44 L. Von Hagen et al.


FIG. 4 A fuzzy cognitve map of variables related to human–elephant conflict based on the authors’ knowledge of the local context, expertise of local villagers, and literature. Variables are linked together through connecting lines (edges) with the strength of association represented by the thickness of the lines. To read the model, take any variable with an arrow originating from it and with an increase of said variable it will have either a positive and increasing (a plus (+) sign) or negative and decreasing (a minus (–) sign) causal influence on the variable it is connected to. CSA, climate smart agriculture.


elephant population as an example in Fig. 2: the arrow originating from this variable to human–elephant conflict has a positive sign, meaning that a higher elephant popu- lation had an increasing effect or influence on human– elephant conflict. The thicker line means it has more influence than other variables with thin connections. An ex- ample of a negative influence is the relationship of education on elephants and human–elephant conflict, meaning that more education about elephants decreases the impact of human–elephant conflict. Of the six study villages, Bungule had the highest complexity score (a potential


indicator of complex systems thinking) and Miasenyi had the highest number of overall variables, connections, driver variables and ordinary variables, and the lowest density (Table 1). In the qualitative aggregation for the four categories of variables (economic, environmental interactions, social, and policy and management), environmental interactions emerged as the leading source of grouped variables, fol- lowed by policy and management (Fig. 3, Supplementary Fig. 6). These four variable categories differed significant- ly (F3,20 = 23.86,P,0.001), and post hoc analysis revealed significant differences between environmental–economic


TABLE 1 Summary metrics of mental model components related to human–elephant conflict taken from participatory model sessions from six villages in the Kasigau Wildlife Corridor, Kenya, as part of fuzzy cognitive map construction.


Order V1


V2 V3 V4 V5 V6


Village


Makwasinyi Kisimenyi Bungule Buguta Itinyi


Miasenyi Co-model


Number of variables


18 24 28 30 43 52 21


Connections1 28


46 57 59 84


103 73


Drivers2 6


4 7 9


14 15 2


1How many times variables were linked (degree of interaction with other variables). 2Variables that affect interactions or variables but are not affected by other variables. 3The number of concepts that influence and are influenced by other variables. 4The number of connections compared to all possible connections.


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


Ordinary3 10


17 18 18 24 31 19


Density4 0.09


0.08 0.08 0.07 0.05 0.04 0.17


Complexity score


0.33 0.75 0.43 0.33 0.36 0.33 0.00


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