IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v510y2025ics0304380025003175.html

A mechanistic stage-structured model for estimating maturation, mortality, and recruitment parameters of three economically significant fish species in Canadian waters

Author

Listed:
  • Shuaib, Sherif Eneye
  • Rutayisire, Ghislain
  • Han, Qing
  • Veprauskas, Amy
  • Kong, Jude Dzevela

Abstract

This study designs and analyzes a discrete-time, stage-structured model to estimate key life-history parameters (recruitment, maturation, and mortality) for three economically significant fish species in Canadian waters: Chinook Salmon (Oncorhynchus tshawytscha), Capelin (Mallotus villosus), and Cod (Gadus morhua). The analysis encompasses model wellposedness, the net reproductive number (R0), and the global stability of equilibria. Sensitivity analysis using Partial Rank Correlation Coefficients (PRCC) was performed to assess the influence of key parameters on R0 and long-term fish population abundance. Our findings reveal that recruitment and adult survival are the primary drivers of long-term population sustainability across all three species. While maturation transitions contribute positively to population growth, their influence is secondary compared to recruitment and survival. These results highlight the importance of effective management strategies that prioritize improving recruitment and adult survival while also supporting successful transitions between life stages to maintain stable fish populations and ensure the ecological and economic sustainability of fisheries.

Suggested Citation

  • Shuaib, Sherif Eneye & Rutayisire, Ghislain & Han, Qing & Veprauskas, Amy & Kong, Jude Dzevela, 2025. "A mechanistic stage-structured model for estimating maturation, mortality, and recruitment parameters of three economically significant fish species in Canadian waters," Ecological Modelling, Elsevier, vol. 510(C).
    • Handle: RePEc:eee:ecomod:v:510:y:2025:i:c:s0304380025003175
    DOI: 10.1016/j.ecolmodel.2025.111331
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380025003175
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2025.111331?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Cole C Monnahan & Kasper Kristensen, 2018. "No-U-turn sampling for fast Bayesian inference in ADMB and TMB: Introducing the adnuts and tmbstan R packages," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-10, May.
    2. Bonelwa Sidumo & Energy Sonono & Isaac Takaidza, 2024. "Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data," Annals of Data Science, Springer, vol. 11(3), pages 803-817, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kala Studens & Benjamin M. Bolker & Jean-Noël Candau, 2024. "Predicting the Temperature-Driven Development of Stage-Structured Insect Populations with a Bayesian Hierarchical Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(3), pages 536-552, September.
    2. Ding, Yihong & Robinson, Elizabeth & Balcombe, Kelvin, 2025. "The role of experience in climate adaptation: Evidence from a field experiment in China," China Economic Review, Elsevier, vol. 94(PB).
    3. Aaron Osgood‐Zimmerman & Jon Wakefield, 2023. "A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling," International Statistical Review, International Statistical Institute, vol. 91(2), pages 318-342, August.
    4. Henrique Aparecido Laureano & Ricardo Rasmussen Petterle & Guilherme Parreira da Silva & Paulo Justiniano Ribeiro Junior & Wagner Hugo Bonat, 2024. "A multinomial generalized linear mixed model for clustered competing risks data," Computational Statistics, Springer, vol. 39(3), pages 1417-1434, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:510:y:2025:i:c:s0304380025003175. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.