© 2002 by CRC Press LLC Section IV Evaluation and Outlook This final section presents an overview of the current field and of options for future developments. The concepts and data presented in the preceding chapters and in the literature have been analyzed in view of the criticisms of SSDs that have been voiced in the past, and during the Interactive Poster Session that was held in 1999 at the 20th Annual Meeting of the Society of Environmental Toxicology and Chemistry in Philadelphia, Pennsylvania. In the concluding outlook chapter, all preceding chapters have been reconsidered to determine the prospects for resolving the criticisms and problems of SSDs. Some of these issues, those that seem amenable to solution, have been extrapolated to the near future, to stimulate discussion and thought on further SSD evolution. © 2002 by CRC Press LLC Issues and Practices in the Derivation and Use of Species Sensitivity Distributions Glenn W. Suter II, Theo P. Traas, and Leo Posthuma CONTENTS 21.1 The Uses of SSDs 21.1.1 SSDs for Derivation of Environmental Quality Criteria 21.1.2 SSDs for Ecological Risk Assessment 21.1.2.1 Assessment Endpoints and the Definition of Risk 21.1.2.2 Ecological Risk Assessment of Mixtures 21.1.3 Probability of Effects from SSDs 21.2 Statistical Model Issues 21.2.1 Selection of Distribution Functions and Goodness-of-Fit 21.2.2 Confidence Levels 21.2.3 Censoring and Truncation 21.2.4 Variance Structure 21.3 The Use of Laboratory Toxicity Data 21.3.1 Test Endpoints 21.3.2 Laboratory to Field Extrapolation 21.4 Selection of Input Data 21.4.1 SSDs for Different Media 21.4.2 Types of Data 21.4.3 Data Quality 21.4.4 Adequate Number of Observations 21.4.5 Bias in Data Selection 21.4.6 Use of Estimated Values 21.5 Treatment of Input Data 21.5.1 Heterogeneity of Media 21.5.2 Acute–Chronic Extrapolations 21.5.3 Combining Data for a Species 21.5.4 Combining Data across Species 21.5.5 Combining Taxa in a Distribution 21 © 2002 by CRC Press LLC 21.5.6 Combining Data across Environments 21.5.7 Combining Data across Durations 21.5.8 Combining Chemicals in Distributions 21.6 Selection of Protection Levels 21.7 Risk Assessment Issues 21.7.1 Exposure 21.7.2 Ecological Issues 21.7.3 Joint Distributions of Exposure and Species Sensitivity 21.8 The Credibility of SSDs 21.8.1 Reasonable Results 21.8.2 Confirmation Studies 21.8.3 SSD vs. Alternative Extrapolation Models 21.9 Conclusions Abstract — As is clear from the preceding chapters, species sensitivity distributions (SSDs) have come to be commonly used in many countries for setting environmental quality criteria (EQCs) and assessing ecological risks (ERAs). However, SSDs have had their critics, and the critics and users of SSD models have raised conceptual and methodological concerns. This chapter evaluates issues raised in published critiques of SSDs (e.g., Forbes and Forbes, 1993; Hopkin, 1993; Smith and Cairns, 1993; Chapman et al., 1998), in a session at the 1999 SETAC Annual Meeting (Appendix A), and in the course of preparing this book. The issues addressed include conceptual issues, statistical issues, the utility of laboratory data, data selection, treatment of data, selec- tion of protection levels, and the validity of SSDs. When considering these issues, one should be aware that the importance and implications of these issues may depend on the context and use of an SSD. The consequences of this evaluation for further devel- opment of SSDs are elaborated in Chapter 22. 21.1 THE USES OF SSDS Models of species sensitivity distributions (SSDs) with respect to a toxic substance can be used in two conceptually distinct ways (Chapters 1 and 4). The first use is to estimate the concentration that affects a particular proportion of species, the HC p . This is the older so-called inverse use, and is employed in the derivation of envi- ronmental criteria. The second use is the forward use of SSDs, which estimates the potentially affected fraction (PAF) of species, or the probability of effects on a species (PES) at a given concentration. The PAF or PES can be calculated for single chemicals and these values can be aggregated to a single value for mixtures of chemicals. In any of these uses, it is assumed that protection of species and communities may be assured by considering the distribution of sensitivities of species tested individually. Although some regu- latory agencies have embraced the concept of risk embedded in the use of SSDs (Chapters 2 and 3) the assumption that SSD-derived criteria are protective is an open question. The definition and interpretation of risk as defined previously (Suter, 1993; Chapters 15 through 17) play a major part in the interpretation of the outcome of SSD methods, as discussed below. © 2002 by CRC Press LLC 21.1.1 SSD S FOR D ERIVATION OF E NVIRONMENTAL Q UALITY C RITERIA As discussed in the introductory chapters, SSDs were developed to derive criteria for the protection of ecological entities in contaminated media. That is, criteria are set at an HC p or an HC p modified by some factor. Such criteria may be interpreted as, literally, levels that will protect 1 – p % of species or simply as consistent values that provide reasonable protection from unspecified effects. If the criteria are interpreted as protecting 1 – p % of species from some effect with defined confidence, then they are potentially subject to scientific confirmation. Some studies have attempted to confirm SSD-based quality criteria in the last decade by comparing them to contaminant effects in the field (Chapter 9 and Section 21.8.2). However, if criteria derived from SSDs are inter- preted simply as reasonable and consistent values, their utility is confirmed in that sense by a record of use that has been politically and legally acceptable. That is, if they were not reasonable and consistent, they would be struck down by the courts or replaced due to pressures from industry or environmental advocacy groups. The U.S. Environmental Protection Agency (U.S. EPA) National Ambient Water Quality Criteria and the Dutch Environmental Risk Limits for water, soil, and sediment have achieved at least the latter degree of acceptance. A general acceptance of the SSD methodology is not necessarily negated by challenges incidentally posed to individual SSD-based criteria such as the challenge of the environmental quality criterion (EQC) for zinc by European industries (RIVM/TNO, 1999). The general acceptance of SSD-derived criteria should not suggest a uniformity of methods around the globe. Adopted methods for deriving EQCs vary in many ways among countries, including the choice and treatment of input data, statistical models, and choice of protection level (Chapters 10 through 20; Roux et al., 1996; Tsvetnenko, 1998; Vega et al., 1997; Tong et al., 1996; ANZECC 2000a,b; etc.). One homology is that SSDs defined by unimodal distribution functions are the basis for deriving EQC in several countries. Polymodality of the data may, however, occur for compounds with a taxon-specific toxic mode of action (TMoA) (Section 21.5.5), and Aldenberg and Jaworska (1999) suggested polymodal model for EQC derivation. The HC p values in the protective range of use (e.g., 5th percentile) estimated with this model were shown to be numerically fairly robust toward deviations from unimodality in some selected cases (Aldenberg and Jaworska, 1999). For compounds with a specific TMoA, it can be argued that the variance in species sensitivity as estimated from the total data set is larger and not representative of the variance of the target species. This would lead to overprotective criteria since the HC p is very sensitive to this variance. On the other hand, it can be argued that the total variance may lead to more protective criteria, providing some safety against unknown or unexpected side effects. Conclusive numerical data remain to be presented in this matter. On non-numerical grounds, but driven by considering the assessment end- points, the estimate of a specific HC p for a target taxon may be preferred over an HC p based on the total data set (Chapter 15). The diversity of operational details and the invention of new approaches like polymodal statistics suggest that discussions will proceed in the use of SSD for deriving environmental quality standards. The history of SSD use (Chapters 2 and 3) © 2002 by CRC Press LLC teaches that it is important to distinguish clearly in the discussion between issues related to assessment endpoints, methodological details of SSDs, and choices within the SSD concept related to the policy context. 21.1.2 SSD S FOR E COLOGICAL R ISK A SSESSMENT The goal of risk assessment is to estimate the likelihood of specified effects such as death of humans or sinking of a ship. The growing use of SSDs in ecological risk assessments and the diverse terminology used so far (Chapter 4; Chapters 15 through 20) necessitate a sharp definition of the outcome of SSDs in terms of predicted risks for specific ecological endpoints. Also, unlike criteria, risk assessments must deal with real sites, which requires modeling the effects of mixtures. SSDs have been incorporated into formal ecological risk assessment methods developed by the Water Environment Research Foundation (WERF, Parkhurst et al., 1996), the Aquatic Risk Assessment and Mitigation Dialog Group (ARAMDG, Baker et al., 1994), and the Ecological Committee on FIFRA Risk Assessment Methods (ECOFRAM, 1999a,b). 21.1.2.1 Assessment Endpoints and the Definition of Risk The appropriateness of SSDs in risk assessment depends on the endpoints of the assessment as well as the use of the SSDs in the inferential process. Assessment endpoints are the operational definition of the environmental values to be protected by risk-based environmental management (Suter, 1989; U.S. EPA, 1992). They consist of an ecological entity such as the fish assemblage of a stream and a property of that entity such as the number of species. Assessment endpoints are estimated from numerical summaries of tests (i.e., test endpoints such as LC 50 values) or of observational studies (e.g., catch per unit effort). The extrapolation from these measures of effect to an assessment endpoint is performed using a model such as an SSD. If SSDs are used inferentially to estimate risks to ecological communities, it is necessary to define the relationship of the SSD to the assessment endpoint, given the input data (test endpoints). Currently, two types of test endpoints are most often used, acute LC 50 values* and chronic no-observed-effects concentrations (NOECs) or chronic values (CVs), which yield acute (SSD LC50) and chronic (e.g., SSD NOEC ) SSDs with different implications. The acute LC 50 values are based on mortality or equivalent effects (i.e., immo- bilization) on half of exposed organisms. Hence, this test endpoint implies mass mortality of individuals. At the population level, it could be interpreted as approx- imately a 50% immediate reduction in abundance of an exposed population. As discussed in Chapter 15, some populations recover rapidly from this loss, but other populations are slow to recover. The immediate consequences of mass mortality are, however, often unacceptable in either case. Hence, if such SSDs are considered to be estimators of the distribution of severe effects among species in the field, then the acute SSDs (SSD LC50 ) may be considered to predict the proportion of species experiencing severe population reductions following short-term exposures. An example * For brevity, we use LC 50 to signify both acute LC 50 and EC 50 . © 2002 by CRC Press LLC of the relationship between SSD and an acute assessment endpoint is shown in Chapter 9, where SSD LC50 values for chlorpyrifos are compared with SSDs for arthropod density in experimental ditches. In this specific example, the SSD model seemed to adequately predict the assessment endpoint “arthropod density” in acute exposures. This shows that SSDs based on acute toxicity data for toxicants with a defined TMoA can adequately predict acute changes in appropriate measures of effect. These SSDs likely predict that something will happen, and also (approxi- mately) what (a degree of mortality). The situation is more difficult for chronic assessments. As discussed below (Section 21.3.1), the conventional chronic endpoints represent thresholds for statis- tical significance and have no biological interpretation. Assessors commonly assume that they represent thresholds for significant effects (Cardwell et al., 1999), but that assumption is not supportable. Conventional chronic endpoints correspond to a wide range of effects on populations (Barnthouse et al., 1990). Hence, the relationship of chronic SSDs to measures of effects in the field is less clear than for acute SSDs. Further, ecosystem function and recovery are not embraced in conventional chronic tests or in the SSD models that utilize them. It is important to apply SSDs to endpoints for which they are suited, and not to overinterpret their results. The chronic SSDs may simply predict the proportion of species experiencing population reduc- tions ranging from slight to severe following long-term exposures. Ecological risk assessors have tended to focus on techniques and to avoid the inferential difficulties of defining and estimating assessment endpoints. For example, the aquatic ECOFRAM (1999a) report provides methods for aquatic ecological risk assessment that rely heavily on SSDs but does not define the assessment endpoints estimated by those methods. Rather, it discusses population and ecosystem function and suggests that they will be protected when 90% of species are protected from effects on survival, development, and reproduction. Similar ambiguities occur in the ARAMDG and WERF risk assessment methods (e.g., Baker et al., 1994; Parkhurst et al., 1996). The ambiguity in the relationship of SSDs to assessment endpoints is due in part to the lack of guidance from the regulatory agencies. The U.S. EPA has not defined the valued environmental attributes that should serve as assessment endpoints (Troyer and Brody, 1994; Barton et al., 1997). The risk managers must identify the target and then risk assessors can design models and select data to hit it. However, the U.S. EPA and other responsible agencies have been reluctant to be more specific than “protect the environment,” “abiotic integrity,” “ecosystem struc- ture and function,” or “ecosystem health.” It is not surprising that risk assessors have tended to be equally vague when specifying what is predicted by SSD models. The lack of a clear relationship of SSDs to assessment endpoints is less prob- lematical if the goal of an assessment is simply comparison or ranking (e.g., Manz et al., 1999). For example, SSDs based on NOECs are used in the Netherlands for mapping regional patterns of relative risks (Chapter 16). In particular, the PAF NOEC was hypothesized to be a measure of the relative risk to the clear ecological endpoint, vascular plant diversity. Risk characterization need not be based solely on SSDs, but on a weighing of multiple lines of evidence. In those cases SSDs may play a supporting role rather than serving as the sole estimator of risk (De Zwart et al., 1998; Hall and Giddings, © 2002 by CRC Press LLC 2000). In particular, effects may be estimated from biosurveys or field experiments and the laboratory data may indicate the particular chemicals that cause the effect. For example, in an assessment of risks to fish in the Clinch River, Tennessee, effects were estimated using survey data, the toxicological cause of the apparent effects was established from toxicity tests of ambient waters and biomarkers, and SSDs were used simply to establish the plausibility of particular contaminants as contrib- utors to the toxicity (Suter et al., 1999). The assessment endpoint was a “reduction in species richness or abundance or increased frequency of gross pathologies.” A 20% or greater change measured in the field or in toxicity tests of site waters was considered significant. The chronic SSDs for individual chemicals were considered reasonably equivalent to this endpoint, because chronic tests include gross pathol- ogies (when they occur) and the chronic test endpoints correspond to at least 20% change in individual response parameters, which in combination, over multiple generations, may result in local population extinction (Suter et al., 1987; Barnthouse et al., 1990). SSDs have been suggested as a key tool in a proposed formal tiered risk assess- ment scheme for contaminated soils, where multisubstance PAFs (msPAFs) functions in a “weight of evidence” approach, in which none of the parameters is able to present the whole “truth.” In this context, the msPAF is considered along with bioassay and field inventory results (De Zwart et al., 1998), arraying them on a dimensionless 0 to 1 scale. When all results point in a similar direction, the inves- tigations are ended at the lowest possible tier with a conclusion. A risk-based approach using SSDs as one line of evidence may also be used to derive environmental criteria for specific sites. The guidelines for water quality in Australia and New Zealand recommend the use of bioassessment and toxicity tests of effluents or ambient media along with SSD-based trigger values to derive defen- sible regulatory values (ANZECC, 2000a). Risk assessment approaches may also be used in the enforcement of criteria. The interpretation of criteria is usually binary (i.e., the criterion is or is not exceeded) or in terms of an exceedence factor (e.g., the concentration exceeds the criterion by 5 times). However, a more risk-based alternative would use an SSD to determine the increase in the number or proportion of species at risk as a result of exceeding the criterion (Knoben et al., 1998). 21.1.2.2 Ecological Risk Assessment of Mixtures Because SSDs have historically been based on single-chemical toxicity tests, they have been criticized for not incorporating the combined effects of mixtures of chemicals (Smith and Cairns, 1993). Since mixtures are the rule rather than the exception in field conditions, this subject requires attention. Since single-chemical test data are the major source of data to construct SSDs, methods have been developed to predict the joint risk of chemicals in a mixture (Chapters 16 and 17). They extend the SSD methodology with concepts from toxi- cology and pharmacology (Plackett and Hewlett, 1952; Könemann, 1981). This is technically feasible, since the units in which risks are quantified (PAFs, or similar expressions used in this book) are dimensionless. The resulting fraction of species © 2002 by CRC Press LLC exposed beyond test endpoint concentrations, given exposure from multiple chem- icals, can thus (at least theoretically) be defined, and we propose the term “multi- substance-PAF” (msPAF) for this concept. The ability to calculate msPAFs as measures of mixture risks relates to the classification of pollutants according to their TMoA (e.g., Verhaar et al., 1992; Vaal et al., 1997). For compounds with the same TMoA, concentration addition rules are applied subsequent to SSD analyses in various forms (Chapters 4, 16, and 17). For compounds with different modes of action the rule of response addition has been used (Chapter 16). Conceptually, the transfer of the toxicological models to the risk assessment context may need further investigation. First, the TMoA is defined in relation to specific sites of toxic action within species, but it may not be constant across species. For example, a photosynthesis inhibitor has a clear dominant TMoA in plants and algae, but it may simultaneously be a narcotic agent for species lacking photosynthesis capacities. The numerical outcome of these approaches is determined by the algorithms to calculate PAFs for nonspecific and specific modes of action and for aggregation into msPAF. The algorithms encountered in this book have not as yet been rigorously tested for their conceptual soundness (e.g., application of toxicological principles to communities rather than to individuals) or for their predictive ability for specific species assemblages. A drawback of calculating msPAF from measured concentrations of compounds is that often many compounds go unnoticed, since they are not in the standard measurement array, or their concentrations are below technical detection limits. Alternatively, an msPAF can be derived experimentally. An effluent, complex mate- rial, or contaminated ambient medium is tested at different dilutions (or concentra- tion steps) with a sufficient number of species to derive an SSD for that mixture, so that nonidentified chemicals are also taken into account (Chapter 18). For example, an acute criterion was calculated for aqueous dilutions of petroleum, expressed as total petroleum hydrocarbons, using the U.S. EPA methodology (Tsvetnenko, 1998). Trends across time or space in risks from mixtures can be analyzed in this way, again most likely as a relative scaling of toxic stress. In this experimental context, it has been observed (Slooff, 1983; Chapter 18) that SSDs from tests of complex mixtures generally have steeper slopes than the SSDs of the individual chemicals in the mixture (Figure 21.1). A probable cause is that the single chemicals in a complex cocktail of contaminants not only act as chemicals with a specific toxicity but also contribute to joint additive toxicity, when they are present below their threshold concentration (Hermens and Leeuwangh, 1982; Verhaar et al., 1995). This is often referred to as baseline toxicity. The results of the experimental study by Pedersen and Petersen (1996) seems to be in accordance with this theory. They observed that the standard deviation of a set of toxicity data for a set of five laboratory test species tended to decrease (i.e, the slope of the SSD, plotted as a cumulative distribution function, or CDF, would increase) with an increasing number of chemicals in the mixture, although the number of species in these experiments was small compared to many SSDs or species in field communities. The relationships between the calculated and measured msPAFs and between these msPAFs and measures of community responses in the field are complicated © 2002 by CRC Press LLC and have not as yet been demonstrated clearly. Variance in the composition of the mixture may lead to varying effects on communities, depending on the dominant modes of action and the taxa present. Obviously, the relation between observed toxicity and the toxicity of mixtures predicted with SSDs requires further develop- ment of concepts and technical approaches, to yield outcomes beyond the level of relative measures of risks (Chapter 22). 21.1.3 P ROBABILITY OF E FFECTS FROM SSD S The criteria generated from SSDs and the risks estimated from SSDs (PAFs or PESs) are often described as probabilistic without defining an endpoint that is a probability (Suter, 1998a,b). This issue relates to the problem discussed above that the users of SSDs often do not clearly define what they are estimating when they use SSDs. The issue becomes important when communicating SSD-based results to risk managers or other interested parties. When SSDs are used as models of the PES for an individual species, the sensitivity of the species is treated as a random variable. The species that is the assessment endpoint is assumed to be a random draw from the same population of species as the test species used to estimate the distribution (Van Straalen, 1990; Suter, 1993). The output of the model is evidently probabilistic, namely, an estimate of the PES on the endpoint species. For example, the probability of toxic effects on rainbow dace given an ambient concentration in a water body may be estimated from the distribution of the sensitivity of tested fish. As with the use of SSDs as models of communities (i.e., to calculate PAFs), uncertainties and variability are associated with estimating a PES. Given the parameter uncertainty due to sampling FIGURE 21.1 SSDs for single compounds and a large mixture, showing the steepness ( β ) of the CDF for the large mixture as compared to individual compounds. (Based on data from De Zwart, Chapters 8 and 18.) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 Log 10 Toxic Units Complex mixture, β = 0.17 Nonpolar Narcotics, β = 0.39 Organophosphates, β = 0.71 Potentially Affected Fraction © 2002 by CRC Press LLC and sample size, a confidence interval for the PES can be calculated (Chapters 5 and 17; Aldenberg and Jaworska, 2000). That is, one could calculate the probability that the PES is as high as P z . However, at present, none of the standard SSD-based assessment methods claims to estimate risks to individual species. More commonly, SSDs are used to generate output that is not a probability. That is, when calculating HC p , p is the proportion of the community that is affected, not a probability . Similarly, when calculating a PAF, the F is a fraction (or equivalently, a proportion) of the community affected, not a probability. If we estimate the distributions of these proportions, then we can estimate the probability of a pre- scribed proportion. Hence, one could estimate the probability that the PAF is a high as F x or the HC p is as low as C y given variance among biotic communities, uncertainty due to model fitting, or any other source of variability or uncertainty. Parkhurst et al. (1996) describe a method to calculate the probability that the PAF is as large as F x at a specified concentration given the uncertainty due to model fitting. The calculation of confidence intervals on HC p to calculate conservative criteria is conceptually equivalent (Van Straalen and Denneman, 1989; Aldenberg and Slob, 1993). The practical implications of this become apparent when considering the need to explain clearly the results of risk assessments to decision makers and interested parties (Suter, 1998b). One must explain that the probabilities resulting from various SSD-based methods are probabilities of some event with respect to some source of variance or uncertainty. In the explanation of SSD results, it should be clear that there are various ways by which the SSD approach may analyze sources of uncer- tainty and variability (see Chapters 4 and 5), and many sources that may be included or excluded. Hence, risk assessors should be clear in their own minds and in their writings concerning the endpoint that they intend to convey. 21.2 STATISTICAL MODEL ISSUES 21.2.1 S ELECTION OF D ISTRIBUTION F UNCTIONS AND G OODNESS - OF -F IT The choice of distribution functions has been the subject of much debate in published critiques of the use of SSDs. Smith and Cairns (1993) objected to the fact that there is no good basis for selecting a distribution function when, as is often the case, the number of observations is small. Many users of SSDs simply employ a standard distribution that has been chosen earlier by a regulatory agency or by the founders of their preferred assessment method. This can lead to SSDs that badly fit the data. See, for example, Figure 21.2, or Aldenberg and Jaworska (1999). Although the use of a standard model can be defended as easy, consistent, and equitable, poor fits cast doubt on the appropriateness of the method. There are various alternatives for selecting distribution functions. First, a chosen function may be considered acceptable based on failure to reject the null hypothesis that the distribution of the data is the same as the distribution defined by the function. Fox (1999) correctly raised the objection to this criterion that failure to reject the null hypothesis does not mean that the function is a good fit to the data. Statistical inference does not allow one to accept a null hypothesis based on failure to reject. [...]... aggregation of species within genera eliminates the common problem of predominance of certain taxa, particularly the genus Daphnia, in ecotoxicological data sets Aggregation of species inevitably results in a reduction of the number of input data This can be counteracted by requiring a minimum number of taxa However, combining data across species to reduce overrepresentation should always be considered in view... Rather than eliminating or minimizing extraneous variance, sources of variance may be explicitly acknowledged as part of the SSD methodology For example, in deriving soil screening benchmarks, Efroymson et al (1997a,b) recognized that variance in test soils was significant, so they considered their distributions to be distributions of species/ soil combinations (see also Section 21.5.1) Such inclusiveness... experimentation study on nematode species composition in copperand zinc-contaminated plots indicates that species composition can change while system function stays relatively unharmed (Klepper et al., 1998; Smit et al., in press) Acclimation of individuals or adaptation of populations may change the relative sensitivity of species and increase mean sensitivity over time, as noted in microand mesocosm studies... uncertainty are included in the calculation 21.2.3 CENSORING AND TRUNCATION Because of the symmetry of most of the distribution functions used in SSDs, asymmetries in the data can affect the results in unintended ways In particular, even after log conversion, many ecotoxicological data sets contain long upper tails due to highly resistant species (see, e.g., Figure 21.2) If these data are used in fitting... could cause an anticonservative bias in estimates of low percentiles 21 .4. 6 USE OF ESTIMATED VALUES Toxicity data estimated from models have been used in cases where the available test data are not sufficiently numerous to derive an SSD Models have been developed for compound-to-compound, SSD-to-SSD, or species- to -species extrapolation Models may be used for compound-to-compound extrapolation Van Leeuwen... sensitivity distributions of functional endpoints (FSDs) and microbial responses in mesocosms exhibited as pollution-induced community tolerance (PICT; Van Beelen et al., 2001) Versteeg et al (1999) compared distributions of single -species NOECs (or equivalent test endpoints) for 11 chemicals to distributions of NOECs from model ecosystems ranging from 2-l flasks to 15-m2 ponds They concluded that the HC5... The assignment of species to a medium may be unrealistic, since some species are exposed through various environmental compartments, either during their whole life cycle (e.g., a mammal that drinks water and feeds from terrestrial food webs) or during parts thereof (amphibians) This may pose specific problems related to combining different species in an SSD and the use of one species in SSDs for more... increasing availability of data (e.g., Chapter 8), the splitting of taxa is gaining support These observations suggest that use of SSDs to assess specific assessment endpoints may be considerably improved by splitting taxa or other groups among SSDs However, lumping SSDs may still be desirable for deriving EQCs 21.5.6 COMBINING DATA ACROSS ENVIRONMENTS When there are insufficient data for terrestrial species. .. aquatic species (Chapter 14) This is done by assuming that exposures to soil contaminants are in actuality exposures to the aqueous phase (Løkke, 19 94) Given that assumption, the aqueous exposure concentration for soils is estimated using partitioning coefficients Then, the soil-normalized aquatic data are combined with soil test data A similar strategy is followed in the Netherlands when there are insufficient... scenarios (e.g., 24, 48 , and 96 h, Campbell et al., 2000) It is common practice to lump tests into categories of acute and chronic For example, Solomon et al (2001) used 2 4- to 96-h test data in their assessment of cotton pyrethroids Defining exposure intervals of chronic tests is more difficult The most common strategy when deriving chronic SSDs is to use all nominally chronic data That is, include all tests . Treatment of Input Data 21.5.1 Heterogeneity of Media 21.5.2 Acute–Chronic Extrapolations 21.5.3 Combining Data for a Species 21.5 .4 Combining Data across Species 21.5.5 Combining Taxa in a Distribution 21 . Extrapolation 21 .4 Selection of Input Data 21 .4. 1 SSDs for Different Media 21 .4. 2 Types of Data 21 .4. 3 Data Quality 21 .4. 4 Adequate Number of Observations 21 .4. 5 Bias in Data Selection 21 .4. 6 Use of. properly applied in assessments of short-term exposures, as in spills or upsets in treatment operations. No-observed-effect concentrations (NOECs) and lowest-observed-effect concen- trations (LOECs)