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Investigation of Shark Bycatch in the Hawaii-based Longline Fishery, and an Extension of Analyses of Catch Data from Widely Separated Areas in the Pacific Ocean

Progress reports (PDF): FY 2010, FY 2009, FY 2008, FY 2007

This proposal describes biometrical research with two overall goals. The first is to improve understanding of shark bycatch in the Hawaii-based longline fishery. The second is to elucidate variation across broad spatial scales in catch per unit effort (CPUE) for several highly migratory Pacific fishes as a contribution to the development of ecosystem-based fishery management. These intentions are relevant to priorities recommended for pelagic fisheries research as summarized by Sibert et al. (2005) in the following respects. Improved understanding of the composition of shark bycatch may permit use of the diversity of these species as an indicator of ecosystem status, while an improved understanding of the magnitude of shark bycatch, in the aggregate and by species, is fundamental background information required to evaluate the efficacy of bycatch reduction efforts. Comparisons of catch trends across broad spatial scales may contribute to the definition of the functional sizes of Pacific Ocean ecosystems and improved ecosystem monitoring because such trends may vary both within and among species and fisheries. This second aspect represents a request for an extension of an existing PFRP project "Comparisons of Catch Rates for Target and Incidentally Taken Fishes in Widely Separated Areas of the Pacific Ocean", in order to resume uncompleted activities.

Sharks Bycatch
(1) The first objective will be to use the fishery observer catch data to describe and quantify the species composition of shark bycatch in this fishery. The underlying purpose is to avoid either over- or underestimation of the number of these species taken by this fishery because such errors could engender spurious ecological inferences.
(2) The second objective, which will also use the fishery observer catch data, will be to investigate the fate of released sharks. This should yield a species-specific summary of the observer catch data and notes to complement previous PFRP-supported investigations of post-tagging mortality.
(3) The third objective will be to estimate catches and CPUE for those sharks (blue shark, mako sharks, thresher sharks, and oceanic whitetip shark) that are specifically reported in Hawaii-based longline logbooks. This should generate updated results for blue shark, previously studied from March 1994 through December 1997 (Walsh and Kleiber 2001; Walsh et al. 2002), and comparable results for the other species, which have yet not been studied.
(4) The fourth objective will be to assess whether regulatory actions implemented for reasons unrelated to shark catch rates (e.g., time-area closures or gear restrictions intended to minimize interactions with sea turtles) tend to exacerbate problems with bias in the self-reported catch data. The underlying purpose for this objective is to elucidate the extent to which regulatory changes may elicit unexpected and undesirable consequences.

CPUE Comparisons
(1) The first objective of the comparative analyses will be to develop statistical models of catch rates for wahoo, mahimahi, yellowfin and skipjack tuna from the Hawaii Longline Observer Program catch data and then apply the model coefficients to the logbook data from the Hawaii-based longline fishery. This should serve to increase the number of species with corrected, well-documented catch histories in the data archives of the Pacific Islands Fisheries Science Center.
(2) The second objective is to determine whether, and if so, to what extent, intra- and interspecific CPUE for several species are correlated in various regions of the Pacific Ocean. This will entail comparisons of catch rates from several fisheries for the four aforementioned species and blue marlin by use of appropriate correlation and time series techniques. Likely data sources include offshore creel survey records from the Guam Division of Aquatic and Wildlife Resources, longline logbook records from California and American Samoa, and longline observer catch statistics from the Secretariat of the Pacific Community.
(3) The third objective will be to use the corrected CPUE trends to test predictions from ecosystem models (ECOSIM, ECOSPACE) for the North Pacific Ocean that suggest compensatory responses by fishes at lower trophic levels (e.g., mahimahi, skipjack tuna, wahoo) to declines in higher trophic level predators (e.g. blue marlin, blue shark).

Shark Bycatch
The first two objectives will entail study and summarization of the fishery observer catch data and notes. In addition, the reliability of observers will be assessed by establishing a suite of criteria to be used in evaluating species identifications, particularly among those species that have rarely been caught in this fishery. Results from the checks against these criteria will be tabulated and the various species categorized with respect to their occurrence in this fishery as definite, probable, possible, unlikely, or absent.
The quantitative analyses of shark bycatch and incidental catch will follow Walsh et al. (2002) and Walsh et al. (2005), respectively. The assessment of whether logbook reporting behavior tends to be adversely affected by altered regulatory schemes will entail checks on the logbooks from both observed and unobserved trips before and after such changes in management.

CPUE Comparisons
The comparative catch rate analyses will be initiated by fitting generalized additive models (GAMs) to updated data from the Hawai'i Longline Observer Program for the aforementioned teleosts of interest according to the methods described in Walsh and Kleiber (2001). These models will then be applied to logbooks from unobserved trips according to the methods described in Walsh et al. (2005).
The corrected CPUE trends will be used to compute correlations with catch trends from the other widely separated fisheries. Significant positive correlations would suggest that relative abundance of the species of interest varies in association with factors operating on an oceanic scale. In contrast, significant negative correlations could indicate that catch trends are either influenced primarily by local factors or by different levels of covariates resulting from geographic separation.
The corrected CPUE trends will also be used to investigate ecosystem impacts on the species of interest. Ecosystem models (ECOSIM, ECOSPACE) for the North Pacific Ocean suggest that reduced abundance of one (or more) top trophic level predator(s) (e.g. blue marlin, blue shark) would permit increased abundance of lower trophic level species (mahimahi, skipjack, wahoo) through compensatory effects (Cox et al. 2002; T. Essington, personal communication). Time-series of ratios between species at high and low trophic levels will be computed to test these ecological hypotheses. These time-series will be computed on three spatial scales: 1) Hawaii-based fishery, 2) Guam troll fishery, and 3) from sub-regions in the western and central Pacific or Pacific Ocean using the results from stock assessments.

Year 1 funding for this 2-year project estimated to be available mid-2006.

Literature cited:

Cox, S.P., T.E. Essington, J.F. Kitchell, S.J.D. Martell, C.J. Walters, C. Boggs and I. Kaplan. 2002. Reconstructing ecosystem dynamics in the central Pacific Ocean, 1952-1998. II. A preliminary assessment of the trophic impacts of fishing and effects on tuna dynamics. Canadian Journal of Fisheries and Aquatic Sciences 59:1736-1747.
Sibert, J., S. McCreary, and E. Poncelet. 2005. Pacific Ocean Connections: Priorities for Pelagic Fisheries Research in the Twenty-First century. Report of PFRP Research Priorities Workshop November 16-18, 2005. SOEST 06-01. JIMAR Contribution 06-358.
Walsh, W.A., P. Kleiber. 2001. Generalized additive model and regression tree analyses of blue shark (Prionace glauca) catch rates by the Hawaii-based commercial longline fishery. Fisheries Research 53:115-131.
Walsh, W.A., P. Kleiber, and M. McCracken. 2002. Comparison of logbook reports of incidental blue shark catch rates by Hawaii-based longline vessels to fishery observer data by application of a generalized additive model. Fisheries Research 58:79-94.
Walsh, W.A., R.Y. Ito, K.E. Kawamoto, and M. McCracken. 2005. Analysis of logbook accuracy for blue marlin (Makaira nigricans) in the Hawaii-based longline fishery with a generalized additive model and commercial sales data. Fisheries Research 75:175-192.


Principal Investigators:
Dr. William A. Walsh
National Marine Fisheries Service
PIFSC - Honolulu Laboratory
2570 Dole Street
Honolulu, Hawaii 96822 USA
Phone (808) 983-5346
FAX (808) 983-2902
email: William.Walsh@noaa.gov

Mr. Keith Bigelow
National Marine Fisheries Service
PIFSC - Honolulu Laboratory
2570 Dole Street
Honolulu, Hawaii 96822 USA
Phone (808) 983-5388
FAX (808) 983-2902
email: Keith.Bigelow@noaa.gov

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This page updated October 4, 2010