Age and growth of North Pacific albacore (Thunnus alalunga): Implications for stock assessment

2013 ◽  
Vol 147 ◽  
pp. 55-62 ◽  
Author(s):  
R.J. David Wells ◽  
Suzanne Kohin ◽  
Steven L.H. Teo ◽  
Owyn E. Snodgrass ◽  
Koji Uosaki
Keyword(s):  
North Pacific ◽  
Stock Assessment ◽  
Age And Growth ◽  
Thunnus Alalunga

Age and growth of albacore Thunnus alalunga in the North Pacific Ocean

2012 ◽  
Vol 80 (6) ◽  
pp. 2328-2344 ◽  
Author(s):  
K.-S. Chen ◽  
T. Shimose ◽  
T. Tanabe ◽  
C.-Y. Chen ◽  
C.-C. Hsu
Keyword(s):  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Age And Growth ◽  
The North ◽  
Thunnus Alalunga ◽  
The North Pacific

Detecting and forecasting complex nonlinear dynamics in spatially structured catch-per-unit-effort time series for North Pacific albacore (Thunnus alalunga)

2011 ◽  
Vol 68 (3) ◽  
pp. 400-412 ◽  
Author(s):  
Sarah M. Glaser ◽  
Hao Ye ◽  
Mark Maunder ◽  
Alec MacCall ◽  
Michael Fogarty ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
North Pacific ◽  
Stock Assessment ◽  
North Pacific Ocean ◽  
Catch Per Unit Effort ◽  
Data Set ◽  
Unit Effort ◽  
Thunnus Alalunga ◽  
Low Dimensional

The presence of complex, nonlinear dynamics in fish populations, and uncertainty in the structure (functional form) of those dynamics, pose challenges to the accuracy of forecasts produced by traditional stock assessment models. We describe two nonlinear forecasting models that test for the hallmarks of complex behavior, avoid problems of structural uncertainty, and produce good forecasts of catch-per-unit-effort (CPUE) time series in both standardized and nominal (unprocessed) form. We analyze a spatially extensive, 40-year-long data set of annual CPUE time series of North Pacific albacore ( Thunnus alalunga ) from 1° × 1° cells from the eastern North Pacific Ocean. The use of spatially structured data in compositing techniques improves out-of-sample forecasts of CPUE and overcomes difficulties commonly encountered when using short, incomplete time series. These CPUE series display low-dimensional, nonlinear structure and significant predictability. Such characteristics have important implications for industry efficiency in terms of future planning and can inform formal stock assessments used for the management of fisheries.

Age and growth of the roughtail skate Bathyraja trachura (Gilbert 1892) from the eastern North Pacific

2007 ◽  
Vol 80 (2-3) ◽  
pp. 325-336 ◽  
Author(s):  
Chanté D. Davis ◽  
Gregor M. Cailliet ◽  
David A. Ebert
Keyword(s):  
North Pacific ◽  
Age And Growth

Transoceanic migration rates of young North Pacific albacore, Thunnus alalunga, from conventional tagging data

2008 ◽  
Vol 65 (8) ◽  
pp. 1681-1691 ◽  
Author(s):  
Momoko Ichinokawa ◽  
Atilio L. Coan, ◽  
Yukio Takeuchi
Keyword(s):  
North Pacific ◽  
Seasonal Migration ◽  
Western Pacific ◽  
Migration Routes ◽  
Migration Rates ◽  
The North ◽  
Maximum Likelihood Methods ◽  
Westward Movement ◽  
Thunnus Alalunga ◽  
The North Pacific

This study summarizes US and Japanese historical North Pacific albacore ( Thunnus alalunga) tagging data and uses maximum likelihood methods to estimate seasonal migration rates of young North Pacific albacore. Previous studies related to North Pacific albacore migration have found that the frequency of albacore migrations is difficult to quantify because of inadequate amounts of tags released by the US tagging program in the western Pacific. Use of the combined Japan and US tagging data solves this problem. This study also incorporates specific seasonal migration routes, hypothesized in past qualitative analyses, to avoid overparameterization problems. The estimated migration patterns qualitatively correspond to those from previous studies and suggest the possibility of frequent westward movements and infrequent eastward movements in the North Pacific. This frequent westward movement of young albacore in the North Pacific would correspond to a part of albacore life history in which immature fish recruit into fisheries in the western and eastern Pacific and then gradually move near to their spawning grounds in the central and western Pacific before maturing.

scholarly journals Age determination and growth of Atlantic redfish (Sebastes marinus and S. mentella): bias and precision of age readers and otolith preparation methods

2005 ◽  
Vol 62 (4) ◽  
pp. 655-670 ◽  
Author(s):  
Christoph Stransky ◽  
Sif Gudmundsdóttir ◽  
Thorsteinn Sigurdsson ◽  
Svend Lemvig ◽  
Kjell Nedreaas ◽  
...  
Keyword(s):  
Stock Assessment ◽  
Age Determination ◽  
Growth Curves ◽  
Age And Growth ◽  
Preparation Methods ◽  
Irminger Sea ◽  
Coefficients Of Variation ◽  
Commercial Species ◽  
Almost All

Abstract Age determination of Atlantic redfish (Sebastes spp.) has proven difficult and has led to inconsistent age and growth estimates in the past. Using otoliths of the two major commercial species, golden redfish (Sebastes marinus) and deep-sea redfish (S. mentella), a series of exchange schemes was carried out to assess bias and precision of age readings between four readers and between two preparation methods. Considerable bias between readers and moderate precision were observed for the S. marinus readings, especially for ages >20 years, with coefficients of variation (CV) of 7.7–12.0% and average percent error (APE) of 5.4–8.5%. Agreement between readers increased from 17–28% to 45–61% when allowing deviations of ±1 year, and to 80–92% with ±3 years tolerance. The age of S. marinus determined from broken and burnt otoliths was estimated to be slightly lower than when the age of the same individuals was determined from thin-sectioned otoliths. The bias and precision estimates obtained from the S. mentella material were generally poorer than for S. marinus (CV 8.2–19.1%, APE 5.8–13.5%), but similar to reported values for other long-lived fish species. Better than 50% agreement was only achieved with ±3 years tolerance. Growth rates differed significantly between species, confirming slower growth for S. mentella. For S. marinus, only one reader comparison revealed significantly different growth functions, whereas almost all S. mentella reader pairs showed significant differences in growth curves. Section and break-and-burn readings of S. marinus did not differ significantly. Average ages of around 9–10 years were determined for juvenile S. mentella 24–30 cm long, which were likely to have migrated from East Greenland into the Irminger Sea, based on earlier observations. As some of the error in the age determinations presented could be attributed to interpretation differences between readers, further intercalibration of redfish ageing is urgently needed in order to provide consistent input data for stock assessment.

scholarly journals Selecting the best growth model for elasmobranches

2015 ◽  
Author(s):  
Kwang-Ming Liu ◽  
Chiao-Bin Wu ◽  
Shoou-Jeng Joung ◽  
Wen-Pei Tsai
Keyword(s):  
Growth Model ◽  
Growth Models ◽  
Stock Assessment ◽  
Gompertz Model ◽  
Information Criterion ◽  
Age And Growth ◽  
Deep Waters ◽  
Extended Longevity ◽  
Best Fit ◽  
Two Parameter

Age and growth information is essential for accurate stock assessment of fish, and growth model selection may influence the result of stock assessment. Previous descriptions of the age and growth of elasmobranches relied mainly on the von Bertalanffy growth model (VBGM). However, it has been noted that sharks, skates and rays exhibit significant variety in size, shape, and life-history traits. Given this variation, the VBGM may not necessarily provide the best fit for all elasmobranches. This study attempts to improve the accuracy of age estimates by testing four growth models—the VBGM, two-parameter VBGM, Robertson (Logistic) and Gompertz models—to fit observed and simulated length-at-age data for 37 species of elasmobranches. The best growth model was selected based on corrected Akaike’s Information Criterion (AICc), the AICc difference, and the AICc weight. The VBGM and two-parameter VBGM provide the best fit for species with slow growth and extended longevity (L∞ > 100 cm TL, 0.05 < k < 0.15 yr-1), such as pelagic sharks. For fast-growing small sharks (L∞ < 100 cm TL, kr or kg > 0.2 yr-1) in deep waters and for small-sized demersal skates/rays, the Robertson and the Gompertz models provide the best fit. The best growth models for small sharks in shallow waters are the two-parameter VBGM and the Robertson model, while all the species best fit by the Gompertz model are skates and rays.

scholarly journals PRODUKSI DAN PRODUKTIVITAS TUNA OLEH KAPAL TUNA LONGLINE YANG BERBASIS DI PPN PALABUHANRATU

2020 ◽  
Vol 10 (2) ◽  
pp. 173-181
Author(s):  
Mohammad Imron ◽  
Roza Yusfiandayani ◽  
Mulyono S. Baskoro
Keyword(s):  
Stock Assessment ◽  
Unit Effort ◽  
Thunnus Obesus ◽  
Thunnus Alalunga ◽  
Tuna Longline

Produktivitas penangkapan tuna dapat dilihat dari produksi penangkapan yang didaratkan di pelabuhan (landing) per upaya penangkapan (effort). Pelabuhan Perikanan Nusantara (PPN) Palabuhanratu menjadi salah satu pelabuhan perikanan yang aktivitas perikanannya tergolong aktif di wilayah pesisir selatan Pulau Jawa dan menjadi salah satu pusat kegiatan perikanan tangkap di wilayah Propinsi Jawa Barat. Produksi ikan tuna di PPN Palabuhanratu mengalami peningkatan yang cukup signifikan dari tahun 2010 sampai tahun 2019. Pada tahun 2014-2018 produksi ikan tuna di PPN Palabuhanratu mengalami penurunan yang cukup drastis. Pada tahun 2019, produksi kembali meningkat menjadi 1,091,612 ton. Landing Per Unit Effort (LPUE) digunakan dalam penelitian perikanan untuk mengindikasikan kelimpahan sumberdaya yang digunakan untuk melakukan stock assessment ketika mengestimasi kelimpahan relatif dari suatu spesies yang dieksploitasi. Komposisi hasil tangkapan tuna oleh kapal tuna longline terdiri atas ikan tuna sirip kuning (Thunnus albacores), tuna mata besar (Thunnus obesus), ikan tuna albakor (Thunnus alalunga). Produksi tuna yang didaratkan di PPN Palabuhanratu dari tahun 2010-2019 mengalami fluktuasi. Pada tahun 2010 produksi ikan tuna sirip kuning sebesar 444,952 ton, ikan tuna mata besar sebesar 979,189 ton, ikan tuna albakor sebesar 122,671 ton. Pada tahun 2019 produksi ikan tuna sirip kuning sebesar 617,992 ton, ikan tuna mata besar sebesar 240,487 ton, ikan tuna albakor sebesar 233,133 ton. Produktivitas tertinggi terjadi pada ikan tuna sirip kuning tahun 2014 dengan nilai LPUE sebesar 6,09 dengan produksi sebesar  2,448,171 ton dengan jumlah effort 402. Produktivitas mengalami fluktuasi setiap tahunnya.

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