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Standard dynamic energy budget model parameter sensitivity

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  • Matyja, Konrad

Abstract

The Dynamic Energy Budget model describes energy and mass balance in living organisms. It has found many applications in biological and ecological sciences. The model parameters can be connected to a single underlying biological process, and this mechanistic approach is helpful in understanding how complex biological systems work. However, the large number of model parameters makes it difficult to estimate their value, especially from data limited only to adult growth and reproduction at abundant food and constant temperature. Therefore, in this study, the sensitivity of the model solution to primary parameter values and the sensitivity of parameters to the perturbation in the data were analyzed. It was shown that the first-order sensitivity coefficients are different for each of the analyzed primary parameters and depend on their values and configuration in the equation. The sensitivity of parameters to data changes across analyzed time intervals, reaching minima and maxima. Moreover, the influence of each data point is smaller with an increasing number of data points. The recognition of the impact of parameters on the model solution, as well as the identification of data points with the strongest influence on estimates, can be helpful in experimental design and evaluation of the model.

Suggested Citation

  • Matyja, Konrad, 2023. "Standard dynamic energy budget model parameter sensitivity," Ecological Modelling, Elsevier, vol. 478(C).
    • Handle: RePEc:eee:ecomod:v:478:y:2023:i:c:s0304380023000327
    DOI: 10.1016/j.ecolmodel.2023.110304
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    References listed on IDEAS

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    1. Lika, Konstadia & Augustine, Starrlight & Kooijman, Sebastiaan A.L.M., 2020. "The use of augmented loss functions for estimating dynamic energy budget parameters," Ecological Modelling, Elsevier, vol. 428(C).
    2. Jager, Tjalling & Zimmer, Elke I., 2012. "Simplified Dynamic Energy Budget model for analysing ecotoxicity data," Ecological Modelling, Elsevier, vol. 225(C), pages 74-81.
    3. Jager, Tjalling, 2020. "Revisiting simplified DEBtox models for analysing ecotoxicity data," Ecological Modelling, Elsevier, vol. 416(C).
    4. Accolla, Chiara & Vaugeois, Maxime & Rueda-Cediel, Pamela & Moore, Adrian & Marques, Gonçalo M. & Marella, Purvaja & Forbes, Valery E., 2020. "DEB-tox and Data Gaps: Consequences for individual-level outputs," Ecological Modelling, Elsevier, vol. 431(C).
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