Year : 2011 | Volume
| Issue : 4 | Page : 299-310
Logging or conservation concession: Exploring conservation and development outcomes in Dzanga-Sangha, Central African Republic
Marieke Sandker1, Bruno Bokoto-de Semboli2, Philipp Roth3, Cyril Péllisier4, Manuel Ruiz-Pérez5, Jeff Sayer6, Andrea K Turkalo7, Ferdinand Omoze3, Bruce M Campbell8
1 Autonomous University of Madrid, Madrid, Spain; Center for International Forestry Research, Bogor, Indonesia
2 Center for International Forestry Research, Bogor, Indonesia; World Wide Fund for Nature, Bayanga, Central African Republic
3 Deutsche Gesellschaft für Technische Zusammenarbeit, Bayanga, Central African Republic
4 World Wide Fund for Nature, Bayanga, Central African Republic; World Wide Fund for Nature, Kinshasa, Democratic Republic of Congo
5 Autonomous University of Madrid, Madrid, Spain
6 James Cook University, Cairns, Australia; International Union for Conservation of Nature, Gland, Switzerland
7 Wildlife Conservation Society, Bayanga, Central African Republic
8 Center for International Forestry Research, Bogor, Indonesia; Challenge Program for Climate Change, Agriculture and Food Security, University of Copenhagen, Copenhagen, Denmark
Autonomous University of Madrid, Madrid, Spain; Center for International Forestry Research, Bogor, Indonesia
Source of Support: None, Conflict of Interest: None
|Date of Web Publication||21-Jan-2012|
| Abstract|| |
The Dzanga-Sangha landscape consists of a national park surrounded by production forest. It is subject to an integrated conservation and development project (ICDP). In collaboration with the ICDP personnel, a participatory model was constructed to explore wildlife conservation and industrial logging scenarios for the landscape. Three management options for the landscape's production forest were modelled: (I) 'predatory logging', exploitation by a logging company characterised by a lack of long-term plans for staying in the landscape, (II) sustainable exploitation by a certified logging company, and (III) conservation concession with no commercial timber harvesting. The simulation outcomes indicate the extreme difficulties to achieve progress on either conservation or development scenarios. Both logging scenarios give best outcomes for development of the local population. However, the depletion of bushmeat under the predatory logging scenario negatively impacts the population, especially the BaAka pygmy minority who most strongly depend on hunting for their income. The model suggests that conservation and development outcomes are largely determined by the level of economic activity, both inside and outside the landscape. Large investments in the formal sector in the landscape without any measures for protecting wildlife (Scenario I) leads to some species going nearly extinct, while investments in the formal sector including conservation measures (Scenario II) gives best outcomes for maintaining wildlife populations. The conservation concession at simulated investment levels does not reduce poverty, defined here in terms of monetary income. Neither does it seem capable of maintaining wildlife populations since the landscape is already filled with settlers lacking economic opportunities as alternatives to poaching.
Keywords: Congo basin, integrated conservation and development project, participatory modelling, poaching, STELLA, Tri-National de la Sangha
|How to cite this article:|
Sandker M, Semboli BB, Roth P, Péllisier C, Ruiz-Pérez M, Sayer J, Turkalo AK, Omoze F, Campbell BM. Logging or conservation concession: Exploring conservation and development outcomes in Dzanga-Sangha, Central African Republic. Conservat Soc 2011;9:299-310
|How to cite this URL:|
Sandker M, Semboli BB, Roth P, Péllisier C, Ruiz-Pérez M, Sayer J, Turkalo AK, Omoze F, Campbell BM. Logging or conservation concession: Exploring conservation and development outcomes in Dzanga-Sangha, Central African Republic. Conservat Soc [serial online] 2011 [cited 2019 Oct 17];9:299-310. Available from: http://www.conservationandsociety.org/text.asp?2011/9/4/299/92141
| Introduction|| |
The Congo basin forests constitute the second largest area of moist tropical forest in the world, after the Amazon, covering a total area of about two million sq. km (Congo Basin Forest Partnership 2006). About 12% of this area is under protection (Laporte et al. 2007) while 76% consists of production forest (Congo Basin Forest Partnership 2006). These production forests are often located in proximity to national parks, forming a buffer between the protected area and more intensively used agricultural areas. The Dzanga-Sangha landscape located in the south-western part of the Central African Republic (CAR) is typical of this situation. Clark et al. (2009) investigated the effect of logging on mammal populations in the proximity of this area and concluded that production forests, if managed appropriately, can extend the effective habitat of many of Central Africa's most threatened species.
Despite its high biological diversity, the Congo basin ranks economically amongst the poorest regions of the world (World Bank 2010). CAR has a gross national income of merely EUR 290 per capita and ranks amongst the lowest income countries (World Bank 2009). The forestry sector is of major importance to the CAR's economy; timber contributed 41% of the national export revenues in 2007, and the sector is, after the government, the most important provider of employment nationwide (de Wasseige et al. 2009). In remote rural areas, logging companies are by far the most important providers of salaried jobs (de Wasseige et al. 2009) and often take over the role of the state in providing services (e.g., electricity and hospitals) in the villages where they operate. For this reason, local people often see their arrival as an opportunity to advance local development. Logging concessions in the Congo basin thus have a dual function, buffering and extending protected areas while boosting the local economy. But the centres of development that are stimulated by forestry operations have also been linked to an upsurge in the use of wildlife for bushmeat (Bennett 2004). Not all of the vast production forest in the Congo basin is currently being logged; in 2007 only 36% was legally allocated as logging concessions. Africa's timber production is projected to increase substantially (FAO 2003); thus, an increase in the number of licenses and area exploited is expected. The arrival of logging companies in these remote forest areas will affect the local economy and wildlife populations.
Ever since the creation of the national park in 1990, an integrated conservation and development project (ICDP) has been operating in the Dzanga-Sangha landscape. Discussions on how to manage the production forest are on-going between the CAR government and proponents of the ICDP. The most likely scenario is that a license will be issued to a logging company for the extraction of timber under a rotation cycle. However, one conservation organisation leading the ICDP has advocated making the area a 'conservation concession'. The concept is defined as follows by Rice (2003: 1): "Under a conservation concession agreement, national authorities or local resource users agree to protect natural ecosystems in exchange for a steady stream of structured compensation from conservationists or other investors". With conservation and development experts from the ICDP, we constructed a systems dynamics model for the Dzanga-Sangha landscape to explore different management options for the production forest and their consequences for wildlife and the local economy.
| Methods|| |
Conservation and development in the Dzanga-Sangha landscape
Situated in the extreme southwest of the CAR, the Dzanga-Sangha landscape covers a total area of 4,643 sq. km. Of this area, 27% is made up by the national park (Sangha and Ndoki sector), 4% is pre-park (a 2 km wide buffer zone around the parks), 55% is production forest (to be allocated to a logging concession), 13% is a communal hunting zone and 1% is reserved for agriculture [Figure 1]. 70% of the production forest is also reserved for safari hunting, although no safari hunting companies currently operate in the area.
|Figure 1: The Dzanga-Sangha landscape (modifi ed from GTZ-GFA Unpublished)|
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The forest of Dzanga-Sangha constitutes an integral part of the sites recognised as critical for the conservation of dense forests in the Afro-tropical region (de Monza 1996). It hosts approximately 105 terrestrial mammal species, out of which 16 are primates, 14 are ungulates and 14 are carnivores (Blom 2001). The region has a remarkably high density of forest elephants (Loxodonta cyclotis) with 0.2-0.3 individuals per sq. km and of western-lowland gorillas (Gorilla gorilla gorilla) with 1.05 individuals per sq. km (Blake 2005). The region is important for Bongo antelopes (Tragelaphus euryceros) with an estimated density of 0.3 individuals per sq. km (Klaus-Hügi 1998). Hunting pressure is high in the landscape resulting in rapidly declining populations of several wildlife species (Blake 2005). The worsening wildlife situation in Bayanga is illustrated by the withdrawal of the last active safari hunting company in 2007 (and it is unlikely that safari hunting will resume in the near future). The employment provided by the safari company at the time it was operational was rather limited (<10 permanent jobs) and safari tax revenues represented a tiny fraction of the forestry taxes (e.g., 0.25-1.5% of forestry taxes for the period 2000-2003). For this reason, safari hunting has not been included in the model.
In 2005, the Dzanga-Sangha landscape had a population of about 6,850 people (2 people/sq. km) living in 12 villages (Kamiss 2006; GTZ unpublished). Before the arrival of a logging company in 1972, Bayanga was a small fishing village (Kamiss 2006). The population at that time consisted of BaAka pygmies (mainly hunter-gatherers) and Sangha Sangha Bantu (mainly fishermen). These original people make up about one third of the present total population, the rest being Bantu migrants (Kamiss 2006). Principal income sources of the local population are agriculture, hunting, gathering and local sale of non-timber forest products (NTFPs) (like Gnetum spp.), fishing and employment in ecotourism and the ICDP project. Ecotourism is well developed in the landscape; tourists are offered various programmes (e.g., elephant spotting from the mirador, visit to a group of habituated gorillas and net-hunting with the BaAka), there is a relatively luxurious lodge, and there is an airstrip for small planes, attracting high-budget tourists. In 2007, about 580 well-spending tourists visited the area generating 72 million CFA francs (FCFA) (EUR 110,000) of local revenue. Of this, about 12% was directly captured by local people through salaries (Roth 2008 pers. comm.). Though this amount may seem substantial, the annual salary revenue generated by the logging company when it was operational was much higher at 270 million FCFA (EUR 411,600) (Czesnik 2007).
Bayanga-where the sawmill is located-is the largest village, home to 57% of the total population of the landscape. The sawmill was closed in 2004 and will resume operations only after a licence is auctioned to a new logging company. However, previous logging activities have attracted many migrants into the landscape comprising roughly two thirds of the current population (Kamiss 2006). Between 1972 and 2004, four different logging companies exploited the logging concession in the Dzanga-Sangha landscape. Most of the logging companies operated only for short periods, given that this is a remote area and the financial returns on logging are severely limited by the costs of extraction, transportation to ports and taxes. The departure of these logging companies was often abrupt, leaving its local employees unemployed with several months of unpaid salaries. The intervals during which the concession remained closed usually ranged between one and four years. The majority of the workforce remained in the village or in mining camps just north of the landscape, hoping to be paid for their past work and hoping to secure employment in a new logging company (Noss 1995). However, with each re-opening, only a fraction of the old workforce was usually re-employed and many new people arrived from outside the landscape (Kamiss 2007 pers. comm.). The 2004 sawmill closure resulted in an increase in households practicing agriculture from 39 to 76% (Kamiss 2006). Many laid-off employers turned to hunting (Doungoubé 1990). Logging by itself also augmented hunting pressure through increased access to markets and hunting by family members of sawmill employees (Bennett 2004). However, with its history of logging and the high number of migrants this attracted who settled in and around Bayanga, the hunting pressure is already high in absence of logging activities.
Between 2004 and 2009, under the guidance of model experts, an exploratory landscape model was built by non-model expert conservation and development actors working in the Dzanga-Sangha landscape. The objectives of participatory modelling include stimulating the exchange of knowledge and visions between the actors, unravelling complex interactions, identifying drivers of change, understanding trade-offs between conservation and development outcomes, and exploring plausible future scenarios (Van den Belt 2004; Sandker et al. 2010). The model has been developed in the user-friendly modelling language STELLA 8.0 (High Performance Systems 1996). STELLA is a system dynamics modelling language whose basic components are stocks (such as forest area, elephant population and annual budgets), flows (e.g., change from forest to agriculture or birth and death of elephants) and convertors that moderate these flows (including laws, interventions and price variations). The scenarios to be simulated with the model were defined during workshops with conservation and development actors working in the Dzanga-Sangha landscape.
The model structure
The model reflects nature-society interactions. Model building was preceded by visioning exercises like historical trend analysis (Sayer et al. 2007) to identify trends of socio-economic and biological variables, as well as past responses to specific events. These exercises helped to select the main stocks and flows for the model. We did not attempt for the model to be all-inclusive, rather the stakeholders were urged to select only those variables and interactions they considered most important for the outcomes of concern to the ICDP. This resulted in a model existing consisting of 12 interconnected sectors (sub-models), containing a total of 260 variables. [Figure 2] gives a schematic representation of some simplified relations between variables in the model determining per capita income. It demonstrates how many relations are ambiguous; e.g., investment in logging provides an alternative to poaching through employment but at the same time attracts potential poachers through increased immigration. Many such relations are regulated through interventions either by the ICDP or by a certified logging company.
|Figure 2: Schematic representation of relations between some variables in the model|
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The model sectors with variables and their relations are given in [Table 1], which also gives data sources. In the model, households get income from agriculture, fishing, hunting, NTFPs, employment and commerce. With the exception of NTFP and fishing, the income contributions per household are all dynamic. Households are simulated to turn to poaching and agriculture in the absence of formal employment [Figure 2]. An increase in salaried jobs, on the other hand, results in agricultural products being sold at higher prices and commerce being stimulated. The model can be downloaded at: http://www.cifor.cgiar.org/conservation/_ref/research/research.2.5.htm.
The data entered in the model
Data from existing project monitoring, and extracted from scientific publications and ongoing studies underway in the reserve (e.g., recent data on duiker population), were used in the model (see [Table 1]). Additional information to fill some data gaps was gathered through 50 interviews held between April and June, 2006 with the head of household (chef de ménage); 20 interviews concern BaAka and 30 Bantu households. The interviewed households were randomly selected in three different villages (Bayanga, Bomandjokou and Mossapoula) characterised respectively as: a village with a sawmill (though closed at the time of the interview, but with a higher concentration of migrant households), a village with employment from ecotourism and a village without access to formal employment. The questions focused on income generating activities, and number and species of animals hunted. Income per activity was approximated from the respondent's estimate of the total production of the household, the share sold and the price per product. When respondents had difficulties using numbers in their replies, marbles were used to quantify importance of activities. Given the small sample size and the possible large error with estimated values, the interview results are of indicative value only and we deemed it necessary to discuss and validate the results with local experts [Table 1]. Remaining data gaps were filled by informed estimates obtained through discussions with local experts working in the area. Thus, the simulation results of this study are based on the best available information to us. We recommend further profound and solid research to fill knowledge gaps.
Indicators of conservation and development outcomes in the landscape
The states of key attributes indicating the status of conservation and development in the landscape were selected and plotted on graphs to visualise the consequences of the different scenarios for conservation and development (simulation outcomes). Elephant and duiker populations were selected as proxies indicating the general state of conservation. Elephants have low reproduction rates and their hunting is prohibited; they are mainly poached with large calibre guns. Duikers have high reproduction rates and their hunting is legal under certain conditions; however, they are mainly captured with cable snares or hunted with small home-made guns, both of which are illegal. Duikers form the most important source of bushmeat; the 2006 household surveys revealed that of the small animals hunted, 9 out of 10 were duikers. Noss (1998) collected data on bushmeat hunting in Bayanga in 1994 and concluded at that time that hunting levels were likely to be unsustainable for duikers. When simulating the situation from 1994 to today, based on Noss's approximated hunting pressure for mid-range duiker densities and maximum reproduction rates, the results showed that there would be no duikers left outside the protected area. However, 16 years after Noss's study, bushmeat markets are still abundantly supplied with duikers, and the 2006-2008 mapping of hunting zones by the ICDP (Dzanga-Sangha Project unpublished) showed that hunting areas still exist in the proximity of the villages, providing little evidence of their depletion. Furthermore, the 2006 household questionnaires [Table 1] indicated that the number of duikers hunted per hunter is almost the same as the number found by Noss. This is why we have chosen rather high values for the possible range of duiker density and reproduction rate as given in Noss's study [Table 1].
Household cash income was used as a proxy indicating the state of local development. Though this only gives a rough approximation in a situation where subsistence income is of great importance, the actors building the model still thought this was the best indicator to approximate the development situation of local people. Since about half of the cash captured by BaAka comes from bushmeat (2006 household surveys, [Table 1]), monetary income does reflect their access to this important forest product.
The outcomes of the simulation often led to discussions among stakeholders and revisions of the model until consensus was reached on plausible future scenarios. Considering the process adopted for the elaboration and revision of the latter, the results presented in this article do not represent accurate predictions, but are indicative and should serve as a basis for reflection and discussion with regard to the future of the Dzanga-Sangha and similar landscapes.
Scenario I: 'Predatory logging' (Business as usual)
This scenario simulates logging companies characterised by a lack of long-term plans for staying in the landscape and minimal investments in local salaries, infrastructure and facilities. The continuous arrival and departure of logging companies simulate an extension of what the landscape has experienced since 1972. In our simulated model, the opening of the sawmill attracts many job seekers and their families (2,000 people), while the closure results in only some of them leaving (500 people). The simulation assumes opening of the sawmill in years 2 and 13, and closure in years 10 and 18.
Scenario II: Logging by certified company
This scenario simulates the re-opening of the sawmill by a company obtaining the Forest Stewardship Council (FSC) certification. We assume their sustainable exploitation practice to result in a long-term forestry enterprise, thus no closure is simulated. This feature was confirmed by Desmet (2009 pers. comm.) of the neighbouring logging company Congolaise Industrielle des Bois who claimed that without the FSC certificate the company would have most likely collapsed due to the financial crisis of 2008. The reason is that FSC certified products have a niche market which fluctuates less, and are sometimes subject to supply agreements, thus providing stable prices and a guaranteed timber off-take for the logging companies. Furthermore, the FSC certification requires the company to provide basic services in the villages located in the concession, pay higher salaries and invest resources in controlling poaching (Forest Stewardship Council 2007).
Scenario III: Conservation concession (No logging)
This scenario simulates the production forest in the Dzanga-Sangha landscape as a conservation concession. Following the earlier given definition of Rice (2003) of a conservation concession, we assume that the compensation the conservation investor pays to the state would be equivalent to the taxes and royalties paid by a traditional concessionaire, amounting on average to 207 million FCFA/year (EUR 316,000) for the period 2000-2003 (Czesnik 2007). We believe this would be the minimum investment of a conservation concession that would be approved by the government, since ideally a conservation concession should also compensate for the foregone salaries of logging company personnel. We assume ecotourism to develop under this scenario, with the number of eco-tourists increasing by five times the current number after 10 years.
| Results|| |
Local economy and population dynamics
The cash investment in the formal sector is substantial under both logging scenarios [Figure 3]. With each closure of the logging company under Scenario I (predatory logging), the investment drops below that of Scenario III (conservation concession); this difference is a result of the larger investment in ecotourism employment under Scenario III.
|Figure 3: Investment in the formal sector (salaries from ecotourism, the ICDP and the logging concession) under the three scenarios explored (I: predatory logging, II: certifi ed logging, III: conservation concession)|
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The human population increases substantially under all scenarios [Figure 4]a. Due to the politically unstable and economically weak situation in the CAR, immigration into the landscape is expected to be high even in the absence of opportunities for paid labour. Under the conservation concession scenario, the annual population growth rate is 2.5%. Scenario II has the highest population growth attracted by the large cash investment in the formal sector [Figure 3]. Though the cash investment under Scenario I is much lower than under Scenario II, population size is comparable due to the recurring cycle of opening and closing of the sawmill; with each opening (years 2 and 13) attracting more people than those leaving after each closure (years 10 and 18).
|Figure 4: (a) Total human population and (b) number of hunters in the Dzanga-Sangha landscape under the three scenarios explored (I: predatory logging, II: certifi ed logging, III: conservation concession)|
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The number of hunters [Figure 4]b is calculated as a percentage of the unemployed male population in the landscape [Table 1]. The number of hunters falls back when people are employed by the company after re-opening (years 2 and 13). However, the number of hunters increases substantially each time the predatory logging company shuts down (years 10 and 18) on account of laid-off workers turning to poaching.
The BaAka are poorer than the Bantu in monetary terms, capturing about one third of the cash the Bantu capture per capita [Table 1] and [Figure 5]. The BaAka cash income shows a downward trend that is mainly attributed to the declining duiker population. Their access to bushmeat diminishes, which currently contributes to 50% of their cash income (2006 household surveys; [Table 1]). Though the BaAka only capture 5-10% of the jobs in the logging concessions, their per capita cash income is quite dramatically affected since they are less in number and because their average per capita cash income is relatively low [Figure 5]a. Since [Figure 5]a displays average values, it doesn't show the large income differentiation for BaAka under Scenario I with only some capturing the benefits (salaried jobs) but all suffering the costs (reduced access to bushmeat). The Bantu cash income also shows a downward trend; this is due to investments in the formal sector being divided over a larger number of people-since there is an increase in population, mainly due to immigration-thereby lowering the per capita cash capture [Figure 2]. Even though the cash investment in the formal sector continuously increases under Scenario II [Figure 3], the migrant inflow exceeds the availability of new jobs at the logging concession, resulting in the slight decrease in per capita cash income.
|Figure 5: Average annual per capita cash income for (a) BaAka pygmies and (b) Bantu under the three scenarios explored (I: predatory logging, II: certifi ed logging, III: conservation concession)|
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Under the predatory logging and conservation concession scenarios, the simulated elephant population declines by 85% and 50% respectively after 20 years [Figure 6]a. Such dramatic losses have been seen in the past in the landscape when the elephant population decreased by 70% from 2,855 individuals in the 1980s (Carroll 1986) to 869 individuals in 2004 (Blake 2005). In year 7 of the simulation under Scenario I, and in year 8 under Scenario III, the elephant density inside the park drops below 0.2/sq. km, resulting in a decreasing hunting success from this moment onwards [Figure 6]a. In year 10 under Scenario I, the elephant population declines faster due to the increase in poachers [Figure 4]b as a result of the closure of the logging company. The same feature is seen with the closure in year 18 under Scenario I, though the elephant population is already so weak that hunting elephants becomes quite difficult. Under Scenario III, the pressure on elephants increases steadily due to the increasing human population and the limited alternative income generating activities in the landscape. Although Scenario II provides the best option to conserve the elephant population, it still entails a 35% decline.
|Figure 6: Total number of (a) forest elephants and (b) duikers in the Dzangha-Sangha reserve under the three scenarios explored (I: predatory logging, II: certifi ed logging, III: conservation concession)|
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The duiker population [Figure 6]b is strongly related to the number of hunters [Figure 4]b under the three scenarios. For the first ten years of the simulation, the conservation concession gives the least favourable results for the duiker population because of the lack of alternative income generating activities for the local population, as a result of which a large share is dedicated to bushmeat hunting. The closure of the logging concession in years 10 and 18 (Scenario I) results in a sudden increase in duiker hunting and thus a sudden decrease in its population [Figure 6]b as people compensate forgone income from their lost jobs with bushmeat hunting. At the start of the simulation, the duiker population is already slightly below 40 animals/sq. km, so the hunting success diminishes linearly with the decrease in duiker density. Again, Scenario II provides the best outcomes for the duiker population, although duiker densities drop to less than half the current values.
The predatory logging scenario bares a more dramatic effect on elephants than duikers in comparison to the other scenarios. This scenario was modelled with an accompanying increase in import of large calibre guns used for elephant poaching through the opening of roads and increased traffic, and because the employers will have more money to purchase guns [in line with findings of Bennett (2004)]. With a certified logging company however, we expect the company to establish control points on the roads, limiting the transport of guns and bushmeat, and close logging roads that are no longer used. This is being done by neighbouring certified companies in Cameroon and Congo.
A sensitivity analysis showed that wildlife population outcomes are sensitive to levels of human migration, and duiker population outcomes are also sensitive to duiker density and reproduction rate. If, instead of the assumed 1-3% immigration rate, we assume an out-migration rate of 2%, e.g., as a result of economic opportunities in the town of Berberati or Bangui, the elephant population is 12-70% higher at the end of the simulation. Assuming this out-migration occurs under the scenario of a certified logging company, the elephant population becomes comparable to the current population size after 20 years. Using minimal duiker densities with maximum reproduction rates [as given by Noss (1998)] results in similar downward population trends when comparing the three scenarios; only they would go extinct under Scenarios I and III after 19 and 20 years respectively, while under Scenario II a small population of around 10,000 duikers would remain after 20 years. Assuming minimum densities and reproduction rates [as given by Noss (1998)] results in minimal differences between the scenarios, with duikers going extinct under all scenarios.
| Discussion|| |
Local and national economy
Overall, peoples' incomes decline in the absence of a major entrepreneur/employer like a logging company, mainly because the landscape lacks the economic activities needed to support the increasing human population. Bushmeat hunting is unlikely to provide a sustainable cash flow for an increasing local population. Current modelled hunting levels are unsustainable [Figure 5]b and hunting pressure from outside the area might increase in the future as improved roads provide better access to the landscape. For a large proportion of the population, agricultural production is not likely to provide a feasible income alternative either. The Dzanga-Sangha landscape has mainly ferralitic soils which are rapidly impoverished by slash-and-burn when not altering cycles of cassava cultivation with other crops (Kokamy-Yambere et al. 2007) as is the current practice. This, together with the administrative restrictions placed on agricultural expansion, limits the potential of agriculture to become a major income source. We did not model soil fertility declines nor restrict agricultural expansion to remain within the small administrative agriculture zone. A separate modelling exercise showed that applying an agricultural rotation or agricultural expansion-as needed to keep agricultural production at current levels-will already be restricted by the limits of the agriculture zone within the next five years. If the boundaries of the agriculture zone are not revised and the current practice of continued mono-cropping continues, household incomes might decline more dramatically than suggested by the simulation results.
Investments in the area are not obvious. As the history of the Dzanga-Sangha landscape illustrates, with the repeated closure of the sawmill, logging companies are faced with the difficulty of having to bear high transportation costs, given the long distances to ports and generally worsening quality of the inland roads. In remote locations like those of Dzanga-Sangha, logging companies tend to export higher value timber in order to reduce the relative share of transportation costs (Ezzine-de Blas 2007). It is therefore in these locations especially that FSC certification increases the robustness of logging companies to withstand world market fluctuations, guaranteeing the feasibility of their continuance in the locality.
The sensitivity of the data to human migration levels in and out of the landscape illustrates the importance for rural areas of growth in the national economy (Frost et al. 2007), as this reduces pressure on natural resources. At the same time, national development also brings new conservation challenges for rural areas because of increased access through infrastructure development (Laurance et al. 2006).
If the socio-political turmoil in the country continues, without a significant surge in economic opportunities, the Dzanga-Sangha landscape will become a likely refuge for people who will make a living from some bushmeat hunting, agriculture and other activities. If, in such a situation, the flow of people coming to Dzanga-Sangha is even larger than the modelled 1-3% rate of immigration, it would result in very pessimistic outcomes for the landscapes' wildlife populations and impoverish the human population further.
The current conservation concession scenario assumes a compensation of forgone forestry taxes of EUR 316,000/year or EUR 1.23 per ha/year [Table 1]. The Cameroonian government has been unable to find investors ever since 2001 who would be willing to pay EUR 1.4 per ha for 830,000 ha of almost entirely intact forest bordering a national park as a conservation concession (The Economist 2008; Sandker 2009 pers. obs.). Likewise, the Peruvian government tried to auction off 800,000 ha of forest as a conservation concession but could not find investors willing to pay between EUR 0.7-2.8 per ha (Hardner & Rice 2002). The Dzanga-Sangha concession concerns largely logged-over forest so it might be overly optimistic to assume that investors will be found for the simulated EUR 1.2 per ha/year.
According to Karsenty (2007), opportunity costs of forgone wages for the Cameroonian 830,000 ha would be as much as EUR 6.4 million per year (EUR 7.8 per ha) in addition to the taxes and royalties paid. Even if investors are willing to pay the EUR 316,000 in the CAR, given the current situation of bad governance, direct payments to the government will do little for the local population.
One could argue that ecotourism will provide economic opportunities to the local population (although ecotourism could probably be developed under the certified logging scenario as well). Under our most optimistic scenario (assuming a 400% increase in tourists), ecotourism will contribute EUR 55,850 to the local economy, whereas the logging concession contributes between EUR 364,100 and EUR 885,200. A willingness-to-pay study in the Dzanga-Sangha landscape (Tieguhong 2009) revealed that tourists are willing to spend about 1.8 times more than they currently do, but even by doubling the revenue from ecotourism, it will still contribute less to the local economy than logging would. Furthermore, a large share of the population living far from the project area may have no access to income from ecotourism. Often, income from ecotourism cannot compete with income from more destructive activities (Oates 1999). Perhaps carbon payments can increase the investment in forest conservation, though research by World Growth (2009) found that the value of carbon credits at best brings in only a quarter of what can be secured from more effective economic use of forest land by developing countries.
Logging activities might lead to a larger increase in hunting pressure than predicted by our model because of intensified commercial hunting through increased traffic and cash income in the villages and an increase in bushmeat demand from concession workers. This is likely to alter the difference between Scenarios I and II, but we do not expect this to change the overall trends under the three scenarios. Also, the scenarios don't envisage large scale mining to develop in the landscape. Recently, mining concessions have been on the increase in neighbouring landscapes in Cameroon and Congo, similar to that of Dzanga-Sangha. If mining concessions are given out in Dzanga-Sangha, hunting pressure is likely to be much higher than simulated in the current scenarios.
One ecologist working in the landscape believed that the decrease in elephant population could be even more dramatic than the simulation results suggest (Turkalo 2010 pers. comm.). We assumed that elephant hunting will become less successful once elephant density drops to below 0.2 elephants per sq. km, but since elephants concentrate on forest clearings (salines), hunting success might start decreasing at a lower elephant density. Furthermore, the number of elephants might decrease more drastically than simulated in the three scenarios if the elephants migrate to neighbouring Congo or Cameroon, as elephants have been known to move out of dangerous areas (Blake 2005).
Van Vliet & Nasi (2008) report how different methods to approximate duiker densities result in highly variable results. Noss (1998) compared his own findings based on line transect surveys and net hunt encounters with those of other studies using different methods to approximate duiker densities (i.e., radio-telemetry, pellet and track counts). He indicates that densities of C. monticola could vary in a range of 11-79 animals per sq. km in the community hunting zone of the Dzanga-Sangha landscape according to different methods used to approximate duiker density. The density range he gives for C. callipygus and C. dorsalis shows an even larger variation, with highest approximated densities being 25 to 30 times the most conservative estimates. As explained under the method section, based on the hunting off-take rates in combination with duikers apparently still being abundant, we have used a high reproduction rate and relatively high densities for duikers in the model [Table 1]. When interpreting the results, one should consider the significant uncertainty concerning duiker density. This study therefore seeks to assess a realistic trend in duiker population. For an accurate prediction of duiker densities, we would recommend a more in-depth study on the population.
Given the large variation in possible duiker densities, Noss (1998) was not certain about whether hunt off-takes in 1994 of C. monticola and C. dorsalis were unsustainable. He did find hunting of C. callipygus to be at unsustainable levels even after assuming the most optimistic density and reproduction figures. When comparing Noss's (1998) data on snare-captured duiker species gathered in 1994 with the most hunted species according to the 50 household interviews in 2006, we observe that the capture of C. monticola has doubled in proportion to that of C. callipygus. Furthermore, the share of C. dorsalis (constituting 10% in weight of the duikers captured in 1994) has gone down by almost three times (3.6% in weight of the total hunted in 2006). A study carried out by Van Vliet et al. (2007) comparing duiker inventories in Gabon in the 1980s and 2006 demonstrated that C. dorsalis has become locally extinct as a result of hunting pressure. Thus, the relative decrease of C. dorsalis compared to the other duiker species in 2006 might reflect local depletion of this species due to over-hunting. We hypothesise that as a result of the depletion of one species of duiker, other species of duiker (notably C. monticola with its high reproduction rate) might increase as they have a higher carrying capacity and are exposed to less inter-species competition. These are, however, speculative findings since the data of Noss (1998) and the 2006 survey data were assessed in different ways and the standard hunting method for small animals has altered between 1994 and 2006 with small home-made guns gaining popularity. Further, the household hunting estimates from the 2006 questionnaires are thought to be too rough to allow drawing any solid conclusions. We recommend further research to be done to test the hypothesis we pose. All the same, if a resilient animal like the duiker shows a dramatic decline, it is quite likely that other less resilient bushmeat animals will locally disappear with high hunting pressure.
| Conclusion|| |
The simulation results oblige one to temper expectations on best outcomes for wildlife conservation and local development in the Dzanga-Sangha landscape. Even the most optimistic conservation scenario suggests dramatic wildlife losses over the 20 years to come, while the income situation for local people only improves a little. On a positive note, there does appear to be room for synergies, with Scenario II giving the best, or least bad, outcomes for both conservation and development.
Contrary to the suggestion of Niesten & Rice (2004), the simulation results suggest that at current investment levels, conservation concessions do not have the potential to provide economic development. In the situation of the Dzanga-Sangha landscape as well, the conservation concession is not capable of maintaining wildlife populations, since a majority of the settlers in the landscape already lack alternative income generating activities. Economic development outside the landscape, and economic investments in the landscape (notably logging or mining), have major consequences for wildlife densities as they determine the extent of human migration in and out of the landscape and determine the economic alternatives for the people living in the landscape. External economic investments have a higher impact on the size of wildlife populations than current conservation interventions. A key criticism of past integrated conservation and development initiatives has been their failure to recognise the importance of external forces whose impact dwarfs that of the on-site interventions of conservation agencies (McShane & Wells 2004). In situations like the Dzanga-Sangha landscape, the ICDP plays an important role in negotiating with the government and possibly other actors on the choice of logging company that gets to exploit the concession. The ICDP should inform the government about the consequences of their choices for wildlife and the social-economic situation. This choice will be the main determinant of conservation and development outcomes for the landscape.
The results of this study suggest that rather than seeking to expand the protected area networks in the Congo basin, both wildlife conservation and local development outcomes are better served by an appropriate balance between sustainable use and protection of the forest. This study therefore illustrates the importance of initiatives like Forest Law Enforcement, Governance and Trade (FLEGT), promoting timber certification and the allocation of Reducing Emissions from Deforestation and forest Degradation (REDD) funding for sustainable management as well as protection.
| Acknowledgement|| |
We would like to thank the Livelihoods and Landscapes initiative of the International Union for Conservation of Nature (IUCN) and the Center for International Forestry Research (CIFOR) for financing part of this research. We thank the Dzanga-Sangha population for sharing information with us through the 2006 household surveys. We would also like to thank the ICDP personnel in Bayanga for their contributions, and two anonymous reviewers for their constructive criticism. 
| References|| |
|1.||Bennett, E.L. 2004. Seeing the wildlife and the trees: Improving timber certification to conserve tropical forest wildlife. Washington, DC: World Bank. |
|2.||Blake, S. 2005. Long term system for monitoring the illegal killing of elephants (MIKE). Central African forests: Final report on population surveys 2003-2004. Washington DC: CITES MIKE Programme. |
|3.||Blom, A. 2001. Ecological and economic impacts of gorilla-based tourism in Dzanga-Sangha, Central African Republic. Ph.D. thesis. Wageningen University, Wageningen, The Netherlands. |
|4.||Carroll, R.W. 1986. The status, distribution and density of the lowland gorilla (Savage and Wyman), forest elephant and associated dense forest fauna in southwestern Central African Republic: Research towards the establishment of a reserve for their protection. Ph.D. thesis. Yale School of Forestry and Environmental Studies, Yale University, London, UK. |
|5.||Clark, C.J., J.R. Poulsen, R. Malonga and P.E. Elkan. 2009. Logging concessions can extend the conservation estate for Central African tropical forests. Conservation Biology 23(5): 1281-1293. |
|6.||Congo Basin Forest Partnership. 2006. Les forêts du Bassin du Congo-Les états des forêts 2006. Kinshasa: CBFP. |
|7.||Congolaise Industrielle des Bois. 2006. Plan d'amenagement de l'Unité Forestière d'Aménagement de Kabo (2005-2034). Kabo: CIB. |
|8.||Czesnik, F. 2007. Mission d'appui-conseil financier pour le renforcement de la coopération avec la Commune de Bayanga. Bayanga: GFA Consulting Group and Dzanga-Sangha Project. |
|9.||de Monza, J.P. 1996. Conservation de la forêt tropicale humide. Yaoundé: International Union for Conservation of Nature (IUCN) Cameroon. |
|10.||de Wasseige, C., D. Devers, P. de Marcken, R. Eba'a-Atyi, R. Nasi and P. Mayaux. (eds.). 2009. Les Forêts du Bassin du Congo - Etat des Forêts 2008. Office des publications de l'Union européenne. http://www.observatoire-comifac.net/edf2008.php. Accessed on July 1, 2010. |
|11.||Doungoubé, G. 1990. Central African Republic: The Dzanga-Sangha dense forest reserve. In: Living with wildlife: Wildlife resource management with local participation in Africa (ed. Kiss, A.). Pp. 75-79. Washington, DC: World Bank. |
|12.||Dzanga-Sangha project unpublished. 2006-2008 workshops of the Deutsche Gesellschaft für Technische Zusammenarbeit in the villages with the objective to map hunting zones. |
|13.||Deutsche Gesellschaft für Technische Zusammenarbeit. Unpublished. 2005-2007 population census for the 12 villages in the Dzanga-Sangha landscape. Bayanga: Deutsche Gesellschaft für Technische Zusammenarbeit. |
|14.||Deutsche Gesellschaft für Technische Zusammenarbeit-GFA. Unpublished. Map with administrative zones produced in 2009. Bayanga: Deutsche Gesellschaft für Technische Zusammenarbeit and GFA Consulting Group. |
|15.||Ezzine-de Blas, D. 2007. El Corazón de Madera de África: Dinámicas de Concesiones Forestales en la Cuenca del Congo. Ph.D. thesis. Universidad Autónoma de Madrid, Madrid, Spain. |
|16.||Food and Agriculture Organization of the United Nations (FAO). 2003. African forests: A view to 2020. Forestry Outlook Study for Africa. Rome: European Commission (EC)/African Development Bank/FAO. |
|17.||Frost, P., B. Campbell, M. Luckert, M. Mutamba, A. Mandondo and W. Kozanayi. 2007. In search of improved rural livelihoods in semi-arid regions through local management of natural resources: Lessons from case studies in Zimbabwe. World Development 35(11): 1961-1974. |
|18.||Forest Stewardship Council. 2007. Systeme de certification Forest Stewardship Council: Referentiel de gestion forestiere adapte pour le Cameroun. Yaoundé: RF03 FSC GF Cameroun version 1.3. Bureau Veritas Certification/Eurocertifor. |
|19.||Garreau, J.M. 1994. Enquête sur le revenu des ménages à Bayanga et Moussapoula. Bayanga: Dzanga-Sangha Project. |
|20.||Hardner, J. and R. Rice. 2002. Rethinking green consumerism. Scientific American 287: 89-95. |
|21.||High Performance Systems. 1996. STELLA and STELLA Research Software. Hanover, NH: High Performance Systems. |
|22.||Kamiss, A. 2006. Recensement de la population des villages de la Réserve Spéciale de Dzanga Sangha de l'année 2005. Bayanga: Dzanga-Sangha Project. |
|23.||Karsenty, A. 2007. Overview of industrial forest concessions and concession-based industry in Central and West Africa and considerations of alternatives. Montpellier: Centre de Coopération Internationale pour la Recherche en Développement. |
|24.||Klaus-Hügi, C. 1998. Home range, feeding behaviour and social organization of the bongo (Tragelaphus euryceros) in the rain forest of the Central African Republic. Ph.D. thesis. University of Zürich, Zürich, Switzerland. |
|25.||Kokamy-Yambere, S., E. Bango, P. Doko and R. Kanimbia. 2007. Rapport de Prospection Pedologique et de Cartographie des Terroirs Agricoles Villageois dans la Commune de Yobe-Sangha. Bayanga: GFA Consulting Group. |
|26.||Laporte,T.N., A. Jared, A.J. Stabach, R. Grosch, S.T. Lin and J.S. Goetz. 2007. Expansion of industrial logging in Central Africa. Science 316: 1451. |
|27.||Laurance, W.F., B.M. Croes, L. Tchignoumba, S.A. Lahm, A. Alonso, M.E. Lee, P. Campbell, et al. 2006. Impacts of roads and hunting on Central African rainforest mammals. Conservation Biology 20(4): 1251-1261. |
|28.||McShane, T. and M. Wells. (eds.). 2004. Getting biodiversity projects to work: Towards more effective conservation and development. New York, NY: Columbia University Press. |
|29.||Niesten, E. and R. Rice. 2004. Sustainable forest management and conservation incentive agreements. International Forestry Review 6: 56-60. |
|30.||Noss, A.J. 1995. Duikers, cables and nets: A cultural ecology of hunting in a Central African forest. Ph.D. thesis. University of Florida, Gainesville, FL, USA. |
|31.||Noss, A.J. 1998. The impact of cable snare hunting on wildlife population in the forests of the Central Africa. Conservation Biology 12(2): 390-398. |
|32.||Oates, J.F. 1999. Myth and reality in the rain forest: How conservation strategies are failing West Africa. Berkeley, CA: University of California Press. |
|33.||Rice, R. 2003. Conservation concessions - concept description. V th World Parks Congress: Sustainable Finance Stream. Durban: Conservation International. |
|34.||Sandker, M., B.M. Campbell, M. Ruiz-Pérez, J. Sayer, R. Cowling, H. Kassa and A.T. Knight. 2010. The role of participatory modeling in landscape approaches to reconcile conservation and development. Ecology and Society 15: 13. |
|35.||Sayer, J., B. Campbell, L. Petheram, M. Aldrich, M. Ruiz-Perez, D. Endamana, Z. Nzooh-Dongmo, et al. 2007. Assessing environment and development outcomes in conservation landscapes. Biodiversity and Conservation 16: 2677-2694. |
|36.||The Economist. 2008. The price of conservation: The unkindest cut. The Economist. http://www.economist.com/node/10688618. Accessed on October 15, 2011. |
|37.||Tieguhong, J.C. 2009. Ecotourism for sustainable development: Economic valuation of recreational potentials of protected areas in the Congo basin. Ph.D. thesis. University of KwaZulu-Natal, Pietermaritzburg, South Africa. |
|38.||Turkalo, A. 2005. Rapport d'activité. Bayanga: Dzanga-Sangha Project. |
|39.||United Nations. 2009. World Population Prospects: The 2008 Revision. New York: UN Department of Social and Economic Affairs. |
|40.||Van den Belt, M. 2004. Mediated modeling: A system dynamics approach to environmental consensus building, Washington DC: Island Press. |
|41.||Van Vliet, N. and R. Nasi. 2008. Why do models fail to assess properly the sustainability of duiker (Cephalophus spp.) hunting in Central Africa? Oryx 42: 392-399. |
|42.||Van Vliet, N., R. Nasi, L. Emmons, F. Feer, P. Mbazza and M. Bourgarel. 2007. Evidence for the local depletion of bay duiker Cephalophus dorsalis, within the Ipassa Man and Biosphere Reserve, north-east Gabon. African Journal of Ecology 45(3): 440-443. |
|43.||World Bank. 2009. Gross national income per capita 2008, Atlas method and PPP. Washington, DC: World Bank. |
|44.||World Bank. 2010. Poverty statistics and indicators. http://go.worldbank.org/JIO7WY61V0. Accessed on October 10, 2010. |
|45.||World Growth. 2009. Forestry and the poor: How forestry reduces poverty. World Growth. http://www.worldgrowth.org/assets/files/WG%20Forestry%20Report%208_09.pdf. Accessed on October 15, 2011. |
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
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