-Yellowfin tuna (Thunnus albacares) are known to preferentially occupy the surface mixed layer above the thermocline and it has been suggested that they are physiologically restricted to water temperatures no more than 8• C colder than surface waters. However, we here report for dive data acquired from a large yellowfin tuna which demonstrate for the first time that this species is indeed capable of making prolonged dives into deep cold waters. A yellowfin tuna (134 cm fork length) caught near an anchored fish aggregating device (FAD) in the Seychelles (Western Indian Ocean) was equipped with an internally implanted archival tag and released. The fish was recaptured 98 days later. As predicted for this species, this fish spent 85% of its time shallower than 75 m (maximum thermocline depth experienced by the fish) but, over the course of the track, it performed three deep dives to 578 m, 982 m and 1160 m. Minimum ambient water temperatures recorded at these depths were 8.6• C, 7.4 • C and 5.8• C respectively and varied by up to 23.3• C from surface temperatures. The fish spent 8.3% of its time in waters more than 8 • C colder than the surface layer and daily experienced a wide range of sea temperatures (mode at 15−16• C) and of temperatures of the gut cavity (mode at 6• C). The reason for these dives can not be known. These depths and temperatures significantly exceed those reported in the literature so far and clearly demonstrate that this species has the physiological and behavioral ability to penetrate deep cold sections of the ocean.

No abstract

The Indian Ocean Tuna Tagging Program (IOTTP) provided a unique opportunity to assess the viability of estimating the age of tropical tunas from the micro-structural features of otoliths. Here, we analyzed the length measurements and micro-increment counts collected for 506 sagittal otoliths, of which 343 were chemically marked with oxytetracycline, for bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis), and yellowfin tuna (Thunnus albacares). Our results show that the otoliths of tropical tunas grow more slowly than the rest of the body. Our findings confirm that both yellowfin and juvenile bigeye deposit daily increments in their otoliths, though ages are underestimated for large bigeye (>100 cm) when derived from micro-increment counts. Our results also indicate that skipjack otoliths are not suitable for age estimations during the adult phase, as evidenced by the poor agreement between micro-increment counts and days-at-liberty. We hypothesize that the income breeding strategy of skipjack could explain the variability observed in the deposition rates. Due to their complex microstructural patterns, the reading of tropical tuna otoliths requires a degree of interpretation that can result in poor count precision and large variability in micro-increment counts, both among and within teams of readers. Age estimates were found to vary between readers, a factor which can eventually affect growth estimates and ultimately, impact on fisheries management decisions and outcomes. To address this, we recommend that reference collections of otoliths are developed, with a view to standardizing the reading process. Further, alternative methods, such as annual age estimations (as opposed to daily), and alternative structures, such as dorsal spines for skipjack, should be used to improve the accuracy of age estimations and the speed with which they can be made. Highlights► We confirm that yellowfin and juvenile bigeye deposit daily micro-increments in otoliths. ► Our results support previous findings that deposition rates in adult skipjack are highly variable. ► Age estimates are characterized by very low precision and strongly dependent on the reader. ► Variability in otolith reading can affect growth estimates. ►Reference collections are required to standardize the reading of tuna micro-increments.

Age estimates, typically determined by counting periodic growth increments in calcified structures of vertebrates, are the basis of population dynamics models used for managing exploited or threatened species. In fisheries research, the use of otolith growth rings as an indicator of fish age has increased considerably in recent decades. However, otolith readings include various sources of uncertainty. Current ageing methods, which converts an average count of rings into age, only provide periodic age estimates in which the range of uncertainty is fully ignored. In this study, we describe a hierarchical model for estimating individual ages from repeated otolith readings. The model was developed within a Bayesian framework to explicitly represent the sources of uncertainty associated with age estimation, to allow for individual variations and to include knowledge on parameters from expertise. The performance of the proposed model was examined through simulations, and then it was coupled to a two-stanza somatic growth model to evaluate the impact of the age estimation method on the age composition of commercial fisheries catches. We illustrate our approach using the saggital otoliths of yellowfin tuna of the Indian Ocean collected through large-scale mark-recapture experiments. The simulation performance suggested that the ageing error model was able to estimate the ageing biases and provide accurate age estimates, regardless of the age of the fish. Coupled with the growth model, this approach appeared suitable for modeling the growth of Indian Ocean yellowfin and is consistent with findings of previous studies. The simulations showed that the choice of the ageing method can strongly affect growth estimates with subsequent implications for age-structured data used as inputs for population models. Finally, our modeling approach revealed particularly useful to reflect uncertainty around age estimates into the process of growth estimation and it can be applied to any study relying on age estimation.

The Indian Ocean Tuna Tagging Program provided a unique opportunity to collect demographic data on the key commercially targeted tropical tuna species in the Indian Ocean. In this paper, we focused on estimating growth rates for one of these species, yellowfin (Thunnus albacares). Whilst most growth studies only draw on one data source, in this study we use a range of data sources: individual growth rates derived from yellowfin that were tagged and recaptured, direct age estimates obtained through otolith readings, and length-frequency data collected from the purse seine fishery between 2000 and 2010. To combine these data sources, we used an integrated Bayesian model that allowed us to account for the process and measurement errors associated with each data set. Our results indicate that the gradual addition of each data type improved the model's parameter estimations. The Bayesian framework was useful, as it allowed us to account for uncertainties associated with age estimates and to provide additional information on some parameters (e.g., asymptotic length). Our results support the existence of a complex growth pattern for Indian Ocean yellowfin, with two distinct growth phases between the immature and mature life stages. Such complex growth patterns, however, require additional information on absolute age of fish and transition rates between growth stanzas. This type of information is not available from the data. We suggest that bioenergetic models may address this current data gap. This modeling approach explicitly considers the allocation of metabolic energy in tuna and may offer a way to understand the underlying mechanisms that drive the observed growth patterns