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Updated: Feb 16, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
Published on: July 30, 2019
Bioconcentration, bioaccumulation, biomagnification and trophic magnification: a modelling perspective
Donald Mackay1, Alena K D Celsie2, David E Powell3
1Chemical Properties Research Group, Department of Chemistry, Trent University, Peterborough, ON K9L OG2, Canada. alenacelsie@trentu.ca.
Abstract:
We present a modelling perspective on quantifying metrics of bio-uptake of organic chemicals in fish. The models can be in concentration, partition ratio, rate constant (CKk) format or fugacity, Z and D value (fZD) format that are shown to be exactly equivalent, each having it merits. For most purposes a simple, parameter-parsimonious one compartment steady-state model containing some 13 parameters is adequate for obtaining an appreciation of the uptake equilibria and kinetics for scientific and regulatory purposes. Such a model is first applied to the bioaccumulation of a series of hypothetical, non-biotransforming chemicals with log KOW (octanol-water partition ratio) values of 4 to 8 in 10 g fish ranging in lipid contents to deduce wet-weight and lipid normalized concentrations, bioaccumulation and biomagnification factors. The sensitivity of biomagnification factors to relative lipid contents is discussed. Second, a hypothetical 5 species linear food chain is simulated to evaluate trophic magnification factors (TMFs) showing the critical roles of KOW and biotransformation rate. It is shown that lipid normalization of concentrations is most insightful for less hydrophobic chemicals (log KOW < 5) when bio-uptake is largely controlled by respiratory intake and equilibrium (equi-fugacity) is approached. For more hydrophobic chemicals when dietary uptake kinetics dominate, wet weight concentrations and BMFs are more insightful. Finally, a preferred strategy is proposed to advance the science of bioaccumulation using a combination of well-designed ecosystem monitoring, laboratory determinations and modelling to confirm that the perceived state of the science contained in the models is consistent with observations.
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