Thesis (PhD)--Stellenbosch University, 2011. / ENGLISH ABSTRACT: Species differ in their sensitivities to toxicants and these differences are exploited in ecological risk assessment methods such as species sensitivity distributions (SSDs). The most commonly used endpoints for ecotoxicity testing and thus to generate data for use in SSDs are on the whole-organismal level, and usually include the evaluation of survival and reproduction. However, suborganismal biomarker responses are in many instances more sensitive than these whole-organismal responses. Therefore, this study investigated and compared responses on various biological organisational levels to determine their suitability for use in SSDs. Five terrestrial oligochaete species (earthworms) were selected as model test organisms, and were exposed to a range of concentrations of a well-studied pesticide, copper oxychloride. The investigated responses included survival, biomass change and reproduction on the whole-organismal level. In order to investigate responses on the suborganismal level, cells (coelomocytes) were extracted non-invasively. The spectrophotometric neutral red retention (NRR) assay was used to determine cell survival and the MTT assay to determine mitochondrial metabolic activity of the coelomocytes. The alkaline single cell gel electrophoresis (comet) assay was used to assess DNA integrity in these cells. The amount of Cu taken up by earthworms was also determined and compared to their responses. Species differences were observed in all responses, and EC50 and EC10 values were calculated for the whole-organismal endpoints and used to generate SSDs. From these SSDs, the hazardous concentrations where 5% of all species would be detrimentally affected (HC5) were calculated, which indicated that the most sensitive whole-organismal endpoint was mass change, followed by reproduction and survival. It was found that earthworms avoided feeding on the contaminated substrate in high copper oxychloride concentration exposures. The concentration where this behaviour occurred could be estimated for each species, and an SSD was constructed with these data. The HC5 value indicated that this response is more sensitive than earthworm survival, but less sensitive than the other responses. It was shown that the earthworms regulated their body Cu concentrations in a species-specific manner. This regulation of Cu was reflected in the suborganismal responses, and the species that had taken up the highest amount of Cu was the most sensitive species for all three suborganismal assays. Due to this regulation of Cu, the resulting dose-responses for the suborganismal endpoints did not allow for the calculation of EC50 values in most of the species and such data could thus not be used to generate SSDs. Sufficient EC10 values were however generated to construct SSDs from the results of the NRR and comet assays. The HC5 values obtained from SSDs constructed with EC10 values for both suborganismal and whole-organismal endpoints indicated that the NRR assay was the most sensitive endpoint, followed by both the comet assay and earthworm mass change, and subsequently the other whole-organismal endpoints. In conclusion, the majority of the responses on the various levels of biological organisation investigated during the present study were shown to be suitable to determine species sensitivity relationships in the terrestrial oligochaete species studied. / AFRIKAANSE OPSOMMING: Spesies verskil van mekaar ten opsigte van hulle sensitiwiteit vir toksikante, en hierdie verskille word in ekologiese risikobepalingsmetodes soos spesie-sensitiwiteitsverspreidings (SSVs) gebruik. Die mees algemene eindpunte vir ekotoksisiteitstoetse, en wat dus gebruik word om data te genereer vir SSVs, is op die heelorganismevlak, en sluit gewoonlik die bepaling van oorlewing en voortplanting van die toetsorganismes in. Hierdie eindpunte is egter in die meeste gevalle minder sensitief as suborganismiese biomerker-response. Hierdie studie het dus die response op verskeie vlakke van biologiese organisasie ondersoek en vergelyk om te bepaal of hulle geskik is vir gebruik in SSVs. Vyf terrestriële spesies van die klas Oligochaeta is gekies as toetsorganismes en is blootgestel aan 'n reeks konsentrasies van die goed bestudeerde pestisied koperoksichloried. Die response oorlewing, massaverandering en voortplanting is op die heelorganismevlak ondersoek. Vir die suborganismiese response is selle (selomosiete) met behulp van 'n nie-ingrypende proses vanuit die erdwurms geïsoleer. Die suborganismese toetse wat op hierdie selle gedoen is, was die neutraalrooi-retensietoets (NRR toets) om sel-oorlewing te bepaal, die MTT toets om mitochondriese metabolisme te bepaal en die alkaliese komeettoets om DNS-integriteit te bepaal. Die hoeveelheid Cu wat die erdwurms opgeneem het, is ook bepaal en met hulle response vergelyk. Verskille is tussen die spesies waargeneem vir al die response. Beide EK50 en EK10 waardes is bereken vir die heelorganismiese eindpunte om SSVs te genereer. Vanaf hierdie SSVs kon die gevaarlike konsenstrasie, waar 5% van alle spesies nadelig beïnvloed kan word (GK5), bereken word. Hierdie GK5 waardes het aangedui dat massaverandering die mees sensitiewe heelorganismiese eindpunt was, gevolg deur voortplanting en oorlewing. Die erdwurms het opgehou vreet aan die gekontamineerde substraat by hoë koperoskichloriedkonsentrasies. Die konsentrasie waar hierdie gedrag plaasgevind het kon vir elke spesie vasgestel word, en 'n SSV is met behulp van hierdie data genereer. Hierdie GK5 waarde het aangedui dat hierdie respons meer sensitief was as oorlewing, maar minder sensitief as die ander response. Die erdwurms kon die konsentrasie van Cu in hulle liggame op 'n spesie-spesifieke manier reguleer. Hierdie regulering van interne Cu is weerspieël in die suborganismiese response, waar die spesie wat die meeste Cu opgeneem het, ook die mees sensitiewe was vir al drie suborganismiese toetse. As gevolg van hierdie regulering van Cu en die gevolglike dosis-responsverhoudings, kon EK50-waardes nie vir al die spesies bereken word nie, en dus was daar geen EK50-data beskikbaar om SSVs mee te genereer nie. Genoegsame EK10 waardes kon egter bereken word vir die NRR- en komeettoetse, en gebruik word om SSVs te genereer. Die GK5-waardes wat bereken kon word vanuit die SSVs met EK10 waardes vir beide suborganismese en heelorganismiese response, het aangedui dat die mees sensitiewe eindpunt die NRR toets was, gevolg deur beide die komeettoets en massaverandering van erdwurms, en daarna die ander heelorganismiese eindpunte. Die gevolgtrekking is dat daar aangetoon kon word dat die meerderheid van die response wat gedurende hierdie studie ondersoek is, geskik is om sensitwiteitsverhoudings van hierdie groep spesies te bepaal.

Uncertainty is pervasive in risk assessment. In ecotoxicological risk assessments, it arises from such sources as a lack of data, the simplification and abstraction of complex situations, and ambiguities in assessment endpoints (Burgman 2005; Suter 1993). When evaluating and managing risks, uncertainty needs to be explicitly considered in order to avoid erroneous decisions and to be able to make statements about the confidence that we can place in risk estimates. Although informative, previous approaches to dealing with uncertainty in ecotoxicological modelling have been found to be limited, inconsistent and often based on assumptions that may be false (Ferson & Ginzburg 1996; Suter 1998; Suter et al. 2002; van der Hoeven 2004; van Straalen 2002a; Verdonck et al. 2003a). In this thesis a Generalised Linear Modelling approach is proposed as an alternative, congruous framework for the analysis and prediction of a wide range of ecotoxicological effects. This approach was used to investigate the results of toxicity experiments on the effect of 2,4-Dichlorophenoxyacetic Acid (2,4-D) formulations and 4-Chlorophenol (4-CP, an associated breakdown product) on Daphnia carinata. Differences between frequentist Maximum Likelihood (ML) and Bayesian Markov-Chain Monte-Carlo (MCMC) approaches to statistical reasoning and model estimation were also investigated. These approaches are inferentially disparate and place different emphasis on aleatory and epistemic uncertainty (O'Hagan 2004). Bayesian MCMC and Probability Bounds Analysis methods for propagating uncertainty in risk models are also compared for the first time. For simple models, Bayesian and frequentist approaches to Generalised Linear Model (GLM) estimation were found to produce very similar results when non-informative prior distributions were used for the Bayesian models. Potency estimates and regression parameters were found to be similar for identical models, signifying that Bayesian MCMC techniques are at least a suitable and objective replacement for frequentist ML for the analysis of exposureresponse data. Applications of these techniques demonstrated that Amicide formulations of 2,4-D are more toxic to Daphnia than their unformulated, Technical Acid parent. Different results were obtained from Bayesian MCMC and ML methods when more complex models and data structures were considered. In the analysis of 4-CP toxicity, the treatment of 2 different factors as fixed or random in standard and Mixed-Effect models was found to affect variance estimates to the degree that different conclusions would be drawn from the same model, fit to the same data. Associated discrepancies in the treatment of overdispersion between ML and Bayesian MCMC analyses were also found to affect results. Bayesian MCMC techniques were found to be superior to the ML ones employed for the analysis of complex models because they enabled the correct formulation of hierarchical (nested) datastructures within a binomial logistic GLM. Application of these techniques to the analysis of results from 4-CP toxicity testing on two strains of Daphnia carinata found that between-experiment variability was greater than that within-experiments or between-strains. Perhaps surprisingly, this indicated that long-term laboratory culture had not significantly affected the sensitivity of one strain when compared to cultures of another strain that had recently been established from field populations. The results from this analysis highlighted the need for repetition of experiments, proper model formulation in complex analyses and careful consideration of the effects of pooling data on characterising variability and uncertainty. The GLM framework was used to develop three dimensional surface models of the effects of different length pulse exposures, and subsequent delayed toxicity, of 4-CP on Daphnia. These models described the relationship between exposure duration and intensity (concentration) on toxicity, and were constructed for both pulse and delayed effects. Statistical analysis of these models found that significant delayed effects occurred following the full range of pulse exposure durations, and that both exposure duration and intensity interacted significantly and concurrently with the delayed effect. These results indicated that failure to consider delayed toxicity could lead to significant underestimation of the effects of pulse exposure, and therefore increase uncertainty in risk assessments. A number of new approaches to modelling ecotoxicological risk and to propagating uncertainty were also developed and applied in this thesis. In the first of these, a method for describing and propagating uncertainty in conventional Species Sensitivity Distribution (SSD) models was described. This utilised Probability Bounds Analysis to construct a nonparametric 'probability box' on an SSD based on EC05 estimates and their confidence intervals. Predictions from this uncertain SSD and the confidence interval extrapolation methods described by Aldenberg and colleagues (2000; 2002a) were compared. It was found that the extrapolation techniques underestimated the width of uncertainty (confidence) intervals by 63% and the upper bound by 65%, when compared to the Probability Bounds (P3 Bounds) approach, which was based on actual confidence estimates derived from the original data. An alternative approach to formulating ecotoxicological risk modelling was also proposed and was based on a Binomial GLM. In this formulation, the model is first fit to the available data in order to derive mean and uncertainty estimates for the parameters. This 'uncertain' GLM model is then used to predict the risk of effect from possible or observed exposure distributions. This risk is described as a whole distribution, with a central tendency and uncertainty bounds derived from the original data and the exposure distribution (if this is also 'uncertain'). Bayesian and P-Bounds approaches to propagating uncertainty in this model were compared using an example of the risk of exposure to a hypothetical (uncertain) distribution of 4-CP for the two Daphnia strains studied. This comparison found that the Bayesian and P-Bounds approaches produced very similar mean and uncertainty estimates, with the P-bounds intervals always being wider than the Bayesian ones. This difference is due to the different methods for dealing with dependencies between model parameters by the two approaches, and is confirmation that the P-bounds approach is better suited to situations where data and knowledge are scarce. The advantages of the Bayesian risk assessment and uncertainty propagation method developed are that it allows calculation of the likelihood of any effect occurring, not just the (probability)bounds, and that the same software (WinBugs) and model construction may be used to fit regression models and predict risks simultaneously. The GLM risk modelling approaches developed here are able to explain a wide range of response shapes (including hormesis) and underlying (non-normal) distributions, and do not involve expression of the exposure-response as a probability distribution, hence solving a number of problems found with previous formulations of ecotoxicological risk. The approaches developed can also be easily extended to describe communities, include modifying factors, mixed-effects, population growth, carrying capacity and a range of other variables of interest in ecotoxicological risk assessments. While the lack of data on the toxicological effects of chemicals is the most significant source of uncertainty in ecotoxicological risk assessments today, methods such as those described here can assist by quantifying that uncertainty so that it can be communicated to stakeholders and decision makers. As new information becomes available, these techniques can be used to develop more complex models that will help to bridge the gap between the bioassay and the ecosystem.