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The stock assessment theory of relativity: deconstructing the term “data-limited” fisheries into components and guiding principles to support the science of fisheries management

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Abstract

The term “data-limited fisheries” is a catch-all to generally describe situations lacking data to support a fully integrated stock assessment model. Data conditions range from data-void fisheries to those that reliably produce quantitative assessments. However, successful fishery assessment can also be limited by resources (e.g., time, money, capacity). The term “data-limited fisheries” is therefore too vague and incomplete to describe such wide-ranging conditions, and subsequent needs for management vary greatly according to each fishery’s context. Here, we acknowledge this relativity and identify a range of factors that can constrain the ability of analyses to inform management, by instead defining the state of being “data-limited” as a continuum along axes of data (e.g., type, quality, and quantity) and resources (e.g., time, funding, capacity). We introduce a tool (the DLMapper) to apply this approach and define where a fishery lies on this relativity spectrum of limitations (i.e. from no data and no resources to no constraints on data and resources). We also provide a ranking of guiding principles, as a function of the limiting conditions. This high-level guidance is meant to identify current actions to consider for overcoming issues associated with data and resource constraints given a specific “data-limited” condition. We apply this method to 20 different fisheries to demonstrate the approach. By more explicitly outlining the various conditions that create “data-limited situations” and linking these to broad guidance, we aim to contextualize and improve the communication of conditions, and identify effective opportunities to continue to develop and progress the science of “limited” stock assessment in support of fisheries management.

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Data availability

The datasets generated during and/or analyzed during the current study are available at the following repository: https://github.com/shcaba/DL-Mapper

Notes

  1. This tool can be accessed at https://connect.fisheries.noaa.gov/DLMapper/; tool development code can be found at https://github.com/shcaba/DL-Mapper.

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Acknowledgements

We are particularly grateful to Brent Wise for his role in initiating the World Fisheries Congress session on data poor fisheries and to all the conveners and organisers of that conference despite the ongoing global challenges. We very much thank the following scientists for providing us with information on data and resource constraints for our selected fishery examples; Daniel Johnson, Ashley Fowler, Steve Newman, David Fairclough, Matias Braccini, Anthony Hart. We thank all that attended and contributed to the data-limited session of the World Fisheries Congress, despite the challenges of juggling time zones via a remote meeting.

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Authors and Affiliations

  1. NOAA Fisheries, Northwest Fisheries Science Center, Seattle, WA, 98103, USA

    Jason M. Cope

  2. CSIRO Oceans and Atmosphere, Hobart, Tasmania, 7001, Australia

    Natalie A. Dowling & Pia Bessell-Browne

  3. Western Australian Fisheries and Marine Research Laboratories, Department of Primary Industries and Regional Development, Government of Western Australia, PO Box 20, North Beach, Western Australia, 6920, Australia

    Sybrand A. Hesp

  4. Virginia Institute of Marine Science, William and Mary, Gloucester Point, VA, 23062, USA

    Kristen L. Omori

  5. Virginia Polytechnic Institute and State University, Blacksburg, Virginia, 24061, USA

    Leandro Castello

  6. New South Wales Department of Primary Industries, Port Stephens Fisheries Institute, Taylors Beach Rd, Taylors Beach, NSW, 2315, Australia

    Rowan Chick

  7. The Nature Conservancy, Corvallis, OR, 97330, USA

    Dawn Dougherty

  8. National Institute of Water and Atmospheric Research Limited, 301 Evans Bay Parade, Greta Point, Wellington, New Zealand

    Steven J. Holmes

  9. South Australian Research and Development Institute, West Beach, South Australia, 5022, Australia

    Richard McGarvey

  10. School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, 98195, USA

    Daniel Ovando

  11. Bridge Environment, LLC, Seattle, Washington, 98125, USA

    Josh Nowlis

  12. Biospherics P/L, PO Box 168, South Fremantle, WA, 6162, Australia

    Jeremy Prince

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  1. Jason M. Cope
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Contributions

JM. Cope was a co-lead developer of the paper concept, provided lead writing of the Introduction and Methods sections, produced several figures, edited the final version of the manuscript and developed the software application DLMapper. NA. Dowling, SA. Hesp, and KL. Omori were co-lead developers of the paper concept, providing lead writing of the results and discussion sections, produced figures and tables, provided major editing of the manuscript, and provided significant feedback on the DLMapper application. The remaining authors provided fishery examples to use in the paper and/or editorial assistance with the paper along with review of the DLMapper application.

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Correspondence to Jason M. Cope.

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Cope, J.M., Dowling, N.A., Hesp, S.A. et al. The stock assessment theory of relativity: deconstructing the term “data-limited” fisheries into components and guiding principles to support the science of fisheries management. Rev Fish Biol Fisheries 33, 241–263 (2023). https://doi.org/10.1007/s11160-022-09748-1

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