic (11 per cent of the Central African forest block). In West Africa, we obtained very little information on the presence of the forest buffalo in the residual forest block, suggesting that the conservation status of the forest buffalo in this region is very worrisome.

African buffalo herd size varies across their distribution range from as few as 5–10 in the rainforests of West and Central Africa to as many as 2000 individuals in the floodplains of eastern and southern Africa. The home range size of African buffalo also varies greatly, with those of savanna buffalo herds generally ranging between 50 and 350 km2. The larger home ranges are generally observed in areas where resources are spatially segregated, and where herds are forced to undertake seasonal movements. In contrast, forest buffalo exhibit smaller home ranges (<10 km2) due to a less pronounced seasonality of the environment, and a more homogeneous spatial arrangement of resources. African buffalo are ruminants, essentially feeding on grass and roughage. This species is capable of subsisting on pastures too coarse and too tall for most other herbivores. The African buffalo occupies an important niche, opening up habitats that are preferred by short-grass grazers. Although the African buffalo primarily is a grazer, savanna buffalo can partially switch their diet to browse when grasses become tall and lignified. The ability of the African buffalo to cope with contrasting environmental conditions throughout most sub-Saharan ecosystems, by modulating a large array of biological traits, highlights a high degree of behavioural plasticity.