Bats are natural reservoirs of a variety of zoonotic viruses, many of which cause severe human diseases. Characterizing viruses of bats inhabiting different geographical regions is important for understanding their viral diversity and for detecting viral spillovers between animal species. Herein, the diversity of DNA viruses of five arthropodophagous bat species from Argentina was investigated using metagenomics. Fecal samples of 29 individuals from five species (Tadarida brasiliensis, Molossus molossus, Eumops bonariensis, Eumops patagonicus, and Eptesicus diminutus) living at two different geographical locations, were investigated. Enriched viral DNA was sequenced using Illumina MiSeq, and the reads were trimmed and filtered using several bioinformatic approaches. The resulting nucleotide sequences were subjected to viral taxonomic classification. In total, 4,520,370 read pairs were sequestered by sequencing, and 21.1% of them mapped to viral taxa. Circoviridae and Genomoviridae were the most prevalent among vertebrate viral families in all bat species included in this study. Samples from the T. brasiliensis colony exhibited lower viral diversity than samples from other species of New World bats. We characterized 35 complete genome sequences of novel viruses. These findings provide new insights into the global diversity of bat viruses in poorly studied species, contributing to prevention of emerging zoonotic diseases and to conservation policies for endangered species.

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Identification of stable and high-yielding genotypes is a real challenge in peach breeding, since genotype-byenvironment interaction (GE) masks the performance of the materials. The aim of this work was to evaluate the effectiveness of parameter estimation and genotype selection solving the LMM under frequentist and Bayesian approaches. Fruit yield of 308 peach genotypes were assessed under different seasons and replication numbers arranged in a completely randomized design. Under the frequentist framework the restricted maximum likelihood method to estimate variance component and genotypic prediction was used. Different models considering environment, genotype and GE effects according to the likelihood ratio test and Akaike information criteria were compared. In the Bayesian approach, the mean and the variance components were assumed to be random variables having a priori non-informative distributions with known parameters.According the deviance information criteria the most suitable Bayesian model was selected. The full model was the most appropriate to calculate parameters and genotypic predictions, which were very similar in both approaches. Due to imbalance data, Cullis's method was the most appropriate to estimate heritability. It was calculated at 0.80, and selecting above 5% of the genotypes, the realized gain of 14.81 kg.tree 1 was attained.Genotypic frequentist and Bayesian predictions showed a positive correlation (r = 0.9991; P = 0.0001). Since the Bayesian method incorporates the credible interval for genetic parameters, genotypic Bayesian prediction would be a more useful tool than the frequentist approach and allowed the selection of 17 high-yielding and stable genotypes.