Influence of Rhodolith-Forming Species and Growth-Form on Associated Fauna of Rhodolith Beds in the Central-West Gulf of California, México

Abstract.  Rhodoliths provide a stable and three-dimensional habitat to which other seaweeds and invertebrates can attach. Although ecological factors affecting rhodolith beds have been studied, little is known about the effect of rhodolith species and growth-form on associated fauna. Experiments were conducted at three rhodolith beds in the central-west Gulf of California. Faunal abundance differed significantly in relation to rhodolith-forming species, but no significant differences were observed between different growth-forms. Rhodolith structure differs between the species Lithophyllum margaritae and Neogoniolithon trichotomum , and the combination of structure differences and rhodolith abundances may be responsible of the significant differences in faunal abundance and richness. Crustaceans, polychaetes and molluscs were the most important taxa in all three rhodolith beds. The amphipod species Pontogeneia nasa and the cnidarian Aiptasia sp. were dominant in both rhodolith beds, El Requesón and Isla Coyote, in Bahía Concepción. The Isla Coronados rhodolith bed was dominated by an unidentified harpacticoid copepod (Copepoda sp.1). Rhodolith species is more important than growth-form in determining abundance and richness of the associated fauna. Nevertheless, factors such as wave motion, depth, bioturbation and others should be considered when studying organisms associated with rhodolith beds.     

On the Nature of the Variable Gamma-Ray Sources at Low Galactic Latitudes

Population studies of EGRET gamma-ray sources indicate that there is a distinctive population of bright sources at low galactic latitudes. The sources have a distribution consistent with that of young galactic objects, with a concentration toward the inner spiral arms. There is a subgroup that displays strong variability with timescales from days to months. Following an earlier suggestion by Kaufman Bernadó et al. (2002), we explore the possibility that these sources could be high-mass microquasars. Detailed models for the gamma-ray emission that include inverse Compton interactions of electrons in the relativistic jets and photons from all local fields (stellar UV photons, synchrotron photons, soft X-ray photons from the accretion disk, and hard X-ray photons from a corona) are presented. We conclude that microquasars are excellent candidates for the parent population of the subgroup of variable low-latitude EGRET sources.     

Estimating suspended sediment concentrations from turbidity measurements and the calibration problem

In situ turbidity meters are being increasingly used to generate continuous records of suspended sediment concentration in rivers. However, the usefulness of the information obtained depends heavily on the existence of a close relationship between fluctuations in suspended sediment concentration and turbidity and the calibration procedure that relates suspended sediment concentration to the turbidity meter's signal. This study assesses the relationship between suspended sediment concentration and turbidity for a small (1·19 km²) rural catchment in southern Brazil and evaluates two calibration methods by comparing the estimates of suspended sediment concentration obtained from the calibrated turbidity readings with direct measurements obtained using a USDH 48 suspended sediment sampler. With the first calibration method, the calibration relationship is derived by relating the turbidity readings to simultaneous measurements of concentration obtained from suspended sediment samples collected from the vicinity of the turbidity probe during flood events. With the second method, the calibration is based on the readings obtained from the turbidity meter when the probe immersed in samples of known concentration prepared using soils collected from the catchment. Overall, there was a close link between fluctuations in suspended sediment concentration and turbidity in the stream at the outlet of the catchment, and the estimates of sediment concentration obtained using the first calibration method corresponded closely with the conventionally measured sediment concentrations. However, use of the second calibration method introduced appreciable errors. When the estimated sediment concentrations were compared with the measured values, the mean errors were ± 122 mg l^?1 and + 601 mg l^?1 for the first and second calibration procedures respectively. Copyright © 2007 John Wiley & Sons, Ltd.     

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets

In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.     

Persistence and long-term memories of daily maximum and minimum temperatures in southern South America

The persistence and long-term memories in daily maximum and minimum temperature series during the instrumental period in southern South America were analysed. Here, we found a markedly seasonal pattern both for short- and long-term memories that can lead to enhanced predictability on intraseasonal timescales. In addition, well-defined spatial patterns of these properties were found in the region. Throughout the entire region, the strongest dependence was observed in autumn and early winter. In the Patagonia region only, the temperatures exhibited more memory during the spring. In general, these elements indicate that nonlinear interactions exist between the annual cycles of temperature and its anomalies. Knowledge of the spatiotemporal behaviour of these long-term memories can be used in the building of stochastic models that only use persistence. It is possible to propose two objective forecast models based on linear interactions associated with persistence and one that allows for the use of information from nonlinear interactions that are manifested in the form of forerunners.