An agent-based model of an endangered population of the Arctic fox from Mednyi Island
This work addresses ecological challenges for conservationists by providing insights into population decline and evolutionary mechanisms in a high-density environment, though it is incremental as it applies existing AI techniques to a new biological dataset.
The researchers tackled the problem of understanding the catastrophic degradation and persistence of an endangered Arctic fox population on Mednyi Island by developing an agent-based model combined with probabilistic graphical models, resulting in a realistic simulation that analyzes survival and population dynamics under various conditions.
Artificial Intelligence techniques such as agent-based modeling and probabilistic reasoning have shown promise in modeling complex biological systems and testing ecological hypotheses through simulation. We develop an agent-based model of Arctic foxes from Medniy Island while utilizing Probabilistic Graphical Models to capture the conditional dependencies between the random variables. Such models provide valuable insights in analyzing factors behind catastrophic degradation of this population and in revealing evolutionary mechanisms of its persistence in high-density environment. Using empirical data from studies in Medniy Island, we create a realistic model of Arctic foxes as agents, and study their survival and population dynamics under a variety of conditions.