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Dynamic energy budgets in population ecotoxicology: Applications and outlook

Author

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  • Jager, Tjalling
  • Barsi, Alpar
  • Hamda, Natnael T.
  • Martin, Benjamin T.
  • Zimmer, Elke I.
  • Ducrot, Virginie

Abstract

Most of the experimental testing in ecotoxicology takes place at the individual level, but the protection goals for environmental risk assessment are at the population level (or higher). Population modelling can fill this gap, but only models on a mechanistic basis allow for extrapolation beyond the conditions in the experimental tests. The life-history traits of individuals form the basis of population dynamics, and population modelling thus requires a proper understanding of the individual's behaviour. The dynamic energy budget (DEB) theory offers a flexible platform for the development of models at the individual level. Linking DEB models to population models can thus provide a mechanistic basis for extrapolation. Here, we provide a conceptual overview of DEB theory, with emphasis on its applications in ecotoxicology. Furthermore, we briefly review the applications in which a DEB-based individual model has been linked to structured population dynamics. Finally, we discuss some of the most important areas for further research in this context.

Suggested Citation

  • Jager, Tjalling & Barsi, Alpar & Hamda, Natnael T. & Martin, Benjamin T. & Zimmer, Elke I. & Ducrot, Virginie, 2014. "Dynamic energy budgets in population ecotoxicology: Applications and outlook," Ecological Modelling, Elsevier, vol. 280(C), pages 140-147.
    • Handle: RePEc:eee:ecomod:v:280:y:2014:i:c:p:140-147
    DOI: 10.1016/j.ecolmodel.2013.06.024
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    References listed on IDEAS

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    1. Jager, Tjalling & Zimmer, Elke I., 2012. "Simplified Dynamic Energy Budget model for analysing ecotoxicity data," Ecological Modelling, Elsevier, vol. 225(C), pages 74-81.
    2. Billoir, Elise & Péry, Alexandre R.R. & Charles, Sandrine, 2007. "Integrating the lethal and sublethal effects of toxic compounds into the population dynamics of Daphnia magna: A combination of the DEBtox and matrix population models," Ecological Modelling, Elsevier, vol. 203(3), pages 204-214.
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    1. 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).
    2. Haberle, Ines & Marn, Nina & Geček, Sunčana & Klanjšček, Tin, 2020. "Dynamic energy budget of endemic and critically endangered bivalve Pinna nobilis: A mechanistic model for informed conservation," Ecological Modelling, Elsevier, vol. 434(C).
    3. Jager, Tjalling, 2020. "Revisiting simplified DEBtox models for analysing ecotoxicity data," Ecological Modelling, Elsevier, vol. 416(C).
    4. Jager, Tjalling & Salaberria, Iurgi & Hansen, Bjørn Henrik, 2015. "Capturing the life history of the marine copepod Calanus sinicus into a generic bioenergetics framework," Ecological Modelling, Elsevier, vol. 299(C), pages 114-120.
    5. Grechi, Laura & Franco, Antonio & Palmeri, Luca & Pivato, Alberto & Barausse, Alberto, 2016. "An ecosystem model of the lower Po river for use in ecological risk assessment of xenobiotics," Ecological Modelling, Elsevier, vol. 332(C), pages 42-58.
    6. Guillaumot, Charlène & Saucède, Thomas & Morley, Simon A. & Augustine, Starrlight & Danis, Bruno & Kooijman, Sebastiaan, 2020. "Can DEB models infer metabolic differences between intertidal and subtidal morphotypes of the Antarctic limpet Nacella concinna (Strebel, 1908)?," Ecological Modelling, Elsevier, vol. 430(C).

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