Arsenic toxicity to cladocerans isolated and associated with iron: implications for aquatic environments.

Arsenic is an ametal ubiquitous in nature and known by its high toxicity. Many studies have tried to elucidate the arsenic metabolism in the cell and its impact to plants, animals and human health. In aqueous phase, inorganic arsenic is more common and its oxidation state (As III and As V) depends on physical and chemical environmental conditions. The aim of this study was to evaluate toxicity of arsenic to Daphnia similis and Ceriodaphnia silvestrii, isolated and associated with iron. The results showed differences in toxicity of As III and As V to both species. Effective concentration (EC50) mean values were 0.45 mg L-1 (As III) and 0.54 mg L-1 (As V) for D. similis, and 0.44 mg L-1 (As III) and 0.69 mg L-1 (As V) for C. silvestrii. However, As V IC25 mean value was 0.59 mg L-1, indicating that C. silvestrii has mechanisms to reduce arsenic toxicity. On the other hand, when associated with iron at 0.02 and 2.00 mg L-1, EC50 values decreased for D. similis (0.34 and 0.38 mg L-1) as well as C. silvestrii (0.37 and 0.37 mg L-1), showing synergistic effect of these substances.

Multiple-trait, random regression, and compound symmetry models for analyzing multi-environment trials in maize breeding.

An efficient and informative statistical method to analyze genotype-by-environment interaction (GxE) is needed in maize breeding programs. Thus, the objective of this study was to compare the effectiveness of multiple-trait models (MTM), random regression models (RRM), and compound symmetry models (CSM) in the analysis of multi-environment trials (MET) in maize breeding. For this, a data set with 84 maize hybrids evaluated across four environments for the trait grain yield (GY) was used. Variance components were estimated by restricted maximum likelihood (REML), and genetic values were predicted by best linear unbiased prediction (BLUP). The best fit MTM, RRM, and CSM were identified by the Akaike information criterion (AIC), and the significance of the genetic effects were tested using the likelihood ratio test (LRT). Genetic gains were predicted considering four selection intensities (5, 10, 15, and 20 hybrids). The selected MTM, RRM, and CSM models fit heterogeneous residuals. Moreover, for RRM the genetic effects were modeled by Legendre polynomials of order two. Genetic variability between maize hybrids were assessed for GY. In general, estimates of broad-sense heritability, selective accuracy, and predicted selection gains were slightly higher when obtained using MTM and RRM. Thus, considering the criterion of parsimony and the possibility of predicting genetic values of hybrids for untested environments, RRM is a preferential approach for analyzing MET in maize breeding.