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June 1, 2026Aquaculture Reports0 citationsOpen Access

Combining physiological experiments and multimodal optimization to estimate DEB parameters for intensively farmed bivalves in the Yellow Sea

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LWLinjie WangFLFan LinAAAntonio Agüera

Key Points

  • To determine the key Dynamic Energy Budget (DEB) parameters for two bivalve species in the Yellow Sea.
  • Conducted controlled physiological experiments on Pacific oysters and Manila clams.
  • Utilized multimodal optimization for parameter calibration.
  • Combined local core parameter measurements with mathematical optimization techniques.
  • Obtained shape coefficients δ m of 0.178 for Pacific oysters and 0.374 for Manila clams.
  • Calculated volume-specific maintenance rates of 22.49 J/(cm³·d) for Pacific oysters and 50.87 J/(cm³·d) for Manila clams.
  • Demonstrated that different calibration approaches result in physiologically divergent parameter sets despite similar goodness-of-fit.

Abstract

The key Dynamic Energy Budget (DEB) energetic parameters were determined for two intensively cultured bivalve species in the Yellow Sea, the Pacific oyster Magallana gigas (Thunberg, 1793) (= Crassostrea gigas ) and the Manila clam Ruditapes philippinarum (Adams & Reeve, 1850), through controlled physiological experiment. The parameters obtained include the shape coefficient δ m (0.178 and 0.374, respectively), volume-specific cost for structure E G (1675 and 5688 J/cm³), maximum storage density E M (2493 and 2159 J/cm³), and volume-specific maintenance rate ṗ M (22.49 and 50.87 J/(cm³·d)). These parameters were subsequently calibrated using multimodal optimization. Different calibration strategies produced statistically equivalent fits but yielded parameter sets with divergent physiological interpretations, demonstrating that goodness-of-fit alone is insufficient to guarantee biological plausibility. We recommend the integrated calibration approach which first constrains the model with locally measured core parameters, then refines the remaining coefficients via mathematical optimization. This strategy produces parameter sets that are both physiologically interpretable and predictive, thereby improving the reliability of DEB based individual models in aquaculture applications.

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Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6a1d216202fbce91306376b3https://doi.org/10.1016/j.aqrep.2026.103679
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