The Eberhart and Russel’s Bayesian method used as an instrument to select maize hybrids

Adaptability and stability analysis methods that use a priori information allow identifying and selecting potentially productive genotypes with greater accuracy. The aim of the current study is to use the Eberhart and Russel’ Bayesian method as an instrument to analyze the adaptability and stability of hybrid maize cultivars and to assess the efficiency of using the distribution of informative and non-informative priors to select cultivars. Twenty-five (25) hybrid maize cultivars were assessed in 11 environments located in the Brazilian Northeastern region, during 2012 and 2013, according to a complete randomized block design, with two repetitions. The Eberhart and Russel’s methodology was performed in the GENES software, whereas the Bayesian procedure was implemented in the free software R, by using the MCMCregress function of the MCMCpack package. The adaptability and stability parameters values and the credibility intervals have shown that the Eberhart and Russel’s method via Bayesian technique has shown greater stability-estimation accuracy and greater efficiency in recommending cultivars adapted to favorable and unfavorable environments. The Bayesian methods using priories informative (M1) and few informative (M2) distributions have presented the same genotype classifications in the comparison between a priori distributions; however, according to the Bayes Factor, the M1 was the most adequate distribution to help finding more reliable estimates.     

Molecular phylogeny of three subfamilies of the Neanuridae (Insecta, Collembola) and the position of the antarctic species Friesea grisea Schäffer.

The nuclear 28S rRNA and the mitochondrial COII gene were used to establish phylogenetic relationships among species of the family Neanuridae, with special emphasis on species of the subfamily Neanurinae. Phylogenetic analysis was conducted using genetic distances, parsimony and likelihood methods. The D3-D5 fragment of the rRNA gene was very conserved, both in sequence and in secondary structure features. This fragment supplied little information on relationships at this level. The phylogenetic reconstruction based on 1st and 2nd codon positions of the COII gene was partly in accordance with morphological data, but it was discordant for the placement of some species. Relationships among the subfamilies Frieseinae, represented by the Antarctic species Friesea grisea, Pseudachorutinae and Neanurinae were uncertain. The subfamily Neanurinae and its tribes Neanurini and Paleonurini were shown as monophyletic taxa. Relationships between three species of the genus Bilobella were in accordance with morphological and biochemical data. Relationships between genera within the Neanurini were more controversial. In accordance with morphological hypotheses, a basal position of Thaumanura was suggested, but the molecular data placed Neanura muscorum in a derived position, in sharp contrast with morphological evidence. A close relationship was suggested between Deutonura conjuncta, Cansilianura malatestai and Lathriopyga longiseta. The disagreement between molecular and morphological data suggests that one or both data sets might be affected by a certain degree of homoplasy and that these data should be interpreted with caution in phylogenetic reconstructions.     

Effectiveness of field-exposed attract-and-kill devices against the adults of Popillia japonica (Coleoptera: Scarabaeidae): a study on duration,form and storage

BACKGROUND

The Japanese beetle Popillia japonica Newman is an insect pest native to Japan that has spread into North America, the Azores and, recently, into continental Europe. Here, we present a study assessing the effectiveness of a long-lasting insecticide-treated net (LLIN), assembled in semiochemical-baited attract-and-kill devices (A&Ks) as a low environmental impact means to control P. japonica in the field. We compared the attractiveness of three different forms of A&Ks that were left outdoors throughout the summer, and the residence time of P. japonica landing on them. Moreover, we performed a preliminary study testing the effectiveness of new LLINs after storage. Collected data also allowed us to investigate the beetles' diel flight patterns in relation to meteorological conditions.

RESULTS

Killing effectiveness of the field-exposed A&Ks declined steadily over the flight season (from 100% to 37.5%) associated with a decrease in residues of α-cypermethrin, the active ingredient in the LLINs. The different A&K forms (pyramidal, octahedral and ellipsoidal) attracted similar numbers of beetles. Individual beetles' residence time ranged from 75 to 95 s and differed slightly between A&K forms. Effectiveness of LLINs decreased by ≈30% after 1 year storage. Based on numbers landing on A&Ks, the beetles' flight activity peaked about 14:30 h and was inversely correlated with relative humidity.

CONCLUSION

This study indicates that semiochemical-baited A&Ks are effective for controlling P. japonica in the field. Because of active ingredient decay, the LLINs should be replaced after 30–40 days of field exposure to ensure that the A&Ks remain fully functional. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel (Anguilla anguilla L.) intensive rearing system

One of the main problems in the management of fishfarms with water recirculating system is the forecasting and control of ammonia concentration in order to minimise the fish stress status. This paper examines methodologies of prediction in a real-time environment for an eel intensive rearing system. Approaches based on linear multiple regression, univariate time series models (exponential smoothing and autoregressive integrated moving average (ARIMA) models) and computational neural networks (ANNs) are developed to predict the daily average ammonia concentration in rearing tanks with water recirculating. The models are established using actual data from an eel fishfarm in southern Spain. The input variables used in the models (multiple regression, Holt smoothing model, ARIMA models and ANN models) are the ammonia concentration of previous days. In ANN models, the training method used is a standard back-propagation variation known as extended-delta-bar-delta (EDBD). Different neural architectures, whose learning is carried out by crossvalidation and controlling several threshold determination coefficients, are compared. Globally, the nonlinear ANN model approach is shown to provide a better prediction of daily average ammonia concentration than linear multiple regression and univariate time series analysis when the correlation between data series is low and when the models were obligated to predict in a situation for which specifically had not been calibrated. The best results were obtained by 5:10s:15s:1l ANN model in the pre-growth series.     

Gambusia holbrooki,the ‘tadpolefish’: The impact of its predatory behaviour on four protected species of European amphibians

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