Interactive Simulations to Codesign with Villagers an Agent-Based Model of Bushmeat Hunting in Cameroon

A companion modeling process was conducted to assess the impacts of hunting activities in the region of the Korup National Park (South-West Cameroon). Bushmeat hunting in African tropical forests is an essential survival means for rural populations. At the same time, the hunting, which is primarily done using snare traps, is relatively unselective, affects many different wildlife species, and consequently negatively impacts biodiversity.

Computational models of socio-ecosystems can use concepts, terms of relationship that are not meaningful to the local stakeholders whose behavior is being modeled (Sterling et al. in review). Furthermore, regional population dynamics models used to determine sustainable hunting pressures may not include information meaningful to resource users at the local scale. Yet, for the same general level of hunting pressure, the system “hunter-animal-hunting territory” can be sustainable or not depending on the spatial and temporal distribution of hunting and of hunted individuals (Van Vliet & Nasi, 2008). Therefore this study adopted spatially-explicit individual-based models to investigate the sustainability of bushmeat hunting parametrized using information from a combination of stakeholders and scientific experts.

An agent-based model (ABM) was co-designed and used with local populations to raise their awareness about the sustainability of bushmeat hunting activities. It focuses on the population of blue duikers (Cephalophus monticola), a common game in Cameroon, considered as an important bioindicator species. The purpose of designing and using an ABM with the local villagers was to turn the question of bushmeat hunting sustainability into a matter of common concern at a sub-regional scale (a group of 7 villages), and to stimulate villagers to engage in community-based hunting management. General objectives were to promote non-judgmental, non-directive public discussion and reflection, and to collectively envision possible management options for the sustainability of blue duikers hunting. The specific objective of the PM workshops was to share information on:

  • the biology and behavior of blue duikers in a non-hunted habitat;
  • the potential impact of snare-trap hunting on the blue duiker population;
  • the elicitation and specification of hunting practices through collective discussions during the presentation of the computer simulation model;
  • the feasibility and potential impact of different hunting management rules.


Process: A step-by-step interactive design of the ABM

Village meetings were structured in three successive steps. During the first step, an abstract representation of a village surrounded by a portion of forest was co-designed by directly manipulating the computer interface displaying a spatial grid. The model used the Cormas software ( which enables various types of interactions with users (see Bommel et al, 2015). Then, knowledge about the life-cycle and behavior of blue duikers was shared through the demonstration of the individual-based population dynamics module of the ABM (previously constructed by biologists on the project). This first step was meant to illustrate a basic model to the villagers and to progressively engage them in further collaborative and interactive design, particularly for the development of the hunting module in the second step of the meetings. This second step elicited snare-trapping practices through interactive simulations, and calibrated the hunting module by setting a value for the probability of a blue duiker being caught by a snare trap. In a third step, a more realistic version of the ABM was introduced. The seven villages included in the process were located in the GIS-based spatial representation, and the number of “Hunter” agents for each village in the ABM was set according to the results of a survey. The demonstration of this more realistic version triggered discussion about possible management scenarios. The modeling results of those scenarios, obtained with a final version of the ABM, were discussed during later village meetings.


The project’s team was mainly composed of a wildlife biologist from the University of Dschang (Cameroon), and an ABM modeler from Cirad (France). They constructed the first version of the individual-based module for the blue duikers. 187 hunters were identified in the study area and 65 (35%) of them were monitored for hunting behaviors. While farming remains the main activity, hunting is performed by male villagers (from 15 to 60 years old) mainly during the wet season: on average a trapper sets around 100 snares.

Three workshops were organized in three villages: Abat, Mgbegati and Bakut. Four other communities were also involved in the three workshops. Any villager interested in attending the workshop was welcome. Participants were from 60 to 80 people and demographically diverse (male hunters, but also women, children, and the elderly). The three workshops all started in early afternoon and lasted over three hours. Just before and just after the interactive demonstration of the ABM, a total of 42 participants (most of them belonging to the group of 65 hunters whose activity was previously monitored) were asked a short list of questions, to assess the effects of attending the workshops.


In the three workshops, the participants reacted positively. The reality and the magnitude of the overhunting problem were acknowledged by a large majority of participants. Before the workshops, 20 out of the 42 interviewed participants expressed skepticism about the risk of extinction of the blue duiker population in the region. After the workshops, this number fell to 9. Education and raising awareness were stressed by some other participants as being crucial. They argued that the population should be made aware of the long term dangers of over-hunting and that youths should be better educated in agriculture, forest sciences, and biodiversity conservation. Survey measurements also indicated that a significant number of people experienced measurable learning gains about the biology and the ethology of the blue duiker; 15 people improved their understanding about the longevity of the species, and 11 people improved their understanding about its territoriality.

In terms of using the model, 37 out of the 42 interviewed participants declared that they enjoyed its demonstration, three found it difficult to follow and understand, and 36 felt that it was a fair representation of reality (Ngahane 2013). Thirty-seven interviewed participants volunteered to be involved in the next stages of the process. By the end of the first workshops, the participants had already started to discuss additional possible scenarios to be tested with the ABM. Three main management options were discussed, including (1) restriction of foreign hunters, (2) reducing the number of snares per hunter, and (3) the establishment of a reserve zone. Thus the primary output of the model provided a learning context for critical thinking and sparking creativity, and identifying and clarifying the impacts of potential solutions to a given problem (Brugnach 2010).

In terms of general conclusions, there is still a gap from the post-model debriefing discussions to the formulation of decision-making outcomes. The level of abstraction required by explaining generalities is high for participants, who tend to focus on their peculiar situation. As an individual, it may be difficult to think in terms of behaviors representative of a group of individuals. The approach advocates for the early and interactive use of a stylized scale model as an intermediate object to facilitate this activity with local stakeholders.

Further references are available in Le Page et al. (2015, The computer code and the full documentation (including ODD) are available from the CoMSES Net Computational Model Library.

  • : Pierre Bommel, Christophe Le Page