Application of Geographical Information Science to the Northwest Pacific Albacore (Thunnus alalunga) Fishery: Biological and Physical Interactions.

Michael Thompson- Marine Resource Management, College of Oceanography and Atmospheric Sciences, Oregon State University

Abstract:

Albacore tuna (Thunnus alalunga) are a commercially important fish species which migrate annually from the waters of the Western Pacific to the waters off the U.S. Pacific Northwest coast. Despite their economic importance, little is known about the biological and physical interactions that take place during this migratory period. This project was designed to investigate the physical interactions between age class and sea-surface temperatures (SST) and biological interactions, including age class and lipid content relationships. Albacore that were collected in the summer of 2003 were found to be concentrated within a narrow range of SST, between 16˚ and 19.94˚ Celsius, and age class did not significantly correlate with SST (p=0.574). Some trends are present with regard to spatial interactions between age classes, however, more research needs to be done to determine if albacore schools are age specific. Lipid content does correlate with the date of capture (p<0.001) but does not show any spatial trend or relationship with size (p=0.818).

Figure 1: The locations of all albacore collected during the summer of 2003, including fish collected for handling and quality project and lipid analysis. Fish located north-west of Hawaii were collected during June and those off the Pacific coast were collected between July and October.

Introduction:

The albacore fishery is considered one of the last open-access fisheries in the U.S. and due to it’s highly migratory nature, little is actually known about the biological and physical interactions that occur within this species. On their migration across the Pacific they have been found to follow the transition zone between the warmer Central Pacific gyre waters and the cooler waters of the North Pacific as the warmer water moves north and east during the spring and summer (Bartoo and Foreman 1994, Polovina et. al 2001). During this time the U.S. surface troll fleet has access to the fishery and usually begin their annual season during June and continue to follow the fish along the West Coast through October, when they begin their return migration (Laurs and Dotson 1992). Beyond this limited information very little else is known about their behaviors and how they interact with the physical parameters of the ocean. Observations during the summer seemed to indicate that age classes tend to school together, since fish of approximately the same size were caught at the same times and locations. Expanding our knowledge through the use of Geographical Information Science (GIS) of this commercially important tuna, which is coming under increasing pressure due to the decline in other tuna species populations, is vital if we want to maintain healthy stocks and provide new tools for the international regulation of albacore (some work has been done with GIS on blue fin tuna by Schick 2002). In addition to providing new tools for fishery managers, including the use of GIS analytical techniques, GIS applications may provide new marketing opportunities for the fishing industry and allow, both fishery managers and the fishing industry, to work together in providing a sustainable level of harvest in the albacore fishery.

Figure 2: Albacore collected between August 24th and 31st of 2003 and their relation to SST. SST GeoTIFF was provided by MODIS Terra AVHRR satellite imagery in 8 day intervals with a 4 km resolution. Contours were added with ESRI spatial analysis tools.

Materials and Methods:

The 371 albacore used for this project were collected during the summer of 2003 for two separate experiments, a quality and handling experiment and a lipid quantification study. Fish used for the quality and handling study were collect by the researcher on-board the vessel and those used for the lipid study were collect by fishermen. ESRI ArcGIS 8.2 software was used to spatially analyze the data and S-PLUS 6.1 was used to conduct the statistical analysis. Geo-reference TIFF’s were obtained for SST through NASA (http://podaac-esip.jpl.nasa.gov/poet/) from MODIS Terra AVHRR satellite imagery in 8 day intervals with 4 km resolution.

SST was interpolated for individual days using the spatial analyst tools within ArcGIS in order to provide resolution higher than the 4 km satellite imagery. Spatial interpolation was also used for age class relationships. Individual days were used due to the highly migratory nature of albacore and the unlikelihood that they would stay within the same location on subsequent days. Radial Basis Functions were used to provide prediction fields for separate age classes.

Figure 3: Spatially interpolated age class layers, using mean age, showing some spatial relationships between fish of the same ages. A) fish caught on August 27, 2003; B) fish caught on August 26, 2003; C) fish caught on August 25, 2003. Fish of the same age class tend to be caught in similar locations but some variability can be seen, especially between different days.

Figure 7: Radial Basis Function showing prediction fields for age classes. However, due to the variability, predictions of age class specific harvest would be limited.

Results:

The results of the age class SST analysis show no correlation between age class and ocean surface temperatures (p=0.574) using a linear regression model. The spatial distribution of tuna of all age classes does not show any recognizable trend and no relation to strong ocean fronts was found to exist. Discrepancies were found between the SST values form the satellite imagery and those collected with in-situ devices on board the fishing vessels. Satellite derived SST’s were approximately 4 degrees below those recorded on the vessels, even though night-time SST data were used (which tend to reduce the variability of day-time SST due to backscattering and refraction of light).

Figure 4: Age class vs. SST fitted value plot showing no correlation between the age of the fish and the water temperature that they were caught in (using on-board temperature measurements).

Figure 5: Lipid percentage vs. Date of capture fitted value plot showing a positive correlation between higher fat content and later capture dates.

Figure 6: Trend analysis for age class and location using the geo-statistical analyst in ArcGIS 8.2. Fish of the same age tend to be caught at similar times and location, however, some variability does exist.

Spatial interpolation of age class fields did indicate the possibility that individual age classes did school together. Trends were observed on-board and can be seen in the trend plots for the individual days. Spatial interpolation and raster layers show that age classes tend to be caught in the same locations and linear regression analysis comparing age class and location was significant (p<0.01). However, the correlation was not high (R2=0.136). Lipid percentage did not show a relationship with location or age class, but was shown to be positively correlated with the date of capture (p<0.001).

Figure 8: Age class point features converted to raster with spatial analyst tools. From fish caught on August 25, 2003. Temporal dimension has not been taken into consideration so actual trends may not be recognizable. Age classes go from light (2 years) to dark (4 years).

Discussion:

Utilizing GIS tools to analyze the interactions of marine fish with their biological and physical environment can lead to a greater understanding of their behaviors and provide, both the fishing industry and fishery managers with a valuable tool for building sustainable fisheries around the world. The results of this project, although inconclusive, do provide some evidence that age classes tend to school together and that fish caught later in the season tend to have higher lipid concentrations. It also provides evidence that GIS applications can be used to investigate these and other questions about the interactions that fish have with their environments. Since many vessels already collect spatial information about their fishery operations, the use of this information can be beneficial to, not only the fishermen, but fishery managers by expanding the amount of data they have access to when making critical decisions about the health and composition of fish stocks.

References:Bartoo, N. and Foreman, T. 1994. A synopsis of the biology and

fisheries for North Pacific albacore tuna. In Interactions of Pacific Tuna Fisheries. R. Shomura, J. Majkowski and S. Langi, eds. Proceedings of the first FAO expert consultation on interactions pf Pacific tuna fisheries. 3-11 December 1991. Noumea, New Caledonia. FAO Fish. Tech. Paper. 336(2): 173-187.

Laurs, M. and Dotson, R. 1992. Albacore. In California’s living marine resources and their utilization. W.S. Leet, C.M. Dewees and C.W. Hauger, eds. Pp. 136-138.

Polovina, J., Howell, E., Kobayashi, D. and Seki, M. 2001. The transition zone chlorophyll front, a dynamic global feature defining migration and forage habitat for marine resources. Progress in Oceanography. 49: 469-483.

Schick, R. 2002. Using GIS to track whales and bluefin tuna in the Atlantic ocean. In Undersea with GIS. Dawn Wright, ed. ESRI Press, Redlands, CA. 2002.

Figure 9: Interpolated field for lipid percentage and spatial location showing no correlation between migration from west to east.