Fuzzy adaptive management of social and ecological carrying capacities for protected areas

Abstract

Commonly used methods of evaluating the degree of consistency of protected area ecosystems with social and ecological carrying capacities are likely to result in decision errors. This occurs because such methods do not account for imprecision and uncertainty in inferring the degree of ecosystem consistency from an observed ecosystem indicator. This paper proposes a fuzzy adaptive management approach to determine whether a protected area ecosystem is consistent with ecological and social carrying capacities and, if not, to identify management actions that are most likely to achieve consistency when there is uncertainty about the current degree of consistency and how alternative management actions are likely to influence that consistency. The proposed approach is illustrated using a hypothetical example that uses an ecosystem indicator that reflects combinations of different levels of user satisfaction and conservation of threatened and endangered species. Application of the proposed fuzzy adaptive management approach requires a protected area manager to: (1) identify alternative management actions for achieving ecosystem consistency with social and ecological carrying capacities in each of several management zones in a protected area; (2) randomly assign alternative management actions to management zones; (3) define fuzzy sets for the ecosystem indicator and degree of ecosystem consistency, and fuzzy relations between the ecosystem indicator and the degree of ecosystem consistency; (4) monitor the indicator in each management zone; (5) define fuzzy sets based on the observed indicator in each management zone; and (6) combine the fuzzy sets defined on the observed indicator and the fuzzy relations between the indicator and the degree of ecosystem consistency to reach conclusions about the most likely degree of consistency for alternative management actions in each management zone. The fuzzy adaptive management approach proposed here is advantageous when the benefits of avoiding the decision errors inherent with crisp and stochastic decision rules outweigh the added cost of implementing the approach.