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Environmental problems - Chemical approaches
RESEARCH ARTICLE (Open Access)

Does toxicity test variability support bioavailability model predictions being within a factor of 2?

Gwilym A. V. Price https://orcid.org/0000-0003-3261-1298 A B * , Jenny L. Stauber B C , Sarah Stone A B , Darren J. Koppel D , Aleicia Holland B C and Dianne Jolley B
+ Author Affiliations
- Author Affiliations

A Faculty of Science, University of Technology Sydney, Ultimo, NSW 2007, Australia.

B CSIRO Land and Water, Lucas Heights, NSW, Australia.

C Department of Environment and Genetics, School of Life Science, Centre for Freshwater Ecosystems, La Trobe University, Albury/Wodonga Campus, Vic., Australia.

D Australian Institute of Marine Science, Crawley, WA, Australia.

* Correspondence to: gwilym.price@csiro.au

Handling Editor: Kevin Wilkinson

Environmental Chemistry 19(4) 177-182 https://doi.org/10.1071/EN22050
Submitted: 13 May 2022  Accepted: 11 July 2022   Published: 8 September 2022

© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Environmental context. Having appropriate and robust models used for developing water quality guidelines is critical for sound environmental management. Methods used to validate models have only been demonstrated appropriate for a small portion of data types used in these models. This study has found that models using certain data types would be more appropriately validated using alternative evaluation criteria. This study serves as an important reference for developing and evaluating robust models.

Rationale. Bioavailability-based toxicity models for metals often have performance assessed by whether it can predict toxicity data within a factor of 2 of their paired observed toxicity data. This method has only been verified for median effect values (EC50) for acute fish and daphnia data, however toxicity models have been developed for a much broader range of effect levels (i.e. EC10/EC20) and species (e.g. microalga). This study tested whether the factor-of-2 rule is appropriate for a wider range of organisms and effect concentrations than previously studied.

Methodology. Toxicity estimate data from repeated tests conducted under the same conditions were collated to assess variation in results and compare this variation to a range of 4 (a factor of 2 above and below the mean) and a range of 9 (a factor of 3 above and below the mean) to assess if a factor-of-3 rule may be more appropriate for some species and effect levels.

Results and discussion. Overall, the factor-of-2 rule is broadly applicable for metal toxicity to a range of species for EC50 data. The EC10 datasets highlighted that larger variability exists in low effect levels and supported the use of a factor-of-3 rule, while the either the factor-of-2 or factor-of-3 rule could be applied to microalgae. The level of performance evaluation chosen may depend on the application of the bioavailability model. This study also found that while repeated toxicity test data is routinely generated, it is rarely published. Publication of such data would enable expansion of the present study to include inter-laboratory comparisons, an important consideration as most bioavailability models are based on data pooled from multiple sources.

Keywords: bioavailability, biotic ligand model, ecotoxicology, metal toxicity, model predictions, model validation, reference toxicants, water quality.


References

Batley GE, Van Dam RA, Warne M, Chapman J, Fox DR, Hickey CW, Stauber JL (2018) Technical Rationale for Changes to the Method for Deriving Australian and New Zealand Water Quality Guideline Values for Toxicants - Update of 2014 version. Prepared for the revision of the Australian and New Zealand Guidelines for Fresh and Marine Water. (CSIRO: Canberra, ACT, Australia)

Besser JM, Ivey CD, Steevens JA, Cleveland D, Soucek D, Dickinson A, Genderen EJ, Van Ryan AC, Schlekat CE, Garman ER, Middleton ET, Santore RC (2021). Modeling the Bioavailability of Nickel and Zinc to Ceriodaphnia dubia and Neocloeon triangulifer in Toxicity Tests with Natural Waters. Environmental Toxicology and Chemistry 40, 3049–3062.
Modeling the Bioavailability of Nickel and Zinc to Ceriodaphnia dubia and Neocloeon triangulifer in Toxicity Tests with Natural Waters.Crossref | GoogleScholarGoogle Scholar |

Brix KV, Deforest DK, Tear L, Grosell M, Adams WJ (2017). Use of Multiple Linear Regression Models for Setting Water Quality Criteria for Copper: A Complementary Approach to the Biotic Ligand Model. Environmental Science and Technology 51, 5182–5192.
Use of Multiple Linear Regression Models for Setting Water Quality Criteria for Copper: A Complementary Approach to the Biotic Ligand Model.Crossref | GoogleScholarGoogle Scholar |

Brix KV, DeForest DK, Tear L, Peijnenburg W, Peters A, Middleton ET, Erickson R (2020). Development of Empirical Bioavailability Models for Metals. Environmental Toxicology and Chemistry 39, 85–100.
Development of Empirical Bioavailability Models for Metals.Crossref | GoogleScholarGoogle Scholar |

Brix KV, Tear L, Santore RC, Croteau K, DeForest DK (2021). Comparative Performance of Multiple Linear Regression and Biotic Ligand Models for Estimating the Bioavailability of Copper in Freshwater. Environmental Toxicology and Chemistry 40, 1649–1661.
Comparative Performance of Multiple Linear Regression and Biotic Ligand Models for Estimating the Bioavailability of Copper in Freshwater.Crossref | GoogleScholarGoogle Scholar |

Croteau K, Ryan AC, Santore R, DeForest D, Schlekat C, Middleton E, Garman E (2021). Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the Toxicity of Nickel to Aquatic Freshwater Organisms. Environmental Toxicology and Chemistry 40, 2189–2205.
Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the Toxicity of Nickel to Aquatic Freshwater Organisms.Crossref | GoogleScholarGoogle Scholar |

DeForest DK, Brix KV, Tear LM, Adams WJ (2018). Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines. Environmental Toxicology and Chemistry 37, 80–90.
Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines.Crossref | GoogleScholarGoogle Scholar |

DeForest DK, Brix KV, Tear LM, Cardwell AS, Stubblefield WA, Nordheim E, Adams WJ (2020). Updated Multiple Linear Regression Models for Predicting Chronic Aluminum Toxicity to Freshwater Aquatic Organisms and Developing Water Quality Guidelines. Environmental Toxicology and Chemistry 39, 1724–1736.
Updated Multiple Linear Regression Models for Predicting Chronic Aluminum Toxicity to Freshwater Aquatic Organisms and Developing Water Quality Guidelines.Crossref | GoogleScholarGoogle Scholar |

Di Toro DM, Allen HE, Bergman HL, Meyer JS, Paquin PR, Santore RC (2001). Biotic Ligand Model of the Acute Toxicity of Metals. 1. Technical Basis. Environmental Toxicology and Chemistry 20, 2383–2396.
Biotic Ligand Model of the Acute Toxicity of Metals. 1. Technical Basis.Crossref | GoogleScholarGoogle Scholar |

Erickson RJ, Benoit DA, Mattson VR, Leonard EN, Nelson Jr HP (1996). The effects of water chemistry on the toxicity of copper to fathead minnows. Environmental Toxicology and Chemistry 15, 181–193.
The effects of water chemistry on the toxicity of copper to fathead minnows.Crossref | GoogleScholarGoogle Scholar |

Garman ER, Meyer JS, Bergeron CM, Blewett TA, Clements WH, Elias MC, Farley KJ, Gissi F, Ryan AC (2020). Validation of Bioavailability-Based Toxicity Models for Metals. Environmental Toxic and Chemistry 39, 101–117.
Validation of Bioavailability-Based Toxicity Models for Metals.Crossref | GoogleScholarGoogle Scholar |

Kassambara A (2020) Package ggpubr; ‘ggplot2’ Based Publication Ready Plots. R package version 0.4.0. Available at https://CRAN.R‐project.org/package=ggpub

Meyer JS, Traudt EM, Ranville JF (2018). Is the Factor-of-2 Rule Broadly Applicable for Evaluating the Prediction Accuracy of Metal-Toxicity Models?. Bulletin of Environmental Contamination and Toxicology 100, 64–68.
Is the Factor-of-2 Rule Broadly Applicable for Evaluating the Prediction Accuracy of Metal-Toxicity Models?.Crossref | GoogleScholarGoogle Scholar |

Peters A, Merrington G, Schlekat C, De Schamphelaere K, Stauber J, Batley G, Harford A, van Dam R, Pease C, Mooney T, Warne M, Hickey C, Glazebrook P, Chapman J, Smith R, Krassoi R (2018). Validation of the nickel biotic ligand model for locally relevant species in Australian freshwaters. Environmental Toxicology and Chemistry 37, 2566–2574.
Validation of the nickel biotic ligand model for locally relevant species in Australian freshwaters.Crossref | GoogleScholarGoogle Scholar |

Peters A, Merrington G, Stauber J, Golding L, Batley G, Gissi F, Adams M, Binet M, McKnight K, Schlekat CE, Garman E, Middleton E (2021). Empirical Bioavailability Corrections for Nickel in Freshwaters for Australia and New Zealand Water Quality Guideline Development. Environmental Toxicology and Chemistry 40, 113–126.
Empirical Bioavailability Corrections for Nickel in Freshwaters for Australia and New Zealand Water Quality Guideline Development.Crossref | GoogleScholarGoogle Scholar |

Price GAV, Stauber JL, Holland A, Koppel DJ, Van Genderen EJ, Ryan AC, Jolley DF (2022). The influence of hardness at varying pH on zinc toxicity and lability to a freshwater microalga, Chlorella sp. Environmental Science: Processes & Impacts 24, 783–793.
The influence of hardness at varying pH on zinc toxicity and lability to a freshwater microalga, Chlorella sp.Crossref | GoogleScholarGoogle Scholar |

RStudio Team (2020) RStudio: Integrated Development for R. Available at http://www.rstudio.com/

Santore RC, Ryan AC (2015). Development and application of a multimetal multibiotic ligand model for assessing aquatic toxicity of metal mixtures. Environmental Toxicology and Chemistry 34, 777–787.
Development and application of a multimetal multibiotic ligand model for assessing aquatic toxicity of metal mixtures.Crossref | GoogleScholarGoogle Scholar |

Santore RC, Di Toro DM, Paquin PR, Allen HE, Meyer JS (2001). Biotic ligand model of the acute toxicity of metals. 2. Application to acute copper toxicity in freshwater fish and Daphnia. Environmental Toxicology and Chemistry 20, 2397–2402.
Biotic ligand model of the acute toxicity of metals. 2. Application to acute copper toxicity in freshwater fish and Daphnia.Crossref | GoogleScholarGoogle Scholar |

Santore RC, Croteau K, Ryan AC, Schlekat C, Middleton E, Garman E, Hoang T (2021). A Review of Water Quality Factors that Affect Nickel Bioavailability to Aquatic Organisms: Refinement of the Biotic Ligand Model for Nickel in Acute and Chronic Exposures. Environmental Toxicology and Chemistry 40, 2121–2134.
A Review of Water Quality Factors that Affect Nickel Bioavailability to Aquatic Organisms: Refinement of the Biotic Ligand Model for Nickel in Acute and Chronic Exposures.Crossref | GoogleScholarGoogle Scholar |

Stone S, Koppel D, Binet MT, Simpson SL, Jolley DF (2022). Pulse-Exposure Toxicity of Ammonia and Propoxur to the Tropical Copepod Acartia sinjiensis. Environmental Toxic and Chemistry 41, 208–218.
Pulse-Exposure Toxicity of Ammonia and Propoxur to the Tropical Copepod Acartia sinjiensis.Crossref | GoogleScholarGoogle Scholar |

Stephan CE, Mount DI, Hansen DJ, Gentile JH, Chapman GA, Brungs WA (1985) ‘Guidelines for deriving numerical national water quality criteria for the protection of aquatic organisms and their uses.’ (US Environmental Protection Agency: Washington, DC, USA)

Wickham H (2016) ‘Elegant Graphics for Data Analysis.’ (Springer-Verlag: New York, USA)