Size composition, growth and sexual maturity of bigeye tuna, Thunnus obesus (Lowe), from the Japanese long-line fishery in the Eastern Pacific Ocean

Size composition data of bigeye tuna taken from the eastern tropical Pacific Ocean by Japanese Prefectural experimental training vessels from 1958 to 1964 are examined. A gradient of increasing fish size from east to west is noted. Males increase in ratio over females for the entire range of lengths examined, and beyond 170 cm comprise more than 75 per cent of the total. The first semester of the year is important as a bigeye spawning season. A general relationship between sexual maturity and thermal structure of the water is discussed. At the end of their 12th quarter of life bigeye are about 114 cm long, by the 16th quarter, 137 cm and at the end of 20 quarters, about 153 cm. The long-line fishery in the eastern Pacific has had a marked effect on the size composition of the stocks of bigeye, but whether the fishing has driven the stocks below a point which could afford a maximum sustainable yield could not be determined. (PDF contains 55 pages.)

Analysis of variance and standardization of longline hook rates of bigeye (Thunnus obesus) and yellowfin (Thunnus albacares) tunas in the Eastern Pacific Ocean during 1975-1987

ENGLISH: Longline hook rates of bigeye and yellowfin tunas in the eastern Pacific Ocean were standardized by maximum depth of fishing, area, and season, using generalized linear models (GLM's). The annual trends of the standardized hook rates differ from the unstandardized, and are more likely to represent the changes in abundance of tunas in the age groups most vulnerable to longliners in the fishing grounds. For both species all of the interactions in the GLM's involving years, depths of fishing, areas, and seasons were significant. This means that the annual trends in hook rates depend on which depths, areas, and seasons are being considered. The overall average hook rates for each were estimated by weighting each 5-degree quadrangle equally and each season by the number of months in it. Since the annual trends in hook rates for each fishing depth category are roughly the same for bigeye, total average annual hook rate estimates are possible with the GLM. For yellowfin, the situation is less clear because of a preponderance of empty cells in the model. The full models explained 55% of the variation in bigeye hook rate and 33% of that of yellowfin. SPANISH: Se estandardizaron las tasas de captura con palangre de atunes patudo y aleta amarilla en el Océano Pacífico oriental por la profunidad máxima de pesca, área, y temporada, usando modelos lineales generalizados (MLG). Las tendencias anuales de las tasas de captura estandardizadas son diferentes a las de las tasas no estandardizadas, y es más que representen los cambios en la abundancia de los atunes en los grupos de edad más vulnerables a los palangreros en las áreas de pesca. Para ambas especies fueron significativas todas las interacciones en los MLG con año, profundidad de pesca, área, y temporada. Esto significa que las tendencias anuales de las tasas de captura dependen de cuál profundidad, área, y temporado se está considerando. Para la estimación de la tasa de captura general media para cada especie se ponderó cada cuadrángulo de 5 grados igualmente y cada temporada por el número de meses que contiene. Ya que las tendencias anuales en las tasas de captura para cada categoría de profundidad de pesca son aproximadamente iguales para el patudo, son posibles estimaciones de la tasa de captura anual media total con el MLG. En el caso del aleta amarilla, la situación es más confusa, debido a una preponderancia de celdas vacías en el modelo. Los modelos completos explican el 55% de la variación de la tasa de captura de patudo y 33% de la del aleta amarilla. (PDF contains 19 pages.)

Some observations on bigeye tuna (Thunnus obesus) caught by the surface and longline fisheries for tunas in the Eastern Pacific Ocean

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Croissance du patudo (Thunnus obesus) de l'Océan Atlantique intertropical oriental

The growth of big eye tuna (Thunnus obesus) in the Eastern Tropical Atlantic Ocean was studied using Petersen's method, by the analysis of the length frequency data of the F.I.S. (French-Ivorian-Senegalese) surface tuna fleet from 1969 to 1977. The results are in agreement with a previous study by Champagnat and Pianet (1973) and with observations made in the Pacific Ocean.

The potential use of time-area closures to reduce catches of bigeye tuna (Thunnus obesus) in the purse-seine fishery of the eastern Pacific Ocean

Skipjack (Katsuwonus pelamis), yellowfin (Thunnus albacares), and bigeye (Thunnus obesus) tunas are caught by purse-seine vessels in the eastern Pacific Ocean (EPO). Although there is no evidence to indicate that current levels of fishing-induced mortality will affect the sustainability of skipjack or yellowfin tunas, fishing mortality on juvenile (younger than 5 years of age) bigeye tuna has increased, and overall fishing mortality is greater than that necessary to produce the maximum sustainable yield of this species. We investigated whether time-area closures have the potential to reduce purse-seine bigeye catches without significantly reducing skipjack catches. Using catch and effort data for 1995–2002, we identified regions where the ratio of bigeye to skipjack tuna catches was high and applied simple closed-area models to investigate the possible benefits of time-area closures. We estimated that the most optimistic and operationally feasible 3-month closures, covering the equatorial region of the EPO during the third quarter of the year, could reduce bigeye catches by 11.5%, while reducing skipjack tuna catches by 4.3%. Because this level of bigeye tuna catch reduction is insufficient to address sustainability concerns, and larger and longer closures would reduce catches of this species signficantly, we recommend that future research be directed toward gear technology solutions because these have been successful in many other fisheries. In particular, because over 50% of purse-seine catches of bigeye tuna are taken in sets in which bigeye tuna are the dominant species, methods to allow the determination of the species composition of aggregations around floating objects may be important.

Effects of El Niño–Southern Oscillation events on catches of Bigeye Tuna (Thunnus obesus) in the eastern Indian Ocean off Java

The effects of El Niño–Southern Oscillation events on catches of Bigeye Tuna (Thunnus obesus) in the eastern Indian Ocean (EIO) off Java were evaluated through the use of remotely sensed environmental data (sea-surface-height anomaly [SSHA], sea-surface temperature [SST], and chlorophyll a concentration), and Bigeye Tuna catch data. Analyses were conducted for the period of 1997–2000, which included the 1997–98 El Niño and 1999–2000 La Niña events. The empirical orthogonal function (EOF) was applied to examine oceanographic parameters quantitatively. The relationship of those parameters to variations in catch distribution of Bigeye Tuna was explored with a generalized additive model (GAM). The mean hook rate was 0.67 during El Niño and 0.44 during La Niña, and catches were high where SSHA ranged from –21 to 5 cm, SST ranged from 24°C to 27.5°C, and chlorophyll-a concentrations ranged from 0.04 to 0.16 mg m–3. The EOF analysis confirmed that the 1997–98 El Niño affected oceanographic conditions in the EIO off Java. The GAM results indicated that SST was better than the other environmental factors (SSHA and chlorophyll-a concentration) as an oceanographic predictor of Bigeye Tuna catches in the region. According to the GAM predictions, the highest probabilities (70–80%) for Bigeye Tuna catch in 1997–2000 occurred during oceanographic conditions during the 1997–98 El Niño event.

Dynamics of bigeye (Thunnus obesus) and yellowfin (T. albacares) tuna in Hawaii’s pelagic fisheries: analysis of tagging data with a bulk transfer model incorporating size-specific attrition

Tag release and recapture data of bigeye (Thunnus obesus) and yellowfin tuna (T. albacares) from the Hawaii Tuna Tagging Project (HTTP) were analyzed with a bulk transfer model incorporating size-specific attrition to infer population dynamics and transfer rates between various fishery components. For both species, the transfer rate estimates from the offshore handline fishery areas to the longline fishery area were higher than the estimates of transfer from those same areas into the inshore fishery areas. Natural and fishing mortality rates were estimated over three size classes: yellowfin 20–45, 46–55, and ≥56 cm and bigeye 29–55, 56–70, and ≥71 cm. For both species, the estimates of natural mortality were highest in the smallest size class. For bigeye tuna, the estimates decreased with increasing size and for yellowfin tuna there was a slight increase in the largest size class. In the Cross Seamount fishery, the fishing mortality rate of bigeye tuna was similar for all three size classes and represented roughly 12% of the gross attrition rate (includes fishing and natural mortality and emigration rates). For yellowfin tuna, fishing mortality ranged between 7% and 30%, the highest being in the medium size class. For both species, the overall attrition rate from the entire fishery area was nearly the same. However, in the specific case of the Cross Seamount fishery, the attrition rate for yellowfin tuna was roughly twice that for bigeye. This result indicates that bigeye tuna are more resident at the Seamount than yellowfin tuna, and larger bigeye tunas tend to reside longer than smaller individuals. This may result in larger fish being more vulnerable to capture in the Seamount fishery. The relatively low level of exchange between the Sea-mount and the inshore and longline fisheries suggests that the fishing activity at the Seamount need not be of great management concern for either species. However, given that the current exploitation rates are considered moderate (10–30%), and that Seamount aggregations of yellowfin and bigeye tuna are highly vulnerable to low-cost gear types, it is recommended that further increases in fishing effort for these species be monitored at Cross Seamount.

Movements, behavior, and habitat selection of bigeye tuna (Thunnus obesus) in the eastern equatorial Pacific, ascertained through archival tags

Ninety-six bigeye tuna (88– 134 cm fork length) were caught and released with implanted archival (electronic data storage) tags near fish-aggregating devices (FADs) in the equatorial eastern Pacific Ocean (EPO) during April 2000. Twenty-nine fish were recaptured, and the data from twenty-seven tags were successfully downloaded and processed. Time at liberty ranged from 8 to 446 days, and data for 23 fish at liberty for 30 days or more are presented. The accuracy in geolocation estimates, derived from the light level data, is about 2 degrees in latitude and 0.5 degrees in longitude in this region. The movement paths derived from the filtered geolocation estimates indicated that none of the fish traveled west of 110°W during the period between release and recapture. The null hypothesis that the movement path is random was rejected in 17 of the 22 statistical tests of the observed movement paths. The estimated mean velocity was 117 km/d. The fish exhibited occasional deep-diving behavior, and some dives exceeded 1000 m where temperatures were less than 3°C. Evaluations of timed depth records, resulted in the discrimination of three distinct behaviors: 54.3% of all days were classified as unassociated (with a floating object) type-1 behavior, 27.7% as unassociated type-2 behavior, and 18.7% as behavior associated with a floating object. The mean residence time at floating objects was 3.1 d. Data sets separated into day and night were used to evaluate diel differences in behavior and habitat selection. When the fish were exhibiting unassociated type-1 behavior (diel vertical migrations), they were mostly at depths of less than 50 m (within the mixed layer) throughout the night, and during the day between 200 and 300 m and 13° and 14°C. They shifted their average depths in conjunction with dawn and dusk events, presumably tracking the deep-scattering layer as a foraging strategy. There were also observed changes in the average nighttime depth distributions of the fish in relation to moon phase.

Catches per unit of effort of bigeye tuna: a new analysis with regression trees and simulated annealing

ENGLISH: We analyzed catches per unit of effort (CPUE) from the Japanese longline fishery for bigeye tuna (Thunnus obesus) in the central and eastern Pacific Ocean (EPO) with regression tree methods. Regression trees have not previously been used to estimate time series of abundance indices fronl CPUE data. The "optimally sized" tree had 139 parameters; year, month, latitude, and longitude interacted to affect bigeye CPUE. The trend in tree-based abundance indices for the EPO was similar to trends estimated from a generalized linear model and fronl an empirical model that combines oceanographic data with information on the distribution of fish relative to environmental conditions. The regression tree was more parsimonious and would be easier to implement than the other two nl0dels, but the tree provided no information about the nlechanisms that caused bigeye CPUEs to vary in time and space. Bigeye CPUEs increased sharply during the mid-1980's and were more variable at the northern and southern edges of the fishing grounds. Both of these results can be explained by changes in actual abundance and changes in catchability. Results from a regression tree that was fitted to a subset of the data indicated that, in the EPO, bigeye are about equally catchable with regular and deep longlines. This is not consistent with observations that bigeye are more abundant at depth and indicates that classification by gear type (regular or deep longline) may not provide a good measure of capture depth. Asimulated annealing algorithm was used to summarize the tree-based results by partitioning the fishing grounds into regions where trends in bigeye CPUE were similar. Simulated annealing can be useful for designing spatial strata in future sampling programs. SPANISH: Analizamos la captura por unidad de esfuerzo (CPUE) de la pesquería palangrera japonesa de atún patudo (Thunnus obesus) en el Océano Pacifico oriental (OPO) y central con métodos de árbol de regresión. Hasta ahora no se han usado árboles de regresión para estimar series de tiempo de índices de abundancia a partir de datos de CPUE. EI árbol de "tamaño optimo" tuvo 139 parámetros; ano, mes, latitud, y longitud interactuaron para afectar la CPUE de patudo. La tendencia en los índices de abundancia basados en árboles para el OPO fue similar a las tendencias estimadas con un modelo lineal generalizado y con un modelo empírico que combina datos oceanográficos con información sobre la distribución de los peces en relación con las condiciones ambientales. EI árbol de regresión fue mas parsimonioso y seria mas fácil de utilizar que los dos otros modelos, pero no proporciono información sobre los mecanismos que causaron que las CPUE de patudo valiaran en el tiempo y en el espacio. Las CPUE de patudo aumentaron notablemente a mediados de los anos 80 y fueron mas variables en los extremos norte y sur de la zona de pesca. Estos dos resultados pueden ser explicados por cambios en la abundancia real y cambios en la capturabilidad. Los resultados de un arbal de regresión ajustado a un subconjunto de los datos indican que, en el OPO, el patudo es igualmente capturable con palangres regulares y profundos. Esto no es consistente con observaciones de que el patudo abunda mas a profundidad e indica que clasificación por tipo de arte (palangre regular 0 profundo) podría no ser una buena medida de la profundidad de captura. Se uso un algoritmo de templado simulado para resumir los resultados basados en el árbol clasificando las zonas de pesca en zonas con tendencias similares en la CPUE de patudo. El templado simulado podría ser útil para diseñar estratos espaciales en programas futuros de muestreo. (PDF contains 45 pages.)

Estimating relative abundance from catch and effort data, using neural networks

We develop and test a method to estimate relative abundance from catch and effort data using neural networks. Most stock assessment models use time series of relative abundance as their major source of information on abundance levels. These time series of relative abundance are frequently derived from catch-per-unit-of-effort (CPUE) data, using general linearized models (GLMs). GLMs are used to attempt to remove variation in CPUE that is not related to the abundance of the population. However, GLMs are restricted in the types of relationships between the CPUE and the explanatory variables. An alternative approach is to use structural models based on scientific understanding to develop complex non-linear relationships between CPUE and the explanatory variables. Unfortunately, the scientific understanding required to develop these models may not be available. In contrast to structural models, neural networks uses the data to estimate the structure of the non-linear relationship between CPUE and the explanatory variables. Therefore neural networks may provide a better alternative when the structure of the relationship is uncertain. We use simulated data based on a habitat based-method to test the neural network approach and to compare it to the GLM approach. Cross validation and simulation tests show that the neural network performed better than nominal effort and the GLM approach. However, the improvement over GLMs is not substantial. We applied the neural network model to CPUE data for bigeye tuna (Thunnus obesus) in the Pacific Ocean.

An Evaluation of the area stratification used for sampling tunas in the eastern Pacific Ocean and implications for estimating total annual catches

The Inter-American Tropical Tuna Commission (IATTC) staff has been sampling the size distributions of tunas in the eastern Pacific Ocean (EPO) since 1954, and the species composition of the catches since 2000. The IATTC staff use the data from the species composition samples, in conjunction with observer and/or logbook data, and unloading data from the canneries to estimate the total annual catches of yellowfin (Thunnus albacares), skipjack (Katsuwonus pelamis), and bigeye (Thunnus obesus) tunas. These sample data are collected based on a stratified sampling design. I propose an update of the stratification of the EPO into more homogenous areas in order to reduce the variance in the estimates of the total annual catches and incorporate the geographical shifts resulting from the expansion of the floating-object fishery during the 1990s. The sampling model used by the IATTC is a stratified two-stage (cluster) random sampling design with first stage units varying (unequal) in size. The strata are month, area, and set type. Wells, the first cluster stage, are selected to be sampled only if all of the fish were caught in the same month, same area, and same set type. Fish, the second cluster stage, are sampled for lengths, and independently, for species composition of the catch. The EPO is divided into 13 sampling areas, which were defined in 1968, based on the catch distributions of yellowfin and skipjack tunas. This area stratification does not reflect the multi-species, multi-set-type fishery of today. In order to define more homogenous areas, I used agglomerative cluster analysis to look for groupings of the size data and the catch and effort data for 2000–2006. I plotted the results from both datasets against the IATTC Sampling Areas, and then created new areas. I also used the results of the cluster analysis to update the substitution scheme for strata with catch, but no sample. I then calculated the total annual catch (and variance) by species by stratifying the data into new Proposed Sampling Areas and compared the results to those reported by the IATTC. Results showed that re-stratifying the areas produced smaller variances of the catch estimates for some species in some years, but the results were not significant.

Résumé des connaissances actuelles sur la biologie et la pêche des thons tropicaux en Atlantique

This work summarizes the present knowledge on biology and fisheries of Atlantic tuna such as yellowfin (Thunnus albacares), skipjack (Katsuwonus pelamis) and to a lesser extent, bigeye (Thunnus obesus) and albacore (Thunnus alalunga). Current appraisal of these stocks

Effects of lunar cycle and fishing operations on longline-caught pelagic fish: fishing performance, capture time, and survival of fish

Commercial longline fishing data were analyzed and experiments were conducted with gear equipped with hook timers and timedepth recorders in the Réunion Island fishery (21°5ʹS lat., 53°28ʹE long.) to elucidate direct and indirect effects of the lunar cycle and other operational factors that affect catch rates, catch composition, fish behavior, capture time, and fish survival. Logbook data from 1998 through 2000, comprising 2009 sets, indicated that swordfish (Xiphias gladius) catch-per unit of effort (CPUE) increased during the first and last quarter of the lunar phase, whereas albacore (Thunnus alalunga) CPUE was highest during the full moon. Swordfish were caught rapidly after the longline was set and, like bigeye tuna (Thunnus obesus), they were caught during days characterized by a weak lunar illumination—mainly during low tide. We found a significant but very low influence of chemical lightsticks on CPUE and catch composition. At the time the longline was retrieved, six of the 11 species in the study had >40% survival. Hook timers indicated that only 8.4% of the swordfish were alive after 8 hours of capture, and two shark species (blue shark [Prionace glauca] and oceanic whitetip shark [Carcharhinus longimanus]) showed a greater resilience to capture: 29.3% and 23.5% were alive after 8 hours, respectively. Our results have implications for current fishing practices and we comment on the possibilities of modifying fishing strategies in order to reduce operational costs, bycatch, loss of target fish at sea, and detrimental impacts on the environment.

Increases in the relative abundance of mid-trophic level fishes concurrent with declines in apex predators in the subtropical North Pacific, 1996–2006

Catch rates for the 13 most abundant species caught in the deep-set Hawaii-based longline fishery over the past decade (1996–2006) provide evidence of a change among the top North Pacific subtropical predators. Catch rates for apex predators such as blue shark (Prionace glauca), bigeye (Thunnus obesus) and albacore (Thunnus alalunga) tunas, shortbill spearfish (Tetrapturus angustirostris), and striped marlin (Tetrapturus audax) declined by 3% to 9% per year and catch rates for four midtrophic species, mahimahi (Coryphaena hippurus), sickle pomfret (Taractichthys steindachneri), escolar (Lepidocybium flavobrunneum), and snake mackerel (Gempylus serpens), increased by 6% to 18% per year. The mean trophic level of the catch for these 13 species declined 5%, from 3.85 to 3.66. A shift in the ecosystem to an increase in midtrophic-level, fast-growing and short-lived species is indicated by the decline in apex predators in the catch (from 70% to 40%) and the increase in species with production to biomass values of 1.0 or larger in the catch (from 20% to 40%). This altered ecosystem may exhibit more temporal variation in response to climate variability.

Age and growth of three species of Lake Victoria fish determined by means of otolith daily growth rings

The sagittal otoliths of Lates niloticus, Haplochromis obesus, and Oreochromis niloticus from Lake Victoria were examined for daily growth rings using scanning electron microscopy. In the three species the increments were clear and thick enough to allow future studies with light microscopy. The daily nature of the increments seems supported by the rhythmic growth that were found.