An integrated simulation modeling and operations research approach to spatial management decision making

Significant declines, and in some cases total collapses, of about 70 % of the worlds exploited fish populations (FAO 1995) are considered among the most threatening global resource problems currently faced. The state of world fisheries indicates that traditional fishery management models and control mechanisms have been largely ineffectual in creating sustainable fisheries (Ludwig & al. 1993; Sharp 1995; Caddy 1996; Ault & al. 1998a). A serious deficiency of these traditional methods is that they have focused solely on the temporal processes driving fish stock productivity, while ignoring the complex spatial dynamics of both fish stocks and human activities that impact them. Fish stock spatial dynamics, such as preferential habitat utilization by species, movement and migratory behavior, prey searching, predator avoidance, and larval dynamics and drift, result in fish stocks being heterogeneously distributed throughout the oceans (Rothschild 1986; Longhurst & Pauly 1987; MacCall 1990; Mann & Lazier 1991; Bakun 1996; Humston & al. 2000). In fact, over 50 % of the world’s fish production occurs in less than 5 % of the ocean’s area (Ryther 1969). In addition, spatially explicit human activities (e.g., fishing, dredging, etc.) occur over selective temporal periods and spatial extents, thus exacerbating the already complex spatial dynamics of fish stocks. Innovative tools are needed that allow fishery managers to explicitly integrate spatial processes into management decision making. We have developed a generalized framework that builds upon traditional fishery management methodologies by utilizing knowledge from the fields of computer simulation modeling and operations research. We have built a spatial simulation model that extends a traditional population dynamics simulation model into space to alAn integrated simulation modeling and operations research approach to spatial management decision making