IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v407y2019ic2.html
   My bibliography  Save this article

Modelling long-term fisheries data to resolve the attraction versus production dilemma of artificial reefs

Author

Listed:
  • Roa-Ureta, Ruben H.
  • Santos, Miguel N.
  • Leitão, Francisco

Abstract

The main role of artificial reefs (ARs) is to enhance the productivity and sustainability of coastal fisheries by creating new fish biomass. From a modelling point of view, the creation of new fish biomass would be realized by a shift to a state of higher carrying capacity of the environment (K) for aquatic populations and communities. However, it has not been possible to demonstrate unequivocally rising K as a result of AR deployment because of the difficulty in disentangling enhancements due to simple distributional changes (the attraction hypothesis) versus total abundance rise (the production hypothesis). Here we develop a modelling framework based on simple, inexpensive fisheries data to quantify the impact of ARs, disentangling attraction from production by assessing the rise in regional K. The rationale is that if attraction to ARs from the wider region was the main driver of increased abundance in the ARs then regional K would have remained constant before, during and after deployment of the ARs. Therefore an increase in regional K disproves the hypothesis of attraction. The study case is the fishery for the two-banded seabream Diplodus vulgaris in southern Portugal. Monthly time series of 27 years of landings, 20 years of fishing effort, were available from three small-scale fleets: one was the artisanal fleet operating on the ARs and the other two were semi-industrial fleets operating on the wider continental shelf. The model that we developed and applied incorporated the data from all fleets so it evaluated the change in regional K. We show that regional K for D. vulgaris increased by 35% after final deployment of the ARs and it did so in linear fashion during four years. From a fisheries perspective the result was more nuanced because although the deployment succeeded in raising regional K, stock biomass and thereby enhancing the artisanal fishery, it also led to a substantial rise in total fishing mortality and exploitation rate because the semi-industrial fleets operating offshore increased their harvest rate nearly 3-fold. Our modelling framework has wide applicability in other regions due to the elementary nature of the necessary fishing monitoring data.

Suggested Citation

  • Roa-Ureta, Ruben H. & Santos, Miguel N. & Leitão, Francisco, 2019. "Modelling long-term fisheries data to resolve the attraction versus production dilemma of artificial reefs," Ecological Modelling, Elsevier, vol. 407(C), pages 1-1.
    • Handle: RePEc:eee:ecomod:v:407:y:2019:i:c:2
    DOI: 10.1016/j.ecolmodel.2019.108727
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380019302273
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2019.108727?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Russell B. Millar & Renate Meyer, 2000. "Non‐linear state space modelling of fisheries biomass dynamics by using Metropolis‐Hastings within‐Gibbs sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 327-342.
    2. Campbell, Matthew D. & Rose, Kenneth & Boswell, Kevin & Cowan, James, 2011. "Individual-based modeling of an artificial reef fish community: Effects of habitat quantity and degree of refuge," Ecological Modelling, Elsevier, vol. 222(23), pages 3895-3909.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haolin Yu & Jie Feng & Wei Zhao & Tao Zhang & Haiyan Wang & Yunlong Ji & Yanli Tang & Liyuan Sun, 2024. "Evaluation of the Residency of Black Rockfish ( Sebastes schlegelii ) in Artificial Reef Areas Based on Stable Carbon Isotopes," Sustainability, MDPI, vol. 16(5), pages 1-15, March.
    2. Jorge Ramos & Benjamin Drakeford & Ana Madiedo & Joana Costa & Francisco Leitão, 2024. "A Bayesian Approach to Infer the Sustainable Use of Artificial Reefs in Fisheries and Recreation," Sustainability, MDPI, vol. 16(2), pages 1-16, January.
    3. Barrett, Luke T. & Theuerkauf, Seth J. & Rose, Julie M. & Alleway, Heidi K. & Bricker, Suzanne B. & Parker, Matt & Petrolia, Daniel R. & Jones, Robert C., 2022. "Sustainable growth of non-fed aquaculture can generate valuable ecosystem benefits," Ecosystem Services, Elsevier, vol. 53(C).

    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. Watkins, Katherine Shepard & Rose, Kenneth A., 2017. "Simulating individual-based movement in dynamic environments," Ecological Modelling, Elsevier, vol. 356(C), pages 59-72.
    2. Chaloupka, Milani & Balazs, George, 2007. "Using Bayesian state-space modelling to assess the recovery and harvest potential of the Hawaiian green sea turtle stock," Ecological Modelling, Elsevier, vol. 205(1), pages 93-109.
    3. Christoph Berninger & Almond Stocker & David Rugamer, 2020. "A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction," Papers 2006.05750, arXiv.org, revised Feb 2021.
    4. Min-Je Choi & Do-Hoon Kim, 2020. "Assessment and Management of Small Yellow Croaker ( Larimichthys polyactis ) Stocks in South Korea," Sustainability, MDPI, vol. 12(19), pages 1-17, October.
    5. Watkins, Katherine Shepard & Rose, Kenneth A., 2013. "Evaluating the performance of individual-based animal movement models in novel environments," Ecological Modelling, Elsevier, vol. 250(C), pages 214-234.
    6. Gimenez, Olivier & Rossi, Vivien & Choquet, Rémi & Dehais, Camille & Doris, Blaise & Varella, Hubert & Vila, Jean-Pierre & Pradel, Roger, 2007. "State-space modelling of data on marked individuals," Ecological Modelling, Elsevier, vol. 206(3), pages 431-438.
    7. Ji-Hoon Choi & Jae-Bong Lee & Sang-Chul Yoon & Do-Hoon Kim, 2021. "A Bioeconomic Analysis of the Sandfish ( Arctoscopus japonicus ) Management Policies of the Eastern Sea Danish Fishery in Korea," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
    8. Ruiz, Javier & Prieto, Laura & Astorga, Diana, 2012. "A model for temperature control of jellyfish (Cotylorhiza tuberculata) outbreaks: A causal analysis in a Mediterranean coastal lagoon," Ecological Modelling, Elsevier, vol. 233(C), pages 59-69.
    9. Su, Zhenming & Peterman, Randall M., 2012. "Performance of a Bayesian state-space model of semelparous species for stock-recruitment data subject to measurement error," Ecological Modelling, Elsevier, vol. 224(1), pages 76-89.
    10. I. B. J. Goudie & M. Goudie, 2007. "Who captures the marks for the Petersen estimator?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 825-839, July.
    11. Dunham, Kylee & Grand, James B., 2016. "Effects of model complexity and priors on estimation using sequential importance sampling/resampling for species conservation," Ecological Modelling, Elsevier, vol. 340(C), pages 28-36.
    12. Axel Finke & Ruth King & Alexandros Beskos & Petros Dellaportas, 2019. "Efficient Sequential Monte Carlo Algorithms for Integrated Population Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 204-224, June.
    13. Russell B. Millar, 2004. "Sensitivity of Bayes Estimators to Hyper-Parameters with an Application to Maximum Yield from Fisheries," Biometrics, The International Biometric Society, vol. 60(2), pages 536-542, June.
    14. Ruth King & Stephen P. Brooks & Chiara Mazzetta & Stephen N. Freeman & Byron J. T. Morgan, 2008. "Identifying and diagnosing population declines: a Bayesian assessment of lapwings in the UK," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(5), pages 609-632, December.

    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:407:y:2019:i:c:2. 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.