Pelagic Fishery Research Program (PFRP)
Joint Institute for Marine and Atmospheric Research
School of Ocean and Earth Science and Technology
University of Hawaii
Project title: Local Pelagic Catch and Effort Data Analysis and Integrated Modeling to Quantify the Effects of Local Fisheries on Fish Availability (RCUH project 2041)
Funding agency: NOAA, NMFS
Principal Investigator: Dr. Christofer Boggs NMFS/SWFSC Honolulu Laboratory
Staff: Dr. Xi He, Dr. Quanhe Yang, Xian Zhou, Weining Wu, and Daniel Curran
Purpose of the project:
1. Establish a comprehensive catch and effort database for Hawaii pelagic fisheries. Provide annotated databases that include all available historical data on catch and effort by different components of Hawaii's pelagic fisheries.
2. Establish a meaningful time series of pelagic fish availability and fishing pressure in Hawaii. Compute time series (1948-1992) of catch per unit effort (CPUE) and standardized fishing effort, at monthly resolution, for yellowfin and bigeye tuna
s, blue and striped marlins, mahimahi, and ono (wahoo).
3. Develop analytical models for variation in pelagic fish availability (CPUE) in Hawaii. Analyze the CPUE time series in relation to potential influences including total local fishing effort and effort by specific fishery components.
Progress through May 1996:
Funding was received at the University in August 1993, and staff began work in February-April 1994. This project assembled all available data on catch and effort statistics for Hawaii pelagic fisheries, with emphasis on the six most valuable pelagic spe
cies. Confidential annotated data-bases were created from Hawaii's Division of Aquatic Resources (HDAR) data and from wholesale market data. Nonconfidential monthly and yearly summaries of catch of pelagic species were produced. Time series analysis of
HDAR data did not show negative effects from an increase in fishing.
Creation of catch per unit effort indices for Hawaii's handline and troll fisheries:
Commercial catches reported to HDAR are the only continuous long-term time series of data on Hawaii's handline and troll fisheries. The validity of HDAR data used has often been called into question. Specifically, HDAR data suffers from the lack of a spe
cific measure of effort due to the reporting of catches by date, and until 1989 information on unsuccessful fishing trips (zero catch trips) was absent from the database. To address these problems we used dates of catch from 1970-92 as a measure of effor
t (fishing days) and examined intervals between dates fished as a second variable (Fig. 1). In 1978-79, catch per days fished (CPDF) was anomalous for catches reported one month apart, strongly suggesting that catches for many days fished were summed in
monthly reports. The time series of all six species examined were improved by elimination of the monthly reports in those years. For other years, CPDF data stratified by intervals between reports, showed similar trends for different intervals. Thus, co
rrected CPDF data appear to provide a useful index of fluctuations in troll and handline fishery performance.
Frequency distributions of CPDF for any one species are sometimes highly skewed and include substantial frequencies of zero CPDF. A modified negative binomial distribution fit to the CPDF provided mean catch rates that differed very little from simple a
rithmetic means. Other analyses showed that mean CPDF, excluding days which caught no species at all, was highly predictive of mean CPDF including zero catch days (Fig. 2). Thus, lack of data on fishing days with no catch throughout much of the time ser
ies does not invalidate the use of CPDF as an index of fishery performance.
The HDAR data were appended with individual licensee identifiers to enable us to track fishermen individually or as groups through time. Cluster analysis was used to classify fishermen by gear use and species composition of catch. Individual long-term f
ishermen with consistent reporting habits were also identified for contact during a survey of operational characteristics of different fishery sectors.
Estimation of under-reporting rates for HDAR data:
Information from independent fish dealers, wholesale market monitoring projects, and previous studies were used to gauge the amount of fish not reported to HDAR by different fishery sectors. Previous studies found substantial under-reporting problems by H
awaii longline vessels from 1979 to 1989. The accuracy of reports by Hawaii's handline and troll fleets had never been carefully examined.
Comparative information on handline and troll catch was available for only three years (1980, 1981, and 1988). These years showed that under-reporting by handline and troll fishermen was not too severe with reported catches to HDAR representing about 80%
of actual total catch. Only a few years of comparative data for handline and troll fisheries exists making any quantification of trends in under-reporting difficult. Since the possible comparisons did show that reported catches were not grossly inaccura
te, we focused our under-reporting study on the longline sector of Hawaii's pelagic fishery.
A set of raising factors were estimated to correct for under-reporting by longliners to HDAR. Total catches of six species were estimated from HDAR-independent data and compared with existing HDAR catch reports to determine suggested raising factors (Tabl
e 1). Individual species comparisons were not available for all years, forcing us to base estimates on a single species or on a species grouping. From 1984 to 1986 no comparative data were available and suggested raising factors were interpolated for ea
ch species. Bigeye tuna and yellowfin tuna raising factors were interpolated from 1984 to 1987.
Simulation modeling and time series analysis of catch and CPUE:
Hawaii commercial fisheries data only provide time series of total catch and indices of CPUE. We examined a tool that can be used to estimate fisheries impacts on local fishery performance when only time series of catch and CPUE data are available. We us
ed simulations to model relationships between total catch and CPUE and patterns of these relationships under different scenarios of key parameters: migrations rates, fishing mortality, and catchability. We then analyzed time series of total catch and CP
UE produced by these models using time series transfer function models. We also applied the transfer function models to real commercial fisheries data from Hawaii's yellowfin tuna fisheries. Finally, we compared the results from the simulation models an
d the real data were used to estimate the power of the transfer function models to detect local fishery impacts.
The simulation models were run at monthly time steps for 30 years. The models include stochastic processes in migration rates, fishing mortality, and catchability. Each scenario was run 1000 times. Total catches of yellowfin tuna from 1962 to 1994 were
estimated for three fishing gear types: longline, troll, and handline. Results indicated that total catches by Hawaii's fishery sectors have low probabilities of affecting local CPUE for yellowfin tuna. Further analysis is focusing on the same data, b
ut at finer spatial and temporal scales.
Curran, D. S., C. H. Boggs, and X. He., 1996. Catch and Effort From Hawaii's Longline Fishery Summarized by Quarters and Five Degree Squares. U.S. Dep. Commer., NOAA Tech. Memo. NMFS-SWFSC-225, 68 p.
He, X., C. H. Boggs, and K. A. Bigelow, 1996. Cluster analysis of longline sets and fishing strategies within the Hawaii-based Fishery. Submitted to the journal Fisheries Research.
He, X., and C. H. Boggs, 1996. Time Series Analysis on Hawaii's Tuna Fisheries: Do Local Catches Affect Local Abundance? Second FAO Expert Consultation on Interactions of Pacific Tuna Fisheries. Shimizu, Japan. Paper No: 5-24 (In press).
He, X., and C. H. Boggs, 1996. Estimating Fisheries Impacts Using Commercial Fisheries Data: Simulation Models and Time Series Analysis of Hawaii's Yellowfin Tuna Fisheries. For the Proceedings of the Second World Fisheries Congress July 28-August 1, 19
96, Brisbane, Australia.
Summary of accomplishments, results, and plans for future research:
This project has been successful in compiling internally consistent catch and effort time series data for catches of yellowfin and bigeye tunas, blue and striped marlins, ono (wahoo), and mahimahi in Hawaii. Catch per unit of effort (CPUE) has remained hi
gh when total catch was high implying that the fishery is having little impact on the stock. Various approaches to the problem of non-reporting of zero-catch trips were attempted; biases from this source are consistent and correctable. The problem of int
erpreting CPUE in the longline fishery when the vessels change their targeting practices was resolved by applying multivariate analysis techniques to the catches. A spatial search model showed that the spatial distribution of fishing may be a good proxy f
or the spatial distribution of target species. Several presentations have been made at a Pelagic Fisheries Research Program Symposium (November 1995) in Honolulu, Hawaii and at the 46th and 47th Annual Tuna Conferences (May 1995 and 1996) in Lake Arrowhe
ad, California. Future work will emphasize publication of existing results, more sophisticated time series analysis, analysis of other pelagic species, and a survey of long-term fishermen to gain an understanding of the changes in operational characteri
stics of different fisheries sectors over time. Appropriate models for the analysis of these data will be developed and used to recommend suitable levels of fishing effort for these fisheries. Due to departure of some staff the project is taking longer
than expected to complete. Funds already received will support continuation of this project.
Table 1.--Suggested raising factors for HDAR longline catch data, by year and taxa, compared with raising factors derived from previous NMFS estimates of longline catch. When no ratio was calculated for the raising factor, raising factors based on other
taxa or combined taxa are suggested, as indicated by an asterisk. For species not listed, the factor for combined taxa is recommended. In some years raising factors were derived by interpolation between years as indicated by parentheses.
Year Bigeye Yellowfin Billfish Mahimahi Wahoo Combined taxa
This study NMFS
1970-78 1.19 1.19* 1.19* 1.19* 1.19* 1.19* 1.00
1979 1.72* 1.72* 1.68 2.41 1.76 1.72 2.76
1980 7.47* 7.47* 8.11 5.20 2.74 7.47 6.90
1981 11.46* 11.46* 11.85 9.81 4.60 11.46 10.50
1982 12.50 20.29 22.90 19.29 8.83 14.35 11.38
1983 5.28 5.46 8.46 6.37 6.02 5.81 5.68
1984 (6.17) (6.27) (7.84) (5.97) (5.74) (5.99) 7.92
1985 (7.10) (7.08) (7.22) (5.57) (5.47) (6.17) 9.00
1986 (7.96) (7.88) 6.60 5.17 5.19 6.35 5.28
1987 8.85 8.69 14.83 7.12 6.58 10.09 11.03
1988 9.42 5.94 7.22 6.21 7.36 9.04 8.86
1989 2.25 1.75 2.05 2.06 2.13 2.05 2.19
1990 1.26 1.11 1.31 1.14 1.48 1.25 1.38
1991 1.25 1.06 1.09 1.63 1.40 1.15 1.31
1992 1.34 1.04 1.00 1.62 1.24 1.10 1.41
Figure 1. Time series of yellowfin tuna CPDF by Hawaii handline fishery 1970-1992. (A) Shows all trips and trips stratified by reporting interval. (B) Shows all trips with monthly summary trips for 1978-79 deleted and monthly trips as a separate
Figure 2. Time series of yellowfin tuna CPDF by Hawaii handline and troll fisheries from 1970-92 with and without zero catch trip data.