Species non-exchangeability for ecotoxicological risk assessment [poster spotlight]

In aquatic based chemical risk assessments, there is a wealth of statistical techniques for use in lower tier risk assessment. In particular, we focus on estimation of the hazardous concentration to x% of an ecological community (HCx); a concept based on the idea of Species Sensitivity Distribution (SSD). The SSD is typically assumed to act as a proxy distribution to model the inter-species variation in the biological assemblage. Over time, a number of criticisms have been made of the SSD concept, but we focus on one in particular – species non-exchangeability. The concept was first discussed within a semi-probabilistic setting by an opinion of the European Food Safety Authority (EFSA) Scientific Panel on Plant Production products and their Residues (EFSA Journal, 2005). We build on their findings to demonstrate, statistically, that the Rainbow trout (Oncorhynchus mykiss) is not exchangeable with other species. By this term, we mean that, a priori, before observing the toxicity value of the species, we do not believe it to be a realisation from the same distribution as the other species in the assemblage. In fact, the Rainbow trout is typically more sensitive than the average fish species across a wide range of substances. In addition, we briefly demonstrate how to exploit historical databases of toxicity data featuring the Rainbow trout to quantify this non-exchangeability in order to derive new estimators for the HCx

Species non-exchangeability in probabilistic ecotoxicological risk assessment

Current ecotoxicological risk assessment for chemical substances is based on the assumption that tolerances of all species in a specified ecological community are a priori exchangeable for each new substance. We demonstrate non-exchangeability by using a large database of tolerances to pesticides for fish species and extend the standard statistical model for species tolerances to allow for the presence of a single species which is considered non-exchangeable with others. We show how to estimate parameters and adjust decision rules that are used in ecotoxicological risk management. Effects of parameter uncertainty are explored and our model is compared with a previously published less tractable alternative. We conclude that the model and decision rules that we propose are pragmatic compromises between conflicting needs for more realistic modelling and for straightforwardly applicable decision rules

Distributed Branching Bisimulation Minimization by Inductive Signatures

We present a new distributed algorithm for state space minimization modulo branching bisimulation. Like its predecessor it uses signatures for refinement, but the refinement process and the signatures have been optimized to exploit the fact that the input graph contains no tau-loops. The optimization in the refinement process is meant to reduce both the number of iterations needed and the memory requirements. In the former case we cannot prove that there is an improvement, but our experiments show that in many cases the number of iterations is smaller. In the latter case, we can prove that the worst case memory use of the new algorithm is linear in the size of the state space, whereas the old algorithm has a quadratic upper bound. The paper includes a proof of correctness of the new algorithm and the results of a number of experiments that compare the performance of the old and the new algorithms

Extending the SSD concept to explore some foundational model limitations: a Bayesian hierarchical approach

Species sensitivity distributions are statistical constructs which model interspecies variation of sensitivity to a particular toxic stressor. The current REACH technical guidance document permits the application of SSDs in risk assessment, subject to a number of criteria. Notwithstanding noteworthy criticism received, the SSD is considered by regulators to be a pragmatic model for extrapolating to environmental toxicant concentrations of concern. The manner in which SSDs are currently applied is implicitly dependent on a number of (overlapping) statistical and ecological assumptions. These include (but are not limited to): (1) the measured species toxicity values being precisely known; (2) independence of SSDs for each separate chemical risk assessment; (3) a priori exchangeability of species toxicity values; (4) no correlation between species. In this research we propose a model which generalizes the SSD concept to include chemical effects and shared species effects. It offers flexibility to address or refine each assumption by hierarchically adding layers into the model. Models are fitted to RIVM and US EPA acute-effect toxicity databases under a Bayesian statistical framework to allow for transparent quantification of and flexible propagation of uncertainty. Important insight is gained from the inclusion of ‘species effects’ modelling which, expectedly, indicates increasing differences as taxonomic distances in SSDs increase. The magnitude of measurement error estimated, based on within taxa homogeneity, which also properly accounts for censored measurements, is likely to be of significance to risk assessors and warrant further consideration in either modelling framework. The current quasi-meta-analysis approach towards aggregating multiple chemical-species data points is untenable from an uncertainty viewpoint. Initial results indicate deficiencies in the current SSD concept, thus reducing the credibility and meaningfulness of any subsequently derived hazardous concentrations. Other recent model proposals which act as precursory tools to SSD modelling may not sensibly propagate uncertainty and/or succumb to modelling contradictions. A hierarchical model may overcome this, however will require a more radical approach to defining protection goals and environmental concentrations of concern

Coverage of endangered species in environmental risk assessments at EFSA

The EFSA performs environmental risk assessment (ERA) for single potential stressors such as plantprotection products, genetically modified organisms and feed additives, and for invasive alien speciesthat are harmful to plant health. This ERA focusses primarily on the use or spread of such potentialstressors in an agricultural context, but also considers the impact on the wider environment. It isimportant to realise that the above potential stressors in most cases contribute a minor proportion ofthe total integrated pressure that ecosystems experience. The World Wildlife Fund listed the relativeattribution of threats contributing to the declines in animal populations as follows: 37% fromexploitation (fishing, hunting, etc.), 31% habitat degradation and change, 13% from habitat loss, 7%from climate change, and only 5% from invasive species, 4% from pollution and 2% from disease. Inthis scientific opinion, the Scientific Committee gathered scientific knowledge on the extent of coverageof endangered species in current ERA schemes that fall under the remit of EFSA. The legal basis andthe relevant ecological and biological features used to classify a species as endangered areinvestigated. The characteristics that determine vulnerability of endangered species are reviewed.Whether endangered species are more at risk from exposure to potential stressors than other non-target species is discussed, but specific protection goals for endangered species are not given. Due toa lack of effect and exposure data for the vast majority of endangered species, the reliability of usingdata from other species is a key issue for their ERA. This issue and other uncertainties are discussedwhen reviewing the coverage of endangered species in current ERA schemes. Potential tools, such aspopulation and landscape modelling and trait-based approaches, for extending the coverage ofendangered species in current ERA schemes, are explored and reported

Variation in the level of protection afforded to birds and crustaceans exposed to different pesticides under standard risk assessment procedures

First-tier risk assessment for pesticides is often based on the quotient of the toxicity divided by the predicted environmental concentration or dose. This ratio is compared to a fixed assessment factor (AF) to decide whether the pesticide is to be allowed on the market or whether further research is needed. Often, a high value (e.g., the 90th percentile) is assumed for the predicted environmental concentration, and the lowest available value is chosen to represent toxicity; yet, the real level of protection is not known. Therefore, it is also not known whether the first tier is conservative enough or too conservative. By using 2 large toxicity databases and assuming a log-logistic species sensitivity distribution for each pesticide, the percent of species not covered by the AF is estimated in the scenario, where exposure is at the maximum level allowable in the first tier. In the case of crustaceans, the median estimate of the fraction of species not covered by the AF of 100 in the first-tier scenario is 3.4%, on average, for 72 pesticides. In other words, on average, 3.4% of the crustacean species will be exposed above their median lethal concentration (LC50) and median lethal dose (LD50) value in 10% of receiving surface waters that receive the maximum allowable exposure to an individual pesticide. The estimated level of protection varies widely between pesticides. For 10% of the pesticides, the estimated fraction of species not covered is ≥10% (maximum = 41.4%). For 28% of the pesticides, 99.9% of the species will have the assumed level of protection. For birds, the median estimate of the fraction of species exposed above their median lethal dose for the first-tier scenario (AF = 10) is 3.0% on average, when the AF is applied to the lower of the toxicity values for the 2 standard test species. For 11% of the pesticides, the median estimate is ≥10% (maximum = 15.7%). When the AF is applied instead to the geometric mean of the toxicity values for the 2 standard species, the median estimate of the fraction of species not covered by the AF is increased to 7.4% on average; for 31% of the pesticides, this fraction is ≥10% (maximum = 33.4%). This variation in the level of protection should be considered when defining the assumptions, assessment factors, and decision criteria in regulatory risk assessment. Integr Environ Assess Manag 2011;7:459–465. © 2011 SETA

The Dutch Objective Burden Inventory:Validity and reliability in a Canadian population of caregivers for people with heart failure

Evidence suggests that caregivers of people with heart failure (HF) often experience caregiver burden and emotional distress. However, these studies measured the caregiving experience using generic tools since a disease-specific tool was not available. Recently, the Dutch Objective Burden Inventory (DOBI) was developed as a disease-specific tool measuring objective caregiver burden in a Dutch HF population of caregivers. Using a cross-sectional design, caregivers of HF patients attending an outpatient HF clinic completed the DOBI, the Hosptial Anxiety and Depression Scale (HADS) and the Caregiver Reaction Assessment (CRA). Caregivers (n=47) were mainly female (72%) and spouses (72%) of the HF patients with a mean age of 63.1 (+/-10.4) years. Patients were older (mean age 72.7; +/-10.6), 64% male and had advanced HF. Feasibility for the objective portion of the DOBI was excellent with .80 for all DOBI subscales. The DOBI is the only disease-specific tool that measures burden for caregivers of HF patients. The objective portion of the DOBI showed evidence of adequate internal consistency and construct validity in a Canadian population of caregivers of HF patients attending a HF Clinic. Further testing is needed to determine floor and ceiling effects for DOBI items and responsiveness of this tool. (C) 2010 European Society of Cardiology. Published by Elsevier B.V. All rights reserved