Valery E. Forbes

The use and misuse of biomarkers in ecotoxicology

Substantial efforts have been devoted to developing and applying biomarkers for use in ecotoxicology. These efforts have resulted partly from a desire for early warning indicators that respond before measurable effects on individuals and populations occur and partly as an aid to identifying the causes of observed population- and community-level effects. Whereas older biomarkers focused on measures of organism physiology or biochemistry, advances in molecular biology are extending the biomarker philosophy to the level of the genes (i.e., ecotoxicogenomics). However, the extent to which biomarkers are able to provide unambiguous and ecologically relevant indicators of exposure to or effects of toxicants remains highly controversial. In the present paper, we briefly discuss the application of biomarkers in ecotoxicology and ecological risk assessment, and we provide examples of how they have been applied. We conclude that although biomarkers can be helpful for gaining insight regarding the mechanisms causing observed effects of chemicals on whole-organism performance and may, in some cases, provide useful indicators of exposure, individual biomarker responses should not be expected to provide useful predictions of relevant ecological effects--and probably not even predictions of whole-organism effects. Suites of biomarkers are only likely to provide increased predictability if they can be used in a comprehensive mechanistic model that integrates them into a measure of fitness. Until this can be achieved, biomarkers may be useful for hypothesis generation in carefully controlled experiments. However, because the aims of environmental monitoring and ecological risk assessment are to detect and/or predict adverse chemical impacts on populations, communities, and ecosystems, we should be focusing our efforts on improving methods to do this directly. This will involve developing and testing models of appropriate complexity that can describe real-world systems at multiple scales.

Species Sensitivity Distributions Revisited: A Critical Appraisal

We revisit the assumptions associated with the derivation and application of species sensitivity distributions (SSDs). Our questions are (1) Do SSDs clarify or obscure the setting of ecological effects thresholds for risk assessment? and (2) Do SSDs reduce or introduce uncertainty into risk assessment? Our conclusions are that if we could determine a community sensitivity distribution, this would provide a better estimate of an ecologically relevant effects threshold and therefore be an improvement for risk assessment. However, the distributions generated are typically based on haphazard collections of species and endpoints and by adjusting these to reflect more realistic trophic structures we show that effects thresholds can be shifted but in a direction and to an extent that is not predictable. Despite claims that the SSD approach uses all available data to assess effects, we demonstrate that in certain frequently used applications only a small fraction of the species going into the SSD determine the effects threshold. If the SSD approach is to lead to better risk assessments, improvements are needed in how the theory is put into practice. This requires careful definition of the risk assessment targets and of the species and endpoints selected for use in generating SSDs.

A critique of the use of distribution-based extrapolation models in ecotoxicology

A fundamental unanswered question in ecotoxicology concerns the extent to which ecosystem-level effects of pollutants can be understood or predicted from tests at lower levels of organization (Forbes & Forbes 1993). Much attention during the 1970s and early 1980s has been directed towards developing new and better test methods and identifying ideal test species, indicator organisms and biomarkers. Given the impracticality of testing all species for their sensitivity to toxicants, reliance has usually been put on data gathered for a few selected species. In contrast to toxicological studies where data for a few surrogate species are extrapolated to humans, ecotoxicological testing requires extrapolation from a small number of test species to a vast number of species varying in taxonomy, size, life history, physiology and geographic range (Cairns & Mount 1990). In this paper we examine the features and assumptions of recently developed distribution-based extrapolation models that are currently in use (OECD 1991; Van Leeuwen et al. 1992). All these presume an underlying interspecific distribution of sensitivities to a toxicant. We suggest that the rational use of these models as ecotoxicological tools for environmental regulation requires additional basic knowledge in two areas. The first concerns the relationship between structure and function in ecosystems. The second involves the nature of the statistical distribution of toxicity end-points in natural assemblages of species. Because of deficiencies in basic knowledge in these areas, statistical extrapolation does not presently offer an improvement over much simpler arbitrary assessment factors. We conclude that the added complexity inherent in their use is not outweighed by the benefits obtained.

Development of a framework based on an ecosystem services approach for deriving specific protection goals for environmental risk assessment of pesticides

General protection goals for the environmental risk assessment (ERA) of plant protection products are stated in European legislation but specific protection goals (SPGs) are often not precisely defined. These are however crucial for designing appropriate risk assessment schemes. The process followed by the Panel on Plant Protection Products and their Residues (PPR) of the European Food Safety Authority (EFSA) as well as examples of resulting SPGs obtained so far for environmental risk assessment (ERA) of pesticides is presented. The ecosystem services approach was used as an overarching concept for the development of SPGs, which will likely facilitate communication with stakeholders in general and risk managers in particular. It is proposed to develop SPG options for 7 key drivers for ecosystem services (microbes, algae, non target plants (aquatic and terrestrial), aquatic invertebrates, terrestrial non target arthropods including honeybees, terrestrial non-arthropod invertebrates, and vertebrates), covering the ecosystem services that could potentially be affected by the use of pesticides. These SPGs need to be defined in 6 dimensions: biological entity, attribute, magnitude, temporal and geographical scale of the effect, and the degree of certainty that the specified level of effect will not be exceeded. In general, to ensure ecosystem services, taxa representative for the key drivers identified need to be protected at the population level. However, for some vertebrates and species that have a protection status in legislation, protection may be at the individual level. To protect the provisioning and supporting services provided by microbes it may be sufficient to protect them at the functional group level. To protect biodiversity impacts need to be assessed at least at the scale of the watershed/landscape.

Ecological models in support of regulatory risk assessments of pesticides: developing a strategy for the future.

Abstract This brief communication reports on the main findings of the LEMTOX workshop, held from 9 to 12 September 2007, at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. The workshop brought together a diverse group of stakeholders from academia, regulatory authorities, contract research organizations, and industry, representing Europe, the United States, and Asia, to discuss the role of ecological modeling in risk assessments of pesticides, particularly under the European regulatory framework. The following questions were addressed: What are the potential benefits of using ecological models in pesticide registration and risk assessment? What obstacles prevent ecological modeling from being used routinely in regulatory submissions? What actions are needed to overcome the identified obstacles? What recommendations should be made to ensure good modeling practice in this context? The workshop focused exclusively on population models, and discussion was focused on those categories of population models that link effects on individuals (e.g., survival, growth, reproduction, behavior) to effects on population dynamics. The workshop participants concluded that the overall benefits of ecological modeling are that it could bring more ecology into ecological risk assessment, and it could provide an excellent tool for exploring the importance of, and interactions among, ecological complexities. However, there are a number of challenges that need to be overcome before such models will receive wide acceptance for pesticide risk assessment, despite having been used extensively in other contexts (e.g., conservation biology). The need for guidance on Good Modeling Practice (on model development, analysis, interpretation, evaluation, documentation, and communication), as well as the need for case studies that can be used to explore the added value of ecological models for risk assessment, were identified as top priorities. Assessing recovery potential of exposed nontarget species and clarifying the ecological relevance of standard laboratory test results are two areas for which ecological modeling may be able to provide considerable benefits.

Toxicant Impacts on Density-Limited Populations: A Critical Review of Theory, Practice, and Results

Most natural populations experience some density dependence, and long- term average rates of population growth are likely to be close to zero (i.e., steady state). An essential question, therefore, is how and to what extent do density-dependent effects influence the responses of populations to toxicant impacts? Here we consider three general types of interaction between density dependence and toxicant effects: additive, less than additive, and more than additive. If we know enough about the life-history dynamics of an organism and how its life-history traits are affected by density and toxicant exposure, we should be able to use life-history models to predict responses of populations living under density-dependent control to chemical exposure. However, because the number of factors influencing the outcome is large, we demonstrate that simple, general, a priori predictions are not feasible. A review of the literature confirms that a variety of interactions has been observed in experimental systems. It is essential that experiments are designed so that the interactions of interest can be determined. We review these critically. Experi- mental designs that are appropriate for exploring density-toxicant interactions on individual physiological performance may be unable to detect potential compensatory interactions at the population level. Designs most likely to simulate the dynamics of field populations will be unable to determine the mechanistic bases underlying population-level impacts. To facilitate an ecologically correct interpretation of density-toxicant interactions, it is there- fore essential that the limitations of the chosen experimental designs be recognized and made explicit.

A Weight-of-Evidence Framework for Assessing Sediment (Or Other) Contamination: Improving Certainty in the Decision-Making Process

A basic framework is presented for the ecological weight-of-evidence (WOE) process for sediment assessment that clearly defines its essential elements and will improve the certainty of conclusions about whether or not impairment exists due to sediment contamination, and, if so, which stressors and biological species (or ecological responses) are of greatest concern. The essential “Certainty Elements” are addressed in a transparent best professional judgment (BPJ) process with multiple lines-of-evidence (LOE) ultimately quantitatively integrated (but not necessarily combined into a single value). The WOE Certainty Elements include: (1) Development of a conceptual model (showing linkages of critical receptors and ecosystem quality characteristics); (2) Explanation of linkages between measurement endpoint responses (direct and indirect with associated spatial/temporal dynamics) and conceptual model components; (3) Identification of possible natural and anthropogenic stressors with associated exposure dynamics; (4) Evaluation of appropriate and quantitatively based reference (background) comparison methods; (5) Consideration of advantages and limitations of quantification methods used to integrate LOE; (6) Consideration of advantages and limitations of each LOE used; (7) Evaluation of causality criteria used for each LOE during output verification and how they were implemented; and (8) Combining the LOE into a WOE matrix for interpretation, showing causality linkages in the conceptual model. The framework identifies several statistical approaches for integrating within LOE, the suitability of which depends on physical characteristics of the system and the scale/nature of impairment. The quantification approaches include: (1) Gradient (regression methods); (2) Paired reference/test (before/after control impact and ANOVA methods); (3) Multiple reference (ANOVA and multivariate methods); and 4) Gradient with reference (regression, ANOVA and multivariate methods). This WOE framework can be used for any environmental assessment and is most effective when incorporated into the initial and final study design stages (e.g., the Problem Formulation and Risk Characterization stages of a risk assessment) with reassessment throughout the project and decision-making process, rather than in a retrospective data analysis approach where key certainty elements cannot be adequately addressed.

Extrapolation in Ecological Risk Assessment: Balancing Pragmatism and Precaution in Chemical Controls Legislation

M national and international regulatory bodies require assessment of the ecological risks of industrial and agricultural chemicals. For example, the US Toxic Substances Control Act requires ecological risk assessment for new and existing industrial chemicals (Zeeman and Gilford 1993), and the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) makes similar requirements for pesticides (Touart 1995). Since the 1990s, the European Union has likewise developed legislation requiring that ecological risk assessment be conducted for industrial and agricultural chemicals. The risk assessment process has come under increasing attack for being both too slow and too simplistic scientifically. For example, although the European Union legislation for existing chemicals has been in place for about a decade, risk assessment has been performed on fewer than 10 chemicals, a small number considering that 100,000 or so chemicals have been registered by industry. Currently, the risk assessment process is under review, and there is pressure from various stakeholders to replace the scientific approach with one based more upon the “precautionary principle,”that is, applying controls to chemicals in advance of scientific understanding if there is a presumption that harm will be caused. This issue highlights the dilemma of balancing industrial output, and its many benefits, with environmental protection and challenges the ability of the scientific approach to properly inform this process. In this article we briefly describe the process of ecological risk assessment as applied in the control of chemicals. We pay particular attention to the extrapolation methodology as a pragmatic way to develop an assessment of effects on ecological systems with the minimum amount of empirical information. The use of extrapolation in ecological effects assessment has been reviewed before (Chapman et al. 1998, Duke and Taggart 2000), but we not only describe extrapolation methodology but also critically consider the rationale behind it, with the aim of producing a more refined though still pragmatic approach.

How do physiological responses to stress translate into ecological and evolutionary processes

Abstract Identifying physiological adaptation (or absence of it, which we define as stress) to environmental variables requires an explicit link with effects on Darwinian fitness. This can be effected by simultaneous observations on physiological and fitness variables; but the relationships are likely to be complex. Specific hypotheses on functional links between physiology and fitness need to be formulated (and incorporated into models) and tested. The genetic basis of these relationships and the possibility of evolution of tolerance need to be considered.

Toxic effects and bioaccumulation of nano-, micron- and ionic-Ag in the polychaete, Nereis diversicolor

There is increasing concern about the toxicities and potential risks, both still poorly understood, of silver nanoparticles for the aquatic environment after their eventual release via wastewater discharges. In this study, the toxicities of sediment associated nano (<100 nm)-, micron (2-3.5 μm)- and ionic (AgNO(3))-Ag on the sediment-dwelling polychaete, Nereis diversicolor, were compared after 10 days of sediment exposure, using survival, DNA damage (comet assay) and bioaccumulation as endpoints. The nominal concentrations used in all exposure scenarios were 0, 1, 5, 10, 25, and 50 μg Ag/g dry weight (dw) sediment. Our results showed that Ag was able to cause DNA damage in Nereis coelomocytes, and that this effect was both concentration- and Ag form-related. There was significantly greater genotoxicity (higher tail moment and tail DNA intensities) at 25 and 50 μg/g dw in nano- and micron-Ag treatments and at 50 μg/g dw in the ionic-Ag treatment compared to the controls (0μg/g dw). The nano-Ag treatment had the greatest genotoxic effect of the three tested Ag forms, and the ionic-Ag treatment was the least genotoxic. N. diversicolor did accumulate sediment-associated Ag from all three forms. Ag body burdens at the highest exposure concentration were 8.56 ± 6.63, 6.92 ± 5.86 and 9.86 ± 4.94 μg/g dw for worms in nano-, micron- and ionic-Ag treatments, respectively, but there was no significant difference in Ag bioaccumulation among the three treatments.