Developing and analyzing species sensitivity distributions in ecological risk assessment
Anastasia Del Signore
Del Signore, A. 2015. Developing and applying species sensitivity distributions for ecological risk assessment. PhD thesis, Radboud University Nijmegen, the Netherlands. © 2015 Anastasia Del Signore. All rights reserved. ISBN: 978-90-6464-889-2 Layout and cover: Aleksandra Fedorenkova and Anastasia Del Signore; cover picture source: colorlovers.com RubyBird Printed by: GVO Drukkers & Vormgevers, Ede, The Netherlands Developing and applying species sensitivity distributions
for ecological risk assessment
Proefschrift
ter verkrijging van de graad van doctor aan de Radboud Universiteit Nijmegen op gezag van de rector magnificus prof. dr. Th.L.M. Engelen, volgens besluit van het college van decanen in het openbaar te verdedigen op donderdag 3 september 2015, om 10.30 uur precies
door
Anastasia Del Signore
geboren op 18 april 1984
te Narva, Estland
Promotoren |
A.J. Hendriks
Prof. dr. A.M. Breure
Copromotoren |
Dr. R.S.E.W. Leuven
Dr. H.J.R. Lenders
Manuscriptcommissie |
Prof. dr. M.A.J. Huijbregts
Prof. dr. W. Admiraal (Universiteit van Amsterdam)
Dr. L. Posthuma (RIVM)
Paranimfen |
Dr. Loes Geelen
Dr. Laura Golsteijn
The research described in this thesis was performed within project RCiERA (Research Collaboration in Ecological Risk Assessment). The project was financially supported by a grant of The National Institute for Public Health and the Environment (RIVM) in Bilthoven and Radboud University Nijmegen.
Take from the altars of the past the fire – not the ashes
| Jean Jaures
The difficulty in most scientific work lies in framing the questions rather than in finding the answers
| Arthur Edwin Boycott
Contents
Chapter 1 | General introduction | 8
Chapter 2 | Ecotoxicogenomics: bridging the gap between genes and | 23 populations
Chapter 3 | Tolerance of native and non-native fish species to chemical | 39 stress: a case study for the river Rhine
Chapter 4 | Ranking ecological risks of multiple chemical stressors on | 56 amphibians
Chapter 5 | Size-mediated effects of water-flow velocity on riverine fish | 72 species
Chapter 6 | Development and application of the SSD approach in | 92 scientific case studies for ecological risk assessment
Chapter 7 | Synthesis and conclusions | 128
Appendices | | 143
Summary | | 199
Samenvatting | | 206
About the author | | 213
Acknowledgements | | 214
Chapter 1 General introduction
1.1. Introduction
The world faces a major challenge in determining the impact of the multitude of chemical and physical stressors on organisms in the environment. Such stressors may act on different scales, e.g., on local scale (point source chemical pollutants), landscape scale (threats of invasive species) or global scale (climate change). To protect ecosystems from adverse impacts of stressors Ecological Risk Assessment (ERA) is used as an approach to compare the relative risks contributed by different stressors to ecological entities. While physical stressors, such as temperature rise, may impact ecosystem structure and functioning, ERA has mainly been developed and used to investigate the possible effects of toxic chemical stressors released into ecosystems. Until now, ERA is extensively used to develop EU legislations on existing and new chemicals (EC 2003; EC 2006) and to derive national environmental quality criteria for toxic compounds (EQC) in several countries. ERA combines hazard identification and effect assessment with predictions of fate and exposure of chemicals (Figure 1.1), providing a better understanding of the potential of chemicals to cause harm and the likelihood of that potential being realized, i.e., risk characterization (Calow 1998). The different phases of ERA dealing with hazard identification leading to risk characterization can be considered as the scientific part of the risk assessment process; subsequent risk management is related to policy making and establishing regulations.
Figure 1.1. Ecological Risk Assessment. The scientific part includes phases from risk identification to characterization; risk management is related to policy making. The process of risk assessment and management has an iterative character where exchange of knowledge takes place (see paragraph 2).
The premise of ERA is that ecosystems can be protected from environmental impact of chemicals by an approach based on the measurement of direct effects of chemicals in simple “one substance - one species” toxicity tests. Given the impossibility of testing the effects of all chemical compounds on all species, traditional approaches to risk assessment are based on observing the effects of chemicals on the survival, growth, and reproduction of a few selected test species (Forbes and Forbes 1993). The results of such toxicity tests are extrapolated statistically to “ecologically relevant” level impacts including populations, communities and ecosystems (Calow 1998; Forbes et al. 2008; Figure 1.2). One of the common approaches to extrapolating single-species toxicity data
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to the multiple-species ecosystem level is the use of Species Sensitivity Distributions (SSDs). Another approach for extrapolating results from single-species toxicity tests to ecosystem levels is to apply an assessment factor method. In this method, the sensitivity of the most sensitive species is assessed using assessment factors to address the uncertainty in extrapolation of available data (Verdonck et al. 2005). The focus of the present thesis is only on the SSD approach and its potential and application in the scientific part of the ERA process.
Figure 1.2. The aim of ERA is to protect higher levels of biological organization, e.g., populations, communities, and ecosystems. In standard toxicity tests the effects of chemicals are assessed on individual levels by obtaining, e.g., LC50 or NOEC. For extrapolation from lower to higher levels of biological organisation the SSD approach can be used.
1.2. Species Sensitivity Distributions in ecological risk assessment
Development of the SSD approach was the result of interactions between science and policy. Around 1980, European and North American regulatory authorities were developing quantitative approaches for derivation of environmental quality criteria (Suter 2002; Van Straalen and Van Leeuwen 2002; Vighi et al. 2006). Scientists developed extensive ecotoxicological data sets and explored patterns in the data (Van Straalen and Souren 2002). It appeared that species sensitivity endpoints, e.g., LC50 (Lethal Concentration causing mortality to 50% of the individuals tested), were distributed consistently for most chemicals resembling a log-normal distribution (Suter 2002). Thus, the SSD approach is based on the common observation that species differ in their sensitivity to the same chemical stressor and that inter-species differences can be large (Posthuma et al. 2002a). Several authors have contributed to the development and improvement of the methodology for the SSD, e.g., Kooijman 1987; Van Straalen
Introduction | 9
and Denneman 1989; Wagner and Løkke 1991; Aldenberg and Slob 1993, Aldenberg and Jaworska 2000; Posthuma et al. 2002b; Suter 2002.
The use of the SSD approach is recommended in the risk assessment of chemicals in the United States (Stephan 1985), Europe (Aldenberg and Slob 1993), Australia and New Zealand (Hose and Van den Brink 2004). The SSD approach has been adopted in risk assessment in Europe and the US to derive regulatory environmental quality criteria, benchmarks for screening assessments, and to estimate ecological risks (Solomon et al. 1996; Sijm et al. 2002; Stephan 2002). A more detailed overview of the implementation of the SSDs in environmental regulations is presented in Posthuma et al. (2002b).
An SSD curve is basically a cumulative distribution function of laboratory-derived toxicity data for a single chemical. SSD curves with respect to a chemical compound can be used to derive environmental quality criteria (EQC) and to quantify ecotoxicological risk (Figure 1.3).
For derivation of EQC in risk assessment, SSDs are used to estimate a level of exposure that is not to be exceeded, e.g., the HC5 in several European legislations (Posthuma et al. 2002b). The hazardous concentration for 5% of the species (HC5) predicts an environmental concentration below which only a small proportion of species (5%) would be affected. At concentrations of a compound below the HC5, more than 95% of the tested set of species will be protected against effects as determined by the toxicity tests (e.g., growth, reproduction, mortality) (Aldenberg and Jaworska 2000). Effect levels, or toxicity endpoints, are commonly defined as No Observed Effect Concentration (NOEC) or Effect Concentration for x% (ECx). The NOEC represents the concentration of a chemical that will not harm the species involved, with respect to the effect that is studied. The ECx-concentration is the concentration showing effect in x% of organisms tested.
As a risk estimate, SSDs are used to estimate the potentially affected fraction (PAF), which is the proportion of species exposed to a measured or expected concentration generating an adverse effect, because of exceedance of the effect concentration of the exposed species (Klepper et al. 1998; De Zwart and Posthuma 2005). Thus, a PAF indicates a fraction of test species that would potentially suffer at a given exposure (Posthuma and Suter 2011).
When sufficient data are available, it is possible to derive a cumulative distribution for a specified endpoint (NOEC, ECx for growth or reproduction etc.). The geometric mean of this distribution represents the average sensitivity of the set of species considered and the standard deviation represents the variation among species (Smit et al. 2001).
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Often such a distribution curve is drawn to the logarithm of the concentration. Due to the assumption that the data obtained for test species can be considered as a sample from the community to be protected and that only a limited number of species is included in the sample, high importance must be given to statistical uncertainties in estimation of hazardous concentrations (Van Straalen and Van Leeuwen 2002).
Figure 1.3. Species Sensitivity Distributions (SSDs) can be used for the derivation of hazardous concentrations x% of species (HCx) and setting quality criteria, and to assess the potentially affected fraction (PAF) of species in an ecosystem due to exposure to a given concentration of a chemical in the environment.
1.3. Problem setting
The management of environmental problems, such as contamination by potentially toxic chemicals, can benefit from communication of knowledge and interactions between science and policy. Communication of scientific knowledge for risk assessment involves production, transfer and utilization of knowledge. Obviously, following a rational perspective new scientific knowledge would improve the decision making process and risk management (Van Straalen and Souren 2002). The development of risk assessment procedures is an example of such interactions between science and policy. Currently, the goal of risk management generally relates to an acceptable risk level for the relevant protection targets. Decision-makers need scientists to explain the possibilities for and exact meaning of protection levels, and the uncertainties involved, as well as to elaborate on technical procedures, models, protocols and related quality standards (Swartjes 2011).
Although the SSD approach in ecological risk assessment is accepted by policy makers, scientific discussion continues concerning the lack of “ecology” in the concept, various technical and statistical issues, and risk interpretation (Suter et al. 2002). Several of
Introduction | 11
these issues are addressed in the present thesis. The SSD approach has been defined as “interesting and powerful” due to its practical usefulness in applications in management problems and decision making (Van Straalen 2002). However, the potential of this approach for application in scientific research has not been fully investigated. Therefore, this thesis will also explore applications of the SSD approach in different case studies in scientific research for ecological risk assessment.
1.4. Connecting different levels of biological organization
Despite the use of the SSDs in the regulatory field, the approach potentially has several drawbacks. For example, a general critique on the use of SSDs is that, in general, they are based on individual-level endpoints, which may not be directly or consistently related to risks for populations, which are the ultimate targets for protection in ERA (Forbes and Forbes 1993; Forbes and Calow 2002; Kefford et al. 2005). As a possible solution to link the effects from different levels of biological organization a new paradigm has been developed for risk assessment, the so called AOP (Adverse Outcome Pathways). In this AOP paradigm, modern molecular techniques are applied to make use of observations on molecular, cellular, and metabolic responses caused by chemical stressors to assess the likelihood of impact on higher levels of biological organization (Ankley et al. 2010). The AOP has been defined as a set of plausible connections that lead all the way from the molecular initiating event to an adverse effect on higher levels of biological organization relevant for risk assessment. So far, however, it is not clear if sub-organism endpoints can be used in ERA to derive EQC or to estimate the ecological risks (Figure 1.5). For example, at the HC5 level derived from SSDs based on whole organism endpoints, the fraction of species showing sub-cellular biomarker response was around 80% (Smit et al. 2009) meaning that sub-cellular responses can indicate potential hazardous effects due to exposure to a chemical and can be used as an early warning indicator. Therefore, there is a need to understand the link between the effects of stressors on sub-organism levels and the effects on higher levels of biological organization.
1.5. Application of the SSD approach for ranking stressors
Apart from application of the SSD approach for derivation of EQC and as a tool to estimate risks, SSDs can be used for ranking of chemicals based on their potential risk to ecosystems. The ranking of chemicals can be used for prioritization schemes in establishment of guidelines or standards as well as in allocation of research and monitoring resources (Whiteside et al. 2008). Despite this practical application of the SSD approach, the focus on ranking the chemical stressors has received little attention in scientific literature. Examples of the use of SSDs for this purpose are described by Harbers et al. (2008), who used multimedia fate modelling and the SSDs to calculate
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the overall PAFs of high-production volume chemicals on the North Sea coastal ecosystems. Whiteside et al. (2008) have ranked 206 pesticides based on their potential risk to aquatic life dividing the estimated average environmental concentration by HC5 values calculated for fish, crustacean, insect, algae, and macrophyte species. Similarly, Henning-De Jong et al. (2008) have ranked practical application patterns of 200 pesticides used in the Rhine-Meuse-Scheldt catchments concerning their potential toxic impact calculated as PAFs on the North Sea coastal ecosystem. The results of such ranking exercises can be useful for field-impact-based risk management of pesticides. However, the ranking so far has focused on identifying the relative potential of a stressor to cause toxic impact on general aquatic ecosystems. It would be interesting to apply the ranking method for a specific group of species exposed to multiple stressors in their actual environment and to investigate how the ranking would change for different species groups. Such specific ranking can underpin local risk management or rehabilitation plans for a certain species.
1.6. Assessment of effects from non-toxic stressors
Another important scientific discussion concerns the assessment of effects caused by non-toxic (non-chemical) stressors. Procedures developed for the assessment of chemicals by SSDs have been applied to estimate the effects from single non-toxic stressors, for example temperature (De Vries et al. 2009). Focusing solely on chemical stressors may under- or overestimate the impacts of chemical stressors since non- chemical stressors like temperature, pH, flow velocity or shipping induced waves may affect the sensitivity to toxic chemicals (Heugens et al. 2001). To our knowledge, however, assessment of the combined impact of chemical and non- chemical stressors by the SSD approach has not yet been developed. Moreover, it is not clear how the effects of non-chemical stressors can be assessed, allowing for comparison of severity of effects between these types of stressors. The risk assessment would benefit from integral assessments of total impact on ecosystems caused by chemical and non- chemical stressors. For example, current ERA focuses on the impacts on species caused by pollution in their habitats by chemical stressors aiming at improving habitat conditions. Yet, degradation of habitat itself and change of habitat heterogeneity may also affect species therein, e.g., homogeneous water velocity in river habitats may result in disappearance of nursery areas or spawning areas with different velocity requirements for different fish species (Sukhodolov et al. 2009). So far, methods for quantification of such stressors have not been developed for risk assessment. Thus, it would be interesting to investigate whether the SSD approach could be a suitable method for evaluating the risks for different types of stressors.
Introduction | 13
1.7. Development in application of the SSD approach in scientific research
Scientific discussions on the SSDs have concerned technical issues, such as quality and quantity of test data, handling multiple test data for a single species, selection of test endpoints (e.g., LC50s or NOECs). Also, the choice of the statistical approach, risk limits (e.g., 5%) to derive standards, and the inclusion of different taxonomic groups have been discussed (Posthuma et al. 2002a). The discussions and criticism concerning the SSD approach have been worthwhile in a scientific sense, since they have led to improvements in the approach. For example, traditionally the SSD approach was based on single chemical toxicity tests and has been criticized for not incorporating the combined effects of mixtures of chemicals. Since organisms in a polluted environment generally are exposed to complex mixtures of chemicals, the SSD approach was extended to predict the joint risk of chemicals in a mixture to derive multi-substance PAF (msPAF) (Traas et al. 2002; De Zwart and Posthuma 2005). Moreover, originally SSDs were based on laboratory tests with animals, however, new applications of the SSDs include the use of field data based on the sensitivity of organisms, e.g., in pollution induced community tolerance (e.g., Rutgers and Breure 1999).
In recent years, the SSD approach has been explored in scientific research for different environmental problem definitions, e.g., to determine the variation in sensitivity among various species groups across chemical compounds (Maltby et al. 2005). Therefore, the question on how to apply the SSD approach in non-standardized settings becomes a scientific discussion in its own right and it would be interesting to know the horizon of the possibilities of using this approach for various non-regulatory problem definitions in scientific research. Another aspect interesting to investigate is how technical issues are dealt with when the SSD approach is applied for purposes other than derivation of HC5 for a chemical compound.
1.8. Objective and outline
The objective of the present thesis is to develop and apply species sensitivity distributions for ecological risk assessment of toxic and non-toxic stressors. More specifically the aim of the present thesis is:
- To quantify the difference in sensitivity at different levels of biological organisation - To quantify the difference in sensitivity between native and non-native species - To quantify the difference in sensitivity between species to non-toxic stressor - To quantify the difference in contribution of single stressors to ecological risk
The framework for the thesis is presented in Figure 1.4. Each chapter can be allocated along the complexity gradient evolving with the development in the SSD approach and its application (indicated by the arrow), e.g., from risk assessment of a single chemical
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to multiple stress including non-toxic (non-chemical) stressors. Chapter 2 concerns a single chemical stressor including an assessment of the effects on the lowest levels of biological organization, i.e., gene level. Chapter 3 focuses on higher levels of biological organization such as individuals and populations exposed to multiple chemical stressors moving further from the original aim of the SSD application. In Chapters 4 and 5, complexity is added by integrating the assessment of non-chemical stressors. Additionally, in chapter 5 field-based effect data are included. Chapter 6 reflects on the evolution in the SSD approach from the original policy needs to new topics of scientific research where the approach has been applied. Chapter 7 integrates the findings from the previous chapters providing general conclusions and recommendations for future research. The following sections describe the backgrounds of these topics and specify the research questions.
Figure 1.4. Framework for the present PhD thesis.
1.9. From sub-cellular endpoints to adverse effects on higher levels of biological organisation
With growing demand of toxicity data, together with the demands to diminish animal testing and speed up the toxicity testing (REACH), a new paradigm has been developed for risk assessment that uses modern molecular techniques to make use of observations on sub-cellular responses caused by chemical stressors indicating impacts on higher levels of biological organization (Figure 1.5). Responses on sub-cellular levels may serve as early-warning indicators of exposure to chemicals, but the real meaning in terms of potential effects on populations is largely unknown (Vighi et al. 2006). The
Introduction | 15
focus of this research topic is on the quantitative and mechanistic relationships between sub-cellular, i.e., gene level and population responses to chemical exposure.
The research question in Chapter 2 is:
In what way do the responses at the sub-cellular level predict the effects on higher levels of biological organization?
In this chapter, the link between gene level responses and the population level response based on exposure of aquatic species to cadmium is explored. Mechanistic links connecting specific endpoints on all levels of biological organisations as well as correlative relationships using the SSD approach with endpoints on gene level and LC50 endpoints on organism level are presented.
Figure 1.5. Extrapolation of responses on gene and cell level derived in new toxicity tests to higher levels of biological organization.
1.10. Application of the SSD approach in case studies for ecological risk assessment
In scientific research the SSDs are becoming a tool in studies, where the research questions deviate from the original purposes of policy need such as EQC setting.
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Estimating the differences in sensitivity to environmental stressors between various species groups may indicate the most vulnerable species or populations in relation to a specific environmental variable, which may be important for ecological rehabilitation. In the last decades, ecological rehabilitation of many ecosystems has been hindered by the spread of non-native species (Van der Velde et al. 2002). One of the characteristics considered to make a non-native species become invasive in a new environment is the higher tolerance to environmental stressors compared to native species (Karatayev et al. 2009). Applying the SSD approach for case studies concerning relative sensitivities of non-native and native species may reveal potential differences in sensitivity to environmental stressors and possibly explain which environmental variables facilitate the spread of non-native species. Therefore, the focus of this research topic is on the comparison of potential impacts of water pollution with pesticides and metals on native and non-native fish species in a case study for the river Rhine.
In Chapter 3 the research question is raised:
Can the SSD approach be applied to reveal potential differences in sensitivity between native and non-native species to certain environmental stressors?
In this chapter, the native and non-native fish species sensitivity to pesticides, metals and ammonium in the river Rhine are compared by the application of the SSD approach and derivation of the PAFs based on environmental concentrations of chemicals in the previous decades.
1.11. Application of the SSD approach for impact assessment of multiple stressors and ranking
The basic assumption of the SSD approach is that the laboratory-derived sensitivities of a set of species follow a statistical distribution that can be used to predict the probability of risk that species in the field are exposed to concentrations exceeding their sensitivities. Since species in their habitat are exposed to a combination of stressors, it is hard to identify, which of the stressors pose the major threat to the species of interest. Focusing on a specific assemblage of species and ranking the stressors present in their actual habitat posing the highest risk may result in better management and conservation of threatened populations.
Chapter 4 of the present thesis will answer the research question:
Can the SSD approach be used to rank stressors measured in actual and potential habitats of a specific taxonomic group?
In this chapter, we analyse the impact of multiple chemical stressors and acidification on populations of anuran species in the Netherlands. First, we combine the measured
Introduction | 17
concentrations of the stressors in the actual habitats where anuran species were observed with the SSDs for anuran species to derive ecological risks. Second, we rank the stressors according to their potential risk in order to indicate the major threat to anuran populations and to guide risk managers on which stressor needs to be tackled first.
1.12. Application of the SSD approach for non-chemical stressors
A role of scientists is to generate new knowledge and communicate it to the decision makers to improve risk assessment. Although the original ERA application of the SSDs aims at protecting ecosystems from toxic chemical threats, other, non-chemical, stressors can also affect them. Unlike for toxic chemical stressors, standard protocols for deriving endpoints to assess the effects on different species do not exist for non- chemical stressors. In several earlier studies, the SSD approach has been applied to quantify the impacts of non-chemical stressors, e.g., of suspended clays and sediment grain size (Smit et al. 2008) or desiccation (Collas et al. 2014). Other non-chemical stressors that do not affect toxicity of chemicals have not been assessed in a way that would allow for comparison of severity of effects between these types of stressors. Moreover, several aspects of how to apply SSD for non-chemical stress remain unclear, e.g., endpoints (for example equivalent to LCx or any other), distribution type (normal or logistic). Therefore, the focus of this research topic is on the potentials and limitations of application of the SSD approach for non-chemical stress to be used in ERA.
The research question for Chapter 5 is formulated:
What are the potential and limitations of the application of the SSD approach to non-chemical stress in ERA?
In this chapter SSDs are applied to assess the effects of a non-chemical stressor: water- flow velocity. First, SSDs are constructed using field and laboratory based endpoints, i.e., the maximum water-flow velocity level fish species can tolerate. Second, the PAFs based on field monitoring data from the river Rhine are derived. The importance of inclusion of non-chemical stress in ERA is discussed.
1.13. Development in application of the SSD approach
Despite the increased use of SSDs in regulations and scientific research, the approach potentially has several drawbacks. The main criticism that has been discussed in Forbes and Calow (2002) and Posthuma et al. (2002a) include technical aspects of constructing an SSD, such as the minimum number of species for constructing an SSD, distribution model choice, quality and representativeness of the data points. The focus of this research topic is on how these technical issues have been tackled in the recent years and on the ways SSD has been applied to study environmental problems.
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In Chapter 6 we analyse the evolution of application of the SSD approach for purposes other than deriving EQS, and answer the research question:
How are SSDs being applied in scientific research for risk assessment of (non-)chemical stress on aquatic and terrestrial ecosystems?
In Chapter 7 the synthesis and general discussion of the results are presented.
1.14. References
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Introduction | 19
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Schroer AFW, Belgers D, Brock TCM, Matser AM, Maund SJ, Van den Brink PJ. 2004. Comparison of laboratory single species and field population-level effects of the pyrethroid insecticide lambdacyhalothrin on freshwater invertebrates. Arch Environ ContamToxicol 46, 324-335. Schuler LG, Rand GM. 2008. Aquatic risk assessment of herbicides in freshwater ecosystems of South Florida. Arch Environ Contam Toxicol 54, 571-583. Selck H, Riemann B, Christoffersen K, Forbes VE, Gustavson K, Hansen BW, Jacobsen JA, Kusk OK, Petersen S. 2002. Comparing sensitivity of ecotoxicological effect endpoints between laboratory and field. Ecotox Environ Saf 58, 2245-2255. Sijm DTHM, van Wezel AP, Crommentuijn T. 2002. Environmental risk limits in the Netherlands. In: Posthuma L, Suter GW, Traas TP, eds, Species-Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, FL, USA, pp 221-254. Smit MGD, Hendriks AJ, Schobben JHM, Karman CC, Schobben HPM. 2001. The variation in slope of concentration-effect relationships. Ecotox Environ Saf 48, 43- 50. Smit, MGD, Bechmann RK, Hendriks AJ, Skadsheim A, Larsen BK, Baussant T, Bamber S, Sanni S. 2009. Relating biomarkers to whole-organism effects using species sensitivity distributions: A pilot study for marine species exposed to oil. Environ Toxicol Chem 28, 1104-1109. Smit MGD, Holthaus KIE, Trannum HC, Neff JM, Kjeilen-Eilertsen G, Jak RG, Singsaas I, Huijbregts MAJ, Hendriks AJ. 2008. Species sensitivity distributions for suspended clays, sediment burial, and grain size change in the marine environment. Environ Toxicol Chem 27, 1006-1012. Solomon KR, Baker DB, Richards RP, Dixon, DR, Klaine SJ, La Point TW, et al. 1996. Ecological risk assessment of atrazine in North American surface waters. Environ Toxicol Chem 15, 31-74. Stephan CE. 1985. Are the “Guidelines for deriving numerical national water quality criteria for the protection of aquatic life and its uses” based on sound judgments? In: Cardwell RD, Purdy R, Bahner RC, eds, Aquatic Toxicology and Hazard Assessment: Seventh Symposium, ASTM STP 854 , American Society for Testing and Materials, Philadelphia, PA, pp 515-526. Stephan CE. 2002. Use of species sensitivity distributions in the derivation of water quality criteria for aquatic life by the US Environmental Protection Agency. In: Posthuma L, Suter GW, Traas TP, eds, Species-Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, FL, USA, pp 211-220. Sukhodolov A, Bertoldi W, Wolter C, Surian N, Tubino M. 2009. Implications of channel processes for juvenile fish habitats in alpine rivers. Aquatic Sci 71, 338-349. Suter GW II. 2002. North American history of species sensitivity distributions. In: Posthuma L, Suter GW, Traas TP, eds, Species-Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, FL, USA, pp 11-17. Suter GW II, Traas TP, Posthuma L. 2002. Issues and practices in the derivation and use of the sepcoesdsemitivitydistriburion. In: Posthuma L, Suter GW, Traas TP, eds, Species-Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, FL, USA, pp 437-474.
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Swartjes FA. 2011. Introduction to Contaminated Site Management. In: Swartjes FA, ed, Dealing with Contaminated Sites. Springer Science+Business Media B.V. pp 3- 89. Traas TP, Van de Meent D, Posthuma L, Hamers T, Kater BJ, De Zwart D, Aldenberg T. 2002. The potentially affected fraction as a measure of ecological risk. In: Posthuma L, Suter GW II, Traas TP (eds), Species Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, pp 315-344. Van der Velde G, Nagelkerken I, Rajagopal S, Bij de Vaate A. 2002. Invasions by alien species in inland freshwater bodies in Western Europe: the Rhine delta. In: Leppäkoski E, Gollasch S, Olenin S, eds, Invasive aquatic species of Europe. Distribution, impacts and management. Kluwer Academic Publishers, Dordrecht, The Netherlands. pp 360-372. Van Straalen NM, Denneman CAJ. 1989. Ecotoxicological evaluation of soil quality criteria. Ecotox Environ Saf 18, 241-251. Van Straalen NM, Souren AF. 2002. Life-cycle of interactions between science and policy - the case of risk assessment for zinc. Australasian J Ecotoxicol 8, 27-33. Van Straalen NM, Van Leeuwen CJ. 2002. European history of species sensitivity distributions. In: Posthuma L, Suter GW, Traas TP, eds, Species-Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, FL, USA, pp 19-34. Van Straalen NM. 2002. Theory of ecological risk assessment based on species sensitivity distributions. In: Posthuma L, Suter GW, Traas TP, eds, Species- Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, FL, USA, 37-48. Verbrugge LNH, Schipper AM, Huijbregts MAJ, van der Velde G, Leuven RSEW. 2012. Sensitivity of native and non-native mollusc species to changing river water temperature and salinity. Biol Invas 14, 1187-1199. Verdonck FAM, Van Sprang PA, Vanrolleghem PA. 2005. Uncertainty and precaution in European environmental risk assessment of chemicals. Water Sci Technol 52, 227-234. Vighi M, Finizio A, Villa S. 2006. The evolution of the environmental quality concept: From the US EPA red book to the European water framework directive. Environ Sci Pollut Res 13, 9-14. Wagner C, Løkke H. 1991. Estimation of ecotoxicological protection levels from NOEC toxicity data. Wat Res 25, 1237-1242.
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Chapter 2 Ecotoxicogenomics: bridging the gap between genes and populations
Fedorenkova A, Vonk JA, Lenders HJR, Ouborg NJ, Breure AM, Hendriks AJ
Published in Environmental Science and Technology 44: 4328-4333, 2010
Chapter 2 | Ecotoxicogenomics | 23
Chapter 2 | Abstract
Ecotoxicogenomics might help solving open questions that cannot be answered by standard ecotoxicity tests currently used in environmental risk assessment. Changes in gene expression are claimed to serve potentially as early warning indicators for environmental effects and as sensitive and specific ecotoxicological endpoints. Ecotoxicogenomics focus on the lowest rather than the highest levels of biological organization. Our aim was to explore the links between gene expression responses and population level responses, both mechanistically (conceptual framework) and correlatively (Species Sensitivity Distribution). The effects of cadmium on aquatic species were compared for gene level responses (Lowest Observed Effect Concentrations) and individual level responses (median Lethal Concentrations, LC50, and No Observed Effect Concentrations, NOEC). Responses in gene expression were on average four times above the NOEC and eleven times below the LC50 values. Currently, use of gene expression changes as early warning indicators of environmental effects is not underpinned due to a lack of data. To confirm the sensitivity claimed by ecotoxicogenomics more testing at low concentrations is needed. From the conceptual framework, we conclude that for a mechanistic gene population link in risk management, research is required that includes at least one meaningful endpoint at each level of organization.
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2.1. Introduction
The potential and actual damage of chemicals to the environment is estimated in risk assessment (Power and Adams 1997; Calow 1998; Moore 2002). To cover different levels of organization, effects on molecules, cells, organs, individuals, populations, and ecosystems may be taken into account (Markert and Breure 2003). In ecotoxicity tests, however, the most commonly used endpoints are survival and reproduction of individuals (Miracle and Ankley 2005). These standard tests have been proven useful and efficient in current risk assessments. However, they do not provide sufficient information in all situations, and for early warning they may respond too slowly (Moriarty 1999; Walker et al. 2006; Robbens et al. 2007). As a result, endpoints on subcellular and molecular levels have been proposed to provide additional support for risk assessment. Since the late 1990s, when “omics” technology emerged and was applied in ecotoxicology, research on effects of various chemicals at the genetic level has given rise to the field of ecotoxicogenomics (Neumann and Galvez 2002). Ecotoxicogenomics is the study of gene and protein expression integrating transcriptomics, proteomics, and metabolomics into ecotoxicology (Snape et al. 2004). Application of ecotoxicogenomic techniques in chemical screening, environmental monitoring, and risk assessment is currently being explored. These techniques have been applied, e.g., to study the interactions of different substances on organisms (Poynton and Vulpe 2009) and to determine how chemicals affect molecular pathways and biological processes within individuals (Lettieri 2006; Kammenga et al. 2007).
The potential applications are widely recognized and it has been suggested that these “omics” technologies can provide a broader understanding and prediction of the effects of chemicals on populations and ecosystems (Steinberg et al. 2008). Changes in gene expression are claimed to serve as early warning indicators for environmental effects and as useful biomarkers for chemical exposure (Bartosiewicz et al. 2001; Thomas et al. 2001; Snell et al. 2003; Pennie et al. 2004), because they can be detected at low concentrations of chemicals and before morphological or reproductive effects become visible (Nuwaysir et al. 1999; Klaper and Thomas 2004), Overall, recent reviews concerning ecotoxicogenomics share a common opinion that this field is one of the most promising for environmental risk assessment in which responses in gene expression to chemicals can be considered as a new sensitive, specific, and informative ecotoxicological endpoint (Aardema and MacGregor 2002; Ankley et al. 2006; Calzolai et al. 2007; Poynton et al. 2008). However, sceptical attitudes toward application of “omics” research tools into ecotoxicology and the use of gene expression effects in risk assessment are also present (Forbes et al. 2006; Menzel et al. 2009).
While ecotoxicogenomics is developing, the focus of this field appears to be on the lowest rather than the highest levels of biological organization. Yet, a comprehensive
Chapter 2 | Ecotoxicogenomics | 25
overview of attempts to link these levels carried out so far is lacking. The aim of the present study was therefore to explore potential relationships, both correlative and mechanistic, between gene expression responses and population level responses to be used in risk assessment.
2.2. Materials and methods
Literature concerning “omics” in ecotoxicology was explored in the Web of Science, Scopus and Scirus databases using different combinations of the keywords “ecotoxicogenomic”, “ecotoxicology”, “risk assessment”, “gene expression”, “microarray”, “genomics”, “transcriptomics”, “metabolomics”, and “heavy metals”. Searches with keywords were supplemented with examination of the literature cited in the articles found. Priority was given to articles with (a claim of) an application in ecotoxicology. For the construction of a framework linking genes to populations, we investigated studies on plant and animal species from various terrestrial and aquatic systems exposed to different stressors. For the statistical analysis illustrating potential ways for gene to population extrapolation, we focused on effects of cadmium on aquatic species only, because of the relative large number of publications available. Data on toxic effects of cadmium at different levels of organization were collected from articles, reports, and from the database of the Dutch National Institute for Public Health and the Environment (RIVM): e-toxBase (Crommentuijn et al. 1997; RIVM). For the gene level response, we selected articles containing data on whole-genome expression profiling or specific gene expression in aquatic organisms under exposure to cadmium. If more than one concentration of cadmium was tested, the lowest concentration with a response deviating significantly from the control was used. This concentration served as the Lowest Observed Effect Concentration (LOEC) (Table A3 of Appendix A). Obviously, one cannot exclude that lower concentrations, if tested, would have induced an effect as well. For the individual level response, median Lethal Concentrations (LC50) and No Observed Effect Concentrations (NOEC) for aquatic species were collected (Table A4 of Appendix A). The data were plotted log-logistically to create Species Sensitivity Distributions (SSDs), representing the cumulative distribution of test endpoints data (LOEC, NOEC, or LC50) (Posthuma et al. 2002). SSDs are used to represent stress to the ecosystem caused by chemicals. These distributions can be derived at the species level from toxicity data obtained from acute or chronic tests. Also, other endpoints on different levels such as effects on gene expression can be used. The Potentially Affected Fraction (PAF) in these distribution curves shows the proportion of the species affected as a function of stressor concentration. The “potential fraction” indicates the fraction of species estimated to be exposed beyond an effective concentration (Posthuma et al. 2002). The average sensitivity of the species is represented by the 50% hazardous concentration (HC50) (Newman et al. 2000). The median hazardous concentration
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values (HC50) and 5% hazardous concentrations (HC5) were calculated according to Aldenberg and Jaworska (2000) and compared in t-tests.
2.3. Results 2.3.1. Linking levels in a hierarchical framework
Research in ecotoxicogenomics focuses on the genome-wide expression analysis under exposure to various contaminants resulting in chemical-specific patterns of gene expression and on the development of a mechanistic understanding of chemical toxicity on various organisms (Table A2 of Appendix A). Potentials and advantages of ecotoxicogenomics are recognized, and its usefulness for application in ecotoxicology is accepted, but evidence and successful examples of these aspects are often missing. This potential can be accomplished by making links between gene expression profiles, cellular level responses, and observed biological responses known to impair adverse impacts at individual and population level (Jha 2008). In general, biomarkers are considered most useful for environmental risk assessment if they can predict the effects on survival, growth, or reproduction (Roh et al. 2006). Thus, to test the ecotoxicological relevance of changes in gene expression and to use these as biomarkers or as new endpoints, it is necessary to ensure that the changes in gene expression are integrated with individual and population level endpoints.
The linkages from gene to population level effects can be presented in a hierarchical framework (Figure 2.1). Following cascade effects, processes at one level are considered to be caused by processes at a lower level and result in consequences at a higher level (Newman and Unger 2003; Spurgeon et al. 2005). The initial responses to a chemical interacting with the site of action can be observed at low levels of organization, e.g., gene transcription and protein synthesis (Spurgeon et al. 2008). In case of continuing or increasing stress, effects on the molecular level will result in a local cellular response (Poynton et al. 2008). Cellular effects in turn may lead to tissue damage and to physiological, biochemical, or behavioural changes at the whole organism level. Damage at these levels can potentially affect population dynamics and community structure (Newman and Unger 2003). Ecotoxicogenomic studies typically focus on the lower end of the framework (Table A2 of Appendix A). Changes in gene expression can result from toxicity as either a direct or indirect response to chemical exposure (Van Straalen and Roelofs 2008). Some genes are turned on or off. Alternatively, the level of expression of some genes is altered (Walker et al. 2006). Toxic endpoints at the cellular level such as inflammation, apoptosis, necrosis, and cellular differentiation are preceded by specific alterations in gene expression (Pennie et al. 2004). Gene expression effects are generally specific for certain chemicals. By moving up in the organizational hierarchy, observed effects become less specific to the chemical tested (Shugart et al. 1998). The upper part of the framework relates to the
Chapter 2 | Ecotoxicogenomics | 27
area of classical ecotoxicology. For assessment and understanding the direct effects of toxicants on these levels, many approaches have been developed (Forbes and Forbes 1994). The most commonly used test endpoints for organism level are the LC50 and the NOEC (Kimball and Levin 1985; Forbes and Calow 2002). Often, however, environmental risk assessment aims to protect populations rather than individuals (Suter 1993; Forbes and Calow 1999; Forbes and Calow 2002b). Basic endpoints at the level of populations may include variables like the population density, productivity, and probability of extinction (Newman 2003). Community level effects are often described by changes in species composition (types, diversity) (Calow 1998). Effects on populations and communities are ecologically relevant but they often lack mechanistic explanations (Magrini et al. 2008).
Figure 2.1. Hierarchical framework linking genes to populations throughout all levels of biological organization (vertical arrows). Ecotoxicogenomics investigate effects of chemicals at the gene level. Ecotoxicology covers the response at the individual and population level. The major gap identified in the present study is between gene and cell responses on the one hand and the individual and population responses on the other (white arrow). Endpoints at several levels may help to link these parts and fill the gap (dotted boxes).
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2.3.2. Bridging the gaps in individual studies
Examples of recent ecotoxicogenomic studies in the area of environmental toxicology, ecotoxicology and risk assessment are given in Table A1 of Appendix A. In these studies, gene expression was used to distinguish the type of contamination or the mechanism of action of the chemicals rather than used to predict effects on exposed populations. Some of these studies report the effects on oxidative stress, detoxification, immune response, and energy metabolism, thereby qualitatively linking chemical-gene- cellular interactions. Some studies also incorporated effects at gene level with response at higher levels of biological organization. Magrini et al. (2008) investigated gene expression in Arabidopsis thaliana grown in soils contaminated by copper and lead in combination with individual level responses. Genes related to metallothioneins, heat shock proteins, protein synthesis, cellular transport, and wound stress response were up-regulated. Gene expression was used as an indication of soil contamination, which was also supported by reduced plant growth. This result suggests that exposure to heavy metals can trigger specific gene activation as well as changes in general stress response genes expression. In another study, rainbow trout (Oncorhynchus mykiss) showed unique response patterns when exposed to a series of model toxicants (Hook et al. 2006). The majority of responsive genes was specific to a single chemical, showing a link between gene expression profiles and the toxic mode of action (Hook et al. 2006). The specificity of gene expression in response to chemicals, the link between genes expression and mode of action provide another example that “omics” technologies can be potentially used for chemical screening. Roelofs et al. (2007) identified differentially expressed genes in springtails (Orchesella cincta) exposed to cadmium in their food. These genes were associated with general stress response and involved in cadmium detoxification. They also found that different metals induced the same gene expression, which is similar in different species (Roelofs et al. 2007). Watanabe et al. (2007) examined gene expression of Daphnia magna under the influence of CuSO4 and H2O2.
It was found that a high dose of CuSO4 induced a similar gene expression profile as a high dose of H2O2. Some heavy metals, copper and cadmium, were also found to trigger similar gene expression patterns in fish (Woo et al. 2009). Sheader et al. (2006) identified 27 up-regulated genes in European flounder (Platichthys flesus) under cadmium exposure and some candidate genes were selected as potential biomarkers for cadmium exposure.
These studies demonstrate how gene expression and “omics” technologies have been applied to study the effects of toxicants on biological pathways in organisms. In a few studies, changes in gene expression were measured and accompanied by simultaneous monitoring the responses at higher levels. In a study with Daphnia magna exposed to different concentrations of cadmium, changes in gene expression were combined with effects on other levels: cellular energy allocation, growth, energy reserve availability,
Chapter 2 | Ecotoxicogenomics | 29
and energy consumption (Soetaert et al. 2007). At higher concentrations and after prolonged exposure, the number of genes expressed differently increased and net energy budget and growth decreased. Changes in gene expression were associated with molecular pathways involved in immune response, stress response, digestion, oxygen transport, cuticle metabolism, and embryo development. Menzel et al. (2009) combined gene expression profiling of Caenorhabditis elegans exposed to different levels of heavy metals and organic pollution in river sediments with the effects on other levels, including endocrine disruption and reproduction. They showed how changes in gene expression can be used as supplementary assay to screen pollution in “real world”. Connon et al. (2008) noted gene expression in Daphnia magna under cadmium exposure and linked this expression to somatic growth, development, and population growth rate. In another study, Caenorhabditis elegans gene expression was integrated with organism and population level endpoints exposed to silver nanoparticles (Roh et al. 2009). This experiment gives an example of how expression of specific gene can be related to decreasing reproduction potential.
By use of earthworms (Lumbricus rubellus) Spurgeon et al. (2005) provided an example of how effects at different levels can be linked by the cascade concept. The earthworms were exposed to different concentrations of zinc, and their gene expression effects were the most sensitive endpoints. Destabilization of lysosomal membrane was the next level at which the effect of zinc was measured. Less sensitive were individual level effects (changes in reproduction at EC50) followed by population size effects.
2.3.3. Bridging the gaps across individual studies
In addition to linking effects at different levels within the same study, gene expression responses and population responses to the same substance can also be compared by merging data. In ecotoxicology, results from single species tests are often combined statistically by creating a SSD (Posthuma et al. 2002).
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Figure 2.2. SSD with the PAF vs. cadmium concentration for NOECs and median LC50s for individual level responses as well as for LOEC for gene expression. The HC50s of 16.5 μg/L (NOEC), 69.3 μg/L (LOEC), and 791.4 μg/L (LC50) differ significantly, whereas the standard deviation equals 1.28 (NOEC), 1.17 (LOEC), 0.98 (LC50). The corresponding HC5s are 0.23 μg/L (NOEC), 0.72 μg/L (LOEC), and 18.3 μg/L (LC50). Aquatic species included in these SSDs are listed in Table A3 (gene level LOEC) and Table A4 of Appendix A (individual level LC50 and NOEC).
In Figure 2.2, the potentially affected fraction of species is plotted as a function of the cadmium concentration for endpoints on different levels of organization. The response concentration LOEC for gene expression is roughly in between the NOEC and LC50 for individual level effects. Differences between gene expression responses and whole organism responses based on NOECs, and between gene expression responses and whole organism responses based on LC50 are statistically significant (Figure 2.2). The standard deviation of distributions reflects slopes of the SSDs and represents variation in sensitivity of the endpoints.
2.4. Discussion 2.4.1. Perspectives for mechanistic links
Studies have shown that gene to cell level types of response are better traceable and can be easier linked to a specific cause than effects at higher levels. Each organism has specific mechanisms to cope with external stressors and to balance normal physiological conditions (Van Straalen and Roelofs 2006). Under stressful conditions, an organism will activate these stress-defence mechanisms acting on molecular up to organism levels. Mechanism-specific endpoints, however, are not necessarily predictive
Chapter 2 | Ecotoxicogenomics | 31
of an adverse outcome on ecologically relevant levels (Ankley et al. 2009), and thus effects of chemicals should be tracked at each level. Examples of common stress responses known on different organizational levels are used as biomarkers of chemical exposure or effect. For instance, effects of chemicals can be marked by phase I and phase II metabolic enzymes, metallothioneins, antioxidant enzymes, and heat shock proteins (Spurgeon et al. 2008). Genes encoding these elements are commonly found to be differentially expressed upon chemical exposure too and can thus reveal respective cellular effects.
It should be possible to link cell stress reactions to organism level responses, by measuring metabolic products and processes such as proteins involved in digestion, oxygen transport, and total hydrocarbons concentration, because toxic defence and repair mechanisms are metabolically costly (Calow 1991). As metabolic rates may be increased by some pollutants and decreased by others, the amount of energy left for survival, growth, and reproduction may be a better indicator (Hendriks et al. 2005; Soetaert et al. 2006). Reallocation of energy to stress-specific responses might represent a general mechanism that occurs under stress exposure (Hoffmann and Parsons 1989). Thus, mechanistic links could potentially be explained by the energy budget models describing the effects of chemical stress on energy fluxes to maintain reproduction and survival of individuals and thus dynamics and existence of populations (Kooijman and Metz 1984).
To be useful for implementation of such a mechanistic gene-population link in risk management, empirical research should include at least one meaningful endpoint at each level of organization. These endpoints may vary as shown in the Figure 2.1 but they will provide mechanistic information for quantifying effects of stressor if measured simultaneously.
The major goal for these endpoints to be mechanistically linked to each other is to relate gene expression endpoints to parameters such as reproduction and growth and to ecologically relevant population and community responses.
2.4.2. Perspectives for correlative links
Data on gene expression LOECs collected in this study are less well standardized than individual-based NOECs and LC50s. Comparing HC50s calculated in this study showed that cadmium induced changes in gene expression at concentrations of about 4 times above the NOEC and 11 times below the LC50 for effects on individual level. At the HC5 level, LOECs for gene expression and NOECs for individual level effects differed by a factor of 3. This indicates that cadmium effects on gene expression have not been observed at the NOEC level, the HC5 of which is usually considered to be the threshold for protection of ecological structure (Van Straalen and Denneman 1989).The
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observed difference between gene expression LOEC and individual level effects NOEC is not always reflected in data for the same species. Gene expression LOECs and individual level NOECs differed by a factor 5 in Daphnia magna and about 250 in Oncorhynchus mykiss. However, in Chironomus tentans gene expression LOEC was 1.5 times more sensitive than individual level NOEC. On individual level, NOEC differs generally by a factor of about 2 from the LOEC for the same chemical compound (De Bruijn et al. 1999). Thus, if we had compared LOECs on individual levels with the obtained LOECs for gene expression, the difference would still be a factor 2. This comparison indicated that response concentrations in gene assays were relatively high. The NOECs and LC50 were obtained for the same groups of species (Pisces, Crustacea, Insecta, and Mollusca) for which LOECs at gene level were available. Obviously, the limited number of input data might have decreased the accuracy of the NOEC and LC50 SSDs. However, the SSDs for individual level effects based on data for all aquatic species available in the RIVM e-toxBase were comparable with the SSDs for this limited group of aquatic species (Figure A1 of Appendix A). The differences in sensitivity between gene responses and individual level were similar in both cases (Table 2.1), with gene expression LOECs about 2 times less sensitive than individual level NOECs.
In a similar SSD approach, cellular biomarkers were found to be a factor of 35-50 more sensitive to oil than individual based endpoints (NOEC) (Smit et al. 2009). In addition, in the example of Spurgeon et al. (2005) it was shown that gene expression response was the most sensitive endpoint and effects of zinc at low concentrations were detected. These studies suggest that high sensitivity in gene expression responses may indeed be achievable. However, the currently available data for cadmium exposure on aquatic organisms do not yet confirm that responses on gene expression level (LOEC) are more sensitive than on individual level (NOEC). Overall, the use of gene expression responses as an early warning of population level effects and as a sensitive endpoint measured at low, environmentally realistic concentrations as suggested in the past (Galay-Burgos et al. 2003; Reynders et al. 2006) is thus not underpinned by the available data yet. We conclude that more testing at low concentrations is needed to confirm the sensitivity claimed. This would also require standardization of gene expressions assays to obtain meaningful endpoints at the gene level and confirmation for other chemicals and species. Such an endpoint may be, for example, a No Observed Transcriptional Effect Level (NOTEL) obtained after a standard exposure period to be compared with existing toxicity data for individual-population level (Lobenhofer et al. 2004; Poynton et al. 2008). This will allow the derivation of an “extrapolation factor” linking directly gene and population responses, which is not yet possible with currently available data. Without such standardization, we should keep in mind that variability in data also reflects differences between protocols and endpoints, in addition to differences
Chapter 2 | Ecotoxicogenomics | 33
between species. While empirical underpinning is required, our study shows that SSD can provide a tool for a gene-population response linking, albeit of a correlative nature.
Table 2.1. HC50 and HC5 values (μg/L Cd) including number of species used in analysis (n) and standard deviation of the SSDs for LOECs (gene expression) and NOECs and LC50 (both individual level response) based on selected groups of aquatic species for which LOEC values were available and for the latter two also on all aquatic species present in the RIVM e-toxBase. NC- not calculated.
Selected species groups All species NOEC HC50 16.5 32.3 HC5 0.23 0.24 n 26 43 SD 1.28 1.12 LOEC HC50 69.3 NC HC5 0.72 NC n 12 NC SD 1.17 NC LC50 HC50 791.4 859.1 HC5 18.3 13.2 n 20 448 SD 0.98 0.99
Acknowledgments
We thank Arjen Wintersen and Mijke van Oorschot for providing the data for the SSDs, Leo Posthuma, Mark Huijbregts, and three anonymous reviewers for useful comments on earlier versions of the manuscript.
2.5. References Aardema MJ, MacGregor JT. 2002. Toxicology and genetic toxicology in the new era of “toxicogenomics”: Impact of “-omics” technologies. Mutat Res Fund Mol M 499, 13-25. Aldenberg T, Jaworska JS. 2000. Uncertainty of the hazardous concentration and fraction affected for normal species sensitivity distributions. Ecotoxicol Environ Saf 46, 1-18. Ankley GT, Bencic DC, Breen MS, Collette TW, Conolly RB, Denslow ND, Edwards SW, Ekman DR, Garcia-Reyero N, Jensen KM, Lazorchak JM, Martinovic D, Miller DH, Perkins EJ, Orlando EF, Villeneuve DL, Wang RL, Watanabe KH. 2009. Endocrine disrupting chemicals in fish: Developing exposure indicators and predictive models of effects based on mechanism of action. Aquat Toxicol 92, 168- 178.
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Ankley GT, Daston GP, Degitz SJ, Denslow ND, Hoke RA, Kennedy SW, Miracle AL, Perkins EJ, Snape J, Tillitt DE, Tyler CR, Versteeg D. 2006. Toxicogenomics in regulatory ecotoxicology. Environ Sci Technol 40, 4055-4065. Bartosiewicz M, Penn S, Buckpitt A. 2001. Applications of gene arrays in environmental toxicology: Fingerprints of gene regulation associated with cadmium chloride benzo(a)pyrene and trichloroethylene. Environ Health Perspect 109, 71-74. Calow P. 1998. Ecological risk assessment: Risk for what? How do we decide Ecotoxicol Environ Saf 40, 15-18. Calow P. 1991. Physiological costs of combating chemical toxicants - ecological implications. Comp Biochem PhysC 100, 3-6. Calzolai L, Ansorge W, Calabrese E, Denslow N, Part P, Lettieri T. 2007. Transcriptomics and proteomics: Applications to ecotoxicology. Comp Biochem Phys D 2, 245-249. Clements WH. 2000. Integrating effects of contaminants across levels of biological organization: an overview. J Aquat Ecosyst Stress Recov 7, 113-116. Connon R, Hooper HL, Sibly RM, Lim FL, Heckmann LH, Moore DJ, Watanabe H, Soetaert A, Cook K, Maund SJ, Hutchinson TH, Moggs J. De Coen W, Iguchi T, Callaghan A. 2008. Linking molecular and population stress responses in Daphnia magna exposed to cadmium. Environ Sci Technol 42, 2181-2188. Crommentuijn T, Polder MD, Vande P, lassche EJ. 1997. Maximum permissible concentrations and negligible concentrations for metals taking background concentrations into account. RIVM Report 60150100: Bilthoven NL. De Bruijn J, Crommentuijn T, Van Leeuwen K, Van der Plassche E, Sijm D, Van der Weiden M. 1999. Environmental Risk Limits in the Netherlands RIVM. Report 601640001: Bilthoven NL. Forbes VE, Calow P. 1999. Is the per capita rate of increase a good measure of population-level effects in ecotoxicology. Environ Toxicol Chem 18, 1544-1556. Forbes VE, Calow P. 2002b. Population growth rate as a basis for ecological risk assessment of toxic chemicals. Phil Trans R Soc London B Biol Sci 357, 1425, 1299- 1306. Forbes VE Calow P. 2002. Species sensitivity distributions revisited: a critical appraisal. Hum Ecol Risk Assess 8, 473-492. Forbes VE, Forbes TL. 1994. Ecotoxicology in Theory and Practice Chapman & Hall: London. Forbes VE, Palmqvist A, Bach L. 2006. The use and misuse of biomarkers in ecotoxicology, Environ Toxicol Chem 25, 272-280. Galay-Burgos M. Spurgeon DJ, Weeks JM, Stu¨rzenbaum SR, Morgan AJ, Kille P. 2003. Developing a new method for soil pollution monitoring using molecular genetic biomarkers. Biomarkers 8, 229-239. Hendriks AJ, Maas-Diepeveen JLM, Heugens EHW, Van Straalen NM. 2005. Meta- analysis of intrinsic rates of increase and carrying capacity of populations affected by toxic and other stressors. Environ Toxicol Chem 24, 2267- 2277. Hoffmann AA, Parsons PA. 1989. Selection for increased desiccation resistance in Drosophila melanogaster - additive genetic control and correlated responses for other stresses. Genetics 122, 837-845.
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Hook SE, Skillman AD, Small JA, Schultz IR. 2006. Gene expression patterns in rainbow trout Oncorhynchus mykiss exposed to a suite of model toxicants. Aquat Toxicol 77, 372-385. Jha AN. 2008. Ecotoxicological applications and significance of the comet assay. Mutagenesis 23, 207-221. Kammenga JE, Herman, MA, Ouborg NJ, Johnson L, Breitling R. 2007. Microarray challenges in ecology. Trends Ecol Evol 22, 273-279. Kimball KD, Levin SA. 1985. Limitations of laboratory bioassays - the need for ecosystem-level testing. Bioscience 35, 165-171. Klaper R, Thomas MA. 2004. At the crossroads of genomics and ecology: The promise of a canary on a chip. Bioscience 54, 403-412. Kooijman SALM, Metz JAJ. 1984. On the dynamics of chemically stressed populations: the deduction of population consequences from effects on individuals. Ecotoxicol Environ Saf 8, 254-274. Lettieri T. 2006. Recent applications of DNA microarray technology to toxicology and ecotoxicology. Environ Health Perspect 114, 4-9. Lobenhofer EK, Cui XG, Bennett L, Cable PL, Merrick BA, Churchill GA, Afshari CA. 2004. Exploration of low-dose estrogen effects: Identification of No Observed Transcriptional Effect Level NOTEL. Toxicol Pathol 32, 482- 492. Magrini KD, Basu A, Spotila JR, Avery HW, Bergman LW, Hammond R, Anandan S. 2008. DNA microarrays detect effects of soil contamination on Arabidopsis thaliana gene expression. Environ Toxicol Chem 27, 2476-2487. Markert BA, Breure AM, Zechmeister HG. 2003. Bioindicators and Biomonitors: Principles Concepts and Applications. Elsevier: Amsterdam. Menzel R, Swain SC, Hoess S, Claus E, Menzel S, Steinberg CEW, Reifferscheid G, Sturzenbaum SR. 2009. Gene expression profiling to characterize sediment toxicity - a pilot study using Caenorhabditis elegans whole genome microarrays. BMC Genomics 10, 160. Miracle AL, Ankley GT. 2005. Ecotoxicogenomics: Linkages between exposure and effects in assessing risks of aquatic contaminants to fish. Reprod Toxicol 19, 321- 326. Moore MN. 2002. Biocomplexity: the post-genome challenge in ecotoxicology. Aquat Toxicol 59, 1-15. Moriarty F. 1999. Ecotoxicology: the Study of Pollutants in Ecosystems. 3rd ed., Academic Press: San Diego. Neumann NF, Galvez F. 2002. DNA microarrays and toxicogenomics: Applications for ecotoxicology. Biotechnol Adv 20, 391-419. Newman MC, Ownby DR, Mezin LCA, Powell DC, Christensen TRL, Lerberg SB, Anderson BA. 2000. Applying species-sensitivity distributions in ecological risk assessment: Assumptions of distribution type and sufficient numbers of species. Environ Toxicol Chem 19, 508-515. Newman MC, Unger MA. 2003. Fundamentals of Ecotoxicology. 3rd ed., Lewis Publishers: Boca Raton FL. Nuwaysir EF, Bittner M, Trent J, Barrett JC, Afshari CA. 1999. Microarrays and toxicology: The advent of toxicogenomics. Mol Carcinogen 24, 153-159.
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Pennie W, Pettit SD, Lord PG. 2004. Toxicogenomics in risk assessment: An overview of an HESI collaborative research program. Environ Health Perspect 112, 417-419. Posthuma L, Suter GW. Traas TP. 2002. Species Sensitivity Distributions in Ecotoxicology. Lewis Publishers: Boca Raton FL. Power M, Adams SM. 1997. Perspectives of the scientificcommunity on the status of ecological risk assessment. Environ Manage 21, 803-803. Poynton HC, Loguinov AV, Varshavsky JR, Chan S, Perkins EI, Vulpe CD. 2008. Gene expression profiling in Daphnia magna part I: Concentration-dependent profiles provide support for the No Observed Transcriptional Effect Level. Environ Sci Technol 42, 6250-6256. Poynton HC, Vulpe CD. 2009. Ecotoxicogenomics: emerging technologies for emerging contaminants. J Am Water Res Assoc 45, 83-96. Poynton HC, Wintz H, Vulpe CD. 2008. Progress in ecotoxicogenomics for environmental monitoring mode of action and toxicant identification: In Comparative Toxicogenomics, Hogstrand C, Kille P, Eds. Elsevier: Amsterdam. Reynders H, Van der Ven K, Moens LN, Van Remortel P, De Coen WM, Blust R. 2006. Patterns of gene expression in carp liver after exposure to a mixture of waterborne and dietary cadmium using a custom-made microarray. Aquat Toxicol 80, 180-193. RIVM e-toxBase Database for Ecotoxicological Risk Assessment National Institute for Public Health and the Environment. Available at http://wwwe-toxbasecom. Robbens J, Van der Ven K, Maras M, Blust R, De Coen W. 2007. Ecotoxicological risk assessment using DNA chips and cellular reporters. Trends Biotechnol 25, 460-466. Roelofs D, Marien J, Van Straalen NM. 2007. Differential gene expression profiles associated with heavy metal tolerance in the soil insect Orchesella cincta. Insect Biochem Mol Biol 37, 287-295. Roh JY, Lee J, Choi J. 2006. Assessment of stress-related gene expression in the heavy metal-exposed nematode Caenorhabditis elegans: A potential biomarker for metal- induced toxicity monitoring and environmental risk assessment. Environ Toxicol Chem 25, 2946-2956. Roh JY, Sim SJ, Yi J, Park K, Chung KH, Ryu DY, Choi J. 2009. Ecotoxicity of silver nanoparticles on the soil nematode Caenorhabditis elegans using functional ecotoxicogenomics. Environ Sci Technol 43, 3933-3940. Sheader DL, Williams TD, Lyons BP, Chipman JK. 2006. Oxidative stress response of European flounder Platichthys flesus, to cadmium determined by a custom cDNA microarray. Mar Environ Res 62, 33-44. Shugart L, Theodorakis C. 1998. Newtrends in biological monitoring: application of biomarkers to genetic ecotoxicology. Biotherapy 11, 119-127. Smit MGD, Bechmann RK, Hendriks AJ, Skadsheim A, Larsen BK, Baussant T, Bamber S, Sanni S. 2009. Relating biomarkers to whole-organism effects using species sensitivity distributions: a pilot study for marine species exposed to oil. Environ Toxicol Chem 28, 1104-1109. Snape JR, Maund SJ, Pickford DB, Hutchinson TH. 2004. Ecotoxicogenomics: the challenge of integrating genomics into aquatic and terrestrial ecotoxicology. Aquat Toxicol 67, 143-154.
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Snell TW, Brogdon SE, Morgan MB. 2003. Gene expression profiling in ecotoxicology. Ecotoxicology 12, 475-483. Soetaert A, Moens LN, Van der Ven K, Van Leemput K, Naudts B, Blust R, De Coen WM. 2006. Molecular impact of propiconazole on Daphnia magna using a reproductionrelated cDNA array. Comp Biochem Phys C 142, 66-76. Soetaert A, Vandenbrouck T, Van der Ven K, Maras M, Van Remortel P, Blust R, De Coen WM. 2007. Molecular responses during cadmium-induced stress in Daphnia magna: Integration of differential gene expression with higher-level effects. Aquat Toxicol 83, 212-222. Spurgeon DJ, Morgan AJ, Kille P. 2008. Current research in soil invertebrate ecotoxicogenomics: In Comparative Toxicogenomics, Hogstrand C, Kille P, Eds. Elsevier: Amsterdam. Spurgeon DJ, Ricketts H, Svendsen C, Morgan AJ, Kille P. 2005. Hierarchical responses of soil invertebrates earthworms, to toxic metal stress. Environ Sci Technol 39, 5327- 5334. Steinberg CEW, Stu¨rzenbaum SR, Menzel R. 2008. Genes and environment: Striking the fine balance between sophisticated biomonitoring and true functional environmental genomics. Sci Total Environ 400, 142-161. Suter GW II, Barnthouse LW. 1993. Ecological Risk Assessment. Lewis Publishers: Boca Raton FL. Thomas RS, Rank DR, Penn SG, Zastrow GM, Hayes KR, Pande K, Glover E, Silander T, Craven MW, Reddy JK, Jovanovich SB, Bradfield CA. 2001. Identification of toxicologically predictive gene sets using cDNA microarrays, Mol Pharmacol 60, 1189-1194. Van Straalen NM, Denneman CAJ. 1989. Ecotoxicological evaluation of soil quality criteria. Ecotoxicol Environ Saf 18, 241-251. Van Straalen NM, Roelofs D. 2008. Genomics technology for assessing soil pollution. J Biol 7, 6-19. Van Straalen NM, Roelofs TFM. 2006. An Introduction to Ecological Genomics Oxford University Press: Oxford. Walker CH, Hopkin SP, Sibly RM, Peakall DB. 2006. Principles of Ecotoxicology 3rd ed. CRC: Boca Raton FL. Watanabe H, Takahashi E, Nakamura Y, Oda S, Tatarazako N, Iguchi T. 2007. Development of a Daphnia magna DNA microarray for evaluating the toxicity of environmental chemicals. Environ Toxicol Chem 26, 669-676. Woo S, Yum S, Park HS, Lee TK, Ryu JC. 2009. Effects of heavy metals on antioxidants and stress-responsive gene expression in Javanese medaka Oryzias javanicus. Comp Biochem Phys C 149, 289-299.
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Chapter 3 Tolerance of native and non-native fish species to chemical stress: a case study for the river Rhine
Fedorenkova A, Vonk JA, Breure AM, Hendriks AJ, Leuven RSEW Published in Aquatic Invasions 8: 231-241, 2013
Chapter 3 | Non-native fish | 39
Chapter 3 | Abstract
Freshwater ecosystems can be impacted by invasive species. Non-native species can become invasive due to their high tolerance to environmental stressors (e.g., pollution and habitat modifications). Yet, tolerance of native and non-native fish species exposed simultaneously to multiple chemical stressors has not been investigated. To quantify tolerance of native and non-native fish species in the Delta Rhine to 21 chemical stressors we derived Species Sensitivity Distributions (SSDs). Differences in tolerance between the two species groups to these stressors were not statistically significant. Based on annual maximum water concentrations of nine chemical stressors in the Delta Rhine the highest contribution to the overall Potentially Affected Fraction (PAF) of both species groups was noted for ammonium, followed by azinphos-methyl, copper, and zinc. PAFs of both groups for metals and ammonium showed a significant linear decrease over the period 1978-2010. Deriving a PAF for each species group was a useful tool for identifying stressors with a relatively highest impact on species of concern and can be applied to water pollution control. Species traits such as tolerance to chemical stress cannot explain the invasiveness of some fish species. For management of freshwater ecosystems potentially affected by non-native species, attention should be given also to temperature, hydrological regimes, and habitat quality.
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3.1. Introduction
Large numbers of species have been introduced in habitats outside their native areas (Lodge 1993). About ten percent of these non-native species can become highly invasive (Jeschke and Strayer 2005; Ricciardi and Kipp 2008). One of the characteristics considered to make a non-native species become invasive is a high tolerance to environmental stressors, both natural and human induced, compared to native species (Karatayev et al. 2009; Leuven et al. 2011; Verbrugge et al. 2012). Yet, research on relative tolerances of non-native species to physico-chemical stressors is severely limited for three main reasons. Firstly, most of the studies focus on a single non-native species, usually an invertebrate (Piola and Johnston 2006; Karatayev et al. 2009; Weir and Salice 2012). Secondly, most attention is given to a single physico- chemical stressor at a time, for example temperature, organic waste, salinity, single chemical compounds, or nutrient pollution (Menke et al. 2007; Früh et al. 2012). Finally, there is a lack in comparative and quantitative assessment of tolerance of native and non-native species to (multiple) stressors (Vila-Gispert et al. 2005; Scott et al. 2007; Alonso and Castro-Diez 2008).
The number of non-native fish species in the river Rhine strongly increased over the 20th century due to the unintentional and deliberate introductions of species (Leuven et al. 2011). During this century, pollution of the river has also increased and reached maximum levels in the period 1960-1980. After the environmental rehabilitation of the Rhine, an improvement in water quality has been observed (IKSR 2012 http://www.iksr.org/). Yet, pollution with organic substances from agricultural activities and with metals from historically polluted sediments continues to be problematic (Nienhuis et al. 2002; IKSR 2012 http://www.iksr.org/). A comparison of potential impact of water pollution on native and non-native fish species has not been performed.
The aim of our study was three-fold. Firstly, we examined quantitatively whether native and non-native fish species differed in tolerance to a broad range of chemical stressors occurring in their habitat. Secondly, we investigated the overall effect of multiple chemical stressors on native and non-native species and ranked the chemical stressors according to their potential impact on each species group. Thirdly, we determined the trends in impact of chemical stressors in the river for both species groups over time, calculated as the potentially affected fraction (PAF) of species. To address this aim, we focused on the native and non-native fish species in the distributaries of the river Rhine in the Netherlands (Delta Rhine) (Figure 3.1). We quantified effects of multiple chemical stressors on fish species by combining Species Sensitivity Distributions (SSDs) derived from acute toxicity tests and environmental concentrations measured in the Delta Rhine in the period 1978-2010. We constructed SSDs for native and non-
Chapter 3 | Non-native fish | 41
native fish species of the Delta Rhine to analyze their tolerance to chemical stressors. Then, we combined the SSDs with monitoring data on chemical distribution in the river to estimate the effects of environmental exposure for each fish species group. Relevance of our quantitative approach for management of native and non-native fish species and for water pollution control is discussed.
3.2. Methods 3.2.1. Deriving Species Sensitivity Distributions from acute toxicity tests
A list of native and non-native fish species occurring in the freshwater sections of the river Rhine distributaries Waal, Nederrijn and IJssel in the Netherlands (Delta Rhine) was derived from Leuven et al. (2011). In total, 60 fish species were recorded in the Delta Rhine over the years 1900-2010, of which 36 were native species and 24 were non-native species (See Appendix B).
Species Sensitivity Distributions (SSDs) were derived to analyze the variation in tolerance of native and non-native fish species to multiple chemical stressors based on acute toxicity data from laboratory studies. Data on the tolerance to chemical stressors (Table 3.1) of fish species of the Delta Rhine were collected from the RIVM e-toxBase and the US EPA ECOTOX database (http://www.e-toxbase.com/; http://cfpub.epa.gov/ecotox/). Both databases comprised acute median Lethal Concentration (LC50) values, i.e., concentrations with mortality effect for 50% of the test organisms. Chronic No Observed Effect Concentrations, i.e., a level of exposure which does not cause observable harm to the organism (Posthuma et al. 2002), were not considered in the current study due to the lack of data. To obtain sufficient toxicity data, test results for different life stages of fish were included. If multiple LC50 values were reported for one species and different life stages, the geometric mean for the same life stage with the lowest LC50 was taken for further analysis, as suggested by US-EPA (TenBrook et al. 2009). The geometric mean represents the best estimate of a toxicity value (TenBrook et al. 2009). For 21 chemicals, toxicity data were available for at least four different fish species and were included in this study to derive SSDs. This number of test species was sufficient since our study focused on a single taxonomic group (fish) and not on the whole aquatic community (ranging from bacteria to mammals). The variation in sensitivity to chemical stressors within a single taxonomic group is generally lower than over a community (Von der Ohe and Liess 2004).
The SSDs were derived for each stressor for each species group, plotted as the cumulative log-normal distribution of LC50 test concentrations. In the log-normal distributions, the standard deviations (SD) describe the variation in tolerance among species. The Hazardous Concentration for 50% (HC50) and 5% (HC5) of the species, commonly used for regulatory purposes, were calculated according to Aldenberg and
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Jaworska (2000). If log10-transformed toxicity values from both species groups were normally distributed according to Shapiro-Wilk tests, potential differences in tolerance to stressors between native and non-native fish species were compared with Independent t tests (D'Agostino and Pearson 1973; Razali and Wah 2011). The difference in variation between species groups was compared by Levene’s tests. Differences were considered statistically significant at p<0.05.
3.2.2. Environmental concentrations of chemicals
Monitoring data on water concentrations in the Delta Rhine were available only for nine stressors out of those for which toxicity data were collected (Table 3.2). The monitoring data were obtained for the period 1978-2010 for metals and ammonium and for the period 1992-2010 for pesticides from the International Commission for the Protection of the Rhine (IKSR 2012, http://maps.wasserblick.net:8080/iksr-zt/). The data represent the concentrations of each chemical measured monthly in the surface water of the main river Rhine channel at gauging station Lobith near the Dutch-German border (Figure 3.1). Since we used effect concentrations based on acute toxicity tests, maximum annual concentrations of each chemical were selected for further analysis.
Figure 3.1. The river Rhine (a) and location of the Lobith gauging station (b).
Chapter 3 | Non-native fish | 43
3.2.3. Effect assessment
In environmental risk assessment, SSDs are used to estimate the Potentially Affected Fraction (PAF) of species at a certain level of exposure (Traas et al. 2002). As such the PAF represents the fraction of species potentially affected above their LC50 level at measured environmental concentrations, depending on the mean toxicity, the variation in sensitivity among species, and the environmental concentration of the stressor.
Monitoring data on nine stressors were available to estimate the PAFs of the native and non-native species group as the fraction of each species group exposed beyond the LC50 endpoint at the specific river location (Lobith) (Aldenberg and Jaworska 2000). The PAFs of species at the measured exposure concentration can be considered a quantification of the severity of effect. The PAFs were calculated for each stressor per year for the period 1992-2010 to facilitate comparison among stressors. The average PAF over this period was used to rank the stressors according to the potential effect they have on each species group. The total PAF, or multi-substance msPAF, for pesticides and total msPAF for metals were calculated by response addition based on PAFs from individual stressor as:
푛 푚푠푃퐴퐹 = 1 − ∏(1 − 푃퐴퐹푖) 푖=1 with n the number of stressors and PAFi the PAF for each stressor individually (Traas et al. 2002).
The differences in PAFs between native and non-native species were compared by Wilcoxon signed rank test (at p < 0.05). The trends of PAFs and chemical concentrations in water over time were analyzed using linear regressions. All statistical tests were performed with SPSS 15.0 for Windows.
3.3. Results 3.3.1. Toxicity data on individual species
In total, 21 chemical stressors were included in the analysis, i.e., 3 metals, 16 pesticides, and 2 phenols. The amount of toxicity data available for native fish species (total 142 acute LC50 values) was larger than for non-native (total 129 acute LC50 values), with a maximum per compound of 13 for native fish species (for zinc) and of 9 for non- native species (for ammonium) (Table 3.1). Toxicity data were available for the same subset of fish species (Appendix B).
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3.3.2. Variation in tolerance to chemical stressors between native and non-native fish species
The HC50 values derived from SSDs for each chemical stressor did not differ significantly (t-test, all p > 0.05) between the native and non-native species (e.g., Figure 3.2 for copper). Yet, non-native species tended to be slightly more tolerant than native species for the majority of stressors studied (Table 3.1). The HC50 of non-native species to metals was consistently higher and HC50 to ammonium and phenols lower than for native species.
Figure 3.2. Species Sensitivity Distribution for copper used to derive Hazardous Concentrations for 5% and 50% (HC5 and HC50, respectively) of native and non-native fish species. PAF is Potentially Affected Fraction in percentage.
The standard deviations (SD) of 12 stressors out of 21 was larger for non-native than for native species while the SD for the remaining 9 stressors was smaller (Table 3.1), (all non-significant, Levene’s test, p > 0.05). In general, the differences between the most sensitive and the most tolerant fish species ranged from a factor of 2 to 800 for the non-native species, and from a factor of 4 to 1200 for the native species. In the non- native species group, Oncorhynchus mykiss and Micropterus salmoides were among the most sensitive species and Carassius auratus (Linnaeus, 1758) and Cyprinus carpio the most tolerant. In the native species group Salmo trutta was one of the most sensitive and Gasterosteus aculeatus (Linnaeus, 1758) and Rutilus rutilus (Linnaeus, 1758) were among the most tolerant. On average, the difference between the most sensitive species of the non-native and native species was a factor of 3, and between the most tolerant species of both groups a factor of 4.
Chapter 3 | Non-native fish | 45
Table 3.1. The Hazardous Concentration (mg/L) at 5% (HC5) and 50% (HC50) of chemicals and the standard deviations (SD) derived from SSDs for non-native and native fish species occurring in the Delta Rhine. Also, the number of species (n) for which toxicity data were available per chemical is provided. Non-native species Native species Log Log Log Log Chemical stressor SD n SD n HC5 HC50 HC5 HC50 Zinc -0.54 0.58 0.64 7 -0.15 0.51 0.39 13 Ammonium -1.00 0.64 1.04 9 -0.66 0.86 0.92 10 DDT -3.00 -1.40 1.14 7 -2.70 -1.4 0.84 8 Malathion -0.85 0.45 0.76 8 -2.00 -0.16 1.23 8 Lindane -2.00 -0.78 0.74 7 -2.00 -1.22 0.55 8 Trichlorfon -1.10 0.85 1.11 7 -1.40 0.47 1.09 8 Copper -1.40 -0.05 0.79 7 -1.52 -0.51 0.58 7 Phenol 0.31 1.25 0.53 6 0.72 1.32 0.35 8 Cadmium -0.59 0.58 0.68 7 -2.00 0.28 1.32 6 Pentachlorophenol -2.00 -0.80 0.72 5 -1.70 -0.92 0.42 8 Endosulfan -4.00 -2.07 1.18 7 -3.00 -1.98 0.55 6 Deltamethrin -2.40 -1.55 0.46 4 -3.00 -1.16 1.27 8 Chlorpyrifos -2.40 -0.94 0.81 4 -3.00 -1.44 0.84 8 Carbaryl 0.47 0.99 0.30 6 0.08 0.79 0.40 5 Endrin -3.70 -2.26 0.90 6 -3.22 -1.91 0.76 5 Azinphos-methyl -2.70 -0.48 1.27 6 -4.00 -1.59 1.44 5 Methoxychlor -2.00 -1.17 0.43 5 -2.00 -1.38 0.50 5 Heptachlor -2.39 -0.90 0.83 6 -2.40 -1.43 0.56 4 Atrazine 0.76 1.40 0.37 6 1.12 1.62 0.27 4 Diazinon 0.06 0.63 0.32 5 -1.40 0.07 0.84 5 Fenitrothion 0.19 0.55 0.20 4 -0.59 0.49 0.59 4
3.3.3. Ranking stressors according to Potentially Affected Fractions
Based on the monitoring data, the average PAFs over the period 1992-2010 related to nine chemical stressors were calculated for native and non-native species (Table 3.2). The msPAF based on effects addition from all stressors was slightly higher for the native species (38.97%) than for the non-native species (31.86%). The differences between the PAFs of native and non-native species were not statistically significant (Wilcoxon signed rank tests, Z = -0.27, p > 0.05). Ammonium had the highest PAFs for
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both species groups (native 35.43%, non-native 29.34%). The ranking of the stressors slightly differed between species groups (Table 3.2). The fraction of the native species affected by the combined pesticides was higher than that of the non-natives (native 2.62%, non-native 0.12%). In contrast, the fraction of non-native species affected by metals was larger (native 2.94%, non-native 3.46%). Azinphos-metyl contributed most to the msPAF for pesticides, and copper dominated the msPAF for metals.
Table 3.2. The average potentially affected fraction PAFs (%) for native and non-native fish species in the river Rhine at Lobith per chemical stressor for the years 1992-2010. Average of annual maximum water concentrations (log transformed, mg/L) over the period 1992-2010 and SD for 9 stressors in the Rhine at Lobith. Changes in annual maximum water concentrations, tested using linear regression (p-values, significant at p < 0.001, slopes and intercepts are shown) for pesticides over the period 1992-2010 and for ammonium, phenol, and metals over the period 1978-2010. n.s. - not significant. River water Chemical PAF (%) PAF (%) Log p-value Slope Intercept stressor Non-native Native SD max Ammonium 29.34 35.43 -0.50 0.27 <0.001 -0.06 150.75 Azinphos- 0.12 2.62 -1.84 0.29 n.s. 1E-03 -1.21 metyl Copper 2.45 2.58 -2.02 0.17 n.s. 0 0.95 Cadmium 1.4E-07 0.36 -3.74 0.24 <0.001 -5.4E-05 0.11 Zinc 1.03 0.01 -1.29 0.17 <0.001 -3.0E-03 6.01 Malathion 2.4E-12 1.4E-03 -4.85 0.31 n.s. -2.5E-06 0.01 Pentachloro- 1.3E-04 4.4E-14 -4.62 0.35 n.s. -2.6E-06 0.01 phenol Fenitrothion 6.2E-20 4.5E-16 -1.92 0.38 n.s. 0 1.98 Atrazine 9.4E-48 1.7E-92 -4.23 0.45 <0.001 -9.5E-06 0.02
3.3.4. Temporal trends in Potentially Affected Fractions
Over the studied period, water concentrations decreased for all chemical stressors, however, the decrease was significant for cadmium, zinc, ammonium, and atrazine only (Table 3.2). The msPAF for metals and the PAF for ammonium corresponding to the annual maximum water concentrations in the river Rhine at Lobith showed a significant linear decrease over the period 1978-2010 for both native and non-native species (Figure 3.3). For pesticides, there was no significant change in the PAF for the period
Chapter 3 | Non-native fish | 47
1992-2010 for either species group. Since we used the same species to determine the tolerance over time, these trends in PAFs were related to changes in concentrations of chemicals in water.
Figure 3.3. Potentially Affected Fraction (PAF) for ammonium (a) and total metals (b) for native and non-native fish species calculated using the maximum annual concentrations in the river Rhine at Lobith. Statistical specifications of linear regressions PAF = a+b∙year: Ammonium only since 1993 native b = -1.21, a = 2459, r2 = 0.70, p < 0.001; non-native b = -0.63, a = 1281, r2 = 0.70, p < 0.001. Metals native b = -0.27, a = 550, r2 = 0.45, p < 0.001; non-native b = -0.13, a = 259, r2 = 0.43, p = 0.001.
Figure 3.3 shows that the drop in water concentrations of ammonium and metals in the river Rhine resulted in comparable values of the PAFs in the last decade for the native
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and non-native species, however, for ammonium it remains still high. The variability in the PAFs over the period 1978-1990 was rather low due to low variability in ammonium concentration over this period (e.g., between 1.2 and 2.0 mg/l), relatively low steepness of the species sensitivity curves and the PAF-scale used in the graph.
3.5. Discussion 3.5.1. Uncertainties
In general, the lack of available toxicity data can limit the number and diversity of species for SSD development (Raimondo et al. 2008). This limited availability of toxicity data for native and non-native species could introduce an uncertainty into the derived SSDs and HC50. Usually, this uncertainty decreases with increasing number of species included in the HC50 calculations (Posthuma and Suter II 2011; Golsteijn et al. 2012). To deal with such data uncertainty, sample sizes can be enhanced by increasing the number of laboratory experiments. However, this is expensive, unfeasible for endangered/protected species and ethically controversial. Additional toxicity data for native and non-native fish species could be generated for chemical stressors of concern by using e.g., quantitative structure-activity relationships between chemicals (Devillers and Devillers 2009) or interspecies correlation estimation models (Dyer et al. 2008; Henning-de Jong et al. 2009; Golsteijn et al. 2012).
3.5.2. Tolerance and comparison with other studies
Previous studies on tolerance of native and non-native species have shown different results indicating that tolerance to abiotic stressors may vary from case to case. Studies on invertebrates have demonstrated different tolerances between native and non-native species to various stressors (Piola and Johnston 2009; Verbrugge et al. 2012; Weir and Salice 2012). Contrasting results in tolerance of fish species have also been obtained, either with no differences in tolerance between native and non-native species to an organic compound (Jin et al. 2011) or a native fish species more tolerant to polycyclic aromatic hydrocarbons than a non-native species (Gevertz et al. 2012). Until now, however, no study has compared the tolerance of a whole fish community consisting of native and non-native fish species to a range of chemical stressors, including several metals, pesticides, and ammonium. The current study was based on the most comprehensive data available and showed no significant difference in tolerance between native and non-native fish species in the Delta Rhine to multiple chemical stressors. This lack of difference in tolerance to chemical stressors between native and non-native fish species may be related to introduction pathways, since many of fish species in the river Rhine were deliberately introduced. Species that survive harsh environmental conditions during dispersal routes (e.g., in ballast water or migration
Chapter 3 | Non-native fish | 49
through canals between river basins) to become invasive elsewhere may, however, be more tolerant than native ones (Piscart et al. 2011).
In the last two decades, the chemical pollution in the Delta Rhine has decreased, whereas the number of non-native fish species has increased. The appearance of non- native fish species and disappearance of some native species in the Delta Rhine since 1980 cannot be explained by their tolerance to chemical stressors. However, the lack of toxicity data for many non-native species that have inhabited the Delta Rhine in the last two decades only induces uncertainty in tolerance to chemicals for this species group. Possible reasons for the changes in the non-native species may be the increase in water temperature, known to affect non-native species less than native species (Leuven et al. 2011). Additionally, the opening of the Main-Danube canal in 1992 connecting the river Rhine with the river Danube may have accelerated the distribution of non-native species into the Delta Rhine (Leuven et al. 2009).
Overall, tolerance to chemical stress was not a trait that could explain the invasiveness of fish species. Other traits such as tolerance to high water temperature, trophic status, maximum adult size and prior invasion success may play a more important role (Marchetti et al. 2004; Leuven et al. 2011). It should be noted, however, that comparative studies on traits between native and non-native species should account for the phylogenetic differences because closely related species may share a similar suite of traits through common ancestry (Jennings et al. 1999; Alcaraz et al. 2005). However, the amount of toxicity data available did not allow additional subdivision of fish species into phylogenetic groups in the current study.
3.5.3. Tendency in different tolerance between native and non-native fish species
Although differences in tolerance between native and non-native fish species were not statistically significant, a certain tendency was observed indicating possible difference in tolerance to different types of stressors. This might relate to the fact that most non- native species used in toxicity tests were from subtropical or tropical climate zones (Leuven et al. 2011; Appendix B). Tropical species have been shown to be less tolerant to ammonium and phenol and more tolerant to metals than species from temperate climate (Brix et al. 2001; Kwok et al. 2007). These different responses of fish species toward stressors might be related to temperature-induced differences in toxicokinetics and toxicodynamics of the stressors among fish species (Smit et al. 2001; Kwok et al. 2007).
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3.5.4. Combined effects of chemical and physical stressors
Even though the results of the current study suggest that there is no significant difference in tolerance between native and non-native fish species to certain chemical stressors, they may be important in combination with physical, morphological and ecological features of the habitats for the adaptation of non-native fish species to a new environment (Den Hartog et al. 1992). For example, the physical degradation of the river Rhine and the rise in water temperature has enabled a large number of new stagnant water fish and thermophilous species to establish themselves in this river (Den Hartog et al. 1992). Water temperature may have less impact on non-native fish species than on natives because non-native fish species of the Delta Rhine were found earlier to be more tolerant to high water temperatures than the native species (Leuven et al. 2011). Combined high water temperature and chemical contamination may have a different effect on native and non-native fish species. All these environmental factors may be interrelated, and tolerance of native and non-native species should be considered for multiple types of stressors together.
3.5.5. Effects of chemical stressors
The ecological condition of the river Rhine has improved since the 1970s (Den Hartog et al. 1992; Nienhuis et al. 2002). With decreasing concentrations of chemical stressors in the water column, the PAF of fish species has been decreasing too. Current concentrations of metals in the Delta Rhine are lower in comparison to those in the period 1978-1985, yet, this yielded similar affected fractions for native and non-native species. The higher values of PAF for native species at higher metal concentrations indicate that native species might be less tolerant to high concentration of metals than non-native species. Figure 3.2 may underline this assumption indicating that at low metal concentrations the PAFs for both species groups are close but divergent at higher concentrations.
Although the concentration of ammonium in the river Rhine at Lobith has decreased from 1.4 mg/L to 0.2 ml/L over the period 1970-2010, it is currently still an important stressor causing the highest PAF of both native and non-native fish species. The relative importance of other stressors studied varied between native and non-native fish species. In the native species group azinphos-methyl and copper contributed significantly to the overall PAF, whereas in the non-native species group copper and zinc were more important.
In the current study the location-specific SSD approach was applied to quantify and compare tolerance of native and non-native fish species to chemical stressors occurring in the river Rhine. Deriving the PAF for each species group was used to rank stressors and can be a useful tool for identifying stressors with the relatively highest impact on
Chapter 3 | Non-native fish | 51
the species of concern. This information can also be applied to water pollution control. The retrospective analysis of the trends of the PAF showed that at higher water concentrations of some chemical stressors there might be different responses between native and non-native species (Figure 3.2).
No significant difference was observed in the tolerance between native and non-native fish species to a range of chemical stressors. For the management of freshwater ecosystems potentially affected by introduced species, attention should therefore be given to other environmental variables such as temperature, hydrological regimes, morphological habitat quality (Moyle and Light 1996; Holway et al. 2002; Leuven et al. 2011; Verbrugge et al. 2012; Früh et al. 2012) and to combined chemical and physical stressors.
In case sufficient data are available, additional analyses of difference in tolerance to chemical stressors between different life stages as well as between non-native fish that spawn and that do not spawn in the target location are recommended.
3.6. References
Alcaraz C, Vila-Gispert A, García-Ferthou E. 2005. Profiling invasive fish species: the importance of phylogeny and human use. Diversity Distributions 11, 289-298. Aldenberg T, Jaworska JS. 2000. Uncertainty of the hazardous concentration and fraction affected for normal species sensitivity distributions. Ecotoxicol Environ saf 46, 1-18. Alonso A, Castro-Diez P. 2008. What explains the invading success of the aquatic mud snail Potamopyrgus antipodarum Hydrobiidae, Mollusca? Hydrobiologia 614, 107- 116. Brix KV, DeForest DK, Adams WJ. 2001. Assessing acute and chronic copper risk to freshwater aquatic life using species sensitivity distributions for different taxonomic groups. Environ Toxicol Chem 20, 1846-1856. D'Agostino R, Pearson ES. 1973. Tests for Departure from Normality. Empirical Results for the Distributions of b2 and √b1. Biometrika 60, 613-622. Den Hartog C, Van den Brink FWB, Van der Velde G. 1992. Why was the invasion of the river Rhine by Corophium curvispinum and Corbicula species so successful? J. Nat. History 26, 1121-1129. Devillers J, Devillers H. 2009. Prediction of acute mammalian toxicity from QSARs and interspecies correlations. Environ Res 20, 467-500. Dyer SD, Versteeg, DJ, Belanger SE, Chaney, JG Raimondo, S Barron, MG. 2008. Comparison of species sensitivity distributions derived from interspecies correlation models to distributions used to derive water quality criteria. Environ Sci Tech 42, 3076-3083. Früh D, Stoll S, Haase P. 2012. Physicochemical and morphological degradation of stream and river habitats increases invasion risk. Biol Invasions 14, 2243-2253.
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Gevertz AK, Tucker AJ, Bowling AM, Williamson CE, Oris JT. 2012. Differential tolerance of native and non-native fish exposed to ultraviolet radiation and fluoranthene in Lake Tahoe California/Nevada., USA. Environ Toxicol Chem 31, 1129-1135. Golsteijn L, Hendriks HWM, Van Zelm R, Ragas AMJ, Huijbregts MAJ. 2012. Do interspecies correlation estimations increase the reliability of toxicity estimates for wildlife? Ecotox Environ Saf 80, 238-243. Henning-de Jong I, Ragas AMJ, Hendriks HWM, Huijbregts MAJ, Posthuma L, Wintersen A, Hendriks AJ. 2009. The impact of an additional ecotoxicity test on ecological quality standards. Ecotox Environ Saf 72, 2037-2045. Holway DA, Suarez AV, Case TJ. 2002. Role of abiotic factors in governing susceptibility to invasion: A test with argentine ants. Ecology 83, 1610-1619. Jennings S, Reynolds JD, Polunin NVC. 1999. Predicting the vulnerability of tropical reef fishes to exploitation with phylogenies and life histories. Conserv Biology 13, 1466-1475. Jeschke JM, Strayer DL. 2005. Invasion success of vertebrates in Europe and North America. Proceedings of the National Academy of Sciences of the United States of America 102, 7198-7202. Jin XW, Zha JM, Xu YP, Wang ZJ, Kumaran SS. 2011. Derivation of aquatic predicted no-effect concentration PNEC for 2,4-dichlorophenol: Comparing native species data with non-native species data. Chemosphere 84, 1506-1511. Karatayev A, Burlakova L, Padilla D, Mastitsky S, Olenin S. 2009. Invaders are not a random selection of species. Biol Invasions 11, 2009-2019. Kwok KWH, Leung KMY, Lui GSG, Chu VKH, Lam PKS, Morritt D, Maltby L, Brock TCM, Van den Brink PJ, Warne MStJ, Crane M. 2007. Comparison of tropical and temperate freshwater animal species' acute sensitivities to chemicals: implications for deriving safe extrapolation factors. Integr Environ Ass Manag 3, 49-67. Leuven RSEW, Van der Velde G, Baijens I, Snijders J, Van der Zwart C, Lenders HJR, Bij de Vaate A. 2009. The river Rhine: a global highway for dispersal of aquatic invasive species. Biol invasions 11, 1989-2008. Leuven RSEW, Hendriks AJ, Huijbregts MAJ, Lenders HJR, Matthews J, Van der Velde G. 2011. Differences in sensitivity of native and exotic fish species to changes in river temperature. Curr Zoology 57, 852-862. Lodge DM 1993. Biological invasions - lessons for ecology. Trends Ecol Evol 8, 133- 137. Marchetti MP, Moyle PB, Levine R. 2004. Invasive species profiling? Exploring the characteristics of non-native fishes across invasion stages in California. Freshwater Biol 49, 646-661. Menke SB, Fisher RN, Jetz W, Holway DA. 2007. Biotic and abiotic controls of Argentine ant invasion success at local and landscape scales. Ecology 88, 3164- 3173. Moyle PB, Light T. 1996. Fish invasions in California: Do abiotic factors determine success? Ecology 77, 1666-1670.
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Nienhuis PH, Buijse AD, Leuven RSEW, Smits AJM, de Nooij RJW, Samborska EM. 2002. Ecological rehabilitation of the lowland basin of the river Rhine NW Europe. Hydrobiologia 478, 53-72. Piola RF, Johnston EL. 2006. Differential tolerance to metals among populations of the introduced bryozoan Bugula neritina. Marine Biol 148, 997-1010. Piola RF, Johnston EL. 2009. Comparing differential tolerance of native and non- indigenous marine species to metal pollution using novel assay techniques. Environ Pollut 157, 2853-2864. Piscart C, Kefford BJ, Beisel JN. 2011. Are salinity tolerances of non-native macroinvertebrates in France an indicator of potential for their translocation in a new area? Limnologica 41, 107-112. Posthuma L , Suter GW II , Traas TP (eds) 2002b. Species Sensitivity Distributions in Ecotoxicology, Lewis Publishers, CRC Press, Boca Raton, FL. Posthuma L, Suter II GW. 2011. Ecological risk assessment of diffuse and local soil contamination using species sensitivity distributions. In: Swartjes FA, ed., Dealing with contaminated sites. From theory towards practical application. Springer Publishers, Dordrecht, pp 625-691. Raimondo S, Vivian DN, Delos C, Barron MG. 2008. Protectiveness of species sensitivity distribution hazard concentrations for acute toxicity used in endangered species risk assessment. Environ Toxicol Chem 27, 2599-2607. Razali NM, Wah YB. 2011. Power comparisons of Shapiro-Wilk, Kolmogorov- Smirnov, Lilliefors and Anderson-Darling tests. J Stat Model Analyt 2, 1, 21-33. Ricciardi A, Kipp R. 2008. Predicting the number of ecologically harmful exotic species in an aquatic system. Diversity Distributions 14, 374-380. Scott DM, Wilson RW, Brown JA. 2007. Can sunbleak Leucaspius delineatus or topmouth gudgeon Pseudorasbora parva disperse through saline waters? J Fish Biol 71, Supplement sd, 70-86. Smit MGD, Hendriks AJ, Schobben JHM, Karman CC, Schobben HPM. 2001. The variation in slope of concentration-effect relationships. Ecotoxicol Environ Saf 48, 43-50. TenBrook PL, Tjeerdema RS, Hann P, Karkoski J. 2009. Methods for deriving pesticide aquatic life criteria. Rev Environ Contam Toxicol 199, 1-92. Traas TP, Van de Meent D, Posthuma L, Hamers T, Kater BJ, De Zwart D, Aldenberg T. 2002. The potentially affected fraction as a measure of ecological risk. In: Posthuma L, Suter GW II, Traas TP eds., Species Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, pp 315-344. Van Damme D, Bogutskaya N, Hoffmann RC, Smith C. 2007. The introduction of the European bitterling Rhodeus amarus to west and central Europe. Fish Fisheries 8, 79-106. Verbrugge LNH, Schipper AM, Huijbregts MAJ, Van der Velde G, Leuven RSEW. 2012. Sensitivity of native and non-native mollusc species to changing river water temperature and salinity. Biol Invasions 14, 1187-1199. Vila-Gispert A, Alcaraz C, Garcia-Berthou E. 2005. Life-history traits of invasive fish in small Mediterranean streams. Biol Invasions 7, 107-116. Von der Ohe PC, Liess M. 2004. Relative sensitivity distribution of aquatic invertebrates to organic and metal compounds. Environ Toxicol Chem 23, 150-156.
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Weir SM, Salice CJ. 2012. High tolerance to abiotic stressors and invasion success of the slow growing freshwater snail, Melanoides tuberculatus. Biol Invasions 14, 385- 394.
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Chapter 4 Ranking ecological risks of multiple chemical stressors on amphibians
Fedorenkova A, Vonk JA, Lenders HJR, Creemers RCM, Breure AM, Hendriks AJ Published in Environmental Toxicology and Chemistry 31: 1416-1421, 2012
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Chapter 4 | Abstract Populations of amphibians have been declining worldwide since the late 1960s. Despite global concern, no studies have quantitatively assessed the major causes of this decline. In the present study, species sensitivity distributions (SSDs) were developed to analyze the sensitivity of anurans for ammonium, nitrate, heavy metals (cadmium, copper), pesticides (18 compounds), and acidification (pH) based on laboratory toxicity data. Ecological risk (ER) was calculated as the probability that a measured environmental concentration of a particular stressor in habitats where anurans were observed would exceed the toxic effect concentrations derived from the species sensitivity distributions. The assessment of ER was used to rank the stressors according to their potential risk to anurans based on a case study of Dutch freshwater bodies. The derived ERs revealed that threats to populations of anurans decreased in the sequence of pH, copper, diazinon, ammonium, and endosulfan. Other stressors studied were of minor importance. The method of deriving ER by combining field observation data and laboratory data provides insight into potential threats to species in their habitats and can be used to prioritize stressors, which is necessary to achieve effective management in amphibian conservation.
Chapter 4 | Ranking stress | 57
4.1. Introduction
For several decades, there has been concern about the decline of populations of amphibians around the world (Barinaga 1990; Blaustein and Wake 1990; Wake 1991; Collins and Storfer 2003). Combining developments among different populations of amphibians, Houlahan et al. (2000) has presented quantitative evidence that rapid declines occurred in the late 1960s and 1970s on a global scale. Several factors have been suggested to contribute to the decline of populations of amphibians, including habitat destruction, climate change, increasing levels of ultraviolet radiation, diseases, introduction of non-native species, contamination with chemical compounds, and environmental acidification (Alford and Richards 1999; Blaustein and Kiesecker 2002; Alford 2010). Yet, no single factor could be identified as a major cause for declining populations of amphibians. Although habitat destruction has been assumed to be a dominant cause for decline, amphibians in available habitats can be affected significantly by other stressors (Gibbons et al. 2000; Stuart et al. 2004). Acidification has been shown to be a serious problem in potentially suitable habitats (Freda 1986; Leuven et al. 1986; Rowe and Freda 2000). Moreover, many amphibian habitats suffer from nitrogen pollution, pesticides, and heavy metals (Linder and Grillitsch 2000; Adolfo and Ortiz-Santaliestra 2009; Lehman and Williams 2010). At the same time, presence of a stressor does not necessarily imply direct loss of amphibians due to this stressor. Therefore, quantitative analyses are needed to sort out the potential impacts of different stressors on amphibians. Such analyses can be used for “prioritization” of stressors, necessary to achieve effective management in conservation of amphibians. Amphibians may be at increased risk to different stressors compared to other vertebrates, because of the lack of dermal defences, such as scales, feathers, or fur, and their skin is highly permeable to gaseous exchange and fluid uptake (Henry 2000). Multiple life stages (eggs, larvae, and adults) increase susceptibility of amphibians to stressors in both aquatic and terrestrial habitats (Henry 2000). Despite the worldwide interest, there are no studies known by the authors allowing for a quantitative assessment of the major causes of their decline (Frias-Alvarez et al. 2010).
In the present study, a number of examples of different chemical stressors potentially affecting amphibian species were selected based on available toxicity data and data that are included in the national surface water surveys. Thus, the impact of ammonium, nitrate, heavy metals (cadmium, copper), pesticides (18 different compounds), and acidification (pH) on various species of anurans (frogs and toads) has been evaluated. Other orders of amphibians were excluded due to a lack of toxicity data. The aim was to determine the sensitivity of anuran species to these stressors and to rank these stressors according to their potential risks, based on a case study of Dutch freshwater bodies.
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First, we developed Species Sensitivity Distributions (SSDs) (Posthuma et al. 2002) to analyse the variation in sensitivity of anurans for each stressor based on toxicity data from laboratory studies. Then, we calculated the ecological risk (ER) (Aldenberg et al. 2002) of single stressors as the probability that measured environmental concentrations in habitats where anurans were observed, exceeded toxic effect concentrations derived from the SSDs. The calculated ERs were used to rank the stressors according to their potential risk to anurans. This method is a useful tool for location-specific assessment but can also be used for generic risk assessments.
4.2. Methods 4.2.1. Laboratory effect concentrations of chemicals
Data on acute and chronic toxic effects (median Lethal Concentrations, LC50s, and no- observed-effect concentration, NOEC, respectively) of ammonium, nitrate, cadmium, copper, and pesticides on anuran embryos or larvae were taken from the Dutch National Institute for Public Health and the Environment e-toxBase database (www.e- toxbase.com) representing the largest ecotoxicity database in its kind (Table 4.1 and Table C1 in Appendix C). Pesticides were included in the analysis if toxicity data were available for at least three different anuran species. This is a lower number of test species than advised in the OECD and EPA guidelines for water quality, however, our study focuses on a single taxonomic group (anurans) and not on the whole aquatic community (ranging from bacteria up to mammals). The variation in sensitivity to toxic stressors within a single taxonomic group is generally lower than over a complete community (Von der Ohe and Liess 2004). Acidification levels corresponding to LC50s on anuran embryos were taken from literature (Leuven et al. 1986; Pauli et al. 2000). If multiple LC50 values were reported for one species, the geometric mean was taken for further analysis.
4.2.2. Environmental concentrations of chemicals
The national database Limnodata Neerlandica of the Dutch Water Boards (www.limnodata.nl) was consulted for monitoring data on physical-chemical parameters of the Dutch surface waters. We retrieved average annual field concentrations of ammonium, nitrate, cadmium, copper, and pH monitored in the period from 1990 to 2008. For metals, levels were reported as concentrations after filtration. Records were obtained for 7,205 locations in different water types known to be potentially suitable habitats for anurans. These included ponds, lakes, oxbow lakes, heathland lakes, ditches, small canals, springs, outlet waters, drinking pools (for cattle), scour holes, ditches and urban waters. All these monitoring locations represented “potential habitats”, i.e., locations in which concentrations of stressors were measured and which anurans can potentially occupy, or were suitable amphibian habitats in the
Chapter 4 | Ranking stress | 59
past. Monitoring data on pesticides in Dutch surface waters for the period from 1997 to 2008 were collected from the Dutch pesticides atlas (www.bestrijdingsmiddelenatlas.nl) for the same water types (i.e., potential habitats) as mentioned above. The dataset included 218,282 records of measured concentrations at 1,570 locations for the 18 pesticides mentioned in Table 4.1. The maximum and the average annual concentrations of each pesticide were derived from the number of observations recorded per year and per location. In cases when concentrations were below detection limits, half of the detection limit was used.
4.2.3. Field occurrence of anuran species
Data on occurrence of anurans in the Netherlands were taken from the RAVON (Reptile, Amphibian and Fish Conservation Netherlands) database for the period 1990 to 2008. In total, we derived 21,374 combinations of locations and years at which any anurans were observed for in total 11 species (Alytes obstetricians, Pelobates fuscus, Bufo bufo, Bufo calamita, Hyla arborea, Rana arvalis, Rana temporaria, Rana lessonae, Rana esculenta, Rana ridibunda, and Rana catesbeiana).
4.2.4. Data treatment
The anurans survey data were matched with the data set for environmental concentrations. Concentrations of each stressor were linked by geographic coordinates and year with the records of the occurrence of anurans. The coordinates were matched on a 1 km × 1 km scale for the pesticides and on a 0.1 km × 0.1 km scale for the other stressors studied. For each stressor, this yielded at least 42 (for cadmium) to at most 445 (for lindane) “realized habitats,” i.e., locations where at least one stressor was measured and at least one anuran species was observed in the same year (Table 4.1). The differences between concentrations of stressors in potential and realized habitats were assessed by t-tests with significance level of α = 0.05.
Laboratory effect concentrations as well as field concentrations can be described by distributions. Such concentration-effect functions are also known as Species Sensitivity Distributions (SSD) describing empirically observed differences in sensitivity of species (Posthuma et al. 2002). In the present study, SSDs were assumed to follow a log-normal distribution of LC50, and goodness-of-fit was assessed for normality by Anderson-Darling test (α = 0.05). In log-normal distributions, the variation in sensitivity among species is described by the standard deviation (SD) (Kooijman 1981).
Data on chronic toxic effects were scarce for anurans; NOEC values were obtained for six pesticides only. Therefore, NOEC levels were set by applying an acute to chronic ratio (ACR). Acute to chronic ratios might vary depending on the taxonomic group and chemical concerned. In the present study, the ACR for anurans calculated for six
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pesticides varied from 2 to 27 and on average was equal to 7.2. Average ACRs for various chemicals have been reported to be 10.5 for fish, 7.0 for daphnids, and 5.4 for algae (Ahlers et al. 2006). Hence, in our analysis chronic values were derived from acute data using an ACR of 10 (De Zwart 2002).
The ecological risk (ER) of exposure was calculated as a probability of a randomly selected field concentration to exceed a randomly selected species sensitivity concentration according to Aldenberg et al. (2002). The ecological risk thus expresses the probability that species are exposed to stressors in the field at concentrations exceeding their LC50s or NOECs (De Zwart 2002). This probability corresponds to the area under the curve of the product (potential overlap) of the probability density function (PDF) of the field concentrations and the cumulative SSD function (CDF) (Aldenberg et al. 2002). This probability can be expressed as
∞
Pr(푥1 > 푥2) = ∫ 푃퐷퐹푥1 (푥) × 퐶퐷퐹푥2(푥)푑푥 −∞ where x1 is a random variable of the logarithm of field concentrations, and the x2 is a random variable of the logarithm of species sensitivity concentrations.
This approach takes into account the differences in shapes of PDF and SSD curves. The total ecological risk was calculated by combining different types of effect of all stressors together by
푛
Total ER = 1 − ∏(1 − 퐸푅푖) 푖=1 where n is number of stressors and ERi is ecological risk for each stressor individually (Van Straalen 2002).
4.3. Results 4.3.1. Variation in sensitivity to stressors and their distributions in habitats
Differences among stressors
For all stressors, the toxicity data were log-normal distributed (all Anderson-Darling tests p > 0.05). The standard deviations (SD) and means for each stressor are presented in Table 4.1. From the ranges in SDs, it is clear that anuran species show variation in their sensitivity to a given stressor. High values of SD indicate a large variation in sensitivity among species to the stressors considered. The largest variation was found for diazinon and the smallest for diuron (1.8 and 0.03, respectively). Stressors combined by groups relevant for specific modes of action (Table 4.1, organochlorine insecticides,
Chapter 4 | Ranking stress | 61
organophosphates, herbicides, pyrethroids, carbamates, fungicide) did not show homogeneity in SD values.
Differences between laboratory and field concentrations
The mean LC50s were significantly higher than the mean concentrations of the stressors in the realized habitats (t-test, α = 0.05, all p < 0.001), except for pH where the value was significantly lower (t-test, α = 0.05, p < 0.001). The variation in sensitivity between anurans to pH, nitrate, carbendazim, lindane, diuron, and parathion-methyl was smaller than the SD of field concentrations in the realized habitats. For the other stressors studied, the SD of toxic effects was larger than SD of field concentrations in the realized habitats (Table 4.1).
Differences between realized and potential habitats
The mean levels of pH, nitrate, deltamethrin, carbofuran, diuron, and alachlor were lower in the realized compared to potential habitats (t-test, α = 0.05, all p < 0.001, Table 4.1). For the other stressors studied, concentrations in habitats where anurans actually occurred did not differ significantly from the concentrations in the potential habitats. For pesticides, the maximum annual concentrations in realized habitats were noted to be a factor of six higher than the annual average concentrations in some locations for carbofuran and lindane and a factor of two higher for carbendazim and DDT. Maximum annual concentrations in potential habitats were on average 28 times higher than maximum concentrations in realized habitats, varying from two times for DDT to 134 times for diuron. The SD for concentrations of all studied stressors was on average lower in realized habitats than in potential habitats.
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Table 4.1. Descriptive statistics for realized habitats, potential habitats, toxic effects, and ER for all studied stressors. Data derived from monitoring surveys for environmental concentrations and for occurrence of anuran species in Dutch freshwaters, and from laboratory toxicity tests on anuran species. Realized habitats Potential habitats Toxic effects Stressor MoA ERa % (LC50) ERa % (NOEC) # meanb SD meanb SD meanb SD n pH* 117 6.8 1.13 7.8 0.72 4 0.53 16 1.06 6.67 copper 52 -2.73 0.30 -2.59 0.32 -0.43 1.03 9 0.83 6.12 diazinon OP 402 -5.03 0.38 -5.04 0.48 -0.01 1.80 3 0.31 1.42 ammonium 216 -0.58 0.48 -0.43 0.45 1.69 0.66 7 0.27 5.96 endosulfan OCl 407 -5.58 0.71 -5.47 0.80 -0.61 1.78 6 0.12 0.76 nitrate* 194 -0.72 0.58 -0.22 0.65 1.60 0.45 6 0.07 3.44 cadmium 42 -4.17 0.42 -4.13 0.41 -0.13 1.17 9 0.06 0.74 malathion OP 331 -4.99 0.46 -5.01 0.51 -0.37 1.28 7 0.03 0.39 endrin* OCl 289 -5.90 0.50 -5.66 0.72 -1.13 1.04 12 0.002 0.06 deltamethrin* Pyr 126 -5.16 0.32 -4.85 0.50 -2.26 0.54 4 2.0·10-4 0.11 dieldrin OCl 289 -5.86 0.51 -6.03 0.62 -0.69 0.81 8 3.0·10-6 6.2·10-5 carbendazim Fung 271 -4.46 0.96 -4.41 0.82 1.64 0.52 3 1.2·10-6 1.5·10-5 azinphos-methyl OP 100 -4.93 0.49 -4.81 0.54 0.15 0.67 6 <1.0·10-6 <1.0·10-6 DDT OCl 232 -5.75 0.50 -5.52 0.80 0.43 0.80 8 <1.0·10-6 <1.0·10-6 atrazine Herb 122 -5.38 0.11 -4.97 0.46 0.99 0.93 4 <1.0·10-6 <1.0·10-6 carbaryl Carb 59 -4.71 0.31 -4.73 0.44 1.10 0.61 5 <1.0·10-6 <1.0·10-6
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parathion-ethyl OP 353 -4.71 0.36 -4.87 0.58 0.37 0.44 8 <1.0·10-6 <1.0·10-6 carbofuran* Carb 272 -5.08 0.33 -5.01 0.49 1.52 0.56 3 <1.0·10-6 <1.0·10-6 lindane OCl 445 -5.51 0.48 -5.35 0.52 0.59 0.34 6 <1.0·10-6 <1.0·10-6 permethrin Pyr 74 -5.15 0.22 -5.01 0.34 0.92 0.50 3 <1.0·10-6 <1.0·10-6 diuron* Herb 390 -4.79 0.51 -4.57 0.58 1.34 0.03 3 <1.0·10-6 <1.0·10-6 parathion-methyl OP 380 -4.97 0.31 -5.02 0.44 0.97 0.13 4 <1.0·10-6 <1.0·10-6
alachlor* Herb 209 -5.20 0.14 -5.19 0.21 0.89 0.22 3 <1.0·10-6 <1.0·10-6
Total ER 2.73 23.17 n = number of anuran species tested # = number of realized habitats a ER= ecological risk, probability (%) that stressor concentrations in realized habitats exceed LC50 or NOEC b Concentrations are in mg/L and log transformed *significant differences (p < 0.001) in stressor concentrations between realized and potential habitats MoA= Mode of action of pesticides Pyr= pyrethroids Carb= carbamates OP= organophosphates OCl= organochlorine insecticides Herb= herbicides Fung= fungicide
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4.3.2. Ecological risk
The ecological risks for the investigated individual stressors ranged from <1.0∙10- 6 to 1.06 %. The ER was the highest for pH followed by copper, diazinon, ammonium, and the rest of the stressors studied (Table 4.1). A graphical representation of the calculated ER for pH and copper is provided in Figure 4.1. There is an overlap of distributions of field pH values (PDF realized habitat) and of LC50s (SSD). The surface underneath the overlapping curves indicates the ER. The calculated ER was equal to 1.06% reflecting the probability that pH in the realized habitats exceeds the LC50 values for anuran species (Figure 4.1a). Figure 4.1b shows the distribution of copper concentrations in the realized and potential habitats (PDFs) together with the respective SSD. The probability that copper concentrations in the realized habitats are higher than LC50s was equal to 0.83%.
The total ER, additively combining the risks of all stressors together, was equal to 2.73%. These ER values were based on the acute LC50 data. However, chronic exposure to lower concentrations may induce non-lethal effects in anurans. Calculations of ER using SSDs based on NOECs (derived as LC50 divided by ACR of 10) resulted in substantially higher values for all stressors. For example, the ERs based on NOEC values were six times higher for pH and almost 50 times higher for ammonium compared to the ERs based on LC50 values. The ranking of compounds based on chronic ER changed slightly, with pH still having the highest value, followed by copper, ammonium, nitrate, diazinon, and the rest of the stressors studied (Table 4.1). The total ER based on NOECs was 23.17%, which is a factor of almost nine higher than the ER based on LC50.
4.4. Discussion 4.4.1. Ecological risk and ranking stressors
The ecological risk of individual stressors at the LC50 level, based on the available data and as calculated for the case study in Dutch freshwater systems, was found to be 2.73%. The highest values were noted for pH, copper, diazinon, ammonium, and endosulfan. The highest risk among pesticides was calculated for diazinon, banned as of 1 March 2011 (EU 2010). At the NOEC level, the ER for each stressor decreases in the order pH, copper, ammonium, nitrate, diazinon, followed by the other stressors studied. Concentrations of nitrate, endrin, deltametrin, carbofuran, diuron, and alachlor were lower in the realized habitats than in potential habitats, indicating that there might be a higher risk for anurans in other potential habitats.
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Figure 4.1. Ecological risk (dotted line) for a. pH (1.06%) and b. copper (0.83%) defined as probability that concentrations in realized anurans habitats (solid line) exceed LC50s for anuran species (SSD, dashed line). Probability density distributions (PDF) of these stressors in potential habitats for anurans are indicated by dash-dotted lines. The left y-axis refers to SSD curve and the right y-axis to both PDF and ER.
Differences in maximum annual concentrations for pesticides between potential and realized habitats may explain the absence of anurans in the potential habitats. Although concentrations of stressors in the realized habitats, i.e., where anuran species were found, were not as high as concentrations causing lethal effects in larvae or embryos, their reproduction fitness might be reduced.
The differences observed between pH distributions in potential and realized habitats may be explained by the fact that some anuran species such as Rana arvalis, Rana lessonae, Pelobates fuscus, Bufo calamita, and Hyla arborea have a preference for
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mesotrophic weakly buffered water bodies and avoid eutrophic or hypertrophic waters (Creemers and Van Delft 2009). However, the results indicate that the pH in some realized habitats poses a risk to anuran species.
4.4.2. Variation in sensitivity and mechanistic underpinning
In general, it was found in previous studies that compounds with a narcosis mode of action have a steeper concentration-effect curve than compounds with a receptor- mediated mechanism of action (De Zwart 2002; Smit et al. 2001), with the pooled standard deviation of all aquatic ecotoxicity data of RIVM e-toxBase being 1.02 (Hollander et al. 2011). On average, SD per compound varies from 0.5 for narcosis to about 1 for specific modes of action (De Zwart 2002). This means that variation in sensitivity of different species would be low for narcosis acting compounds and high for pesticides and heavy metals (Smit et al. 2001). However, for the anuran species in our study, slopes for some pesticides were also steep. On average, for carbamates, pyrethroids, herbicides, ammonium, nitrate, and pH, the SDs were about 0.5. This may be caused by the relatively low number of species for which data were available. Sensitivity distributions based on a few species tend to be somewhat steeper (De Zwart 2002). The SD of a distribution depends on the number of species, and the SD of a compound with a specific mode of action levels off to a value characteristic for this mode of action when sufficient number of species are tested (about 25 and more) (De Zwart 2002). Another explanation might be that these stressors do not have a specific mode of action in anuran species, but instead affect them by narcosis. For the pesticides, in addition to direct toxic effects on anurans, they may induce indirect changes in the aquatic community that can indirectly affect anuran populations. For example, herbicides may cause reductions in the heterogeneity, biomass, and structure of the macrophyte communities that are used by anurans for the attachment of their eggs (Lehman and Williams 2010). Also, herbicides may reduce the algal food base of a community reducing the abundance of all species in the food web and thus having negative impact on anuran populations (Boone and James 2003). Although indirect impacts of pesticide are not reflected in the ER, they may therefore still be important stressors.
The values of about 1 for the SDs of organophosphates, organochlorine insecticides, and heavy metals indicate a broad range of variation in sensitivity of anuran species to these stressors. This might reflect the large variability in toxicokinetics and toxicodynamics of these compounds in different anuran species (Smit et al. 2001).
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4.4.3. Uncertainties
The methodology for deriving the ER depends on sensitivity of different species to stressors and levels of stressors concentrations in the environment. The approach to construct SSDs relies on toxicity data, which are mostly derived from species of temperate regions (Schiesari et al. 2007). Several studies have shown that different species are not significantly more or less sensitive to various stressors when compared by different climate zones or regions and that SSD approach using standard species can be used for species for which toxicity data are not available (Maltby et al. 2005; Raimondo et al. 2008; De Hoop et al. 2011).
According to the ER, threats to anuran populations in the Netherlands may be caused by (1) pH, (2) copper, (3) ammonium and nitrate, and (4) certain pesticides. In the analysis, we considered the toxicity data based on total dissolved concentrations. However, effects of metals in ecotoxicological tests are determined by the bioavailable concentrations (Verschoor et al. 2011). This may influence the ER for copper because its bioavailability was not taken into account, and corrections for bioavailability of copper may yield in a lower ER (Hayashi and Kashiwagi 2011).
Data on pH toxicity for anurans usually apply to the embryonic development because the egg stage is the most sensitive to acidity. For other stressors, this might not be the case (Edginton et al. 2004). In the SSDs, we combined both embryos and larvae data. In addition, acidification of aquatic habitats may not only induce direct effect in anurans but also influence the toxicity of other chemicals in the water (Freda 1986; Rowe and Freda 2000).
For the analysis, we used annual concentrations of stressors. However, concentrations of nitrate, ammonium, and pesticides are variable throughout the year and peaks related to agricultural runoffs might be high and coincide with the breeding period of anurans. In the present study, the maximum annual concentrations were noted to be six times higher than the annual average in several locations for carbofuran and lindane and two times higher for carbendazim and DDT. These differences, however, only marginally influenced the calculated ERs.
Our results are in line with the previous studies that showed the decreases in ranges of anuran species in the Netherlands since 1950 by 25 to 80% for different species. These decreases were assumed to be caused by acidification, eutrophication, pollution with pesticides, and changes in vegetation in water habitats (Creemers and Van Delft 2009).
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4.5. Conclusion
In the present study, we derived ecological risks of multiple stressors for anurans to investigate the relative importance of individual stressors that possibly influence populations of anurans. The method of deriving ER by combining field and laboratory data gives insight into potential threats to species in their actual environment and helps to set up conservation measures. Overall, the present results suggest that acidification, ammonium and certain pesticides such as organophosphates (diazinon) and organochlorine insecticides (endosulfan) should be a high-priority concern to maintain populations of anurans in Dutch freshwaters.
Acknowledgment
We thank Dik van de Meent for great help with calculations, Frank van Herpen and Martina Vijver for providing us the environmental concentration data and two anonymous reviewers for useful comments on an earlier version of the manuscript.
4.6. References
Adolfo M, Ortiz-Santaliestra M. 2009. Pollution: Impact of reactive nitrogen on amphibians (nitrogen pollution). In Heatwole H, Wilkinson JW, eds, Amphibian Biology Volume 8. Amphibian Decline: Diseases, Parasites, Maladies and Pollution. Surrey Beatty & Sons, Baulkham Hills, NSW, Australia, pp 3112- 3144. Ahlers J, Riedhammer C, Vogliano M, Ebert RU, Kuhne R, Schuurmann G. 2006. Acute to chronic ratios in aquatic toxicity - Variation across trophic levels and relationship with chemical structure. Environ Toxicol Chem 25, 2937-2945. Aldenberg T, Jaworska JS, Traas TP. 2002. Normal species sensitivity distributions and probabilistic ecological risk assessment. In Posthuma L, Suter II GW, Traas TP, eds, Species Sensitivity Distributions in Ecotoxicology. Lewis Publishers, Boca Raton, FL, USA, pp 49-102. Alford RA, Richards SJ. 1999. Global amphibian declines: A problem in applied ecology. Annu Rev Ecol Syst 30, 133-165. Alford RA. 2010. Declines and the global status of amphibians. In Sparling DW, Linder G, Bishop CA, Krest KS, eds, Ecotoxicology of Amphibians and Reptiles, 2nd ed. Society of Environmental Toxicology and Chemistry (SETAC) Press, Pensacola, FL, USA, pp 13-46. Barinaga M. 1990. Where have all the froggies gone? Science 247:1033-1034. Blaustein AR, Kiesecker JM. 2002. Complexity in conservation: Lessons from the global decline of amphibian populations. Ecol Lett 5, 597-608. Blaustein AR, Wake DB. 1990. Declining amphibian populations - a global phenomenon. Trends Ecol Evol 5, 203-204. Boone MD, James SM. 2003. Interactions of an insecticide, herbicide, and natural stressors in amphibian community mesocosms. Ecol Appl 13, 829-841. Collins JP, Storfer A. 2003. Global amphibian declines: Sorting the hypotheses. Divers Distrib 9, 89-98.
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Commission decision 2010/71/EU of 8 February 2010 concerning the non- inclusion of diazinon in Annex I, IA or IB to Directive 98/8/EC of the European Parliament and of the Council concerning the placing of biocidal products on the market. OJ L 36, 9.2.2010. Creemers RCM, Van Delft JJCW. 2009. De Amfibieën en Reptielen van Nederland. Nederlandse Fauna 9. Nationaal Natuurhistorisch Museum Naturalis, European Invertebrate Survey, Leiden, The Netherlands. De Hoop L, Schipper AM, Leuven RSEW, Huijbregts MAJ, Olsen GH, Smit MGD, Hendriks AJ. 2011. Sensitivity of polar and temperate marine organisms to oil components. Environ Sci Technol 45, 9017-9023. De Zwart D. 2002. Observed regularities in SSDs for aquatic species. In Posthuma L, Suter II GW, Traas TP, eds, Species Sensitivity Distributions in Ecotoxicology. Lewis Publishers, Boca Raton, FL, USA, pp 133-154. De Zwart D. 2005. Ecological effects of pesticide use in the Netherlands: Modeled and observed effects in the field ditch. Integr Environ Assess Manag 1:123-134. Edginton AN, Sheridan PM, Stephenson GR, Thompson DG, Boermans HJ. 2004. Comparative effects of pH and Vision® herbicide on two life stages of four anuran amphibian species. Environ Toxicol Chem 23, 815-822. Freda J. 1986. The influence of acidic pond water on amphibians: A review. Water Air Soil Poll 30, 439-450. Frias-Alvarez P, Zuniga-Vega JJ, Flores-Villela O. 2010. A general assessment of the conservation status and decline trends of Mexican amphibians. Biodivers Conserv 19, 3699-3742. Gibbons JW, Scott DE, Ryan TJ, Buhlmann KA, Tuberville TD, Metts BS, Greene JL, Mills T, Leiden Y, Poppy S, Winne CT. 2000. The global decline of reptiles, Deja Vu amphibians. Bioscience 50, 653-666. Hayashi TI, Kashiwagi N. 2011. A Bayesian approach to probabilistic ecological risk assessment: Risk comparison of nine toxic substances in Tokyo surface waters. Environ Sci Pollut Res 18, 365-375. Henry PFP. 2000. Aspects of amphibian anatomy and physiology. In Sparling D, Linder G, Bishop CA, eds, Ecotoxicology of Amphibians and Reptiles. Society of Environmental Toxicology and Chemistry (SETAC) Press, Pensacola, FL, USA, pp 71-110. Hollander A, Bruinen De Bruin Y, Vonk JA, Vermeire T, Van De Meent D. 2011. Exposure based waiving in environmental risk assessment: A tiered approach. Human Ecol Risk Assess 17:1173-1192. Houlahan JE, Findlay CS, Schmidt BR, Meyer AH, Kuzmin SL. 2000. Quantitative evidence for global amphibian population declines. Nature 404, 752-755. Kooijman SALM. 1981. Parametric analyses of mortality rates in bioassays. Water Res 15, 107-119. Lehman CM, Williams BK. 2010. Effects of current-use pesticides on amphibians. In Sparling D, Linder G, Bishop CA, eds, Ecotoxicology of Amphibians and Reptiles, 2nd ed. Society of Environmental Toxicology and Chemistry (SETAC) Press, Pensacola, FL, USA, pp 167-202.
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Leuven RSEW, Den Hartog C, Christiaans MMC, Heijligers WHC. 1986. Effects of water acidification on the distribution pattern and the reproductive success of amphibians. Experientia 42, 495-503. Linder G, Grillitsch B. 2000. Ecotoxicology of metals. In Sparling D, Linder G, Bishop CA, eds, Ecotoxicology of Amphibians and Reptiles. Society of Environmental Toxicology and Chemistry (SETAC) Press, Pensacola, FL, USA, pp 325-408. Maltby L, Blake N, Brock TCM, Van Den Brink PJ. 2005. Insecticide species sensitivity distributions: Importance of test species selection and relevance to aquatic ecosystems. Environ Toxicol Chem 24, 379-388. Pauli BD, Perrault JA, Money SL. 2000. RATL: A database of reptile and amphibian toxicology literature. Technical Report Series No. 357. Canadian Wildlife Service, Headquarters, Hull, Québec, Canada. Posthuma L, Suter II GW, Traas TP. 2002. Species Sensitivity Distributions in Ecotoxicology. Lewis Publishers, Boca Raton, FL, USA. Raimondo S, Vivian DN, Delos C, Barron MG. 2008. Protectiveness of species sensitivity distribution hazard concentrations for acute toxicity used in endangered species risk assessment. Environ Toxicol Chem 27, 2599-2607. Rowe CL, Freda J. 2000. Effects of acidification on amphibians at multiple levels of biological organization. In Sparling D, Linder G, Bishop CA, eds, Ecotoxicology of Amphibians and Reptiles. Society of Environmental Toxicology and Chemistry (SETAC) Press, Pensacola, FL, USA, pp 545-572. Schiesari L, Grillitsch B, Grillitsch H. 2007. Biogeographic biases in research and their consequences for linking amphibian declines to pollution. Conserv Biol 21, 465-471. Smit MGD, Hendriks AJ, Schobben JHM, Karman CC, Schobben HPM. 2001. The variation in slope of concentration-effect relationships. Ecotox Environ Safe 48, 43-50. Stuart SN, Chanson JS, Cox NA, Young BE, Rodrigues AS L, Fischman DL, Waller R W. 2004. Status and trends of amphibian declines and extinctions worldwide. Science 306, 1783-1786. Van Straalen NM. 2002. Theory of ecological risk assessment based on species sensitivity distributions. In Posthuma L, Suter II GW, Traas TP, eds, Species Sensitivity Distributions in Ecotoxicology. Lewis Publishers, Boca Raton, FL, USA, pp 37-48. Verschoor AJ, Vink JPM, De Snoo GR, Vijver MG. 2011. Spatial and temporal variation of watertype-specific no-effect concentrations and risks of Cu, Ni, and Zn. Environ Sci Technol 45, 6049-6056. Von Der Ohe PC, Liess M. 2004. Relative sensitivity distribution of aquatic invertebrates to organic and metal compounds. Environ Toxicol Chem 23, 150- 156. Wake DB. 1991. Declining amphibian populations. Science 253, 860-860.
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Chapter 5 Size-mediated effects of water-flow velocity on riverine fish species
Del Signore A, Lenders HJR, Hendriks AJ, Vonk JA, Mulder Ch, Leuven RSEW Published in River Research and Applications DOI: 10.1002/rra.2847, 2014 (in press)
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Chapter 5 | Abstract
We applied species sensitivity distributions (SSDs), commonly used in chemical risk assessment, to quantify the impact of water-flow velocity on the presence of fish species in a river. SSDs for water-flow velocity were derived from observational field data (maximal velocity at which species occur, Vmax) and laboratory measurements (critical swimming velocity, Vcrit). By calculating the potentially affected fraction of the fish species of the river Rhine, effects of water-flow velocity on different life stages and guilds were estimated. Vmax values for adults were significantly higher than those for juveniles and larvae. At water-flow velocity of 60 cm/s, half of the adults were affected, while half of the non-adult life stages were affected at velocities of 25 to 29 cm/s. There was a positive correlation between body size and fish tolerance to water-flow. As expected, rheophilic species tolerated higher water-flow velocities than eurytopic and limnophilic species. Maximal velocities measured in littoral zones of the Rhine were, on average, 10 cm/s, corresponding to an affected fraction of 2%. An increase in water- flow velocity up to 120 cm/s as a result of passing vessels caused an increase in affected species to 75%. For a successful ecological river management, the SSD method can be used to quantify the trait-mediated effects of water-flow alterations on occurring species enabling to compare and rank the effects of chemical and physical stress.
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5.1. Introduction
The distribution and abundance of freshwater fish are influenced by a multitude of physical and chemical stream variables (Poff and Allan 1995). The influence of chemical variables, such as salinity, dissolved oxygen, organic compounds and heavy metals, on characteristics of fish assemblages in rivers, e.g., presence, abundance and species diversity, has been demonstrated by various authors (e.g., Hughes and Gammon 1987; Thiel et al. 1995; Marshall and Elliott 1998). Numerous studies regarding physical variables, have shown that fish assemblages are influenced by water depth, water-flow velocity, substrate, the amount of available cover in vegetation and debris, and thermal regime (Fischer and Paukert 2008; Pease et al. 2012; Nakagawa 2014). Quantification of the impact of a chemical substance on fish distribution is established by calculating lethal concentrations (Posthuma and De Zwart 2006) and is usually performed for ecological risk assessment in order to protect natural ecosystems from adverse impacts.
The relationships between physical variables and characteristics of fish species assemblages are often of a qualitative descriptive nature and predictive models of quantitative relations between a physical variable and the distribution of fish species are rare. Xenopoulos and Lodge (2006) have present a statistical model that relates fish species richness to river discharge and can be used as a forecasting tool for evaluating the regional and global changes in riverine fish richness from reduction in discharges due to various anthropogenic drivers. Jellyman et al. (2013) related spatial and temporal variability in fish biomass and assemblage structure to a gradient of natural flood- related disturbances indicating that bed-movement measures explained the most variance in fish assemblage metrics. Numerous studies have examined the contributions of environmental and spatial factors in structuring local riverine fish communities (Nakagawa 2014 and references therein). Such non-quantitative studies on structure of fish assemblage of various rivers, for example, often report the presence/absence or the frequency of occurrence of fish species in correlation with substrate composition, depth, water velocity, diversity of flow-depth regimes (Thiel et al. 1995; Pease et al. 2012; Nakagawa 2014).
Applying an approach to quantify the impact of physical variables in a similar way to ecotoxicological assessments would allow for comparing different types of impacts on either natural or artificial ecosystems (Struijs et al. 2011 and Stewart et al. 2013, respectively) and prioritizing the results for a better ecological management. Because water-flow velocity has been described as a key factor explaining the patterns in fish assemblage structure across biomes (Facey and Grossman 1992; Donaldson et al. 2013; Nakagawa 2014), it is important to include this factor in quantitative ecological risk assessment to protect ecosystems from adverse changes. However, up till now no
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quantitative predictive model has been developed to assess the influence of water-flow velocity on fish species assemblages in rivers.
Increase in water-flow velocity can be caused by channelization of the riverbed (Den Hartog et al. 1992; Hodgson and O'Hara 1994), vessel traffic (Bhowmik et al. 1995; Wolter and Arlinghaus, 2003) or climate change (Milly et al. 2005). It is further known that rapid water-flows may constitute barriers that exceed the behavioural capabilities of certain fish species, leading to constrained populations (Haro et al. 2004). Fish eggs are more likely to be washed away in locations with higher velocities. Also, juveniles and larvae can be displaced by high velocities, hampering them to reach nursery and shelter habitats (Sukhodolov et al. 2009). The tolerance levels to water-flow velocities vary among species and individuals of different size groups (Sagnes and Statzner 2009). Individuals of larger-sized species are expected to tolerate higher water-flow velocities. At the same time, the metabolic cost of holding position and swimming increases and foraging success decreases at higher velocity (Hill and Grossman 1993). Therefore, a fish species benefits when occupying a specific velocity niche where the net energy intake is maximized to support growth and reproductive output (Hill and Grossman 1993) and suffers energy losses at locations with higher velocities. Thus, the capacity of single fish species to withstand and survive certain water-flow velocities determines their distribution.
In ecological risk assessment, Species Sensitivity Distributions (SSDs) are commonly used to quantify effects of chemicals. The basic principle in using SSDs is that each species differs in its tolerance to the same stressor. SSDs are often used as descriptive statistics of species tolerance data describing among others the variation in tolerance across species (Posthuma et al. 2002). The SSD approach may also be a promising tool to quantify effects of physical variables on biodiversity (Smit et al. 2008). So far, it has already been applied to estimate the effects of water temperature on fish (De Vries et al. 2009; Leuven et al. 2011) and molluscs (Verbrugge et al. 2012), of desiccation on molluscs (Collas et al. 2014), and of suspended clays, sediment burial, and grain size change on marine species (Smit et al. 2008). To date, however, SSDs for water velocity are not available.
The goal of the present study was to quantitatively assess the effect of water-flow velocity on occurring fish species. Although water-flow velocity is directly related to river discharge, which in its turn may influence the fish distribution in rivers (Xenopoulos et al. 2006), water-flow velocity show high spatial variability within river systems. Moreover, data on tolerance of fish species to river discharges are lacking. Therefore, the approach in the present study can be useful to assess local and sub- regional effects from microhabitat to river sections and from main river bed to water bodies in its floodplain. We developed SSDs for water-flow velocity from
Chapter 5 | Water-flow velocity | 75
biogeography to determine ecological effects of water-flow velocity on fish in rivers. Thereby, we also discussed the question whether the impacts of chemical and physical variables can be compared in a similar way. Such a comparative approach may help to investigate which type of variables (i.e., chemical vs. physical) has more severe impact on fish species and, hence, on their distribution in rivers. Firstly, we built a database based on field and laboratory observations concerning the impact of water-flow velocity on fish species of different ecological guilds (i.e., rheophilic, eurytopic, and limnophilic according to Aarts and Nienhuis (2003), life stages and body sizes. Secondly, we developed SSDs based on the selected data to quantify the effects of water-flow velocity for different ecological guilds and different life stages of fish. Thirdly, we examined the relationship between the maximal water-flow velocity a fish tolerates and its body length. Additionally, we elaborated a case study for the River Rhine to demonstrate how these SSDs can be applied by comparing the effects of water velocity with effects of chemical variables and water temperature reported previously for the Rhine. Finally, uncertainties present in the SSDs for physical variables and their use in risk assessment were discussed.
5.2. Methods 5.2.1. Data collection
In order to construct SSDs for water-flow velocity, effect level data (level of a stressor that cause a specific effect on an organism, e.g., mortality) were collected for fish species occurring in Atlantic Europe. The selection of freshwater fish species was based on Freyhof and Brooks (2011) and Leuven et al. (2011). For chemical variables, standardized test protocols exist to determine effect levels (e.g., chemical concentrations causing effect (mortality) in 50% of the individuals of a species [EC50s]). However, for the testing of physical variables such protocols are not commonly available (Smit et al. 2008). Therefore, water velocity records from various sources were collected to construct two datasets: one based on field observations and one based on laboratory measurements.
Firstly, we derived empirical relationships between fish species and water-flow velocity by collecting field data from peer-reviewed publications on the maximal water-flow velocity at which a given species was recorded. All journal papers were obtained by searching within Web of Science (WoS, accessed October 2012), using different combinations of keywords: scientific names of species and “velocity”, “speed”, “current”, “water”, “flow”. This procedure resulted in 18 publications. Additional references were obtained from the reference lists of web-retrieved publications. From all the sources, we selected those that specifically reported the life stage of a given fish species and the water-flow velocity at which it was observed in the field. Reports and ecological atlases on life-history traits addressing water-flow velocities that fish species
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are most likely to tolerate were used as well. All together the data from 49 sources were used for further analysis. If the water-flow velocity value given for a species was presented as a range or more than one record was found in literature, the highest value was selected. Hence, the field maximal velocity tolerance for a species (Vmax) is the maximal water-flow velocity at which individual species were recorded in the field. Data were collected for four life stages: eggs, larvae, juveniles and adults, and lumped into ecological guilds (rheophilic, eurytopic, or limnophilic)
Secondly, effect levels based on laboratory measurements representing critical swimming velocity were collected for the same species for which field data were available. During velocity tests, fish is exposed to an increasing water-flow and forced to swim. The water-flow velocity, and hence the swimming velocity, is stepwise increased until exhaustion occurs (Bain et al. 1988). Theoretically, different water-flow velocities can be considered analogous to varying chemical dose levels, and the velocity at which 50% of the fish have exhausted can be compared to EC50 values (Hammer 1995). However, 50% effect responses for water-flow velocities are never reported in literature. Therefore, we collected only 100% effect response data, reported in literature as critical swimming velocity and defined as the maximal swimming velocity that a fish can sustain for a certain period of time (Bain et al. 1988), or as the water-flow velocity at which a fish can no longer maintain its position and becomes exhausted (He et al. 2013). Such measurements are referred to as critical velocity tolerances, Vcrit. These Vcrit data were collected for different life stages.
Data on average total length [cm] (TL) was obtained from Fishbase (www.fishbase.org) to relate the body length of fish to their field tolerance of water velocity (Vmax). We used average total length of male adult fish as this length was reported for the majority of species in contrast to other length measures (e.g., standard length, fork length, or length of females, larvae or juveniles). The median adult size of all species examined was 20 cm TL, and fish species were therefore divided into two size groups: fish smaller than 20 cm at the adult stage and fish larger than 20 cm at the adult stage. In all studies measuring Vcrit, body length of fish was reported and these values were thus used directly to relate the length of fish to their critical velocity tolerance.
5.2.2. Data treatment
SSDs were constructed to analyse the variation in tolerance of fish species to water- flow velocity. When constructing an SSD, often the distribution curve is plotted against the logarithm of the concentration based on the best-fit results because effect concentrations of chemicals differ among species by several orders of magnitude. In the present study, the differences in velocity tolerance among species (Vmax) were more than one order of magnitude, and therefore a log-normal distribution was fit to the
Chapter 5 | Water-flow velocity | 77
original data (Aldenberg and Jaworska 2000). First, the data were log-transformed and the mean and SD were calculated for each species set, e.g., per life stage, size groups, and ecological guild. The cumulative distribution functions were plotted based on the means and SD following the general equation for a cumulative normal distribution:
1 푥 − 휇 퐹(푥) = [1 + 퐸푅퐹 ( )] 2 휎√2 The individual velocity data points were also plotted cumulatively at (i-0.5)/n, with i rank order, and n the number of species tested (Aldenberg and Jaworska 2000).
In ecological risk assessment, SSDs may be applied to predict the potentially affected fraction of species at a certain level of exposure (Posthuma et al. 2002). When field observations are used for the construction of SSDs, the fraction of species affected is addressed as the empirical Potentially Not Occurring Fraction (PNOF) representing the fraction of species potentially excluded from the studied area due to a specific variable (Struijs et al. 2011; Verbrugge et al. 2012). Based on laboratory measurements, the fraction of species affected is addressed as the Potentially Affected Fraction (PAF), indicating the fraction of a set of test species that is exposed to levels of a specific variable beyond the effect levels (Posthuma et al. 2002). Therefore, PNOF and PAF of fish species for water-flow velocity are quantitative measures of the effect indicating the fraction of species that is not able to tolerate a specific water-flow velocity and thus potentially may not occur at the specific environmental conditions.
SSDs based on Vmax were derived for four life stages: eggs, larvae, juveniles, and adults, whereas SSDs based on Vcrit were created for both the adults and juveniles together due to a general lack of data. To facilitate the comparison of water velocity effects based on Vmax and Vcrit for adults and Vmax between different life stages and ecological guilds, the Effect Levels of 50% (EL50) were calculated indicating the water-flow velocity affecting 50% of the species. Differences in tolerance based on Vmax and Vcrit, between life stages, and among rheophilic, eurytopic, and limnophilic species were investigated with the Mann-Whitney U test or independent-samples 2- tailed t-test. The standard deviations (SD) in log-normal distributions, indicating the steepness of the SSD curve, reflect the variation in tolerance among the species. Difference in variance based on Vmax and Vcrit were compared with the Levene’s test. Differences were considered statistically significant at p < 0.05. The correlation between species length and tolerance of water velocity was assessed by the Spearman’s rank order correlation test. All tests were performed using SPSS 17.0 for Windows.
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5.2.3. Case study: water-flow velocity effects in the River Rhine
Using the actual water-flow velocity and velocity changes in combination with SSDs allows for quantification of impacts of water-flow velocity on presence of fish species at a specific location. As an illustration of practical application of the derived SSD for assessment of the impact of water-flow velocity, we measured water velocities in the littoral zone of the Rhine at different locations. To assess the extent of the impact of changes in water velocity, we used measurements on velocity before and during periods when vessels were passing.
We chose the River Rhine for velocity measurements because fish species for which Vmax and Vcrit were collected are characteristic for this river (Leuven et al. 2011). Water-flow velocities [cm/s] were measured at different locations in the littoral zone of the Rhine and the Meuse-Waal Canal (Appendix D Table D1). Groyne fields, inlet points of side-channels and riprap banks were selected for illustrating possible variations in water velocities. The water-flow velocity was measured at 25 cm below water surface using a TAD-micro flow velocity meter (probe: W16, Höntzsch GmbH, Germany). These data were then used to determine the maximal water-flow velocity during a reference period (no vessels passing) and the maximal velocity during vessels passages. Using the measured velocity during reference periods and during vessels passages, the PNOF and PAF were calculated representing the fraction of fish species potentially not occurring when water-flow velocities exceed the Vmax or the Vcrit, respectively.
5.3. Results 5.3.1. SSD based on Vmax and Vcrit
We obtained Vmax data for 62 fish species (of which 57 at the adult, 25 at the juvenile, 29 at the larval and 26 at the egg stage) reflecting a representative selection of the ichthyofauna, not only of the Rhine but of Western and Central European river basins in general, and included rheophilic, eurytopic, and limnophilic species (Appendix D Table D2). Vcrit data were found for 11 species (Appendix D Table D3). The SSDs based on Vmax for adults and Vcrit (Figure 5.1) showed no statistically significant difference between the mean values (Mann-Whitney U test U = 222, p = 0.21). This EL50 (i.e., water-flow velocity exceeding tolerance levels for 50% of the species) was 60 cm/s (47-77, 5-95% confidence intervals) and 47 cm/s (32-68, 5-95% confidence intervals) based on Vmax and Vcrit, respectively. The standard deviation based on log- transformed velocity values for Vmax was larger than for Vcrit (Figure 5.1) (Levene’s test for equality of variance p = 0.04). However, the difference between the means and variances were not statistically significant when Vcrit and Vmax were compared for the
Chapter 5 | Water-flow velocity | 79
same 11 species (t-test, p > 0.05, Levene’s test p > 0.05, respectively; Appendix D Figure D1).
Figure 5.1. Species Sensitivity Distributions of fish species for maximal water-flow velocity (Vmax solid line, n = 57, SD = 0.4) and critical velocity (Vcrit dashed line, n = 11, SD = 0.2). PNOF: potentially not occurring fraction in percentages based on field observations; PAF: potentially affected fraction based on laboratory measurements.
5.3.2. SSDs for different life stages and ecological guilds of fish
The SSDs based on field observations (Vmax) for the different life stages of fish showed that the maximal water-flow velocities for adult fish were significantly higher than velocities for juveniles, larvae and eggs (Mann-Whitney U test U = 371; 485; 483, respectively, all p < 0.05, Figure 5.2). The EL50 for adults was 60 cm/s, while the EL50s for all non-adult life stages were comparable and not statistically significantly different (for juvenile, larva and egg life stages EL50s (5-95% confidence intervals) were 25 (15- 41), 26 (16-43), and 29 (17-49) cm/s). For a clear visualisation, the (intermediate) SSD curve for larvae was omitted from Figure 5.2 because there was no significant difference with juveniles and eggs. Figure 5.2 showed that at water-flow velocities of 100 cm/s, approximately 90% of the fish species was potentially affected at the juvenile life stage, i.e., that 90% could not tolerate velocities exceeding 100 cm/s. Interestingly, the highest maximal velocity tolerance for all life stages was similar and around 200 cm/s, as indicated by the upper ends of the SSDs (Figure 5.2).
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Figure 5.2. Species Sensitivity Distributions for maximal water-flow velocity (Vmax) of different life-stages. Adults are presented by two size groups, smaller and larger than 20 cm. PNOF: potentially not occurring fraction in percentages. Adults < 20 cm n = 29, SD = 0.4, adults > 20 cm n = 28, SD = 0.4; juveniles n = 25, SD = 0.5; larvae n = 29, SD = 0.6 (not drawn); eggs n = 26, SD = 0.6.
To illustrate the effect of size on velocity tolerance, fish species were divided into two size groups, i.e., smaller than and larger than 20 cm at adult life stage (Median length = 12 cm, SD = 5.2 and Median length = 36.5 cm, SD = 28.2, respectively). Larger species tended to tolerate significantly higher velocities (Median = 50) than smaller species (Median = 100) (Mann-Whitney U test U = 218, p = 0.003, Figure 5.2).
The SSDs for rheophilic, eurytopic, and limnophilic species are presented in Figure 5.3 (adults only). As expected, rheophilic species tolerate higher velocities than eurytopic and limnophilic species (Mann-Whitney U test, U = 125, p < 0.05 and U = 0, p < 0.05, respectively). There was no significant difference between eurytopic and limnophilic species in their tolerance to water velocities (Mann-Whitney U test, U = 42, p = 0.78). The length of rheophilic species (Median length = 30 cm, SD = 32.2) was significantly higher than the length of eurytopic (Median length = 20 cm, SD = 11.6) and limnophilic species (Median length = 9.9 cm, SD = 11.5) (Mann-Whitney U test U = 198, p = 0.03 and U = 38, p = 0.01, respectively).
Chapter 5 | Water-flow velocity | 81
Figure 5.3. Species Sensitivity Distributions for maximal water-flow velocity (Vmax) of adults of rheophilic (solid line, n =28, SD = 0.2), eurytopic (dashed line, n = 22, SD = 0.4), and limnophilic (dotted line, n = 7, SD = 0.2) fish species. PNOF: potentially not occurring fraction in percentages.
5.3.3. Size-velocity relationship
The swimming velocity of fish increases with its body size following the general equation Y = aXb, where Y is the swimming velocity [cm/s] and X is the total length [cm] (Peake, 2004). The critical velocity (Vcrit) tolerance and the length of fish at different life stages were positively correlated based on the length of several or single individuals per species (Spearman's Rank Order test, rs = 0.505, p = 0.02; Figure 5.4). The maximal velocity (Vmax) tolerance and the length of fish at adult stage was also positively correlated (Spearman's Rank Order test, rs = 0.401, p = 0.004; Figure 5.4). When 11 species were compared, for which both Vmax and Vcrit were available, the differences between their respective length (Appendix D Table D3) were not statistically significant (Mann-Whitney U test, all p > 0.05).
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Figure 5.4. Relation between water-flow velocity tolerance and the length of fish representing maximal velocity tolerance based on field observations (Vmax, solid line, n = 57, y =14.1x0.48) and critical swimming velocity (Vcrit, dashed line, n = 21, y=18.5x0.40).
5.3.4. Quantifying the effects of water-flow velocity in the littoral zone of the River Rhine
The maximal measured velocity in the littoral zone of the River Rhine in the reference period when no vessels were passing through the main channel was on average 10 cm/s (Table 5.1). However, during vessel passages, the water-flow velocities increased 12 times reaching velocities up to 117 cm/s (Table 5.1).
Table 5.1. PNOFs and PAFs (%) for fish tolerance of maximal water-flow velocity (Vmax) and critical water velocity (Vcrit) at difference life stages in littoral zone of the River Rhine during reference period, average velocity 10 cm/s, SD = 2, and during vessel passages, average velocity 117 cm/s, SD = 29. Reference During vessel
period passages PNOF juvenile Vmax 22 87 PNOF adult, Vmax 2 74 PNOF adult 0 53 rheophilic, Vmax PNOF adult 6 90 eurytopic, Vmax PNOF adult 9 99 limnophilic,Vmax PAF Vcrit 0 95
Chapter 5 | Water-flow velocity | 83
Calculated PNOFs based on Vmax for adults and juveniles and for different ecological guilds, and PAF based on Vcrit are presented in Table 5.1. Increase in water-flow velocity during vessel passages lasted around 50 s at the peak velocity (Figure 5.5). At the increased velocity during vessel passages, the PNOF for adults increased on average by two orders of magnitude and the PNOF for juveniles by a factor around 3. Comparing the PNOFs between different life stages showed that the PNOF for juveniles were on average a factor of 15 higher than for adult fish at reference period, while during vessel passages the PNOFs for juveniles were on average a factor 3 higher than the PNOFs for adults (Appendix D Table D1).
No effects of water-flow velocity based on Vcrit were found in the reference period. During vessel passages, however, PAFs reached values up to 95%, meaning that 95% of the species would potentially be affected at locations with velocities higher than 117 cm/s. Such a large increase in PAF based on Vcrit was explained by the steepness of the SSD curve and a relatively small variation in critical velocity tolerances of fish species.
Figure 5.5. Typical example of changes in water-flow velocity pattern after a vessel passage, resulting in an increase in the water velocity for around 50 s at the peak of the wave followed by water displacement, bow, stern and propeller waves, as measured in the littoral zone of a groyne field in the River Rhine. Arrow indicates the passage of a vessel.
5.4. Discussion 5.4.1. Uncertainties
The SSDs for Vmax were based on observational data reporting the presence of species at a specific water-flow velocity in their habitat. The assumption was that if the species was not observed at higher water velocity, the highest reported velocity could be used as maximal tolerance level for that species. However, the use of field observations to
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derive tolerance levels is open to debate (Von Stackelberg and Menzie 2002; Verbrugge et al. 2012), as it cannot be excluded that the species is able to tolerate higher velocities than indicated by the field observations. The calculated PNOF values provide thus a conservative estimation of the effect levels of water-flow velocity on fishes.
Constructing SSDs based on effect levels derived from laboratory settings, however, contains uncertainties as well. The critical swimming velocity of fish depends on many environmental and physiological factors. Thus, it becomes difficult to compare experimental results because Vcrit changes with size, sex, light, temperature, oxygen availability, feeding etc. (Hammer 1995). Also we collected the data on Vcrit from studies using different experimental settings where the timing or the intervals between the increasing water velocities varied, although all the tests were performed in otherwise optimal condition for each species (water pH, hardness and temperature). A lack of standardization in the derivation of effect levels for water velocity leads to possibly considerable uncertainties in effect assessments.
In our study, we only examined the potential trait-mediated influence on the occurring fish of water-flow velocities. The actual presence of fish species, however, depends on several other parameters, mostly concomitant, such as pollution, blocked migration routes and absence of necessary habitats as nursery, shelter or spawning grounds (Arlinghaus et al. 2002; Haro et al. 2004; Fischer and Paukert 2008). Additionally, in all rivers there are still lees where even the least tolerant species can temporarily find shelter, for instance in some parts of groyne fields and in connected floodplain lakes and side channels. Our study highlights the importance of spatial heterogeneity of water-flow velocities for different riverine fishes. In the investigation of the fish species distribution in the lower Rhine along the gradient of water-flow velocities and depth, Grift et al. (2003) have found that within the rheophilic guild, barbel and gudgeon dominated the habitats with highest flow velocities whereas ide and asp prevailed in deeper habitats with lower flow velocities. Within the eurytopic guild, species also were spatially separated by flow velocity where bleak occurred in the habitats with higher flow velocities than pikeperch and bream (Grift et al. 2003).
5.4.2. Effects of water-flow velocity
Adult fish were less affected by high velocities than juveniles, larvae and eggs. The calculated EL50s indicate that water velocities of around 25 and 60 cm/s may potentially affect 50% of the species in their juvenile and adult life stage, respectively. These calculated effects may be useful to assess the limiting flow velocities for fish in river sections where the flow has been modified.
Chapter 5 | Water-flow velocity | 85
The effects of the increased flow velocity become more severe when a vessel passes along the water channel. The magnitude of vessel induced increase in velocity can exceed background flow-velocities by one order of magnitude (Appendix D Table D1). Considering the pattern of the flow-velocity change during a passage of a vessel, fishes are exposed to high velocities for around 50 s at the peak of the high velocity wave. Fast waves occur at short timescales since in the Rhine vessels pass on average every three minutes (600 vessels per day at German-Dutch border, http://ccr- zkr.org/12030100-nl.html; Grift et al. 2003). Due to frequently passing vessels, fish are almost constantly exposed to increased velocities and may not be able to withstand them (Arlinghause et al. 2002). During a wave of high velocity a fish use its burst swimming velocity to manoeuvre through the water. This is the highest speed of which fish is capable, and can be maintained only for short periods <20 s (Beamish 1978).
Although there was no significant difference between the water-flow velocity tolerance based on field observations and laboratory tests, the respective PNOFs and PAFs calculated on these two effect levels were not the same. Higher PAFs based on Vcrit compared to PNOFs based on Vmax are the result of smaller variation in Vcrit among species. Therefore, it is important for the effect assessment to consider the differences between the effect levels and endpoints.
5.4.3. Comparing the PAFs for water-flow velocity, water temperature and chemicals
Applying the SSD approach for quantifying the effects of various variables should in theory allow comparing and ranking of these variables. Previous studies on fish species in the River Rhine have reported PAFs for water temperature and a set of different chemicals (Leuven et al. 2011; Fedorenkova et al. 2013). Combined effects of nine chemicals (multi-substance PAF) were based on annual average maximal concentrations in the River Rhine as measured in the Dutch part of the river and were between 30-40% (Fedorenkova et al. 2013). The PAF corresponding to the maximal annual water temperature in the Rhine varied between 30-60% since the year 2000 (Leuven et al. 2011). The average water-flow velocity in the main channel of the Rhine in the Netherlands varies from 50 to 150 cm/s (RIWA 2000). The corresponding PAFs based on Vcrit for the average velocities would be equal to 55% and 89%, respectively. This means that, on average, the effects of water-flow velocities and increases therein would be much higher than the effects of chemical variables and high water temperature.
Direct comparison among PAFs depends on the effect levels used as input data for constructing the SSDs. In the study of Leuven et al. (2011) the effect levels for temperature (maximal temperature that a given species can withstand derived during short term exposure) were comparable to the Vcrit in the present study and therefore,
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the PAFs can be directly compared. However, the PAFs of fish species in the Rhine for the chemicals present in the river were based on laboratory derived EC50s with mortality as the effect indicator (Fedorenkova et al. 2013). In the present study, Vcrit was defined as the effect level representing the velocity at which 100% of the fish tested have been exhausted. If we assume that the exhaustion of a species due to high velocity is comparable to mortality of a species due to chemical exposure, then the velocity at which 50% of the fish have been exhausted is the response comparable to an EC50 value. Thus, deriving PAFs based on Vcrit for the 50% effect level instead of 100%, which would become higher than the PAF calculated in the present study, would allow for comparison and ranking the effects of water velocity and chemicals. Alternatively, other endpoints for chemical effect levels not leading to mortality of individuals but comparable to exhaustion could be used. Therefore, standardization in derivation of effect levels for physical variables similar to chemical variables is essential for the comparison of effects caused by these types of variables.
5.4.4. Individual body sizes
There was a positive correlation between the fish body size and the tolerance of water- flow velocity with exponents of 0.48 and 0.40 based on Vmax and Vcrit, respectively. These values are within the range established in previous studies showing that the relationship of swimming velocity is proportional to the length of a fish with exponents of 0.4 and 0.58 (Peters 1983; Hammer 1995, and references therein). Such allometric relations between body length and water-flow velocity tolerance could explain the differences between SSD curves for adults and juveniles if we assume that roughly the length of the juveniles is 1/5 of the length of the adults. Moreover, in the present study rheophilic species, being significantly larger than the eurytopic and limnophilic species, were found to tolerate higher velocities than eurytopic and limnophilic species. Certainly, the species ecological demands associated with general habitat preferences and different life strategies influence the tolerance of a species to water-flow velocities (Ohlberger et al. 2006). However, the functional trait such as body size (body mass) can explain much variability among species (Hendriks 2013). The body size and its distribution in community can be used as a measure to reflect the effects of external variables (Mulder et al. 2011). In the study of Welch et al. (2013) with Fundulus notatus adult fish inhabiting streams were found to be generally larger than fish inhabiting lakes. Although the body size of fish species can explain the variability in tolerance to water- flow velocity, other factors such as body morphology, sex, age are also important, which, however, were not considered in the present study (Fischer-Rousseau et al. 2010).
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5.4.5. Applicability of SSD approach
The SSD approach presented in this study offers a promising tool to estimate adequate flow velocities needed for different fish species and to quantify the consequences of alterations in water-flow velocity on potential presence fish species. This method can be used to estimate the conditions of habitats with specific water-flow velocities and can be applied to underpin management actions for river rehabilitation, e.g., by establishing suitable variation in abiotic conditions, including water-flow velocities, to promote certain species. For example, for the construction of secondary channels with permanent low-flow conditions required for the re-establishment of typical riverine species, SSD estimation would indicate 25cm/s as limiting water-flow velocity for juvenile fish species.
Temporal alteration in water-flow velocity caused by vessel traffic was used in the present study to illustrate the application possibilities of the SSD approach. We showed that this approach developed for quantifying the impacts of chemicals can be applied to estimate the impact of physical variables previously not evaluated in such a quantitative manner. In earlier studies applying SSDs to quantify the impacts of physical variables (e.g., De Vries et al. 2009; Verbrugge et al. 2012; Collas et al. 2014) the effect levels varied from EC50 to EC100 with mortality as endpoint, making it difficult to compare these SSDs directly with the effects caused by chemicals. However, the application of the SSD approach to physical variables as such can complement risk assessments. Including both types of variables affecting natural habitats of species in one overall assessment may facilitate the comparison of impact and give possibilities to rank and prioritize among various variables. The success of the application of the SSD approach depends, however, on the quantity and quality of the available input data. Moreover, in order to include the estimation of effects from physical variables in ecological risk assessment, standardization in derivation of effect levels for physical variables analogous to chemical variables is required.
Acknowledgements
We thank Remon Koopman and Frank Collas for helping measuring water-flow velocities during the field study. Christian Mulder was supported by the 7th Framework EU Project “SOLUTIONS”. We also thank two anonymous reviewers for comments and suggestions that substantially improved the manuscript.
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Chapter 6 Development and application of the SSD approach in scientific case studies for ecological risk assessment
Del Signore A, Hendriks AJ, Lenders HJR, Leuven RSEW, Breure AM Submitted
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Chapter 6 | Abstract
Species Sensitivity Distributions (SSDs) are used in Ecological Risk Assessment (ERA) for extrapolation of the results of toxicity tests with single-species to determine a toxicity threshold considered protective of ecosystem structure and functioning. The attention to and importance of the SSD approach has increased in scientific and regulatory communities since the 1990s. It triggered discussions and critics on the concept of the approach as well as its technical aspects (e.g., distribution type, number of toxicity endpoints). However, various questions remain unanswered, especially with regard to, e.g., different endpoints, statistical methods and protectiveness of threshold levels derived from SSDs. Furthermore, overall success of application of the SSD approach in scientific research has not been evaluated explicitly. In the presented literature review (covering the period 2002-2013), we therefore explore case studies, where the SSD approach was applied and investigate how endpoint types, species choice and data availability affect SSDs. We also investigate, which statistical methods may be used to construct reliable SSDs and whether or not HC5s from a generic SSD can be protective for a specific local community. It was shown that taxonomic groups had the greatest effect on estimated protective concentrations and not the statistical method used to construct the distribution. Based on comparisons between semi-field and lab-based SSDs, the output from lab-SSD was protective of semi-field communities in majority of studies.
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6.1. Introduction
The world faces a major challenge in determining the consequences of the widespread occurrence of toxic chemical compounds in the environment. Ecological Risk Assessment (ERA) deals with this challenge to protect ecosystems by combining the understanding of the potential of chemicals to cause harm with the likelihood of that potential being realized (Calow 1998). ERA aims to find an exposure level or a toxicity threshold (e.g., PNEC, predicted no-effect concentration), below which an ecosystem will not suffer unacceptable damage.
The problem, however, is how to assess such thresholds if only data on a limited number of species is available (Van der Hoeven 2004). Given the impossibility of testing the effects of all chemical compounds on all species, traditional approaches to risk assessment are based on observations of effects of individual chemicals on the survival, growth, and reproduction of a few selected test species (Forbes and Forbes 1993; Hendriks 2013). The results of such toxicity tests are then extrapolated statistically to ecologically relevant levels including populations, communities and ecosystems (Calow 1998; Forbes et al. 2008). One of the approaches for extrapolation of the results from single-species toxicity tests to ecosystem level is the use of Species Sensitivity Distributions (SSDs). SSDs are cumulative plots of effect concentrations, e.g., median effect concentration (EC50) or no observed effect concentration (NOEC), fitted to a statistical distribution. The lower 5th percentile of the estimated hazardous concentration based on NOECs, HC5, is generally divided by an application factor ranging from 1 to 5 to determine a toxicity threshold considered protective of ecosystem structure and function (Iino et al. 2005). The SSD approach has become a practical method in decision-making processes for the derivation of environmental quality criteria (EQCs), benchmarks for screening assessments, estimation of ecological risks (Stephan 2002) and an accepted instrument in ERA around the world (Schmitt-Jansen and Altenburger 2005). In the USA, the SSD concept is being used as the basis for the screening benchmarks for contaminants in water for National Ambient Water Quality Criteria (TenBrook et al. 2009). In Canada SSDs are recommended in the Canadian Water Quality Guidelines for the Protection of Aquatic Life (TenBrook et al. 2009). The use of SSDs has been approved in the guidelines of several regulations of the European Union, including REACH (Registration, Evaluation, Authorization and restriction of CHemicals), registration of plant protection products (Regulation EC 2009), and the Water Framework Directive (Directive 2000). Its application is also recommended by public organizations in Australia and New Zealand (TenBrook et al. 2009).
The importance of SSDs in ecotoxicity assessment has steadily grown since the 1990s, resulting from a rising popularity of SSDs in both the regulatory and scientific communities. However, in that period intensive discussions have taken place on the
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basic assumptions, statistics, data limitations and applications (e.g., Forbes and Forbes 1993; Posthuma et al. 2002). Although the discussions and critics were fruitful in terms of improvements in the SSD approach, various questions remain unanswered. For example, Forbes and Calow (2002) argued that common test species might be more sensitive and that some endpoints might not be relevant for ecologically important population responses. Also, no consensus has been reached on statistical aspects for SSD construction such as minimum number of data points. Moreover, due to exclusion of biotic and abiotic interactions acting in real ecosystems, the protectiveness of threshold levels, e.g., HC5, for actual ecosystems is questionable. To evaluate the overall success of applying the SSD approach in scientific research the present review focuses on the following research questions:
1. How are SSDs being applied in scientific research for risk assessment of (non- ) chemical stress on aquatic and terrestrial ecosystems? 2. How does selection of various types of endpoints, species and data availability affect SSDs? 3. Which statistical methods should be used to construct a reliable SSD? 4. Is an HC5, derived from a generic SSD, protective for a specific local community?
We seek to answer the abovementioned questions by analyzing available literature since 2002, as developments until that year are described by Posthuma et al. (2002) and Forbes and Calow (2002). More specifically, we analyze, how statistical aspects (model choice, data points) have been tackled, when applying the SSD approach in scientific research in different case studies. To that end, we analyzed case studies that employed SSD for various research questions related to structure and functioning of aquatic and soil ecosystems rather than studies solely performing risk assessment for deriving EQCs for different chemical compounds. We focus on single chemicals rather than mixtures because the application of the SSD approach for mixtures involves many additional technical questions that require separate and extensive examination (e.g., concerning the use of concentration or response addition approach based on various modes of action of chemicals). We excluded studies applying the SSD approach in life cycle impact assessment because they do not focus on the SSD methodology per se.
6.2. Method
To capture relevant scientific publications, the database Web of Science was searched for primary studies on SSDs found by the key phrase “species sensitivity distribution” from January 2002 through December 2013. This effort resulted in 317 publications from which we selected relevant studies for the review. Of these, 150 publications were excluded as not useful for our objectives because SSDs were only mentioned rather than
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actually derived or discussed. Publications related to mixture toxicity, life cycle assessment or simply generation of toxicity data were also excluded. The remaining papers were assessed and allocated into separate groups according to their focus, which helped sorting the information regarding our research questions (Table 6.1). One group of papers included publications where the SSD approach was applied for deriving EQCs, risk limits, benchmarks for chemicals, ecological risks, or other case studies (related to research question 1). To get an impression of how the SSD approach has been developed and applied in scientific research, we focused in detail on case studies with purposes other than merely deriving EQCs or assessing ecological risks. The main findings from these case studies were discussed. Since such case studies do not concern derivation of quality standards or risk estimation for a specific chemical, guidance requirements on how to construct the SSD were usually not followed (e.g., concerning technical aspects such as number of data point, “default” log-normal or log-logistic distribution). To review how these technical aspects have been dealt with, we reviewed every study in detail and determined the distribution assumed, the endpoints observed, and the sample size used. Another group of publications included discussions on statistical methods and underlying data for the construction of SSDs (related to research question 2 and 3). In a third group, we allocated publications on ecological relevance and validation of the SSDs (related to research question 4).
6.3. Results and discussion
6.3.1. Application of the SSD approach in scientific research for risk assessment
The majority of the found publications can be allocated by their common focus in several research topics. 18 publications were related to discussions on how to improve the SSD approach for setting quality criteria for sediment, soil, and groundwater, which was not discussed in detail in the present review. The SSD approach has been applied also in scientific research in various case studies. We identified numerous publications (Table 6.1) that applied the SSD approach for deriving EQCs (Appendix E Table E1) and ecological risks for various chemicals (Appendix E Table E2) or in case studies with other goals, so called “non-standard” case studies, e.g., comparing sensitivity among taxonomic groups or species from different geographic areas (Appendix E Table E3).
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Table 6.1. Number of scientific publications found by the key phrase “species sensitivity distribution” in database Web of Science (2002 - 2013) which were analysed in the present review. Total publications 317 Excluded from review 150 General discussion on SSDs, applications for sediment, soil, 26 groundwater quality criteria setting Case study focus on EQS 44 Case study focus on RA 46 Other case studies 26 Statistical methods 13 Ecological relevance and validation 12 of the SSDs
In the following sections, we explore the application of the SSD approach in “non- standard” case studies and summarize their main findings focusing on research question 1. Table 6.3 summarizes the influences of the topics considered in these case studies on the derived SSDs and HC5s. Figure 6.1 visualizes the overall topics covered by the case studies discussed in the following sections.
The majority of the studies deriving EQCs focused on freshwater ecosystems, followed by soil and marine ecosystems. Less attention was given to sediment EQCs. Zinc, copper and cadmium were the most studied chemicals, followed by different pesticides (Appendix E Table E1). Geographically, most of the studies were performed in China and Japan, followed by the USA and the Netherlands (Appendix E Table E2). Other studies focused on several countries in Europe, Australia and South Africa. The geographical distribution of research may be related to the general distribution of scientists in the field of ecotoxicology (Schiesari et al. 2007) and attention to a limited number of chemical compounds is obviously related to the general lack of toxicity data (Van der Hoeven 2004). In several studies the derived ecological risks were used to prioritize different chemicals or sites of concern indicating the major threats to a specific group of species at a certain location (e.g., Tsushima et al. 2010; Hayashi et al. 2011; Verschoor et al. 2011; Fedorenkova et al. 2012).
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Figure 6.1. Overall topics covered by case studies discussed in the following sections.
6.3.2. Comparison of species from different geographic areas
The approach to construct SSDs most often relies on toxicity data derived from species of temperate regions. Several studies have shown that sensitivity of species to various chemical compounds may or may not differ when comparing climate zones or regions. Hose and Van den Brink (2004) found no significant difference in sensitivity of Australian and non-Australian arthropods and fish to endosulfan by comparing SSD curves and HC5 values. Similarly, no significant differences were found in the sensitivity distributions of tropical and temperate vertebrates, arthropods and non- arthropod invertebrates for 16 pesticide compounds (Maltby et al. 2005).
Rico et al. (2010; 2011) compared tropical and temperate fish and invertebrate species sensitivity distributions of malathion and carbendazim, and parathion-methyl, respectively. They found that Amazonian species were equally sensitive to malathion and parathion-methyl as their temperate counterpart. However, Amazonian fish appeared to be slightly less sensitive for carbendazim than temperate fish. Amazonian invertebrates, however, were significantly less sensitive for this substance than temperate species (Rico et al. 2011).
Olsen et al. (2011) found no difference in sensitivity of arctic and temperate marine species from 7 taxonomic classes to 2-methyl naphthalene, neither at the species level nor at the community level. De Hoop et al. (2011) showed that HC50s of polar and temperate marine species for oil and oil components differed less than a factor 3. However, differences in sensitivity for naphthalene of arthropods, chordates, and echinoderms were significant.
Iwasaki et al. (2012) compared SSDs based on inter-continental field observations of macro-invertebrate species in relation to several metal concentration gradients based on
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lab-derived toxicity values. The estimated hazard concentration values for copper, zinc, and cadmium overlapped closely with laboratory-derived values available from water quality criteria in the USA, UK and EU. The results not only add considerable confidence to the application of existing metal standards, but illustrate also how standard values might be widely applied geographically (Iwasaki et al. 2012). However, in their investigation of regional differences in acute sensitivities of marine invertebrates to four metals, Chapman et al. (2003) concluded that there was no universal, predictable regional pattern of sensitivity for metals among species.
Kefford et al. (2005) investigated whether related taxa of freshwater macroinvertebrates from South Africa and Australia show similar sensitivities to salinity. They concluded that the broad similarity in sensitivity within most taxa at the orders level suggests that it may be acceptable, in the absence of other information, to assume similar salinity sensitivity in different geographic locations within families and orders. Likewise, Van Dam et al. (2004) found no significant difference between acute and chronic toxicity of the herbicide tebuthiuron between northern hemisphere temperate and Australian tropical aquatic species (fish and green algae). These studies compile a growing body of literature showing that the sensitivity of organisms to toxicants is independent of their geographic origin and that there is no consistent geographical pattern in species sensitivity.
6.3.3. Comparison of rare and endangered species
Kefford et al. (2012) studied relative sensitivity of freshwater macroinvertebrates to salinity. They found that locally rare macroinvertebrates tended to be more tolerant than locally common ones. The authors argued that the relationship between rarity and salt tolerance may be due to the tendency of rare species to belong to particular taxa, such as Coleoptera and Odonata, leaning towards salinity tolerant (Kefford et al. 2003). The authors hypothesized that assuming that rare and common species tend to be K- and r- selected, respectively, the inability for K-selected species to recover rapidly from disturbances should be a strong selection pressure to develop resistance to environmental extremes. Raimondo et al. (2008) compared the sensitivity of endangered species with surrogate species commonly used in toxicity tests for 68 chemicals. The authors concluded that SSD approaches using standard species can be used for protections of endangered species for which toxicity data are not available.
6.3.4. Comparison of species from different habitats
Most available ecotoxicological data apply to aquatic species from freshwater habitats. For marine, estuarine and terrestrial species toxicity data are generally scarce giving higher uncertainty in risk assessment for the saltwater and terrestrial environment than for the freshwater environment (Wheeler et al. 2002b; Golsteijn et al. 2013). For
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terrestrial risk assessment, in case of a lack of data, the estimations of effects on soil organism can be based on the data on pore water concentrations and aquatic toxicity data (Golsteijn et al. 2013). Similarly, a general strategy to assess the risk of chemicals for salt water environments is to apply safety factors to the risk level calculated based on freshwater organisms. However, the sensitivity to chemical compounds of species from different habitats may differ. Therefore, an important question to consider in risk assessment is whether protection levels derived from species living in one type of habitats would be protective also for species typical for other types of habitats.
Driven by the relevance for regulation, several authors have investigated potential differences in sensitivity of species from different habitats. For example, Hose (2005) found significant differences in the sensitivity of aquatic taxa living in surface water habitats compared to taxa from ground water habitats to atrazine and chlorpyrifos, whereas Maltby et al. (2005) showed similar sensitivities among arthropod species to eight pesticides in lentic and lotic habitat types. In a study based on large dataset, Golsteijn et al. (2013) showed that for 38 of 47 organic chemicals there was no statistical difference between the HC50s of aquatic and terrestrial species.
Majority of studies, however, were attributed to comparisons between species sensitivities from freshwater and marine habitats. Maltby et al. (2005) found no significant difference in median HC5 values estimated from SSDs constructed using either freshwater or marine taxa for 10 chemical compounds. However, the median HC5 estimates for marine arthropods tended to be smaller than those derived using freshwater arthropods. Comparing within compounds, the sensitivity distributions for freshwater and marine arthropods were significantly different for permethrin and chlorpyrifos, although this difference was not significant when the analysis was restricted to crustaceans alone (Maltby et al. 2005).
Bollmohr et al. (2007) showed that marine organisms (arthropods and fish) were more sensitive (a factor between 1.5 and 2.8 based on HC5s) to pesticides cypermethrin, endosulfan, chlorpyrifos and fenvalerate than freshwater organisms. Wheeler et al. (2002b) compared freshwater and marine datasets based on HC5 and regression parameter values (slopes and intercepts). Although overall sensitivity between freshwater species and marine species was not significantly different, freshwater species exhibited slightly greater sensitivity to ammonia and metals than marine species (crustaceans and fish) (Wheeler et al. 2002b). In contrast, for pesticide and narcotic compounds, marine species tended to be more sensitive than freshwater species. The HC5 values for the anti-fouling biocide tributyltin for marine fish, invertebrates, algae were significantly lower (approximately a factor 8) than that for freshwater species, indicating that marine species might be more susceptible to tributyltin than their freshwater counterparts (Leung et al. 2007). Yet, it should be noted that the means of a
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(log-normal) SSD represent true sensitivities of species. Thus, comparing the HC5 between different species groups indicate the protectivness level for one group based on the data derived for another and not average difference in sensitivity. Zhang et al. (2008) found that freshwater primary producers, however, were around factor 9 more sensitive to an antifouling paint than their marine counterparts. Verbruggen et al. (2008) found no significant differences for freshwater sediment and marine sediment species to total petroleum hydrocarbons.
Considering all available toxicity data for various chemical compounds, De Zwart (2005) concluded that approximately one third of the marine fish, invertebrate and algal species were more sensitive (by a factor 2 or more) than their freshwater counterparts. Biological and physicochemical factors may contribute to differences in freshwater and marine species sensitivities including chemical differences in each medium, especially bioavailability, but also methodological differences in toxicity tests. Overall, the chemical’s activity may differ between species, and can be targeted at a specific species group. Therefore, the types of species in the toxicity datasets may be an important determinant for the observed differences between species (Golsteijn et al. 2013). Yet, the results of available studies do not indicate systematic or consistent differences in the sensitivity of marine versus freshwater taxa. Thus, protection levels, e.g., HC5 derived from freshwater species only, may still be uncertain for marine species. Further research is needed to investigate the protectiveness of the threshold levels derived for species from one type of habitats for species from other types.
6.3.5. Comparison of species sensitivity to non-toxic stress
Although originally application of SSDs in ERA aims at protecting ecosystems from toxic chemical stress, other, non-chemical and non-toxic stressors can affect ecosystems, too. As for chemical compounds, data for non-chemical stressors may be derived in laboratories for various endpoints. Smit et al. (2008), for instance, developed SSDs for suspended clays, burial by sediment, and change in sediment grain size for marine species and estimated Potentially Affected Fractions (PAFs) to communicate potential risks related to drilling of oil and gas wells in the North Sea. De Vries et al. (2009) showed that the SSD approach is also suitable to estimate the risk of thermal effects, especially based on site-relevant species for location-specific assessment. Empirical occurrence data from field studies can be applied also to derive SSDs for non-toxic stress. For example, Struijs et al. (2011) used field observations of many macroinvertebrate genera occurring at various levels of total phosphorus in Dutch inland waters to derive disappeared fraction of species due to elevated levels of phosphorus. By applying this field-based SSD to measured phosphorus in rivers, the authors calculated macroinvertebrate diversity losses to be around 3-9% in different Dutch rivers in year 2000. In a similar approach, Azevedo et al. (2013a) using the
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occurrence of freshwater species at specific total phosphorus concentrations, showed that the relative species richness of streams generally decreased more rapidly than that of lakes with increasing phosphorus levels. Applying SSDs to non-toxic stress, Azevedo et al. (2013b) derived SSDs based on the occurrence data of plant species in relation to soil pH to quantify the fraction of species potentially lost due to soil acidification. The authors concluded that regions within the (sub)tropical moist broadleaf forest may suffer great changes in species richness following a soil acidification.
Other studies on non-toxic stress focused on comparison of sensitivities of native and non-native species. Leuven et al. (2011) applied the SSD approach for a location- specific assessment of fish diversity in relation to river temperature conditions. Their study focused on comparison of tolerance levels to water temperature of native and non- native fish species in the river Rhine. They concluded that temperature tolerance of non- native fish species was consistently higher than that of the native species, indicating that non-native species are better adapted to warm water conditions. However, the differences based on the mean tolerance values were not statistically significant. Furthermore, no significant differences between native and non-native fish species in the river Rhine were found in tolerance to low oxygen concentrations (Elshout et al. 2013) indicating that such conditions do not facilitate the spread of invasive fish species. However, maximum temperature tolerance of molluscs was significantly higher for non-native than for native species (Verbrugge et al. 2012). The mean maximum salinity tolerance was not significantly different between native and non-native species (Verbrugge et al. 2012).
These case studies show that the SSD approach can be applied to quantify the impact of non-toxic stressors such as changing temperature, hypoxia, salinity, and changes in sediment particle size, soil acidification and aquatic eutrophication. Applying the SSD approach to quantify the impact of non-chemical stress in a similar way to ecotoxicological assessments may allow for comparing different types of impacts on ecosystems and prioritizing the results for a better ecological management.
6.4. Underlying toxicity data and statistical methods to construct SSDs 6.4.1. Determining the sensitivity of different taxonomic groups
Following research question 2 we reviewed the available studies related to different species used in SSDs to investigate how different species may influence the construction of SSDs (Table 6.2). Here we analyze the findings from 26 publications discussing variation among different taxonomic groups, different endpoints and effect levels.
The SSD approach presumes that the sensitivity of a community depends on the sensitivity of the individual species of which it is composed, taking into account that
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some species are more susceptible to stress than others (Forbes and Calow 2002). Moreover, in risk assessment laboratory tests species act as surrogates for taxa in the target ecosystems, under the premise that the distribution of sensitivities of laboratory taxa represents the distribution of sensitivities in a generic and diverse community (Posthuma et al. 2002; Staples et al. 2004). Therefore, taxonomic groups that are included in an SSD, should be selected carefully, because the taxonomic composition of the species assemblage may have a significant influence on the assessment of hazard (Duboudin et al. 2004; Van den Brink et al. 2006). However, the practice shows that the non-random selection of laboratory test species results in laboratory data over- representing particular taxonomic groups (Forbes and Calow 2002; Posthuma et al. 2002; Hendriks et al. 2013).
Inter-species variation in sensitivity of different taxonomic groups has been studied widely (e.g., Von der Ohe and Liess 2004; Luttik et al. 2011) and can be attributed partly to the specific toxic mode of action of a chemical. For example, in the case of insecticides, obviously aquatic arthropods (crustaceans and insects) are most sensitive (Maltby et al. 2005), while algae and macrophytes tend to be more sensitive for herbicides (Van den Brink et al. 2006). In addition, within closely related taxonomic groups some groups of species may be more sensitive than others, for example, within arthropods insects appear to be more sensitive than micro-crustaceans to some novel types of insecticides (e.g., neonicotinoids) (Beketov and Liess 2008; Roessink et al. 2013).
Sensitivity to a compound of different taxonomic groups and species within one taxonomic group may vary widely (Von der Ohe and Liess 2004). For example, in their comparison of relative sensitivity of broad taxonomic groups to a wide range of chemical compounds, Raimondo et al. (2008) showed that crustaceans were generally the most sensitive taxa to all mode of actions of 68 chemicals tested compared to molluscs, fish, amphibians, and insects. Similarly, Crane et al. (2003) found that crustaceans were amongst the most sensitive species towards chlorpyrifos, followed by insects and fish while flatworms, snails and rotifers were the least sensitive in the distribution. The water flea Ceriodaphnia dubia was the most sensitive species among all species for which toxicity data were available for chlorpyrifos (Crane et al. 2003). Wong et al. (2009) compared the sensitivities of cladocerans and copepods to the metals Cd, Cu, Pb, Ni, and Zn, and indicated that cladocerans were consistently more sensitive than copepods to Cd and Cu.
For the organochlorine pesticide lindane, there was no significant difference in sensitivity of arthropods and fish, but both groups were significantly more sensitive than non-arthropod invertebrates (Maltby et al. 2005). Weltje et al. (2013) compared the relative sensitivity of amphibians and fish to 55 chemicals based on acute and
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chronic toxicity data. Their results indicate that fish species were significantly more sensitive than amphibians in acute and chronic tests. In the study of Framptom et al. (2006) with soil organisms, the standard test earthworm species, Eisenia fetida, was the least sensitive to insecticides based on acute mortality, whereas the standard Collembola test species, Folsomia candida, was among the most sensitive species for a broad range of toxic modes of action (biocide, fungicide, herbicide, and insecticide).
Analysis of species sensitivity to the fungicide triphenyltin acetate indicated that there was no significant difference in sensitivity between aquatic primary producers, invertebrates, and vertebrates (Roessink et al. 2006). The authors concluded that every aquatic community can be expected to include taxa sensitive to this fungicide. Also, in case of chronic exposure to ionic radiation no statistical differences were revealed between sensitivity of species from terrestrial, marine and freshwater ecosystems (Larsson et al. 2008).
In a study on species sensitivity within one taxonomic group Bossuyt et al. (2005) concluded that generic cladoceran SSDs for Zn and Cu were not significantly different from the SSD based on the cladoceran species representative to a specific location. The authors suggested that the community sensitivity of different cladoceran populations is similar among aquatic systems and is not dependent on the species composition. Hence, the generic SSD can be used as a model for a range of aquatic systems. Concerning the sensitivity of different fish species, several authors concurred that, while no one species is consistently the most sensitive, rainbow trout and other salmonids are generally more sensitive to a range of chemicals than standard test species used in toxicity tests, e.g., fathead minnow, sheepshead minnow, catfish, and bluegill (Buckler et al. 2005; Raimondo et al. 2008). However, Van den Brink et al. (2006) demonstrated no difference in sensitivity between standard aquatic plant species and other primary producers.
Overall, assessment of the differences between species is important for the SSD approach, because selection of species will influence the derivation of levels protective or not to the whole community (Forbes and Calow 2002). According to Maltby et al. 2005 and Van den Brink et al. 2006, only the most sensitive taxonomic groups should be used for risk assessment based on SSDs for substances with a specific mode of action, when clear gaps exist between the sensitivities of different taxonomic groups (e.g., only primary producers for herbicides; arthropods in case of insecticides). The HC5 or PAF values are then related to effects on the most sensitive group of organisms. The motivation for such an approach is regulation driven, as it is important for risk assessors to be precautious and conservative. This approach, however, may result in protecting “95%” of the most sensitive species of a selected taxon rather than the whole community. Obviously, including less sensitive groups will increase the HC5
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(Frampton et al. 2006; Jensen et al. 2007). The variation in sensitivity of different taxonomic groups and species within one taxonomic group is reflected in the SSD curve. Including the data for only one taxonomic group with little variation in sensitivity among species will result in a steep SSD curve. Increased variation from additional taxonomic groups will be indicated by a gentler SSD curve. Thus, it is important to specify the most sensitive species in the ecosystem to be protected. To do so, for example, the rapid toxicity testing (Kefford et al. 2005) as described in the previous section could be used. Deriving HC5 from an SSD based the most sensitive taxonomic group may be, however, over-conservative for the entire community.
Over the range of chemicals with various modes of action, crustaceans (cladocerans) appeared to be among the most sensitive taxa. The reported variability among species may be attributed partially to the size of species and the exposure time in toxicity tests (Verschoor et al. 2012; Tennekes and Sánchez-Bayo 2013). However, Maltby et al. (2005) concluded that, depending on the group of chemicals, different taxonomic groups are more or less sensitive. They suggested that differences in the sensitivity of different taxonomic groups are most likely to occur for toxicants (such as insecticides or herbicides) that have a specific toxic mode of action. Yet, comparison may be difficult as Hendriks et al. (2013) have argued that the mode of action varies among groups of organisms. Even well-studied toxicants such as organophosphate insecticides may not form a homogeneous group regarding their mode of action.
6.4.2. Test endpoints and extrapolation from acute to chronic data
The endpoints measured in the toxicity tests on which SSDs are based usually represent the most sensitive endpoints that are toxicologically and ecologically relevant (Forbes and Calow 2002). Acute toxicity data mostly address mortality and immobility as the most frequently studied endpoints for animals, while biomass and growth rate are mostly used for primary producers. Chronic toxicity data mostly address reproduction, feeding rate and growth as endpoints in animals, and again biomass and growth rate for primary producers. These are standard endpoints required by international guidelines for assessment of effects from chemicals (e.g., OECD 2015).
While lethality is undoubtedly a response with important ecological consequences, toxic substances may cause other ecologically important effects (Posthuma et al. 2011). These may include reduced feeding, impairment of reproduction and growth and behavioral changes. The advantage of using acute EC50 data (e.g., mortality) is the possibility to describe distinct exposure effects of population responses rather than describing the absence of effects (NOEC). The disadvantage of using acute EC50 data is that they do not capture chronic and delayed toxic effects (Jager 2012). However, the chronic NOECs in their turn have been criticized too by several authors, for being a
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fundamentally invalid interpretation of hypothesis testing, because they strongly depend on the experimental design (Crane and Newman 2000). These values were suggested to no longer be produced and used in scientific studies (Warne and Van Dam 2008; Landis and Chapman 2011).
The relevance of using different endpoints for construction of SSDs and their relation to population change has been pointed out earlier. However, no study has been performed examining the meaning of HC5 derived from SSDs based on different endpoints, such as for example, 50% reduction in feeding or 50% reduction in growth. Yet, several authors have investigated the differences between effect levels, i.e., acute EC50 versus chronic NOEC. Studies on effect levels have been performed merely to analyze the adequacy of using acute-to-chronic ratios (ACR), which are commonly used when insufficient chronic data are available to perform long-term exposure assessments (Ahlers et al. 2006). In general, the ACR varies depending on the taxonomic group and chemical concerned (Duboudin et al. 2004). The ACR for anuran species for six pesticides varied from 2 to 27 with an average of 7.2. Average ACRs for various chemicals have been reported to be 10.5 for fish, 7.0 for daphnids, and 5.4 for algae (Fedorenkova et al. 2012). Dom et al. (2012) illustrated that even for certain simple organic compounds with a designated mode of action (i.e., narcotic toxicity) unexpected differences in acute and chronic toxicity can be observed. For instance, acute to chronic ACRs for methanol and ethanol were shown to be species dependent, and varied from 10 to 1000. The authors stressed that in risk assessment procedures more attention should be given to acute to chronic extrapolations. Raimondo et al. (2007) found that invertebrate ACRs generally were more variable than fish ACRs and therefore some species may be at an increased risk of underestimated chronic toxicity when mean or median ACRs are used. Dom et al. (2012) illustrated that fixed ACRs are not able to account for the inter-chemical (a set of chlorinated anilines) and interspecies differences. This variability in ACR can be explained taking into account not only physico-chemistry but also toxicokinetics and toxicodynamics, which are not necessarily the same in acute and chronic exposure (Dom et al. 2012; Fox and Billoir 2013).
Overall, the use of NOEC and LC50 endpoints is driven by regulatory requirements allowing for standardization and comparison among species and chemicals. In principle, each endpoint could form the basis of an SSD including responses at cellular biomarker level and genome level (Smit et al. 2009; Fedorenkova et al. 2010). As the difference between endpoints can be of certain magnitude, SSDs based on different endpoints will have different positions relative to the x axis (Posthuma and De Zwart 2012). The results from such SSDs must be interpreted with caution considering their ecological relevance for population responses. However, few data are usually available
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and presently SSDs are limited largely to the prediction of sensitivity in terms of mortality (Baird and Van den Brink 2007). Extrapolation from LC50 to NOEC endpoints using an ACR of about 2 to 10 depending on the species can be performed. However, high awareness to diversity and irregularities in acute to chronic extrapolations is required.
6.4.3. Number of the underlying toxicity data
The criteria to select a minimum sample size to describe an SSD and to estimate an HCx is often an arbitrary policy decision (Pennington 2003). For example, the US EPA requires at least eight species, the EU between five and eight and Australia and New Zealand five species (TenBrook 2009). The minimum number of data points as estimated a minimum of 10 to 15 data points per toxicant needed to derive a reliable estimate of a particular endpoint (e.g., HC5). Smaller datasets give greater uncertainty in the model output, e.g., HC50 values, which can be significantly reduced if the sample size includes at least 4 data points (Van Zelm et al. 2009; Golsteijn et al. 2012; Hendriks et al. 2013). Overall, low data numbers imply wider confidence intervals (Pennington 2003; Posthuma and Suter 2011) also influencing the test for normality of the data. For scientific case studies, justification for a certain number of data points should be made in view of a specific problem definition (Pennington 2003; Wu et al. 2013). If a chemical has been tested on a few species, the mean and standard deviation of the SSD derived can be underpinned by a comparison to values noted for compounds with the same mode of action. If no empirical data are available, the typical values provided in Hendriks et al. (2013) may serve as a first indication of the SSD characteristics to be expected.
The discussions on the data quantity for the SSDs are triggered by a general lack of ecotoxicity data in risk assessment of chemicals. Therefore, solutions to the lack of toxicity test data are sought by various authors by combining already available data with ecotoxicological modeling (Hendriks 2013), e.g., Interspecies Correlation Models (ICMs) and Quantitative Structure Activity Relationships (QSARs) (Dyer et al. 2006, 2008; Awkerman et al. 2008; Aldenberg and Rorije 2013). ICMs allow the prediction of acute toxicity values for a wide variety of species based on the input of a single acute toxicity value, which can be used to develop SSD and HC5 (Dyer et al. 2006). Recent research has shown that ICM can be used to populate SSDs by providing toxicity estimates for a diversity of species. Awkerman et al. (2008) and Raimondo et al. (2007, 2010) validated ICM for both aquatic and wildlife species. They showed that HCx derived from SSDs using toxicity values derived from ICM were similar to hazard levels derived from SSDs of measured data for aquatic organisms and wildlife. For acute Zn toxicity, the ICM-based HC5 was about twice as high as the measured HC5 although not significantly different (Feng et al. 2013). Dyer et al. (2006) also have
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shown that in general, the ICM-based SSDs had HC5 values within one order of magnitude of the measured HC5 values based on three surrogate species (Pimephales promelas, Onchorynchus mykiss, Daphnia magna) and chemical of diverse modes of action (dodecyl linear alkylbenzenesulfonate (LAS), nonylphenol, fenvalerate, atrazine, and copper). Thus, the application of ICMs was recommended as a valid approach for generating SSDs and hazard concentrations for chemicals with limited toxicity data (Dyer et al. 2008; Barron et al. 2012).
QSARs can be used in the absence of experimental test data to predict the aquatic toxicity of untested chemicals based on their structural similarity to chemicals for which aquatic studies are available (Johnson et al. 2007). Wu et al. (2013) applied QSARs to develop a set of predictive relationships, based on physical and chemical characteristics of metals, and successfully predicted acute toxicities of each species for five phyla and eight families of organisms for 25 metals or metalloids. However, Dom et al. (2012) assessed the chronic toxicity predictions and extrapolations for a set of chlorinated anilines and illustrated that QSARs were not able to account for inter-chemical and interspecies differences. Although the potential application of ICM to increase the number of data for SSDs and the use of QSARs for effects assessments based on chemical structure alone have been demonstrated, this approach needs further evaluation for different species and chemicals.
6.4.4. Toxicity data used in case studies
In the case studies discussed in the previous sections, the number of data points used to construct SSDs and to derive HC5 varied greatly with a minimum of four data points per set (Appendix E Table E3). The acute mortality endpoints (LC50) were most frequently used for the SSD construction in case studies compared to other endpoints (NOEC, LOEC) due to overall lack of sub-lethal effect data. The majority of the studies did not follow any regulatory requirements for a minimum data set (fish, invertebrates, algae). Buckler et al. (2005) stated that although minimum data sets provide satisfactory prediction of toxicity values, it is obviously desirable to have high quality data for as many species as possible. In the majority of the case studies the toxicity data were extracted from large databases such as the US EPA ECOTOX database and RIVM e- toxBase (EPA 2015; RIVM 2015).
6.4.5. Statistical method to fit the SSD curves
Grist et al. 2002 argued that with increasing use of SSDs in ecological risk assessment, it is important that the scientific community agrees on appropriate methods for their derivation. Focusing on our research question 3, we investigated, which statistical distributions and methods to fit the SSD curve are recommended.
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Several authors analyzed, which distributions are the most appropriate for fitting toxicity data into SSD (e.g., Van Sprang et al. 2004; Wang et al. 2008). Yet, Aldenberg and Jaworska (2000) have stated that from the point of toxicity, there is no theoretical justification for any distribution. Moreover, one cannot statistically decide between different distributions at small sample size. However, the choice of an appropriate method for SSD analysis is important because the various methods may generate different HC5 values. Often log-normal or log-logistic distributions are used because effect concentrations can differ between species by several orders of magnitude. The logistic distribution is very much like the normal distribution. However, the logistic has more extended tails and can therefore be regarded a more conservative assumption in comparison to the normal distribution (Aldenberg and Slob, 1993).
The choice of the distribution function to fit the data may be based on goodness-of-fit tests. However, the choice of distribution types is constrained to a few standard distributions for which goodness-of-fit tests are available (Forbes and Calow 2002). Yet, if one needs to select the best fit distribution type, e.g., normal or logistic, which are used for regulatory purposes, several tests are usually applied. The most common procedures to check the normality assumption for the data are Shapiro-Wilk test, Kolmogorov-Smirnov test, Anderson-Darling test, and Lilliefors test. Shapiro-Wilk was shown to be the most powerful normality test, followed by Anderson-Darling, Lilliefors, and Kolmogorov-Smirnov test (Razali and Wah 2011). However, the power for all tests to detect deviation from normality is low for small data sets (Farrell and Rogers-Stewarta 2006). Shapiro-Wilk test can be used for the sample size between 3 and 5000, but for sample sizes of 30 or less the power at 5% significance level is less than 40% (Razali and Wah 2011).
Despite violations of statistical guidelines, such as ignoring criteria that describe an acceptable goodness-of-fit of a model for data, unimodal models appear to provide reasonable estimates of the HC5 (Pennington 2003). Above approximately the 5th percentile (HC5), the common log-normal, log-logistic and log-triangular parametric distributions are similar. Differences between such parametric representations are generally reflected in the tails (Van Straalen 2002). Estimates of the HC5 therefore are not considered to depend strongly on the selection of a typical distribution model (Pennington 2003). However, the uncertainty around HC5 will be strongly dependent on the number of test results taken into account.
Considering the limitations of goodness-of-fit tests, some authors argue that there is no reason to assume an underlying distribution for species sensitivities (Forbes and Forbes 1993) because an alternative resampling (non-parametric bootstrap) method can be used, which does not rely on any assumed distribution (Wheeler et al. 2002). It is has been shown that the non-parametric bootstrap approach can fit the data better than a
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parametric approach (Wang et al. 2008). However, this is a limited approach requiring at least 20 data points to obtain HC5 and associated confidence intervals (Wheeler et al. 2002). As a compromise between the power of resampling and fitting an underlying distribution, Grist et al. (2002) described the bootstrap regression method based on a log-logistic regression model. This hybrid-technique allows for the use of smaller toxicity datasets and the calculation of confidence intervals around the point estimate. Comparing the SSDs derived by four methods, i.e., based on log-normal, log-logistic, bootstrap and bootstrap regression models for 15 chemicals with different modes of action, Wheeler et al. (2002) showed that differences in the HC5 values were within a factor of 2. Wang et al. (2008) also showed that the HC5 estimated from SSDs based on the same parametric and non-parametric models coincided well with each other with the standard deviations mostly within a factor of 2. Thus, the estimates of the HC5 are not highly influenced by the selection of one of these models.
Other authors have investigated different models for SSD construction. Van Straalen (2002) explored the possibility of introducing a true no-effect principle in the SSD framework by considering models with a finite lower threshold using the data set for 21 species for Zn. Four distributions analyzed in this study (the uniform, triangular, exponential and Weibull) tended to underestimate the data in the low concentration range. The estimates of HC0 obtained using these threshold models varied within a range that included the HC5 estimates from the infinite tail logistic and normal models. The advantages of these threshold models is the derivation of HC0, which may be better communicable as a true no-effect concentration of a community compared to the approach based on 95% protection. Van Sprang et al. (2004) showed that non-threshold distribution models (logistic, inverse Gaussian, extreme value, Weibull, gamma, Pearson VI, and normal distributions) tended to overestimate toxicity for Zn in the lower tail, while threshold models such as Pareto, beta, and triangular produced higher, less conservative thresholds. Chen (2004) proposed a distribution free method for calculating HC5 based on asymmetric loss function. This method yields conservative HC5 values but requires relatively large data set (at least 19 data points).
Hickey et al. (2008) described and analyzed several models from a Bayesian perspective to construct SSDs. This Bayesian approach can include all information to determine HCx values and allows expert opinions to be introduced for taxonomic groups with little or no data. Overall, by applying a Bayesian approach that incorporates expert knowledge the uncertainty in SSD estimation can be reduced (2006). The Bayesian statistical approach treats data as fixed and allows one to use the data to update prior distributions on the unknown parameters to obtain posterior distributions. Hickey et al. (2008) compared these new models with a Kaplan-Meier and a log-normal distribution using a large data set on the salinity sensitivity of freshwater macroinvertebrates from
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Australia. The maximum likelihood Kaplan-Meier survival function estimator allows censored data (endpoints from interval concentration and exceeding the reported concentrations) to be included in the model. The log-normal analysis produced an SSD that slightly overestimated (around factor 1.5.) the hazard to species relative to the Kaplan-Meier survival function and Bayesian analyses. Similarly, Dowse et al. (2013) analyzed the influence of quality of the toxicity data and the statistical models Kaplan- Meier survival function, Bayesian statistical models based on the log-normal assumption and Burr type III on the derivation of HC5. The Burr III distribution is a flexible three-parameter distribution that can provide good approximations to many commonly used distributions such as the log-normal, log-logistic, and Weibull (Shao 2000). In the analysis of Dowse et al. (2013) on the salinity sensitivity of freshwater macroinvertebrates from Australia they included modeled data from concentration- response curves generated from toxicity testing (uncensored data) and censored data. The most conservative protective concentrations were estimated with Burr type III and uncensored data (28 data points). There was an increase in HC5 values when censored data was included in the SSDs. The overall conclusion of their study was, however, that the taxonomic groups included had the greatest effect on estimated protective concentrations and not the model type.
Van der Hoeven (2004) argued that much energy is put into choosing the best distribution for the data and refining the estimates of the confidence intervals. However, the most serious problem with HCx estimation methods is the assumption that the species for which data are available are a random sample from all species in the ecosystem. A priori, this assumption may be false. Often some taxonomic groups are over-represented, for instance fish, whereas insects are almost always under- represented (Forbes and Calow 2002; Hendriks et al. 2013). Related to this issue, Douboudin et al. (2004) analysed the effects of taking into account intra-species variation and proportions of taxonomic groups (vertebrates, invertebrates and algae) and statistical method of calculation of the HC5. They concluded that the choice of data (intra-species variation and proportions between taxonomic groups) had more effect on the value of the HC5 than the statistical method used to construct the distribution. Similarly, Hickey et al. (2008) demonstrated that the introduction of a weighting factor to account for the richness (or importance) of taxonomic groups using the Bayesian model influenced the calculated hazard estimates. The Bayesian methods presented by Duboudin et al. (2004), Grist et al. (2006) and Hickey et al. (2008) can be used to control for the contribution of data from different taxonomic groups.
Although the advantages and disadvantages of applying different statistical models to construct SSDs and derive hazardous thresholds have been described in literature, no specific model has been identified as a “default” or “the best fit”. Overall, results of
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several studies showed that estimates of the HC5 are not strongly dependent on the selection of a distribution model, but rather depend on the species selection. To balance the influence of the taxonomic groups, several options can be followed, e.g., setting a minimum number of taxa to be included, plotting average of all species within taxon or using a weighting factor to avoid overrepresentation of well-tested taxa.
6.4.6. Model choice in case studies
Although various statistical methods to construct SSD have been applied in recent literature, in case studies described in the previous sections the method relying on log- normal distribution was most frequently used (Appendix E Table E3). Several studies used a bootstrap approach to estimate a hazardous concentration without assuming any distribution. Some users of the SSD approach assumed a distribution without apparent testing, whether it provided a good fit to the data and simply followed the method provided by Aldenberg and Jaworska (2000). Others choose for log-logistic models for SSD development, because it often provides the best overall fit to toxicity datasets and more conservative HC5 (Raimondo 2008; Rand et al. 2010). When the data were checked for the best fit to a distribution type, the Anderson-Darling test was most commonly used (Appendix E Table E3) because it places more emphasis on tail values (Caldwell et al. 2008).
6.5. Validation of the SSD predictions
The ability of the SSDs to predict effects in the field is of prime concern. Conceptual discussions on the SSD approach and its validity mainly focus on the following questions: (1) are SSD derived standards sufficiently protective, and (2) can any extrapolation of laboratory test data to estimate ecological impacts in the field be valid (Posthuma et al. 2012). However, the validation of estimated impacts based on laboratory test data to the “real” impacts in the field is rarely performed. Here we focus on the studies related to the research question 4 to investigate, whether thresholds derived from SSDs (HC5) are protective of the “real” ecosystems.
Posthuma and De Zwart (2006) investigated the relationship between the predicted risks based on the SSD approach and observed impacts on fish communities in Ohio rivers. Their validation study was based on a large data set and confirmed that chemical impacts estimated by the SSDs were related to degradation of fish diversity. Kefford et al. (2006) tested macroinvertebrates collected from the Murray-Darling river basin in Australia for salinity tolerance in the lab to create SSDs and compared these with the loss of riverine macroinvertebrate species due to increasing salinity. The SSD approach predicted the decline of species with increasing salinity accurately, confirming that SSDs can be used to indicate the fraction of species affected in the field. As a lack of
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monitoring data usually restricts such field validation studies, the majority of studies aiming at investigating the protectiveness of individual species based on SSDs, e.g., a level of 95% of species to protect ecosystem structure and functioning, rely on micro- and mesocosm experiments. Micro- and mesocosms are semi-natural model ecosystems used for risk assessment and are known as higher-tier risk assessment testing systems. In these tests, artificial ponds, streams or enclosed parts of natural waters are sprayed with a compound under investigation at different concentrations to identify concentration-effect relationship at the population and community level.
In recent years, a number of case studies has been conducted that illustrate that HC5s derived from SSDs appear to be protective for (semi-natural) ecosystems (Maltby et al. 2009). Summaries of the studies are provided in Table 6.2.
Based on comparisons between endpoints derived from semi-field studies and lab-based SSDs, the output from SSD as HC5 is a factor of 1.4 to 75 lower than the NOEC based on the most sensitive endpoints measured in the field. In a few cases, HC5 calculated from toxicity data for semi-fields was a factor of 1.1 to 4 lower than the HC5 calculated from laboratory data.
The majority of the mesocosm studies with invertebrates exposed to several chemicals, mainly pesticides, showed that HC5 levels derived from SSDs can be protective for “real world” ecosystems based on such studies. Versteeg et al. (1999) discussed the likeliness of lower sensitivities of organisms in mesocosm studies than in laboratory studies. They argued that the lack of random species selection and the development of toxicity tests with sensitive taxa for use in laboratory tests may be one explanation. Furthermore, differences in water quality and availability of habitat or shelter in laboratory and semi-field studies is likely to favour greater sensitivity under laboratory conditions (Versteeg et al. 1999). Moreover, the SSD approach ignores ecological relationships between species, assuming that such interactions do not influence the sensitivity of ecosystems. In experimental ecosystems and enclosures, toxic effects at the population- and community level were found to be determined by the inherent sensitivity of the species present and the ecological relationships between the species (Fleeger et al. 2003, De Laender et al. 2008). Hence, knowledge on ecological interactions should be incorporated in ecological effect assessments to estimate ecosystem effects of chemicals more accurately. Additional confirmation of protectiveness of HC5s from a generic lab-based SSD for local communities based on aquatic vertebrates, soil invertebrates and other types of chemicals would strengthen the use of SSD approach in risk assessment (Jansch et al. 2006).
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Table 6.2. Overview of the studies where results from semi-natural systems were compared with the outcome from SSDs based on laboratory toxicity data. Description Stressors Species studies HC5 or lowest Input data for lab- HC5 from lab Reference of semi- tested (endpoints NOEC SSD based SSD natural measured) system Enclosure in Tributyltin Phytoplankton NOEC (M. edulis NOECs PNEC Selck et al. a bay (TBT) (photosynthesis, post larval shell Marine TBT: 0.03 ng/L (2002) Linear biomass, PICT), growth) phytoplankton, LAS: 2.1 µg /L alkylbenzene Bacteria, molluscs TBT: 0.3 ng/L bacteria, sulfonates larvae and adult ( LAS: 0.052 mg /L arthropods, (LAS) mortality, molluscs, fish abundance, shell growth) Field soil Zinc Nematode NOEC Soil microbial HC5 Smit et al. plots abundance and 56 mg total Zn/kg community 61 mg (2002) species dry soil (according to Dutch total Zn/kg composition soil protection system) Artificial Endosulfan Macroinvertebrate HC5 LC50 HC5 Hose and stream on the abundances (per 8 1.57 µg/L Arthropods, non- 0.19 µg/L Van den banks of a arthropod taxa) arthopods, (arthropods Brink river amphibians, fish only) (2004) HC5 0.021 µg/L (all data with different safety factors)
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Micro- and Atrazine, Densities/biomass NOEC (one EC50 and NOECs ChronicHC5 Van den mesocosm of simazine, of algae and aquatic example Atrazine) Freshwater primary for one Brink et al. different metribuzin, vascular plants 5 µg/L producers example (2006) designs metamitron, and/or dissolved atrazine linuron, oxygen and pH 3 µg/L diuron, diquat, values 2,4-D, pendimethalin. Enclosures in Gamma- Macroinvertebrates, NOEC LC50 HC5 Van experimental cyhalothrin zooplankton, (macroinvertebrates Insects and 2.12 ng/l Wijngaarden ditches phytoplankton , community crustaceans et al. 2009 macrophytes dynamics) 5 ng/l Divers Imidacloprid Various NOEC (no effect LC50 HC5 Nagai and mesocosms (I) invertebrates entire community) Arthropods I: 0.43 μg/L Yokoyama designs Fipronil (Fi) I: 0.6-1.6 μg/L Fi: 0.084 μg/L (2012) Fenitrothion Fi: 0.15 μg/L F: 0.44 μg/L (F) F: 1.1 μg/L Enclosures in Metamitron Phytoplankton, Mm: Chronic NOEC and LC50 Mm: Brock et al. experimental (Mm) Zooplankton NOEC Algae, macrophytes 1.39 μg/L (2004) ditches Metribuzin (species (phytoplankton) (chronic HC5) (Mb) composition) 5.6 μg/L 7.38 μg/L Periphyton (acute HC5) (Chlorophyll-a) μg/L Macrophytes Mb:NOEC (different (community endpoints) metabolism) Mb: Macro- 280 μg/L 667 μg/L (acute invertebrates HC5)
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Indoor glass Fluazinam Phytoplankton, NOEC (no effect LC10 and LC50 HC5 Van cylinder Zooplankton entire community) invertebrates 0.6 μg/L Wijngaarden microcosms macroinvertebrates 2 μg/L (LC10) et al. (2010) macrophyte 3.9 μg/L (LC50) Enclosures in Triphenyltin Macroinvertebrates, HC5 EC50 HC5 Roessing et experimental acetate zooplankton, 0.3 to 0.6 μg /L Macroinvertebrates, 1.3 μg /L al. (2006) ditches phytoplankton, zooplankton, algae, macrophytes macrophytes
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6.6. General conclusions
Out of 10 studies comparing species sensitivities to diverse chemical compounds in different geographic locations (arctic- temperate- tropic), only two cases indicated significant differences between species. Nevertheless, caution should be taken when assessing ecological risk in regions for which no toxicity data for local species are available considering differences in taxonomic composition and possible consequences for HC5 values (Table 6.3).
Literature comparison indicates that marine species may be a factor 1.5 to 9 more sensitive than freshwater species, up to a factor 9 less sensitive than freshwater species, or not significantly different from freshwater species. Thus, protection levels, e.g., HC5 derived from freshwater species only, may be still uncertain for marine species. Further research would be needed to investigate the protectiveness of the threshold levels derived for species from one type of habitats for species from different types of habitats.
The acute to chronic extrapolation ratio (ACR) was shown to be taxon dependent. HC50 for SSDs on acute LC50s were 2 to 1000 higher than those for chronic NOECs. Average ACRs for various chemicals have been reported to be 10.5 for fish, 7.2 for anuran species, 7.0 for daphnids, and 5.4 for algae. More variation was reported in invertebrate ACRs than fish ACRs. Therefore, some species may be at an increased risk of underestimated chronic toxicity when mean or median ACRs are used. High awareness in risk assessment procedure should be given to ACRs due to their diversity and irregularities.
Chasing the most sensitive species or taxon to base the risk assessment on, by deriving HC5 from the SSD approach should be carried out with caution (e.g., derive HC5 based only on primary producers for herbicides; arthropods in case of insecticides). This approach may result in protecting “95%” of the most sensitive species of a selected taxon and be over-conservative for the entire community (Table 6.3).
Smaller sets of toxicity data give greater uncertainty in the SSD output, which can be significantly reduced if the sample size includes at least 4 data points.
By using ICM to populate SSDs, several authors have shown that ICM-based HC5s were within an order of magnitude of the measured HC5 values for chemicals of diverse modes of action.
From a vast number of studies investigating the influence of different methods to fit the toxicity data into SSD and derive HC5 values, a general conclusion can be dawn that taxonomic groups (intra-species variation and proportions between taxonomic groups)
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had the greatest effect on estimated protective concentrations and not the statistical method used to construct the distribution.
Based on comparisons between endpoints derived from semi-field studies and lab-based SSDs, generally the output from SSD as HC5 is a factor of 1.4 to 75 lower than the NOEC based on the most sensitive endpoints measured in the field. However, in a few cases, HC5 calculated from toxicity data from semi-field data was a factor of 1.1 to 4 lower than the HC5 calculated from laboratory data.
The horizon of application of the SSD approach has been widened in recent scientific research and novel applications concerned non-chemical stressors and deriving the effect levels from field monitoring data.
Table 6.3. Different aspects important to consider for the SSD approach based on the overview of the studies explored in the present study. The influence on the SSD curve and HCx is indicated by +++ very important, ++ important, + less important. SSD Aspect to HC5/ curv Remark Example consider HC50 e Including less sensitive group will increase the Curve can be multi- Species HC5. Variation in modal if sensitive group +++ +++ sensitivity within a taxon taxonomic groups are selection is lower (thus, SSD curve included is steeper) than among taxa. Macroinvertebrates from static and lotic habitats did Mainly similar not differ in their sensitivity, but marine sensitivity to pesticides Habitat species were found + + whereas sensitivity of type more often with higher species from freshwater sensitivity than and marine habitats may freshwater species vary depending on a stressor. No significant difference in sensitivity of Australian and non-Australian arthropod and fish to Geographic Mainly similar + + endosulfan. Amazonian location sensitivity invertebrates significantly less sensitive than their temperate counterparts to carbendazim.
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Sensitivity of plants to a Mode of Importance of MoA specific mode of action of action of +++ +++ varies per taxonomic a herbicide is higher than chemicals group of other taxonomic groups. Overall lack of Uncertainty in HCx Number of ++ +++ toxicity data for SSD decreases when number of data points curves data point > 4. For acute toxicity data on Cu and Zn for cladoceran species, Weibull, uniform Depends on number of and beta distributions gave Distributio data points. Goodness- underestimation and log- + ++ n type of-fit tests should be logistic and triangular performed distribution gave overestimation in the lower tail compared to a log-normal distribution. Uncertainty in acute-to- Any endpoint can be chronic ratios depends on Endpoint used. NOECs are species and mode of action (NOEC vs. + +++ required by regulations of chemical. SSD position LC50) to derive HC5 and relative to x-axis will vary EQC depending on input data.
6.7. References
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Chapter 7 Synthesis and conclusions
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7.1. Introduction
The objective of the present thesis was to develop and apply species sensitivity distributions (SSD) for ecological risk assessment of toxic and non-toxic stressors. Several critiques on the application of the SSD approach for Ecological Risk Assessment (ERA) have been identified, which are also presented in chapter 6 of this thesis. It can be concluded that the potential of the SSD approach has not been fully explored. In the SSD approach, the effects of stressors on the individual species in laboratory toxicity tests are extrapolated statistically to the responses on populations and ecosystems. However, the link between the effects on different levels of biological organization is often unclear. Moreover, species assemblages used in the SSD approaches do not reflect the diversity of the species and their interactions in the environment. Still, the Potentially Affected Fractions (PAFs) and hazardous concentrations (HCx) are used to predict the impact on a community in terms of a fraction of test species affected at a given exposure. Nevertheless, the PAFs and HCx can be applied to rank risks caused by various stressors on a specific (group of) species or at different locations for better field-impact-based risk management. Despite this practical application, risk ranking based on the SSD approach has received little attention in scientific literature. The SSD approach is usually applied for studying the impact of chemical stressors; physical stressors are rarely considered. Therefore, the present thesis focuses on several research questions:
1. In what way do the responses at the sub-cellular level predict the effects on higher levels of biological organization? (Chapter 2) 2. Can the SSD approach be applied to reveal potential differences in sensitivity between native and non-native species to certain environmental stressors? (Chapter 3) 3. Can the SSD approach be used to rank stressors measured in actual and potential habitats of a specific taxonomic group? (Chapter 4) 4. What are the potential and limitations of the application of the SSD approach to non-toxic stressors? (Chapter 5) 5. How are SSDs being applied in scientific research for risk assessment of (non- )chemical stressors on aquatic and terrestrial ecosystems? (Chapter 6)
This chapter provides a discussion on the results from the previous chapters of the present thesis, followed by general conclusions and recommendations. In section 7.2, the relationship between responses on different levels of biological organization is investigated. In section 7.3 and 7.4, we explore the use of the SSD approach based on presence of species in their habitats exposed to various stressors. These stressors are ranked according to their potential to cause harm to local species assemblages. In section 7.5 the question, whether the impacts of chemical and physical stressors can be
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compared in a similar way is discussed. Such a comparative approach reveals which type of stressor (i.e., chemical vs. physical) has more severe impacts on ecosystems. The application of the SSD approach in case studies for ecological risk assessment is explored in section 7.6. Finally, sections 7.7 draws general conclusions and recommendations for further research.
7.2. Connecting different levels of biological organization
Traditionally, the SSD approach is based on the data on the effects of chemicals on the survival, growth, and reproduction of a few selected test species, e.g., NOEC or LC50 endpoints. In principle, each endpoint can form the basis of an SSD including responses at cellular biomarker level and genome level (Smit et al. 2009; Chapter 2). The responses at sub-cellular levels may be detected at low concentrations of chemicals and before morphological or reproductive effects become visible. Therefore, specific responses on sub-cellular levels could serve as early-warning biomarkers representing changes in organisms due to exposure to a chemical stressor. An SSD based on sub- cellular endpoints is expected to be shifted left as compared to the one based on the endpoints such as reproduction and growth, and likewise, a HC5 for this endpoint type would be lower than the ones based on NOECs (Posthuma and De Zwart 2012).
To answer research question 1, the ecotoxicological relevance of responses at the sub- cellular level, i.e., changes in gene expression, and their potential to predict the effects on higher levels of biological organization were investigated in chapter 2 using an example of aquatic species exposed to cadmium. It was found that cadmium concentrations causing significant changes at genome level were on average a factor of two higher than NOEC at the whole organism level. The SSD curve based on gene-level endpoints was shifted to the right of the curve based on NOECs and subsequently the related HC5 was higher. Therefore, based on the analysis of the available data, the use of gene expression changes as early warning indicators for effects on higher levels of biological organisation has not been proven. However, other sub-cellular biomarkers, such as DNA damage and oxidative stress, were shown in previous research to be more sensitive in indicating potential effects on the whole organism level, responding to chemical exposure at concentrations of a factor 35-50 lower than the organism NOECs (Smit et al. 2009). In the study of Guendel et al. (2012) on phenanthrene effects on zebrafish, it was shown that sub-cellular endpoints like protein abundance alteration were comparable to LC10 values on organism level and thus being more sensitive responses compared to traditional lethal endpoints. Moreover, in Chapter 2, a major gap was identified between gene and cell responses on the one hand and the individual and population responses on the other (Figure 7.1). The complications of extrapolating from sub-organism to ecologically relevant levels were found to be more profound than in standard extrapolations from organism to ecosystem.
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Overview of the studies investigating the responses on gene level to chemical stress (ecotoxicogenomics) has shown that gene expression had been used to distinguish the type of contamination or the mechanism of action of the chemicals rather than used to predict effects on exposed populations. As a possible solution to understand the link between the effects on sub-cellular levels and higher levels of biological organisation, a new approach in ecological risk assessment established as Adverse Outcome Pathway (AOP) can be used (Ankley et al. 2010, Forbes and Calow 2012). Once the connections from the molecular initiating event to an adverse effect on higher levels of biological organization are established, observations on (sub-)cellular responses caused by chemical stressors could be used to predict the likelihood of impact on higher levels of biological organization (Ankley et al. 2010). However, currently the AOP approach focuses merely on assessment of intrinsic hazard of chemicals, by studying their potential to lead to adverse effects, mainly on organism level, via initial response indicated by expression of specific genes. Therefore, the challenge for the future research would be to develop methods to link organism effects to responses on higher levels of biological organization, as the same impact on individual-level endpoints in different species may result in different population-level outcomes.
Figure 7.1. Ecotoxicogenomics investigate effects of chemicals at the genome level. Ecotoxicology covers the response at the individual and population level. The major gap identified in Chapter 2 is between gene and cell responses on the one hand and the individual and population responses on the other (white arrow).
7.3. Application of the SSD approach in case studies for ecological risk assessment
In the case study presented in Chapter 3, the application of the SSD approach was explored to identify possible differences between tolerance of native and non-native fish
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species in the river Rhine to diverse chemical compounds including ammonium, pesticides and metals. For 21 chemicals, toxicity data (LC50) were available for at least four different fish species, which were used to derive SSDs. The HC50 values derived from SSDs for each chemical compound did not differ significantly between the native and non-native species. In a similar case study applying the SSD approach for comparing sensitivity of fish species to low oxygen concentrations, no difference in sensitivity between native and non-native fish species occurring in the river Rhine were observed, indicating that this stressor does not facilitate further spread of non-native fish species (Elshout et al. 2013). Additionally, Jin et al. (2011) found no significant difference between HC5s for native Chinese and non-native invertebrates and fish to 2,4-dichlorophenol. A related application of SSDs to restrict the spreading of non-native species was derived by Smit et al (2008b). By constructing a time-dependent SSD, they derived the level of hydrogen peroxide for the elimination of non-native species to treat ballast water and prevent these species from spreading in new environments. In other case studies on non-native species it was shown that the mean maximum salinity tolerance was not significantly different between native and non-native molluscs found in the Netherlands (Verbrugge et al. 2012). However, the maximum temperature tolerance of non-native molluscs species in the Netherlands was significantly higher than for native species (Verbrugge et al. 2012). Similarly, Leuven et al. (2011) showed that non-native fish species in the Netherlands can tolerate higher water temperatures than native fish. These examples of case studies, where the SSD approach was applied in a similar way to compare sensitivities of native and non-native species to various stressors, suggest that water temperature may have more influence than chemical pollution on the establishment of non-native fish and mollusc species in comparison with native species. Therefore, for the management of freshwater ecosystems potentially affected by introduced species, attention should be given to environmental stressors such as temperature, hydrological regimes and habitat quality, and to combined effects of chemical and physical stressors (importance of these stressors was discuss in chapter 5). Overall, an increasing number of case studies applying the SSD approach in recent years indicates a success of this approach in dealing with diverse research questions in ecological risk assessment (130 publications since 2002, Chapter 6).
7.4. Application of the SSD approach for risk ranking
The SSD approach generating a set of HC5 values for different chemical compounds can facilitate ranking of these compounds in their relative hazard for assemblages of tested species (Posthuma and De Zwart 2012). In chapter 4, ecological risks (ERs) of multiple stressors including metal, pesticides and pH were derived for anuran species in the Dutch water bodies to investigate the relative importance of individual stressors that possibly influence populations of anurans. The ER was quantified as the probability that
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a measured environmental concentration of a particular stressor in habitats where anurans were observed would exceed the toxic effect concentrations derived from the SSDs. The stressors causing the major potential impact on anurans were identified and ranked. The highest values of ER were noted for pH, copper, diazinon, ammonium and endosulfan. It was shown that, deriving ER by combining field observations and laboratory data gives insight into potential threats to species in their actual environment. Such an approach can be used for rankings of potential ecological impact magnitudes for different stressors, prioritizing the stressors, which is necessary to achieve effective management in amphibian conservation.
Another approach for ranking stressors for the assemblage of fish species characteristic for the river Rhine was explored in chapter 3. Ranking of several chemical stressors was based on the average Potentially Affected Fraction (PAF) over the period of almost two decades (1992-2010). Based on annual maximum water concentrations in the river Rhine, the highest contribution to the overall PAF based on LC50 for fish species was noted for ammonium, followed by azinphos-methyl, copper, and zinc.
The derived measure of potential impact of chemical stressors on anuran and fish species was based on measured concentrations in Dutch surface waters, combined with the toxic effect concentrations (LC50). It allows for comparing how the risk ranking varies between taxonomic groups (Table 7.1). The comparison of ranking was possible for six chemicals only, indicating the highest risk to anurans due to copper and to fish due to ammonium. Uncertainty in deriving measures of risk and large differences in values between risk due to ammonium may be caused by different sources of monitoring data as well as by the toxicity data. The monitoring data as measured in the river Rhine reflect NH3 and NH4 together, whereas the toxicity tests on fish include NH4 only. Therefore, it should be noted that by combining these sets of data, the risk to fish of ammonium exposure could be over-estimated.
Recalculating the ER for anurans into PAFs based on average concentrations over the studied period provides similar results. Thus, the use of a single environmental concentration or a whole range of measured concentrations in the actual habitats of anurans influences the measure of risk only marginally. The advantage of the PAF approach is that it does not require extensive data on environmental concentrations of stressors and the calculations are fast and easy. It was shown that the risk levels decreased in different order for both taxonomic groups. Comparing the toxicity data and the method for deriving the risk, may explain the differences in ranking of stressors between anuran and fish species. The ERs for anurans were derived based on the average annual water concentrations (1997-2008), whereas for fish the PAFs were based on the average maximal annual concentration (1992-2010). When considering the toxicity data for azinphos-methyl, for example, the LC50 for the most sensitive anuran
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species (Bufo woodhousii) was a factor of 210 higher than the LC50 for the most sensitive fish species (Esox lucius). In previous research, it was shown that fish and amphibian toxicity data are highly correlated and that fish are more sensitive to 39 of 55 chemicals than amphibians (median sensitivity ratio of 0.52) (Weltje et al. 2013). Thus, the derived higher values of risk for fish compared to anurans is consistent with the previous findings indicating that fish may be overall more sensitive than anurans.
Table 7.1. Comparison of chemicals ranking for anuran and fish species in the Netherlands using the Ecological risk (ER) and Potentially Affected Fraction (PAF in %) Stressor Anurans ER Anurans PAF Fish PAF Ammonium 0.27 0.23 35.43 Azinphos-methyl < 1.0E-06 < 1.0E-06 2.62 Copper 0.83 0.89 2.58 Cadmium 0.06 0.06 0.36 Malathion 0.03 0.05 1.4E-03 Atrazine < 1.0E-06 < 1.0E-06 < 1.0E-06
In a similar approach, Verschoor et al. (2011) derived 372 site-specific SSDs for copper, nickel and zinc in the Netherlands for several taxonomic groups including fish, invertebrates, algae, insects, molluscs, and amphibians. By expressing HC5 as total dissolved metal concentration, a straightforward comparison between HC5 and field measurements was enabled. Comparing HC5 at different sites, the authors showed that only representatives of one or two taxa were affected although some taxa were more sensitive at some sites than at others to any of the three metals. Hayashi et al. (2011) compared and ranked the ecological risks of nine major toxic substances (ammonia, bisphenol-A, chloroform, copper, hexavalent chromium, lead, manganese, nickel and zinc) in Tokyo surface waters by deriving expected PAFs based on NOECs for various taxonomic groups. The estimated PAF values suggested that the risk from nickel was highest followed by zinc and ammonia. Such ranking outputs from the SSD approach in the form of ER, PAF or HC5 can yield important information for decision making, indicating the major threats to a specific group of species or the most vulnerable species (taxon) at a certain location. This approach may help prioritizing resources for better management of polluted sites as well as of rehabilitation projects.
7.5. Application of the SSD approach for non-chemical stressors
Applying the SSD approach to quantify the impact of non-chemical stress in a similar way to ecotoxicological assessments may allow for comparing different types of impacts on ecosystems and prioritizing the results for better ecological management. In Chapter 5, the effects of water-flow velocity on fish species were assessed quantitatively
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by deriving SSDs based on observational data reporting the presence of species at a specific water-flow velocity in their habitat. It was found that the maximal velocity tolerance and the length of fish at the adult stage were positively correlated. Also, rheophilic species, being significantly larger than the eurytopic and limnophilic species, were found to tolerate higher velocities than eurytopic and limnophilic species. By combining the SSDs with the average measured water velocity in the littoral zone of the river Rhine, the PAF of fish species were estimated to be 39%. Comparing the PAF for chemical stressors derived in chapter 3 helps to understand, which type of stressors (i.e., chemical vs. non-chemical) has a more severe impact on fish species and, hence, on their distribution in rivers. The PAF estimated in chapter 3 from combined effects of nine chemicals, based on annual average maximal concentrations in the Rhine, were between 30 and 40%. This means that, on average, the effects of water-flow velocities would be similar to or higher than the effects of chemical stressors. However, the time parameter gives uncertainty in estimations of PAFs as the duration of exposure to these diverse stressors is highly variable both in the laboratory tests and actual field exposure. It should be considered, that short term exposure to high velocity waves might be insufficient to induce significant effects on fish, indicating that the use of single maximum values may result in overestimated PAF/PNOFs. Furthermore, the combined effects from various stressors could also be overestimated due to the fact that spatial and temporal heterogeneity in abiotic conditions in the river were not taken into account.
Earlier case studies on deriving SSDs to quantify the impact of non-chemical and non- toxic stressor focused on changing temperature, desiccation, hypoxia, pH, and changes in sediment particle size. Smit et al. (2008) developed SSDs for suspended clays, burial by sediment, and change in sediment grain size for marine species and estimated PAF to communicate potential risk related to drilling of oil and gas wells in the North Sea. Leuven et al. (2011) applied the SSD approach for a location-specific assessment of fish diversity in relation to river temperature conditions, showing that native fish species were more sensitive to changes in water temperature than non-native species. Elshout et al. (2013) explored differences in hypoxia tolerance among different life stages of fish species using SSD approach indicating that juvenile fish had higher tolerance to low oxygen levels than adults. Collas et al. (2014) derived SSDs and Potentially Not Occurring Fraction (PNOF) to predict effects of desiccation on mollusc assemblages in rivers during low discharge events. The authors showed that the PNOFs for desiccation explained up to 65% of the not occurring fraction of the mollusc species in the river Rhine. The SSD-based approach for non-chemical stressors has been applied not only for ERA but it has been adopted in Life Cycle Analysis (LCA) also, as assessment method for environmental pressures of, e.g., thermal pollution, groundwater extraction, water consumption (Verones et al. 2010; Hanafiah et al. 2011; Van Zelm et al. 2011).
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In case studies applying SSDs to quantify the impacts of non-chemical stressors the effect levels varied from EC50 to EC100 with mortality as endpoint, or time needed to reach 50% or 100% effect, making it difficult to compare them directly with the effects caused by chemicals. However, the application of the SSD approach to non-chemical stressors as such may complement risk assessments. Including both types of stressors affecting natural habitats of species in one overall assessment may facilitate the comparison of impact and give possibilities to rank and prioritize among various stressors. The success of the application of the SSD approach depends, however, on the quantity and quality of the available input data. Moreover, standardization in the derivation of effect levels for non-chemical stressors similar to chemical stressors is essential for the comparison of effects caused by these types of variables.
7.6. Development in application of the SSD approach
In Chapter 6, we explored 130 papers published in scientific journals since 2002 that applied the SSD approach for deriving environmental quality criteria (EQC) and ecological risks for various chemicals or in case studies with other goals, so called “non- standard” case studies, e.g., comparing sensitivity among taxonomic groups or species from different geographic areas. Chapter 6 gives an extended overview of case studies showing that the complexity and diversity of topics, where the SSD approach is applied, has grown in the last two decades. The majority of the studies deriving EQC were focused on freshwater ecosystems, followed by soil and marine ecosystems. In case studies investigating the sensitivities between species in different geographic locations, most attention went to species from tropical (Amazonian) versus their temperate counterparts, arctic versus temperate, and Australian versus species from other parts of the world. In all studies data were derived for fish and invertebrates; aquatic macrophytes or algae were rarely studied. A low diversity in case studies comparing sensitivity of species from different geographic locations is caused by the dependency of the SSD on the availability of data. The production of the data in its turn is geographically restricted as allocation of resources for research is uneven throughout the world.
Exploration of the historical case studies and developments in the method itself has shown several gaps and potential niches for further exploration and improvement. For example, only a limited number of chemical compounds have received attention in case studies with SSDs such as metals (cadmium, copper, lead, mercury, nickel and zinc) and organic compounds (pesticides), while there are over 65,000,000 registered chemicals and thousands of chemicals for which data are available in toxicity databases (Hendriks et al. 2013). Additional investigation of the scientific literature has shown that the potential of the SSD approach in sediment, soil, air and groundwater risk assessment is not yet fully explored and realized due to a lack of toxicity data for species
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characteristic to these systems. This might be due to less attention generally paid to sediment, soil and groundwater systems compared to aquatic systems. The importance of the SSD approach in scientific research, for policy makers and societal impacts has triggered a wide range of studies on possible improvements in the approach, focusing on improving classical SSD approaches based on laboratory test input data as well as on novel applications such as field data-based SSDs (e.g., Van Sprang et al. 2004, Grist et al. 2006, Struijs et al. 2011; Verbrugge et al. 2012; Azevedo et al. 2013, Schipper et al. 2014). The concept of the SSD approach appears to be flexible and dynamic, where new ideas help improving the methodology and widen the areas for its application.
7.7. Overall conclusions and recommendations for future research
The research outlined in this PhD thesis contributes to understanding of the potential, effectiveness and limitations in the SSD approach. The potential of the SSD approach has been realized in diverse case studies focusing on a single chemical stressor and effects on sub-cellular level (Chapter 2), single taxonomic groups and multiple chemical stressors (Chapter 3 and 4) or non-chemical stressors and effect levels derived from field monitoring studies (Chapter 5). Examples of a successful application of the SSD approach deriving risks for various stressors, in different geographic locations, comparing habitat types and taxonomic groups were explored in Chapter 6. Limitations were identified in the areas of the approach concerning the effect levels for non- chemical stressors, methodology of the SSD approach per se and availability of the input data (e.g., for sediment, soil species, species from non-temperate climate zones).
As a summary of the previous chapters, the answers to the research questions of the present thesis are given in a condense manner based on the discussion in previous section of the current chapter. Suggestions for potential future research are also given.
In what way do the responses at the sub-cellular level predict the effects on higher levels of biological organization?
Based on the analysis of the available data used in chapter 2, the use of gene expression changes as early warning indicators for effects on higher levels of biological organisation has not been proven. The responses at the sub-cellular level can be used to distinguish the type of contamination or the mechanism of action of the chemicals, rather than use to predict effects on exposed populations. It would be interesting to investigate in the future research how a HC5 derived from an SSD based on sub-cellular level corresponds to the effect at the whole organism level, similarly to the approach, where a multi-species SSD is related to the effects observed in a mesocosm systems (see Chapter 6 on comparison of HC5 derived based on laboratory and mesocosm studies).
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Can the SSD approach be applied to reveal potential differences in sensitivity between native and non-native species to certain environmental stressors?
The SSD approach can be applied to mechanistically compare sensitivities between native and non-native species to various stressors. The differences revealed by this approach were related to water temperature between native and non-native fish and mollusc species. That is, as non-native fish species were found to be less sensitive to water temperatures than native species. Similar differences in sensitivity to high temperature were reported for non-native and native mollusc species of the river Rhine. Comparing sensitivity of species to other stressors, no significant differences were revealed between the assemblages of native and non-native fish species characteristic to the river Rhine to 21 chemical compounds including ammonium, pesticides and metals. In a comparison of a similar set of fish species, no differences in sensitivity between native and non-native species were found for low oxygen concentrations. Similarly, sensitivity to desiccation and water salinity did not differ between native and non-native mollusc species. Currently, the research on native and non-native species applying the SSD as a tool to investigate the differences in species sensitivity focuses on the fish and molluscs of the river Rhine. In future research, more attention could be given to other taxonomic or functional groups, non-natives to other regions and stressors such as alteration of hydrological regimes, habitat quality and combined effects of chemical and physical stressors. Such investigation can help understanding, which environmental variables facilitate the spread of non-native species for better management of freshwater ecosystems potentially affected by introduced species. Furthermore, in case sufficient data are available, additional analyses of difference in tolerance to chemical stressors between different life stages as well as between non- native fish that spawn and that do not spawn in the target location are recommended.
Can the SSD approach be used to rank stressors measured in actual habitats of a specific taxonomic group?
A straightforward comparison can be made between an SSD for a specific taxonomic group and field measurements in its habitats to derive a measure of magnitude of potential ecological impact for different stressors. Such measure of impact can be expressed as PAF, ER or HC5 enabling the relative ranking of the stressors. Figure 7.2 presents a framework for the procedure for ranking stressors according to PAFs. Ranking of a set of chemical compounds measured in the habitats of anuran and fish species in the Netherlands was performed in chapters 3 and 4 of the present thesis showing that risk ranking for these taxonomic groups varied with the highest risk to fish due to ammonium, and to anurans due to copper. However, the methods deriving the measure of risk were different in these studies. Therefore, it would be interesting to
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investigate further, applying the same methodology, the potential differences in ranking of stressors for various taxonomic groups.
Figure 7.2. A framework of the procedure for ranking stressors according to PAFs at a specific site (habitat A).
What are the potential and limitations of the application of the SSD approach to non-chemical stressors?
The application of the SSD approach to non-chemical stressors as such may complement ecological risk assessments. Including both types of stressors affecting habitats of species in one overall assessment may facilitate the comparison of impact and give possibilities to rank and prioritize among various stressors. The success of the application of the SSD approach depends, however, on the quantity and quality of the available input data. However, the data for non-chemical stressors that can be directly applied as input for SSDs are limited. Therefore, standardization in the derivation of effect levels for non-chemical stressors similar to chemical stressors should be established in the future for the comparison of effects caused by these types of variables.
How are SSDs being applied in scientific research for risk assessment of (non- )chemical stressors on aquatic and terrestrial ecosystems?
A review of the research papers published in scientific journals in the last two decades has shown that the topics, where the SSD approach is applied, can be diverse and complex. The SSD approach is not used only for deriving environmental quality criteria
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and ecological risks for various chemicals, but also applied in various “non-standard” case studies. By applying the SSD approach, numerous researchers examine the sensitivities between species in different geographic locations, habitat types (e.g., marine vs. freshwater, lentic vs. lotic); between native and non-native, endangered and common species; among various taxonomic and functional groups depending on the mode of action of chemicals; or rank stressors and polluted sites according to the potential impact on certain species. The application of the SSD approach is also explored for non-chemical stressors and by deriving the effect levels from field-based data. The majority of the studies focuses on applying SSDs for risk assessment in freshwater ecosystems, followed by soil and marine ecosystems, whereas sediment, soil and groundwater risk assessment received less attention. Further research would be needed to investigate the protectiveness of the threshold levels derived for species from one type of habitats for species from different types of habitats. Additional attention should also be given to the exploration of ICM or other models to populate SSDs to overcome the lack of empirical toxicity data.
7.8. References
Ankley GT, Bennett RS, Erickson RJ, Hoff DJ, Hornung MW, Johnson RD, Mount DR, Nichols JW, Russom CL, Schmieder PK, Serrrano JA, Tietge JE, Villeneuve DL. 2010. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 29, 730-41. Azevedo LB, van Zelm R, Hendriks AJ, Bobbink R, Huijbregts MAJ. 2013. Global assessment of the effects of terrestrial acidification on plant species richness. Environ Pollution 174, 10-15. Collas FPL, Koopman KR, Hendriks AJ, Verbrugge LNH, Van der Velde G, Leuven RSEW. 2014. Effects of desiccation on native and non-nativemolluscs in rivers. Freshwater Biol 59, 41-55. Elshout PMF, Pires LMD, Leuven RSEW, Wendelaar Bonga SE, Hendriks AJ. 2013. Low oxygen tolerance of different life stages of temperate freshwater fish species. J Fish Biol 83, 190-206. Forbes VE, Calow P. 2012. Promises and problems for the new paradigm for risk assessment and an alternative approach involving predictive systems Models. Environ Toxicol Chem 31, 2663-2671. Grist EPM, O'Hagan A, Crane M, Sorokin N, Sims I, Whitehouse P. 2006. Bayesian and time-independent species sensitivity distributions for risk assessment of chemicals. Environ Sci Technol 40, 395-401. Gündel U, Kalkhof S, Zitzkat D, Von Bergen M, Altenburger R, Küster E. 2012. Concentration-response concept in ecotoxicoproteomics: effects of different phenanthrene concentrations to the zebrafish (Danio rerio) embryo proteome. Ecotoxicol Environ Saf 76, 11-22.
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Hanafiah MM, Xenopouos MA, Pfister S, Leuven RSEW, Huijbregts MAJ. 2011. Characterization factors for water consumption and greenhouse gas emissions based on freshwater fish species extinction. Environ Sci Technol 45, 5272-5278. Hayashi TI, Kashiwagi N. 2011. A Bayesian approach to probabilistic ecological risk assessment: Risk comparison of nine toxic substances in Tokyo surface waters. Environ Sci Pollut Res 18, 365-375. Hendriks AJ, Awkerman JA, De Zwart D, Huijbregts MAJ. 2013. Sensitivity of species to chemicals: Dose-response characteristics for various test types (LC50, LR50 and LD50) and modes of action. Ecotoxicol Environ Saf 97, 10-16. Hunka AD, Meli M, Thit A, Palmqvist A, Thorbek P, Forbes VE. 2013. Stakeholders’ perspective on ecological modeling in environmental risk assessment of pesticides: challenges and opportunities. Risk Analysis 33, 68-79. Jin X1, Zha J, Xu Y, Wang Z, Kumaran SS. 2011. Derivation of aquatic predicted no- effect concentration (PNEC) for 2,4-dichlorophenol: comparing native species data with non-native species data. Chemosphere 84, 1506-11. Leuven RSEW, Hendriks AJ, Huijbregts MAJ, Lenders HJR, Matthews J, Van der Velde G. 2011. Differences in sensitivity of native and exotic fish species to changes in river temperature. Current Zoology 57, 852-862. OECD 2015. Guidelines for the testing of chemicals, section 2. Effects on biotic systems. ISSN: 2074-5761 (online); DOI : 10.1787/20745761. Posthuma L, De Zwart D. 2012.Predicted mixture toxic pressure relates to observed fraction of benthic macrofauna species impacted by contaminant mixtures. Environ Toxicol Chem 31, 2175-88. Schipper AM, Posthuma L, De Zwart D, Huijbregts MAJ. 2014. Deriving field-based species sensitivity distributions (f-SSDs) from stacked species distribution models (s-SDMs). Environ Sci Technol 48, 14464-14471. Smit MGD, Holthaus KIE, Trannum HC, Neff JM, Kjeilen-Eilertsen G, Jak RG, Singsaas I, Huijbregts MAJ, Hendriks AJ. 2008a. Species sensitivity distributions for suspended clays, sediment burial, and grain size change in the marine environment. Environ Toxicol Chem 27, 1006-1012. Smit MG, Ebbens E, Jak RG, Huijbregts MA. 2008b. Time and concentration dependency in the potentially affected fraction of species: the case of hydrogen peroxide treatment of ballast water. Environ Toxicol Chem 27, 746-53. Smit MGD, Bechmann RK, Hendriks, AJ, Skadsheim, A, Larsen, B K, Baussant, T, Bamber, S, Sanni S. 2009. Relating biomarkers to whole-organism effects using species sensitivity distributions: a pilot study for marine species exposed to oil. Environ Toxicol Chem 28, 1104-1109. Struijs J, De Zwart D, Posthuma L, Leuven RSEW, Huijbregts MAJ. 2011. Field sensitivity distribution of macroinvertebrates for phosphorus in inland waters. Integr Environ Assess Manage 7, 280-286. Van Sprang PA, Verdonck FAM, Vanrolleghem PA, Vangheluwe ML, Janssen CR. 2004. Probabilistic environmental risk assessment of zinc in Dutch surface waters. Environ Toxicol Chem 23, 2993-3002. Van Zelm R, Schipper AM, Rombouts M, Snepvangers J, Huijbregts MAJ. 2011. Implementing groundwater extraction in life cycle impact assessment:
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characterization factors based on plant species richness for the Netherlands. Environ Sci Technol 45, 629-635. Verbrugge LNH, Schipper AM, Huijbregts MAJ, Van der Velde G, Leuven RSEW. 2012. Sensitivity of native and non-native mollusc species to changing river water temperature and salinity. Biol Invasions 14, 1187-1199. Verones F, Hanafiah M, Pfister S, Huijbregts MAJ, Pelletier G, Koehler A. 2010. Characterization factors for thermal pollution in freshwater aquatic environments. Environ Sci Technol 44, 9364-9369. Verschoor AJ, Vink JPM, De Snoo GR, Vijver MG. 2011. Spatial and temporal variation of water type-specific no-effect concentrations and risks of Cu, Ni, and Zn. Environ Sci Technol 45, 6049-6056.
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Appendices
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APPENDIX A | Supporting information for chapter 2
Figure A1. Species Sensitivity Distribution with the Potentially Affected Fraction versus cadmium concentration for No Observed Effect Concentrations (NOEC) and median Lethal Concentrations (LC50) for individual level responses as well as for Lowest Observed Effect Concentrations (LOEC) for gene expression in all aquatic species present in the e-toxBase. The HC50s of 32.3 μg/L (NOEC), 69.3 μg/L (LOEC), and 859.1 μg/L (LC50) differ significantly (2- tailed t-test, α=0.05), whereas the standard deviation equals 1.12 (NOEC), 1.17 (LOEC), 0.99 (LC50). The corresponding HC5s are 0.24 μg/L (NOEC), 0.72 μg/L (LOEC), and 13.2 μg/L (LC50). These values are comparable to the HCx-values based on fauna species only, as presented in Figure 1.
Table A1. Recent ecotoxicogenomic studies where gene expression was used to investigate the toxic mechanism of action or to identify specific gene expression patterns caused by chemicals. Relationships between gene expression responses and other levels are indicated: C- cell, T-tissue, I- individual, P- population levels.
Species Stressor Gene level Links to other Reference levels Arabidobsis Pb, Cu, Cr Cellular transport, Plants growth rate [1] thaliana protein synthesis, (I) oxidative stress response, heat shock proteins Daphnia Cd Digestion, oxygen Cellular energy, [2] magna transport, cuticula allocation (C) metabolism, signal Growth, transduction energy budget (I) 144 |
Daphnia Cd Metabolism, ion Population growth [3] magna transport, oxidative rate (P) stress Daphnia Cu, Cd, Zn Digestive, oxidative Chitinas [4] magna stress enzymes, e immune enzyme response, activity metallothioneins (C)
Caenorhabditi Heavy Phase I or phase II DNA damage (C) [5] s elegans metals, metabolism genes, Endocrine organic heat disruption, pollutants shock proteins, reproduction (I) others C. elegans Silver Stress related genes Survival, growth, [6] nanoparticl (heat shock reproduction (I) es proteins, metallothioneins etc.) C. elegans Cd, Pb, Cr, Stress related genes Growth, [7] As reproduction, mortality (I) C. elegans Cd, Cu, Zn Metallothioneins Body size, [8] development, brood size and lifespan (I) Lumbricus Zn Metallothioneins Lysosomal [9] rubellus membrane stability (C) Organic material removal, reproduction (I) Population size, community diversity (P) Lumbricus Cu Energy metabolism Body weight (I) [10] rubellus Salmo trutta Zn, Cu, Cd Metallothioneins, Metallothioneins, [11] oxidative stress protein and response antioxidant enzyme activity in kidney, liver and gills (T)
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Table A2. Examples of articles studying gene expression under exposure to different stressors in different species without correlations to higher levels of biological organization.
Reference Organism Stressor [12] Micropterus salmoides Androgen hormones [13] Eisenia fetida Cadmium, lead, zinc [14] Caenorhabditis elegans Cadmium [15] Folsomia candida, Orchesella cincta Cadmium, phenanthrene, desiccation, heat shock, pH stress, starvation [16] Mytilus spp. Mercury, crude oil mixture [17] Platichthys flesus Cadmium chloride, 3-methylcholanthrene, PCB, tert-butyl-hydroperoxide, lindane, perfluoro-octanoic acid
[18] Lumbricus rubellus Cadmium, copper [19] Gillichthys mirabilis Hypoxia [20] Daphnia magna Ibuprofen [21] Oncorynchus mykiss Chromium VI, diquat, ethynylestradiol [22] Danio rerio 4-nonylphenol [23] Tigriopus japonicus Copper [24] Pimephales promelas Methylmercury [25] Cyprinodon variegatus Endocrine disrupting compounds [26] Tigriopus japonicus Cadmium, copper, silver, arsenic [27] Caenorhabditis elegans Cadmium [28] Arabidobsis thaliana Ozone [29] Cyprinus carpio Endocrine disrupting compounds
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[30] Cyprinus carpio Endocrine disrupting compounds [31] Lumbricus rubellus Cadmium, fluoranthene, atrazine [32] Pimephales promelas Ketoconazole [33] Orchesella cincta Cadmium [34] Arabidobsis thaliana Drought, cold, salinity [35] Platichthys flesus Cadmium [36] Arabidobsis thaliana Abiotic stress [37] Oncorhynchus mykiss Zink, silver, copper, cadmium [38] Daphnia magna Copper sulfate, hydrogen peroxide, pentachlorophenol, naphthoflavone [39] Platichthys flesus Estuaries pollution [40] Platichthys flesus Cadmium, 3-methylcholanthrene,
Table A3. Studies of gene expression in aquatic species under cadmium exposure, showing concentrations and exposure time used in experiments, and the determined LOEC value. Data used for Figure 1.
Species Concentrations Cd µg/L (time) LOEC Cd µg/L Reference (time) Anadara granosa 250 (4, 8, 12, 16 d) 250 (4 d) [41] Chironomus tentans 200; 2000; 20000 (24 h) 200 (24 h) [42] Crassostrea virginica 50 (48 h) 50 (48 h) [43] Cyprinus carpio 9.4; 105; 480 (3 h, 42 h, 7 d, 28 d) 9.4 (3 h) [44] Danio rerio 250 (48 h) 250 (48 h) [45]
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Daphnia magna 6; 20; 37 (24 h) 6 (24 h) [3] Kryptolebias marmoratus 610 (6, 12, 24, 48, 96 h) 610 (48 h) [46] Mytilus edulis 200 (4, 21 d) 200 (4 d) [47] Oncorhynchus mykiss 560 (6 h) 560 (6 h) [48] Oryzias javanicus 0.1; 10; 1000 (24 h) 0.1 (24 h) [49] Platichthys flesus 50 (1, 2, 4, 8, 16 d) 50 (1 d) [40] Takifugu obscurus 2050 (6, 12, 24, 48, 96 h) 2050 (6 h) [50]
Table A4. Aquatic species used for LC50s (acute toxicity tests; e-toxBase) and NOECs (chronic toxicity tests; RIVM report no. 601501-001 / 1997) under cadmium exposure. Data used in Figure 1.
Species (LC50s) Species (NOECs) Amnicola sp. Asellus aquaticus Carassius auratus Ampelisca abdita Ceriodaphnia dubia Channa punctata Chironomus Ceriodaphnia reticulata Chironomus plumosus Chironomus tentans Cyprinodon variegatus Daphnia magna Daphnia pulex tentans Chironomus thummi Cyprinus carpio Daphnia magna Dreissena polymorpha Echinometra mathaei Hyalella azteca Ictalurus Daphnia pulex Fundulus diaphanus Hexagenia rigida Lepomis punctatus Isognomon californicum Jordanella floridae Lepomis gibbosus Morone americana Morone saxatilis Notemigonus macrochirus Lytechinus pictus Mysidopsis bahia Nassarius festivus crysoleucas Oncorhynchus mykiss Simocephalus vetulus Oncorhynchus mykiss Pimephales promelas Potamopyrgus jenkinsi Trichoptera Zygoptera Radix plicatula Rhepoxynius abronius Salvelinus fontinalis Strongylocentrotus droebachie Thermocyclops oithonoides Tisbe battagliai
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salinity stresses using a full-length cDNA microarray. Plant J. 2002, 31 (3), 279- 292. 35. Sheader, D. L.; Williams, T. D.; Lyons, B. P.; Chipman, J. K. Oxidative stress response of European flounder (Platichthys flesus) to cadmium determined by a custom cDNA microarray. Mar. Environ. Res. 2006, 62 (1), 33-44. 36. Swindell, W. R. The association among gene expression responses to nine abiotic stress treatments in Arabidopsis thaliana. Genetics 2006, 174 (4), 1811-1824. 37. Walker, P. A.; Kille, P.; Hurley, A.; Bury, N. R.; Hogstrand, C. An in vitro method to assess toxicity of waterborne metals to fish. Toxicol. Appl. Pharm. 2008, 230 (1), 67-77.
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APPENDIX B | Supporting information for chapter 3
Table B1. Fish species, native (0), non-native (1) recorded in main and side channels of the freshwater sections of Rhine river distributaries (Delta Rhine) in 1900-2010 (From Leuven et al. 2011), number of chemicals tested, and species presence (+) or absence (-) in two selected decades. Synonyms of species names are given in brackets. Climate in region of origin of species in brackets: (S) subtropics, (T) tropics. Species Native/non- Number of 1980- 2000- native chemicals 1990 2010 species tested per species Abramis brama 0 4 + + Acipenser baerii 1 3 - +b Acipenser gueldenstaedtii 1 - + b Acipenser ruthenus 1 1 - + b Acipenser stellatus 1 - + b Acipenser sturio 0 - - Alburnoides bipunctatus 0 - + Alburnus alburnus 0 6 + + Alosa alosa 0 + - Alosa fallax 0 + + Ameiurus melas (Ictalurus melas) 1 10 - - Ameiurus nebulosus (Ictalurus 1 1 nebulosus) - + b Anguilla anguilla 0 7 + + Aspius aspius 1 - + Ballerus sapa (Abramis sapa) 1 - + Barbatula barbatulus 0 3 (Noemacheilus barbatulus) + + Barbus barbus 0 1 + + Blicca bjoerkna (Abramis bjoerkna) 0 + + Carassius carassius 0 8 + + Carassius gibelio (Carassius 1 20 auratus) + + Chondrostoma nasus 0 + + Cobitis taenia 0 + + Coregonus oxyrinchus 0 - + Cottus rhenanus / Cottus perifretum 0 + +
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Ctenopharyngodon idella (S) 1 6 + b + b Cyprinus carpio (S) 1 21 + + Esox lucius 0 13 + + Gasterosteus aculeatus 0 16 + + Gobio gobio 0 3 + + Gymnocephalus cernua 0 2 + + Hypophthalmichthys molitrix 1 6 (Aristichthys molitrix) (S) + b + b Lampetra fluviatilis 0 + + Leuciscus idus 0 12 + + Leuciscus leuciscus 0 2 + + Lota lota 0 + + Micropterus salmoides (S) 1 17 - - Misgurnus fossilis 0 + + Neogobius fluviatilis 1 - + Neogobius kessleri 1 - + Neogobius melanostomus 1 - + Oncorhynchus mykiss (S) 1 20 + b + b Osmerus eperlanus 0 + + Perca fluviatilis 0 3 + + Petromyzon marinus 0 + + Platichthys flesus 0 5 + + Poecilia reticulate (T) 1 21 + b + b Proterorhinus semilunaris 1 - + Pseudorasbora parva 1 1 - + Pungitius pungitius 0 1 + + Rhodeus amarus (Rhodeus sericeus) 1a 1 - + Romanogobio belingi (Gobio 1 albipinatus) - + Rutilus rutilus 0 10 + + Salmo salar 0 14 - + Salmo trutta 0 15 + + Sander lucioperca (Stizostedion 1 1 lucioperca) + + Scardinius erythrophthalmus 0 5 (Rutilus erythrophthalmus) + + Silurus glanis 0 3 + + Squalius cephalus (Leuciscus 0 3 cephalus) + +
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Tinca tinca 0 7 + + Vimba vimba 1 + + a: regarded as non-native species according to Van Damme et al. (2007); b: incidentally recorded.
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APPENDIX C | Supporting information for chapter 4
Table C1. Overview of the toxicity data from the e-toxBase
Stressor Species name Author(s) Year Alachlor Bufo americanus Howe, GE, R Gillis, and RC Mowbray 1998 Bufo bufo japonicus Nishiuchi, Y 1980 Rana pipiens Howe, GE, R Gillis, and RC Mowbray 1998 Ammonium Pseudacris regilla Schuytema, GS, and AV Nebeker 1999 Xenopus laevis Schuytema, GS, and AV Nebeker 1999 Hyla crucifer Diamond, JM, DG Mackler, WJ Rasnake, and D Gruber 1993 Rana pipiens Diamond, JM, DG Mackler, WJ Rasnake, and D Gruber 1993 Hecnar, SJ 1995 Bufo americanus Hecnar, SJ 1995 Bufo bufo Xu, Q, and RS Oldham 1997 Pseudacris Hecnar, SJ 1995 triseriata triseria Atrazine Bufo americanus Birge, WJ, JA Black, and RA Kuehne 1980 Howe, GE, R Gillis, and RC Mowbray 1998 Rana catesbeiana Birge, WJ, JA Black, and RA Kuehne 1980 Rana palustris Birge, WJ, JA Black, and RA Kuehne 1980 Rana pipiens Birge, WJ, JA Black, and RA Kuehne 1980 De Zwart 2008
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Howe, GE, R Gillis, and RC Mowbray 1998 Azinphos- Bufo woodhousei Mayer, FLJ, and MR Ellersieck 1986 methyl fowleri Sanders, HO 1970 Bufo woodhousii De Zwart 2008 Pseudacris regilla Schuytema, GS, AV Nebeker, and WL Griffis 1995 Pseudacris triseriata De Zwart 2008 Mayer, FLJ, and MR Ellersieck 1986 Rana clamitans Harris, ML, CA Bishop, J Struger, B Ripley, and JP Bogart 1998 Rana ridibunda Ozmen, M, S Sener, A Mete, and H Kucukbay 1999 Xenopus laevis Schuytema, GS, AV Nebeker, and WL Griffis 1994 Cadmium Ambystoma gracile Nebeker, AV, GS Schuytema, and SL Ott 1994 Bufo arenarum Ferrari, L, A Salibian, and CV Muino 1993 Muino, CV, L Ferrari, and A Salibian 1990 Gastrophryne carolinensis Birge, WJ 1978 Microhyla ornata Rao, IJ, and MN Madhyastha 1987 Rana ridibunda Loumbourdis, NS, P Kyriakopoulou-Sklavounou, and G 1999 Zachariadis Rana catesbeiana Zettergren, LD, BW Boldt, DH Petering, MS Goodrich, DN Weber, 1991 and JG Zettergren Xenopus laevis Herkovits, J, P Cardellini, C Pavanati, and CS Perez-Coll 1997 Rana luteiventris Lefcort, H, RA Meguire, LH Wilson, and WF Ettinger 1998 Bufo melanostictus Khangarot, BS, and PK Ray 1987 Carbaryl Bufo bufo japonicus Hashimoto, Y, and Y Nishiuchi 1981
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Hyla versicolor Zaga, A, EE Little, CF Rabeni, and MR Ellersieck 1998 Rana clamitans Boone, MD, and CM Bridges 1999 Rana hexadactyla Khangarot, BS, A Sehgal, and MK Bhasin 1985 Rana tigrina De Zwart 2008 Marian, MP, V Arul, and TJ Pandian 1983 Carbendazim Bufo bufo japonicus Nishiuchi, Y, and K Yoshida 1974 Rana hexadactyla Khangarot, BS, A Sehgal, and MK Bhasin 1985 Rana limnocharis Pan, DY, and XM Liang 1993 Carbofuran Microhyla ornata Pawar, KR, and M Katdare 1983 Rana hexadactyla Khangarot, BS, A Sehgal, and MK Bhasin 1985 Rana limnocharis Pan, DY, and XM Liang 1993 Copper Bufo bufo japonicus Nishiuchi, Y 1975 Bufo melanostictus Khangarot, BS, and PK Ray 1987 Bufo woodhousei fowleri Birge, WJ, and JA Black 1979 Gastrophryne carolinensis Birge, WJ 1978 Birge, WJ, and JA Black 1979 Hyla chrysoscelis Birge, WJ, and JA Black 1979 Microhyla ornata Rao, IJ, and MN Madhyastha 1987 Rana hexadactyla Khangarot, BS, A Sehgal, and MK Bhasin 1985 Rana pipiens Birge, WJ, and JA Black 1979 Rana tigrina Khangarot, BS, S Mathur, and VS Durve 1981 DDT Anura sp Okudaira, H 1973 Bufo bufo japonicus Hashimoto, Y, and Y Nishiuchi 1981
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Bufo vulgaris formosus Nishiuchi, Y 1980 Bufo woodhousei Mayer, FLJ, and MR Ellersieck 1986 fowleri Sanders, HO 1970 Bufo woodhousii De Zwart 2008 Pseudacris triseriata De Zwart 2008 Mayer, FLJ, and MR Ellersieck 1986 Pseudacris triseriata Sanders, HO 1970 triseria Rana limnocharis Pan, DY, and XM Liang 1993 Rana temporaria Harri, MNE, J Laitinen, and EL Valkama 1979 Deltamethrin Bufo arenarum De Zwart 2008 Salibian, A 1992 Bufo bufo De Zwart 2008 Rana limnocharis Pan, DY, and XM Liang 1993 Rana temporaria De Zwart 2008 Thybaud, E 1990 Diazinon Bufo bufo japonicus Hashimoto, Y, and Y Nishiuchi 1981 Rana clamitans Harris, ML, CA Bishop, J Struger, B Ripley, and JP Bogart 1998 Rana limnocharis Pan, DY, and XM Liang 1993 Dieldrin Bufo vulgaris formosus Nishiuchi, Y 1980 Bufo woodhousei fowleri Mayer, FLJ, and MR Ellersieck 1986 Sanders, HO 1970 Bufo woodhousii De Zwart 2008 Pseudacris triseriata De Zwart 2008
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Mayer, FLJ, and MR Ellersieck 1986 Pseudacris triseriata Sanders, HO 1970 triseria Rana catesbeiana Schuytema, GS, AV Nebeker, WL Griffis, and KN Wilson 1991 Rana limnocharis Pan, DY, and XM Liang 1993 Rana pipiens Schuytema, GS, AV Nebeker, WL Griffis, and KN Wilson 1991 Xenopus laevis Schuytema, GS, AV Nebeker, WL Griffis, and KN Wilson 1991 Diuron Pseudacris regilla Schuytema, GS, and AV Nebeker 1998 Rana aurora Schuytema, GS, and AV Nebeker 1998 Rana catesbeiana Schuytema, GS, and AV Nebeker 1998 Xenopus laevis Schuytema, GS, and AV Nebeker 1998 Endosulfan Bufo bufo japonicus Hashimoto, Y, and Y Nishiuchi 1981 Bufo melanostictus Vardia, HK, PS Rao, and VS Durve 1984 Bufo vulgaris formosus Nishiuchi, Y 1980 Rana clamitans Harris, ML, CA Bishop, J Struger, B Ripley, and JP Bogart 1998 Rana limnocharis Pan, DY, and XM Liang 1993 Rana tigrina Gopal, K, RN Khanna, M Anand, and GSD Gupta 1981 Endrin Acris crepitans Hall, RJ, and DM Swineford 1981 Bufo americanus Hall, RJ, and DM Swineford 1981 Bufo bufo japonicus Hashimoto, Y, and Y Nishiuchi 1981 Bufo woodhousei fowleri Mayer, FLJ, and MR Ellersieck 1986 Sanders, HO 1970 Bufo woodhousii De Zwart 2008 Pseudacris triseriata De Zwart 2008
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Mayer, FLJ, and MR Ellersieck 1986 Pseudacris triseriata Sanders, HO 1970 triseria Rana catesbeiana Bottger, A 1988 Hall, RJ, and DM Swineford 1981 Thurston, RV, TA Gilfoil, EL Meyn, RK Zajdel, TL Aoki, and GD 1985 Veith Rana hexadactyla Khangarot, BS, A Sehgal, and MK Bhasin 1985 Rana sphenocephala Hall, RJ, and D Swineford 1980 Hall, RJ, and DM Swineford 1981 Rana sylvatica Hall, RJ, and DM Swineford 1981 Rana temporaria Wohlgemuth, E 1977 Lindane Bufo woodhousei fowleri Mayer, FLJ, and MR Ellersieck 1986 Sanders, HO 1970 Pseudacris triseriata Mayer, FLJ, and MR Ellersieck 1986 Pseudacris triseriata Sanders, HO 1970 triseria Rana hexadactyla Khangarot, BS, A Sehgal, and MK Bhasin 1985 Rana limnocharis Pan, DY, and XM Liang 1993 Rana temporaria Thybaud, E 1990 Malathion Bufo arenarum Venturino, A, LE Gauna, RM Bergoc, and AMP D'Angelo 1992 Bufo woodhousei fowleri Mayer, FLJ, and MR Ellersieck 1986 Sanders, HO 1970 Bufo woodhousii De Zwart 2008
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Office of Pesticide Programs 2000 Pseudacris triseriata De Zwart 2008 Mayer, FLJ, and MR Ellersieck 1986 Office of Pesticide Programs 2000 Pseudacris triseriata Sanders, HO 1970 triseria Rana hexadactyla Khangarot, BS, A Sehgal, and MK Bhasin 1985 Rana limnocharis Pan, DY, and XM Liang 1993 Nitrate Bufo americanus Hecnar, S.J. 1995 Bufo bufo Xu, Q., and R.S. Oldham 1997 Pseudacris regilla Schuytema, G.S., and A.V. Nebeker 1999 Pseudacris triseriata Hecnar, S.J. 1995 triseria Rana pipiens Hecnar, S.J. 1995 Xenopus laevis Schuytema, G.S., and A.V. Nebeker 1999 Parathion Anura sp Okudaira, H 1973 Bufo arenarum Anguiano, OL, CM Montagna, M Chifflet de Llamas, L Gauna, and 1994 AM Pechen de D'Angelo Bufo bufo japonicus Hashimoto, Y, and Y Nishiuchi 1981 Bufo woodhousei fowleri Mayer, FLJ, and MR Ellersieck 1986 Bufo woodhousii De Zwart 2008 Pseudacris triseriata De Zwart 2008 Mayer, FLJ, and MR Ellersieck 1986 Pseudacris triseriata Sanders, HO 1970 triseria
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Rana catesbeiana Hall, RJ, and E Kolbe 1980 Rana limnocharis Pan, DY, and XM Liang 1993 Parathion-methyl Pseudacris triseriata De Zwart 2008 Mayer, FLJ, and MR Ellersieck 1986 Rana catesbeiana De Zwart 2008 Rana limnocharis Pan, DY, and XM Liang 1993 Rana tigrina Alam, MN, and M Shafi 1991 Permethrin Bufo bufo japonicus Nishiuchi, Y, H Iwamura, and K Asano 1986 Rana brevipoda porosa Nishiuchi, Y, H Iwamura, and K Asano 1986 Rana catesbeiana Bottger, A 1988 De Zwart 2008 Jolly, AL, JB Graves, JW Avault, and KL Koonce 1978 Jolly, ALJ, JW Avault Jr, KL Koonce, and JB Graves 1978 Thurston, RV, TA Gilfoil, EL Meyn, RK Zajdel, TL Aoki, and GD 1985 Veith
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APPENDIX D | Supporting information for chapter 5
Table D1. PNOFs and PAFs (%) for fish tolerance of water-flow velocity based on Vmax and Vcrit at difference life-stages at different locations during reference period and during the passage of vessels. Groyne Inlet side Location Riprap bank field channel Number of flow records 55 78 67 Average max velocity cm/s 7 9 10 SD 1 1.6 2.3
PNOF juvenile, Vmax 15.0 20.1 22.6
PNOF adult, Vmax 0.9 1.9 2.6
PNOF adult rheophilic, Vmax 0 0 0 PNOF adult eurytopic, Vmax 2.4 4.7 6 PNOF adult limnophilic,Vmax 2.2 6 8.7
Reference period Reference PAF Vcrit 0.03 0.15 0.28
Number of ship passages 6 9 13 Average max velocity cm/s 19 25 117
SD 5 5 29
PNOF juvenile, Vmax 41.2 50.1 89.8
PNOF adult, Vmax 10.6 17.1 76.6
PNOF adult rheophilic, Vmax 0.03 0.2 53.7
PNOF adult eurytopic, Vmax 21 31.3 90.5 PNOF adult limnophilic, Vmax 42.8 62.6 99.9
During ship passing ship During PAF Vcrit 5.3 13.2 95.2
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Table D2. Overview of the data on Vmax for fish species of difference life-stages and the body length (TL) of the adults. Guilds meaning: E- eurytopic, L- limnophilic, R- rheophilic. a Species scientific name Species Guild Vmax (cm/s) Length Reference common adult name (cm) egg larva juven adult ile Abramis bjoerkna White bream E 20 20 Schoone and Van Breugel, 2006 Abramis bjoerkna E 5 -20 5 -20 20 10 Dorenbosch et al., 2011 Abramis bjoerkna E 20 Mann, 1995 Abramis brama Common E 3 5-16 25 Van Emmerik and De Nie, 2006 bream Abramis brama E 20 Mann, 1995 Abramis brama E 5 Lamouroux et al., 1999 Acipenser gueldenstaedtii Danube R 100-150 145 Billard and Lecointre, 2001 sturgeon Acipenser ruthenus Sterlet R 100-200 40 Billard and Lecointre, 2001 sturgeon Acipenser stellatus Starry R 110-190 125 Billard and Lecointre, 2001 sturgeon Alburnoides bipunctatus Schneider R 50 9 Mann, 1995 Alburnoides bipunctatus R 20 Lamouroux et al., 1999 Alburnoides bipunctatus R 70 De Nie, 1996 Alburnus alburnus Bleak E 30 30 50 80 15 Dorenbosch et al., 2011 Alburnus alburnus E 20 Mann, 1995
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Alburnus alburnus E 80 Lamouroux et al., 1999 Alosa alosa Allis shad R 50-200 40 Maitland and Hatton-Ellis, 2003 Alosa fallax Twaite shad R 50-200 40 Maitland and Hatton-Ellis, 2003 Ameiurus melas Black E 4-40 27 Stuber et al., 1982 bullhead Ameiurus nebulosus Brown E 20 25 Crombaghs et al., 2000 bullhead Anguilla anguilla European eel E 30 35 Laffaille et al., 2003 Aspius aspius Asp L 40 Grift et al., 2003 Aspius aspius L 20 55 Mann, 1995 Barbatula barbatula Stone loach R 5 5 20-80 12 Lamouroux et al., 1999 Barbus barbus Barbel R 120 120 10-110 30 Wijmans, 2007 Barbus barbus R 20 30 Grift et al., 2003 Barbus barbus R 49 Mann, 1995 Barbus barbus R 30 40 De Nie, 1996 Carassius auratus Goldfish E 45 10 Li et al., 2008 Chondrostoma nasus Common R 10-50 10-50 50-100 25 Beekman, 2005 nase Chondrostoma nasus R 100 50 90 Mann, 1995 Chondrostoma nasus R 100 De Nie, 1996 Cobitis taenia Spined loach R 15-30 5 De Nie, 1996 Cobitis taenia R 27 27 50 Dorenbosch et al., 2011 Cottus gobio Bullhead R 10-80 10 Tomlinson and Perrow, 2003 Cottus gobio R 10-15 Peters, 2005 Cottus gobio R 20 20 20 100 Dorenbosch et al., 2011
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Ctenopharyngodon idella Grass carp E 60-180 11 Scierman and Smith, 1983 Cyprinus carpio Common carp E 10-120 31 De Wilt and Van Emmerik, 2008 Cyprinus carpio E 5 5 5 10-50 Dorenbosch et al., 2011 Cyprinus carpio E 5 Mann, 1995 Esox lucius Northern pike E 26-44 40 De Laak and Van Emmerik, 2006 Esox lucius E 5 Mann, 1995 Esox lucius E 1 1 1 5 Dorenbosch et al., 2011 Esox lucius E 5 Lamouroux et al., 1999 Gasterosteus aculeatus Three-spined E 30 5 De Nie, 1996 stickleback Gasterosteus aculeatus E 30 Dorenbosch et al., 2011 Gobio albipinnatus White-finned R 50 Banaduc, 2007 gudgeon Gobio gobio Gudgeon R 20 10-55 12 Beers, 2005 Gobio gobio R 20 40 Grift et al., 2003 Gobio gobio R 100 2-80 Mann, 1995 Gobio gobio R 5 Lamouroux et al., 1999 Gymnocephalus cernua Ruffe E 20 12 Specziar and Vida, 1995 Hypophthalmichthys Silver carp E 70 18 Lohmeyer and Garvey, 2008 molitrix Hypophthalmichthys R 230 230 Kolar et al. 2007 nobilis Hypophthalmichthys R 48- 70 Lohmeyer and Garvey, 2008 nobilis 130 Lampetra fluviatilis River R 1-50 100-200 35 Maintland, 2003 lamprey
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Lampetra fluviatilis R 50- 50- 50 200 Dorenbosch et al., 2011 100 100 Lampetra fluviatilis R 200 De Nie, 1996 Lampetra planeri European R 3-50 16 Dorenbosch et al., 2011 brook lamprey Lampetra planeri R 50 50 Maitland, 2003 Lampetra planeri R 50 30 De Nie, 1996 Lepomis gibbosus Pumpkinseed L 25-31 10 Brinsmead and Fox, 2002 Lepomis gibbosus L 5 Lamouroux et al., 1999 Leucaspius delineatus Belica L 38.6 38.6 6 Mann, 1995 Leuciscus cephalus Chub R 140 140 20 60 30 Dorenbosch et al., 2011 Leuciscus cephalus R 5 5 75 Arlinghaus and Wolter, 2003 Leuciscus cephalus R 50 Mann, 1995 Leuciscus cephalus R 5 Lamouroux et al., 1999 Leuciscus idus Ide R 50 50 50 50 30 Dorenbosch et al., 2011 Leuciscus idus R 5 40 20-150 Koopmans and Van Emmerik, 2006 Leuciscus idus R 20 40 Grift et al., 2003 Leuciscus idus R 5-40 5-40 5-40 5-40 De Nie, 1996 Leuciscus leuciscus Common R 50 50 50 60 15 Dorenbosch et al., 2011 dace Leuciscus leuciscus R 2 2 Mann and Bass, 1997 Leuciscus leuciscus R 30 13.5 50 Mann, 1995 Leuciscus leuciscus R 20 Lamouroux et al., 1999 Leuciscus leuciscus R 2 50 De Nie, 1996
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Lota lota Burbot R 4-8 5 150-250 40 Beelen, 2009 Micropterus salmoides Largemouth L 40 4 6 40 Stuber et al., 1982 black bass Micropterus salmoides L 10 27 20 Stuber et al., 1982 Misgurnus fossilis Weatherfish L 10 15 Van Beek, 2003 Neogobius fluviatilis Monkey goby E 10-60 Jurajda et al., 2005 Neogobius melanostomus Round goby E 2-4.4. 10-60 Jurajda et al., 2005 Neogobius melanostomus E 34 Charlebois et al., 1997 Oncorhynchus mykiss Rainbow R 76-156 60 Molony, 2001 trout Osmerus eperlanus European R 150 150 150 150 17 Dorenbosch et al., 2011 smelt Osmerus eperlanus R 200 De Nie, 1996 Perca fluviatilis European E 5-20 5-20 50-100 25 Dorenbosch et al., 2011 perch Perca fluviatilis E 5 Lamouroux et al., 1999 Petromyzon marinus Sea lamprey R 80 158.5 60 Maitland, 2003 Petromyzon marinus R 200 200 80 80 Dorenbosch et al., 2011 Petromyzon marinus R 200 De Nie, 1996 Phoxinus phoxinus Eurasian E 20-30 20-30 7 Mann, 1995 minnow Phoxinus phoxinus E 5 Lamouroux et al., 1999 Poecilia reticulatus Guppy E 48 3 Luyten and Liley, 1985 Proterorhinus semilunaris Western E 40-80 Vassilev et al., 2008 tubenose goby Pseudorasbora parva Stone moroko E 7 8 Sunardi et al., 2007
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Pungitius pungitius Ninespine L 30 7 De Nie, 1996 stickleback Rhodeus sericeus amarus European L 10 5 De Lange and Van Emmerik, bitterling 2006 Rhodeus sericeus amarus L 10 10 10 10 Dorenbosch et al., 2011 Rhodeus sericeus amarus L 5 Lamouroux et al., 1999 Rutilus rutilus Roach E 20 6.9 25 Mann, 1995 Rutilus rutilus E 5 5 25 100 Dorenbosch et al., 2011 Rutilus rutilus E 6.9 6.9 Mann and Bass, 1997 Rutilus rutilus E 5 Lamouroux et al., 1999 Salmo salar Atlantic R 50-65 25-90 38 Hendry and Cragg-Hine, 2003 salmon Salmo salar R 40 60 Crisp, 1995 Salmo trutta Sea trout R 20-80 20-80 20-80 20-80 72 De Laak, 2008 Salmo trutta R 40 Lamouroux et al., 1999 Salvelinus fontinalis Brook trout R 92 26 Stolz and Schnell, 1991 Salvelinus fontinalis R 15 Raleigh, 1982 Sander lucioperca Pike-perch E 10 50 Aarts, 2007 Scardinius Rudd E 10 10 10 10 20 Dorenbosch et al., 2011 erythrophthalmus Scardinius E 5 Mann, 1995 erythrophthalmus Scardinius E 5 Lamouroux et al., 1999 erythrophthalmus Silurus glanis Wels catfish E 10 10 300 Van Emmerik, 2009 Thymallus thymallus Grayling R 40-70 40-70 30 Dorenbosch et al., 2011
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Tinca tinca Tench L 25 20 Beelen, 2006 Tinca tinca L 10 10 10 10 Dorenbosch et al., 2011 Tinca tinca L 5 Mann, 1995 Tinca tinca L 5 Lamouroux et al., 1999 Vimba vimba Vimba bream R 20 20 20 Dorenbosch et al., 2011 Vimba vimba R 60-140 Lusk et al., 2005 a Full references: Aarts TWPM. 2007. Kennisdocument snoekbaars, Sander lucioperca (Linnaeus, 1758). Kennisdocument 16. Sportvisserij Nederland, Bilthoven. Arlinghaus R, Wolter C. 2003. Amplitude of ecological potential: chub Leuciscus cephalus (L.) spawning in an artificial lowland canal. Journal of Applied. Ichthyology 19: 52-54. Bănăduc D. 2007. Important area for fish - Natura 2000 (SCI) for Gobio albipinnatus Lukasch, 1933 species in the Danube Delta (Romania). Acta Oecologica 14: 127-135. Beekman J. 2005. Kennisdocument sneep, Chondrostoma nasus (Linnaeus, 1758). Kennisdocument 4. OVB / Sportvisserij Nederland, Bilthoven. Beelen P. 2006. Kennisdocument zeelt, Tinca tinca (Linnaeus, 1758). Kennisdocument 24. OVB / Sportvisserij Nederland, Bilthoven. Beelen P. 2009. Kennisdocument kwabaal, Lota lota (Linnaeus, 1758). Kennisdocument 28, OVB / Sportvisserij Nederland, Bilthoven. Beers MC. 2005. Kennisdocument riviergrondel, Gobio gobio (Linnaeus, 1758). Kennisdocument 10. OVB / Sportvisserij Nederland, Bilthoven. Billard R, Lecointre G. 2001. Biology and conservation of sturgeon and paddlefish. Reviews in Fish Biology and Fisheries 10: 355- 396. Brinsmead J, Fox MG. 2002. Morphological variation between lake-and stream dwelling rock bass and pumpkinseed populations. Journal of Fish Biology 61: 1619-1638. Cammaerts R, Spikmans F, Van Kessel N, Verreycken H, Chérot F, Demol T, Richez S. 2012. Colonization of the Border Meuse area (The Netherlands and Belgium) by the non-native western tubenose goby Proterorhinus semilunaris (Heckel, 1837) (Teleostei, Gobiidae). Aquatic Invasions 7: 251-258.
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Charlebois PM, Marsden JE, Goettel RG, Wolfe RK, Jude DJ, Rudnika S. 1997. The round goby, Neogobius melanostomus (Pallas), a review of European and North American literature. Illinois-Indiana Sea Grant Program and Illinois Natural History Survey. INHS Special Publication No. 20. Crisp DT. 1995. Dispersal and growth rate of O-group salmon (Salmo salar L.) from point-stocking together with some information from scatter-stocking. Ecology of Freshwater Fish 4: 1-8. Crobmaghs BHJM, Akkermans RW, Gubbels REMB, Hoogerwerf G. 2000. Vissen in Limburgse beken. De verspreiding en ecologie van vissen in stromende water in Limburg. Stichting Natuurpublicaties Limburg, Maastricht. De Laak GAJ, van Emmerik WAM. 2006. Kennisdocument snoek, Esox lucius (Linnaeus, 1758). Kennisdocument 13. OVB / Sportvisserij Nederland, Bilthoven. De Laak GAJ. 2008. Kennisdocument forel, Salmo trutta (Linnaeus, 1758). OVB / Sportvisserij Nederland, Bilthoven De Lange MC, van Emmerik WAM. 2006. Kennisdocument bittervoorn Rhodeus amarus (Bloch, 1782). Kennisdocument 15. OVB / Sportvisserij Nederland, Bilthoven. De Nie HW. 1996. Atlas van de Nederlandse Zoetwatervissen. Media publishing, Doetichem. De Wilt RS, van Emmerik WAM. 2008. Kennisdocument karper, Cyprinus carpio (Linnaeus, 1758). Kennisdocument 22. OVB / Sportvisserij Nederland, Bilthoven. Dorenbosch M, van Kessel N, Kranenbarg J, Spikmans F, Verberk W, Leuven RSEW. 2011. Nevengeulen in uiterwaarden als kraamkamer voor riviervissen. Rapport Ontwikkeling en Beheer Natuurkwaliteit 2011/OBN143-RI. Bosschap, bedrijfschap voor bos en natuur, Driebergen. Grift RE, Buijse AD, Van Densen WLT, Machiels MAM, Kranenbarg J, Klein Breteler JGP, Backx JJGM. 2003. Suitable habitats for 0-group fish in rehabilitated floodplains along the lower river Rhine. River Research and Applications 19: 353-374. Hendry K, Cragg-Hine D. 2003. Ecology of the Atlantic Salmon. Conserving Natura 2000 Rivers Ecology Series No. 7. English Nature, Peterborough. Jurajda P, Černý J, Polačik M, Valová Z, Janáč M, Blažek R, Ondračková M. 2005. The recent distribution and abundance of non- native Neogobius fishes in the Slovak section of the River Danube. Journal of Applied Ichthyology 21: 319-323. Kolar CS, Chapman DC, Courtenay WR, Housel CM, Williams JD, Jennings DP. 2007. Bigheaded carps: a biological synopsis and environmental risk assessment. American Fisheries Society, Special Publication 33, Bethesda, MD. Koopmans, JH, van Emmerik, WAM. 2006. Kennisdocument winde, Leuciscus idus L. Kennisdocument 20. OVB / Sportvisserij Nederland, Bilthoven.
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Laffaille P, Feunteun E, Baisez A, Robinet T, Acou A, Legault A, Lek S. 2003. Spatial organisation of European eel (Anguilla anguilla L.) in a small catchment. Ecology of Freshwater Fish 12: 254-264. Lamouroux N, Capra H, Pouilly M, Souchon Y. 1999. Fish habitat preferences at the local scale in large streams of southern France. Freshwater Biology 42: 673-687. Li J, Blake R, Chan K. 2008. Swimming in four goldfish (Carassius auratus) morphotypes: Understanding functional design and performance through artificial selection. Journal of Fish Biology 75: 591-617. Lohmeyer AM, Garvey JE. 2008. Placing the North American invasion of Asian carp in a spatially explicit context. Biological Invasions 11: 905-916. Lusk S, Luskova V, Halačka K, Šlechtová V, Šlechta V. 2005. Characteristics of the remnant Vimba vimba population in the upper part of the Dyje River. Folia Zoologica 54: 389-404. Luyten PH, Liley NR. 1985. Geographic variation in the sexual behavior of the guppy, Poecilia reticulala (Peters). Behavior 95: 164- 179. Maitland PS, Hatton-Ellis TW. 2003. Ecology of the Allis and Twaite Shad. Conserving Natura 2000 Rivers Ecology Series No. 3. English Nature, Peterborough Maitland PS. 2003. Ecology of the River, Brook and Sea Lamprey. Conserving Natura 2000 Rivers Ecology Series No. 5. English Nature, Peterborough. Mann RHK. 1995. Natural factors influencing recruitment success in coarse fish populations. In the ecological basis for river management (eds. Harper DM, Ferguson AJD), pp. 339-348. Wiley: Chichester. Mann RHK, Bass JAB. 1997. The critical water velocities of larval roach (Rutilus rutilus) and dace (Leuciscus leuciscus) and implications for river management. Regulated Rivers: Research and Management 13: 295-301. Molony B. 2001. Environmental requirements and tolerances of rainbow trout (Oncorhynchus mykiss) and brown trout (Salmo trutta) with special references to South Australia: a review. Fisheries Research Report Western Australia 130: 1-28. Peters JS. 2005. Kennisdocument rivierdonderpad Cottus gobio (Linnaeus, 1758). Kennisdocument 9. OVB / Sportvisserij Nederland, Bilthoven. Raleigh RF. 1982. Habitat-suitability index models: Brook Trout. US Department of Interior, Fish and Wildlife Service, Biological Services Program, Lafayette, LA. Schoone CH, van Breugel M. 2006. Kennisdocument kolblei Abramis (of Blicca) bjoerkna L. Kennisdocument 19. OVB / Sportvisserij Nederland, Bilthoven.
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Shireman JV, Smith CR. 1983. Synopsis of biological data on the grass carp, Ctenopharyngodon idella (Cuvier and Valenciennes, 1884). FAO Fish. Synop. No.135. Specziár A, Vida A. 1995. Comparative study of Gymnocephalus cernuus (Linnaeus, 1758) and G. baloni Holcik & Hensel, 1974 (Pisces, Percidae). Miscellanea Zoologica Hungarica 10: 103-116. Stolz J, Schnell J. 1991. Trout. The Wildlife Series. Stackpole Books, Harrisburg, PA. Stuber RJ, Gebhart G, Maughan OE. 1982. Habitat Suitability Index Models: Largemouth Bass. US Department of Interior, Washington, DC. Sunardi AT, Manatunge J, Fujino T. 2007. The effects of predation risk and current velocity stress on growth, condition and swimming energetics of Japanese minnow (Pseudorasbora parva). Ecological Research 22: 32-40. Tomlinson ML, Perrow MR. 2003. Ecology of the Bullhead. Conserving Natura 2000 Rivers Ecology Series No. 4. English Nature, Peterborough. Van Beek GCW. 2003. Kennisdocument grote modderkruiper (Misgurnus fossilis). Kennisdocument 1. OVB / Sportvisserij Nederland, Bilthoven. Van Emmerik WAM. 2009. Kennisdocument Europese meerval Silurus glanis (Linnaeus, 1758). Kennisdocument 29. OVB / Sportvisserij Nederland, Bilthoven. Van Emmerik WAM, De Nie HW. 2006. De zoetwaterviseen van Nederland, ecologisch bekeken. OVB / Sportvisserij Nederland. Bilthoven. Vassilev M, Trichkova T, Uruche D, Stoica I, Battes K, Zivkov M. 2008. Distribution of Gobiid species (Gobiidae, Pisces) in the Yantra River (Danube Basin, Bulgaria). Proceedings of the Anniversary Scientific Conference of Ecology, Plovdiv, 163-172. Wijmans PADM. 2007. Kennisdocument barbeel, Barbus barbus (Linnaeus, 1758). Kennisdocument 14. Sportvisserij Nederland, Bilthoven
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Table D3. Overview of available data on critical water-flow velocity tolerance (Vcrit) and maximal velocity tolerance (Vmax) and length for 11 fish species.
Fish species Length (cm) Vcrit (cm/s) Length (cm) Vmax (cm/s) Carassius auratus 6.5 45.4 10.0 45.0 Ctenopharyngodon idella 8.6 43.2 10.7 180.0 Cyprinus carpio 6.2 56.2 31.0 120.0 Esox lucius 25.0 30.0 40.0 44.0 Lepomis gibbosus 12.7 38.2 9.9 31.0 Lota lota 25.0 38.3 40.0 250.0 Micropterus salmoides 10.0 38.0 40.0 20.0 12.2 47.6 Oncorhynchus mykiss 41.0 72.0 60.0 76.0 38.0 95.0 34.9 96.2 Poecilia reticulata 1.8 13.7 2.8 48.0 Salmo trutta 35.0 78.0 72.0 80.0 Salvelinus fontinalis 8.2 30.3 26.4 92.0 10.2 47.2 10.4 50.5 10.9 55.9 10.6 57.4 11.0 67.9 11.6 88.7 11.2 92.9
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Figure D1. Species Sensitivity Distributions of fish species for maximal water-flow velocity (Vmax solid line, n = 11, SD = 0.3) and critical velocity (Vcrit dashed line, n = 11, SD = 0.2). PNOF: potentially not occurring fraction in percentages based on field observations; PAF: potentially affected fraction based on laboratory measurements
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APPENDIX E | Supporting information for chapter 6
Table E1. Recent studies where the SSD approach has been applied to derive EQS for a specific chemical compound. Reference Species Stressor Focus [1] Insects, fish, Methyl parathion Freshwater mussels, shrimps, crabs, ecosystem water fleas, algae
[2] Soil nematodes, soil Zinc Sandy soil invertebrates mesocosms [3] Marine plankton Tributyltin (TBT) and Lab data and organisms, from bacteria linear alkylbenzene marine to crustaceans sulfonate enclosures [4] Test for 12 families: toad, Chlorite Freshwater frog, isopod, ostracod, ecosystem copepod, Worm, mosquito larvae, planaria, catfish guppies, cichlids [5] Algae, vascular aquatic Metribuzin and Freshwater plants, invertebrates, fish metamitron [6] Arthropod, nonarthropod Endosulfan Freshwater invertebrate, fish, or amphibian, Australian and non- Australian species [7] 10 aquatic macrophyte Terbutylazine, Freshwater species and a natural metsulfuron-methyl epiphyte community [8] Fish, amphibians, Nonylphenol Freshwater invertebrates (shrimp, ethoxylates, copepod, mussels, midge, nonylphenol ether daphnids), freshwater and carboxylate, marine organisms nonylphenol [9] Crustacea, Barium, cadmium, and Marine Mollusca, total polycyclic sediment Polychaeta, aromatic hydrocarbons Echinodermata, Platyhelminthes
[10] 17 species, fish, Chlorpyrifos Freshwater invertebrates
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[11] Algae, amphipod, fathead High-solubility alkyl Freshwater minnow sulfate [12] Soil invertebrates Atrazine, carbendazim, Soil chlorpyrifos, copper compounds, diazinon, dimethoate, hexachlorocyclohexane, lambda-cyhalothrin, parathion, pentachlorophenol, and propoxur [13-15] Algae, mulluscs, Ionizing contaminants Soil and crustaceans, fish, aquatic amphibians; annelida, ecosystems arthropoda, insects, mammals, reptiles, birds [16] Lymnaea stagnalis and Tributyltin Freshwater freshwater invertebrates and fish [17] Algae Fluoxetine, Freshwater fluvoxamine, sertraline [18] Microbial processes, Metals and organics Soil plants, Collembola, (e.g., DDT, PCP, PAH) Lumbricidae [19] Soil invertebrates and Linear alkylbenzene Soil plants sulphonate [20] Benthic invertebrates Copper Sediment (Lumbriculus variegatus, Tubifex tubifex, Chironomus riparius, Gammarus pulex, Hyalella 178ollus) [21] Marine sediment species Arsenic, cadmium, Marine of Hong Kong chromium, sediment copper, lead, mercury, quality nickel, silver, and zinc, guidelines total PAH, total PCB [22] three plant species, Organic waste Soil collembolans, enchytraeids, earthworms, microorganisms [23] 26 aquatic species (algae, 17R-Ethinyl estradiol Freshwater invertebrates, amphibians, fish)
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[24] Luminescent bacterium, Total PAH marine and mud shrimp, sea urchin, 2 freshwater insects, nematode, benthic amphipod [25] All available species Ammonia Freshwater and marine sediment [26] Green algae, daphnia, Toxins of Primnesium Freshwater rotifer, fish parvum [27] 3 soil invertebrates and 2 Broad-spectrum Soil plants bacteriostatic triclosan [28] Bacteria, microalgae, 2 brominated volatile Freshwater cladocerans, duckweed, organic compounds midge [29] Daphnia magna, Molybdate Freshwater Ceriodaphnia dubia green alga, rotifer, midge, snail (Lymnaea stagnalis), frog (Xenopus laevis), higher plant Lemna minor, fathead minnow, rainbow trout [30] All available toxicity data Boron compounds Freshwater [31] Ceriodaphnia dubia Chloride Freshwater Daphnia magna Oncorhynchus mykiss Pimephales promelas Lumbriculus variegatus Tubifex tubifex Chironomus dilutus Hyallela azteca Brachionus calyciflorus [31] Cladoceran, Sulphate Freshwater Rotifer, amphipod , 3 species of fish, pacific tree frog, 2 plants, moss
[32] All available benthic Copper Sediment species [33] All available data Steroid estrogens Freshwater [34] All available data 4-nonylphenol Freshwater
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[35] All available data Zinc, cadmium, Freshwater hexavalent chromium, benzene, nitrobenzene [36] All available marine molybdate Marine species [37] Chinese aquatic animals Tetrabromobisphenol A Freshwater and plants [38] All available species molybdate Marine and freshwater [39] Fish, amphibians, 2,4-dichlorophenol, Freshwater molluscs, crustaceans, 2,4,6-trichlorophenol algae and pentachlorophenol [40] Freshwater and saltwater Pyrethroid insecticide Freshwater vertebrates and permethrin invertebrates [41] Daphnia magna, Daphnia Mercury Freshwater longispina, Pseudokirchneriella subcapitata, Chlorella vulgaris, Lemna minor, Chironomus riparius [42] 8 species from 5 Glyphosate-base Freshwater taxonomic groups herbicide roundup [43] Microalgae, polychaetes, Mercury, copper, Marine bivalves, crustaceans, cadmium, lead, zinc ecosystems echinoderms, chordates, fish [44] Aphipods, mayflies, Nickel Soil oligochaetes, mussels, midges
Table E2. Recent studies where the SSD approach has been applied for the risk assessment in a specific region. Reference Species Stressor Study area [45] Aquatic species Arsenic, cadmium, 17 North Carolina Not clear, which copper, lead, basins species mercury, and zinc [46] Algae, poriferans, Zinc Dutch surface waters molluscs, crustaceans, insects, fish [47] Fish, amphibians, Herbicide tebuthiuron Freshwater fauna aquatic plants, diatoms and flora of northern Australia’s tropical wetlands
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[48] 2 fish species, Short-chain Aquatic and Daphnia, 2 algae chlorinated paraffins sediment organisms species, shrimp, midge in Japanese Arakawa Tamagawa, Yodogawa rivers [49] Arthropods DDT, DDD, DDE Whole sediment in chlordane and south Florida endosulfan freshwater canals [50] Aquatic species 343 high-production- Coastal North Sea volume chemicals ecosystem [51] Algea, invertebrates, Alcohol ethoxylates Aquatic fish (17 species) environments of Europe and North America [52] Arthropods and fish Cypermethrin, Open estuary, endosulfan, Western Cape, South chlorpyrifos, Africa fenvalerate [53] Algae, hydra, rotifers, Bisphenol A North American and molluscs, crustaceans European surface (both benthic and waters pelagic), insects, annelids, fish, and amphibians [54] Marine sediment Cadmium, copper and Sydney Harbour species zinc Australia [55] Macroalgae, Nitrified sewage Bass Strait, Victoria, microalgae, scallop, effluent Australia sea urchin [56] All species for which 200 pesticides North Sea coastal toxdata were available ecosystem, ranking pesticides [57] Aquatic plants and Herbicides (ametryn, South Florida algae atrazine, simazine, freshwater prometryn, ecosystem hexazinone, metribuzin, diuron, linuron, uracil) [58] 29 marine and Tributyltin Dutch harbours and freshwater species open coastal waters (algae, annelida, 181ollusk181181m, 181ollusk181181mata, 181ollus, 181ollusk181, pisces)
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[59] 2 plants, 3 Retention pond water Edith River, northern invertebrates, 1 fish from a gold mine Australia [60] Algae, crustacean, Unionized ammonia Harbour area in mollusc, echinoderm, Hong Kong fish [61] 19 different species Zinc 9 European river belonging basins in Germany, to fish (eight different Belgium, France species), invertebrates (four crustaceans, one insect, two rotifers, two molluscs) and algae (two different green algae species) [62] All data Zinc Freshwater and marine sites in Japan, ranking sites [63] 21 species of algae, 14 phenols Taihu lake, China, invertebrates and fish ranking phenols [64] Algae, ciliate, Zinc Surface waters in molluscs, crustaceans, Japan amphipods, insects, fish [65] Detritus, DDT Bohay Bay, China Phytopankton, Micozooplankton, Herbivorous feeders, Macrozooplankton, Small mollusca, Small crustacean, Large mollusca, Large crustacean, Small pelagic fish, Demersal fish, Benthic feeders, Top pelagic feeders [66] All available data for Bisphenol-A, Tokyo surface algae and higher chloroform, waters, ranking risks plants, hexavalent invertebrates, or chromium, lead, zinc, vertebrates ammonia, copper, nickel, manganese [67] All available data for Copper, zinc, nickel Surface water type in fish, molluscs, the Netherlands, invertebrates, algae, space and time insects, amphibians ranking
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[68] All available data 60 substances Catalan river basins monitored in the river basin [69] Aquatic plants Herbicides atrazine, South Florida canals bromacil, metolachlor, norflurazon, simazine [70] Anura and caudate Ionizing radiation Japan species [71] 6 Chinese aquatic 2,4,6-trichlorophenol China species [72] All aquatic species organochlorine Lake Chaohu, China pesticides: p,p DDT,γ-HCH, heptachlor, aldrin, endrin, [73] Algae, Daphnia, fish Soil washing effluent Nagano and Niigata waters sites, Japan [74] Anuran species Ammonium, Dutch surface cadmium, copper, 18 waters, risk ranking pesticides, pH [75] All available data PAH River basins in China [76] 6 indigenous Chinese 3 chlorphenols Surface waters in specie (benthic China invertebrates, fish, algae) [77] Bacterial taxa Antibiotics Worldwide ciprofloxacin, erythro mycin, and tetra• cycline [78] Marine algae and fish 2,2’- Inland See of Japan dipyridyldisulfide [79] Marine species antifouling biocide, Hiroshima Bay, pyridine Japan triphenylborane [80] All available data PAHs Lake Chaohu, China, ranking of PAHs [81] Crustaceans, insects, Organochlorine Lake Small spider pesticides HCH, DDT Baiyangdian, China [82] Crustaceans, DDT and its Rivers, lakes, amphibians, fish, algae metabolites reservoirs in Haihe Plain, China [83] Algae and aquatic 30 herbicides and Bay of Vilaine area, plants biocides France
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[84] Unicellular organisms, 5 nanomaterials Surface water, algae, invertebrates, sewage treatment and vertebrate plant effluents, soils, sludge-treated soils [85] Terrestrial plants Ozone North-western Europe [86] Aqautic plants, fish , Antibiotic Farm wastewaters in algae oxytetracycline China [87] Algae, amphibian, Herbicide glyphosate Vineyard river in fish, molluscs and its metabolite western Switzerland [88] Algae, crustaceans, Phthalate esters Lake Chaohu, China insects, molluscs, amphibians, fish
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Table E2 continues
[89] Arthropods, algae and macrophyte Neonicotinoid insecticide Portugese rice plot imidacloprid [90] Fish, invertebrates and algae 15 pesticides monitored in the river Lourens river catchments, Western catchment Cape, South Africa
Table E3. Overview of case studies using SSD (AD - Anderson-Darling test; KS - Kolmogorov-Smirnov test; SW - Shapiro-Wilk; NR- not reported; NA- not applicable). Stressor Species/ species group Min Endpoint Distribution type Data fit Ref. data test points Suspended clays, burial by Marine species 12 LC50, escape Log-normal NR [91] sediment, change in potential sediment grain size Salinity Macroinvertebrates 5 LC50 Burr type III, NR [92] Kaplan-Meier survival function, Bayesian models Salinity Macroinvertebrates 41 LC50 Kaplan-Meier NR [93] method Salinity Macroinvertebrates 48 LC50 Log-normal SW [94] log-logistic, Kaplan-Meier method Temperature Fish 22 Max, min Normal KS [95]
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Temperature, salinity Molluscs 8 Max record in Log-logistic NR [96] the field Increasing water Aquatic species 50 LC50 Normal NR [97] temperature Low oxygen Fish 4 LOEC, Normal KS, SW [98] LC100 15 toxicants Vertebrates, invertebrates, 3 LOEC Log-normal Chi- [99] algae bootstrap square, KS, SW 16 insecticides Vertebrates (fish, 6 LC50 or EC50 Log-normal AD [100] amphibians), arthropods for immobility, (crustaceans, insects; or biomass freshwater-saltwater) 21 chemicals: NH4, metals, Freshwater-saltwater 6 LC50 Log-normal NR [101] pesticides vertabrates, invertabrates log-logistic Endosulfan Invertebrates, fish, 14 LC50 Burr type III NR [6] amphibian 9 herbicides Fish, invertebrate, 6 NOEC, EC50 Log-normal AD [102] macrophytes, algae PAH -2-methyl naphthalene Arctic-temperate marine 17 LC50, NOEC Log-normal AD [103] species Oil components Marine species 10 LC50 Log-normal KS, SW [104] Malathion, carbendazim Fish, arthropods 5 LC50 Log-normal AD [105]
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68 chemicals Saltwater and freshwater 10 LC50 Log-logistic NR [106] fish, invertebrates (mysids, mussels, daphnids) Cu, Zn Cladoceran species 3 LC50 Log-normal AD [107] 17-R-Ethinyl Estradiol Aquatic invertebrates, 26 NOEC Weibull AD [23] fish, amphibian reproduction 18 chemicals (metals, DDT, Soil species from 3 NOEC Log-normal NR [108] naphthalene, endosulphan) freshwater species by soil- water partitioning coefficient Nonylphenol Freshwater and marine 16 Geometric Log-normal NR [8] vertebrate, invertebrate, mean of NOEC algae and LOEC 11 pesticides Soil invertebrates 5 LC50 Log-normal AD [12] Atrazine, prometryn, Micro- and macro- 10 LC50 Log-normal NR [109] isoproturon algae 42 fungicides Aquatic vertebrates, 6 EC50 mortality Log-normal AD [110] invertebrates, macrophytes, or algae immobilization Tributyltin and linear Marine molluscs, chrodata, 4 NOEC and Log-logistic NR [3] alkylbenzene sulfonates arthropod, phytoplankton, LC50 log-normal bacteria Heavy metals, ammonia, Fish, algae, invertebrates 11 NOEC (direct Log-logistic NR [111] household- products or with ACR of 10)
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Alcohol ethoxylates Fish, algae, invertebrates 17 EC10 and Log-normal NR [51] NOEC 10 herbicides Aquatic plants and algae 4 EC50 Log-logistic NR [57] population growth, photo- synthesis rate Metribuzin, Algae, vascular plants, 4 NOEC, LC50 Log-normal AD [5] metamitron invertebrates, fish Zn Algae, molluscs, 21 NOEC Pareto AD [46] crustaceans, insects, fish Linear alkylbenzene Soil invertebrates and 21 NOEC, EC10 Log-normal KS [19] sulphonate plants 7 insecticides Arthropods, fish 6 LC50 Burr type III AD [52] Endosulfan Arthropods, fish 5 NOEC, LC50 Log-logistic NR [112] Chlorpyrifos Fish and invertebrates 22 LC50 Log-normal Chi- [113] square 19 herbicides, 13 Groundwater-dwelling 5 LC50 Burr type III Non- [114] insecticides, 2 fungicides invertebrate orders linear (Crustacea, Rotifera) regr. Pentachlorphenol Fish, crustacean, mollusc, 6 NOEC, LC50 Burr type III AD [115] hydrophyte Petroleum hydrocarbons Marine and freshwater 6 50% LR based ETX program AD [24] sediment invertebrates on internal outcome KS concentrations
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Antifouling paint irgarol Marine and freshwater 21 EC50 Probit model NA [116] autotrophs
Tributyltin Marine and freshwater 7 NOEC and Log-logistic NR [16] species fish and LOEC invertebrates Herbicide tebuthiuron Fish and green alga 5 NOEC and Log-logistic AD [47] EC50 Weibull Log-normal
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species sensitivity distributions. Environmental Toxicology and Chemistry 21:2459-2467. 102. den Brink PJ, Blake N, Brock TCM, Maltby L. 2006. Predictive value of species sensitivity distributions for effects of herbicides in freshwater ecosystems. Human and Ecological Risk Assessment 12:645-674. 103. Olsen GH, Smit MGD, Carroll J, Jaeger I, Smith T, Camus L. 2011. Arctic versus temperate comparison of risk assessment metrics for 2-methyl-naphthalene. Marine Environmental Research 72:179-187. 104. de Hoop L, Schipper AM, Leuven RSEW, Huijbregts MAJ, Olsen GH, Smit MGD, Hendriks AJ. 2011. Sensitivity of Polar and Temperate Marine Organisms to Oil Components. Environmental Science & Technology 45:9017-9023. 105. Rico A, Waichman AV, Geber-Correa R, van den Brink PJ. 2011. Effects of malathion and carbendazim on Amazonian freshwater organisms: comparison of tropical and temperate species sensitivity distributions. Ecotoxicology 20:625-634. 106. Raimondo S, Vivian DN, Delos C, Barron MG. 2008. Protectiveness of species sensitivity distribution hazard concentrations for acute toxicity used in endangered species risk assessment. Environmental Toxicology and Chemistry 27:2599-2607. 107. Bossuyt BTA, Muyssen BTA, Janssen CR. 2005. Relevance of generic and site- specific species sensitivity distributions in the current risk assessment procedures for copper and zinc. Environmental Toxicology and Chemistry 24:470-478. 108. Jong IHD, Ragas AMJ, Hendriks HWM, Huijbregts MAJ, Posthuma L, Wintersen A, Hendriks AJ. 2009. The impact of an additional ecotoxicity test on ecological quality standards. Ecotoxicology and Environmental Safety 72:2037-2045. 109. Schmitt-Jansen M, Altenburger R. 2005. Predicting and observing responses of algal communities to photosystem II-herbicide exposure using pollution-induced community tolerance and species-sensitivity distributions. Environmental Toxicology and Chemistry 24:304-312. 110. Maltby L, Brock TCM, van den Brink PJ. 2009. Fungicide Risk Assessment for Aquatic Ecosystems: Importance of Interspecific Variation, Toxic Mode of Action, and Exposure Regime. Environmental Science & Technology 43:7556-7563. 111. De Zwart D, Dyer SD, Posthuma L, Hawkins CP. 2006. Predictive models attribute effects on fish assemblages to toxicity and habitat alteration. Ecological Applications 16:1295-1310. 112. Rand GM, Carriger JF, Gardinali PR, Castro J. 2010. Endosulfan and its metabolite, endosulfan sulfate, in freshwater ecosystems of South Florida: a probabilistic aquatic ecological risk assessment. Ecotoxicology 19:879-900. 113. Crane M. 2003. Proposed development of Sediment Quality Guidelines under the European Water Framework Directive: a critique. Toxicology Letters 142:195- 206. 114. Hose GC. 2005. Assessing the need for groundwater quality guidelines for pesticides using the species sensitivity distribution approach. Human and Ecological Risk Assessment 11:951-966. 115. Jin XW, Zha JM, Xu YP, Giesy JP, Wang ZJ. 2012. Toxicity of pentachlorophenol to native aquatic species in the Yangtze River. Environmental Science and Pollution Research 19:609-618.
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116. Zhang AQ, Leung KMY, Kwok KWH, Bao VWW, Lam MHW. 2008. Toxicities of antifouling biocide Irgarol 1051 and its major degraded product to marine primary producers. Marine Pollution Bulletin 57:575-586
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Summing up
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Summary | Given the impossibility of testing the effects of all chemical compounds on all species, traditional approaches to risk assessment are based on observing the effects of chemicals on the survival, growth, and reproduction of a few selected test species. The results of such single-species, organism level toxicity tests can be extrapolated to the multiple- species ecosystem level by using the Species Sensitivity Distributions (SSDs) approach. The SSD approach is based on the common observation that species differ in their sensitivity to the same chemical stressor and that inter-species differences can be large. For derivation of environmental quality criteria (EQC) in risk assessment, SSDs are used to estimate a level of exposure that is not to be exceeded, e.g., HC5 in several European legislations. As a risk estimate, SSDs are used to assess the potentially affected fraction (PAF), which is the proportion of species exposed to a measured or expected concentration generating an adverse effect.
Although the SSD approach in ecological risk assessment is accepted by policy makers, scientific discussion continues concerning the lack of “ecology” in the concept, various technical and statistical issues, and risk interpretation. Several of these issues were addressed in the present thesis. The SSD approach has been defined as “interesting and powerful” due to its practical usefulness in applications in management problems and decision making. However, the potential of this approach for application in scientific research has not been fully investigated. Therefore, this thesis explored applications of the SSD approach in different case studies for ecological risk assessment.
In Chapter 2, the relation between responses on different levels of biological organization was investigated. Comparison of tolerance to chemical stressors between native and non-native fish species in the Netherlands was performed in Chapter 3. In chapter 4, various stressors were ranked according to their potential to cause harm to local species assemblages. In chapter 5, the question whether the impacts of chemical and non-chemical (physical) stressors can be compared in a similar way by the means of SSD approach was discussed. Such a comparative approach revealed, which type of stressor (i.e., chemical vs. physical) has more severe impacts on ecosystems. Finally, the application of the SSD approach in case studies for ecological risk assessment was explored in Chapter 6. In conclusion of the thesis, general discussion was provided in Chapter 7.
The basis for the SSD approach has been criticized because the organism level endpoints used in SSDs may not be related directly or consistently to the effects at higher levels of biological organization, e.g., populations and ecosystems, which are the ultimate targets for protection in ecological risk assessment. In chapter 2, the link between the effects observed at different levels of biological organization was explored by
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investigating scientific literature. The ecotoxicological relevance of responses at the sub-cellular, i.e., changes in gene expression, and their potential to predict the effects on higher levels of biological organization was investigated on the example of aquatic species exposed to cadmium. It was found that cadmium concentrations causing significant changes at genome level were on average a factor of two higher than NOEC at the whole organism level. The SSD curve based on the gene-level endpoints was shifted to the right of the curve based on NOECs and subsequently the related HC5 was higher. Therefore, based on the analysis of the available data used in chapter 2, the use of gene expression changes as early warning indicators for effects on higher levels of biological organisation has not been proven. Moreover, in Chapter 2 based on the analysis of studies investigating the effects of chemicals at sub-cellular levels, a major gap was identified between gene and cell responses on the one hand and the individual and population responses on the other. Therefore, the challenge for the future research would be to develop methods to link organism effects to responses on higher levels of biological organization.
In the case study presented in Chapter 3, a potential of the SSD approach was explored to identify possible differences between tolerance of native and non-native fish species in the river Rhine to diverse chemical compounds including pesticides and metals (in total 21 chemicals). The SSD for 9 chemical compounds derived from acute toxicity tests were combined with the environmental concentrations measured in the Delta Rhine (in the Netherlands) in the period 1978-2010. The HC50 values derived from SSDs for each chemical compound did not differ significantly between native and non-native species. The lack of difference in tolerance to chemical stressors between native and non-native fish species may be related to introduction pathways, since many of the fish species in the river Rhine were deliberately introduced. Species that survive harsh environmental conditions during dispersal routes (e.g., in ballast water or migration through canals between river basins) to become invasive elsewhere may, however, be more tolerant than native ones. The PAF based on effects addition from 9 stressors was slightly higher for the native species (38 %) than for the non-native species (31 %). The highest contribution to the overall PAF of both species groups was noted for ammonium, followed by azinphos-methyl, copper, and zinc. Deriving a PAF for each species group was shown to serve as a useful tool for identifying stressors with a relatively highest impact on species of concern and can be applied to water pollution control. Overall, it was concluded in this chapter that for management of freshwater ecosystems potentially affected by non-native species, attention should be given also to temperature, hydrological regimes, and habitat quality.
The SSD approach generating a set of HC5 values for different chemical compounds can facilitate ranking of these compounds in their relative hazard for assemblages of
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tested species. In chapter 4, ecological risks (ERs) of multiple stressors including metal, pesticides and pH were derived for anuran species in the Dutch water bodies to investigate the relative importance of individual stressors that possibly influence populations of anurans. The ER was quantified as the probability that a measured environmental concentration of a particular stressor in habitats where anurans were observed would exceed the toxic effect concentrations derived from the SSDs. The stressors causing the major potential impact on anurans were identified and ranked. The highest values of ER were noted for pH, copper, diazinon, ammonium, and endosulfan. It was shown that the method of deriving ER by combining field observation data and laboratory data provides insight into potential threats to species in their habitats and can be used to prioritize stressors, which is necessary to achieve effective management in amphibian conservation.
Applying the SSD approach to quantify the impact of non-chemical stress in a similar way to ecotoxicological assessments may allow for comparing different types of impacts on ecosystems and prioritizing the results for a better ecological management. In Chapter 5 the effect of water-flow velocity on occurrence of fish species was assessed quantitatively, where an SSD for water-flow velocity related to fish biogeography in rivers was developed. By calculating the potentially affected fraction of the fish species of the river Rhine, effects of water-flow velocity on different life stages and guilds were estimated. It was found that the maximal velocity tolerance and the length of fish at the adult stage were positively correlated. Also, rheophilic species were found to tolerate higher velocities than eurytopic and limnophilic species. Adult fish were less affected by high water-flow velocities than juveniles, larvae and eggs. The calculated HC50s indicate that water-flow of around 25 and 60 cm/s may potentially affect 50% of the species in their juvenile and adult life stage, respectively. Comparison of PAFs for water-flow velocity and chemical stressors revealed that on average, the effects of water-flow velocities would be similar to or higher than the effects of chemical stressors.
Chapter 6 gives an extended overview of case studies showing that the complexity and diversity of topics, where the SSD approach is applied, has grown in the last two decades. The potential of the SSD approach has been realized in various case studies investigating inter-specific variation in sensitivity of different taxonomic groups from different geographic locations and different habitats. The overall findings from various case studies show that the sensitivity of organisms to toxicants is independent of their geographic origin and that there is no consistent geographical pattern in species sensitivity. Application of the SSD approach in several case studies focusing on species from different habitat types did not indicate systematic or consistent differences in the sensitivity of marine versus freshwater taxa. Thus, protection levels, e. g., HC5 derived from freshwater species only, may be still uncertain for marine species. Further research
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would be needed to investigate the protectiveness of the threshold levels derived for species from one type of habitats for species from different types of habitats. The influence of a taxonomic group on deriving the HC5 has been intensively studied in terms of finding the most sensitive group or species that influence the SSD curve the most. Out of all species for which toxicity data exist cladocerans appeared more often than other species as the most sensitive toward various chemical compounds. However, depending on the chemical mode of action any species can be particularly the most or the least sensitive. Obviously, including less sensitive species will increase the HC5. According to the results of several case studies, taxonomic composition and the mode of action of chemicals have the greatest influence on the SSD curve and derived HCx. As much as the areas of research of SSD application varied, the diversity in distribution models can vary greatly, too. Preferences in case studies have been given to log-normal distributions although various statistical methods for fitting SSD curves have been proposed by various authors.
In Chapter 7, overall discussion on the previous chapters has been presented providing the answers to the research questions put forward in the introduction of the present thesis:
In what way do the responses at the sub-cellular level predict the effects on higher levels of biological organization?
Based on the analysis of the available data used in chapter 2, the use of gene expression changes as early warning indicators for effects on higher levels of biological organisation has not been proven. The responses at the sub-cellular level had been used to distinguish the type of contamination or the mechanism of action of the chemicals rather than used to predict effects on exposed populations.
Can the SSD approach be applied to reveal potential differences in sensitivity between native and non-native species to certain environmental stressors?
The SSD approach can be applied to mechanistically compare sensitivities between native and non-native species to various stressors. The differences revealed by this approach were related to water temperature between native and non-native fish and mollusc species. That is, as non-native fish species were found to be less sensitive to water temperatures than native species. Similar differences in sensitivity to high temperature were reported for non-native and native mollusc species of the river Rhine. Comparing sensitivity of species to other stressors, no significant differences were revealed between the assemblages of native and non-native fish species characteristic to the river Rhine to 21 chemical compounds including ammonium, pesticides and metals. In a comparison of a similar set of fish no differences in sensitivity between
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native and non-native species were found for low oxygen concentrations. Similarly, sensitivity to desiccation and water salinity did not differ between native and non-native mollusc species. Currently, the research on native and non-native species applying the SSD as a tool to investigate the differences in species sensitivity focuses on the fish and molluscs of the river Rhine. In future research more attention could be given to other taxonomic or functional groups, non-natives to other regions and stressors such as alteration of hydrological regimes, habitat quality and combined effects of chemical and physical stressors.
Can the SSD approach be used to rank stressors measured in actual and potential habitats of a specific taxonomic group?
A straightforward comparison can be made between an SSD for a specific taxonomic group and field measurement in its habitats to derive a measure of magnitude of potential ecological impact for different stressors. Such measure of impact can be expressed as PAF, ER or HC5 enabling the relative ranking of the stressors. Ranking of a set of chemical compounds measured in the habitats of anuran and fish species in the Netherlands was performed in chapters 3 and 4 of the present thesis showing that risk ranking for these taxonomic groups varied with the highest risk to fish due to ammonium, and to anurans due to copper.
What are the potential and limitations of the application of the SSD approach to non-chemical stressors?
The application of the SSD approach to non-chemical stressors as such may complement ecological risk assessments. Including both types of stressors affecting habitats of species in one overall assessment may facilitate the comparison of impact and give possibilities to rank and prioritize among various stressors. The success of the application of the SSD approach depends, however, on the quantity and quality of the available input data. However, the data for non-chemical stressors that can be directly applied as input for SSDs are limited. Therefore, standardization in the derivation of effect levels for non-chemical stressors similar to chemical stressors is essential for the comparison of effects caused by these types of variables.
How are SSDs being applied in scientific research for risk assessment of (non-) chemical stressors on aquatic and terrestrial ecosystems?
The overview of research papers published in scientific journals in the last two decades has shown that the topics, where the SSD approach is applied, can be complex and diverse. The SSD approach is used not only for deriving environmental quality criteria and ecological risks for various chemicals, but also in various “non-standard” case studies. By applying the SSD approach, numerous researchers examine the sensitivities
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between species in different geographic locations, habitat types (e.g., marine vs. freshwater, lentic vs. lotic); between native and non-native, endangered and common species; among various taxonomic groups depending on the mode of action of chemical compounds; or rank stressors or polluted sites according to the potential impact on certain species. The application of the SSD approach is also explored for non-chemical stressors and by deriving the effect levels from field-based data. The majority of the studies focuses on applying SSDs for risk assessment in freshwater ecosystems, followed by soil and marine ecosystems, whereas sediment, air and groundwater risk assessment receive less attention.
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Samenvatting | Het rechtstreeks bepalen van de effecten van alle chemische stoffen op alle soorten planten en dieren is onmogelijk. Traditionele benaderingen van risicobeoordeling (in het Engels: Risk Assessment) van stoffen zijn daarom vaak gebaseerd op de effecten op overleving, groei en reproductie van een beperkt aantal soorten. De resultaten van dergelijke toxiciteitstesten kunnen echter worden geëxtrapoleerd naar het niveau van ecosystemen met behulp van de Species Sensitivity Distributions (SSD) benadering. Deze SSD-benadering is gebaseerd op het gegeven dat soorten onderling sterk kunnen verschillen in hun gevoeligheid voor dezelfde (chemische) stressor. Voor het afleiden van milieukwaliteitscriteria (met een Engelse afkorting EQCs) ten behoeve van risicobeoordeling, worden SSDs gebruikt voor het maken van een schatting van het niveau van blootstelling dat niet overschreden mag worden, zoals vastgelegd in diverse Europese wetten en regels. Een voorbeeld daarvan is de HC5, de concentratie van een stof waarbij maximaal 5% van de onderzochte soorten een mogelijk effect zou kunnen ondervinden. Ten behoeve van risicoschatting worden SSDs gebruikt om een indicatie te geven van de Potentieel Aangetaste Fractie (Potentially Affected Fraction; PAF). De PAF is het relatieve aantal soorten dat negatieve effecten ondervindt wanneer ze worden blootgesteld aan een gemeten of verwachte concentratie van een schadelijke stof.
Hoewel de SSD-benadering in ecologische risicobeoordeling ten behoeve van het beleid inmiddels geaccepteerd is, gaat de wetenschappelijke discussie over deze benadering nog steeds door. Deze discussie richt zich thans vooral op het gebrek aan “ecologie” in het concept, op diverse technische en statistische onderwerpen en op de interpretatie van risico’s. In het voorliggende proefschrift wordt op een aantal van deze discussiepunten ingegaan. Ondanks de wetenschappelijke kanttekeningen die er bij gemaakt kunnen worden, wordt de SSD-benadering beschouwd als “interessant en krachtig” vanwege haar praktische bruikbaarheid voor beleid en beheer. De mogelijkheden van de benadering voor toepassing in wetenschappelijk onderzoek zijn echter nog niet ten volle onderzocht. Dit proefschrift verkent daarom deze toepassingsmogelijkheden in een aantal case studies van ecologische risicobeoordeling.
Het fundament van de SSD-benadering is aan heftige kritiek onderhevig geweest omdat de effecten op het niveau van individuele organismen niet direct of consistent gerelateerd hoeven te zijn aan effecten op hogere biologische organisatieniveaus zoals populaties en ecosystemen, terwijl ecologische risicobeoordeling zich juist richt op bescherming van die hogere niveaus. In hoofdstuk 2 is de relatie tussen effecten op verschillende niveaus van biologische organisatie onderzocht. De ecotoxicologische betekenis van responses op subcellulair niveau, te weten veranderingen in de expressie van genen, en hun potentie om effecten op hogere biologische organisatieniveaus te voorspellen, is onderzocht aan de hand van aquatische soorten die aan cadmium waren
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blootgesteld. Uit de studie bleek dat cadmiumconcentraties die tot significante veranderingen op het niveau van expressie van het genoom van een organisme leidden, gemiddeld een factor twee hoger lagen dan de NOECs (No Observed Effect Concentrations) voor individuele organismen. De SSD-curve gebaseerd of effecten op het niveau van genetische expressie is daarmee rechts van de curve gebaseerd op NOECs op organisme-niveau gesitueerd. Dientengevolge was ook de HC5 afgeleid van responses op genetisch niveau hoger en wordt dus wellicht onterecht een hogere concentratie als “veilig” beschouwd bij gebruik van data die betrekking hebben op de effecten op subcellulair niveau. Op basis van de analyse van thans beschikbare data kan het nut van responses op genetisch niveau als een soort “early warning” indicatoren voor effecten op hogere biologische organisatieniveaus dus ook nog niet als bewezen worden beschouwd. Gebaseerd op een analyse van de studies die de effecten van chemische stoffen op subcellulair niveau onderzocht hebben, kan bovendien geconcludeerd worden dat er een groot gat bestaat tussen de responses op genetisch en cellulair niveau aan de ene kant en individuele en populatieresponses aan de andere kant. Voor toekomstig onderzoek is het daarom een grote uitdaging om methoden te ontwikkelen waarmee effecten op organismen gerelateerd kunnen worden aan responses op hogere biologische organisatieniveaus.
In hoofdstuk 3 zijn de potenties van de SSD-benadering onderzocht om mogelijke verschillen te identificeren in de tolerantie van vissoorten voor diverse chemische substanties inclusief pesticiden en metalen (in totaal 21 verschillende chemische stoffen). Daarbij zijn inheemse respectievelijk niet-inheemse soorten van de Rijn met elkaar vergeleken. Tevens zijn de SSDs, gebaseerd op acute toxiciteitstesten, voor negen van de onderzochte chemische stoffen vergeleken met de daadwerkelijk in de periode 1978-2010 gemeten concentraties van deze stoffen in de Nederlandse Delta Rijn. De HC50 waarden afgeleid van de SSDs voor elke onderzochte chemische stof bleken niet significant te verschillen voor inheemse en niet-inheemse soorten. Het gegeven dat inheemse en niet-inheemse vissoorten niet significant bleken te verschillen in tolerantie voor chemische stressoren, heeft echter wellicht te maken met de wijze waarop niet-inheemse vissoorten in de Rijn terecht zijn gekomen. Veel soorten zijn immers opzettelijk geïntroduceerd. Soorten die andere dispersieroutes hebben gevolgd met veel zwaardere milieudruk (bijvoorbeeld in ballastwater of via gegraven kanalen tussen rivierstroomgebieden) om vervolgens in hun nieuwe leefgebied invasief te worden, zouden wellicht toleranter kunnen zijn dan inheemse soorten. Wanneer de effecten van de negen verschillende stressoren bij elkaar werden genomen en op basis hiervan een PAF werd berekend, dan bleek deze PAF voor inheemse soorten iets hoger te zijn (38%) dan voor niet-inheemse soorten (31%). Voor beide groepen geldt dat de grootste bijdrage aan de gecombineerde PAF werd gevormd door ammonium, gevolgd door azinphos-methyl, koper en zink. Het op deze wijze afleiden van een PAF voor elke
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soortgroep bleek een bruikbare manier voor het identificeren van de stressoren met de (relatief) hoogste impact op de verschillende soorten en kan een instrument zijn dat kan worden ingezet voor vraagstukken op het gebied van de aanpak van waterverontreiniging. Verder bleek uit deze studie dat het in beschouwing nemen van temperatuur, hydrologische regimes en habitatkwaliteit wellicht relevant is voor het beheer van zoetwaterecosystemen in relatie tot de aanwezigheid van niet-inheemse soorten.
Wanneer met behulp van de SSD-benadering HC5-waarden bepaald worden voor verschillende chemische substanties, dan kunnen deze stoffen op deze wijze gerangschikt worden voor de onderzochte soorten of soortverzamelingen. In hoofdstuk 4 zijn op deze wijze de ecologische risico’s (ERs) van een scala aan stressoren, inclusief metalen, pesticiden en pH, afgeleid voor Anura-soorten (kikkers en padden) in Nederlandse wateren en kon het relatieve belang van de afzonderlijke stressoren voor kikker- en paddenpopulaties bepaald worden. Het ER is daarbij uitgedrukt als de waarschijnlijkheid dat een daadwerkelijk in het leefgebied gemeten concentratie van een bepaalde stressor, de toxische effect concentraties voor padden en kikkers, afgeleid van de SSDs, zou overschrijden. De stressoren met het potentieel grootste effect konden zo geïdentificeerd worden en ten opzichte van elkaar worden gerangschikt. De hoogste ER-waarden werden vastgesteld voor pH, koper, diazinon, ammonium en endosulfan. Uit de studie bleek dat de methode van het afleiden van ER-waarden door waarnemingsdata uit het veld (stoffen en soorten) te combineren met laboratoriumdata, inzicht oplevert in de mogelijke bedreigingen van soorten in hun daadwerkelijke leefgebieden. Bovendien kan de methode gebruikt worden voor het prioriteren van stressoren hetgeen noodzakelijk is voor het effectief beschermen van populaties.
Indien de SSD-benadering ook gebruikt kan worden voor het vaststellen van de impact van niet-chemische stressoren op een wijze die vergelijkbaar is met ecotoxicologische beoordelingen, dan kunnen effecten op ecosystemen van geheel verschillende aard met elkaar vergeleken worden en worden geprioriteerd met het oog op een effectiever beheer. In hoofdstuk 5 is daartoe het effect van stroomsnelheid op het voorkomen van vissoorten in de Rijn op een kwantitatieve wijze vastgesteld met behulp van de SSD- benadering. Door de PAF als functie van de stroomsnelheid te berekenen, konden de effecten van stroomsnelheid op verschillende levensstadia en visgildes worden geschat. De maximaal tolereerbare stroomsnelheid bleek positief gecorreleerd te zijn met de lengte van de vis in het adulte stadium. Verder bleken rheofiele vissoorten hogere stroomsnelheden te tolereren dan eurytope en limnofiele soorten. Vissen in het adulte stadium bleken minder beïnvloed te worden door hoge stroomsnelheden dan juveniele individuen, larven en eieren. De berekende HC50s geven aan dat stroomsnelheden van circa 25 en 60 cm/s effect kunnen hebben op soorten in hun juveniele respectievelijk
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adulte levensstadia. Vergelijking van de PAFs voor stroomsnelheid met chemische stressoren liet verder zien dat gemiddeld genomen de effecten van stroomsnelheid vergelijkbaar of zelfs hoger zijn dan de effecten van chemische stressoren.
Hoofdstuk 6 geeft een overzicht van de toegenomen complexiteit en diversiteit van toepassingen van SSDs zoals die in de afgelopen twee decennia zijn gepubliceerd in de wetenschappelijke literatuur. De mogelijkheden van toepassing van de SSD-benadering worden aangetoond op basis van verschillende case studies waarin de interspecifieke variatie in gevoeligheid is onderzocht voor diverse taxonomische groepen uit verschillende geografische regio’s en verschillende typen leefgebieden. De studies laten zien dat de gevoeligheid van organismen voor toxische stoffen onafhankelijk is van de geografische oorsprong van de onderzochte soorten en dat er geen consistent geografisch patroon in de gevoeligheid van soorten bestaat. Toepassing van de SSD- benadering voor soorten van verschillende typen leefgebieden gaf geen indicatie voor systematische of consistente verschillen in gevoeligheid van soortgroepen uit mariene versus zoetwatermilieus. Een bepaald beschermingsniveau, bijvoorbeeld een HC5, dat zuiver en alleen is afgeleid van zoetwatersoorten biedt dus niet vanzelfsprekend ook bescherming aan mariene soorten. Dat betekent dat nader onderzoek vereist is naar de mate van bescherming die drempelwaarden, afgeleid van soorten van een bepaald leefgebiedtype, bieden aan soorten uit andere typen leefgebieden. Ook de invloed van de bestudeerde taxonomische groepen op het afleiden van een HC5 is intensief onderzocht met het oog op het vinden van de meest gevoelige soortgroepen. Van alle soortgroepen waarvoor toxiciteitsdata beschikbaar zijn bleken watervlooien vaker dan andere groepen het meest gevoelig te zijn. Afhankelijk van de wijze waarop een bepaalde chemische stof werkt in een organisme (mode of action) bleken er echter ook grote verschillen te zijn tussen soorten waarbij dan de ene, dan de andere soort het meest of minst gevoelig bleek te zijn. Het meenemen van minder gevoelige soorten zal – vanzelfsprekend – leiden tot een hogere HC5. De resultaten van diverse case studies laten dan ook zien dat de taxonomische samenstelling van de onderzochte groep van organismen en de werkingswijze van de onderzochte stoffen de grootste invloed hebben op de onderzochte SSD-curve en daarmee dus ook op de daarvan afgeleide HCx. Evenzeer als er grote verschillen waren tussen de diverse onderzoeksvelden waarin SSDs worden toegepast, bleken er ook grote verschillen te zijn in de gebruikte verdelingsmodellen. Veel auteurs bleken log-normale verdelingen te prefereren, maar diverse onderzoekers hebben ook vele andere statistische methoden voorgesteld om SSD-curves zo goed mogelijk te laten fitten.
In hoofdstuk 7 worden de in de inleiding gestelde onderzoeksvragen in samenhang bediscussieerd.
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Tot op welke hoogte voorspelt de respons op een bepaalde stressor op het subcellulaire niveau de effecten op hogere niveaus van biologische organisatie?
De analyse van thans beschikbare data zoals gepresenteerd in hoofdstuk 2 laat zien dat er vooralsnog geen bewijs is dat veranderingen in de expressie van genen gebruikt kunnen worden als “early warning” indicatoren voor effecten op hogere niveaus van biologische organisatie. De respons op subcellulair niveau is tot nu toe meer bruikbaar gebleken voor het onderscheiden van het type verontreiniging of het werkingsmechanisme van chemische stoffen dan voor het voorspellen van effecten op populaties die aan deze chemische stoffen waren blootgesteld.
Kan de SSD-benadering worden toegepast om mogelijke verschillen in gevoeligheid voor een bepaalde stressor aan te tonen tussen inheemse en niet- inheemse soorten?
De SSD-benadering kan worden toegepast om de gevoeligheid van inheemse en niet- inheemse soorten voor diverse stressoren mechanistisch te vergelijken. Op deze wijze konden verschillen worden aangetoond voor de gevoeligheid van inheemse en niet- inheemse vissen en mollusken voor watertemperatuur. Niet-inheemse vissen bleken minder gevoelig te zijn voor bepaalde temperaturen in vergelijking met inheemse soorten. Soortgelijke verschillen zijn gevonden voor de gevoeligheid van inheemse en niet-inheemse mollusken in de Rijn voor hoge watertemperaturen. De gevoeligheid van inheemse en niet-inheemse soorten die in de Rijn leven voor 21 chemische stoffen – waaronder ammonium, pesticiden en metalen – bleek niet significant te verschillen. Ook bleek er bij soortgelijke vergelijkingen geen significant verschil te zijn tussen inheemse en niet-inheemse vissoorten voor lage zuurstofconcentraties. Voor inheemse en niet- inheemse mollusken kon geen significant verschil in gevoeligheid worden aangetoond voor verdroging en saliniteit. Het onderzoek waarbij de SSD-benadering is toegepast om verschillen op te sporen tussen inheemse en niet-inheemse vis- en molluskensoorten heeft zich tot nu toe gericht op soorten van de rivier de Rijn. Toekomstig onderzoek in deze richting zou zich meer moeten richten op andere taxonomische of functionele groepen, op niet-inheemse soorten in andere regio’s en op stressoren zoals verandering van hydrologisch regime, habitatkwaliteit en gecombineerde effecten van chemische en fysieke stressoren.
Kan de SSD-benadering gebruikt worden voor het prioriteren van stressoren in het daadwerkelijke en het potentiële habitat van een specifieke taxonomische groep?
Tussen de SSD voor een specifieke taxonomische groep en directe metingen van stressoren in het veld kan een rechtstreekse vergelijking worden gemaakt om een idee
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te krijgen van de orde van grootte van de mogelijke ecologische gevolgen van bepaalde stressoren. Die mogelijke ecologische gevolgen kunnen dan worden uitgedrukt als PAF, ER of HC5 waarmee het ook mogelijk wordt de stressoren onderling te rangschikken op hun potentiële effecten. Het prioriteren van een set van chemische stressoren gemeten in de habitats van kikkers en padden en van vissen is uitgevoerd in de hoofdstukken 3 respectievelijk 4. Aangetoond kon worden dat het grootste risico voor vissen gevormd werd door ammonium en voor kikkers en padden door koper.
Wat zijn de mogelijkheden en beperkingen van de toepassing van de SSD- benadering voor niet-chemische stressoren?
De toepassing van de SSD-benadering voor niet-chemische stressoren kan een zinvolle aanvulling zijn op de ecologische beoordelingen van chemische stoffen. Het bij elkaar brengen van beide typen stressoren in één beoordeling kan de vergelijking van de impact van beide vergemakkelijken en biedt mogelijkheden voor het rangschikken en prioriteren van verschillende stressoren. De slaagkans voor een dergelijke benadering met SSDs is echter afhankelijk van de kwantiteit en kwaliteit van de input data. Niet- chemische data die rechtstreeks gebruikt kunnen worden in een SSD zijn niet altijd in de gewenste vorm beschikbaar. Het standaardiseren van het afleiden van de effecten voor niet-chemische stressoren op een wijze die vergelijkbaar is met chemische stressoren is van essentieel belang voor de vergelijking van effecten van beide typen stressoren.
Hoe worden SSDs toegepast in wetenschappelijk onderzoek voor de risicobeoordeling van (niet-) chemische stressoren in aquatische en terrestrische ecosystemen?
De review van wetenschappelijke artikelen uit de laatste twee decennia heeft duidelijk gemaakt dat de onderwerpen waarbij de SSD-benadering is toegepast complex en divers zijn. De SSD-benadering blijkt niet alleen gebruikt te worden voor het afleiden van EQCs en ERs voor diverse chemicaliën, maar ook in verschillende typen “niet- standaard” case studies. Talloze onderzoekers hebben de SSD-benadering gebruikt om de gevoeligheid te bepalen van soorten uit verschillende geografische regio’s of habitat typen (bijvoorbeeld mariene versus zoetwatermilieus, stilstaand versus stromend water); voor inheemse en niet-inheemse soorten en voor bedreigde en algemeen voorkomende soorten; voor verschillende taxonomische groepen in relatie tot het werkingsmechanisme van de onderzochte chemische stof; en voor het rangschikken van stressoren of verontreinigde locaties met het oog op het schatten van de potentiële effecten op bepaalde soorten. Het toepassen van de SSD-benadering is ook verkend voor niet-chemische stressoren en voor het afleiden van effectniveaus van veldgegevens. De meerderheid van de onderzochte studies richtte zich op risicobeoordeling met behulp
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van SSDs voor zoetwater-ecosystemen, gevolgd door bodem- en mariene ecosystemen; de beoordeling van risico’s voor sediment, lucht en grondwater krijgen veel minder aandacht.
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About the author | Curriculum vitae
Anastasia Del Signore (née Fedorenkova) was born on the 18th of April 1984 in Narva, Estonia. During 2002-2006 she studies biology at Tartu University following half a year Erasmus exchange program at Wageningen University, the Netherlands. For her Master’s studies, she received a scholarship from the Dutch Ministry of Foreign Affairs (MTEC Scholarships Environmental Sciences) and came back to Wageningen University. There she obtained a MSc in Environmental Science in 2008. In her Master thesis, she focused on the power relations between the EU and UN in governing such environmental problem as oil pollution from shipping. In 2008-2012, Anastasia was working on her PhD project at Environmental Science department at Radboud University Nijmegen under supervision of Prof. dr. Jan Hendriks and Prof. dr. Ton Breure in collaboration with the RIVM. During the years at Radboud University, she was also a member of the PhD committee of the IWWR institute organising monthly guest scientific lectures and annual PhD days. Currently, she is working at BASF SE as ecotoxicologist at crop protection division.
Peer-reviewed (to be) publications
Fedorenkova A, Vonk JA, Lenders HJR, Ouborg NJ, Breure AM, Hendriks AJ. 2010. Ecotoxicogenomics: bridging the gap between genes and populations. Environmental Science and Technology 44: 4328-4333. Fedorenkova A, Vonk JA, Lenders HJR, Ouborg NJ, Breure AM, Hendriks AJ. 2010. Response to Comment on “Ecotoxicogenomics: Bridging the Gap between Genes and Populations”. Environmental Science and Technology 44: 9241-9241. Fedorenkova A, Vonk JA, Lenders HJR, Creemers RCM, Breure AM, Hendriks AJ. 2012. Ranking ecological risks of multiple chemical stressors on amphibians. Environmental Toxicology and Chemistry 31: 1416-1421. Fedorenkova A, Vonk JA, Breure AM, Hendriks AJ, Leuven RSEW. 2013. Tolerance of native and non-native fish species to chemical stress: a case study for the river Rhine. Aquatic Invasions 8: 231-241. Del Signore A, Lenders HJR, Hendriks AJ, Vonk JA, Mulder Ch, Leuven RSEW. 2014. Size-mediated effects of water-flow velocity on riverine fish species. River Research and Applications DOI: 10.1002/rra.2847, 2014 (in press). Del Signore A, Hendriks AJ, Lenders HJR, Leuven RSEW, Breure AM. Development and application of the SSD approach in scientific case studies for ecological risk assessment. Submitted.
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Acknowledgement |
On my way to arrive to this page, I got to meet and know some people, whom I would like to thank here: Dear Jan, thank you for your patience, support and being able to deal with my emotions and of course for letting me write different things in this boekje and now defend them! Ton, thank you for your positive attitude and helping me seeing the light at the end of the tunnel. Rob, thank you for sharing your ideas and data (especially chapter 5) and your help. Bor, thank you for your great support (not only related to amphibians) and your sense of humour. Arie, I dedicate the title of the song of Depeche Mode “Personal Jesus” to you and thank you for being with me at the times when I needed your help, opinion, kind words, or a minute to talk nonsense. Christian sono molto felice di avere una pubblicazione con te e ti ringrazio di cuore per tutte le nostre discussioni e lamentazioni e piu di tutto per le tue visite guidate a TEFAF. Mark H., thank you for your valuable comments on my thesis and being my judge! Leo, first of all, I have to admit I was terrified to meet you for the first time knowing you edited That Book; second, thank you very much for being at my defence! Prof. Wim, thank you for being part of the examination commission (I hope to survive). Loes, An, Pim, Mark L., I am happy I had you at the very beginning of this long street, finally I have arrived, too. Laura, my stick behind the door, thanks a lot for letting me cry on your shoulder and being always super nice and care-full to me. Ligia, Alessandra, Isabel, Rik, Daan, Jon, Loes, Laura, An, Mark L, thanks for great time at the uni and in our big trips. Marlia, Anne, Lisette, Rosalie, Aafke, Mara, Reinier, Thomas, Pieter, Yen, Marieke, Ad, Dik, Gina and all other colleagues, thank you for being nice colleagues :) Special thank goes to the one who helped me moving on when nothing made sense anymore (See figure 1). Оля Ш., тебе спасибо за минутки терапии и понимание! Cosimo + Tamari, thank you for making my Nijmegense life happier : ) Vivian, thank you for taking care of me when I was insane!
Мама и Папа, Даша и Саша, Миша, Анютка, Сонечка, спасибо, что верите в меня, гордитесь мной и поддерживаете меня во всех моих начинаниях и наконец-то заканчиваниях. Только вы знаете, чего мне это стоило, поэтому спасибо вам за терпение и выслушивание вечного моего нытья :)
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Саша, тебе отдельное ещё спасибо за всё наше время тут и там, за твои прилёты, ну, и за обложку, само собой. Giuseppe, ti sei preso cura di me ogni giorno, incoraggiandomi, ascoltandomi, condividendo i tuoi pensieri e le tue conoscenze. Ti ringrazio per questo perche senza il tuo supporto e il tuo amore non sarei mai arrivata cosi lontano.
Figure 1. The one who always helped me to carry on.
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