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Performance of Longline Catchability Models in Assessments of Pacific Highly Migratory Species

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

The proposed study will concentrate on two aspects that largely affect longline catchability - the vertical distribution of hooks and the vertical distribution of a species catchability based on depth and habitat. Emphasis will be given to applying statistical models to the Japanese longline fishery given the long history (>50 yrs) of catch and effort data and the Hawaii-based longline fishery that can be analyzed at a variety of spatio-temporal scales and have additional information on gear configuration from logbook and scientific observer programs. The species of interest include tunas (bigeye, yellowfin and albacore), billfishes (blue and striped marlin, swordfish) and sharks (primarily blue shark).

The major objectives are:
1. Quantifying longline gear depth and variability
2. Reconsideration of environmental parameters that affect longline catchability
3. Improvements to the statHBS and public domain release
4. Depth and habitat catchability comparisons and model validations.

Objective 1. Quantifying longline gear depth and variability
Central to each standardization method is the vertical distribution of longline hooks. Longline maximum fishing depth is a key parameter allowing the estimation of the vertical distribution of longline hooks. However, for a given fishing operation, maximum fishing depth is variable amongst different baskets along the mainline. This variability is a primary consequence of ocean current effects. Thus, for a given fishing operation, maximum fishing depth corresponds to a distribution of depth values observed at the middle of the mainline between buoys. In this context, data from monitored longline experiments carried out in the central south Pacific within the ECOTAP project (1995-1999) will be considered to develop an empirical model for estimating maximum fishing depth distributions according to fishing tactic information and ocean currents from output of Ocean General Circulation Models.

Objective 2. Reconsideration of environmental parameters that affect longline catchability
Predicted fields of key variables (e.g. ambient temperature, oxygen, thermocline gradient, and deep scattering layer) will be produced and efforts will concentrate on several focus areas with extensive archival tag data (e.g. Coral Sea, Hawaii and eastern Pacific Ocean). Specific focus areas may lead to a better determination of environmental preferences or suggest different formulations for use in the statHBS or GLM standardizations. Ocean dynamics occur on a variety of spatial and temporal scales.
Analyses will be initially conducted with environmental data on spatial scales that correspond to fishery data such as 1 and 5 per month data.

Objective 3. Improvements to the statHBS and public domain release
The statHBS model currently exists as undocumented AD Model Builder code. The model will be re-written and improvements will include an interface with R software for increased diagnostic capabilities. Environmental data are required for the model and acquisition of such data could follow similar web-based protocols as implemented in the current Kalman filter geolocation package with SST (Nielsen et al. in press). A composite GLM and statHBS model is also envisaged whereby environmental information are incorporated into a GLM approach with covariates.

The gear component can be parameterized in the statHBS framework. The species composition of the catch has changed over time in the longline fishery due to gear changes and the abundance of individual species. One concept is to take a multispecies approach in the statHBS estimation process as vertical distribution in catchability differs among species. For example, gear and habitat or depth preferences could be estimated for species that have demonstrated niche differences, such as yellowfin (mixed layer, upper thermocline), albacore (upper thermocline) and bigeye tuna (lower thermocline). Gear attributes could be parameterized and bounded by the qualitative results from Objective 1. The habitat preference component of the model will be further developed to include covariates that may influence habitat preference (e.g. ocean bio-ecological area, fish size).

Objective 4. Depth and habitat catchability comparisons and model validations
Analytical comparisons are required to address the depth versus habitat argument. At least three methods of estimating catchability are envisaged: 1) habitat in the statHBS framework, 2) deterministic catchability with depth (coefficients from Ward and Myers 2005) and 3) statistically estimating catchability with depth (statHBS configured with depth instead of habitat). Values of the objective functions will be compared along with temporal and spatial residual analyses.

Longline monitoring studies can further address the depth versus habitat argument and may provide validation for a particular approach. Gear depth and shoaling were documented on 599 longline sets (266 tuna, 333 swordfish) in the Hawaii-based fishery (Bigelow et al. 2006). Sets were monitored with time-depth recorders (TDRs) and scientific observers recorded the hook number and size of each fish caught, consequently the actual vertical distribution is known for ~20,000 individuals representing 13 WPRFMC Pacific Pelagic Management Utilization Species. Models based on vertically distributing a species by depth or habitat could be compared with the detailed hook monitoring.

The robustness of various standardization methods could be assessed by applying standardization models to a simulated fish population. This approach was taken in the Atlantic Ocean by the ICCAT Working Group on Assessment Methods. A simulator was developed to generate Japanese catch and effort data for blue and white marlin from 1956 to 1995 (Goodyear 2003, 2005a). While the project is ongoing, recent results indicate that evaluations of GLM and deterministic habitat-based models using simulated longline data failed to identify a useful CPUE standardization methodology (Goodyear 2005b). This unsatisfactory result could be a consequence of problems with the simulator, the assumptions or data used in the simulations, or the standardization methods themselves.

This proposed project would consider simulations for the Pacific Ocean, but careful planning would be necessary because the methodological considerations are complex. The simulator code has been provided by P. Goodyear to two of the PIs should the project consider the use of model testing through a simulator or operational model.

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

Literature cited:

Bigelow, K., Musyl, M.K., Poisson, F., Kleiber, P., 2006. Pelagic longline gear depth and shoaling. Fish. Res. 77:173-183.
Goodyear, C.P., 2003. SEEPA - A data simulator for testing alternative longline CPUE standardization methods. SCRS/2003/032.
Goodyear, C.P., 2005a. Simulated Japanese longline CPUE for blue and white marlin. SCRS/2005/032.
Goodyear, C.P., 2005b. Performance diagnostics for the longline CPUE simulator. SCRS/2005/080.
Nielsen, A., Bigelow, K., Musyl, M., Sibert. J., In Press. Improving light based geo-location by including sea surface temperature. Fish. Oceangr.
Ward, P., and Myers, R.A. 2005. Inferring the depth distribution of catchability for pelagic fishes and correcting for variations in the depth of longline fishing gear. Can. J. Fish. Aquat. Sci. 62: 1130-1142.


Principal Investigators:
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

Dr. Mark Maunder
Inter-American Tropical Tuna Commission
8604 La Jolla Shores Drive
La Jolla, CA 92037-1508 USA
Phone (858) 546-7027
FAX (858) 546-7133
email: mmaunder@iattc.org

Mr. Adam Langley
Oceanic Fisheries Programme
Secretariat of the Pacific Community
98845 Noumea cedex
email: AdamL@spc.int

Dr. Pascal Bach
BP 171
34203 Sete cedex
email: Pascal.Bach@ird.fr

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This page updated October 4, 2010