The location of the study area is shown in Figure 1. Baulu Conservation Concession is located in the Tshuapa province in central DRC, in the central basin.

Administratively, the Baulu concession is entirely located in the territory: Befale; the sector: Lomako; the Boyela, Loma, and Nsongo-Mboyo groups.

From a geographical point of view, the total area is 277,131 ha. The boundaries of the Baulu Conservation Forest Concession are as follows ([7]):

1) To the North: the left bank of the Lomako River, the part between its confluence with the Maringa River (geographic coordinates 0˚51'35" north latitude and 21˚41'43" east longitude) and its confluence with the Loilaka (geographic coordinates 0˚39'48" north latitude and 21˚27'30" east longitude);

2) To the East: the Loilaka River from its confluence with the Lomako to its source (geographic coordinates of 0˚31'17" North latitude and 21˚29'39" East longitude), then a straight line (1608m direction 191˚) joining the source of the Loilaka River to the source of the unnamed river (geographic coordinates 0˚30'15" North latitude and 21˚29'39" East longitude), then the unnamed river to its confluence with the Lokomo (geographic coordinates 0˚25'19" North latitude and 21˚27'40" East longitude);

3) To the South: the right bank of the Lokomo River, the part between the confluence with an unnamed river (and the confluence with the Maringa River (geographic oordinates 0˚51'35" North latitude and 21˚41'43" East longitude);

4) To the West: the right bank of the Maringa River, the part between the confluences with the Lokomo and Lomako Rivers.

Figure 1. Location of the Baulu Conservation Forest Concession N˚001/24.

The field equipment included navigation equipment (GPS, compass), data collection equipment (smartphones, power bank, waterproof paper sheets), as well as logistics and security equipment (motorcycle, machetes, boots).

A documentary review was conducted, including research reports, scientific publications, historical forest inventories (IFCO, ICCN), and the Environmental and Social Impact Assessment (ESIA) of the concession.

This study is based on a secondary analysis of two complementary field diagnostics, carried out in February and March 2025: an ecological analysis of the fauna and a socioeconomic assessment. The collection of primary data was preceded by obtaining Free, Prior, and Informed Consent (FPIC) in 16 of the 17 villages of the Loma group.

2.4.1. Ecological Analysis of Fauna

A dual methodological approach was used to circumvent logistical and social constraints.

1)Participatory surveys with hunters: Five (5) experienced hunters were randomly selected from each of the 16 villages (n = 80 in total). Semi-structured interviews were conducted using a questionnaire on KoBoToolbox (https://ee.kobotoolbox.org/x/7m7DKRbu) and a species identification guide to identify the wildlife present, an identification scene is shown in Photo 1.

Photo 1. Identification of wildlife by a hunter from Bongila village.

2)Reconnaissance marches (RECCE): A field inventory was carried out on a 24 km transect, the route of which is highlighted in Figure 2. This transect was traversed over 6 days (4 hours/day during twilight periods). As illustrated in Photo 2, the method consisted of following a track or transect of least resistance while maintaining the compass direction, with the deviation angle not exceeding 40˚ ([10]). A team of 7 people (tracker, porters, machete, GPS operator, paper and digital data pointer) systematically noted the signs of direct and indirect presence (footprints, droppings, tracks, vocalizations, direct observations).

Photo 2. Data collection in forests.

Figure 2. Survey plan for reconnaissance walks.

2.4.2. Socioeconomic Assessment

Data collection to assess economic opportunities was structured using a quantitative and qualitative approach. Initially, the sample size was calculated by applying the formula for an infinite population ([12]):

where n is the sample size; z = confidence level according to the reduced centered normal distribution (for a 95% confidence level, z = 1.96); p = estimated proportion of the population that presents the characteristic (when unknown, we use p = 0.5 which corresponds to the worst case, i.e., the greatest dispersion); m = tolerated margin of error (10% in this case).

For a finite population N = 9377 ([6]).

Then, the correction coefficient for the finite population was applied:

with n = 96 initially calculated, the adjusted size amounts to n ′ = 95 households. However, to anticipate unforeseen events in the field (refusal to participate, incomplete data), this number was increased to 110 households during planning.

2.4.3. Adaptation to Field Constraints

The initial methodology had to be adapted in situ due to social constraints. To ensure an equitable distribution of “civilities” (informal financial compensation requested by communities) between villages, a purposive sampling of 10 households per village was adopted, bringing the final sample to 160 households spread across the 16 villages. Structured questionnaires on KoBoToolbox (https://ee.kobotoolbox.org/x/q2mZPPRR, as shown in Photo 3), were administered to collect data on economic activities, income, marketing, and perceptions towards conservation.

Photo 3. Surveys on economic potential in the village of Lofukia.

All primary data was consolidated, cleaned, and organized in a Microsoft Excel database before analysis.

1)EcologicalAnalysisofFauna

The assessment of species richness (SR), corresponding to the total number of species recorded, constituted the first stage of the analysis. This was completed by the study of three recognized biodiversity indices:

Shannon index ( H ′ )

This index measures diversity by integrating both the total number of species and their relative abundance within the community studied ([15]).

Simpson index (D)

It allows the quantification of the possible dominance of one or more species in the population ([13]).

with i: A species in the study environment; s: Specific richness; pi: Proportion of species i in relation to the total number of individuals (N) in the study environment, which is calculated as follows:

where n i is the number of individuals for species i, and N is the total number (individuals of all species).

The method for analyzing data from wildlife biodiversity reconnaissance walks also involved calculating abundance indices. Here, the encounter rate was the unit of analysis. This is the kilometer abundance index (KAI), which made it easier to standardize the various presence indices per kilometer.

2)Socioeconomic Assessment

The variations and distribution of economic activities in relation to the village were generated using Microsoft Office Excel software, using diagrams. Comparisons between activity groups and activities were made using the vioplot function in R Studio, incorporating box plots. Chi-square and Kruskal-Wallis tests were carried out to determine whether there is a dependency between different variables (gender and activity; monthly income and education level; monthly income and activity or activity groups), using RStudio software.

3)Synthetic Assessment Framework

The ecological and socioeconomic results were interpreted qualitatively using the eight criteria defined by the [2] for the recognition of OECMs. A SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was finally used to synthesize in an integrative manner the factors influencing the OECM potential of the site.

The combined evaluation of participatory surveys and reconnaissance walks provided a detailed result of the wildlife community.

3.1.1. Specific Richness

The inventory recorded a total of 43 species of mammals, birds, and reptiles, whose detailed distribution is presented in Table 1. Surveys of the 80 hunters identified 40 species, demonstrating detailed knowledge of the territory. The reconnaissance walks (RECCE) over 24 km confirmed the presence of 23 of these species through the observation of 202 signs of presence, such as fresh feces, illustrated by Photo 3. The majority of which were footprints (58%), tracks (19%), and droppings (11%).

Table 1. Specific faunal richness.

Photo 3. Fresh black duiker droppings.

3.1.2. Conservation Status and Threatened Species

The area is of exceptional ecological importance, with 26.1% of recorded species classified as threatened on the IUCN Red List. This proportion breaks down as follows:

1) 10% of species are Critically Endangered (CR), including the iconic Bonobo (Pan paniscus).

2) 10% are Endangered (EN), such as the Dryad Monkey (Cercopithecus dryas).

3) 10% are Vulnerable (VU), such as the Congo Peacock (Afropavo congensis).

3.1.3. Relative Abundance and Community Structure

As detailed in Table 2, the analysis of the Kilometric Abundance Index (IKA) reveals a community dominated by resilient species such as the Red River Hog (Potamochoerus porcus, IKA = 2.49 ind./km) and the dorsal-banded duiker (Cephalophus dorsalis, IKA = 2.47 ind./km). Conversely, predators such as the African Civet (Civettictis civetta, IKA = 0.03 ind./km) and the Crowned Eagle (Stephanoaetus coronatus, IKA = 0.03 ind./km) are extremely rare.

Table 2.Kilometric abundance indices.

Note: Legend: IKA R: Kilometric index of track abundance. Ind. Km: Indices per kilometer.

3.1.4. Ecosystem Diversity Indices

Biodiversity calculations indicate a complex and relatively preserved ecosystem. The Shannon index value ( H ′ = 2.43 ) and the low Simpson index value (D = 0.134), whose distribution is visualized in Figure 3, confirm a moderate specific diversity and a balanced community, without marked dominance of a species.

Figure 3.Distribution of increasing cumulative frequencies of the workforce.

The survey of 160 households in the 16 villages of the Loma group shed light on living conditions, economic activities, and community perceptions.

3.2.1. Dependence on Natural Resources and Economic Activities

The local economy is characterized by an extreme dependence on natural resources for subsistence. The main activities are slash-and-burn agriculture (38% of reported activities), the collection of Non-Timber Forest Products (NTFPs) (30% of activities), mainly caterpillars (30% of NTFPs) and medicinal plants (25%), and hunting (11%), livestock farming (9%), and fishing (7%) complete this portrait of a harvesting economy.

3.2.2. Gender Distribution of Activities and Income

A very marked division of labor according to gender was observed (Chi-square test, p-value = 3.012e−10). Men are mainly involved in hunting (65% of hunters), while women are responsible for collecting NTFPs (80% of collectors).

Despite this disparity in roles, median incomes are low and homogeneous (50,000 - 100,000 FC/month, ≈ 20 - 40 USD), as shown in Figure 4, with no significant difference related to the level of education (Kruskal-Wallis, p = 0.7329) or the type of activity (p = 0.1063).

Figure 4. Income variation by education level.

3.2.3. Marketing Constraints and Isolation

The area’s isolation is a major obstacle to development. The main obstacles cited are the lack of reliable transportation (32%) and high logistics costs (27%). This situation reinforces dependence on intermediaries (45% of sales) and limits market access, with only 3% of production reaching urban markets.

3.2.4. Community Perceptions and Relationships with Conservation

Perceptions reveal a gap of distrust toward conservation initiatives. A significant portion of respondents (42%) declared themselves “neutral” about the impact of their activities on resources. However, 29% of respondents perceived a decline in wildlife resources, attributing it to agricultural and hunting practices. This distrust was concretely manifested during data collection through the widespread demand for “civility” and the curtailment of ecological inventories due to tensions.

This section applies the CBD’s official Other Effective Area-Based Conservation Measures (OECM) assessment framework to the Baulu Conservation Concession. The assessment is structured in three steps and eight criteria.

Step 1: Is the site important for biodiversity?

Criterion A: Presence of threatened, endemic,or restricted-range species

Assessment:Compliant. The inventory formally identified 43 species, 26.1% of which are classified as threatened on the IUCN Red List. This includes Critically Endangered (CR) species such as the Bonobo (Pan paniscus), the Congo Peafowl (Afropavo congensis), as well as Endangered (EN) and Vulnerable (VU) species. The presence of these species gives the site exceptional biological importance at a global level.

Criterion B: Presence of important, fragile,or critical habitats

Assessment:Compliant. The site is covered by a dense, humid, semi-deciduous forest habitat characteristic of the Congo Basin. This ecosystem is recognized as a critical and irreplaceable habitat for a large number of identified threatened species (primates, elephants, Congo peafowl).

Criterion C: Important ecosystem services

Assessment:Compliant (inferred). As a large, intact tropical forest massif, the concession plays a crucial role in carbon sequestration (climate change mitigation), water cycle regulation (Maringa and Lomako rivers), and soil protection. Although not directly measured in the studies, these services are inherent to this type of ecosystem and provide a solid basis for existing REDD+/carbon projects.

Step 2: Is the site effectively managed and governed for conservation?

Criterion D: Governance framework

Assessment:Non-compliant (major flaw). Governance is currently conflictual, contested, and fragmented. Although a formal framework exists (Conservation Concession awarded to the KNCC company), it is rejected by a portion of the population (1 in 17 villages refuses FPIC). Historical mistrust, tensions during inventories, and the demand for “civility” demonstrate the absence of a legitimate, inclusive framework accepted by all stakeholders. This is the main obstacle to OECM recognition.

Criterion E: Management measures

Assessment:Partially Compliant/Non-Compliant. There is no formalized management plan or deliberate, active conservation measures. Current management relies on traditional practices (hunting, gathering) that are not geared toward a conservation objective and show signs of unsustainability (perceived resource decline). Hunting is unregulated, and there are no no-take zones or quotas. Management is therefore reactive and informal, falling far short of required standards.

Criterion F: Conservation results

Assessment:Partially Compliant. The status of biodiversity is currently good (evidence of past ecosystem effectiveness) but likely declining. The presence of sensitive species is a positive outcome. However, the rarity of some predators and local perceptions of decline indicate that current management measures (or lack thereof) are not effectively maintaining ecological integrity in the long term. The status is stable but vulnerable.

Step 3: Does the site ensure long-term preservation?

Criterion G: Sustainability of arrangements

Assessment: Non-compliant. Current arrangements are neither permanent nor stable. The governance crisis and the lack of tangible economic benefits from conservation for communities make the model untenable. The main economic activities (slash-and-burn agriculture, unsustainable hunting) are incompatible with long-term conservation objectives. Sustainability is not assured without a change in the model.

Criterion H: Equitable sharing of benefits

Assessment: Non-compliant. Inequalities are marked, particularly between the sexes (women carry out the bulk of the NTFP collection work without equitable access to the value created). Incomes are generally low and homogeneous, indicating that the benefits from the resources are not equitably distributed. The current system primarily benefits external intermediaries. Communities bear the opportunity costs of conservation (perceived access restrictions) alone without deriving significant benefits, which fuels mistrust.

Table 3 summarizes the evaluation of the compliance of the Baulu Conservation Forest Concession with the CBD OECM framework.

Table 3. Assessment of the suitability of the area for the CDB OECM framework.

Baulu Conservation Concession is of exceptional biological importance, fully meeting the first stage of the OECM framework. However, it does not currently meet the requirements for recognition as an OECM. Critical deficiencies in governance (D), management (E), sustainability (G), and equity (H) are such that long-term biodiversity conservation is not guaranteed. We believe the site should be considered a priority candidate for a capacity-building project to address these gaps.

The SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) synthesizes the key elements identified in the ecological, socioeconomic, and OECM analyses, as presented in Table 4. It aims to guide decision-making for future actions towards the recognition of the Baulu concession as an OECM.

Table 4. SWOT analysis.

Baulu Conservation Concession reveals a fundamental paradox with exceptional ecological richness facing major socioeconomic and governance challenges. Ecologically, the site meets the basic criteria (A, B, C) of the OECM, with a richness of 43 species of which 26.1% are threatened (e.g., Bonobo, Congo Peafowl), confirming its status as a Congo Basin rainforest critical for global biodiversity. Diversity indices ( H ′ = 2.43 ) indicate a relatively preserved ecosystem, although the predominance of resilient species such as the Banded Duiker (Cephalophus dorsalis) and the River Pig (Potamochoerus porcus) may signal adaptation to moderate anthropogenic pressure ([1]).

Historical community mistrust, inherited from past evictions from the Lomako Reserve, has directly compromised our ecological analysis of wildlife. The forced interruption of inventories by local populations, who perceive these exercises as a threat to their access to resources, has likely led to an underestimation of wildlife richness.

However, this value is seriously threatened by structural weaknesses (W1 - W5) that make OECM integration unfeasible in the short term. The Loma group community has critical resource dependence (38% slash-and-burn agriculture, 30% NTFPs), exposing it to resource degradation and poverty. Marketing barriers (32% lack of reliable transport, 45% intermediaries) drastically limit incomes (median < USD 40/month), even though education is on the rise, contradicting general expectations ([14]) and highlighting the importance of local realities. This economic precarity is reinforced by marked gender inequality, where women dominate NTFP collection (80%), but the benefits are not equitably distributed (non-compliance with criterion H), a dynamic widely observed in rural Africa ([5]; [16]).

The main obstacle is the governance crisis (non-compliance with criterion D) and the historical distrust of communities towards conservation initiatives ([10]). This distrust, exacerbated by the lack of a management plan (criterion E) and unsustainable arrangements (criterion G), results in a predominantly neutral or negative perception of the impact of activities on resources The absence of legitimate and inclusive governance prevents the guarantee of long-term conservation.

Accordingly, the Baulu Concession is considered a “Candidate” or “Emerging” OECM. This incremental approach, although not yet formalized in CBD guidelines, is increasingly advocated in the conservation literature for sites of high biological value but where governance and management arrangements are immature ([9]). Only a transformative roadmap, focused on addressing governance issues and ensuring equity, will realize its potential and achieve international recognition. Our proposal positions Baulu as a case study for the application of a “candidate” approach in the DRC, where a conditional and incremental commitment towards OECM status could catalyze the necessary investments while maintaining high ambition.