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Two pairs of eyes are better than one: Combining individual-based and matrix models for ecological risk assessment of chemicals

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  • Meli, Mattia
  • Palmqvist, Annemette
  • Forbes, Valery E.
  • Groeneveld, Jürgen
  • Grimm, Volker

Abstract

Current chemical risk assessment procedures may result in imprecise estimates of risk due to sometimes arbitrary simplifying assumptions. As a way to incorporate ecological complexity and improve risk estimates, mechanistic effect models have been recommended. However, effect modeling has not yet been extensively used for regulatory purposes, one of the main reasons being uncertainty about which model type to use to answer specific regulatory questions. We took an individual-based model (IBM), which was developed for risk assessment of soil invertebrates and includes avoidance of highly contaminated areas, and contrasted it with a simpler, more standardized model, based on the generic metapopulation matrix model RAMAS. In the latter the individuals within a sub-population are not treated as separate entities anymore and the spatial resolution is lower. We explored consequences of model aggregation in terms of assessing population-level effects for different spatial distributions of a toxic chemical. For homogeneous contamination of the soil, we found good agreement between the two models, whereas for heterogeneous contamination, at different concentrations and percentages of contaminated area, RAMAS results were alternatively similar to IBM results with and without avoidance, and different food levels. This inconsistency is explained on the basis of behavioral responses that are included in the IBM but not in RAMAS. Overall, RAMAS was less sensitive than the IBM in detecting population-level effects of different spatial patterns of exposure. We conclude that choosing the right model type for risk assessment of chemicals depends on whether or not population-level effects of small-scale heterogeneity in exposure need to be detected. We recommend that if in doubt, both model types should be used and compared. Describing both models following the same standard format, the ODD protocol, makes them equally transparent and understandable. The simpler model helps to build up trust for the more complex model and can be used for more homogeneous exposure patterns. The more complex model helps detecting and understanding the limitations of the simpler model and is needed to ensure ecological realism for more complex exposure scenarios.

Suggested Citation

  • Meli, Mattia & Palmqvist, Annemette & Forbes, Valery E. & Groeneveld, Jürgen & Grimm, Volker, 2014. "Two pairs of eyes are better than one: Combining individual-based and matrix models for ecological risk assessment of chemicals," Ecological Modelling, Elsevier, vol. 280(C), pages 40-52.
  • Handle: RePEc:eee:ecomod:v:280:y:2014:i:c:p:40-52
    DOI: 10.1016/j.ecolmodel.2013.07.027
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    References listed on IDEAS

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    1. 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.
    2. Meli, Mattia & Auclerc, Apolline & Palmqvist, Annemette & Forbes, Valery E. & Grimm, Volker, 2013. "Population-level consequences of spatially heterogeneous exposure to heavy metals in soil: An individual-based model of springtails," Ecological Modelling, Elsevier, vol. 250(C), pages 338-351.
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    Cited by:

    1. Li, Yan & Blazer, Vicki S. & Iwanowicz, Luke R. & Schall, Megan Kepler & Smalling, Kelly & Tillitt, Donald E. & Wagner, Tyler, 2020. "Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu✰," Ecological Modelling, Elsevier, vol. 438(C).
    2. Radchuk, Viktoriia & Oppel, Steffen & Groeneveld, Jürgen & Grimm, Volker & Schtickzelle, Nicolas, 2016. "Simple or complex: Relative impact of data availability and model purpose on the choice of model types for population viability analyses," Ecological Modelling, Elsevier, vol. 323(C), pages 87-95.
    3. 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).
    4. Grimm, Volker & Augusiak, Jacqueline & Focks, Andreas & Frank, Béatrice M. & Gabsi, Faten & Johnston, Alice S.A. & Liu, Chun & Martin, Benjamin T. & Meli, Mattia & Radchuk, Viktoriia & Thorbek, Pernil, 2014. "Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE," Ecological Modelling, Elsevier, vol. 280(C), pages 129-139.
    5. Halsey, Samniqueka J. & Cinel, Scott & Wilson, Jared & Bell, Timothy J. & Bowles, Marlin, 2017. "Predicting population viability of a monocarpic perennial dune thistle using individual-based models," Ecological Modelling, Elsevier, vol. 359(C), pages 363-371.

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