Somatic growth of Atlantic bluefin tuna ( Thunnus thynnus ) under global climate variability: evidence from over 60 years of daily resolved growth increments with a simulation study

Somatic growth is integral to fishery stock productivity. Under climate variability, omitting growth variability renders fishery management strategies non-optimal. Based on a multidecadal tag–recapture database, a case study is presented to investigate the potential growth response of the Atlantic b...

Full description

Bibliographic Details
Published in:Canadian Journal of Fisheries and Aquatic Sciences
Main Author: Zhou, Can
Format: Article in Journal/Newspaper
Language:English
Published: Canadian Science Publishing 2022
Subjects:
Online Access:http://dx.doi.org/10.1139/cjfas-2021-0097
https://cdnsciencepub.com/doi/full-xml/10.1139/cjfas-2021-0097
https://cdnsciencepub.com/doi/pdf/10.1139/cjfas-2021-0097
Description
Summary:Somatic growth is integral to fishery stock productivity. Under climate variability, omitting growth variability renders fishery management strategies non-optimal. Based on a multidecadal tag–recapture database, a case study is presented to investigate the potential growth response of the Atlantic bluefin tuna (Thunnus thynnus) to three regionally relevant large-scale climate patterns: the North Atlantic Oscillation, Arctic Oscillation, and Pacific North America pattern. An additional simulation study is conducted to explore the effect of the overall scale and the distribution of measurement error on the detection probability of extrinsic effects and the estimation of growth parameters. Results indicate significant growth response at an intra-annual scale to all three climate indices examined. Identified growth responses to climate variations are highly nonlinear. The projected growth shows increased growth in recent decades under climate variability with respect to the historical mean. Simulation results show a higher probability to detect climate signals when the overall measurement error is low. Substantial bias is expected when the measurement error at tag release is high, cautioning against careless integration of different types of growth data.