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Home , African buffalo

African forest in the Gamba Complex of Protected Areas Gabon.

MSc thesis Jordy Litjens 2016/2017

Supervisors: Pim van Hooft Angelique Todd

Abstract The (Syncerus caffer nanus) in the Gamba Complex of Protected Areas is not very abundant. Even though the species is protected, it is threatened by hunters, poachers and the commercial bush meat trade. The pressure on these is shown in the shy behavior that they exhibit in area. Buffaloes in similar areas without the hunting pressure do not show this shy behavior. To find the buffalo in the area it is best to patrol the large plains at sunrise and sunset, as the buffalo are grazing during these hours. Buffalo were never found in the proximity of human civilization. Larger plains and more area are landscape variables that positively correlated with the presence of buffalo (Anova, F1,96 = 10.9049, p = 0.001). Whilst the amount of water, forest and plantations did not show any effects on the species. The future of the forest buffalo is unclear with Shell leaving the area, as Shell provided work and income for the local people. When Shell leaves the population of Gamba will move with it. The status of the buffalo is unclear in this process. To protect the species in the area steps must be undertaken to reduce the demand for bush meat and with that the hunting pressure. Introduction Gabon is a large and biodiverse country. The country is also very rich in natural resources. The land offers oil, gold, wood etc. and these resources are exploited by the government. Due to this richness in resources the government has no need to develop a sustainable eco-tourism with which they can protect nature (van de Veen 2001). However, due to the worldwide changes in nature laws the government established several nature reserves throughout Gabon. The area between Loango National Park and Moukalaba-Doudau National Park, called the Gamba Complex of Protected Areas (GCoPA) also gained a protected status. The area is very rich in animals and it is one of the few places in the world where the land animals live in a close relationship with the sea. Hippos ( amphibius), (Crocodylus niloticus), (Loxodonta cyclotis), gorillas (Gorilla gorilla) and buffalo can all be found on the beach and in the waves. However, there is still a lot of illegal poaching within the GCoPA and control against poaching is very limited due to the low funds available for patrolling the area and other forms of law enforcement. Gamba and Shell Gamba is a town that supports the oil industry of Shell Gabon with >9000 inhabitants (Lee et al. 2006, Alonso et al. 2014). The local population, including Shell Gabon employees and contractors, put negative pressure on the local wildlife, including poaching and traffic disturbances. Remnants of poached buffalo have been found near Gamba, on the shell oil terminal, which means the buffalo are not protected efficiently in this area. Buffalo presence is negatively influenced by highly active oil industry (Laurence et al. 2006). Other large , including elephants, and red river hogs, are more abundant in open industrial areas (Vanthomme et al. 2013). The influence of oil extraction has been researched by Rabanal et al. in 2010. Here Rabanal found that elephants stayed clear of areas with seismic activity due to oil extraction. It is unknown how buffalo respond to the seismic activity, but expected is that they stay clear from the seismic activity and therefor the oil industry.

Poaching During the last 20 years the demand for bush meat in the urban areas of Gabon has increased and a fluent trade in bush meat developed in the Gamba area. Per capita, Gamba inhabitants are the biggest bush meat consumers in Gabon (Thibault and Blaney, 2003; Van der Veen. 2001). Buffaloes are being poached by humans. They are large mammals that can weigh over a 500kg. The meat of the forest buffalo is seen as a delicacy. The Katavi-Rukwa National Park/ Reserve in Western has legal and illegal hunting on the forest buffalo (Waltert et al. 2009). Here up to 10% of the buffalo population is victimized by the illegal hunt, however the poaching did not significantly influence the habitat use of the buffalo in that area. Between 1997 and 1998 a total of 54 poached buffalo were found on the market of Gamba, with an overall weight of 28350 kg (Thibault and Blaney, 2003). This was only the number of buffalo found on the market. Poaching can have a lot of effects on behavior and species composition within an area. In the Udzungwa Mountains of Tanzania, the species composition and abundance of two forest areas were compared with another (Hegerl et al. 2017). The areas were ecologically similar. The one difference between them was their protection status. The first was a national park, which is a protected and controlled area. The other was a forest reserve in which there is almost no law enforcement, which means there is a hunting pressure in this area. The number of species was 40% lower in the forest reserve area. Also, animal abundance was lower in the reserve than in the national park. There is clearly a negative effect of poaching on animal abundance and species richness. Poaching takes away the prey items from large . In Gabon, the large is the (Panthera pardus). Leopard density increases with little to no poaching (Ramesh et al. 2016). This is another example of how poaching influences the ecosystem. In areas that are actively hunted and/or poached the abundance of large carnivores decreases. It is possible that the carnivore switches its hunting strategy and target species to prevent conflict with poachers, as is the case with the Persian leopard in Golestan National Park in Iran (Ghoddousi et al. 2017).

Goal Ad hoc observations in the GCoPA near the Shell Gabon oil concession and the town of Gamba suggests that the local density of African buffalo, syncerus caffer nanus, is very low compared to the national parks in Gabon. Based on the ad hoc observations, L. Korte reports that buffaloes are uncommon in the GCoPA and that group size is smaller than found in a 2002-2004 study in Lopé NP (Korte 2008b). The goal of this study is to determine the status of S. caffer nanus on the plains near Gamba. Since little is known on S. caffer nanus, more information is needed to improve the knowledge on and protection of the species. At the start of the study the following questions were stated: 1) What is the abundance of African forest buffalo on Vera Plaine, a highly disturbed area near Shell Gabon and in the Gamba Complex? 2) Are buffalo more abundant near waterways? 3) Does the amount of open landscape in an area influence buffalo presence and group size? 4) Does the distance to villages and traffic influence buffalo densities and are there signs of buffalo poaching? 5) Are buffalo always found near a forest edge, do they venture away from the edge either into open spaces or deeper into the forest? Following the questions, these expectations were stated: 1) Buffalo densities are negatively influenced by human disturbances, especially due to the threat of poaching. In areas in which there is little human disturbance, more buffalo can be found. 2)There are more buffaloes near waterways and on open plains as there are relatively more resources (particularly water and fresh grass). 3) Buffalo stay close to the edge of the forest when on the plains as the forest provides safety from hunters. Method African Forest Buffalo Knowledge on the African forest buffalo is limited in comparison to the closely related Cape buffalo (Syncerus caffer caffer). The forest buffalo is of a shy nature and is quite elusive. Its habitat consists of , wetlands and rainforest. However, it prefers open clearings to dense forests (Blake, 2002). The forest buffalo primarily feeds on grasses (Poaceae) and sedges (Cyperaceae), which make up 97% of their diet during the wet season (Halley & Minagawa 2005; Van der Hoek et al. 2013). Buffalo groups are stable over the year and can contain up to 10-20 individuals (Korte, 2008b). Group size is dependent on the home range of the buffalo and the amount of open landscape in this home range. Larger can support larger groups. Group composition consists mostly of multiple females and a couple of males. Furthermore, all age classes can be represented. The horns of the African forest buffalo are swept backwards to prevent them from snagging on branches and they cover the forehead. The ears are fringed with long hairs and they have a large dorsal mane. The gestation period of buffalo takes 11-12 months. Buffalo have only one offspring per gestation. Breeding is done all year around, but most calves are born in the wet season as there will be more food available. Calves are weaned till an age of 6 months. African forest buffalo have no significant natural enemies, except for the leopard (Panthera pardus). Buffaloes are 25% of the diet of the leopard, which mainly predate on buffalo calves, when buffalo are very abundant (Henschel et al. 2005). African are burned during the to maintain open area and to provide high-quality forage for herbivores. The rise of high-quality forage attracts buffalo to the open spaces during the wet season. The buffalo are highly dependent on open areas and in forests they need more spacing between the trees as they prefer to rest in groups (Melletti et al. 2007). During the wet season, the buffalo rest more closely together in open areas and less closely in forest patches (Melletti et al. 2009). Circuit observations To measure group size of buffalo in open grassland and marshlands, three circuit observations were performed. The three circuits were established at the start of the research. Circuits are routes across savannah and wetland areas, which were driven by car. The circuits were on average 40 km in length. Overall duration of a circuit was approximately two hours. The locations of the circuits were chosen by the availability of roads. The circuit included some off-road parts to observe a larger area on the plains. The speed of the vehicle varies depending on the road condition, presence of buffalo and the amount of open plain. When buffalo were found, the car was stopped to measure coordinates with a GPS (GARMIN, GPSmap 62s). To derive the coordinates of the buffalo the distance from the car was measured using a Rangefinder (NIKON, Forestry Pro). A compass (Recta DP2, Switzerland) was used to measure the angle of the observation compared to the North. The measurements were made only once per group as the buffalo did not stay in place due to their shy nature and often fled into the forest. When buffalo fled before the measurements were taken, the location of the buffalo was visited and the GPS coordinated were taken directly on the fresh buffalo tracks on the ground. For all buffalo observed during the circuits; location, date, time and number of buffalo were recorded. If possible, sex and age class were noted as well. Photographs of buffalo were taken when possible to inspect age class and sex. Age classes consist of infants: <12 months; juveniles: 1-3 years; subadults: 3-4 years; young adults: 4-5 years and adults: >5 years (Pienaar, 1969; Korte, 2008b) (table 2, appendix 5). The photos were also used to identify individuals. Identification was done by the shape and size of the ears and horns, the gender and the color of their coat. Buffalo that were observed together at multiple times at different moment were considered to belong to a single herd. In an earlier study, more buffalo were observed grazing in grasslands in the morning (Korte, 2008a). Therefore, to maximize the probability of observations, circuit observations started at sunrise. When observed within 20 meters from the vehicle, other signs of buffalo, such as feces and footprints were recorded as well. Six camera traps were used to record buffalo in areas where high buffalo activity was observed. This was done to record group size and composition as well as other animals near the buffalo groups. Camera traps were deployed following paragraph “3.2 camera deployment” of the protocol by Jansen et al. (2014). The camera’s stood up to 19 days in the field and each camera was placed twice. In total 12 spots were sampled with 4 spots per circuit. Analysis GPS coordinates of buffalo observations were analyzed using ArcGIS. A buffer with a two-kilometer radius was created, using the buffer tool in ArcGIS pro, around all direct and indirect observations. The radius was determined on the twelve-squared kilometer home range of the African forest buffalo, found in the study of L. Korte 2008. Within this buffer zone the intertabulate function was used to determine the amount of water, inundated savanna, forest and the other landscape variables within this area. Group size and observations were both modelled against the landscape variables using (Glm’s and lmer’s) in R-studio, with R i386 3.2.2. The count-distribution variables were modelled with a Poisson glm against the independent landscape variables. Non-significant (p > 0.05) explanatory variables were dropped to achieve the best possible model and the different best models will be compared using the AIC values to determine which one has the best fit. Abundance was determined by the chance to observe a group of buffalo during a circuit. The amount of buffalo that could be found in the area was determined by dividing the number of buffalo observed in total by the total surface area and the number of days the buffalo were observed. The chance that a single group was observed multiple times was determined by the group that was observed most often. The number of times this group was observed was divided by the number of circuits in the area. The effect of roads was determined by the data of Hadrien Vanthomme, 2015. The roads were classified in 5 categories; “Tres frequentes” (more than 11 vehicles/day), Frequentee (1-10 vehicles/day), Moyenne (1-7 vehicles/week), Peu (1-4 vehicles/month) and Rare (<1 vehicle/month). The amount of each type of road was measured within the 2-km radius buffer around the buffalo using the intertabulate function of ArcGIS. The total amount of roads with each category added was also used as a variable. The group size was modelled against each type of road using a linear model.

Results Abundance During the circuits, 125 buffalo were observed directly. Some of these buffalo were observed multiple times and detracting these repeated measurements the observed minimum population size was obtained. The observed population size of the buffalo was 104 animals. In a couple of areas where no direct observations were done, indirect observations were found (dung and tracks). This implies that buffalo can be found in these areas, but the animals themselves were not found. All circuits had a comparable overall surface area in plains (table 1). When comparing the three circuits, circuit 2 (Mbouda) had the largest chance of observing any signs of buffalo (0.381). Vèra plain or circuit 1 had a lower chance (0.198) and circuit 3 had the lowest chance (0.144). Circuit 3 was the coastal track and was closest to human civilization. In this area, no direct observations were made. Only a small number of individuals was observed more than once during the circuits. The group that was observed most often was observed three times out of thirty. This results in a chance of 1 out of 10 to observe this group and a 9 out of 10 chances not to observe this group. Because of the high chance of not observing a group there is clearly an underestimation of the population size in the area. Expected is that 1 out of 10 groups have been seen more than once. With 104 individual buffalo and a chance of 9/10 not to observe the buffalo you would expect a population size of 936 buffalo on the plains around Gamba. Almost all buffalo that were found, were found outside of a 10km radius of any human civilization (Figure 1). This indicates that buffaloes are more abundant in places that are removed far from human settlements. Signs of poaching were found in the same area as the buffalo. The signs were found in the proximity of buffalo. This was most likely a causal relationship as the poachers hunt the buffalo and therefore need to be close to the population. The poaching signs were found on chance. For example; a standing car, a single snare or an in a snare. Direct contact with poachers was prevented. Table 1: The intensity of buffalo per circuit. Circuit two on the road to Mbouda has the highest frequency of buffalo.

Circuit Surface Buffalo/ Dung/ km2/day Tracks/ km2/day Buffalo signs/ area plains km2/day km2/day (km2) C1 29.611 0.124 0.005 0.070 0.198 C2 26.946 0.173 0.001 0.198 0.381 C3 26.461 0.000 0.0353 0.108 0.144

Figure 1: Map of the study area. Brown dots are indirect buffalo observations. Light blue dots are direct buffalo observations. The black circles are the 10km radius around all villages. Most buffalo observations were done outside a 10km radius from human civilization.

Group size The group size of the buffalo varied throughout my entire study. The African forest buffalo is known to split their herd into sub-groups, fusion-fission dynamics (Couzin and laidre, 2009). During the study, a lot of smaller groups were found (Figure 2). A couple of large groups of 13 or higher were found. This indicates that the larger groups were split into these sub-groups.

Figure 2: A histogram of group size. Most groups found during the study were relatively small. Only a small number of large groups was found of which the largest group existed of 17 animals.

The landscape surface variables within the 2 kilometer radius of the observation were modelled using a glm. A lmer was tried with the random variables; weather and type of observation. However, the lmer (AIC = 431.18) had a lower fit then the glm (AIC = 430.45), so the simples model was chosen. All insignificant variables were dropped stepwise from the model. When testing the model with an ANOVA with a 90% confidence interval. The model shows that when there is more water in an area, that the group size was smaller (Anova, F1,77 = 5.8976, p = 0.01756). A larger amount of savanna or open area showed larger group sizes of buffalo (Anova, F1,76 = 3.6597, p = 0.05956). Inundated forest had a negative effect on the group size (Anova, F1,75 = 5.1333, p = 0.02636). This indicates that groups of buffalo were smaller in areas with a lot of inundated forest. The distances to all different landscape variables were insignificant (p > 0.05). The different group sizes that I found were dependent on the surface area of the plain. Larger plains can provide larger groups of buffalo with food (Anova, F1,77 = 9.9404, p = 0.0023).

Figure 3: Number of buffalo in a group is shown against the surface in km2 of the plains where the buffalo were found. Larger plains showed larger group sizes. Buffalo presence The GPS points were analyzed using GIS and R. The observations were modeled against the closest distance to the different landscape variables. The best possible model had significant effects for the distance to the Atlantic Ocean, the distance to human villages and the distance to the edge of the forest. Buffalo were more likely to be found away from the ocean (Anova, F1,103 = 34.068, p < 0.001) and from human villages (Anova, F1,102 = 92.354, p < 0.001). However, buffalo were more likely to be found near the edge of a forest (Anova, F1,101 = 15.700, p < 0.001). The surface area variables were modelled using a glm, with all types of surface areas within a 2-km radius included. Only the amount of ocean was insignificant for the presence of buffaloes and was therefore dropped from the model. The data was also modelled using an lmer with the type of observation and the weather as random variables. However, the lmer (AIC = 377.35) had a lower fit than the glm (AIC = -13.68) and therefor the glm was used. From this model, the important landscape variables were derived to explain buffalo presence. The following surface variables had a negative effect on the presence of buffalo; amount of water (Anova, F1,103 = 59.8183, p < 0.001), inundated savanna (Anova, F1,102 = 2.7535, p =

0.100), savanna (Anova, F1,101 = 28.5670, p < 0.001), inundated forest (Anova, F1,100 = 50.2871, p <

0.001), forest (Anova, F1,99 = 23.7743, p < 0.001), village (Anova, F1,98 = 8.8665, p = 0.003) and plantation (Anova, F1,97 = 25.7065, p < 0.001). This indicates for each of these variables that when there is a larger surface area of each, within a 2-km radius, it is less likely to find buffalo in the area. The total surface area of a plain however, showed a positive relationship. Indicating that buffalo are more likely to be found on larger plains (Anova, F1,96 = 10.9049, p = 0.001).

Roads The influence of roads seemed to be small on the buffalo group size. Roads with a low intensity (Peu and rare) had no significant effect on the group size (p > 0.1) and were dropped from the model. The total amount of roads did not show a significant effect (p > 0.1), but was kept in the model (AIC = 562.5007) as it gave a better fit, than without this variable (AIC = 567.4545). Very busy roads (Tres frequentes), showed a strong negative relationship between group size and the length of the road within the 2-km radius (Anova, F1,100 = 5.1789, p = 0.02500). Frequently visited roads (Frequentee) also showed a negative relationship between the amount of roads and the group size (Anova, F1,100 = 5.4744, p = 0.02128). The same effect was found for roads with an average usage (Moyenne)(Anova,

F1,100 = 2.8538, p = 0.09427).

Figure 4: The effect of roads was determined by the data of Hadrien Vanthomme, 2015. The roads were classified in 5 categories; “Tres frequentes” (more than 11 vehicles/day), Frequentee (1-10 vehicles/day), Moyenne (1-7 vehicles/week), Peu (1-4 vehicles/month) and Rare (<1 vehicle/month). Also, the total amount of road within the 2-km radius if the buffalo was used as a variable.

Camera traps On the 12 camera traps that were placed near spots with high buffalo activity, only two cameras observed buffalo. In total, these 2 cameras caught 5 buffalo of which one individual twice. The combined number of trapping days was 234 days and this resulted in 2269 photos. The low number of buffalo indicates a very low efficiency of camera trapping to gather information on forest buffalo. However, information was found on the species of animals that appeared near these buffalo spots. Species that were most common in the area near this buffalo activity were; Blue (Philantomba monticola), Elephant (Loxodonta Africana), Red capped mangabey (Cercocebus torquatus), ( porcus) and the (Cephalophus dorsalis).

Discussion Behavior and hunting pressure Buffalo on the plains around Gamba showed very shy behavior. The moment the buffaloes noticed the observer, they started running for the edge of the forest in which they disappeared. This behavior is most likely due to the hunting pressure in the area. In most locations where we found buffalo, signs of poaching were found as well. This indication of poaching was most likely present due to the presence of the buffalo in this area. Meat of the buffalo sells at a price of 2200 CFA-Franc (Local guide). The hunting season was closed during the study, but the buffalo were still threatened by snares and opportunistic hunters. Limited hunting was still allowed in Gamba to sustain oneself. Local fishermen were carrying guns on the road to their fishing spot to shoot buffalo when encountered. In the surrounding national parks buffalo are more abundant then on the plains near Gamba. In Moukalaba-Doudou National Park, 200 buffalo can be observed within an hour (Todd. 2016). Within the national park there is no hunting pressure on the buffalo and the population size is a lot larger. The shyness of the buffalo on the plains around Gamba is not observed in the Rabi shell concession. The area is only accessible for shell personnel and there are strict regulations on moving through the area. In this area, there is no hunting pressure which is shown in the behavior of the buffalo. The buffalo in this area walk on the landing strip of the airfield and must be chased of using fire trucks. This happens multiple times a week and has become a game for the buffalo. After their game, the buffalo walk onto the football field to graze. Without the hunting pressure, the buffaloes show bold behavior near humans, as these humans form no threat. The same pressure of human activity is shown with other large herbivores as human density often correlates with increased threats to, and more competition with wildlife. In 2009, Urquiza-Haas et al. found that large bodied vertebrate density in the Yucatan Peninsula, Mexico is negatively correlated with human density. In Nepal, a large ungulate species, the ( gaurus frontalis), is very rare in areas with human occupation (Bhattarai & Kindlmann, 2013). The buffalo in the area have no competition with livestock as there is no livestock in the area, however a lot of their habitat goes lost by the burning of patches of forests to create plantations. Buffalo do not eat fruits and therefor do not need to be on these plantations and they probably avoid these partly by the increased risk of snares and hunting.

Landscape variables The negative effect shown by Savanna and inundated savanna was highly unexpected. This effect is most likely shown due to the method of this analysis. Within the 2-km radius of the buffalo the surface area of each different variable always added up to the same amount. When one type of surface area decreased, the others would increase. The variables were therefor not independent from one another. The buffalo were observed at a single moment in time. As the buffalo moved around constantly, it is impossible to know where in the home range the buffalo stood. This could have been near the edge of the home range or in the middle of it. Buffalo seemed to be less present in areas that contained a large source of water. Because it was the wet season with high rainfall the buffalo did not need to go near large water sources to drink. Flooded areas of savanna and forest provided the buffaloes with sufficient drinking water. The buffalo never strayed far from the edge of the forest. The forest provides a form of safety from hunters. Villages seem to be avoided by buffalo, however it is also possible that most buffalo near villages have been hunted and therefore could not be found. There were also no signs of buffalo presence near the villages. However there have been found some tracks on the shell terminal outside of the circuits.

Observation method Both transects and camera trapping seemed to have a very low effectiveness on capturing buffaloes. The circuits performed by car improved the chance of observing buffaloes. However, the noise created by the car probably warned buffaloes of our presence before we could see them. Preventing them from leaving the safety of the forest or by fleeing into the forest. This created an underestimation of the population size. Also, due to the two-week interval the buffaloes might have been on one of the other plains and this also added to the underestimation of the population size. This is represented in the nine out of ten chance to observe a buffalo more than once. Pictures were taken hastily and the quality was not good enough to observe sufficient details for the individual identification. Only a small number of individuals were identified and since and the observation of groups has been compromised out of group identification. Some buffalo moved between sub-groups. The measurements done with the compass and rangefinder were not very precise. The rangefinder had a range of 500m. Most buffaloes were observed outside of an 500m range and fled during the approach. To get an exact measurement the buffaloes needed to either stand still within the range, or measurements needed to be taken after the buffalo had disappeared. Camera trapping is supposedly the best method to observe animals and to calculate abundance and species richness (Silveira et al. 2003). During my experiment, I had a small number of cameras and these were placed at places with a lot of buffalo activity and/or signs. However, the number of buffaloes captured by this method was very small. Buffaloes had a lot of different paths to enter the plains from the forest edge. With the number of cameras that I had, this method was very ineffective.

Sampling design. The sampling period of my research was quite small. The actual observation took 9 weeks and within this time 125 buffaloes were observed. To gain sufficient insight on the buffalo population of the area the sampling period should be a lot larger, up to two years. Since buffalo divide into sub-groups, each buffalo needs to be observed at least 4 times to gain sufficient insight in its group dynamics (Korte 2008b). Time was very limited due the combination with another project. Each week a single circuit was observed, whilst it would have been better to randomize the order of circuits throughout the study to prevent a pattern for the buffalo. Eventually, the pattern did not seem to have an effect as the number of observations in week one, two and three of circuit 2 were comparable. Circuit 3 had no direct observations at all. Only circuit 1 had a decrease in the number of observations.

Shell Shell is a company that does damage to an ecosystem. However, Shell is the best in a bad situation as the company does invest in nature conservation. In Gamba, the Smithsonian institution was almost completely financed by Shell. The company protects its newest claims (Rabi) with strict regulations and limited access. Shell has no direct negative effects on the buffalo population. However due to the construction of roads to reach distant oil concessions, a large area has been opened to the public. Hunters can reach a large area, which is impossible to be patrolled due to the lack of money. Rangers have no money for transport or weapons and only have their badge to protect themselves from armed poachers (Local guides). Shell Gabon sold all land based claims in Gabon to Assala Energy Holdings, because the oil in the area is running out. Shell will leave the area in the duration of 2017. The new owner of the oil concessions is most likely less concerned with the environment and will quickly draw the last drops of oil from the ground. Most likely the economy of the area will plummet as there will be less jobs and the population of Gamba will have to move away to search for new job opportunities. Most locations including the Rabi concession will be made accessible to the hunters and the wildlife in the area will be ripe for the picking.

Recommendations In future research, I would advise the implementation of drones to observe the buffaloes (Koh and Wich, 2012). Since drones can fly silently over the savannas of Gabon, they will not scare the buffalo. When observing from a car your sight is limited, due to the height differentiations in the landscape. The patchy forest-savanna landscape also limits the line of sight from a car. When obtaining an aerial view, a larger area can be observed. Due to GPS technics, the drone can fly automatically over the plains using GPS tracks. The camera can be calibrated to observe buffalo and other large animals. The use of drones limits the need for human interference as hardly any human effort needed to survey the plains. To get the distance more precise during circuit observations, a rangefinder with a larger range is needed. The plains with buffalo are large and the rangefinder should reflect this. Working with drones should eradicate this problem. To introduce a better analysis of individuals a good camera with a long lens should be used. One that can see details in distances over 500 meters. Since all the buffalo observations were only momentarily shots, it shows only a small part of the buffalo locations. Buffaloes keep moving around and the observation is only a small representation of their habitat. It is better to track the entire home range of the buffalo with the use of tracking collars and to do the habitat analysis afterwards as this will give a more complete overview of the buffalo’s habitat. Another method to get better insight in the buffalo presence would be by placing multiple camera traps around a single plain. Covering a large part of all the wildlife trails around the plain. This however would require a large number of cameras as the plains are very large. Therefor I still think that the circuit method was the best method for the situation on the Véra plains. Shell would do a great job by closing all access roads that they created, but this is unlikely to happen as a new company will take over in the area. With shell leaving the area the future for the local flora and fauna is unclear. Will the local population leave the area with Shell or will they leave a wave of destruction to scavenge every bit of bush meat? Can the buffalo population recover from the high hunting pressure of the last years? And finally, will there be a viable future for eco-tourism and nature conservation after all these years of exploitation? There are a lot of unknowns and the role of the buffalo is unclear. However, the African forest buffalo is an iconic species in the area and must be protected. Acknowledgement Lots of thanks and appreciation for Pim van Hooft and Angelique Todd for their supervision of my project. Furthermore, I would like to thank Hadrien Vanthomme for the advice and help on my analysis. Thanks to Tobi Ellie, Anne Vader, Martin and Guy Roger for their assistance in the field. Finally, I’d like to thank the Smithsonian Institution in Gabon for the time in and the aid during the project. References “African Forest Buffalo Fact sheet”. Library.sandiegozoo.org. N.p., 2016. Web. 18 July, 2016.

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Appendix

Table 2: Age classes, indicating differences between males and females and all 5 age classes (African forest buffalo fact sheet)

Age class Age Body weight (kg) Body length (cm) Horns Male Female Male Female Infants <1 year 64-84 64-84 132-151 121-150 Straight and v-shaped.

Juveniles 1-2 years 259-283 259-323 178-194 178-196 30-46 cm, curve lightly outwards.

Subadults 2-3 years 282-483 314-493 191-225 191-219 41-86 cm, tips grow towards one another. The boss (rounded area at the base) takes shape Young adults 4-5 years 378-617 397-576 215-238 211-255 Tips of begin to sweep backwards. Boss is thickening Adults 5> years 488-768 312-637 225-267 215-249 Up to 104 cm in width Tips of horns are widely spread; the boss is well-shaped