of Shark Bycatch in the Hawaii-based Longline Fishery, and an Extension
of Analyses of Catch Data from Widely Separated Areas in the Pacific
reports (PDF): FY
2010, FY 2009,
FY 2008, FY
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.
(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
(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
(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).
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
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.
funding for this 2-year project estimated to be available mid-2006.
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.