Connectivity and population structure of albacore tuna across southeast Atlantic and southwest Indian Oceans inferred from multidisciplinary methodology

Abstract

Albacore tuna (Thunnus alalunga) is an important target of tuna fisheries in the Atlantic and Indian Oceans. The commercial catch of albacore is the highest globally among all temperate tuna species, contributing around 6% in weight to global tuna catches over the last decade. The accurate assessment and management of this heavily exploited resource requires a robust understanding of the species’ biology and of the pattern of connectivity among oceanic regions, yet Indian Ocean albacore population dynamics remain poorly understood and its level of connectivity with the Atlantic Ocean population is uncertain. We analysed morphometrics and genetics of albacore (n = 1,874) in the southwest Indian (SWIO) and southeast Atlantic (SEAO) Oceans to investigate the connectivity and population structure. Furthermore, we examined the species’ dispersal potential by modelling particle drift through major oceanographic features. Males appear larger than females, except in South African waters, yet the length–weight relationship only showed significant male–female difference in one region (east of Madagascar and Reunion waters). The present study produced a genetic differentiation between the southeast Atlantic and southwest Indian Oceans, supporting their demographic independence. The particle drift models suggested dispersal potential of early life stages from SWIO to SEAO and adult or sub-adult migration from SEAO to SWIO.

Stepping up to genome scan allows stock differentiation in the worldwide distributed blue shark Prionace glauca

“…Tagging data on yellowfin tuna revealed the migration of individuals from the western equatorial Indian Ocean to the southern Benguela region off western South Africa (Eveson et al, 2015; Murua et al, 2015). Previous studies on cosmopolitan large pelagic sharks in particular (e.g., Bester‐van der Merwe et al, 2017; Portnoy et al, 2010), and other cosmopolitan large pelagic bony fishes in general (e.g., Mullins & Shaw, 2018; Nikolic et al 2020b), also found similar population structure patterns and pointed to the potential role of the South African region in connecting populations from the Atlantic and Indian oceans.…”

“…Off the southern tip of Africa, the Agulhas current retroflects eastward into the Indian Ocean. This retroflection takes the form of an unstable jet that can shed warm eddies into the Atlantic Ocean (Nikolic et al 2020b). Studies on bigeye tuna reported a lack of a clear physical barrier preventing mutual exchange between the Atlantic and Indo‐West Pacific regions and the co‐occurrence of two separate populations off the southern tip of Africa (Chow et al, 2000; Durand et al, 2005).…”

Otolith chemical fingerprints of skipjack tuna (Katsuwonus pelamis) in the Indian Ocean: First insights into stock structure delineation

“…otolith microchemistry, genetics, biophysical models…), proved to be more effective to fully understand the spatial ecology of highly migratory fish species, and, hence increased the resolving power of stock discrimination [ 69 – 71 ]. For instance, including passive drift trajectory simulations may also be helpful to understand potential patterns of skipjack larval dispersal in the Indian Ocean [ 72 ]. The combination of otolith chemistry data coupled with genetic analyses can increased details on connectivity patterns, as both techniques provide information on complementary timescales (individual for otolith chemistry, and evolutionary for genetics), unravelling otherwise hidden patterns [ 73 ].…”

Klasterisasi Spasial Keragaman Spesies Tuna di Perairan Selatan Jawa

“…Berdasarkan informasi tersebut dapat dinyatakan bahwa periode September-November merupakan periode migrasi beberapa spesies tuna pada perairan Samudera Hindia bagian selatan Pulau Jawa. Secara umum, migrasi mampu merefleksikan preferensi spasial (Humston et al, 2000); dengan ditemukannya pola distribusi spesies tuna pada perairan ini maka dapat dinyatakan bahwa tuna bermigrasi sepanjang tahun (Nikolic et al, 2020). Oleh karena itu perlu dilakukan analisis klasterisasi kembali pada perairan tersebut di musim penangkapan yang berbeda (temporal).…”