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ABSTRACT. Changes in abundance and distribution of anchovy and other species of pelagic fish of the Northern Humboldt Current System (NHCS) are driven by environmental forcing variations in different time and spatial scales between the coastal 'cold' ecosystem and the 'warm' oceanic one. Data to study these fluctuations have come mostly from the fishery to show how anchovy (Engraulis ringens) increases when sardine (Sardinops sagax) declines and vice versa. However, using acoustic data on latitudinal biomass we show that other species as mackerel (Scomber japonicus) and jack mackerel (Trachurus murphyi) also follow the same opposed trend, then the fishery data hides the true dimension of the balance of abundance among species. Based on Hovmoller diagrams we scrutinized the changes in interannual latitudinal acoustic biomass, landings and influence of El Niño events from 1966 to 2009 in order to describe: 1) how the anchovy decadal distribution pattern moved from south to north since the 1960's; 2) how there have been produced concomitant changes in the latitudinal abundance and distribution of other species such as sardine, jack mackerel and mackerel before, during and after El Niño events; and 3) what was the overall effect of the succession of El Niño events on all these pelagic species. We concluded that: a) every El Niño event has had an effect on the expansion or contraction of pelagic species distribution and abundance, with different latitudinal effects; and b) the El Niño 1997-98 did not trigger but accelerated a decline phase on the abundance of sardine, jack mackerel and mackerel by a reduction of their ideal habitat due to an expansion of the coastal ecosystem caused by a shallower location of the upper limit of the Oxygen Minimum Zone (OMZ) These findings observed using past data might be taken into consideration for fishery management purposes when considering future scenarios. Keywords: acoustics, small pelagic fish, landings, biomass, latitudinal distribution, El Niño, Perú. Tendencias espacio-temporales en la distribución de la biomasa de anchoveta peruana y de otros peces pelágicos pequeños entre 1966 y 2009RESUMEN. Los cambios en la abundancia y distribución de anchoveta y de otras especies de pequeños peces pelágicos de la región norte del Sistema de la Corriente de Humboldt (NHCS) son el producto de la variación de forzantes ambientales en diferentes escalas de tiempo y espacio que influyen entre el ecosistema 'frío' costero y el oceánico 'cálido'. La información para estudiar estas fluctuaciones provienen mayormente de las pesquerías, y muestran que la anchoveta (Engraulis ringens) incrementa su abundancia cuando la de sardina (Sardinops sagax) declina y viceversa. Sin embargo, utilizando datos acústicos de biomasa latitudinal se muestra que otras especies como la caballa (Scomber japonicus) y el jurel (Trachurus murphyi) también siguen la misma tendencia opuesta a anchoveta, lo que en principio indica que los datos pesqueros disponibles no indican la verdadera dimensión del balance de abund...

The fall armyworm, Spodoptera frugiperda Smith & Abbot (Lepidoptera: Noctuidae), is a key pest of corn, Zea mays L. (Poales: Poaceae), in Mexico. The development of genetically modified (GM) corn hybrids for resistance to this insect, with the inclusion of several genes coding for proteins Cry1Ab, Vip3Aa20, and mCry3A of Bacillus thuringiensis Berliner (Bacillales: Bacillaceae) (Bt), offer an alternative to conventional insecticides to control this pest. Resistance to fall armyworms of the GM corn hybrids Agrisure 3000 GT, Agrisure Viptera 3110, and Agrisure Viptera 3111 was evaluated in 4 locations at Sinaloa for a 3 yr period. Damage evaluation showed that the maize hybrids with the Bt gene insertion were not affected by the fall armyworm as compared with their respective isolines, which were seriously damaged. The results reaffirm the insect control benefits provided by this technology and provide a baseline for resistance management.

The paper considers the problem of estimating the dependence function of a bivariate extreme survival function with standard exponential marginals. Nonparametric estimators for the dependence function are proposed and their strong uniform convergence under suitable conditions is demonstrated. Comparisons of the proposed estimators with other estimators are made in terms of bias and mean squared error. Several real data sets from various applications are used to illustrate the procedures. Academic PressAMS 2000 subject classifications: 62G20, 62E20, 62G30, 62G32, 60G15, 60G70, 60F15.

This study proposes a control chart based on functional data to detect anomalies and estimate the normal output of industrial processes and services such as those related to the energy efficiency domain. Companies providing statistical consultancy services in the fields of energy efficiency; heating, ventilation and air conditioning (HVAC); installation and control; and big data for buildings, have been striving to solve the problem of automatic anomaly detection in buildings controlled by sensors. Given the functional nature of the critical to quality (CTQ) variables, this study proposed a new functional data analysis (FDA) control chart method based on the concept of data depth. Specifically, it developed a control methodology, including the Phase I and II control charts. It is based on the calculation of the depth of functional data, the identification of outliers by smooth bootstrap resampling and the customization of nonparametric rank control charts. A comprehensive simulation study, comprising scenarios defined with different degrees of dependence between curves, was conducted to evaluate the control procedure. The proposed statistical process control procedure was also applied to detect energy efficiency anomalies in the stores of a textile company in the Panama City. In this case, energy consumption has been defined as the CTQ variable of the HVAC system. Briefly, the proposed methodology, which combines FDA and multivariate techniques, adapts the concept of the control chart based on a specific case of functional data and thereby presents a novel alternative for controlling facilities in which the data are obtained by continuous monitoring, as is the case with a great deal of process in the framework of Industry 4.0.