Temporal variation of genetic diversity and differentiation of albacore, Thunnus alalunga, in the Mediterranean Sea

This study is on albacore (Thunnus alalunga, Bonnaterre 1788), an epi- and mesopelagic oceanic tuna species cosmopolitan in the tropical and temperate waters of all oceans including the Mediterranean Sea, extending in a broad band between 40°N and 40°S. What it’s known about albacore population structure is based on different studies that used fisheries data, RFLP, mtDNA control region and nuDNA markers, blood lectins analysis, individual tags and microsatellite. At the moment, for T. alalunga six management units are recognized: the North Pacific, South Pacific, Indian, North Atlantic, South Atlantic and Mediterranean stocks. In this study I have done a temporal and spatial comparison of genetic variability between different Mediterranean populations of Thunnus alalunga matching an historical dataset ca. from 1920s composed of 43 individuals divided in 3 populations (NADR, SPAIN and CMED) with a modern dataset composed of 254 individuals and 7 populations (BAL, CYP, LIG, TYR, TUR, ADR, ALB). The investigation was possible using a panel of 94 nuclear SNPs, built specifically for the target species at the University of Basque Country UPV/EHU. First analysis done was the Hardy-Weinberg, then the number of clusters (K) was determined using STRUCTURE and to assess the genetic variability, allele frequencies, the average number of alleles per locus, expected (He) and observed (Ho) heterozygosis, and the index of polymorphism (P) was used the software Genetix. Historical and modern samples gives different results, showing a clear loss of genetic diversity over time leading to a single cluster in modern albacore instead of the two found in historical samples. What this study reveals is very important for conservation concerns, and additional research endeavours are needed.

Spatio-temporal diversity of Megistozoobenthos in the Antalya Gulf and relationships with environmental features

Antalya Gulf is situated in the Levantine Sea, the second biggest and most eastern basin in the Mediterranean Sea. This area is an ultra-oligotrophic basin, strongly affected by anthropogenic inputs, in particular in the fishing areas. For this characteristic, in the Levantine Sea, there is a strong pressure on the natural resources and benthic assemblages. Furthermore, many alien species enter from Suez Canal and are well established in the area. All these pressures are leading to a degradation of the Levantine Sea. For this reason it is important to have tools to study and monitoring the functioning of the marine ecosystem. Benthic organisms are superior to many other biological groups for their response to environmental stresses. The variability of benthic assemblages on a site can reflect, in an integrative mode, the entire functioning of the marine ecosystem. In this study, that wants to analyze the spatial and temporal distribution of the benthic macrofaunal assemblages of Antalya Gulf, 90 benthic species divided in 8 taxa (Annelida, Cnidaria, Echinodermata, Echiura, Mollusca, Porifera, Sipunculida and Tunicata) were found. All the analyses conducted on the entire benthic class and later on Mollusca and Echinodermata separately highlighted the importance of depth on structuring benthic community.

Temporal dynamics of benthic assemblages along a gradient of ocean acidification at a CO2 vent’s system of the Ischia Island

Global warming and ocean acidification, due to rising atmospheric levels of CO2, represent an actual threat to terrestrial and marine environments. Since Industrial Revolution, in less of 250 years, pH of surface seawater decreased on average of 0.1 unit, and is expected to further decreases of approximately 0.3-0.4 units by the end of this century. Naturally acidified marine areas, such as CO2 vent systems at the Ischia Island, allow to study acclimatation and adaptation of individual species as well as the structure of communities, and ecosystems to OA. The main aim of this thesis was to study how hard bottom sublittoral benthic assemblages changed trough time along a pH gradient. For this purpose, the temporal dynamics of mature assemblages established on artificial substrates (volcanic tiles) over a 3 year- period were analysed. Our results revealed how composition and dynamics of the community were altered and highly simplified at different level of seawater acidification. In fact, extreme low values of pH (approximately 6.9), affected strongly the assemblages, reducing diversity both in terms of taxa and functional groups, respect to lower acidification levels (mean pH 7.8) and ambient conditions (8.1 unit). Temporal variation was observed in terms of species composition but not in functional groups. Variability was related to species belonging to the same functional group, suggesting the occurrence of functional redundancy. Therefore, the analysis of functional groups kept information on the structure, but lost information on species diversity and dynamics. Decreasing in ocean pH is only one of many future global changes that will occur at the end of this century (increase of ocean temperature, sea level rise, eutrophication etc.). The interaction between these factors and OA could exacerbate the community and ecosystem effects showed by this thesis.

Temporal and spatial variation of nitrogen transformations in nitrogen-saturated soils of a central Appalachian hardwood forest

We studied temporal and spatial patterns of soil nitrogen (N) dynamics from 1993 to 1995 in three watersheds of Fernow Experimental Forest, W.V.: WS7 (24-year-old, untreated); WS4 (mature, untreated); and WS3 (24-year-old, treated with (NH4)2SO since 1989 at the rate of 35 kg Nha–1year–1). Net nitrification was 141, 114, and115 kg Nha–1year–1, for WS3, WS4, and WS7, respectively, essentially 100% of net N mineralization for all watersheds. Temporal (seasonal) patterns of nitrification were significantly related to soil moisture and ambient temperaturein untreated watersheds only. Spatial patterns of soil water NO3–of WS4 suggest that microenvironmental variabilitylimits rates of N processing in some areas of this N-saturated watershed, in part by ericaceous species in the herbaceous layer. Spatial patterns of soil water NO3–in treated WS3 suggest that later stages of N saturation may result inhigher concentrations with less spatial variability. Spatial variability in soil N variables was lower in treated WS3 versus untreated watersheds. Nitrogen additions have altered the response of N-processing microbes to environmental factors, becoming less sensitive to seasonal changes in soil moisture and temperature. Biotic processes responsible forregulating N dynamics may be compromised in N-saturated forest ecosystems.

Day-to-night time differences in the relationship between cardiorespiratory coordination and heart rate variability

Heart rate variability (HRV) and cardiorespiratory coordination, i.e. the temporal interplay between oscillations of heartbeat and respiration, reflect information related to the cardiovascular and autonomic nervous system. The purpose of this study was to investigate the relationship between spectral measures of HRV and measures of cardiorespiratory coordination. In 127 subjects from a normal population a 24 h Holter ECG was recorded. Average heart rate (HR) and the following HRV parameters were calculated: very low (VLF), low (LF) and high frequency (HF) oscillations and LF/HF. Cardiorespiratory coordination was quantified using average respiratory rate (RespR), the ratio of heart rate and respiratory rate (HRR), the phase coordination ratio (PCR) and the extent of cardiorespiratory coordination (PP). Pearson's correlation coefficient r was used to quantify the relationship between each pair of the variables across all subjects. HR and HRR correlated strongest during daytime (r = 0.89). LF/HF and PP showed a negative correlation to a reasonable degree (r = -0.69). During nighttime sleep these correlations decreased whereas the correlation between HRR and RespR (r = -0.47) as well as between HRR and PCR (r = 0.73) increased substantially. In conclusion, HRR and PCR deliver considerably different information compared to HRV measures whereas PP is partially linked reciprocally to LF/HF.

Environmental variability promotes plant invasion

Global environmental change not only entails changes in mean environmental conditions but also in their variability. Changes in climate variability are often associated with altered disturbance regimes and temporal patterns of resource availability. Here we show that increased variability of soil nutrients strongly promotes another key process of global change, plant invasion. In experimental plant communities, the success of one of the world's most invasive plants, Japanese knotweed, is two- to four-fold increased if extra nutrients are not supplied uniformly, but in a single large pulse, or in multiple pulses of different magnitudes. The superior ability to take advantage of variable environments may be a key mechanism of knotweed dominance, and possibly many other plant invaders. Our study demonstrates that increased nutrient variability can promote plant invasion, and that changes in environmental variability may interact with other global change processes and thereby substantially accelerate ecological change

Continental-scale temperature variability during the past two millennia

Past global climate changes had strong regional expression. To elucidate their spatio-temporal pattern, we reconstructed past temperatures for seven continental-scale regions during the past one to two millennia. The most coherent feature in nearly all of the regional temperature reconstructions is a long-term cooling trend, which ended late in the nineteenth century. At multi-decadal to centennial scales, temperature variability shows distinctly different regional patterns, with more similarity within each hemisphere than between them. There were no globally synchronous multi-decadal warm or cold intervals that define a worldwide Medieval Warm Period or Little Ice Age, but all reconstructions show generally cold conditions between ad 1580 and 1880, punctuated in some regions by warm decades during the eighteenth century. The transition to these colder conditions occurred earlier in the Arctic, Europe and Asia than in North America or the Southern Hemisphere regions. Recent warming reversed the long-term cooling; during the period ad 1971–2000, the area-weighted average reconstructed temperature was higher than any other time in nearly 1,400 years.

Satellite-Derived Variability in Chlorophyll, Wind Stress, Sea Surface Height, and Temperature in the Northern California Current System

Satellite-derived data provide the temporal means and seasonal and nonseasonal variability of four physical and biological parameters off Oregon and Washington ( 41 degrees - 48.5 degrees N). Eight years of data ( 1998 - 2005) are available for surface chlorophyll concentrations, sea surface temperature ( SST), and sea surface height, while six years of data ( 2000 - 2005) are available for surface wind stress. Strong cross-shelf and alongshore variability is apparent in the temporal mean and seasonal climatology of all four variables. Two latitudinal regions are identified and separated at 44 degrees - 46 degrees N, where the coastal ocean experiences a change in the direction of the mean alongshore wind stress, is influenced by topographic features, and has differing exposure to the Columbia River Plume. All these factors may play a part in defining the distinct regimes in the northern and southern regions. Nonseasonal signals account for similar to 60 - 75% of the dynamical variables. An empirical orthogonal function analysis shows stronger intra-annual variability for alongshore wind, coastal SST, and surface chlorophyll, with stronger interannual variability for surface height. Interannual variability can be caused by distant forcing from equatorial and basin-scale changes in circulation, or by more localized changes in regional winds, all of which can be found in the time series. Correlations are mostly as expected for upwelling systems on intra-annual timescales. Correlations of the interannual timescales are complicated by residual quasi-annual signals created by changes in the timing and strength of the seasonal cycles. Examination of the interannual time series, however, provides a convincing picture of the covariability of chlorophyll, surface temperature, and surface height, with some evidence of regional wind forcing.

Spatial Patterns of Intraseasonal Variability of Chlorophyll and Sea Surface Temperature in the California Current

Six years of daily satellite data are used to quantify and map intraseasonal variability of chlorophyll and sea surface temperature (SST) in the California Current. We define intraseasonal variability as temporal variation remaining after removal of interannual variability and stationary seasonal cycles. Semivariograms are used to quantify the temporal structure of residual time series. Empirical orthogonal function (EOF) analyses of semivariograms calculated across the region isolate dominant scales and corresponding spatial patterns of intraseasonal variability. The mode 1 EOFs for both chlorophyll and SST semivariograms indicate a dominant timescale of similar to 60 days. Spatial amplitudes and patterns of intraseasonal variance derived from mode 1 suggest dominant forcing of intraseasonal variability through distortion of large scale chlorophyll and SST gradients by mesoscale circulation. Intraseasonal SST variance is greatest off southern Baja and along southern Oregon and northern California. Chlorophyll variance is greatest over the shelf and slope, with elevated values closely confined to the Baja shelf and extending farthest from shore off California and the Pacific Northwest. Intraseasonal contributions to total SST variability are strongest near upwelling centers off southern Oregon and northern California, where seasonal contributions are weak. Intraseasonal variability accounts for the majority of total chlorophyll variance in most inshore areas save for southern Baja, where seasonal cycles dominate. Contributions of higher EOF modes to semivariogram structure indicate the degree to which intraseasonal variability is shifted to shorter timescales in certain areas. Comparisons of satellite-derived SST semivariograms to those calculated from co-located and concurrent buoy SST time series show similar features.

Variability of mesospheric water vapor above Bern in relation to the 27-day solar rotation cycle

Many studies investigated solar–terrestrial responses (thermal state, O₃ , OH, H₂O) with emphasis on the tropical upper atmosphere. In this paper the Focus is switched to water vapor in the mesosphere at a mid-latitudinal location. Eight years of water vapor profile measurements above Bern (46.88°N/7.46°E) are investigated to study oscillations with the Focus on periods between 10 and 50 days. Different spectral analyses revealed prominent features in the 27-day oscillation band, which are enhanced in the upper mesosphere (above 0.1 hPa, ∼64 km) during the rising sun spot activity of solar cycle 24. Local as well as zonal mean Aura MLS observations Support these results by showing a similar behavior. The relationship between mesospheric water and the solar Lyman-α flux is studied by comparing thesi-milarity of their temporal oscillations. The H₂O oscillation is negatively correlated to solar Lyman-α oscillation with a correlation coefficient of up to −0.3 to −0.4, and the Phase lag is 6–10 days at 0.04 hPa. The confidence level of the correlation is ≥99%. This finding supports the assumption that the 27-day oscillation in Lyman-α causes a periodical photo dissociation loss in mesospheric water. Wavelet power spectra, cross-wavelet transform and wavelet coherence analysis (WTC)complete our study. More periods of high common wavelet power of H₂O and solar Lyman-α are present when amplitudes of the Lyman-α flux increase. Since this is not a measure of physical correlation a more detailed view on WTC is necessary, where significant (two sigma level)correlations occur intermittently in the 27 and 13-day band with variable Phase lock behavior. Large Lyman-α oscillations appeared after the solar super storm in July 2012 and the H₂O oscillations show a well pronounced anticorrelation. The competition between advective transport and photo dissociation loss of mesospheric water vapor may explain the sometimes variable Phase relationship of mesospheric H₂O and solar Lyman-α oscillations. Generally, the WTC analysis indicates that solar variability causes observable photochemical and dynamical processes in the mid-latitude mesosphere.

Impact of above- and below-ground invertebrates on temporal and spatial stability of grassland of different diversity

1. Recent theoretical studies suggest that the stability of ecosystem processes is not governed by diversity per se, but by multitrophic interactions in complex communities. However, experimental evidence supporting this assumption is scarce.2. We investigated the impact of plant diversity and the presence of above- and below-ground invertebrates on the stability of plant community productivity in space and time, as well as the interrelationship between both stability measures in experimental grassland communities.3. We sampled above-ground plant biomass on subplots with manipulated above- and below-ground invertebrate densities of a grassland biodiversity experiment (Jena Experiment) 1, 4 and 6 years after the establishment of the treatments to investigate temporal stability. Moreover, we harvested spatial replicates at the last sampling date to explore spatial stability.4. The coefficient of variation of spatial and temporal replicates served as a proxy for ecosystem stability. Both spatial and temporal stability increased to a similar extent with plant diversity. Moreover, there was a positive correlation between spatial and temporal stability, and elevated plant density might be a crucial factor governing the stability of diverse plant communities.5. Above-ground insects generally increased temporal stability, whereas impacts of both earthworms and above-ground insects depended on plant species richness and the presence of grasses. These results suggest that inconsistent results of previous studies on the diversity–stability relationship have in part been due to neglecting higher trophic-level interactions governing ecosystem stability.6. Changes in plant species diversity in one trophic level are thus unlikely to mirror changes in multitrophic interrelationships. Our results suggest that both above- and below-ground invertebrates decouple the relationship between spatial and temporal stability of plant community productivity by differently affecting the homogenizing mechanisms of plants in diverse plant communities.7.Synthesis. Species extinctions and accompanying changes in multitrophic interactions are likely to result not only in alterations in the magnitude of ecosystem functions but also in its variability complicating the assessment and prediction of consequences of current biodiversity loss.

North Pacific Carbon Cycle Response to Climate Variability on Seasonal to Decadal Timescales

Climate variability drives significant changes in the physical state of the North Pacific, and there may be important impacts of this variability on the upper ocean carbon balance across the basin. We address this issue by considering the response of seven biogeochemical ocean models to climate variability in the North Pacific. The models' upper ocean pCO(2) and air-sea CO(2) flux respond similarly to climate variability on seasonal to decadal timescales. Modeled seasonal cycles of pCO(2) and its temperature- and non-temperature-driven components at three contrasting oceanographic sites capture the basic features found in observations (Takahashi et al., 2002, 2006; Keeling et al., 2004; Brix et al., 2004). However, particularly in the Western Subarctic Gyre, the models have difficulty representing the temporal structure of the total pCO(2) seasonal cycle because it results from the difference of these two large and opposing components. In all but one model, the air-sea CO(2) flux interannual variability (1 sigma) in the North Pacific is smaller ( ranges across models from 0.03 to 0.11 PgC/yr) than in the Tropical Pacific ( ranges across models from 0.08 to 0.19 PgC/yr), and the time series of the first or second EOF of the air-sea CO(2) flux has a significant correlation with the Pacific Decadal Oscillation (PDO). Though air-sea CO(2) flux anomalies are correlated with the PDO, their magnitudes are small ( up to +/- 0.025 PgC/yr ( 1 sigma)). Flux anomalies are damped because anomalies in the key drivers of pCO(2) ( temperature, dissolved inorganic carbon (DIC), and alkalinity) are all of similar magnitude and have strongly opposing effects that damp total pCO(2) anomalies.

Spatial and temporal aspects of synaptic plasticity

The notion that changes in synaptic efficacy underlie learning and memory processes is now widely accepted even if definitive proof of the synaptic plasticity and memory hypothesis is still lacking. When learning occurs, patterns of neural activity representing the occurrence of events cause changes in the strength of synaptic connections within the brain. Reactivation of these altered connections constitutes the experience of memory for these events and for other events with which they may be associated. These statements summarize a long-standing theory of memory formation that we refer to as the synaptic plasticity and memory hypothesis. Since activity-dependent synaptic plasticity is induced at appropriate synapses during memory formation, and is both necessary and sufficient for the information storage, we can speculate that a methodological study of the synapse will help us understand the mechanism of learning. Random events underlie a wide range of biological processes as diverse as genetic drift and molecular diffusion, regulation of gene expression and neural network function. Additionally spatial variability may be important especially in systems with nonlinear behavior. Since synapse is a complex biological system we expect that stochasticity as well as spatial gradients of different enzymes may be significant for induction of plasticity. ^ In that study we address the question "how important spatial and temporal aspects of synaptic plasticity may be". We developed methods to justify our basic assumptions and examined the main sources of variability of calcium dynamics. Among them, a physiological method to estimate the number of postsynaptic receptors as well as a hybrid algorithm for simulating postsynaptic calcium dynamics. Additionally we studied how synaptic geometry may enhance any possible spatial gradient of calcium dynamics and how that spatial variability affect plasticity curves. Finally, we explored the potential of structural synaptic plasticity to provide a metaplasticity mechanism specific for the synapse. ^

Spatial and temporal precipitation patterns of the Ebro River Basin, Spain

The Ebro River Basin, with around 85 000 km2 and located in NE Spain, is characterized by the high spatial heterogeneity of its geology, topography, climatology and land use. Rainfall is one of the most important climatic variables studied owing to its non-homogenous behaviour in event and intensity, which creates drought, water runoff and soil erosion with negative environmental and social consequences. In this work we characterized the rainfall variability pattern in the Ebro River Basin using universal multifractal (UM) analysis, which estimates the concentration of the data around the precipitation average (C1, codimension average), the degree of multiscaling behaviour in time (? index) and the maximum probable singularity in the rainfall distribution ( s). A spatial and temporal analysis of the UM parameters is applied to study the possible changes. With this porpoise, 60 daily rainfall series were selected from 132 synthetic series generated by Luna and Balairón (AEMet). These daily rainfall series present a length of 60 years, from 1950 to 2009. Each one of them was subdivided (1950?1970 and 1980?2009) to analyse the difference between the two periods. The range of variation of precipitation amounts and the frequency of dry events between both periods are discussed, as well as the evolution of the UM parameters through the years.

Climate variability effects on field crops in Iberian Peninsula. Predictability

La presente Tesis constituye un avance en el conocimiento de los efectos de la variabilidad climática en los cultivos en la Península Ibérica (PI). Es bien conocido que la temperatura del océano, particularmente de la región tropical, es una de las variables más convenientes para ser utilizado como predictor climático. Los océanos son considerados como la principal fuente de almacenamiento de calor del planeta debido a la alta capacidad calorífica del agua. Cuando se libera esta energía, altera los regímenes globales de circulación atmosférica por mecanismos de teleconexión. Estos cambios en la circulación general de la atmósfera afectan a la temperatura, precipitación, humedad, viento, etc., a escala regional, los cuales afectan al crecimiento, desarrollo y rendimiento de los cultivos. Para el caso de Europa, esto implica que la variabilidad atmosférica en una región específica se asocia con la variabilidad de otras regiones adyacentes y/o remotas, como consecuencia Europa está siendo afectada por los patrones de circulaciones globales, que a su vez, se ven afectados por patrones oceánicos. El objetivo general de esta tesis es analizar la variabilidad del rendimiento de los cultivos y su relación con la variabilidad climática y teleconexiones, así como evaluar su predictibilidad. Además, esta Tesis tiene como objetivo establecer una metodología para estudiar la predictibilidad de las anomalías del rendimiento de los cultivos. El análisis se centra en trigo y maíz como referencia para otros cultivos de la PI, cultivos de invierno en secano y cultivos de verano en regadío respectivamente. Experimentos de simulación de cultivos utilizando una metodología en cadena de modelos (clima + cultivos) son diseñados para evaluar los impactos de los patrones de variabilidad climática en el rendimiento y su predictibilidad. La presente Tesis se estructura en dos partes: La primera se centra en el análisis de la variabilidad del clima y la segunda es una aplicación de predicción cuantitativa de cosechas. La primera parte está dividida en 3 capítulos y la segundo en un capitulo cubriendo los objetivos específicos del presente trabajo de investigación. Parte I. Análisis de variabilidad climática El primer capítulo muestra un análisis de la variabilidad del rendimiento potencial en una localidad como indicador bioclimático de las teleconexiones de El Niño con Europa, mostrando su importancia en la mejora de predictibilidad tanto en clima como en agricultura. Además, se presenta la metodología elegida para relacionar el rendimiento con las variables atmosféricas y oceánicas. El rendimiento de los cultivos es parcialmente determinado por la variabilidad climática atmosférica, que a su vez depende de los cambios en la temperatura de la superficie del mar (TSM). El Niño es el principal modo de variabilidad interanual de la TSM, y sus efectos se extienden en todo el mundo. Sin embargo, la predictibilidad de estos impactos es controversial, especialmente aquellos asociados con la variabilidad climática Europea, que se ha encontrado que es no estacionaria y no lineal. Este estudio mostró cómo el rendimiento potencial de los cultivos obtenidos a partir de datos de reanálisis y modelos de cultivos sirve como un índice alternativo y más eficaz de las teleconexiones de El Niño, ya que integra las no linealidades entre las variables climáticas en una única serie temporal. Las relaciones entre El Niño y las anomalías de rendimiento de los cultivos son más significativas que las contribuciones individuales de cada una de las variables atmosféricas utilizadas como entrada en el modelo de cultivo. Además, la no estacionariedad entre El Niño y la variabilidad climática europea se detectan con mayor claridad cuando se analiza la variabilidad de los rendimiento de los cultivos. La comprensión de esta relación permite una cierta predictibilidad hasta un año antes de la cosecha del cultivo. Esta predictibilidad no es constante, sino que depende tanto la modulación de la alta y baja frecuencia. En el segundo capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de verano en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de maíz en la PI para todo el siglo veinte, usando un modelo de cultivo calibrado en 5 localidades españolas y datos climáticos de reanálisis para obtener series temporales largas de rendimiento potencial. Este estudio evalúa el uso de datos de reanálisis para obtener series de rendimiento de cultivos que dependen solo del clima, y utilizar estos rendimientos para analizar la influencia de los patrones oceánicos y atmosféricos. Los resultados muestran una gran fiabilidad de los datos de reanálisis. La distribución espacial asociada a la primera componente principal de la variabilidad del rendimiento muestra un comportamiento similar en todos los lugares estudiados de la PI. Se observa una alta correlación lineal entre el índice de El Niño y el rendimiento, pero no es estacionaria en el tiempo. Sin embargo, la relación entre la temperatura del aire y el rendimiento se mantiene constante a lo largo del tiempo, siendo los meses de mayor influencia durante el período de llenado del grano. En cuanto a los patrones atmosféricos, el patrón Escandinavia presentó una influencia significativa en el rendimiento en PI. En el tercer capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de invierno en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de trigo en secano del Noreste (NE) de la PI. La variabilidad climática es el principal motor de los cambios en el crecimiento, desarrollo y rendimiento de los cultivos, especialmente en los sistemas de producción en secano. En la PI, los rendimientos de trigo son fuertemente dependientes de la cantidad de precipitación estacional y la distribución temporal de las mismas durante el periodo de crecimiento del cultivo. La principal fuente de variabilidad interanual de la precipitación en la PI es la Oscilación del Atlántico Norte (NAO), que se ha relacionado, en parte, con los cambios en la temperatura de la superficie del mar en el Pacífico Tropical (El Niño) y el Atlántico Tropical (TNA). La existencia de cierta predictibilidad nos ha animado a analizar la posible predicción de los rendimientos de trigo en la PI utilizando anomalías de TSM como predictor. Para ello, se ha utilizado un modelo de cultivo (calibrado en dos localidades del NE de la PI) y datos climáticos de reanálisis para obtener series temporales largas de rendimiento de trigo alcanzable y relacionar su variabilidad con anomalías de la TSM. Los resultados muestran que El Niño y la TNA influyen en el desarrollo y rendimiento del trigo en el NE de la PI, y estos impactos depende del estado concurrente de la NAO. Aunque la relación cultivo-TSM no es igual durante todo el periodo analizado, se puede explicar por un mecanismo eco-fisiológico estacionario. Durante la segunda mitad del siglo veinte, el calentamiento (enfriamiento) en la superficie del Atlántico tropical se asocia a una fase negativa (positiva) de la NAO, que ejerce una influencia positiva (negativa) en la temperatura mínima y precipitación durante el invierno y, por lo tanto, aumenta (disminuye) el rendimiento de trigo en la PI. En relación con El Niño, la correlación más alta se observó en el período 1981 -2001. En estas décadas, los altos (bajos) rendimientos se asocian con una transición El Niño - La Niña (La Niña - El Niño) o con eventos de El Niño (La Niña) que están finalizando. Para estos eventos, el patrón atmosférica asociada se asemeja a la NAO, que también influye directamente en la temperatura máxima y precipitación experimentadas por el cultivo durante la floración y llenado de grano. Los co- efectos de los dos patrones de teleconexión oceánicos ayudan a aumentar (disminuir) la precipitación y a disminuir (aumentar) la temperatura máxima en PI, por lo tanto el rendimiento de trigo aumenta (disminuye). Parte II. Predicción de cultivos. En el último capítulo se analiza los beneficios potenciales del uso de predicciones climáticas estacionales (por ejemplo de precipitación) en las predicciones de rendimientos de trigo y maíz, y explora métodos para aplicar dichos pronósticos climáticos en modelos de cultivo. Las predicciones climáticas estacionales tienen un gran potencial en las predicciones de cultivos, contribuyendo de esta manera a una mayor eficiencia de la gestión agrícola, seguridad alimentaria y de subsistencia. Los pronósticos climáticos se expresan en diferentes formas, sin embargo todos ellos son probabilísticos. Para ello, se evalúan y aplican dos métodos para desagregar las predicciones climáticas estacionales en datos diarios: 1) un generador climático estocástico condicionado (predictWTD) y 2) un simple re-muestreador basado en las probabilidades del pronóstico (FResampler1). Los dos métodos se evaluaron en un caso de estudio en el que se analizaron los impactos de tres escenarios de predicciones de precipitación estacional (predicción seco, medio y lluvioso) en el rendimiento de trigo en secano, sobre las necesidades de riego y rendimiento de maíz en la PI. Además, se estimó el margen bruto y los riesgos de la producción asociada con las predicciones de precipitación estacional extremas (seca y lluviosa). Los métodos predWTD y FResampler1 usados para desagregar los pronósticos de precipitación estacional en datos diarios, que serán usados como inputs en los modelos de cultivos, proporcionan una predicción comparable. Por lo tanto, ambos métodos parecen opciones factibles/viables para la vinculación de los pronósticos estacionales con modelos de simulación de cultivos para establecer predicciones de rendimiento o las necesidades de riego en el caso de maíz. El análisis del impacto en el margen bruto de los precios del grano de los dos cultivos (trigo y maíz) y el coste de riego (maíz) sugieren que la combinación de los precios de mercado previstos y la predicción climática estacional pueden ser una buena herramienta en la toma de decisiones de los agricultores, especialmente en predicciones secas y/o localidades con baja precipitación anual. Estos métodos permiten cuantificar los beneficios y riesgos de los agricultores ante una predicción climática estacional en la PI. Por lo tanto, seríamos capaces de establecer sistemas de alerta temprana y diseñar estrategias de adaptación del manejo del cultivo para aprovechar las condiciones favorables o reducir los efectos de condiciones adversas. La utilidad potencial de esta Tesis es la aplicación de las relaciones encontradas para predicción de cosechas de la próxima campaña agrícola. Una correcta predicción de los rendimientos podría ayudar a los agricultores a planear con antelación sus prácticas agronómicas y todos los demás aspectos relacionados con el manejo de los cultivos. Esta metodología se puede utilizar también para la predicción de las tendencias futuras de la variabilidad del rendimiento en la PI. Tanto los sectores públicos (mejora de la planificación agrícola) como privados (agricultores, compañías de seguros agrarios) pueden beneficiarse de esta mejora en la predicción de cosechas. ABSTRACT The present thesis constitutes a step forward in advancing of knowledge of the effects of climate variability on crops in the Iberian Peninsula (IP). It is well known that ocean temperature, particularly the tropical ocean, is one of the most convenient variables to be used as climate predictor. Oceans are considered as the principal heat storage of the planet due to the high heat capacity of water. When this energy is released, it alters the global atmospheric circulation regimes by teleconnection1 mechanisms. These changes in the general circulation of the atmosphere affect the regional temperature, precipitation, moisture, wind, etc., and those influence crop growth, development and yield. For the case of Europe, this implies that the atmospheric variability in a specific region is associated with the variability of others adjacent and/or remote regions as a consequence of Europe being affected by global circulations patterns which, in turn, are affected by oceanic patterns. The general objective of this Thesis is to analyze the variability of crop yields at climate time scales and its relation to the climate variability and teleconnections, as well as to evaluate their predictability. Moreover, this Thesis aims to establish a methodology to study the predictability of crop yield anomalies. The analysis focuses on wheat and maize as a reference crops for other field crops in the IP, for winter rainfed crops and summer irrigated crops respectively. Crop simulation experiments using a model chain methodology (climate + crop) are designed to evaluate the impacts of climate variability patterns on yield and its predictability. The present Thesis is structured in two parts. The first part is focused on the climate variability analyses, and the second part is an application of the quantitative crop forecasting for years that fulfill specific conditions identified in the first part. This Thesis is divided into 4 chapters, covering the specific objectives of the present research work. Part I. Climate variability analyses The first chapter shows an analysis of potential yield variability in one location, as a bioclimatic indicator of the El Niño teleconnections with Europe, putting forward its importance for improving predictability in both climate and agriculture. It also presents the chosen methodology to relate yield with atmospheric and oceanic variables. Crop yield is partially determined by atmospheric climate variability, which in turn depends on changes in the sea surface temperature (SST). El Niño is the leading mode of SST interannual variability, and its impacts extend worldwide. Nevertheless, the predictability of these impacts is controversial, especially those associated with European climate variability, which have been found to be non-stationary and non-linear. The study showed how potential2 crop yield obtained from reanalysis data and crop models serves as an alternative and more effective index of El Niño teleconnections because it integrates the nonlinearities between the climate variables in a unique time series. The relationships between El Niño and crop yield anomalies are more significant than the individual contributions of each of the atmospheric variables used as input in the crop model. Additionally, the non-stationarities between El Niño and European climate variability are more clearly detected when analyzing crop-yield variability. The understanding of this relationship allows for some predictability up to one year before the crop is harvested. This predictability is not constant, but depends on both high and low frequency modulation. The second chapter identifies the oceanic and atmospheric patterns of climate variability affecting summer cropping systems in the IP. Moreover, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of maize yield variability in IP for the whole twenty century, using a calibrated crop model at five contrasting Spanish locations and reanalyses climate datasets to obtain long time series of potential yield. The study tests the use of reanalysis data for obtaining only climate dependent time series of simulated crop yield for the whole region, and to use these yield to analyze the influences of oceanic and atmospheric patterns. The results show a good reliability of reanalysis data. The spatial distribution of the leading principal component of yield variability shows a similar behaviour over all the studied locations in the IP. The strong linear correlation between El Niño index and yield is remarkable, being this relation non-stationary on time, although the air temperature-yield relationship remains on time, being the highest influences during grain filling period. Regarding atmospheric patterns, the summer Scandinavian pattern has significant influence on yield in IP. The third chapter identifies the oceanic and atmospheric patterns of climate variability affecting winter cropping systems in the IP. Also, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of rainfed wheat yield variability in IP. Climate variability is the main driver of changes in crop growth, development and yield, especially for rainfed production systems. In IP, wheat yields are strongly dependent on seasonal rainfall amount and temporal distribution of rainfall during the growing season. The major source of precipitation interannual variability in IP is the North Atlantic Oscillation (NAO) which has been related in part with changes in the Tropical Pacific (El Niño) and Atlantic (TNA) sea surface temperature (SST). The existence of some predictability has encouraged us to analyze the possible predictability of the wheat yield in the IP using SSTs anomalies as predictor. For this purpose, a crop model with a site specific calibration for the Northeast of IP and reanalysis climate datasets have been used to obtain long time series of attainable wheat yield and relate their variability with SST anomalies. The results show that El Niño and TNA influence rainfed wheat development and yield in IP and these impacts depend on the concurrent state of the NAO. Although crop-SST relationships do not equally hold on during the whole analyzed period, they can be explained by an understood and stationary ecophysiological mechanism. During the second half of the twenty century, the positive (negative) TNA index is associated to a negative (positive) phase of NAO, which exerts a positive (negative) influence on minimum temperatures (Tmin) and precipitation (Prec) during winter and, thus, yield increases (decreases) in IP. In relation to El Niño, the highest correlation takes place in the period 1981-2001. For these decades, high (low) yields are associated with an El Niño to La Niña (La Niña to El Niño) transitions or to El Niño events finishing. For these events, the regional associated atmospheric pattern resembles the NAO, which also influences directly on the maximum temperatures (Tmax) and precipitation experienced by the crop during flowering and grain filling. The co-effects of the two teleconnection patterns help to increase (decrease) the rainfall and decrease (increase) Tmax in IP, thus on increase (decrease) wheat yield. Part II. Crop forecasting The last chapter analyses the potential benefits for wheat and maize yields prediction from using seasonal climate forecasts (precipitation), and explores methods to apply such a climate forecast to crop models. Seasonal climate prediction has significant potential to contribute to the efficiency of agricultural management, and to food and livelihood security. Climate forecasts come in different forms, but probabilistic. For this purpose, two methods were evaluated and applied for disaggregating seasonal climate forecast into daily weather realizations: 1) a conditioned stochastic weather generator (predictWTD) and 2) a simple forecast probability resampler (FResampler1). The two methods were evaluated in a case study where the impacts of three scenarios of seasonal rainfall forecasts on rainfed wheat yield, on irrigation requirements and yields of maize in IP were analyzed. In addition, we estimated the economic margins and production risks associated with extreme scenarios of seasonal rainfall forecasts (dry and wet). The predWTD and FResampler1 methods used for disaggregating seasonal rainfall forecast into daily data needed by the crop simulation models provided comparable predictability. Therefore both methods seem feasible options for linking seasonal forecasts with crop simulation models for establishing yield forecasts or irrigation water requirements. The analysis of the impact on gross margin of grain prices for both crops and maize irrigation costs suggests the combination of market prices expected and the seasonal climate forecast can be a good tool in farmer’s decision-making, especially on dry forecast and/or in locations with low annual precipitation. These methodologies would allow quantifying the benefits and risks of a seasonal weather forecast to farmers in IP. Therefore, we would be able to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. The potential usefulness of this Thesis is to apply the relationships found to crop forecasting on the next cropping season, suggesting opportunity time windows for the prediction. The methodology can be used as well for the prediction of future trends of IP yield variability. Both public (improvement of agricultural planning) and private (decision support to farmers, insurance companies) sectors may benefit from such an improvement of crop forecasting.