Future needs and recommendations in the development of species sensitivity distributions: Estimating toxicity thresholds for aquatic ecological communities and assessing impacts of chemical exposures

Belanger, Scott E.; Barron, Mace G.; Craig, Peter; Dyer, Scott D.; Galay-Burgos, Malyka; Hamer, Mick; Marshall, Stuart; Posthuma, Leo; Raimondo, Sandy; Whitehouse, Paul

doi:10.1002/ieam.1841

Cited by 102 publications

(102 citation statements)

References 23 publications

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“…A common criticism of SSDs is the basic assumption of exchangeability of the species selected allowing their “random sampling.” Craig et al () have shown that for some species, such as Oncorhynchus mykiss or Carassius auratus , this assumption is improper, affecting the HC 5 derivation significantly, which is also visible in Figure . Some methods were provided to tackle this challenge (Craig et al ; Belanger et al ) and should be considered by environmental risk practitioners who use the SSD and PSSD+ methods.…”

Section: Resultsmentioning

confidence: 99%

Systematic Consideration of Parameter Uncertainty and Variability in Probabilistic Species Sensitivity Distributions

Wigger

Kawecki

Nowack

et al. 2019

Integr Envir Assess & Manag

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The calculation of a species sensitivity distribution (SSD) is a commonly accepted approach to derive the predicted no‐effect concentration (PNEC) of a substance in the context of environmental risk assessment. The SSD approach usually is data demanding and incorporates a large number of ecotoxicological values from different experimental studies. The probabilistic SSD (PSSD) approach is able to fully consider the variability between different exposure conditions and material types, which is of great importance when constructing an SSD for any chemical, especially for nanomaterials. The aim of our work was to further develop the PSSD approach by implementing methods to better consider the uncertainty and variability of the input data. We incorporated probabilistic elements to consider the uncertainty associated with uncertainty factors by using probability distributions instead of single values. The new PSSD method (named “PSSD+”) computes 10 000 PSSDs based on a Monte Carlo routine. For each PSSD calculated, the hazardous concentration for 5% of species (HC5) was extracted to provide a PNEC distribution based on all data available and their associated uncertainty. The PSSD+ approach also includes the option to consider a species weighting according to a typically constituted biome. We applied this PSSD+ approach to a previously published data set on C nanotubes and Ag nanoparticles. The evaluation of the uncertainty factor distributions and species weighting have shown that the proposed PSSD method is robust with respect to the calculation of the PNEC value. Furthermore, we demonstrated that the PSSD+ can handle both small and more comprehensive data sets because the PNEC distributions are a close representation of the data available. Finally, the sensitivity testing toward data set variations showed that the maximum variation of the mean PNEC was of a factor of about 2, so that the method is relatively insensitive to missing data points as long as the most sensitive species is included. Integr Environ Assess Manag 2020;16:211–222. © 2019 SETAC

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Section: Resultsmentioning

confidence: 99%

Systematic Consideration of Parameter Uncertainty and Variability in Probabilistic Species Sensitivity Distributions

Wigger

Kawecki

Nowack

et al. 2019

Integr Envir Assess & Manag

View full text Add to dashboard Cite

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“…Different PCB congeners and technical mixtures are without doubt more potent than others, particularly those with higher percentages of the dioxin-like PCBs (77, 81, 126, and 169;Safe 1994;Giesy and Kannan 1998;Van den Berg et al 1998;Kannan et al 2000;Burkhard and Lukasewycz 2008); nonetheless, the LOAER regressions using total PCB concentrations were robust enough across PCB mixtures to be useful with calculated degrees of uncertainty. Similarly, small datasets are considered valid when regulatory assessments are developed for Registration, Evaluation, Authorisation, and Restriction (REACH) in Europe (European Chemicals Agency 2008), water quality criteria in the United States (Stephan et al 1985), and similar assessments in other countries (Belanger et al 2017). Fish exhibit differing sensitivities to dioxins (Elonen et al 1998;Tillitt et al 2017), and differences among species in sensitivity to PCBs were evident in the data we compiled.…”

Section: Discussionmentioning

confidence: 99%

Polychlorinated biphenyl tissue‐concentration thresholds for survival, growth, and reproduction in fish

Berninger

Tillitt

2019

Enviro Toxic and Chemistry

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Polychlorinated biphenyls (PCBs) have left a legacy of environmental contamination. Even though they were banned from production and active use in the 1970s, they persist in the environment and still have the potential to impact aquatic life. Our objective was to identify data from controlled laboratory studies of PCB‐related adverse effects in fish and to conduct a meta‐analysis on mortality, growth, and reproductive (MGR) threshold responses. For each endpoint type, we compiled data on the lowest‐observed‐adverse effect concentration (LOAEC) and the degree of effect at the LOAEC as a percentage of control. The LOAECs were expressed as tissue concentrations, so the term lowest‐observed‐adverse‐effect residue concentration (LOAER) was used to represent PCB exposures. The lower limit of applicability was set at 0.1 μg/g total PCB tissue concentration, below which adverse MGR effects in fish were not supported by the data. Sensitivity distributions identifying the probability of adverse effects in fish populations or communities predicted that 25% of fish species would be impacted between 0.1 and 7.5 μg/g. Concentration–response threshold regressions were developed from the MGR datasets. For example, a 1 μg/g total PCB tissue concentration would predict effects of 17% mortality, 15% growth, and 39% reproductive. The analysis determined the degree of adverse response, with uncertainty estimates, expected across a broad range of PCB tissue exposure concentrations in fish. Data generated from MGR endpoints were combined to determine an approach for overall effect thresholds for PCB‐related injury in fish. The MGR datasets included only laboratory data; however, responses were compared with field‐observed effects. The present review provides a comprehensive assessment of PCB‐induced injury in fish utilizing a data‐inclusive approach. Environ Toxicol Chem 2019;38:712–736. Published 2018 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

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“…Test data sample sizes for subsequent SSD calculations ranged from n = 6 to n = 165. Two of the data sets, chlorine and C12TMAC, would not meet the agreed‐on practices of various regulatory organizations for deriving HC5 values where minimum taxonomic diversity should be more than 8 (Stephan et al ) to more than 10 (Australian and New Zealand Environment and Conservation Council and Agriculture and Resource Management Council of Australia and New Zealand ; European Chemicals Agency ) for SSD application (European Centre for Ecotoxicology and Toxicology of Chemicals ; Belanger et al ). Several chemicals (nickel, copper, and chlorpyrifos) were well above the typical level of species diversity for SSD data sets.…”

Section: Methodsmentioning

confidence: 99%

SSDs revisited: part II—practical considerations in the development and use of application factors applied to species sensitivity distributions

Belanger

Carr²

2019

Enviro Toxic and Chemistry

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Application factors are routinely applied in the extrapolation of laboratory aquatic toxicity data to ensure protection from exposure to chemicals in the natural environment. The magnitude of the application factor is both a scientific and a policy decision, but in any case, it should be rooted in scientific knowledge so as to not be arbitrary. Information‐rich chemicals are often subjected to species sensitivity distribution (SSD) analysis to transparently describe certain aspects of assessment uncertainty and are normally subjected to much smaller application factors than screening information data sets. We describe a new set of tools useful to assess the quality of SSDs. Twenty‐two data sets and 19 chemicals representing agrochemicals, biocides, surfactants, metals, and common wastewater contaminants were compiled to demonstrate how the tools can be used. “Add‐one‐in” and “leave‐one‐out” simulations were used to investigate SSD robustness and develop quantitative evidence for the use of application factors. Theoretical new toxicity data were identified for add‐one‐in simulations based on the expected probabilities necessary to lower the hazardous concentration to 5% of a species (HC5) by a factor of 2, 3, 5, or 10. Simulations demonstrate the basis for application factors in the range of 1 to 5 for well‐studied chemicals with high‐quality SSDs. Leave‐one‐out simulations identify the fact that the most influential values in the SSD come from the extremes of the sensitive and tolerant toxicity values. Mesocosm and field data consistently demonstrate that HC5s are conservative, further justifying the use of small application factors for high‐quality SSDs. Environ Toxicol Chem 2019;38:1526–1541. © 2019 SETAC

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Future needs and recommendations in the development of species sensitivity distributions: Estimating toxicity thresholds for aquatic ecological communities and assessing impacts of chemical exposures

Cited by 102 publications

References 23 publications

Systematic Consideration of Parameter Uncertainty and Variability in Probabilistic Species Sensitivity Distributions

Systematic Consideration of Parameter Uncertainty and Variability in Probabilistic Species Sensitivity Distributions

Polychlorinated biphenyl tissue‐concentration thresholds for survival, growth, and reproduction in fish

SSDs revisited: part II—practical considerations in the development and use of application factors applied to species sensitivity distributions

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