A Decision Support Model for Fisheries Management in Hawaii: A Multilevel and Multiobjective Programming Approach

Abstract

Management of Hawaii's fisheries faces great challenges due to rapid growth that has intensified competition among fisheries and users with different interests. This study develops and applies a multilevel and multiobjective programming model to assist decision making in Hawaii's fisheries. The multilevel aspect of the model incorporates objectives of both policy makers and fishermen. The use of a multiobjective model is essential in fisheries management because the typical fishery policy problem is characterized by more than one objective or goal that decision makers want to optimize. The model covers 9 fleet categories, 5 fishing areas, 4 seasons, and 14 species, of which 10 are targeted species. Catch per unit of effort (CPUE) includes targeted and incidental catch species. A nonlinear relationship between CPUE and effort is incorporated into the model. By use of the various objectives or policy options of fisheries management, the current model provides optimum solutions in fishing effort and its spatial and temporal distribution, as well as the optimal harvest level. The current model has been applied to evaluate several management issues facing Hawaii's fisheries. Application of the model indicates that the trade-offs between recreational and commercial fishing vary by effort level. At the current fishing effort level, an increase of one recreational trip reduces commercial profit by US$12.14. Moreover, the study concludes that the area closure regime designed to reduce conflict between commercial and recreational fishing can cause profit loss to the commercial fisheries in the range of $0.44 million to $0.70 million.

Sea turtle interactions with Hawaii's longline fishery: an extended multi-objective programming model incorporating spatial and seasonal dimensions

“…6 The NISE method is the most effective technique to solve two-objective problems. It employs a weighted objective function to generate a trade-off curve representing the set of noninferior solutions (Pan et al, 2001). The optimal outcome from the trade-off frontier will form the basis of turtle related fishery policy evaluation when compared to the base scenario.…”

“…Therefore, Leung et al (1999) suggested that a multi-objective approach would be essential for policy analysis. Consequently, a multi-objective programming model was developed and applied to examine the relationships between Hawaii's commercial and recreational fisheries by Pan et al (2001). The model aimed to maximize both the profit and recreational opportunities by accounting for fishers' micro-decision behaviours as well.…”

“…Considering these concerns, the multi-objective mathematical programming model by Pan et al (2001) was, therefore, modified to incorporate the issue of sea turtle interactions by Pradhan and Leung (2006a). The work by Pradhan and Leung has been further expanded in this study by adding spatial and seasonal dimensions, as it is presumed that a more refined optimization in terms of economic and environmental efficiency gains may be achieved with these added features.…”

Decision Support to Sustainable Management of Bottom Trawl Fleet

“…The use of multicriteria methods, such as Simple Additive Weighting (SAW), AHP and Multi-Attribute Utility Theory (MAUT), are widely accepted in fisheries, and the known approach in fisheries management is mainly based on the use of multicriteria methods for the selection and evaluation of management instruments, and for analyzing the performance of fisheries management policies (e.g., [10,33,34]). Although DSS and multicriteria methods have been applied to support decision makers in fisheries research (e.g., [1,2,11,12,35]), the techniques have been used separately.…”

Multi-criteria decision-making for fisheries management: A case study of Mediterranean demersal fisheries

“…In this respect, multi-criteria analysis [18] can provide a conceptual framework to support the decision process by allowing decisionmakers to highlight possible trade-offs among conflicting viewpoints and to address a number of objectives that cannot be reduced to a single dimension such as the monetary one [4]. Despite it is widely agreed that the use of a multi-criteria approach is highly desirable [46], the use of multi-objective methods in fisheries research has been scarce over the past decades, although pioneering studies have been conducted since the early 1980s (e.g., [5,8]) and some recent examples can also be found in the literature (e.g., [44,32,29,3]). Nevertheless, most efforts remain directed to the development of analytical tools to evaluate the impact of management strategies from a single-objective perspective.…”