Abstract
Microbial population responses to combined effects of chronic irradiation and other stressors (chemical contaminants, other sub-optimal conditions) are important for ecosystem functioning and bioremediation in radionuclide-contaminated areas. Quantitative mathematical modeling can improve our understanding of these phenomena. To identify general patterns of microbial responses to multiple stressors in radioactive environments, we analyzed three data sets on: (1) bacteria isolated from soil contaminated by nuclear waste at the Hanford site (USA); (2) fungi isolated from the Chernobyl nuclear-power plant (Ukraine) buildings after the accident; (3) yeast subjected to continuous γ-irradiation in the laboratory, where radiation dose rate and cell removal rate were independently varied. We applied generalized linear mixed-effects models to describe the first two data sets, whereas the third data set was amenable to mechanistic modeling using differential equations. Machine learning and information-theoretic approaches were used to select the best-supported formalism(s) among biologically-plausible alternatives. Our analysis suggests the following: (1) Both radionuclides and co-occurring chemical contaminants (e.g. NO2) are important for explaining microbial responses to radioactive contamination. (2) Radionuclides may produce non-monotonic dose responses: stimulation of microbial growth at low concentrations vs. inhibition at higher ones. (3) The extinction-defining critical radiation dose rate is dramatically lowered by additional stressors. (4) Reproduction suppression by radiation can be more important for determining the critical dose rate, than radiation-induced cell mortality. In conclusion, the modeling approaches used here on three diverse data sets provide insight into explaining and predicting multi-stressor effects on microbial communities: (1) the most severe effects (e.g. extinction) on microbial populations may occur when unfavorable environmental conditions (e.g. fluctuations of temperature and/or nutrient levels) coincide with radioactive contamination; (2) an organism’s radioresistance and bioremediation efficiency in rich laboratory media may be insufficient to carry out radionuclide bioremediation in the field—robustness against multiple stressors is needed.
Highlights
Understanding radionuclide effects on non-human biota is important for environmental protection and bioremediation after accidental or malicious (“dirty bomb” attacks) radionuclide releases [1,2,3,4,5,6,7]
We searched the literature for published data sets on microbial populations subjected to chronic irradiation, which met the following conditions: radiation had to be combined with other stressors, and the publications had to contain enough information to conduct a thorough mathematical/statistical analysis
There was no evidence that the composition of bacterial taxa detected in any two samples was correlated with the depth of soil separating the samples: the Mantel test for spatial auto-correlation using 9999 random permutations produced a correlation of only -0.02 with a p-value of 0.81
Summary
Understanding radionuclide effects on non-human biota is important for environmental protection and bioremediation after accidental (e.g. from nuclear power plants or waste storage facilities) or malicious (“dirty bomb” attacks) radionuclide releases [1,2,3,4,5,6,7]. The objective of this paper is to use quantitative mathematical modeling to identify general patterns of microbial responses to radioactive contamination. Understanding and predicting such patterns can guide research on environmental protection and bioremediation after radionuclide releases [5, 6, 8]. We searched the literature for published data sets on microbial populations subjected to chronic irradiation, which met the following conditions: radiation had to be combined with other stressors (e.g. chemical toxicants, oligotrophic conditions, and/or high cell removal rates), and the publications had to contain enough information to conduct a thorough mathematical/statistical analysis. We sought to use data sets on different microbial groups (e.g. bacteria and fungi) and exposure conditions (e.g. field and laboratory), so that integrating the analysis results from all these data sets would help to form a general picture of how microbial communities respond to chronic irradiation combined with other stressors
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