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    ABSTRACT: Sperm whale (Physeter macrocephalus) populations were severely depleted by commercial whaling worldwide in the 18th through the 20th century. Consequently, in 1970, this species was listed in the United States as an endangered species. To date, accurate information on the abundance and distribution of sperm whales in offshore areas of the North Pacific are scant. Sperm whales regularly produce high intensity sounds for navigation, prey detection, and communication. Thus, this species can be very effectively monitored using passive acoustic techniques especially in remote and inaccessible locations such as the Gulf of Alaska (GOA). In this study, a Passive Aquatic Listener (PAL) was deployed at Ocean Station PAPA (50°N, 145°W) in the GOA between 2007 and 2012 to monitor the seasonal occurrence of sperm whales in the area. Preliminary results indicate that within the 5-year deployment period sperm whales were acoustically present year round and that the number of acoustic sperm whale detections showed a seasonal trend with slightly higher numbers during the summer months. We are currently investigating the linkage between the occurrence of sperm whales and environmental conditions (e.g., Pacific Decadal Oscillation index) in the study area. [Funding from the Office of Naval Research.].
    No preview · Article · Nov 2013 · The Journal of the Acoustical Society of America
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    ABSTRACT: The present study deals with the development, application and evaluation of a modelling tool, implemented along with a field sampling program, in a limited coastal area in the Northeast Aegean. The aim was to study, understand and quantify physical circulation and water column ecological processes in a high resolution simulation of a past annual cycle. The marine ecosystem model consists of a three dimensional hydrodynamic component suitable for coastal areas (Princeton Ocean Model) coupled to a simple ecological model of five variables, namely, phytoplankton, nitrate, ammonia, phosphate and dissolved organic carbon concentrations. The ecological parameters (e.g. half saturation constants and maximum uptake rates for nutrients) were calibrated using a specially developed automated procedure. Model errors were evaluated using qualitative, graphic techniques and were quantified with a number of goodness-of-fit measures. Regarding physical variables, the goodness-of-fit of model to field data varied from fairly to quite good. Indicatively, the cost function, expressed as mean value per sampling station, ranged from 0.15 to 0.23 for temperature and 0.81 to 3.70 for current speed. The annual cycle of phytoplankton biomass was simulated with sufficient accuracy (e.g. mean cost function ranging from 0.49 to 2.67), partly attributed to the adequate reproduction of the dynamics of growth limiting nutrients, nitrate, ammonia and the main limiting nutrient, phosphate, whose mean cost function ranged from 0.97 to 1.88. Model results and field data provided insight to physical processes such as the development of a wind-driven, coastal jet type of surface alongshore flow with a subsurface countercurrent flowing towards opposite direction and the formation of rotational flows in the embayments of the coastline when the offshore coastal current speed approaches values of about 0.1 m/s. The percentage of field measurements where the N:P ratio was found over 16:1 varied between stations from 57 to 65%, demonstrating the importance of phosphate as a limiting nutrient for phytoplankton growth. The model also successfully reproduced phytoplankton gradients, e.g. those developed almost all year round between the most eutrophic areas (city harbour with mean chlorophyll-a concentration of 1.08 μg/L and decreasing biomass by over 40 mg C/m3 from surface to the bottom layer) and the oligotrophic open waters (mean chlorophyll-a concentration of 0.14 μg/L).
    No preview · Article · Jul 2013 · Estuarine Coastal and Shelf Science
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    ABSTRACT: The exploration of processes leading to coastal eutrophication is a major challenge in ecological research, particularly in light of important new policies such as the European Water Framework Directive. In the present study primary production (in terms of chlorophyll α – chl α) is modeled based on a number of abiotic parameters using model trees (MTs), a machine learning (ML) approach whereby linear regressions are induced within homogeneous subsets of samples (tree leaves). Standardized regression was applied to determine the relative weight of abiotic parameters in the MT tree leaves whereas the efficiency of the MT method in chl α prediction was tested against neural networks (NNs) which is the most frequently used ML approach, and the classical multiple linear regression (MLR). To assess the efficiency of models to describe eutrophication-related responses under different environmental conditions, the methods were applied on a coastal ecosystem affected by terrestrial runoff for two meteorologically contrasting annual cycles: a typical dry ('04–'05) and a typical wet ('09–'10). MTs showed increased predictive power in chl α prediction attributed to the discrimination of input data space into tree leaves, instead of using a uniform space as in NNs and MLR. By grouping samples of each tested annual cycle (wet and dry) on a seasonal basis into discrete groups/leaves, MTs offer a much more explanatory description of ecosystem status than NNs and MLR. The discriminating variables forming tree leaves and the weighing coefficients of Linear Models (LMs) in each leaf provided a useful scaling of abiotic parameters driving chl α dynamics. The MT method is thus proposed as an efficient tool for obtaining insights into ecosystem processes leading to eutrophication events in coastal ecosystems and a useful component in integrated coastal zone management.
    Full-text · Article · Dec 2012 · Estuarine Coastal and Shelf Science
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