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[EN] This study examined the morphological characteristics of Trifolium repens L. sampled from 64 plots of a mountain meadow located in the North of the province of León (Spain), where a fertilization assay was performed for 30 years. At the end of the experiment, plants of white clover were obtained by manual separation from herbage harvested from each plot in the second cut, and a number of characteristics were determined in leaves (5 parameters), inflorescences (5 parameters), fruits and seeds (3 parameters). The leaf area (measured as the surface of the central leaflet) was the most important morphological characteristic to discriminate the clover plants, whereas other parameters were less important. Multiple linear regression was used to establish the relationships between the doses of fertilizers (N, P, K) applied and the clover yield or the clover leaf area in the plants sampled in each plot. Considering the morphological characteristics studied (mainly the leaf area) plants of white clover were clustered in four groups. Clover plants with a greatest leaf area (205 ± 18 mm2) were obtained from plots with low doses of nitrogen fertilizer (20 kg ha-1 yr-1) and doses of phosphorus and potassium (80 kg ha-1 yr-1 and 160 kg ha-1 yr-1). Conversely, plants with a smallest leaf area (117 ± 6 mm2) were collected from plots fertilized with low doses of phosphorus (25 kg ha-1 yr-1) and low / moderate doses of nitrogen and potassium (79 kg ha-1 yr-1 of both fertilizers)., [ES] Se describen las características morfológicas de Trifolium repens L. en 64 parcelas de un ensayo de fertilización mineral realizado en la Montaña de León durante un periodo de 30 años. Al final de la experiencia, previa separación del trébol blanco en el segundo corte, se analizan 13 características de sus hojas (5), inflorescencias (5), frutos y semillas (3). La superficie foliar (como medida de la superficie del foliolo central de la hoja) es la característica morfológica más importante para la diferenciación de las plantas de trébol mientras que los demás parámetros tienen una importancia menor. Se establece la relación entre la fertilización aportada y la superficie foliar; las dosis de nitrógeno (N), fósforo (P) y potasio (K) actúan como factores de variabilidad y se describen 4 tipos de trébol blanco. Los tipos con mayor superficie foliar (205±18 mm2) se obtuvieron con la mezcla de dosis bajas de N (20 kg ha- 1año-1), moderadas de P (80 kg ha-1año-1) y altas de K (160 kg ha-1año-1). Los de menor superficie foliar (117±6 mm2) se obtuvieron con la mezcla de dosis bajas de P (25 kg ha-1año-1) y bajas/moderadas dosis de N y K (79 kg ha-1año-1) en ambos casos.

Chorological and taxonomical data are reported on several taxa from the province of León. For some we provide the first record for the province, or extend their known distribution.

This paper presents a floristic and vegetation study of the serpentinised territories of the Dominican Republic, corresponding to the Yamasá and Prieta mountains, Dajabón, Puerto Plata and Gaspar Henández. These sites are very important at a local level as they act as speciation centres and have a specific ultramafic flora with a high endemicity rate. The floristic analysis revealed the existence of 219 species, of which 54 (24.7%) are endemic. Twenty-one plots of vegetation distributed in five areas dominated by serpentines have been analysed in this study. The phytosociological approach is based on the Braun-Blanquet method. The study was combined with statistical techniques such as multivariate analyses. The results revealed three different kinds of forests, one dominated by the endemic Bignoniaceae, Tabebuia berterii (CC.) Britton, which is peculiar to the subhumid-humid environments of the Caribbean-Cibensean territory. The second type of forest is dominated by the serpentinophyte Polygonaceae, Leptogonum buchii Urb. which thrives in the humid ombrotype of the north territory. Finally, the third one adopts the form of the pine forest of Dajabón, dominated by Pinus occidentalis Sw. Four associations and three alliances are proposed as new syntaxa based on statistical and phytosociological analyses. © 2013 © 2013 Società Botanica Italiana.

Winter wheat is the main crop on the North China Plain (NCP), and in this region the most limiting factor for the crop is water. The objective of this study was to adapt and test the ability of the FAO-developed AquaCrop model (v3.1) to simulate winter wheat grain yield, biomass, actual evapotranspiration (ETa) and total soil water content (0-120cm). Field experiments were conducted under deficit irrigation at the Luancheng Agro-ecosystem station (NCP) in 1998-2001, and the AquaCrop model was calibrated with treatment D (1999-2000); the rest of the data was used for validation of the model. The AquaCrop model was revalidated with data on measured grain yield from the experimental station for 1990-2010, considering actual field conditions. The second revalidation was done with the statistical grain yield for 1995-2010 in the study region. For the model validation, the significant differences between simulated and observed grain yield, biomass and ETa were in the order of: rainfed treatment>well-watered treatment>moderate water stress. Total soil water simulated by AquaCrop tends to follow closely the trend in the measured data, but with slight underestimations for irrigated treatments and significant overestimations for rainfed treatments. In general, errors in the model's evaluation such as RMSE and Willmot's d statistics were for grain yield (0.58Mgha-1, 0.92), biomass (0.87Mgha-1, 0.95), ETa (33.2mm, 0.93) and soil water content (24.5-37.6mm, 0.85-0.90). The overall results based on extensive validation and revalidation showed that AquaCrop is a valid model and can be used with a reliable degree of accuracy for optimizing winter wheat grain yield production and water requirement on the NCP.

Se estudia el estado fenológico de 11 gramíneas en 14 prados de la Comarca de Riaño (Montaña de León) en tres momentos de corte relacionados con la gestión de los recursos forrajeros: para aprovechamiento directo “en verde”, para conservación mediante ensilado “ensilado temprano” y para conservación más tardía “ensilado tardío”. La caracterización del estado fenológico de cada especie se realizó mediante la descripción de 5-25 tallos. Los estados fenológicos medios en los distintos momentos de corte fueron: 27,4 (final del alargamiento de los tallos), 31,1 (emergencia de la inflorescencia) y 34,0 (alargamiento del pedúnculo de la espiga). Los tres momentos de corte ligados a los aprovechamientos, pueden identificarse atendiendo al estado fenológico predominante que alcanzan tres especies: Anthoxanthum odoratum, Poa pratensis y Lolium perenne. El aprovechamiento temprano “en verde” se identifica con las especies A. odoratum y P. pratensis (en estado de antesis y preantesis respectivamente); el aprovechamiento para “ensilado temprano” con P. pratensis y L. perenne (en preantesis y espiga totalmente emergida) y el aprovechamiento para “ensilado tardío” con A. odoratum y P. pratensis (en estados de postantesis y antesis respectivamente).

Se estudia el estado fenológico (EFM) de 11 gramíneas en 14 prados (comunidades del Orden Arrhenatheretalia) de la Comarca de Riaño (León) que durante los últimos 10 años han recibido fertilización nitrogenada (nivel bajo: <60 kg ha-1 año-1 y alto >60 kg ha-1 año-1) y fosfórica (nivel bajo: <80 kg ha-1 año-1 y alto >80 kg ha-1 año-1). La toma de muestras se realiza en tres momentos sucesivos del ciclo de primavera (previos al tradicional para henificación) que se corresponden con tres formas de aprovechamiento forrajero: corte para aprovechamiento directo “en verde”, para conservación mediante ensilado “ensilado temprano” y para conservación más tardía “ensilado tardío”. La descripción del estado de desarrollo se efectúa sobre 5-25 tallos por especie. La incidencia de la fertilización sobre el estado de desarrollo de la vegetación es reducida en fases tempranas de la producción de forraje; seis especies: Alopecurus pratensis, Anthoxanthum odoratum, Cynosurus cristatus, Holcus lanatus, Lolium perenne y Poa pratensis presentan una mayor relación entre el abonado y el estado de fenológico. La fertilización nitrogenada y fosfórica, afectan positivamente al desarrollo de C. cristatus en momento de corte más temprano (aprovechamiento “en verde”), que alcanza la fase final del encañado con una fertilización elevada frente estadíos previos de alargamiento de los tallos con fertilización baja. Esta misma fertilización, afecta de forma negativa a A. odoratum en el aprovechamiento más tardío (conservación mediante ensilado “ensilado tardío”), que alcanza la fase de antesis con fertilización baja mientras que es de preantesis cuando la fertilización es alta.

Our soil study of the chromic soils located in the strip of land between Sierra Morena and the Guadalquivir Valley reveals a pH-value close to neutral in 25 samples. The pH-values are close to 5.5 only in the samples taken from the arenosols of the granitic batholith of Los Pedroches. However, it is the percentage of clay, sand and silt that explains the occurrence of grassland dominated by Raphanus raphanistrum, in the company of species with a psammophilous character such as Linaria spartea, Linaria amethystea and Brassica barrelieri. Our soil analysis reveals the presence of acid or base-poor soils. Consequently, the community of Raphanus raphanistrum found there displays an acidophilous, neutro-basophilous and subarenicolous character. Different types of sabulicolous plant communities appear as a result of the gradation of the soil texture. Since the Raphanus communities cannot be considered as genuinely sabulicolous, we have coined the term 'subarenicolous' to describe any plant community whose optimum sand content lies between 50% and 70%. The community of Linaria spartea and Raphanus raphanistrum is neutro-basophilous and is found in at least the Marianic-Monchiquensean and North Hispalensean sectors. The floristic composition of the association is made up of Raphanus raphanistrum, Linaria spartea, Linaria amethystea, Medicago polymorpha, Hordeum leporinum, Bromus diandrus, Brassica barrelieri. ©Italian Society for Vegetaion Science.

This study analyzes the mean maximum and minimum temperature trends on a monthly, seasonal, and annual timescale by applying various statistical tools to data from 476 Spanish weather stations during the period between 1961 and 2006. The magnitude of the trends was derived from the slopes of the regression lines using the least squares method, and the nonparametric Mann-Kendall test was used to determine the statistical significance of the trends. Temperature significantly increased in over 60% of the country in March, June, spring, and summer in the case of maximum temperatures and in March, May, June, August, spring, and summer for minimum temperatures. At the annual resolution, temperatures significantly increased in over 90% of Spain with a rise of around 0.3A degrees C/decade. The maximum temperature increased at a higher rate than the minimum temperature from midsummer to early winter as well as in winter, spring, and summer and also on an annual basis.

Monthly, seasonal and annual temperature trends between 1961 and 2010, as well as in the periods 1961-1978, 1979-1993 and 1994-2010 over continental Spain have been calculated in this work, according to the 144 thermopluviometric stations data widespread throughout the territory. The magnitude and sign of the trends have been evaluated with the Sen’s slope estimator and their statistical significances have been determined with the non parametric test of Mann-Kendall. As the main result, it has been confirmed what many other authors have already pointed out: a general increase in temperature (monthly, seasonal and annual) in Spain over the last 50 years, especially in March and summer months and, therefore, spring and summer. By and large, a substantial decrease in some monthly, seasonal and annual mean temperature as well as in continentality has been found in the cycle 1961-1978. In addition, the period 1979-1993 is characterized by statistically non significant increases in tempe¬rature trends (except July, August and spring with significant increases) and the lack of pattern in annual mean temperature, mean¬while a decrease in temperature only significant in March and winter over the period 1994-2010 has been observed in some Spanish areas.

This article deals with a phytosociological survey of marcescent Portuguese oak (Quercus broteroi) forests in southern Portugal, and presents the results of an analysis of 56 releves, combining Braun-Blanquet's methodology with hierarchical cluster analysis (Ward's Hierarchical Clustering, based on Bray-Curtis distance). From these results, a new temporihygrophilous Portuguese oak forest association is described. Largely thermomediterranean, upper dry to subhumid, Ulici welwitschiani-Quercetum broteroi occurs on limestone soils within the Coastal Lusitan-Andalusian Province. Its high conservation value - mainly due to its rarity and floristic structure - supports this forest's integration in Habitat 9240 (Annex B-I from Council Directive 92/43/EEC) in order to ensure its preservation. Based on the results, new biogeographic borders for the Ribatagan-Sadensean Sector are proposed.

This paper determines the isobioclimates existing in the province of Leon (north-western Spain) as the result of combining macrobioclimates, bioclimates, bioclimatic variants, thermotypes, ombrotypes and their corresponding bioclimatic horizons, all obtained from the analysis of eighty-three transects distributed throughout the study area The relations between isobioclimates and climax vegetation are also analysed. Twenty-six different isobioclimates are identified (15 temperate and 11 Mediterranean). Most vegetation series develop in the supratemperate humid and hyperhumid isobioclimates.

Pardiela's stream watershed was selected for a preliminary test of an ecological assessment through the application of the Potentiality Distance Index (PDI) methodology, based on current and potential vegetation maps. This analysis reveals the distance of a given vegetation community from its optimal potential state (climax) and the final PDI values portray the conservation status of the territory and allow the sketching of its needs for recovery or protection. Hence, territory management policies can subsequently be adjusted for better land use and biodiversity conservation. For this reason, this methodology can be an important step for landscape planning because it allows the adjustment of development proposals and activity monitoring. The final PDI value for the study area is 0.2509, indicating that Pardiela's basin conservation status is moderate.

The spatial distribution of recent mean temperature trends over Spain during the period 1961-2006 at monthly, seasonal and annual time scale is carried out in this study by applying various statistical tools to data from 473 weather stations. The magnitude of trends was derived from the slopes of the linear trends using ordinary least-square fitting. The non-parametric Mann-Kendall test was used to determine the statistical significance of trends. Maps of surface temperature trends were generated by applying a geostatistical interpolation technique to visualize the detected tendencies. This study reveals that temperature has generally increased during all months and seasons of the year over the last four decades. More than 60% of whole Spain has evidenced significant positive trends in March, June, August, spring and summer. This percentage diminishes around 40% in April, May and December. Annual temperature has significantly risen in 100% of Spain of around 0.1-0.2 °C/decade according to the Fourth Assessment Report of the IPCC. © 2011 Elsevier B.V.

The main methods for valuating vegetation proposed by different authors over the last three decades are summarized and discussed in this paper. As a result of this fact a new index for calculating the global conservation interest of the vegetation in a place or site is also proposed in this study. The index, based on the Cadiñanos & Meaza's proposals, includes new criteria and synphytosociological concepts and can be a very useful tool to be applied in land management and nature conservation studies.

The application of data mining and machine learning techniques to biological and biomedicine data continues to be an ubiquitous research theme in current bioinformatics. The rapid advances in biotechnology are allowing us to obtain and store large quantities of data about cells, proteins, genes, etc., that should be processed. Moreover, in many of these problems such as contact map prediction, the problem tackled in this paper, it is difficult to collect representative positive examples. Learning under these circumstances, known as imbalanced big data classification, may not be straightforward for most of the standard machine learning methods. In this work we describe the methodology that won the ECBDL’14 big data challenge for a bioinformatics big data problem. This algorithm, named as ROSEFW-RF, is based on several MapReduce approaches to (1) balance the classes distribution through random oversampling, (2) detect the most relevant features via an evolutionary feature weighting process and a threshold to choose them, (3) build an appropriate Random Forest model from the pre-processed data and finally (4) classify the test data. Across the paper, we detail and analyze the decisions made during the competition showing an extensive experimental study that characterize the way of working of our methodology. From this analysis we can conclude that this approach is very suitable to tackle large-scale bioinformatics classifications problems.

The application of data mining and machine learning techniques to biological and biomedicine data continues to be an ubiquitous research theme in current bioinformatics. The rapid advances in biotechnology are allowing us to obtain and store large quantities of data about cells, proteins, genes, etc., that should be processed. Moreover, in many of these problems such as contact map prediction, the problem tackled in this paper, it is difficult to collect representative positive examples. Learning under these circumstances, known as imbalanced big data classification, may not be straightforward for most of the standard machine learning methods. In this work we describe the methodology that won the ECBDL’14 big data challenge for a bioinformatics big data problem. This algorithm, named as ROSEFW-RF, is based on several MapReduce approaches to (1) balance the classes distribution through random oversampling, (2) detect the most relevant features via an evolutionary feature weighting process and a threshold to choose them, (3) build an appropriate Random Forest model from the pre-processed data and finally (4) classify the test data. Across the paper, we detail and analyze the decisions made during the competition showing an extensive experimental study that characterize the way of working of our methodology. From this analysis we can conclude that this approach is very suitable to tackle large-scale bioinformatics classifications problems.

The application of data mining and machine learning techniques to biological and biomedicine data continues to be an ubiquitous research theme in current bioinformatics. The rapid advances in biotechnology are allowing us to obtain and store large quantities of data about cells, proteins, genes, etc., that should be processed. Moreover, in many of these problems such as contact map prediction, the problem tackled in this paper, it is difficult to collect representative positive examples. Learning under these circumstances, known as imbalanced big data classification, may not be straightforward for most of the standard machine learning methods. In this work we describe the methodology that won the ECBDL’14 big data challenge for a bioinformatics big data problem. This algorithm, named as ROSEFW-RF, is based on several MapReduce approaches to (1) balance the classes distribution through random oversampling, (2) detect the most relevant features via an evolutionary feature weighting process and a threshold to choose them, (3) build an appropriate Random Forest model from the pre-processed data and finally (4) classify the test data. Across the paper, we detail and analyze the decisions made during the competition showing an extensive experimental study that characterize the way of working of our methodology. From this analysis we can conclude that this approach is very suitable to tackle large-scale bioinformatics classifications problems.

The application of data mining and machine learning techniques to biological and biomedicine data continues to be an ubiquitous research theme in current bioinformatics. The rapid advances in biotechnology are allowing us to obtain and store large quantities of data about cells, proteins, genes, etc., that should be processed. Moreover, in many of these problems such as contact map prediction, the problem tackled in this paper, it is difficult to collect representative positive examples. Learning under these circumstances, known as imbalanced big data classification, may not be straightforward for most of the standard machine learning methods. In this work we describe the methodology that won the ECBDL’14 big data challenge for a bioinformatics big data problem. This algorithm, named as ROSEFW-RF, is based on several MapReduce approaches to (1) balance the classes distribution through random oversampling, (2) detect the most relevant features via an evolutionary feature weighting process and a threshold to choose them, (3) build an appropriate Random Forest model from the pre-processed data and finally (4) classify the test data. Across the paper, we detail and analyze the decisions made during the competition showing an extensive experimental study that characterize the way of working of our methodology. From this analysis we can conclude that this approach is very suitable to tackle large-scale bioinformatics classifications problems.