Modeling Longline Effort Dynamics and Protected Species Interaction

Related PFRP projects:
A Multilevel and Multiobjective Programming Model of Hawaii Commercial Fisheries

Regulatory Impact Analysis Framework for Hawaii Pelagic Fishery Management: A Multilevel and Multiobjective Programming Model

Progress Reports (PDF):FY 2007 final progress report, FY 2006, FY 2005, FY 2004, FY 2003

Project Overview
With the rapid expansion of the Hawaii longline fleet in the recent decade, the longline fishing industry has been confronting several issues such as gear conflicts in the nearshore fishery, localized overfishing, and impacts on protected sea turtles and birds. These issues have lead to a variety of fishery regulations directed towards the longline fleet, including the establishment of limited entry, area closure, and a recent court decision banning the targeting of swordfish. Increased concerns for environmental impacts of fishing activities continue to pose a serious challenge to fishery managers in devising fishery management policies that are economically efficient, environmentally sound and acceptable to resource users with different interests. Fishery managers are considering more pragmatic regulatory measures to resolve these issues primarily on an ecological front.

While the ecosystem-based fishery management has recently been promoted as the preferred approach for sustainable fishery management, ecosystems modeling is still in its infancy. Although information required for ecosystem-based fishery management is highly scattered, it is clear that an interdisciplinary and learning-by-doing approach can be a precursor to this broad-based fishery management approach. One of the major components in any ecosystem model would be the depiction of the fishers' behavior in relation to their choice of effort levels, target species, fishing location, and entry into/exit out of a fishery. The behavioral aspects of fishers are generally embedded in a fleet dynamic model to derive the optimal fleet mix and effort levels spatially (by area) and temporally (by season) given the estimated amount of fishery resources under various management options and socio-economic conditions faced by the fishery.

A Hawaii fleet dynamic model earlier developed by then-PFRP researcher MinLing Pan provides optimal seasonal and spatial allocation of effort among different fleets as well as the trade-offs between recreational and commercial fisheries and impacts of area closure regime. This model, however, does not consider the plausible impacts of existing fishery management on the biodiversity of the ecosystem. Furthermore, this model was developed using a limited period of data and in a situation where the different kinds of fisheries were allowed to practice without much restriction. The derived optimal outcomes also did not consider interactions with major protected marine species, especially sea turtles. Under the present situation, if the longline fishery and related economic activity were to continue, protected species and other predator-prey relationships must constitute an integral part of the pelagic fishery management planning and implementation. The purpose of this proposed study is to refine and extend the existing Hawaii fleet dynamic model by incorporating the most current available information and up-to-date modeling platform for ease of applications. Project researchers will extend ongoing efforts by current PFRP researchers Xiulin Gu and Sam Pooley by including more pragmatic constraints into the fleet dynamics model.

Specific objectives and tasks are as follows:

1. Extend the longline trip level time-series data set from 1991-1998 to 2001. A longer series of data is essential to test if there exists any patterned behavior or technical-economic interactions of the fishers and the marine species under consideration including protected species, and thus provide increased realism to the fleet dynamic model.
2. Re-estimate the technical and economic interrelationships among different species landed; and the entry/exit behavior. The longer time-series will allow researchers to test if changes have occurred in the above relationships in light of the recent ban in swordfish harvest.
3. Estimate the catch-effort relationships for each species and for each fleet. This is now possible with a longer time-series data set not available in previous modeling efforts.
4. Analyze the factors, rate, and degree of protected species interaction (e.g., turtles and seabirds) with longline fishing activities. Possible factors responsible for protected species interaction such as vessel-specific characteristics, target choices, spatial, temporal, physical environment and other factors will be investigated. The likelihood of protected species interaction associated with each of the above factors will also be estimated utilizing the time series data set. Key factors leading to the protected species exposure will be identified and the probabilities of such species interaction will also be estimated.
5. The information generated above will be incorporated into the existing fleet dynamic model in maximizing fishery welfare and fishing effort considering broader implications on protected species and stock conditions. Finally, the model will be migrated to the MS Excel modeling platform for ease of model implementation, utilization, and future modifications.
   

Year 1 funding for this 2-year project to be awarded in November 2002.

Collaborator:
Dr. Omar El-Gayar
Dakota State University
College of Business and Information Systems

820 N. Washington Ave.
Madison, SD 57042-1799 USA
email: omar.el-gayar@dsu.edu

This page updated September 25, 2007