Consumo de Savia por Melanerpes cactorum y su Rol en la Estructuración de Ensambles de Aves en Bosques Secos

The White-fronted Woodpecker (Melanerpes cactorum) drills holes in branches and trunks to feed on sap flows, providing an energy-rich food resource for other birds. Here we describe ecological and behavioral traits of the White-fronted Woodpecker related to its sap-feeding habits in the semiarid Chaco of Argentina and explore the structure of the avian assemblage in relation to the sap resource. Sap consumption by the White-fronted Woodpecker and other sap-feeding species was strongly seasonal and positively associated with periods of resource scarcity. The White-fronted Woodpecker actively defended the sap wells from smaller birds. Specialist and facultative nectarivores that assimilate sucrose at a high rate represented an important proportion of sap-feeding birds. In this system of woodpecker, sap, and other sap-feeding species, each species’ consumption depends on its physiological and behavioral characteristics as well as on the availability of other food in the surrounding environment.Melanerpes cactorum perfora ramas y troncos de árboles y arbustos para consumir la savia que fluye de las perforaciones, posibilitando a otras especies de aves el acceso a un recurso de alto contenido energé- tico. En este estudio describimos rasgos de la historia natural de M. cactorum relacionados con su alimentación en el Chaco semiárido de Argentina e investigamos la estructuración de ensambles de aves en torno al recurso savia. Para M. cactorum y las especies de aves que consumieron savia, el consumo de savia fue marcadamente estacional, posiblemente asociado a periodos de escasez de recursos. Melanerpes cactorum defendió activamente las perforaciones ante algunas especies de aves cuya masa corporal fue menor a la de los carpinteros. Las especies nectarívoras especialistas y facultativas con alta tasa de asimilación de sacarosa representaron una importante proporción de las aves que consumieron savia. En el sistema carpinteros–savia–aves consumidoras de savia, el consumo de este recurso depende de características fisiológicas y comportamentales de las especies, como así también de la disponibilidad de otros recursos alimenticios en los ambientes que habitan.Fil: Nuñez Montellano, Maria Gabriela. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tucumán; ArgentinaFil: Blendinger, Pedro Gerardo. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tucumán; ArgentinaFil: Macchi, Leandro. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tucumán; Argentin

Extrapolation for Time-Series and Cross-Sectional Data

Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts’ commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased

Predicting hydrological response to climate change in the White Volta Catchment, West Africa

This study uses an ensemble of Regional Climate Model (REMO), to simulate and project the climate at local scale in order to investigate the hydrological impact of possible future climate change in White Volta Catchment (West Africa). The results, obtained from the REMO climate model, were compared to the observational datasets for precipitation and temperature for the period 1995-2008. The projected meteorological variables for the period 2030-2043 were used as input to the Soil and Water Assessment Tool (SWAT) hydrological model which was calibrated (R2 = 0.88 and NSE= 0.84) and validated (R2 = 0.82 and NSE= 0.79) with historical data to investigate the possible impact of climate change in the catchment. The results obtained from the investigation revealed that catchment is sensitive to climate change. With a small increase of 8% and 1.7% of the mean annual precipitation and temperature respectively, annual surface runoff, annual baseflow and evapotranspiration recorded increment of 26%, 24% and 6% respectively

Autophagy in the control and pathogenesis of parasitic infections

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