- An agent-based model (ABM) representing snare trapping of
blue duikers (Cephalophus monticola) was co-designed and used with
local populations to raise their awareness about the sustainability of
bushmeat hunting activities in the region of the Korup National Park
(South-West Cameroon). Village meetings based on interactive
simulations with a stylized scale model 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. Then,
knowledge about the live-cycle traits and the behavior of blue duikers
was shared through the demonstration of the individual-based population
dynamics module of the ABM. The objective of the second step,
introducing the hunting module of the ABM, was to elicit snare trapping
practices through interactive simulation and to calibrate the hunting
module by setting a value for the probability of a blue duiker to be
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 realistic version
triggered discussion about possible management scenarios, whose results
obtained with the finalized version of the ABM will be discussed during
next round of village meetings.We present the pros and cons of the
method consisting in using at an early stage of theprocess interactive
simulations with stylized scale models to specify empirically-based
- Bushmeat Hunting, Participatory Simulation, Community-Based Wildlife Management, Companion Modelling, Qualitative Data
- Computational models of socio-ecosystems often use concepts
that are unlikely to be meaningful to the local stakeholders whose
behavior is being modeled. For command-and-control approaches based on
predictive models meant to support decision makers to select the
"best", "optimal" management option, this may not be an issue. Yet this
conventional approach to natural resource management is increasingly
challenged by environmental problems that are embedded in highly
complex systems with profound uncertainties (Schlüter
et al. 2012). In some situations, no amount of technical
expertise is likely to solve such "wicked" problems that defy
classification (Ludwig 2001).
Each solution will entail costs and lead to unexpected consequences,
and may even worsen the problem.
- Simulation models are more and more used to raise the
awareness of stakeholders about the true nature of the problem they
face. Oriented toward social learning, these models do not pledge for
prediction anymore, but rather aim at fostering critical thinking,
sparking creativity, identifying and clarifying the impacts of
potential solutions to a given problem (Brugnach
2010). From a methodological point of view, the involvement
of stakeholders in the modeling process can take place at any stage
from conceptual design; implementation; use; and simulation outcome
analysis. Moreover, models commonly used to support participatory
modeling processes are from various types (System Dynamics, Bayesian
Networks, ABMs, etc.), leading to a diversity of approaches (Voinov & Bousquet 2010;
Kelly et al. 2013).
- One of these approaches, companion modeling, combines
quantitative tools (empirically-based agent-based models) and
qualitative research methods (role-playing games, surveys and
interviews) to engage stakeholders in adaptive processes, with models
evolving to address the questions and priorities that emerge from
stakeholder engagement (Barreteau
et al. 2003; Etienne 2011).
The companion modeling approach aims at making a "matter of concern"
emerge from a group of participants through the process of co-designing
and using a model whose purpose is to support the multi-stakeholders
- An ongoing companion modeling process related to the sustainability of bushmeat hunting in the Southwest Province of Cameroon is presented in this paper. To integrate qualitative data into the ABM, we used in an interactive way a stylized scale model enabling to co-design the ABM with the villagers. First, the context of the case study is presented, and then the methodology used to conduct the participatory simulation workshops organized in three villages is detailed. These methodological aspects are put into perspective in a general discussion.
A companion modeling process on bushmeat hunting in the Southwest Province of Cameroon
- Bushmeat hunting is an essential survival mean for rural
populations living in Africa (Brashares
et al. 2004). At the same time, bushmeat hunting impacts
biodiversity directly by threatening the viability of all the wildlife
species catchable by snares, the most common hunting technique that is
quite unselective (Wilkie 1999).
For many years, global population dynamics models have been used to
determine sustainable hunting pressures. Yet with a same global 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). This evidence encourages adopting
spatially-explicit individual-based models to investigate the
sustainability of bushmeat hunting (for previous examples of such ABMs
developed in Cameroon, see for instance Bousquet
et al. 2001; Van Vliet et
- Moreover, translating scientific recommendations into sound
hunting practices well accepted by the local populations has repeatedly
proved to be challenging. It is often difficult to establish the
legitimacy of the institution in charge of defining, monitoring and
enforcing the rules when this is putting in question traditional
hunting practices. In Africa, this difficulty is exacerbated by the
lack of sufficient means for the basic functionality of the
institution, for being able to provide compensation for loss of
earnings arising due to restriction or prohibition.
- Put together, these methodological and institutional
considerations have prompted the scientific team working for 10 years
on wildlife in the periphery of Korup National Park (South West region
of Cameroon, see Figure 1) to
engage in a companion modeling process. The objective in 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 (group of 7 villages), and to stimulate villagers to
engage in community-based hunting management.
Figure 1. Study site location
- The blue duiker (Cephalophus monticola),
a very common game in African tropical forests, is considered as a good
bioindicator species. Biological data on blue duikers and
socio-economic data related to bushmeat hunting and to the contribution
of bushmeat to household livelihood were collected in the study area
from May to November 2009 and 2010, and from January to May 2011 (Bobo & Kamgaing 2011).
- 187 hunters were identified in the study area and 65 (35%)
of them were monitored. While farming remains the main activity,
hunting is performed by male villagers (from 15 to 60 years old) mainly
during the wet season (mid-March to mid-November) through intensive
snare trapping: in average, a trapper sets around 100 snares. The
trapping productivity was about 0.66 kg of bushmeat/hunter/day. Around
60% of the harvested bushmeat was meant for sale, representing 12.6% of
the average household revenue (Bobo
& Kamgaing 2011).
- An ABM was designed to assess the impact of this hunting
activity on the population of blue duikers. The CORMAS
platform (Bousquet et al. 1998;
Le Page et al. 2012) was
used to implement the model called "Frotembo" from the local name of
the blue duiker. The computer code and the full documentation
(including ODD) are available from the CoMSES
Net Computational Model Library.
- The modeling process started with a first version of the
model being just a spatially-explicit individual-based population
biology module based on the blue duiker lifehistory characteristics.
Our idea was to showcase this first version to the villagers and to
progressively engage them in the collaborative and interactive design
of the hunting module. The specific objective of these participatory
simulation workshops was to allow sharing of information on the
- 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 in this context.
- Three workshops were organized from the 29th through the 31st of July 2012 in three project villages: Abat, Mgbegati and Bakut. Four other project villages were also involved: villagers from Bajoh and Bayib-ossing attended the first workshop in Abat; participants from Basu joined the second workshop organized in Mgbegati; people from Osselle came to Bakut for the last workshop. The three groups were made to minimize the travelling of participants while ensuring an effective participation of the population. As any villager interested in attending the workshop was welcome, the audiences were important (between 60 and 80 people) and heterogeneous (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 in order to assess the effects of attending the workshops (Ngahane 2013).
A step by step interactive design of the ABM
- This section provides details about the 3-step process that
was carried out in order to facilitate the comprehension of the
computer model by the participants. The main characteristics of the 3
successive versions of the "Frotembo" ABM are given in Table 1.
Table 1: Steps of the interactive design of the "Frotembo" ABM Spatial representation Spatial extent Hunting Step 1 Abstract/Co-designed 1 village 2.25 km2 (15x15 cells) None Step 2 Abstract 2 villages 25 km2 (50x50 cells) Interactive simulation, then 10 hunter agents Step 3 Realistic/ GIS-based 7 villages 288 km2 (160x180 cells) 146 hunter agents
- The first step was meant to introduce the abstract
representation of a village in the forest and the blue duiker
individual-based population module. The different types of land cover
and the notion of cell as a 1-ha portion of space were presented, as
well as the various stages of individual blue duikers. In a second
step, hunting with snares was interactively introduced in a wider
portion of forest (two villages linked by a road, a stylized map still
without any realism). The last step was built on the elements
previously introduced but was based on an explicit representation of
the 7 villages and the Northern periphery of the Korup National Park.
The spatial resolution of the model (the dimension of the smallest
observable detail) was the same in the 3 steps: a cell always
represents 1 ha. The whole portion of space represented in the model
was gradually expanded: 1.5 km * 1.5 km in the first step; 5 km * 5 km
in the second step; 16 km * 18 km in the last step (see Table 1). This
process of zooming out allowed starting focusing on the biology and the
behavior of the blue duiker. The objective was to communicate and to
discuss the related parameters and the underlying assumptions for the
participants to not consider the model as a black box and to become
familiar with it. In the final step of the workshops, the more
realistic representation of the region in the model allowed making the
final discussions more concrete.
- More details about each step are now provided.
First step: introducing an abstract representation of a village in the forest and the blue duiker individual-based population biology module
- A piece of pristine forest was depicted as a regular grid
made of 15*15 hexagonal cells, with a cell having a surface area of
1ha. The spatial extent of this small portion of forest was thus 1.5 km
* 1.5 km. The primary forest land cover was represented by the dark
green background color of the cells. To help them to understand the
degree of simplification and stylization of the computer model,
participants were requested to change this initial cover to indicate
the presence of human beings. One participant was then called up to
show where he/she thinks a village (dark grey background color, see
Tigure 2) could be established
on this artificial landscape representing a virgin forest. Another
participant was requested to locate the farmland (light brown
background color), another one the roads (light grey background color),
the secondary forest (light green background color) and the streams
(blue background color). The results obtained in the three workshops
are shown in Figure 2.
Figure 2. Spatial settings drawn by the participants
- In the model, duikers grow, move, mate, reproduce, and die
on a weekly time-step. Only the growth is similar for all duikers (they
all get older by 1 week each time-step). The other functions depend on
their age and sex. The details of each function are given in a
description based on the ODD protocol, which is available from the
website. To communicate these details to the participants of
the village workshops, we used the tools provided by the Cormas
simulation platform to interactively create and manipulate blue duiker
agents from the spatial grid (see Figure 3).
An adult blue duiker was first introduced on the artificial landscape
(in the forest) and figured as a colored diamond (blue for male, red
for female). Its age was also indicated as a number of weeks.
Figure 3. a) a single 190-week old adult female; b) 4 adult females and 2 adult males; c) 5 weeks later, a 1st territory has been established; d) at t=36, a newborn duiker (in white) appears; e) at t=177, the 7 different stages co-exist
- After having shown that this lone adult duiker just moved
and got older when time was running out (Figure 3a),
5 additional adult duikers were interactively introduced in the
surrounding (Figure 3b). When a
free adult male and a free adult female are able to detect each other
and to find a suitable space (3 ha of unoccupied forest), they mate and
establish a territory that they will occupy until the death of one of
them. To highlight this concept, the corresponding aggregates of 3
cells were displayed in very light grey (see Figure 3c).
After fecundation, the pregnancy of the female lasts for 30 weeks and
then a newborn duiker (small white diamond in Figure 3d)
is delivered. As the weeks go by (see Figure 3e),
the baby duiker grows older and becomes a juvenile (represented in
yellow) and later either a sub adult male (represented in light blue)
or female (represented in orange).
- We ran the simulation step by step, and each time something
new happened on the screen, we took some time to build, from the
reactions of the audience, a shared understanding of its meaning, and
then to introduce and to discuss the corresponding lifehistory
parameter. These feedbacks allowed checking the consistency of the
scientific knowledge with the direct observations and/or beliefs of the
participants. In other words, the values of the biological parameters,
defined prior the workshops to calibrate the model, were acknowledged
as realistic by the participants. The questionnaires used just before
and just after the workshops enabled also us to identify some traits
-like the longevity of the blue duikers- they knew little about,
evidencing the effects of attending the workshops on individual
learning (see next section).
Second step: interactive introduction of the hunting activity
- In a second step, the representation of a bigger population
of blue duikers in a wider portion of forest (5 km × 5 km) was
introduced (see Figure 4).
Figure 4. Stylized portion (5 km * 5 km) of forest with 2 villages in opposite corners connected by a road and a structured population of 535 duikers
- This second version of the model is still a stylized scale
version of the "frotembo" ABM, but it represents a step further towards
realism. First, the spatial resolution remaining unchanged (1 cell = 1
ha), the distance between the two neighboring villages connected by a
road is around 7 km, which is comparable to the actual distances
observed in the study site. Because villagers are used to walk from one
village to another, this relative distance helped them to get a better
sense of the spatial extent of the model. Second, the initial
population of blue duikers was set as an aged-structured population.
When the model is run long enough, the saturation of space in terms of
reproduction territories leads to a convergence of the population
density toward an equilibrium. At equilibrium, the simulated population
is structured due to the effect of the age-dependent natural mortality
rates. 535 individual blue duikers were created as a sample of this
structured population to realistically populate the artificial
landscape shown in Figure 4.
These individuals were randomly located over the spatial grid, with
fewer animals located close to both villages. The corresponding average
blue duiker population density (around 20 individuals per km2)
is still a high estimate of actual densities for hunted blue duiker
populations. Nevertheless, this value was selected as the initial
situation in this second step because when running the simulation for
10 years (520 time-steps) without hunting, the population remains
stable (see blue curve in Figure 5),
which represents a convenient baseline scenario to be used as a
reference when comparing with hunting scenarios.
Figure 5. Evolution of the simulated duiker population density over 520 time-steps without hunting as in Figure 4 (in blue); with 1 trap line (50 traps) set as in figure 6b during the wet seasons (in red); with 10 trap lines (total of 475 traps) set as in Figure 7 during the wet seasons (in green); with 146 trap lines set along trails as in Figure 8 during the wet seasons (in orange)
- To introduce the snare trap hunting module of the
"Frotembo" ABM, a participant was called up to come and show directly
on the projected map where he would locate his snares on the virtual
landscape (see Figure 6a). His
trap line was interactively displayed with black dots representing
snare traps (see Figure 6b).
The facilitator requested him to express the rationales directing his
choices by explicating the decision-making criteria.
Figure 6. a) a participant indicating his traps setting on the virtual landscape; b) 50 black dots representing the snare traps suggested by this participant
- For instance, the participant shown in Figure 6a, who was asked to consider that
his home was located in the bottom-right village, explained he would
walk on the road for a while to go a bit far from the village before
entering the forest and starting to set traps. He then mentioned he
would set around 50 traps along a trail that would retrace his steps
back to the road. Other participants discussed the realism of this
practice, specifically regarding the total number of traps they found
too small, which was consistent with the results obtained from the
surveys (average number of snares per hunter was equal to 107).
- After discussing the spatial aspects and the total number
of snares, the discussion focused on the temporality of using the
snares. It was agreed that the snares should be permanently set during
the wet season from mid-March to mid-November. During that period, all
the snares have to be checked every week to collect caught animals and
to be reset (reactivate) the snares. The snares should then be removed
during the dry season from mid-November to mid-March.
- The catchability of the snares was then discussed by
observing on the spatial interface the blue duikers passing by cells
with snares and disappearing from time to time, according to a
probability set to 0.01 for an active snare to catch an individual
duiker located there for a period of one week. The participants
mentioned that newborns were not concerned. They established a linkage
between this parameter and the resulting bag size (defined as the total
number of animals caught by a trap line in one wet season). For
instance during the first workshop, after running the 35 time-steps
corresponding to a first wet season, the trap line interactively set as
shown in Figure 6b had caught a
total of 9 blue duikers. This simulated value seemed reasonable to the
- To investigate the effects of hunting on the mid-term (10
years), it was decided to repeat 10 times the yearly scheme. We
proposed to run the 10 years by relocating the 50 traps at the exact
same locations that were interactively indicated for the first wet
season. Whereas the participants acknowledged that the interactive mode
would be too time-consuming and understood that this "trick" enabled us
to speed-up the simulation, they clearly mentioned that this was
unrealistic: from one year to another, hunting tracks are changed. For
the research team, this meant that the formalization of an algorithm
accounting for the locations of the snares lines would request
additional work, but at least we collected useful elements to implement
a first version of the hunting module to serve as a reference situation
for hunting, and this also enabled us to make explicit all the
- The results of the 10-year long simulation (red curve in
Figure 5) show that the impact
on the duiker population of such minimal hunting cannot be detected: it
is very similar to the scenario without hunting (blue curve in Figure 5).
- To represent a more intensive hunting pressure on the same
virtual landscape, 10 predefined trap lines (5 from each village) were
then presented. Each individual trap line was made of around 50 snares
(see Figure 7).
Figure 7. 10 predefined trap lines made of a total of 475 snares (black dots)
- The same periodical scheme was applied for 10 years: the
475 traps were repeatedly set exactly at the same location in the
beginning of the 8-month long wet seasons and then these snares were
all removed during the 4 months of the dry seasons. The green curve in
Figure 5 clearly illustrates
the impact of such a hunting pressure on the duiker population: after
10 years, the abundance was halved. This result triggered a discussion
about the risk of extinction of the blue duiker population due to
overhunting. The cumulated catches per year aggregated for the 10
hunters also reflected this impact by exhibiting a clear decreasing
trend (see Figure 8).
Figure 8. Cumulated catches per year by 10 trap lines permanently set during wet seasons (see figure 7)
Third step: introduction of the realistic version of the ABM
- In a third step, we introduced the representation of space
in the ABM based on the relative positioning of the 7 villages of the
study site and also provided the delineation of the river marking the
Eastern border of the Korup National Park (see Figure 9).
Figure 9. Study site area represented in the spatial grid (16 × 18 km) of the "Frotembo" ABM
- The names of the 7 villages were not disclosed at first. We
wanted to check if the participants would easily situate themselves in
this virtual landscape. It was not straightforward and some of them got
confused, but finally the right picture emerged from a collective and
- To conclude the workshops, we explained to the participants that the model could be run on this realistic configuration, by explicitly representing the 146 households from the 7 villages that were identified as practicing snares hunting during the past survey. The objective of this second phase would be to promote non-judgmental, non-directive public discussion and reflection and to collectively envision possible management options for the sustainability of blue duikers hunting.
Effects on the participants
- In the three workshops, the participants reacted
positively: 37 out of the 42 interviewed participants volunteered to be
involved in the next stage of the process (Ngahane
2013). By the ends of the first workshops, the participants
already started to discuss about the possible scenarios to be tested
with the ABM. Three main management options were mentioned:
- foreign hunters should be totally restricted from going into the forest around the villages;
- the number of snares per hunter should be reduced;
- a reserve zone should be created in the forest.
- 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 (Ngahane 2013). Education and
raising awareness were stressed by some other participants as being
crucial. They argued that the population should be sensitized on the
long term dangers of overhunting and that the youths should better
educated in agriculture, forest sciences and biodiversity conservation.
- Learning about the biology and the ethology of the blue
duiker also occurred during the workshops. The expressions of good
understanding increased mainly for the longevity (1 before up to 16
after) and the territoriality (7 before up to 18 after) of the species (Ngahane 2013).
- About the ABM itself, 37 out of the 42 interviewed participants declared to have enjoyed its demonstration, only 3 found it difficult to follow and understand, and 36 felt that it was a fair representation of the reality (Ngahane 2013).
- Agar (2005)
opposes emic models that represent how stakeholders
attached to a given socio-ecological system think things are, to etic
models that are built on an outsider's view of the people and the world
being modeled. There are two main approaches to develop emic models.
- The mainstream one consists in using ethnographic data and
to find ways to distill the essence of the insiders' perspective into
the model (Yang & Gilbert 2008).
The use of social surveys to empirically ground agent-based models is
nowadays recognized as essential, but at the same time, the quest for
generalization beyond case studies remains very challenging (Rounsevell et al. 2012).
- To translate narratives into functions or algorithms
required to implement an ABM, several techniques have been developed.
For instance, Becu and his colleagues (2005)
used knowledge engineering techniques to formalize -from the
transcripts of individual representations acquired through ethnographic
surveys- diagrams made of elements and relations. In a last step of the
methodological sequence, the diagrams are validated by the stakeholders
themselves through playable stories.
- Another one relates strongly on the involvement of the
local stakeholders into the process of designing the model (Voinov & Bousquet 2010).
To involve local stakeholders in the co-design of ABMS, role-playing
games, enabling to represent a context-specific situation in a
particular community, are the basis of one recognized empirical
approach in agent-based modeling (Janssen
& Ostrom 2006). Usually, a participant to a gaming
session plays the role corresponding to its main activity in real life.
The information to be used to develop ABMs is derived from the gaming
session (Bousquet et al. 2002;
Barreteau 2003). In
Kenya, Washington-Ottombre and her colleagues (2010) extracted
narrative and spatially explicit drivers of land-use decisions from a
role-playing game on land adjudication. In Northern Thailand, the
behavioral rules related to land/water use and migration that were
integrated in an ABM were designed with local farmers during several
role-playing game sessions (Naivinit
et al. 2010; Le
Page et al. 2014).
- The post-game debriefing of a role-playing game session is
essential for co-learning to occur. On the one hand, the results are
evaluated by the stakeholders -the players themselves-, who can debate
how the game is different from reality. Taking into account requests to
adjust the game represents opportunities to follow stakeholders'
perspectives. On the other hand, the game organizers can refer to the
behaviors exhibited during the game and the decisions made by the
players to specify rules-based methods for the computerized agents (d'Aquino et al. 2003).
- Yet, there is still a gap from the post-game debriefing discussions to the formulation of decision-making algorithms. 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 described in this paper advocates for the early and interactive use of a stylized scale model as an intermediate object to facilitate this activity with local stakeholders.
- Whereas it is commonly acknowledged that participatory
agent-based simulation promotes learning, the knowledge engendered
during the model design process goes largely unnoticed (Johnston et al. 2007). As
illustrated by the famous Confucius say "I hear and I forget,
I see and I remember, I do and I understand", the learning
during the design stage of a participatory modeling process could well
be more intense than during the simulation stage.
- Following De Kraker and van der Wal (2012), we do believe that more efficient feedbacks between model outcomes and stakeholder choices through interactive visualization or user-friendly scanning tools would facilitate the integration of stakeholder perspectives into models and ultimately enhance social learning. Moreover, more interactive simulation interfaces would also greatly benefit to the all the more delicate phase of ABM checking.
This study was made possible by a grant from the Volkswagen Foundation, Hanover, Germany, through its funding Initiative 'Knowledge for Tomorrow - Cooperative Research Projects in Sub-Saharan Africa'.
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