Here we present the results of a multiple organizational level analysis conceived to identify acclimative/adaptive strategies exhibited by the seagrass Posidonia oceanica to the daily fluctuations in the light environment, at contrasting depths. We assessed changes in photophysiological parameters, leaf respiration, pigments, and protein and mRNA expression levels. The results show that the diel oscillations of P. oceanica photophysiological and respiratory responses were related to transcripts and proteins expression of the genes involved in those processes and that there was a response asynchrony between shallow and deep plants probably caused by the strong differences in the light environment. The photochemical pathway of energy use was more effective in shallow plants due to higher light availability, but these plants needed more investment in photoprotection and photorepair, requiring higher translation and protein synthesis than deep plants. The genetic differentiation between deep and shallow stands suggests the existence of locally adapted genotypes to contrasting light environments. The depth-specific diel rhythms of photosynthetic and respiratory processes, from molecular to physiological levels, must be considered in the management and conservation of these key coastal ecosystems.

This paper proposes a vector sensor measurement model and the related Bartlett estimator based on particle velocity measurements for generic parameter estimation, illustrating the advantages of the Vector Sensor Array (VSA). A reliable estimate of the seabed properties such as sediment compressional speed, density and compressional attenuation based on matched-field inversion (MFI) techniques can be achieved using a small aperture VSA. It is shown that VSAs improve the resolution of seabed parameter estimation when compared with pressure sensor arrays with the same number of sensors. The data considered herein was acquired by a four-element VSA in the 8–14 kHz band, during the Makai Experiment in 2005. The results obtained with the MFI technique are compared with those obtained with a method proposed by C. Harrison, which determines the bottom reflection loss as the ratio between the upward and downward beam responses. The results show a good agreement and are in line with the historical information for the area. The particle velocity information provided by the VSA increases significantly the resolution of seabed parameter estimation and in some cases reliable results are obtained using only the vertical component of the particle velocity.

This paper aims at estimating the azimuth, range and depth of a cooperative broadband acoustic source with a single vector sensor in a multipath underwater environment, where the received signal is assumed to be a linear combination of echoes of the source emitted waveform. A vector sensor is a device that measures the scalar acoustic pressure field and the vectorial acoustic particle velocity field at a single location in space. The amplitudes of the echoes in the vector sensor components allow one to determine their azimuth and elevation. Assuming that the environmental conditions of the channel are known, source range and depth are obtained from the estimates of elevation and relative time delays of the different echoes using a ray-based backpropagation algorithm. The proposed method is tested using simulated data and is further applied to experimental data from the Makai’05 experiment, where 8–14 kHz chirp signals were acquired by a vector sensor array. It is shown that for short ranges, the position of the source is estimated in agreement with the geometry of the experiment. The method is low computational demanding, thus well-suited to be used in mobile and light platforms, where space and power requirements are limited.

A complete understanding of the mechanistic basis of marine ecosystem functioning is only possible through integrative and interdisciplinary research. This enables the prediction of change and possibly the mitigation of the consequences of anthropogenic impacts. One major aim of the European Cooperation in Science and Technology (COST) Action ES0609 “Seagrasses productivity. From genes to ecosystem management,” is the calibration and synthesis of various methods and the development of innovative techniques and protocols for studying seagrass ecosystems. During 10 days, 20 researchers representing a range of disciplines (molecular biology, physiology, botany, ecology, oceanography, and underwater acoustics) gathered at The Station de Recherches Sous-marines et Océanographiques (STARESO, Corsica) to study together the nearby Posidonia oceanica meadow. STARESO is located in an oligotrophic area classified as “pristine site” where environmental disturbances caused by anthropogenic pressure are exceptionally low. The healthy P. oceanica meadow, which grows in front of the research station, colonizes the sea bottom from the surface to 37 m depth. During the study, genomic and proteomic approaches were integrated with ecophysiological and physical approaches with the aim of understanding changes in seagrass productivity and metabolism at different depths and along daily cycles. In this paper we report details on the approaches utilized and we forecast the potential of the data that will come from this synergistic approach not only for P. oceanica but for seagrasses in general.

This paper aims at estimating the azimuth of an underwater acoustic source with a single vector sensor. A vector sensor is a device that measures the scalar acoustic pressure field and the vectorial acoustic velocity field at a single location in space. The actual sensor technology allows to build compact vector sensors, with an operational frequency response ranging from a few hertz to several tens of kilohertz, thus the same device can be used to receive shipping noise upto dolphin whistles. It is demonstrated that one can attain a reliable estimate of the azimuth of a source with a single vector sensor. The method presented is based on the inner product between the sampled acoustic field and the different particle velocity orthogonal components. The method is very simple and low computational demanding thus, well suited to be used in mobile or light platforms where space and/or computational power requirements is of concern. It is shown that the proposed method can be used either in time or in frequency domain, giving rise to easily estimating the azimuth of several sources with non-overlapping frequency bands. The data discussed herein (ship noise, communication signals, tomographic signals) were acquired during the Makai'2005 experiment using a four element vector sensor array. It is shown that the estimates obtained with a single vector sensor are comparable with those obtained with the full vector sensor array and are inline with the expected results as known from the geometry of the experiment.