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Modelling of tuna around fish aggregating devices: The importance of ocean flow and prey

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  • Nooteboom, Peter D.
  • Scutt Phillips, Joe
  • Kehl, Christian
  • Nicol, Simon
  • van Sebille, Erik

Abstract

Catch and distribution of tuna in the ocean are typically investigated with ocean basin-scale models. Due to their large scale, such models must greatly simplify tuna behaviour occurring at a scale below ∼100 km, despite interactions at this level potentially being important to both catch and distribution of tuna. For example, the associative behaviour of tuna with man-made floating objects, that are deployed by fishers to improve their catch rates (Fish Aggregating Devices; FADs), are usually ignored or simplified. Here we present a model that can be used to investigate the influence of tuna dynamics below the ∼100 km scale on larger scales. It is an Individual-Based Model (IBM) of a hypothetical, tuna-like species, that includes their interactions with each other, free-floating FADs and prey. In this IBM, both tuna and FADs are represented by Lagrangian particles that are advected by an ocean flow field, with tuna also exhibiting active swimming based on internal states such as stomach fullness. We apply the IBM in multiple configurations of idealized flow and prey fields, alongside differing interaction strengths between agents. When tuna swimming behaviour is influenced equally by prey and FADs, we find that the model simulations compare well with observations at the ≲100 km scale. For instance, compared to observations, tuna particles have a similar stomach fullness when associated or non-associated to a FAD, tuna colonize at similar timescales at FADs after their deployment and tuna particles exhibit similar variations in continuous residence times. However, we find large differences in emergent dynamics such as residence and catch among different flow configurations, because the flow determines the time scale at which tuna encounter FADs. These findings are discussed in the context of directing future research, and an improved interpretation of tuna catch and other data for the sustainable management of these economically important species.

Suggested Citation

  • Nooteboom, Peter D. & Scutt Phillips, Joe & Kehl, Christian & Nicol, Simon & van Sebille, Erik, 2023. "Modelling of tuna around fish aggregating devices: The importance of ocean flow and prey," Ecological Modelling, Elsevier, vol. 475(C).
    • Handle: RePEc:eee:ecomod:v:475:y:2023:i:c:s0304380022002861
    DOI: 10.1016/j.ecolmodel.2022.110188
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    References listed on IDEAS

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    1. Castellanos, Víctor & Chan-López, Ramón E., 2017. "Existence of limit cycles in a three level trophic chain with Lotka–Volterra and Holling type II functional responses," Chaos, Solitons & Fractals, Elsevier, vol. 95(C), pages 157-167.
    2. Joe Scutt Phillips & Graham M Pilling & Bruno Leroy & Karen Evans & Thomas Usu & Chi Hin Lam & Kurt M Schaefer & Simon Nicol, 2017. "Revisiting the vulnerability of juvenile bigeye (Thunnus obesus) and yellowfin (T. albacares) tuna caught by purse-seine fisheries while associating with surface waters and floating objects," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-18, June.
    3. Langley, Adam & Wright, Andrew & Hurry, Glenn & Hampton, John & Aqorua, Transform & Rodwell, Len, 2009. "Slow steps towards management of the world's largest tuna fishery," Marine Policy, Elsevier, vol. 33(2), pages 271-279, March.
    4. Johann D. Bell & Inna Senina & Timothy Adams & Olivier Aumont & Beatriz Calmettes & Sangaalofa Clark & Morgane Dessert & Marion Gehlen & Thomas Gorgues & John Hampton & Quentin Hanich & Harriet Harden, 2021. "Pathways to sustaining tuna-dependent Pacific Island economies during climate change," Nature Sustainability, Nature, vol. 4(10), pages 900-910, October.
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