Cited by 7Marine and fisheries researchFish Ecology and Management StudiesAquaculture Nutrition and Growth

Toward an environmental predictor of tuna recruitment

Open access
Woodworth‐Jefcoats, Phoebe A., Wren, Johanna L. K.·2020·Fisheries Oceanography

Abstract

Bigeye tuna are of global economic importance and are the primary target species of Hawaii's most valuable commercial fishery. Due to their high commercial value, bigeye tuna are relatively well studied and routinely assessed. Larval and adult bigeye surveys have been conducted for many years and are supported by ongoing research on their physiology and life history. Yet, modeling stock dynamics and estimating future catch rates remain challenging. Here, we show that an appropriately lagged measure of phytoplankton size is a robust predictor of catch rates in Hawaii's bigeye tuna fishery with a forecast window of four years. We present a fishery‐independent tool with the potential to improve stock assessments, aid dynamic fisheries management, and allow Hawaii's commercial longline fishing industry to better plan for the future.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/fog.12487

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The 'critical period' hypothesis was also borne out in the study of Woodworth-Jefcoats and Wren (2020).

    “The “critical period” hypothesis was also borne out in the study of Woodworth-Jefcoats and Wren (2020).”

    From Impacts of phytoplankton availability on bigeye tuna (Thunnus obesus) recruitment in the Indian Ocean (2024) · cites this paper as Woodworth-Jefcoats and Wren, 2020

    The Results report significant correlations between phytoplankton size four years earlier and bigeye tuna catch rates in Hawaii’s longline fishery. The Discussion links this finding to the hypothesis that food availability during early life affects survival, but the study did not directly measure survival during that critical period. The finding is consistent with the hypothesis rather than a direct demonstration of it. (LLM, conf=0.83)

    Experimental — accuracy not yet measured
  • In Woodworth-Jefcoats and Wren (2020), MD50 was identified as an environmental driver of bigeye tuna recruitment and can be an informative predictor of bigeye tuna catch rates in Hawaii's deep-set longline fishery with a 4-year forecast window.

    “In their study, MD50 was identified as an environmental driver of bigeye tuna recruitment and can be an informative predictor of bigeye tuna catch rates in Hawaii’s deep-set longline fishery with a 4-year forecast window, aligning with the results of our study.”

    From Impacts of phytoplankton availability on bigeye tuna (Thunnus obesus) recruitment in the Indian Ocean (2024) · cites this paper as Woodworth-Jefcoats and Wren, 2020

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 25

  1. R E FE R E N C E S Abascal, F. J., Peatman, T., Leroy, B., Nicol, S., Schaefer, K., Fuller, D. W., & Hampton, J. (2018). Spatiotemporal variability in bigeye vertical distribution in the Pacific Ocean. Fisheries Research, 204, 371-379. https://doi.org/10.1016/j.fishr es.2018.03.013 · DOI
  2. Ayers, A. L., Hospital, J., & Boggs, C. (2018). Bigeye tuna catch limits lead to differential impacts for Hawaii longliners. Marine Policy, 94, 93-105. https://doi.org/10.1016/j.marpol.2018.04.032 · DOI
  3. Barnes, C., Irigoien, X., De Oliveira, J. A. A., Maxwell, D., & Jennings, S. (2011). Predicting marine phytoplankton community size structure from empirical relationships with remotely sensed variables. Journal of Plankton Research, 33, 13-24. https://doi.org/10.1093/plank t/ fbq088 · DOI · Paper page
  4. Bigelow, K., Musyl, M. K., Poisson, F., & Kleiber, P. (2006). Pelagic longline gear depth and shoaling. Fisheries Research, 77, 173-183. https://doi. org/10.1016/j.fishr es.2005.10.010 · DOI
  5. Blanchard, J. L., Heneghan, R. F., Everett, J. D., Trebilco, R., & Richardson, A. J. (2017). From bacteria to whales: Using functional size spectra to model marine ecosystems. Trends in Ecology & Evolution, 32(3), 174-186. https://doi.org/10.1016/j.tree.2016.12.003 · DOI
  6. Block, B. A., & Stevens, E. D. (2001). Tuna physiology, ecology, and evolu- tion, 1st ed. San Diego, CA: Academic Press.
  7. Bograd, S. J., Kang, S., Di Lorenzo, E., Horii, T., Katugin, O. N., King, J. R., … Batchelder, H. (2019). Developing a social-ecological-environmental system framework to address climate change impacts in the North Pacific. Frontiers in Marine Science, 6, 333. https://doi.org/10.3389/ fmars.2019.00333 · DOI
  8. Barale, J. F. R. Gower, & L. Alberotanza (eds). Oceanography from Space: Revisited (pp. 273-287). Dordrecht, the Netherlands: Springer. https://doi.org/10.1007/978-90-481-8681-5_16 · DOI
  9. Hobday, A. J., Spillman, C. M., Eveson, J. P., & Hartog, J. R. (2016). Seasonal forecasting for decision support in marine fisheries and aquaculture. Fisheries Oceanography, 25(Suppl 1), 45-56. https://doi. org/10.1111/fog.12083 · DOI
  10. Howell, E. A., Kobayashi, D. R., Parker, D. M., Balasz, G. H., & Polovina, J. J. (2008). TurtleWatch: A tool to aid in the bycatch reduction of loggerhead turtles Caretta caretta in the Hawaii-based pelagic long- line fishery. Endangered Species Research, 5, 267-278. https://doi. org/10.3354/esr00096 · DOI
  11. Kume, S. (1969). Ecological studies on bigeye tuna -V: A critical review on distribution, size composition and stock structure of bigeye tuna in the North Pacific Ocean (north of 16°N). Bulletin of the Far Seas Fisheries Research Lab, 1, 57-75.
  12. Lehodey, P., Chai, F., & Hampton, J. (2003). Modelling climate-related variability of tuna populations from a coupled ocean-biogeochem- ical-populations dynamics model. Fisheries Oceanography, 12, 483- 494. https://doi.org/10.1046/j.1365-2419.2003.00244.x · DOI · Paper page
  13. Lehodey, P., Senina, I., Sibert, J., Bopp, L., Calmettes, B., Hampton, J., & Murtugudde, R. (2010). Preliminary forecasts of Pacific bigeye tuna population trends under the A2 PICC scenario. Progress in Oceanography, 86, 302-315. https://doi.org/10.1016/j.pocean.2010.04.021 · DOI
  14. Llopiz, J. K., & Hobday, A. J. (2015). A global comparative analysis of the feeding dynamics and environmental conditions of larval tunas, mackerel, and billfishes. Deep-Sea Research II, 113, 113-124. https:// doi.org/10.1016/j.dsr2.2014.05.014 · DOI · Paper page
  15. Muhling, B. A., Lamkin, J. T., Alemany, F., García, A., Farley, J., Ingram, G. W., … Carrion, R. L. (2017). Reproduction and larval biology in tunas, and the importance of restricted area spawning grounds. Reviews in Fish Biology and Fisheries, 27, 697-732. https://doi.org/10.1007/ s1116 0-017-9471-4 · DOI · Paper page
  16. Shimizu, Japan: Far Seas Fisheries Research Laboratory, 1985(3), 99. Pacific Islands Fisheries Science Center (2019a). Hawaii DAR Dealer Reporting System Data. https://inport.nmfs.noaa.gov/inpor t/item/5610 Pacific Islands Fisheries Science Center (2019b). The Hawaii and California- based Pelagic Longline Vessels Annual Report for 1 January -31 December, 2018. Data Report DR-19-047. https://doi.org/10.25923/ dq48-ef09 Pacific Islands Fisheries Science Center (2019c) Hawaii Longline Logbook, https://inport.nmfs.noaa.gov/inpor t/item/2721 · DOI
  17. Pikitch, E. K., Santora, C., Babcock, E. A., Bakun, A., Bonfil, R., Conover, D. O., … Sainsbury, K. J. (2004). Ecosystem-based fishery manage- ment. Science, 305(5682), 346-347. https://doi.org/10.1126/scien ce.1098222 · DOI
  18. Polovina, J. J., & Woodworth, P. A. (2012). Declines in phytoplank- ton cell size in the subtropical oceans estimated from satel- lite remotely-sense temperature and chlorophyll, 1998-2007. Deep-Sea Research II, 77-80, 82-88. https://doi.org/10.1016/j. dsr2.2012.04.006 · DOI
  19. Saha, K., Zhao, X., Zhang, H.-M., Casey, K. S., Zhang, D., Baker-Yeboah, S., … Relph, J. M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4 km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset., https:// doi.org/10.7289/v52j68xx · DOI · Paper page
  20. Sathyendranath, S., Grant, M., Brewin, R. J. W., Brockmann, C., Brotas, V., Chuprin, A.… Zibordi, G. (2018) ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 3.1 Data. Centre for Environmental Data Analysis 04 July 2018. https://doi. org/10.5285/9c334 fbe6d 424a7 08cf3 c4cf0 c6a53f5 · DOI
  21. Sculley, M., Ijimi, H., & Chang, Y.-J. (2018). A Base-case Model in Stock Synthesis 3.30 for the 2018 North Pacific Swordfish (Xiphias gladius) Stock Assessment. Pacific Islands Fisheries Science Center (PIFSC) Working Paper, WP-18-005. https://doi.org/10.7289/V5/WP-PIFSC -18-005 · DOI
  22. Tommasi, D., Stock, C. A., Pegion, K., Vecchi, G. A., Methot, R. D., Alexander, M. A., & Checkley, D. M. (2017). Improved manage- ment of small pelagic fisheries through seasonal climate predic- tion. Ecological Applications, 27, 378-388. https://doi.org/10.1002/ eap.1458 · DOI
  23. Woodworth-Jefcoats, P. A., Blanchard, J. L., & Drazen, J. C. (2019). Relative impacts of simultaneous stressors on a pelagic marine eco- system. Frontiers in Marine Science, 6, 383. https://doi.org/10.3389/ fmars.2019.00383 · DOI
  24. Woodworth-Jefcoats, P. A., Polovina, J. J., & Drazen, J. C. (2018). Synergy among oceanographic variability, fishery expansion, and longline catch composition in the central North Pacific Ocean. Fishery Bulletin, 116, 228-239. https://doi.org/10.7755/FB.116.3.2 · DOI
  25. Wren, J. L. K., & Polovina, J. J. (in review) A fishery-dependent recruitment and forecast index for bigeye tuna (Thunnus obesus) in the Hawai'i-based longline fishery.

Cited by

Sorted by citations

Authors
Woodworth‐Jefcoats, P. A., & Wren, J. L. K.
Affiliations
NOAA National Marine Fisheries Service (US)
Corresponding author
Woodworth‐Jefcoats, Phoebe A.
Venue
Fisheries Oceanography
Publisher
Wiley
Published
2020-07-22
Volume
29
Issue
5
Pages
436-441
ISSN
  • 1054-6006 (ISSN-L)
  • 1365-2419
OpenAlex
W3044428993
Cited by
7
References
1
Citations by year
2025: 1 · 2024: 1 · 2023: 2 · 2022: 2 · 2020: 1
FWCI
0.47
Type
journal article
Language
en
Open access
Locations
  • Fisheries Oceanography (publishedVersion, cc-by)
  • NOAA Institutional Repository (submittedVersion, cc-by)
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Marine and fisheries research
  • Physical Sciences › Environmental Science › Nature and Landscape Conservation › Fish Ecology and Management Studies
  • Life Sciences › Agricultural and Biological Sciences › Aquatic Science › Aquaculture Nutrition and Growth
Keywords
bigeye tuna, phytoplankton cell size, environmental predictors, catch rates, stock assessment, fisheries management, longline fishing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review