42 L. Von Hagen et al.
each village agreed on a consensus, and the facilitator en- couraged every participant to contribute their views. We used Mental Modeler 1.0 (Gray et al., 2013, 2017)to
FIG. 1 The Kasigau Wildlife Corridor, Kenya, shown with the 14 community ranches and the locations of the six villages participating in this study.
Table 1). One session per village was conducted on a Friday or Saturday during 21 November–22 December 2020. On days of the sessions, the trilingual facilitator initiated
the participatory sessions in Swahili by introducing the concepts of the research and model building, and clarifying terms that would be used (such as crop raiding/foraging, hu- man–elephant conflict and deterrents) to assure construct validity. Using coloured markers and large sheets of paper, the issue of human–elephant conflict was listed in the centre of the paper and participants were asked to deter- mine system components or variables that affected conflict, with lines drawn to connect these variables. Participants were allowed to add variables through group discussion until they felt they had included all key aspects. The facilita- tor was encouraged not to prompt with specific variables, only suggesting topics for consideration if the participants appeared stuck or confused with the models. The ‘fuzzy’ portion of the model construction was to quantify on a scale of 1 (lowest impact) to 10 (highest impact) how the
two variables related to each other. Negative (−) and posi- tive (+) influences were demarcated on the chart in different colours, red for decreasing, blue for increasing. Normally
this process is done on a valence scale of−1 to +1 with deci- mal intervals (Özesmi & Özesmi, 2004), but for simplicity we used −10 to +10. For each step, the participants from
convert the hand drawn models to fuzzy cognitive maps for each village (Fig. 2, Supplementary Figs 1–5), with the variables randomly placed on the models. The software de- termined the number of variables in each model including the number of connections (indicating the degrees of inter- actions; Özesmi & Özesmi, 2004), transmitter variables (the drivers that affect other variables but are not affected by them), receiver variables (items that only receive impacts and do not affect other components), and ordinary compo- nents (variables that are both receivers and transmitters). The software calculated centrality scores, a measure of im- portance to the overall system dynamics, which is deter- mined by the number of edges or connections for each variable in the system.We also calculated complexity to de- termine the level of complex systems thinking according to previous studies, and density to compare the number of connections in a particular model to all possible connec- tions (Eden, 1992; Özesmi & Özesmi, 2004). To assess if variables were novel or underrepresented, we referred to our collective knowledge of and existing literature on human–elephant interactions. During input of the villagemodels, we noticed complex-
ity seemed to increase with successive sessions. Thus, we evaluated if changes were occurring linearly by plotting the density metric over time and the number of variables, connections, drivers and ordinary components, and tested these with a linear regression. The positive correlation co- efficient values for variables (P = 0.002), connections (P,0.001), drivers (P = 0.006), and ordinary components (P = 0.007) ranged from 0.93 to 0.98, and the density had
a negative correlation coefficient of −0.98 (P,0.001). We therefore concluded that the likely reason for this significant increase in variables and complexity was that the facilitator improved their skills over time, resulting in the models becoming more complex. To address and compensate statistically for this facilitator adaptation and potential bias, we created a qualitative aggre- gation method across villages (Fig. 3; Vasslides & Jensen, 2016; Misthos et al., 2017) by establishing four locally rele- vant categories to group each variable from the respective village models: economic, environmental interactions, social, and policy and management. To better visualize the categories into which each variable fell, we assigned a different colour to each category, resulting in a cognitive colour spectrum (Cholewicki et al., 2019; Supplementary Fig. 6). We calculated the percentage of variables within each category for each village and compared the means of each category with ANOVA, and used a Tukey’s post hoc honest significant difference test for multiple comparison. The lead author (LVH) developed a single co-created model of human–elephant conflict (Fig. 4) based on input
Oryx, 2025, 59(1), 40–49 © The Author(s), 2024. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605324000449
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