In environmental risk assessment, Species Sensitivity Distributions (SSDs) can be applied to estimate a PNEC (Predicted No-Effect Concentration) for a chemical substance, when sufficient data on species toxicities are available. The European Chemicals Agency (ECHA) recommendation is 10 biological species. The question addressed in this paper, is whether QSAR-predicted toxicities can be included in SSD-based PNEC estimates, and whether any modifications need to be made to account for the uncertainty in the QSAR-model estimates. This problem is addressed from a probabilistic modelling point of view. From classical analysis of variation (ANOVA), we review how the error-in-data SSD problem is similar to separation into between-group and within-group variance. ECHA guidance suggests averaging similar endpoint data for a species, which is consistent with group means, as in ANOVA. This exercise reveals that error-in-data reduces the estimation of the between species variation, i.e. the SSD variance, rather than enlarging it. A Bayesian analysis permits the assessment of the uncertainty of the SSD mean and variance parameters for given values of mean species toxicity error. This requires a hierarchical model. Prototyping this model for an artificial five-species data set seems to suggest that the influence of data error is relatively minor. Moreover, when neglecting this data error, a slightly conservative estimate of the SSD results. Hence, we suggest including (model-predicted) data as model point estimates and handling the SSD as usual. The Bayesian simulation of the error-in-data SSD leads to predictive distributions, being an average of posterior spaghetti plot densities or cumulative distributions. We derive new predictive extrapolation constants with several improvements over previous median uncertainty log ₁₀ HC5 estimates, in that they are easily calculable from spreadsheet Student -t functions and based on a more realistic uniform prior for the SSD standard deviation. Other advantages are that they are single-number extrapolation constants and they are more sensitive to small sample size.

High concentrations of cadmium in brown crab are an issue of food safety, and large variations between different areas have been found. To investigate the relative importance of dietary and aqueous uptake regarding the overall accumulation in brown crab, we used stable isotopes to trace the uptake from both routes simultaneously in the same animals. We demonstrated that the analytical challenges regarding background concentrations of natural isotope distribution and polyatomic interferences in the different matrices can be overcome with an appropriate analytical setup and modern mathematical corrections using a computer software. Cadmium was accumulated via both routes and was found in all measured organs at the end of the exposure phase. The obtained data were used to establish accumulation curves for both uptake routes and estimate accumulation parameters for hepatopancreas, as the most important organ in crab regarding total cadmium body burden. Using the estimated parameters in combination with naturally relevant cadmium concentrations in seawater and diet in a model, allowed us to predict the relative importance of the aqueous and dietary uptake route to the total hepatopancreas burden. According to the prediction, the dietary route is the main route of uptake in brown crab with a minimum of 98% of the accumulated cadmium in hepatopancreas originating from diet. Future studies addressing the source and accumulation of cadmium in crab should therefore focus on the uptake from feed and factors connected to foraging.