Modelling the combined effects of ionising radiation and chemical pollutants on wildlife populations

Abstract

A population model is presented to study the combined effects of ionising radiation and chemical pollutants on wildlife. The model is based on first order, non-linear and logistic differential equations combining mortality, morbidity and reproduction phenomena with life history data and ecological interactions. Acclimation is considered as a possible mechanism to study theoretically this effect at low levels of radiation or chemical concentration. Radiation and chemical-induced damages are represented by a ‘repairing pool’ mediating between healthy, damaged, acclimated, and irrecoverable individuals. Damages to population, fecundity and the repairing pool are represented by a linear-quadratic function combining radiation dose and chemical concentration terms. The endpoints of the model are repairable damages (morbidity), impairment of reproductive ability and mortality.

The model is evaluated with a mixed ionising radiation/arsenate demonstration scenario to illustrate the combined effect of radiation and chemical pollutants upon the sustainability of a hypothetical vole population, including the influence of acclimation, given the assumption that the repair of both radiation and toxicity damages share the same mechanism. A sensitivity analysis of the model illustrates the effects of combining radiation dose and chemical concentration on self-repairing and reproductive ability for the population, exploring cases of antagonism and synergism by varying the relevant model parameters.

This model provides a conceptual framework to address mixed radiological and chemical effects to wildlife populations. It can be used to assess the robustness of the benchmarks used in wildlife radiological assessment, informing ongoing regulatory debates on their applicability to mixed stressor situations. Future research will enable to draw conclusions about the most restrictive mixed exposure situations in terms of effects to the population.