pare this estimate with their
pre-defined target abundance. Policy is developed by calling the genetic
algorithm (see below), which works within a manager’s constraints to
find costs for user actions on the resource (e.g., culling, scaring,
etc.) that minimise deviation from the target abundance, as informed by
the predicted consequences of each action on resource abundance and user
action histories. After a suitable policy is found, (4) users perform
actions that affect resources or landscape cells (e.g.