Bridging the gaps: Agent-based modelling for elephant poaching mitigation (version 1.1.0)
African elephants have undergone serious declines in the past century due to demand for ivory. Wildlife managers face significant hurdles when planning poaching mitigation strategies, such as lack of funds, personnel, equipment, and data on how to best use limited resources. Mitigation is also made difficult by the dynamic relationship between elephants, poachers, and law enforcement, as each adaptively responds to the behaviours of the others. The two main modelling techniques applied to this topic to-date – equation-based and game theoretical models – face several limitations. Equation-based models face fundamental difficulties when facing complex and dynamic systems like poaching, and therefore resort to significant simplifications. Game theoretical models have adaptive poachers and rangers, but they do not include any behavioural or ecological information on elephants. They also approach law enforcement interventions with a limited frame of view, typically focussing only on planning effective patrol routes. Agent-based models (ABMs) present a way to fill these gaps. One can explicitly model the complex interdependencies between enforcement strategies, poacher decision-making and adaptations to various strategies, and the ecology and behaviour of elephants. As an illustration of the utility of ABMs, we present a developed a holistic, exploratory ABM that predicts how interactions between elephants, poachers, and law enforcement affect poaching levels within a virtual protected area.
This is a companion discussion topic for the original entry at https://www.comses.net/codebases/3f15f4fa-aa47-4ffe-8c87-7a96505e47c0/releases/1.1.0/