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Investigaciones marinas

versión On-line ISSN 0717-7178

Investig. mar. v.30 n.1 supl.Symp Valparaíso ago. 2002

http://dx.doi.org/10.4067/S0717-71782002030100071 

Spatial Analysis of the Yellowfin Tuna
(Thunnus albacares) Fishery, and its
Relation to El Niño and La Niña Events
in the Tropical Eastern Pacific

Juan Antonio de Anda-Montañez1,
Susana Martínez-Aguilar2,
Alberto Amador-Buenrostro3,
Adriana Muhlia-Almazán1

1Centro de Investigaciones Biológicas del Noroeste,
S.C. (CIBNOR), Apdo. Postal 128, La Paz, B.C.S.,
23000 México, Email: jdeanda@cibnor.mx
2Centro Regional de Investigación Pesquera, INP, km 1
Carr. Pichilingue, La Paz, B.C.S., México
3Centro de Investigaciones Científicas y Enseñanza
Superior de Ensenada (CICESE), Miraflores No. 334,
Fracc. Bellavista, La Paz, B.C.S., México

Objectives

This work evaluates the spatial catchability (q) distribution and CPUE of yellowfin tuna, related to temperature in the tropical eastern Pacific (TEP). The presence or absence of yellowfin tuna can be determined from sea surface temperature (SST). The objectives were therefore to conduct a spatial q analysis, and to identify any potential spatial-temporal patterns under extreme SST conditions.

Results & Discussion

Catchability (q)

The q spatial pattern found reveals that tuna are more vulnerable to fishing near the equator, mainly northwards, between 0° and 10°N. This pattern reflects a thermal preference for the warm water of the equatorial counter-current. Apparently, the q value gradient is strongly related to the thermal TEP gradient. Also associated with this spatial pattern is the high phyto- and zooplankton production prevailing throughout the year (Mann and Lazier, 1991).

It has been documented that tuna migratory patterns are highly variable across the TEP. However, catchability results show a spatial continuity in an approximate West-Northwest direction, practically along the Central America coasts and a portion of the Mexican Pacific. Results suggest that this q pattern has a primary role in the resource assessment. Nevertheless, this type of q estimate is not useful as input into simulation models built with a spatial pattern. So far, and based on information available, they can only be used as q indices showing a spatial trend or pattern with a close relation to the temperature gradient in the TEP.

The CPUE of this fishery has been, and still is, used to evaluate the yellowfin tuna population, under a linearity assumption. The q analysis and the relation to biomass does not reflect any linearity, which should be expected, given that the tuna stock has dynamic migratory movements, and includes shoals with high local densities. If the tuna resource and the fishing fleet have an heterogeneous spatial and temporal distribution, influenced by environmental variability, the assumption of a constant q should be reconsidered, with the aim of improving yield estimations and, eventually, achieving better resource management.

Inter and interannual variability of distribution and relative abundance

All four years analyzed showed a consistent spatial pattern. In the first and fourth quarters, the resource was located east of the 120°W meridion. In the second and third quarters, the resource abundance and fishing operations expanded westwards, reaching up to 150°W.

In this analysis, despite the wide variation of the relative abundance distribution, it was possible to identify high density patches south of Mexico and Central America, along the equator, between 0° and 15°N, and off the South American coast. These abundance patches were found in areas with positive temperature anomalies of up to 4°C, and negative anomalies of up to -5°C, approximately. This suggests a weak correlation, so that temperature is not the only factor limiting the yellowfin distribution. Low catch rates in areas with a similar temperature indicate that there are additional factors influencing the distribution pattern. Therefore, large scale factors such as oceanic fronts and equatorial upwellings, or regional effects such as the upwellings in the Gulf of Tehuantepec and the Costa Rica Dome, play a primary role in determining the tuna distribution patterns.

There are four areas with a high potential for finding yellowfin: the Mexican Pacific; Central America near the Costa Rica Dome; waters off the South America coasts, and mainly in the oceanic area along the equator, up to 15°N.

In Northwest Mexico it is possible to find tuna practically throughout the year, with the highest abundances in the second and third quarters. This area is influenced by the California Current, being enriched by the upwelling process which, on the Western coast of Baja California, is stronger in spring and summer (Mann and Lazier, 1991; Bakun, 1996). The higher tuna abundances coincide with the abundance peaks of red crab (Pleuroncodes planipes) reported by Aurioles-Gamboa (1995), which is the main food source in this area (Galván-Magaña, 1988). In the South of Mexico, fishing operations are also intense, with high tuna abundances (Ortega-García, 1998; Arenas et al., 1999; Hall at al., 1999).

The South of Mexico, particularly the Gulf of Tehuantepec, is characterized by a surface cooling process, due to the intense winter Northern winds (October - March) (Fiedler, 1994). In this study, the presence of cold surface waters, with a tongue-shaped pattern, was observed in the fourth quarter and, at higher intensity, during the first quarter. Such a phenomenon is caused by the presence of a shallow thermocline, allowing the wind-driven entrainment of nutrient-rich sub-surface waters (Trasviña et al., 1995). In summer, this zone is characterized by the presence of the Costa Rica Coastal Current, with temperatures above 27°C, (Ortega-García, 1998), and positive anomalies of up to 4°C. Such anomalies can be observed even in cold years, as found in this study, with values up to +3.5°C.

Another phenomenon found Southwards, helping to enrich the area, is the so-called Costa Rica Dome, which is an increase in the depth of the thermocline, caused by a change in the direction of the Equatorial Counter-Current, when joining the Costa Rica Coastal Current. The whole TEP, and particularly near the Gulf of Tehuantepec and the Costa Rica Dome, is characterized by a shallow thermocline (Cromwell, 1958; Trasviña et al., 1999), which obviously has a higher potential for surface fertilization. These processes play a crucial role for the presence of tuna in the area, in the four quarters of the year.

The other area with significant tuna abundances is located in South America, where coastal upwellings are responsible for high regional productivity, due to winds persisting practically throughout the year. The Southernmost distribution of tuna (15°S) is observed in the fourth and first quarters, reaching up to 20°S in El Niño year 1987, with a temperature higher than 24°C. Generally, this region is characterized by temperatures consistently below 24°C and, as a consequence, strong negative anomalies up to -6.5°C in a La Niña year.

Finally, the oceanic area along the equator, between 0° and 15°N, represents probably the most important region for tuna distribution, where the main currents cause upwelling and divergence of high volumes of nutrient-rich water from below the thermocline (Mann and Lazier, 1991; Vinogradov, 1981).

The influence of El Niño or La Niña events, increases or decreases the thermocline depth, which may affect the enriching TEP patterns. However, the tuna spatial-temporal distribution pattern seems not to be strongly affected by a moderate El Niño event. When El Niño is strong, as it was in 1982-83, the thermocline depth increased across the TEP and, as a consequence, productivity was significantly reduced. This had a strong impact on the tuna fishery, diminishing the fish vulnerability, and is documented on the distribution maps of the third and fourth quarter, 1982, which display the lowest catches in the TEP since the 50's (IATTC, 1998).

The geostatistical analysis of relative abundance showed, in general, that this population has a patched spatial structure, with a spatial-temporal variability. This spatial distribution feature can be explained by the SST variability prevailing in the TEP. Thus, the temperature can explain any persistent pattern observed in the maps.

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