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Boesch, L., R. Mundry, H. Kuehl, and R. Berger 2017. Wild mammals as economic goods and implications for their conservation. Ecology and Society 22(4):36.

Wild mammals as economic goods and implications for their conservation

1Universität Leipzig, Institut für Soziologie, Leipzig, Germany, 2Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, 3Max Planck Institute for Evolutionary Anthropology, Department of Primatology, Leipzig, Germany, 4German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Leipzig, Germany


In social-ecological systems, human activities and animal distribution are interrelated. Any effort at studying wildlife abundance therefore requires the integration of detailed socioeconomic context into species distribution models. Wild mammals have always been an important resource for humankind, and concepts of economic goods provide an analytical framework to deduce relevant socioeconomic factors that shape wild mammal–human relationships and consequences for the spatial distribution patterns of wild mammals. We estimated the effects of the human population on wild mammals in a rural area in the Republic of Guinea, West Africa. We related large mammal survey data via statistical models to detailed socioeconomic information about the human population in the same area. We compared models, taking account of the human population in different ways, and found that wild mammal abundance was better explained by human factors other than human population density. Although human population density had a negative effect on wild mammals, the effect of market integration and food taboos were more important and not accounted for by human population density alone. Additionally, the analysis did not provide evidence of higher mammal abundance in classified forests, which one would assume if conservation interventions aimed at reducing hunting were implemented. Beyond doubt, the relationship between humans and wild mammals is highly complex and species- and context-specific. To understand mammal–human relationships in the wider context of social-ecological systems, an in-depth knowledge of the socioeconomic characteristics of a human population is needed to identify crucial links and driving mechanisms.
Key words: economic good; human influence; mammal abundance; Republic of Guinea, West Africa


Wildlife was “the exclusive source of food, fiber, fuel, and medicine for the first 99% of human history” (Prescott-Allen and Prescott-Allen 1986:1) and still contributes a significant amount to the welfare of societies, even highly industrialized societies (Prescott-Allen and Prescott-Allen 1986, Chardonnet et al. 2002). It is therefore beyond doubt that wild mammals can be seen as an economic good. Excludability and rivalry are the two fundamental properties of any economic good. Excludability refers to the restriction of access to the good, and rivalry refers to the divisibility of the consumption of the good among individuals (Musgrave and Musgrave 1989). Using these criteria, wild mammals can be classified as common goods in many regions of the world. Common goods are nonexcludable and rivalrous. In contrast to common goods, public goods (e.g., lighthouses, national defense) are nonrivalrous, and private goods (e.g., clothing, food) are excludable. Finally, club goods (e.g., pay TV, gyms) are excludable and nonrivalrous. The properties inherent in common goods may lead to a social dilemma, where appropriators of the common good have the incentive to raise the exploitation of the common good without limit, thereby leading to its destruction. This is because each appropriator receives the whole benefit generated due to an increase in the exploitation of the common good, while the costs that occur due to this increase are divided among all appropriators. This is known as Hardin’s “tragedy of the commons”: “Freedom in commons brings ruin to all” (Hardin 1968:162). It is important to note that tragedy of the commons situations are characterized by the fact that the appropriators have the option to act differently: they could exploit the resource in a sustainable way, or they even could abstain from exploiting the resource at all and earn their living in other ways (Gardner et al. 1990). Some societies do indeed manage to establish institutional regimes that allow for a sustainable exploitation of a common good. This is achieved by defining clear group boundaries, matching the rules governing the common good to the local conditions and needs, ensuring participation of those affected by the rules, making sure that the rights of affected community members are respected by outside authorities, developing a system where community members monitor other member’s behavior, sanctioning rule violators, providing means for dispute resolution, and building responsibility for the governance of the common good on the entire system (Ostrom 1990).

Humans can gain economic value from wild mammals in three ways (Chardonnet et al. 2002). First, nutritious value is gained when humans exploit wild mammals in a direct consumptive way: wild mammals are an important source of meat for humans in many parts of the world, and demand for bushmeat has been identified as one factor that is driving wild mammals to extinction (Davies 2002, Milner-Gulland et al. 2003). Second, productive use value is gained when wild mammals are exploited in a direct nonconsumptive way. Examples for the productive use value of wild mammals are numerous, but trade certainly plays the most important role: wild mammals are an important source of income for humans in many parts of the world (Milner-Gulland et al. 2003), and the trade in wild mammals has been identified as a driver of the extinction of endangered wild mammal species (Madhusudan 2005, Milledge 2007, Nijman and Shepherd 2007, TRAFFIC 2008, Nijman 2010, Briceño-Linares et al. 2011). In the African context, the exploitation of wild mammals includes the whole range, from rural consumption, based on subsistence, to purely commercial activities driven by the demand of international trade (Brashares et al. 2011). Third, even if they do not exploit wild mammals directly, humans can still gain indirect nonconsumptive use value from wild mammals. Examples of this value are bird-watching or safari tourism. Finally, wild mammals are an integral part of the ecosystem. Ecosystem functions in turn provide goods and services that are essential for the survival of people. This aspect is not commonly included in the economic value consideration due to the difficulties of quantifying it (De Groot et al. 2002).

The assumption that the property of wild mammals as nonexcludable can be altered has been the foundation for the deer parks in medieval Europe, where the king considered all deer as his private good (Birrell 1992). This assumption is one of the main paradigms of modern conservation policy, which has led to the implementation of strictly protected areas (Gardner et al. 2007), where wild mammals no longer have economic value. The success of protected areas in conserving wild mammal populations is however not guaranteed and depends on substantial efforts (Bruner et al. 2001, Craigie et al. 2010, Tranquilli et al. 2012, 2014), thereby leading to the “mounting realization that protected areas are part of a complex social-ecological system characterized by flux, nonlinear relationships and unpredictable outcomes” (van Wilgen and Biggs 2011:1179). Integrated conservation and development projects therefore assume that the best way to protect wild mammals is to directly involve the local human population: through ownership, economic incentives, and participation, local people should benefit from conservation and support it (Campbell and Vainio-Mattila 2003). This approach is also termed “new conservation” and has been heavily criticized (Soule 2013, Kareiva and Marvier 2014, Marvier 2014).

An understanding of wild mammal–human relationships and the consequences of human activities for the spatial distribution of animals is of major interest for conservation biology and policy because it allows understanding of the relationships between the human population and wild mammals in an area of interest (Elith and Leathwick 2009, Iwamura et al. 2014, Van Vliet et al. 2015). This is an important condition, first, for improving our understanding of social-ecological systems, and second, this understanding is essential for implementing viable conservation programs because the fate of biodiversity and especially wild mammals is closely linked to human behavior and activities (Chazdon et al. 2009, Brncic et al. 2015, Junker et al. 2015). Typically, the set of predictors for modeling species distribution frequently does not include detailed socioeconomic information but only some measure of human population density as a proxy for human activity. Probably this is because spatial information about human population density is easily accessible and does not require the time-consuming collection of detailed spatial socioeconomic context information. However, this approach neglects the fact that some important properties of human populations are not represented by human density. For example, knowing that two areas have the same human population density does not tell us anything about the religious affiliation or the economic activities of the people living in those areas. Not taking account of those differences might lead to biased estimations. Analyzing the relationship between wild mammals and humans within the framework of social-ecological systems can help determine the relevant set of predictors. Ostrom (2007, 2009) proposed a framework where social-ecological systems are made up of four subsystems: the resource system, the resource units, the users, and the governance systems. Although those subsystems are loosely separable, they interact to produce a common outcome at the social-ecological system level. When trying to model the relationship between different factors within a social-ecological system, one should therefore determine the relevant factors from the four subsystems and incorporate them as predictors into the model.

We used a region in the Republic of Guinea as an example to estimate the influence of humans on wild mammal abundance. We compared the predictive value of human population density and other socioeconomic factors on wild mammal abundance. We incorporated the concepts discussed so far into a social-ecological system framework, as proposed by Ostrom (2007, 2009), to derive the relevant factors for our model. Finally, we consider how our socioeconomic approach could be used to increase our understanding of wild mammal–human relationships in other regions.


The Republic of Guinea (Fig. 1A), located in Western Africa, spans an area of 245,720 km2 (World Bank 2016a). Although its mammal fauna is not well-studied, Guinea is believed to have the highest diversity of large mammals in the West African forests on a species per area basis (Barnett and Prangley 1997). Results from a first nationwide chimpanzee survey, conducted from 1996 to 1997, suggested that Guinea was also home to about 18,000 chimpanzees (95% confidence limits: 8113–29,011), the largest countrywide population of chimpanzees in West Africa (Ham 1998). A second large-scale chimpanzee survey conducted by the Wild Chimpanzee Foundation (WCF) in 2012 confirmed such a large chimpanzee population (Regnaut and Boesch 2012).

On the other hand, Guinea is one of the poorest and least developed countries in the world. In 2011, Guinea ranked 178 of 187 in the World Development Indicator (UNDP 2016), with a yearly per capita income of US$447.8 and a life expectancy of 57 years (World Bank 2016b, 2016c). The Guinean economy relies on extractive activities. It has an important mining sector with potential access to one-third of the world’s highest grade bauxite deposits, one untouched high-grade iron ore deposit, and gold, diamonds, platinum, cobalt, nickel, silver, uranium, lead, and zinc (Campbell and Clapp 1995). In 2011, Guinea had mineral rents worth 15.8% of its gross domestic product (GDP) and forest rents worth 13.4% of its GDP (World Bank 2016d, 2016e). While 35% of the Guinean population lived in urban areas in 2011 (World Bank 2016f), the rural population relies on ecosystem services for its survival (Laakso and Tyynela 2006) and practices a slash-and-burn agriculture. Since “the current level of extraction is low compared to the potential indicated by the resource value on the ground” (World Economic Forum 2011:28), extractive economic activities are believed to further increase. Furthermore, population growth was continuously greater than 2% from 2004 to 2014 (World Bank 2016g). Past population growth led to a decrease in the fallow period from traditionally 17 years to 8 years (Sirois et al. 1998). Concerns are high that population and economic growth will have a negative effect on the Guinean wild mammal populations if no appropriate measures are taken.

In 2014, 15.4% of the world’s terrestrial area was classified as protected area (Juffe-Bignoli et al. 2014). In Guinea, there were 124 resource management and protected areas covering 30% of the country’s terrestrial area (Protected Planet 2016). Of these, 98 were classified forests (CFs). These are forests that have been classified by the Guinean state as being of national interest. The exploitation of environmental goods in CFs is regulated in a way as to find an equilibrium between the socioeconomic needs of the local population and the interests of conserving the environment (Ministère de l’agriculture et des ressources animales 1999). Only five Guinean protected areas (Kankan Faunal Reserve [IUCN category IV], Mont Nimba Strict Nature Reserve [IUCN category I], Badiar National Park [IUCN category II], Haut Niger National Park [IUCN category II], and Blanche Island Faunal Reserve [IUCN category IV]) were dedicated to the protection of biodiversity. These five protected areas cover 7050 km2 (2.9%) of Guinea’s terrestrial area, including three of five Guinean ecoregions. Furthermore, not all globally threatened mammals that occur in Guinea are found in these five protected area. These findings highlight the need to increase the number of protected areas that are dedicated to the protection of biodiversity in Guinea (Brugiere and Kormos 2009).

In an effort to create a new national park in the region, the WCF cooperates with the Guinean government, the International Finance Corporation (IFC), and the mining companies Compagnie des Bauxites de Guinée and Global Aluminum Corporation in order to implement a biodiversity offset project in Guinea. The WCF is a nongovernmental organization with the mission “to enhance the survival of the remaining wild chimpanzee populations and their habitat, in West Africa” (WCF 2016). The WCF offset project aims at achieving conservation outcomes from offset programs of the involved mining companies’ activities, according to IFC standards (IFC 2012), through the creation of a new national park in Guinea (WCF 2015). The location of this future national park was selected according to abundance data based on the Guinean WCF Chimpanzee Inventory 2012 as well as feasibility criteria (Regnaut and Boesch 2012). The park is located close to the border of Mali, between the Labe-, the Mamou-, and the Faranah regions, and comprises an already existing network of CFs (Fig. 1A).

Study area, sampling, and field data collection

In 2013 and 2014, data on wild mammals and the human population were collected in the region where the WCF offset project is located to gain a better understanding of the wild mammals, the human population, and human activities in the region. From October 2013 to March 2014, two WCF biomonitoring teams recorded signs and sightings of wild mammals on 184 line transects according to IUCN standards (Kuehl et al. 2008) using a systematic design (systematically segmented track line sampling) and distance sampling methodology (Buckland et al. 2001, Thomas et al. 2010). Transect length was 2.5 km, and spacing between transects was 5.5 km. Total effort was 462.5 km (185 transects), covering 8153 km2. This was the WCF biomonitoring project area (Fig. 1B). From April 2013 to June 2014, one sociological team, consisting of four people and headed by L. B., collected socioeconomic and infrastructure data in the same area. The sociological team focused its effort on a part of the WCF biomonitoring project area. This area is referred to as the study area, and all further details on data and results refer to this study area. The study area comprised 52 transects and 69 villages (Fig. 1C). The transects included in the study area were selected according to the following criteria: they had to be partly located either within a 5-km range of fields or villages, or they had to be within an area surrounded by villages. We conducted long face-to-face interviews with the household heads of the village population and, if necessary and feasible, several other members of the households. The interviews focused on demography, economic practices, and values and beliefs related to the environment (see full questionnaire in Appendix 1). Furthermore, we took GPS track logs of the locations of the villages, their important fields, and the trails and the roads in the study area.


Applying the basic concepts briefly described in the Introduction to the situation in the study area enabled us to formulate hypotheses regarding the influence of the local human population on the wild mammals in the study area. Our underlying assumption was that the local population did not gain indirect use value from wild mammals. This assumption was based on our knowledge of the situation on the ground. We considered only the value the local population could deduce by exploiting wild mammals. Furthermore, we did not consider the relevance of wild mammals for other stakeholders or for the ecosystem.

Using the social-ecological system framework proposed by Ostrom (2007, 2009), we defined the situation and the relevant factors the following way. The outcome of interest was wild mammal abundance. The resource units system was made up of mobile wild mammals. The resource system consisted of the habitat where the wild mammals live. We expected the suitability of the habitat for wild mammals to depend on the habitat type (Tews et al. 2004, Guisan and Thuiller 2005), its access to water (Western 1975, Redfern et al. 2003, DeGama-Blanchet and Fedigan 2006, Chammaillé-Jammes et al. 2007), its accessibility (Malcolm and Ray 2000, Develey and Stouffer 2001, Laurence et al. 2006), and its destruction (Tilmann et al. 1994, Pimm and Raven 2000). The user system was made up of the local population living in the study area. We expected the local population to use wild mammals as a source of meat for private consumption (Davies 2002, Milner-Gulland et al. 2003, Brashares et al. 2011) and a source of income (Milner-Gulland et al. 2003, Madhusudan 2005, Milledge 2007, Nijman and Shepherd 2007, TRAFFIC 2008, Nijman 2010, Briceño-Linares et al. 2011, Brashares et al. 2011). The dependency of the local population on wild mammals depends on viable alternatives (Bennett 2002, Milner-Gulland et al. 2003, Brashares et al. 2011, Junker et al. 2015), which are provided through access to the market as well as fishing activities. Whether the population will use wild mammals is further influenced by normative prescriptions about the appropriateness of eating specific kinds of wild animal meat (McDonald 1977, Balée 1985, Pezzuti et al. 2010, Read et al. 2010, Luzar et al. 2012). While the whole population uses wild mammals and can exploit them, a fraction of the population is professional appropriators (hunters) who are specialized in harvesting wild mammals. They depend on the demand of the local population and the access to the market to earn money. The hunters may have special normative prescriptions related to the killing of wild mammal species (McDonald 1977, Balée 1985, Pezzuti et al. 2010, Read et al. 2010), but we assumed that their hunting activities are based essentially on the demand from the local population and the market. The governance system is shaped by the limited influence of the central government, which is restricted to the CFs. Those are under government control, while the rest of the area is divided among the different village communities and is managed by them through customary rules (Table 1).

Analytical methods

Processing of line transect data

We aggregated the transect sighting raw data in the following way: first, transects longer than 1600 m were split into two equally long segments to account for potential local-scale variation in mammal distribution and the predictor variables (although all transects were designed with a length of 2500 m, it was not always feasible to pass through their entire length. This is why the mean length of the “empirical” transects was 2365 m and nine were shorter than 1600 m). Second, sighting types were classified as ephemeral (direct observation and vocalization) or long-lasting (feces, trace, activity, and nest), and were summed up accordingly. Then, per species, we kept only the more common, ephemeral, or long-lasting sightings, and finally considered only species with sightings that occurred on at least 10 transect segments (Brncic et al. 2015). This was our proxy for abundance. Finally, we further excluded species for which there was no information about home range sizes available because we needed this information for the habitat type control variable (Table 1, Fig. 2, Table 2).

Determination of predictor variables

We interviewed 1389 households (86% of all village households) with a total of 10,463 individuals. We recorded the number of individuals living in households and summed all individuals of all households per village to derive village population sizes. Most villages had approximately 230 inhabitants, but there was large variation in population size. In order to assess whether our village population sizes were trustworthy, we also counted the number of buildings in all villages and controlled whether the village population size correlated with the number of buildings in villages. The Pearson correlation between the number of buildings and the population sizes of the villages was 0.97, which suggested that the population size was indeed trustworthy. We measured the market integration of the village populations by recording monthly shopping trips of individuals and calculating the mean monthly trips to markets of each village population. The mean number of monthly trips to markets of the village populations ranged from 0 to 10.75. We recorded the number of hunters living in a village and summed them at the village level. Forty-three percent of all households possessed a hunting rifle; 17% of them hunted regularly. Overall, 15 households had commercial hunters, who hunted nearly every day. The most frequently hunted animals, in decreasing order, were scrub hare, duiker, cane rat, and bushbuck. An average hunter shot 1.56 duikers per month, whereas the best shot 20. Approximately 193 duikers were shot monthly by the people who were interviewed in the study area. We recorded the number of fishers living in a village and summed them at the village level. Eleven percent of all households fished regularly, and 45 households fished nearly every day. For each village, we recorded the number of household heads who abided to food taboo norms that forbid eating certain wild mammal species, and summed them at the village level. The population in the study area was strongly religious, and animistic beliefs survived side-by-side with the Muslim religion. Species targeted with food taboos were chimpanzees, common wart hogs, Guinea baboons, and patas monkeys. Household heads’ food taboo abidance ranged from 0 to 100% per village (Table 1, Table 3).

Determination of control variables

The studied human population practiced slash-and-burn crop cultivation. The most important crops were rice and peanut. Both were cultivated by approximately 95% of all households during the 2013 growing season. During this season, households harvested an average of 191 kg of rice, with a maximum of 3 tons, and an average of 602.6 kg of peanuts, with a maximum of 6 tons. Other important crops were manioc, millet, and beans. Human–wildlife conflicts were very common because wild mammals and humans competed for the crops in the fields. Ninety-five percent of all households were troubled by wild mammals in their fields, and they all took retaliatory measures when wild mammals entered their fields. We took crop cultivation as a proxy for habitat destruction, and computed the shortest Euclidian distance between transect segments’ midpoints and any field. Access to water was calculated as the shortest Euclidian distance between transect segments’ midpoints and any stream in the study area that had water year-round. Accessibility of the study area was very rudimentary; it was provided by a few dirt roads that were maintained by the local people, and rivers could be crossed only during the dry season (from November to June). We measured the accessibility of the transect segments as the shortest Euclidian distance between transect segments’ midpoints and any road. Four CFs were located within the study area. For each transect segment, we determined its proportion that was located within a CF by using the World Database on Protected Areas layer (IUCN and UNEP-WCMC 2016). On 1 and 2 December 2013, 13 RapidEye Level 3A tiles (Rapideye 2016) of the study area were acquired. We used those satellite images to calculate the Normalized Differenced Vegetation Index (NDVI). The NDVI has been successfully used to predict animal population size (Osborne et al. 2001, Oindo et al. 2002, Zinner et al. 2002), and land cover types can consistently be stratified as a function of the NDVI (Holben 1986). We then extracted the mean NDVI within polygons around each transect. The shortest distance from each point on the edge of the polygons to the transect segment was equivalent to the home range radius for each species (Table 1, Table 3, Fig. 2).

Aggregation of predictor variables at transect segment levels

All predictor variables were further aggregated at the transect segment level. For this process, we first computed the cost distance between all transect segment midpoints and all villages. The cost distance between two points is the path that links the two points with the least traveling effort. The effort was obtained by considering the slope and the distance between two points. We set the slope to 0 on terrain with a road or a trail, and otherwise set it to the steepness of the terrain. We used the costDistance function of the gdistance package in R (van Etten 2015, R Core Team 2016), a Shuttle Radar Topography Mission digital elevation model (Jarvis et al. 2008), our track logs of all roads, trails and villages, and the locations of the transect segments to compute the cost distance between all transect segments and villages. Our main assumption for the aggregation process, based on our experience in the field and other studies (N’Goran et al. 2012), was to define an activity radius of the local population of up to 25 km. This means that we assumed that villagers living outside the 25-km radius around a transect segment had no influence on wild mammals on the respective transect segment, and that the influence of villagers within the 25-km radius around a transect segment decreased with increasing cost distance to the transect segment. We constructed 25-km activity radii around all transect segment midpoints and selected all villages within the activity radii. The values of the predictor variables within the transect segment activity radii were then weighted with the respective inverse cost distance and then were summed per transect (Table 3, Fig. 3).


First, we identified all species with abundance data that followed approximately a Poisson distribution. This was the case for duiker, bushbuck, African civet, crested porcupine, scrub hare, common wart hog, jackal, common genet, Guinea baboon, and patas monkey. Chimpanzee abundance data, on the other hand, were highly overdispersed with an excess number of zeroes and some very high values. We built two data sets, the mixed species abundance data (938 cases) and the chimpanzee abundance data (97 cases). We used mixed effects Poisson regression models to estimate the influence of the human population on the mixed species abundance, and used zero inflated negative binomial regression models to estimate the influence of the human population on chimpanzee abundance (McCullagh and Nelder 1996, Baayen 2008). Because some of the correlations among predictors (population density, market integration, fish supply, hunting pressure, taboo influence) were very high (Table A2.1, A2.2), we were not able to fit models that included all test and control predictors. Instead, we used multimodel inference (Burnham and Anderson 2002). Because the full model with all test and control predictors was characterized by large collinearity (maximum Variance Inflation Factor [VIF] 25.8) (Field 2005), we constructed the set of models in the following way: to begin with, we included a model that comprised the five control predictors only (share classified forests, distance nearest road, distance nearest river, distance nearest field, NDVI) and all models that included all five control predictors and one of the five test predictors (market integration, hunting pressure, fish provision, taboo influence, population density) at a time (six models for the mixed species abundance data and for the chimpanzee abundance data). The model that comprised the control predictors and population density corresponded to a standard ecological model. Since we were specifically interested in the combined effects of taboo influence, market integration, hunting pressure, fish provision, and population density, we added all models that contained combinations of these test predictors and all the control predictors with a maximum VIF ≤ 5 (eight additional models for the mixed species abundance data, leading to 14 models, and two additional models for the chimpanzee abundance data, leading to eight models). Since we wanted to know to what extent the control predictors contributed to mammal abundance, we added all the above models but without the control predictors to the set of models (14 additional models for the mixed species abundance data, leading to 28 models, and eight additional models for the chimpanzee abundance data, leading to 16 models). Note that this led to a model that comprised none of the test or control predictors. We controlled for varying transect segment length by including it (log transformed) as an offset term (McCullagh and Nelder 1996) into all models. The final model set for the chimpanzee abundance data comprised 16 models. For all models on the mixed species abundance data, we included an autocorrelation term as well as a random intercept of transect segment ID and random intercept of species (random slopes of the autocorrelation term within species and transect we kept in all models). Finally, we also replicated the entire set of 27 models (all models apart from the model that comprised only the intercept), and this time also included the random slopes of all predictors within species and added these models to the set. We included these models because we were interested in whether species were affected differentially by the predictors, and we wanted to avoid overconfident models (Barr et al. 2013). The final model set for the mixed species abundance data comprised 55 models (see Tables A3.1 and A3.2 for the full set of candidate models).

All test and control predictors were transformed when necessary (i.e., to achieve approximately symmetrical distributions and to avoid influential cases) and then were standardized to a mean of zero and a standard deviation of 1 prior to estimation to achieve easier interpretable estimates (Schielzeth 2010). In order to control for autocorrelation (which was no issue for the chimpanzee abundance data), we first fitted a full model that included all test and control predictors, apart from taboo influence, and extracted the residuals from it. We then, separately for each data point, averaged the residuals of all other data points of the same respective species, whereby we weighted their contribution by their distance to the respective data point. By this we derived an “autocorrelation term” to be included in the full model. The function that determined the weights when averaging the residuals had the shape of a normal distribution with a mean of zero and a standard deviation determined such that the likelihood of the full model with the derived autocorrelation term included was maximized. This approach is similar to what was done in Fürtbauer et al. (2011). The 55 mixed effects Poisson regression models (Table A3.1) on the mixed species abundance data were fitted using the glmer function of the lme4 package in R (Bates et al. 2015). The 16 zero inflated negative binomial regression models (Table A3.2) on the chimpanzee abundance data were fitted using the zeroinfl function of the pscl package in R (Jackman 2015). For the zero inflated negative binomial regression models, we always included the same predictor and control predictors into the zero part as in the count part. We estimated VIF using the vif function from the car package in R (Fox and Weisberg 2011) The dispersion parameters of the mixed effects Poisson regression models ranged between 1.028 and 1.143. The dispersion parameter of the zero inflated negative binomial regression models ranged between 0.781 and 0.891. All Akaike information criterion (AIC) were calculated with the correction for sample size (AICc) (Burnham and Anderson 2002), and the AIC values for the mixed effects Poisson regression models we additionally corrected for overdispersion (QAICc) (Burnham and Anderson 2002). We centered our inference on delta AIC and the 95% best model confidence set based on Akaike weights (Burnham and Anderson 2002).

Descriptive results

Wild mammal species abundance

In total, 2303 sightings of 18 species were recorded in the study area. The most frequently recorded sighting type was chimpanzee nest, with 994 records, and the most frequently recorded species was the chimpanzee, with 1046 records. The least frequently recorded sighting type was vocalization, with 13 records, and the least frequently recorded species was otter, with one record (Table 2).

Results of statistical analysis

Mixed effects Poisson regression models on species abundance

The 95% best model confidence set of our multimodel inference on mixed species abundance included 17 of 55 models (Table 4). Fifteen of these models included random slopes. This indicates that it is important to account for variation between species in how the predictors influenced their abundance. Sixteen of the models from the confidence set comprised the control predictors. The model that comprised only control predictors was also included in the confidence set: with a delta AIC of 9.094 and an Akaike weight of 0.004, the support for this model was however meager. The fact that most models in the confidence set included the control predictors is strong support for the importance of environmental factors to wild mammal abundance.

The best model had an Akaike weight of 0.372 and included hunting pressure, market integration, taboo influence, and the control predictors (Table 5). In this model, the influence of the market integration varied between the species, having a negative effect on duiker, patas monkey, common genet, and common wart hog abundance, a positive effect on jackal, African civet, crested porcupine, and scrub hare abundance, and no clear influence on Guinea baboon or bushbuck abundance (Fig. 4). The model that included population density and the control predictors was also in the confidence set but ranked only eight and had a delta AIC of 7.242 with an Akaike weight of 0.01 (Table 6). The model averaged coefficients revealed that, across all models, taboo influence and distance to the nearest field had by far the strongest influence on species abundance. The stronger the taboo influence and the larger the distance to the nearest field, the larger the wild mammal species abundance. While the NDVI also had a positive influence on species abundance, the share classified forests, the population density, the hunting pressure, and the distance to the nearest river and road had a negative influence on wild mammal abundance. The influences of fish supply and market integration were very close to zero (Fig. 5).

Zero inflated negative binomial regression models on chimpanzee abundance

The 95% best model confidence set of our multimodel inference on chimpanzee abundance included seven of 16 models (Table 7). None of these models comprised control predictors, which suggests that environmental factors were not of primary importance in predicting chimpanzee abundance. The best model had an Akaike weight of 0.414 and was the model that included only market integration. The model that included population density alone was also in the confidence set. It had a delta AIC of 2.57 and an Akaike weight of 0.114. The model averaged coefficients of the count part of the zero inflated negative binomial models on chimpanzee abundance showed that while market integration clearly had a negative influence on chimpanzee abundance, hunting pressure had a weak positive influence on chimpanzee abundance, and all other coefficients were close to zero (Fig. 6). The model averaged coefficients of the zero part of the zero inflated negative binomial models on chimpanzee abundance showed that the likelihood of no chimpanzee occurrence increased strongly with the market integration (Fig. 7).

Our results revealed that including human socioeconomic factors other than human population density alone increased our capacity to model wild mammal abundance in our study area in Guinea. All human population factors we considered in our analysis were deduced from the framework of wild mammals as economic goods. In the case of the analysis on mixed species abundance, the best model did not contain human population density at all but was made up of taboo influence, market integration, hunting pressure, and the environmental control predictors. Chimpanzee abundance was best modeled by market integration alone.



Although the transect data revealed promising wild mammal abundance in the study area, our results point to serious issues for the WCF biodiversity offset project in the study region. First, the CFs in the study area were not enhancing wild mammal abundance. Instead, they had a negative effect on wild mammal species abundance and did not influence chimpanzee abundance. This might be due to different reasons. For example, the knowledge of the classified forest boundaries was not widespread in the study area, and CF boundaries were not respected, with nine villages being located within them. Furthermore, the success of protected areas in conserving wild mammals depends on considerable effort, especially in law enforcement (Bruner et al. 2001, Tranquilli et al. 2012, 2014). Such effort is lacking in the study area. Our results suggest that the village communities were more successful in controlling the exploitation of wild mammals than the government was in the CFs. When the government does not sufficiently invest in monitoring and controlling its protected areas, those areas might be considered as common ground by the communities surrounding them. The mechanism of the “tragedy of the commons” (Hardin 1968) then leads to overexploitation. This indicates that protected areas without sufficient monitoring and controlling efforts are worse for the conservation of wild mammals than giving the land as property to the local communities (Coase 1960). Or vice versa, if areas with restricted access should remain an important element of conservation projects, the functional regulation and monitoring of restricted access to the area is essential for the protection of wild mammals. Second, the local population relied on slash-and-burn cultivation for their subsistence agriculture. This practice has a detrimental influence on the environment, and the locations of the fields had a strong negative effect on wild mammal species abundance in the study area. It remains unclear if this effect was due to habitat destruction or to conflicts with wild mammals that are attracted by field crops. In any case, long-term conservation planning in the area is constrained if the fields are relocated regularly. Protecting the crops without harming wild mammals, for example by erecting fences around the fields, might prove an efficient tool for wild mammal conservation in the study area (Agrawal et al. 2016). Finally, although the human population was poor and suffered from animal protein deficiency, some did not eat potential game (especially common wart hogs provide plenty of meat) because of religious beliefs. In fact, food taboos had a positive effect on wild mammal species abundance. As suggested by other studies, our results support the notion that food taboos can work as resource management tools to protect wild mammal species (McDonald 1977, Balée 1985, Pezzuti et al. 2010, Read et al. 2010, Luzar et al. 2012). The option to appeal to such beliefs, in cooperation with local religious authorities, should seriously be considered. Unlike Junker et al. (2015), we did not find an effect of fish availability on wild mammal abundance. This suggests that fish availability did not work as a substitute for wild mammal meat in the study area. One reason for this missing effect might be that the amount of fish provided in the study area is not sufficient to substitute bushmeat. Furthermore, the accessibility of the transects did not influence wild mammal abundance. This indicates that the roads might not have affected the remoteness of the area and were used mainly by locals. The effect of hunting was ambiguous. On the one hand, wild mammal species abundance decreased with increasing hunting pressure. On the other hand, chimpanzee abundance increased with growing hunting pressure. The reason for this difference might be that chimpanzees profit from less competition from other wild mammals in areas with increased hunting pressure. Our results regarding chimpanzees are especially interesting because they suggest that in our study area, chimpanzee abundance was not obviously influenced by environmental factors but mainly by human population factors. Especially, the market integration had a clearly negative influence. The situation was similar for common wart hogs. Their abundance decreased strongly with increasing market integration. It seems that for species that lack a local demand, such as chimpanzees and common wart hogs, the market integration of the population compensates for this missing demand and puts pressure on these animals. For other species, such as scrub hares, the same mechanism might have the opposite effect: their abundance increases with market integration. Probably, hunting scrub hare is substituted with buying cheap chicken, which is available only on the market. So overall, the market integration provides alternative sources of income to the local population; however, it also provides additional incentives to exploit wild mammals. If in the long run, the economic development and the market integration of the local population, which was very low, even for Guinean standards, should eventually catch up with the rest of the country, a strategy targeting this issue is necessary. A promising strategy for the WCF biodiversity offset might be to provide alternative sources of income for the local population.


Our results revealed that although environmental factors were important to understanding the abundance of wild mammal species in our study area, it was fundamental to also account for human population factors. In fact, only two of our environmental predictors (NDVI and distance to nearest river) were purely environmental. The other three (distance to nearest road, distance to nearest field, share classified forests) represented human factors. For the chimpanzees, the environmental control predictors did not influence their abundance at all. All in all, this suggests that wild mammal abundance was influenced more by human factors than by environmental factors in our study area. The crucial reason for considering factors other than human population density when estimating the effect of the human population on wild mammals consists not only of optimizing the goodness of fit, as it is shown in our multimodel inference analysis (Table 4, Table 7). Rather, the main reason is to improve our understanding of the relationships between the human population and the wild mammals. This is best exemplified when comparing the best model (Table 5) and the model of rank 8 (Table 6) from our multimodel inference on the mixed species abundance (Table 4). In the best model, wild mammal species abundance was a function of market integration, taboo influence, hunting pressure, and the control predictors. In the model of rank 8 (with a delta AIC of 7.24), wild mammal species abundance was a function of population density and the control predictors. The conclusions drawn from the two models differ substantially. When looking at the population density model (Table 6), one would conclude that the human population had no influence on wild mammal abundance. But the best model, where human population density was replaced by hunting pressure, taboo influence, and market integration, showed a different picture (Table 5, Fig. 4): increasing taboo influence came along with increases in wild mammal abundance. Our analysis furthermore revealed that market integration negatively influenced the abundance of duikers, patas monkeys, common genets, and common wart hogs, positively influenced the abundance of jackals, African civets, crested porcupines, and scrub hares, and had no obvious influence on the abundance of Guinea baboons and bushbucks. Therefore, when planning conservation activities in the area, market activities of the human population must be taken into account very carefully, avoiding the negative effects (additional incentives to exploit wild mammals) and using the positive ones (substitution of wild mammal products with products available on the market). Our results were expectable given that wild mammals and humans are part of a common social-ecological system and influence each other. The framework of wild mammals as economic goods within a social-ecological system is an appropriate tool to help detect important factors that drive the relationship between wild mammals and humans in a diverse range of settings.


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First, we thank the Guinean authorities for providing us with their support and all necessary documents to work in the country. Second, we thank the local population for its kind cooperation. Third, we thank our Guinean assistants Salian Traore, Mohammed Kaba Abdoulaye Diallo, and Ousmane Diallo for their work and advice. Finally, we thank Christophe Boesch for his consultancy and for reviewing the work. This work was funded and facilitated by the Wild Chimpanzee Foundation (WCF) and Deutscher Akademischer Austauschdienst (DAAD).


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Address of Correspondent:
Lukas Boesch
Universität Leipzig, Institut für Soziologie, Beethovenstraße 15, 04107 Leipzig, Germany
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