Contamination by chlorinated pesticides, PCBs and PBDEs in Atlantic spotted dolphin (Stenella frontalis) in western South Atlantic

Chlorinated pesticides, PCBs and PBDEs were analysed in nine blubber samples of Atlantic spotted dolphins, Stenella frontalis, incidentally captured during fishing operations in southern and southeastern Brazil between 2005 and 2007. The majority of compounds analysed were detected, suggesting widespread contamination over the region. Although the samples came from a location far from main coastal industrial areas, the results revealed an influence from such sources. Therefore, levels of PCBs (774-23659 ng g(-1) lipid wt.) and PBDEs (23-1326 ng g(-1) lipid wt.) detected seem to be related to the movement of individuals throughout near-shore and offshore waters. The sample from a lactating female exhibited a lower level of contamination and a distinct pattern, indicating selective transfer favouring less lipophilic compounds.

Prediction with measurement errors in finite populations

We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which points to some difficulties in the interpretation of such predictors. (C) 2011 Elsevier By. All rights reserved.