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

Abstract

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.

Development of integrated habitat indices for bigeye tuna, Thunnus obesus, in waters near Palau

“…The present study used data collected from 77 sampling stations and we considered four environmental variables to develop the IHI, and the weighted average temperature and dissolved oxygen were included in the CPÛ E i prediction model. We suggest that temperature and dissolved oxygen were the key variables influencing the T. obesus distribution (Brill 1994;Bertrand et al 2002;Prince and Goodyear 2006). Otherwise, other variables, such as the depth of the thermocline (Holland et al 1992), the abundance of micronekton (Bertrand et al 2002), deep scattering layers (DSL) (Dagorn et al 2000;Musyl et al 2003) and light levels at depth (Dagorn et al 2000) may influence the behaviour of T. obesus.…”

“…This results from different biological and/or ecological needs of T. obesus to inhabit different depth strata (Song and Zhou 2010), such as e.g. making regular upward excursions (Holland et al 1992;Dagorn et al 2000) to increase muscle temperature (Holland et al 1992) and to withstand low ambient oxygen levels (Lowe et al 2000;Malte et al 2007), or following the organisms and exploiting them as a food resource (Holland et al 1990;Bertrand et al 2002). In three depth strata (i.e.…”

“…Dissolved oxygen was included in the model because the ambient oxygen concentrations were related to T. obesus body temperature (Lowe et al 2000). Moreover, because temperature and dissolved oxygen were highly correlated (Prince and Goodyear 2006) and strong dissolved-oxygen gradients caused the relative abundance of T. obesus to appear higher than in areas with weak oxyclines (Bertrand et al 2002), we suggested to use the weighted average temperature, and dissolved oxygen for the CPUE standardisations to estimate the relative abundance of T. obesus. In the CPUE standardisation and stock assessment, if the basic biological, physiological and oceanographic data are ignored, the results will not differentiate changes in stock size from the changes of environmental variables or fishing capacity, and the fishery management measures would be less effective (Brill 1994).…”

“…Many studies have focussed on the influences of environmental variables, such as temperature, salinity, Chlorophyll a, dissolved oxygen and the abundance of micronekton on the distribution and movement of T. obesus (Dagorn et al 2000;Bertrand et al 2002;Evans et al 2008). Simultaneous analysis of several environmental variables can improve the understanding of their effects on the distribution and movement of tunas (Song et al 2008(Song et al , 2009.…”

Determination of Potential Fishing Grounds of Rastrelliger kanagurta Using Satellite Remote Sensing and GIS Technique

“…Chl-a is known as a very important oceanographic parameter in determining the productivity of the ocean. Chl-a pigment is a convenient index of phytoplankton biomass (Solanki et al 2001) and it could be related to fish production (Bertrand et al 2002). SST is assumed to be an index of the physical environment, which controls the physiology of the living organisms (Solanki et al 2005b) and influences phytoplankton growth (Tang et al 2003).…”