Importancia de la caracterización de la masa de raíces en la simulación de ecosistemas forestales

Los análisis de sensibilidad son una herramienta importante para comprender el funcionamiento de los modelos ecológicos, así como para identificar los parámetros más importantes en su funcionamiento. Además, los análisis de sensibilidad pueden utilizarse para diseñar de forma más efectiva planes de muestreo de campo dirigidos a calibrar los modelos ecológicos. En los estudios de ecosistemas forestales, el análisis cuantitativo de la parte subterránea es mucho más costoso y complicado que el estudio de la parte aérea, en especial el estudio de la dinámica de producción y descomposición de raíces gruesas y finas de los árboles. En este trabajo se muestra un ejemplo de análisis de sensibilidad del modelo forestal FORECAST a parámetros que definen la biomasa, longevidad y concentración de nitrógeno en las raíces de los árboles. El modelo se calibró para simular dos rodales de pino silvestre (Pinus sylvestris) en los Pirineos de Navarra. Los resultados indican que la tasa de renovación de raíces finas es el parámetro más influyente en las estimaciones del modelo de crecimiento de los árboles, seguida de la concentración de N en las mismas, siendo la relación biomasa subterránea/total el parámetro al cual el modelo es menos sensible. Además, el modelo es más sensible a los parámetros que definen el componente subterráneo de la biomasa arbórea cuando simula un sitio de menor capacidad productiva y mayor limitación por nutrientes.

Steric and mass-induced sea level variations in the Mediterranean Sea revisited

The total sea level variation (SLV) is the combination of steric and mass␣induced SLV, whose exact shares are key to understanding the oceanic response to climate system changes. Total SLV can be observed by radar altimetry satellites such as TOPEX/POSEIDON and Jason 1/2. The steric SLV can be computed through temperature and salinity profiles from in situ measurements or from ocean general circulation models (OGCM), which can assimilate the said observations. The mass-induced SLV can be estimated from its time-variable gravity (TVG) signals. We revisit this problem in the Mediterranean Sea estimating the observed, steric, and mass-induced SLV, for the latter we analyze the latest TVG data set from the GRACE (Gravity Recovery and Climate Experiment) satellite mission launched in 2002, which is 3.5 times longer than in previous studies, with the application of a two-stage anisotropic filter to reduce the noise in high-degree and -order spherical harmonic coefficients. We confirm that the intra-annual total SLV are only produced by water mass changes, a fact explained in the literature as a result of the wind field around the Gibraltar Strait. The steric SLV estimated from the residual of “altimetry minus GRACE” agrees in phase with that estimated from OGCMs and in situ measurements, although showing a higher amplitude. The net water fluxes through both the straits of Gibraltar and Sicily have also been estimated accordingly.

EmotiBlog: a finer-grained and more precise learning of subjectivity expression models

The exponential growth of the subjective information in the framework of the Web 2.0 has led to the need to create Natural Language Processing tools able to analyse and process such data for multiple practical applications. They require training on specifically annotated corpora, whose level of detail must be fine enough to capture the phenomena involved. This paper presents EmotiBlog – a fine-grained annotation scheme for subjectivity. We show the manner in which it is built and demonstrate the benefits it brings to the systems using it for training, through the experiments we carried out on opinion mining and emotion detection. We employ corpora of different textual genres –a set of annotated reported speech extracted from news articles, the set of news titles annotated with polarity and emotion from the SemEval 2007 (Task 14) and ISEAR, a corpus of real-life self-expressed emotion. We also show how the model built from the EmotiBlog annotations can be enhanced with external resources. The results demonstrate that EmotiBlog, through its structure and annotation paradigm, offers high quality training data for systems dealing both with opinion mining, as well as emotion detection.

Predictive Models of Alcohol Use Based on Attitudes and Individual Values

Two predictive models are developed in this article: the first is designed to predict people's attitudes to alcoholic drinks, while the second sets out to predict the use of alcohol in relation to selected individual values. University students (N = 1,500) were recruited through stratified sampling based on sex and academic discipline. The questionnaire used obtained information on participants' alcohol use, attitudes and personal values. The results show that the attitudes model correctly classifies 76.3% of cases. Likewise, the model for level of alcohol use correctly classifies 82% of cases. According to our results, we can conclude that there are a series of individual values that influence drinking and attitudes to alcohol use, which therefore provides us with a potentially powerful instrument for developing preventive intervention programs.

Factors that determine the level of commitment of our teaching staff when the implementation of continuous evaluation system takes place

Comunicación presentada en CIDUI 2010, Congreso Internacional Docencia Universitaria e Innovación, Barcelona, 30 junio-2 julio 2010.

How can an invasive grass affect fire behavior in a tropical savanna? A community and individual plant level approach

Some invasive grasses have been reported to change fire behavior in invaded plant communities. Urochloa brizantha is an aggressive invasive grass in the Brazilian Cerrado, an ecosystem where fire is a common disturbance. We investigated the effects of U. brizantha on fire behavior in an open Cerrado physiognomy in Central Brazil. Using experimental burnings we compared fire behavior at both the community and the individual plant level in invaded (UJ) and non-invaded (NJ) areas burned in July. We also assessed the effect of fire season in invaded areas by comparing July (UJ) and October (UO) burnings. We evaluated the following variables: fuel load, fuel moisture, combustion efficiency, maximum fire temperature, flame height, and fire intensity. Additionally, we evaluated the temperatures reached under invasive and native grass tussocks in both seasons. Fuel load, combustion efficiency, and fire intensity were higher in NJ than in UJ, whilst flame height showed the opposite trend. Fuel amount and fire intensity were higher in October than in July. At the individual plant level, U. brizantha moisture was higher than that of native species, however, temperatures reaching ≥600 °C at ground level were more frequent under U. brizantha tussocks than under native grasses. At the community level, the invasive grass modified fire behavior towards lower intensity, lower burning efficiency, and higher flame height. These results provide essential information for the planning of prescribed burnings in invaded Cerrado areas.