Tuna Habitat Research Articles

Estimation of yellowfin tuna (Thunnus albacares) habitat in waters adjacent to Australia’s East Coast: making the most of commercial catch data

The physical environment directly influences the distribution, abundance, physiology and phenology of marine species. Relating species presence to physical ocean characteristics to determine habitat associations is fundamental to the management of marine species. However, direct observation of highly mobile animals in the open ocean, such as tunas and billfish, is challenging and expensive. As a result, detailed data on habitat preferences using electronic tags have only been collected for the large iconic, valuable or endangered species. An alternative is to use commercial fishery catch data matched with historical ocean data to infer habitat associations. Using catch information from an Australian longline fishery and Bayesian hierarchical models, we investigate the influence of environmental variables on the catch distribution of yellowfin tuna (Thunnus albacares). The focus was to understand the relative importance of space, time and ocean conditions on the catch of this pelagic predator. We found that pelagic regions with elevated eddy kinetic energy, a shallow surface mixed layer and relatively high concentrations of chlorophyll a are all associated with high yellowfin tuna catch in the Tasman Sea. The time and space information incorporated in the analysis, while important, were less informative than oceanic variables in explaining catch. An inspection of model prediction errors identified clumping of errors at margins of ocean features, such as eddies and frontal features, which indicate that these models could be improved by including representations of dynamic ocean processes which affect the catch of yellowfin tuna.

Seasonal forecasting of tuna habitat for dynamic spatial management

Capture of the target, bycatch, and protected species in fisheries is often regulated through spatial measures that partition fishing effort, including areal closures. In eastern Australian waters, southern bluefin tuna (SBT, Thunnus maccoyii ) are a quota-limited species in a multispecies longline fishery; minimizing capture by nonquota holders is an important management concern. A habitat preference model (conditioned with electronic tag data) coupled with ocean reanalysis data has been used since 2003 to generate real-time predicted maps of SBT distribution (nowcasts). These maps are used by fishery managers to restrict fisher access to areas with high predicted SBT distribution. Here we use the coupled ocean–atmosphere model, POAMA (predictive ocean atmosphere model for Australia), and a habitat model to forecast SBT distribution at lead times of up to 4 months. These forecasts are comparable with nowcasts derived from the operational system, and show skill in predicting SBT habitat boundaries out to lead-times of 3–4 months. For this fishery, seasonal forecasts can provide managers and fishers with valuable insights into future habitat distributions for the upcoming months, to better inform operational decisions.

Spatio-temporal distributions of tuna species and potential habitats in the Western and Central Pacific Ocean derived from multi-satellite data

In this study, potential tuna habitats in the Western and Central Pacific Ocean (WCPO) area from 1998 to 2005 were identified using a combination of multi-satellite oceanographic data related to sea-surface temperature (SST), chlorophyll-a concentration (Chl-a) and sea-surface height anomaly (SSHA). Histogram analyses of tuna catch per unit effort (CPUE) by the Taiwanese purse seine fishery in relation to the satellite oceanographic data were used to determine tuna's preferred ranges of the three satellite-derived oceanographic parameters. The highest CPUE corresponded to areas where SST ranged from 29.0–29.5°C, Chl-a ranged from 0.0–0.1 mg m−3 and SSHA ranged from 0.0–5.0 cm. Local areas within the WCPO with similar satellite-derived oceanographic parameters were assumed to be potential tuna habitat, and a spatially explicit model was developed to determine their locations. It was found that the potential tuna habitats were distributed in warm pools, in the vicinity of the western cold tongue in the equatorial Pacific. Consequently, by using these potential habitats, tuna-fishery management and protection of habitats of tuna species can be improved, and these can possibly form the basis of fishing-ground forecasting in the future.

Vertical movements, behavior, and habitat of bigeye tuna (Thunnus obesus) in the equatorial eastern Pacific Ocean, ascertained from archival tag data

The results presented in this report are based on analyses of 16,721 days of data downloaded from 96 archival tags recovered from bigeye tuna (Thunnus obesus; 54–159 cm in length, 0.97–5.44 years of age) at liberty from 31 to 1,508 days in the equatorial eastern Pacific Ocean. Analyses of daily timed depth and temperature records resulted in the classification of the data into three daily behavior types: characteristic, associative (associated with floating objects), and other. There is a significant positive correlation between the proportion of time fish exhibit characteristic behavior and increasing length, and significant negative correlations between the proportion of time bigeye exhibit associative and other behavior with increasing length. For the smallest (54–80 cm) to largest (100–159 cm) length classes, the vertical habitats utilized when exhibiting non-associative behaviors were 99 and 98% of the time above the thermocline depth (60 m) during the night, at the same average depth of 34 m, and 60 and 72% of the time below the thermocline during the day at average depths of 163 and 183 m, respectively. For the same smallest to largest length classes, when exhibiting associative behavior, the average nighttime and daytime depths were 25 and 21, and 33 and 37 m, respectively. The apparent effects of the environment on the behavior of the fish are discussed.

Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective

AbstractSkipjack tuna habitat in the western North Pacific was studied from satellite remotely sensed environment and catch data, using generalized additive models and geographic information systems. Weekly resolved remotely sensed sea surface temperature, surface chlorophyll, sea surface height anomalies and eddy kinetic energy data were used for the year 2004. Fifteen generalized additive models were constructed with skipjack catch per unit effort as a response variable, and sea surface temperature, sea surface height anomalies and eddy kinetic energy as model covariates to assess the effect of environment on catch per unit effort (skipjack tuna abundance). Model selection was based on significance of model terms, reduction in Akaike’s Information Criterion, and increase in cumulative deviance explained. The model selected was used to predict skipjack tuna catch per unit effort using monthly resolved environmental data for assessing model performance and to visualize the basin scale distribution of skipjack tuna habitat. Predicted values were validated using a linear model. Based on the four‐parameter model, skipjack tuna habitat selection was significantly (P < 0.01) influenced by sea surface temperatures ranging from 20.5 to 26°C, relatively oligotrophic waters (surface chlorophyll 0.08–0.18, 0.22–0.27 and 0.3–0.37 mg m−3), zero to positive anomalies (surface height anomalies 0–50 cm), and low to moderate eddy kinetic energy (0–200 and 700–2500 cm2 s–2). Predicted catch per unit effort showed a trend consistent with the north–south migration of skipjack tuna. Validation of predicted catch per unit effort with that observed, pooled monthly, was significant (P < 0.01, r2 = 0.64). Sea surface temperature explained the highest deviance in generalized additive models and was therefore considered the best habitat predictor.

Locating tuna forage ground through satellite remote sensing

A technique has been developed to assess tuna habitat using satellite derived ocean colour, water transparency and sea surface temperature. The scientific approach is based on the food and feeding habit and the preferential temperature range of tuna. Time series chlorophyll-a images of Indian Remote Sensing Satellite P4-Ocean Colour Monitor (IRS P4-OCM) have been analysed to define the critical phytoplankton patch size, its persistence and the optimal water transparency depth. Sea surface temperature (SST) data derived from National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) have been analysed to study the optimum temperature range in the surrounding waters. Results based on hindcasting indicate that the tuna forage ground derived from satellite data yielded high catch of tuna (>2% hooking rate). Study reveals that a minimum time delay of 5–7 days is required for a phytoplankton patch to mature to the forage ground. In addition, water transparency for facilitating sight feeding should be about 25–30 m depth. The thermal limit and preferential temperature range observed is 26–30°C and 26–28°C, respectively. Preliminary results based on limited validation are highly promising. However, an experimental forecast is being planned to validate this approach.

Environmental preferences of longlining for yellowfin tuna (Thunnus albacares) in the tropical high seas of the Indian Ocean

AbstractA survey of yellowfin tuna, Thunnus albacares, fishing ground was carried out on board of the Chinese longliners from September 15 to December 12, 2005 in the tropical high seas of the Indian Ocean. The depth at which each yellowfin tuna was hooked was estimated using a stepwise regression analysis of theoretical hook depth and observed average hook depth measured using a temperature depth recorder. Water temperature, salinity, chlorophyll a, dissolved oxygen and thermocline, which are important variables influencing yellowfin tuna habitats, were measured in the survey. Catch rates of yellowfin tuna were then analyzed with respect to depth, temperature, salinity, chlorophyll a, dissolved oxygen and thermocline. We suggest that the optimum ranges of swimming depth, water temperature, chlorophyll a and dissolved oxygen concentration for yellowfin tuna are 100.0–179.9 m, 15.0–17.9°C, 0.090–0.099 μg L−1, 2.50–2.99 mg L−1, respectively; that salinity has less influence on the vertical distribution of adult yellowfin tuna; and that yellowfin tuna are mainly distributed within the thermocline in the high seas of the Indian Ocean. Our results match the yellowfin tuna’s vulnerability to deep longline fishing gear well.

Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data

AbstractSatellite‐based oceanographic data of sea surface temperature (SST), sea surface chlorophyll‐a concentration (SSC), and sea surface height anomaly (SSHA) together with catch data were used to investigate the relationship between albacore fishing ground and oceanographic conditions and also to predict potential habitats for albacore in the western North Pacific Ocean. Empirical cumulative distribution function and high catch data analyses were used to calculate preferred ranges of the three oceanographic conditions. Results indicate that highest catch per unit efforts (CPUEs) corresponded with areas of SST 18.5–21.5°C, SSC 0.2–0.4 mg m−3, and SSHA −5.0 to 32.2 cm during the winter in the period 1998–2000. We used these ranges to generate a simple prediction map for detecting potential fishing grounds. Statistically, to predict spatial patterns of potential albacore habitats, we applied a combined generalized additive model (GAM) / generalized linear model (GLM). To build our model, we first constructed a GAM as an exploratory tool to identify the functional relationships between the environmental variables and CPUE; we then made parameters out of these relationships using the GLM to generate a robust prediction tool. The areas of highest CPUEs predicted by the models were consistent with the potential habitats on the simple prediction map and observation data, suggesting that the dynamics of ocean eddies (November 1998 and 2000) and fronts (November 1999) may account for the spatial patterns of highest albacore catch rates predicted in the study area. The results also suggest that multispectrum satellite data can provide useful information to characterize and predict potential tuna habitats.

Hydrological and trophic characteristics of tuna habitat: consequences on tuna distribution and longline catchability

We studied relationships between tropical tunas (albacore (Thunnus alalunga), bigeye (Thunnus obesus), and yellowfin (Thunnus albacares)) and their biotic and abiotic environments through simultaneous acoustic observations of tunas and their prey, experimental longline catch, and oceanographic data in French Polynesia. Vertical habitat limits were estimated based on temperature and dissolved oxygen at capture data. We then studied tuna-micronekton relationships to better understand how tuna occupy the pelagic space. At a regional scale, tunas were more abundant in areas rich in prey with favourable hydrological conditions. Inside such areas, at the scale of a longline set, however, the longline catches were maximal only when prey were not distributed in dense patches (except for yellowfin tuna). We interpreted this result by considering that areas with high prey abundance attract tunas, but at a small scale, if prey are patchy distributed, tunas are more inclined to feed on them rather than on longline baits. The effect of patches on yellowfin tuna catch per unit effort (CPUE) does not appear likely because this species also feeds on the mixed layer, where patch density was very low. Not only hydrological characteristics, but also prey density and prey patch characteristics, should be taken into account for interpreting longline CPUE data.

Acoustic estimation of longline tuna abundance

The French Polynesia Economic Exclusive Zone is located in an important longline fishing ground for albacore (Thunnus alalunga), yellowfin (T. albacares) and bigeye tuna (T. obesus). Longline tuna abundance estimates using commercial catches are particularly biased when hook depth does not coincide with the depths at which tuna prefer to swim. To avoid catchability problems, a direct acoustic estimate of tuna abundance was made in the French Polynesia EEZ using a 38 kHz echo-sounder with a depth range of 500 m. Several biases can influence individual tuna target selection, such as the threshold effect, the risk of multiple target acceptance, the beam width effect and the reduction in target detection at depth. However, they all appeared to be limited in effect. Comparison with experimental longline catches shows that the acoustically selected targets appear to be representative of longline tuna distribution. A density of 1.33 fish per km2, i.e. about 33.8 kg of tuna per km2, was measured. Such a density is slightly greater than the estimate based on tuna catches, as the whole tuna habitat range is not sampled by most professional longlining.

Effects of Exhausting Exercise on Acid-Base Regulation in Skipjack Tuna (Katsuwonus pelamis) Blood

The effects of exhausting exercise on acid-base balance of skipjack tuna blood were investigated. Following exercise, tuna displayed a mixed respiratory/metabolic acidosis with blood pH being reduced by ∼0.4 units. The respiratory component (51% of the initial acidosis) was compensated following 20 min of recovery, while the blood metabolic acid load (H⁺m; ∼8 mM) was cleared after only 50 min. At that time, there was a great discrepancy between blood lactate load and H⁺m load because blood lactate levels were still increasing. The significance of these results is discussed with reference to the tuna's habitat, behavior, and physiology.