The relationship of telomere length to baseline corticosterone levels in nestlings of an altricial passerine bird in natural populations

Indexación: Web of Science; Scopus.Background: Environmental stressors increase the secretion of glucocorticoids that in turn can shorten telomeres via oxidative damage. Modification of telomere length, as a result of adversity faced early in life, can modify an individual's phenotype. Studies in captivity have suggested a relationship between glucocorticoids and telomere length in developing individuals, however less is known about that relationship in natural populations. Methods: In order to evaluate the effect of early environmental stressors on telomere length in natural populations, we compared baseline corticosterone (CORT) levels and telomere length in nestlings of the same age. We collected blood samples for hormone assay and telomere determination from two geographically distinct populations of the Thorn-tailed Rayadito (Aphrastura spinicauda) that differed in brood size; nestlings body mass and primary productivity. Within each population we used path analysis to evaluate the relationship between brood size, body mass, baseline CORT and telomere length. Results: Within each distinct population, path coefficients showed a positive relationship between brood size and baseline CORT and a strong and negative correlation between baseline CORT and telomere length. In general, nestlings that presented higher baseline CORT levels tended to present shorter telomeres. When comparing populations it was the low latitude population that presented higher levels of baseline CORT and shorter telomere length. Conclusions: Taken together our results reveal the importance of the condition experienced early in life in affecting telomere length, and the relevance of integrative studies carried out in natural conditions.https://frontiersinzoology.biomedcentral.com/articles/10.1186/s12983-016-0133-

Land use and environmental factors affecting red-legged partridge (Alectoris rufa) hunting yields in southern Spain

The red-legged partridge is a small game species widely hunted in southern Spain. Its commercial use has important socioeconomic effects in rural areas where other agrarian uses are of marginal importance. The aims of the present work were to identify areas in Andalusia (southern Spain) where game yields for the red-legged partridge reach high values and to establish the environmental and land use factors that determine them. We analysed 32,134 annual hunting reports (HRs) produced by 6,049 game estates during the hunting seasons 1993/1994 to 2001/2002 to estimate the average hunting yields of red-legged partridge in each Andalusian municipality (n=771). We modelled the favourability for obtaining good hunting yields using stepwise logistic regression on a set of climatic, topographical, land use and vegetation variables that were available as digital coverages or tabular data applied to municipalities. Good hunting yields occur mainly in plain areas located in the Guadalquivir valley, at the bottom of Betic Range and in the Betic depressions. Favourable areas are related to highly mechanised, lowelevation areas mainly dedicated to intensive dry crops. The most favourable areas predicted by our model are mainly located in the Guadalquivir valley

Wireless Communications for Marine Sensor Networks

Peer Reviewe

Magnetic dispersive solid phase microextraction coupled with on-line chemical vapor generation method to extraction/preconcentration of mercury from environmental samples and determination by graphite furnace atomic absorption spectrometry.

Mercury (Hg) is classified as priority hazardous substances. Concentrations found in the aquatic environment are at trace levels as result of natural processes, such as erosion and volcanism, and anthropogenic discharges related mainly to industrial and mining activities. Mercury is one of the most potent neurotoxins known, showing a high number of adverse health effects in animals and humans. For this reason, a simple and rapid method for the determination and preconcentration of mercury in environmental waters is proposed. This work is based on magnetic dispersive solid phase microextraction (MDSPME) coupled with on-line chemical vapour generation (CVG). Graphite furnace atomic absorption spectrometry (GFAAS) was employed for the quantification of Hg. In the preconcentration step, a shell structured Fe3O4@graphene oxide was suspended in the ionic liquid carrier (1-n-butyl-3-metilimidazolium tetrafluoroborate [BMIM][BF4]), obtaining a stable colloidal suspension called ferrofluid. This sorbent possesses as large contact surface area and a high density of polar groups on its surface. The nanoparticles, when finely dispersed in the sample solution, result in almost complete extraction of Hg within a few seconds. All experimental and instrumental variables were optimized and the method was adequately validated by the analysis of certified reference materials of environmental waters. Acknowledgements The authors would like to thank Plan Propio “Proyecto Puente” de la Universidad de Málaga for financial support of this work.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Distribution modelling of wild rabbit hunting yields in its original area (S Iberian Peninsula)

In this work we used the information of the Annual Hunting Reports (AHRs) to obtain a high-resolution model of the potential favourableness for wild rabbit harvesting in Andalusia (southern Spain), using environmental and land-use variables as predictors. We analysed 32,134 AHRs from the period 1993/2001 reported by 6049 game estates to estimate the average hunting yields of wild rabbit in each Andalusian municipality (n5771). We modelled the favourableness for obtaining good hunting yields using stepwise logistic regression on a set of climatic, orographical, land use, and vegetation variables. The favourability equation was used to create a downscaled image representing the favourableness of obtaining good hunting yields for the wild rabbit in 161 km squares in Andalusia, using the Idrisi Image Calculator. The variables that affected hunting yields of wild rabbit were altitude, dry wood crops (mainly olive groves, almond groves, and vineyards), temperature, pasture, slope, and annual number of frost days. The 161 km squares with high favourableness values are scattered throughout the territory, which seems to be caused mainly by the effect of vegetation. Finally, we obtained quality categories for the territory by combining the probability values given by logistic regression with those of the environmental favourability function

Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control

In recent years, the photovoltaic generation installed capacity has been steadily growing thanks to its inexhaustible and non-polluting characteristics. However, solar generators are strongly dependent on intermittent weather parameters, increasing power systems' uncertainty level. Forecasting models have arisen as a feasible solution to decreasing photovoltaic generators' uncertainty level, as they can produce accurate predictions. Traditionally, the vast majority of research studies have focused on the develop- ment of accurate prediction point forecasters. However, in recent years some researchers have suggested the concept of prediction interval forecasting, where not only an accurate prediction point but also the confidence level of a given prediction are computed to provide further information. This paper develops a new model for predicting photovoltaic generators' output power confidence interval 10 min ahead, based on deep learning, mathematical probability density functions and meteorological parameters. The model's accuracy has been validated with a real data series collected from Spanish meteorological sta- tions. In addition, two error metrics, prediction interval coverage percentage and Skill score, are computed at a 95% confidence level to examine the model's accuracy. The prediction interval coverage percentage values are greater than the chosen confidence level, which means, as stated in the literature, the proposed model is well-founded