Robust benchmark dose determination based on profile score methods

We investigate several methods commonly used to obtain a benchmark dose and show that those based on full likelihood or profile likelihood methods might have severe shortcomings. We propose two new profile likelihood-based approaches which overcome these problems. Another contribution is the extension of the benchmark dose determination to non full likelihood models, such as quasi-likelihood, generalized estimating equations, which are widely used in settings such as developmental toxicity where clustered data are encountered. This widening of the scope of application is possible by the use of (robust) score statistics. Benchmark dose methods are applied to a data set from a developmental toxicity study.     

Four priorities for new links between conservation science and accounting research

Article impact statement: New collaborations with accounting research can improve conservation impact of ecosystem-based information systems.     

Predicting Gran alkalinity and calcium concentrations in river waters over a national scale using a novel modification to the G-BASH model

Monthly stream water calcium and Gran alkalinity concentration data from 11 sub-catchments of the Nether Beck in the English Lake District have been used to appraise the transferability of the Scottish, River Dee-based G-BASH model. Readily available riparian zone geochemistry and flow paths were used initially to predict minimum and mean stream water concentrations at the Nether Beck, based on calibration equations from the River Dee catchment data. Predicted values significantly exceeded observed values. Differences in runoff between the two areas, leading to a dilution effect in the Nether Beck, explained most of the difference between observed and predicted values. Greater acid deposition in the Lake District also reduced stream water Gran alkalinity concentrations in that area. If regional differences in precipitation, evapotranspiration and pollutant deposition are incorporated into the model, it may then be used reliably to predict catchment susceptibility to acidification over a wide regional (national) scale.