Comparison of AGNES (absence of gradients and Nernstian equilibrium stripping) and SSCP (scanned stripping chronopotentiometry) for trace metal speciation analysis

The free metal ion concentrations obtained by SSCP (stripping chronopotentiometry at scanned deposition potential) and by AGNES (absence of gradients and Nernstian equilibrium stripping) techniques have been compared and the usefulness of the combination of both techniques in the same electrochemical cell for trace metal speciation analysis is assessed. The free metal ion concentrations and the stability constants obtained for lead(II) and cadmium(II) complexation by pyridinedicarboxylic acid, by 40 nm radius carboxylated latex nanospheres and by a humic acid extracted from an ombrotrophic peat bog were determined. Whenever possible, the free metal ion concentrations were compared with the theoretical predictions of the code MEDUSA and with the free metal ion concentrations estimated from ion selective electrodes (ISE). SSCP values were in agreement with the ones obtained by AGNES, and both of them agreed reasonably with the ISE values and the theoretical predictions. For the lead(II)-humic acid, it was not possible to obtain the stability constants by SSCP due to the heterogeneity effect. However, using AGNES it is possible to obtain, for these heterogeneous systems, the free bulk metal concentration, which allows us to retrieve the stability constant at bulk conditions.

Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudoabsence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study showsthat ifwe do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.