Wildlife management involves uncertainties associated with, e.g., non-equilibrium nature of population dynamics, errors in population census. These uncertainties make implementation of appropriate management measures difficult. One effective approach to this problem is to apply the adaptive management (AM).

I developed a fuzzy-controlled management procedure (fuzzy MP) using fuzzy reasoning based upon the feedback concept of AM. Control using fuzzy reasoning has many applications from household appliances to automatic operation of unmanned trains. Advantages of applying the fuzzy control are: specification of complicated mathematical formulae is not necessary; expert knowledge can be incorporated; uncertainty that humans handle in decision-making can be dealt with in the system.

Performance of the fuzzy MP was evaluated by simulation in a “virtual world” which imitated population dynamics and management of a real population. Using southern bluefin tuna management as a case study, specific operations actually embedded in the fuzzy MP and its performance were described. In contrast with a constant catch management, the fuzzy MP was able to achieve management objectives while continuing fishing and avoiding stock collapse.

Although the fuzzy MP examined was simple, applications to management for a wide range of wildlife populations are possible by refining the MP.