An ecosystem-driven model for spatial dynamics and stock assessment of North Atlantic albacore

Abstract

An application of the Spatial Ecosystem And POpulation DYnamics Model (SEAPODYM) is developed for the North Atlantic albacore (Thunnus alalunga) population. We investigate the spatiotemporal dynamics of this species, distinguishing the influences of environment and international fishing. Incorporating historical data (1960–2008), a maximum likelihood approach allows the estimation of biological parameters (thermal and oxygen tolerance) and stock spatial distribution varying over time. Juvenile albacore are predicted in warm surface waters, whereas adults inhabit cooler and deeper waters. Positive correlations between juveniles and tropical large-scale climate indices highlight the importance of environmental drivers when estimating stock recruitment biology and spatiotemporal distribution. A methodology is proposed to use SEAPODYM outputs to estimate stock abundance and maximum sustainable yield (MSY). MSY is computed taking into account the spatial dynamics of the species and the environmental variability and is based on a mechanistic modelling of larval recruitment. MSY estimates converge towards an asymptotic value (15 997 t) of the same magnitude than standard stock assessment estimates conducted for the international tuna commission. In agreement with all assessment studies, the stock status is estimated from overfished in the 1990s to recovered in the 2000s. Our results show that the stock recovery results both from fishing actions, including total allowable catches established in the 2000s, and from the beginning of a North Atlantic Oscillation warm phase, leading to more favourable recruitment conditions. Following a parsimonious ecosystemic approach, SEAPODYM offers a faithful and spatially dynamic modelling framework that now includes direct tools for spatialized management advice and for distinction between environmental and fishing effects.

Environmental Conditions Are Important Influences on the Recruitment of North Pacific Albacore Tuna, Thunnus Alalunga

“…10). Dragon et al, (2015) identified that the maximum spawning for the North Atlantic albacore tuna occurs at SST of 25.77ºC with a standard error of 1.74ºC. With reference to Fig.…”

“…This gives some indication on the possible mechanisms on how oceanic temperature affects albacore tuna stock abundance and distribution. Dragon et al (2015) used the Spatial Ecosystem and Population Dynamics Model (SEAPODYM) and the maximum likelihood approach to understand the spatial distribution, ecological characteristics and migratory behavior of the North Atlantic albacore tuna in relation to alterations in the environmental patterns. Spawning favorability and foraging behavior of adult fish were shown to be correlated to different oceanic temperature gradients.…”

Modelling South Pacific jack mackerel spatial population dynamics and fisheries

“…The contribution to the negative log‐likelihood function from (i) the discrepancy between observed and predicted CPUE, and from (ii) the discrepancy between observed and predicted proportions of catch at size in the sample data is given by the formulation in Dragon et al. (). This model and its applications have been described in various publications, e.g., Dragon et al.…”

“…This model and its applications have been described in various publications, e.g., Dragon et al. (), Lehodey et al. (, , ), Lehodey, Senina, Calmettes, Hampton, and Nicol (), Senina et al.…”

“…estimates of standard deviation and correlation table were deduced from the Hessian matrix. The contribution to the negative log-likelihood function from (i) the discrepancy between observed and predicted CPUE, and from (ii) the discrepancy between observed and predicted proportions of catch at size in the sample data is given by the formulation in Dragon et al (2015). This model and its applications have been described in various publications, e.g., Dragon et al (2015), Lehodey et al (2008Lehodey et al ( , 2010, Lehodey, Senina, Calmettes, Hampton, and Nicol (2013), Senina et al (2008), Lehodey and Senina (2009) and Sibert, Senina, Lehodey, and Hampton (2012).…”