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Incorporating toxicokinetics into an individual-based model for more realistic pesticide exposure estimates: A case study of the wood mouse

Author

Listed:
  • Liu, Chun
  • Bednarska, Agnieszka J.
  • Sibly, Richard M.
  • Murfitt, Roger C.
  • Edwards, Peter
  • Thorbek, Pernille

Abstract

The potential risk of agricultural pesticides to mammals typically depends on internal concentrations within individuals, and these are determined by the amount ingested and by absorption, distribution, metabolism, and excretion (ADME). Pesticide residues ingested depend, amongst other things, on individual spatial choices which determine how much and when feeding sites and areas of pesticide application overlap, and can be calculated using individual-based models (IBMs). Internal concentrations can be calculated using toxicokinetic (TK) models, which are quantitative representations of ADME processes. Here we provide a population model for the wood mouse (Apodemus sylvaticus) in which TK submodels were incorporated into an IBM representation of individuals making choices about where to feed. This allows us to estimate the contribution of individual spatial choice and TK processes to risk. We compared the risk predicted by four IBMs: (i) “AllExposed-NonTK”: assuming no spatial choice so all mice have 100% exposure, no TK, (ii) “AllExposed-TK”: identical to (i) except that the TK processes are included where individuals vary because they have different temporal patterns of ingestion in the IBM, (iii) “Spatial-NonTK”: individual spatial choice, no TK, and (iv) “Spatial-TK”: individual spatial choice and with TK. The TK parameters for hypothetical pesticides used in this study were selected such that a conventional risk assessment would fail. Exposures were standardised using risk quotients (RQ; exposure divided by LD50 or LC50). We found that for the exposed sub-population including either spatial choice or TK reduced the RQ by 37–85%, and for the total population the reduction was 37–94%. However spatial choice and TK together had little further effect in reducing RQ. The reasons for this are that when the proportion of time spent in treated crop (PT) approaches 1, TK processes dominate and spatial choice has very little effect, and conversely if PT is small spatial choice dominates and TK makes little contribution to exposure reduction. The latter situation means that a short time spent in the pesticide-treated field mimics exposure from a small gavage dose, but TK only makes a substantial difference when the dose was consumed over a longer period. We concluded that a combined TK-IBM is most likely to bring added value to the risk assessment process when the temporal pattern of feeding, time spent in exposed area and TK parameters are at an intermediate level; for instance wood mice in foliar spray scenarios spending more time in crop fields because of better plant cover.

Suggested Citation

  • Liu, Chun & Bednarska, Agnieszka J. & Sibly, Richard M. & Murfitt, Roger C. & Edwards, Peter & Thorbek, Pernille, 2014. "Incorporating toxicokinetics into an individual-based model for more realistic pesticide exposure estimates: A case study of the wood mouse," Ecological Modelling, Elsevier, vol. 280(C), pages 30-39.
  • Handle: RePEc:eee:ecomod:v:280:y:2014:i:c:p:30-39
    DOI: 10.1016/j.ecolmodel.2013.09.007
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    References listed on IDEAS

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    1. Wang, Magnus & Grimm, Volker, 2007. "Home range dynamics and population regulation: An individual-based model of the common shrew Sorex araneus," Ecological Modelling, Elsevier, vol. 205(3), pages 397-409.
    2. Preuss, Thomas Günter & Hammers-Wirtz, Monika & Hommen, Udo & Rubach, Mascha Nadine & Ratte, Hans Toni, 2009. "Development and validation of an individual based Daphnia magna population model: The influence of crowding on population dynamics," Ecological Modelling, Elsevier, vol. 220(3), pages 310-329.
    3. Ashauer, Roman, 2010. "Toxicokinetic–toxicodynamic modelling in an individual based context—Consequences of parameter variability," Ecological Modelling, Elsevier, vol. 221(9), pages 1325-1328.
    4. Engelman, Catherine A. & Grant, William E. & Mora, Miguel A. & Woodin, Marc, 2012. "Modelling effects of chemical exposure on birds wintering in agricultural landscapes: The western burrowing owl (Athene cunicularia hypugaea) as a case study," Ecological Modelling, Elsevier, vol. 224(1), pages 90-102.
    5. Liu, Chun & Sibly, Richard M. & Grimm, Volker & Thorbek, Pernille, 2013. "Linking pesticide exposure and spatial dynamics: An individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes," Ecological Modelling, Elsevier, vol. 248(C), pages 92-102.
    6. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
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