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

Abstract

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

Probabilistic Environmental Risk Assessment for Linear Alkyl Benzene Sulfonate (LAS) in Japan Reduces Assessment Uncertainty

“…that responds at levels approaching the analytical detection limit of LAS and more than a factor of 100 below the existing most sensitive species, rainbow trout. These observations are consistent with many other previously published comparisons and assessments [8,46,47,54,55]. The abundance of data on LAS concentrations in Japanese surface waters provides the most robust dataset the authors are aware of and allows for the evaluation of the likelihood of the environmental concentration exceeding an aquatic effects threshold.…”

“…SSDs were constructed using the SSD in R package developed by P&G (Cincinnati, USA) and discussed in Carr and Belanger [46] and Belanger and Carr [47]. SSDs were fit to a log-logistic model following normalization to C10 to C14 individual CLs and an average environmental exposure CL of C11.5.…”

SSDs Revisited: Part I—A Framework for Sample Size Guidance on Species Sensitivity Distribution Analysis

“…), the nature of the input data and its quality, taxonomic diversity, and so forth (Belanger et al 2017). Predicting the impact of theoretical "new" species toxicity data to provide insight into the expected HC5 is a convenient and straightforward expression of the existing data and distribution (Belanger and Carr 2019). Figure 3 shows the effect of sample size on the estimation of the HC5 for 2 levels of closeness (columns) and a measure of wileyonlinelibrary.com/ETC © 2019 SETAC FIGURE 3: A matrix of plots showing the effect of sample size on closeness to the true value for simulated species sensitivity distributions (SSDs) under a logistic model.…”

Recent Developments in Species Sensitivity Distribution Modeling

“…Following its introduction in the 1980s (Stephan et al 1985; Kooijman 1987; van Straalen and Denneman 1989), the SSD has remained the most widely used method for deriving water quality benchmarks (guidelines, criteria, or standards, depending on the jurisdiction) to characterize effects of chemical contaminants on water quality and/or for ecological risk assessment purposes. The SSD has proved to be a useful, practical, and intuitive tool (Belanger et al 2017; Belanger and Carr 2019), albeit not without numerous limitations (e.g., Organisation for Economic Co‐operation and Development 1992; Forbes and Forbes 1993; Smith and Cairns 1993; Warne 1998; Newman et al 2000; Forbes and Calow 2002; Wheeler et al 2002a, 2002b; Zajdlik 2006; Hickey and Craig 2012; European Centre for Ecotoxicology and Toxicology of Chemicals 2014), including the implausibility of the many assumptions underpinning SSDs and concerns arising from inconsistent statistical results. Despite a significant body of published research and numerous intensive reviews (e.g., Organisation for Economic Co‐operation and Development 1992; Posthuma et al 2002; European Centre for Ecotoxicology and Toxicology of Chemicals 2014; Fisher et al 2019) over the past 20 yr aimed at improving SSD methods, the fundamental SSD approach employed by jurisdictions around the world has remained similar.…”

“…Despite the limitations, SSDs remain a practical tool and, until a demonstrably better inferential framework is available, developments and enhancements to conventional SSD practice will and should continue. Indeed, numerous studies have attempted to address many of the limitations, including issues of sample size, species representativeness and selection, test endpoints, ecological relevance, phylogenetic relatedness, and routes of exposure (e.g., de Zwart and Posthuma 2005; Dyer et al 2006; Fox 2010; Wang et al 2015; Warne et al 2018; Belanger and Carr 2019; Carr and Belanger 2019; Moore et al 2019; Schwarz and Tillmanns 2019). Although certain improvements to formal SSD methods have recently been adopted (i.e., methods typically approved and recommended for use by national, provincial, and state regulatory bodies; see: Warne et al 2018; British Columbia Ministry of Environment and Climate Change Strategy 2019), in general, few of the outcomes of SSD studies from the past 20 yr have been formally adopted.…”