Modeling carrying capacity for national parks

• Published in: Ecological economics Vol. 39; no. 3; pp. 321 - 331
• Main Author: Prato, Tony
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2001; Elsevier
• Series: Ecological Economics
• Subjects: Adaptive ecosystem management; Bayes’ rule; Carrying capacities; Economics; Ecosystems; Environment; Environmental economics; Managers; Modelling; Multiple attribute decision-making; National parks; Utility functions; National parks; Bayes’ rule; Multiple attribute decision-making; Carrying capacities; Adaptive ecosystem management
• ISSN: 0921-8009; 1873-6106
• DOI: 10.1016/S0921-8009(01)00248-8

Units of the National Park System are required to develop a general management plan that is consistent with visitor carrying capacities. Newer definitions of carrying capacities for protected areas, such as national parks and wilderness areas, are in terms of the acceptability of natural resource and human impacts as measured by selected biophysical resource and social conditions, rather than the number of visitors. Most methods for evaluating carrying capacity are non-quantitative and lack analytical rigor. Carrying capacity decisions are easier to justify when based on quantitative methods. This paper describes a carrying capacity modeling system that allows park managers to quantitatively determine whether the current state of a park's ecosystem is in compliance with established standards for carrying capacities and, in cases where it is not, to identify the management action having the greatest likelihood of bringing the ecosystem into compliance with those standards. The modeling system uses an ex post adaptive ecosystem management (AEM) model to determine whether the current state of an ecosystem complies with biophysical and social carrying capacities, and an ex ante multiple attribute scoring test of capacity (MASTEC) to identify the best management action for achieving compliance. The AEM model addresses potential errors that can occur when inferring an ecosystem's state from resource/social conditions. Errors occur when a manager decides that an ecosystem is in one state when in reality it is in another state. The consequences of such errors are greater when resource/social conditions are at the extremes. The AEM model minimizes the likelihood of such decision errors by using Bayes’ rule to determine the state of an ecosystem. The MASTEC method allows a park manager to identify the best management action for bringing an incompliant ecosystem into compliance with carrying capacities. It integrates elements of two carrying capacity methods, Limits of Acceptable Change and Visitor Impact Management, and multiple attribute decision-making. The latter characterizes management actions in terms of their multiple biophysical and social attributes. The best management action maximizes the manager's expected utility function subject to stochastic carrying capacity constraints that require the probability that the value of an attribute exceeds its standard to be no less than a reliability level selected by the manager. Several factors limit the ability of national parks to implement the carrying capacity modeling system. Using a spatial decision support tool to implement the modeling system eases some of these limitations.