964 resultados para Wilson


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Rockfishes (Sebastes spp.) tend to aggregate near rocky, cobble, or generally rugged areas that are difficult to survey with bottom trawls, and evidence indicates that assemblages of rockfish species may differ between areas accessible to trawling and those areas that are not. Consequently, it is important to determine grounds that are trawlable or untrawlable so that the areas where trawl survey results should be applied are accurately identified. To this end, we used multibeam echosounder data to generate metrics that describe the seafloor: backscatter strength at normal and oblique incidence angles, the variation of the angle-dependent backscatter strength within 10° of normal incidence, the scintillation of the acoustic intensity scattered from the seafloor, and the seafloor rugosity. We used these metrics to develop a binary classification scheme to estimate where the seafloor is expected to be trawlable. The multibeam echosounder data were verified through analyses of video and still images collected with a stereo drop camera and a remotely operated vehicle in a study at Snakehead Bank, ~100 km south of Kodiak Island in the Gulf of Alaska. Comparisons of different combinations of metrics derived from the multibeam data indicated that the oblique-incidence backscatter strength was the most accurate estimator of trawlability at Snakehead Bank and that the addition of other metrics provided only marginal improvements. If successful on a wider scale in the Gulf of Alaska, this acoustic remote-sensing technique, or a similar one, could help improve the accuracy of rockfish stock assessments.

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The 1997 reauthorization of the Atlantic Striped Bass Conservation Act (Striped Bass Act) mandated biennial reports to Congress and to the Atlantic States Marine Fisheries Commission (Commission) from the secretaries of the Department of Commerce and the Department of the Interior concerning the progress and findings of studies of Atlantic striped bass (Morone saxatilis). The Striped Bass Act specifically requests updates on studies that include, but are not limited to: annual stock assessments, investigations on the causes of fluctuations in Atlantic striped bass populations, the effects of environmental factors on the recruitment, spawning potential, mortality, and abundance of Atlantic striped bass populations, and investigations of interactions between Atlantic striped bass and other fish. This document is the fifth such report to Congress and includes data available through 2007 with emphasis on the 2005 and 2006 calendar years.

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This study describes fish assemblages and their spatial patterns off the coast of California from Point Arena to Point Sal, by combining the results of the multivariate analyses of several fisheries datasets with a geographic information system. In order to provide comprehensive spatial coverage for the areas of inshore, continental shelf, and continental slope, three fisheries datasets were analyzed: 1) Inshore: the California Department of Fish and Game dataset of fishery-dependent commercial passenger fishing vessel trips that targeted rockfish; 2) Continental Shelf: the National Marine Fisheries Service (NMFS) fishery-independent bottom trawls; and 3) Continental Slope: the NMFS fishery-independent bottom trawls on the continental slope. One-hundred seven species were analyzed. These species represented those captured in at least 5% of the fishing trips or trawls in at least one of the three data sets. We analyzed each of the three datasets separately, and the three sets of results were combined to define 28 species assemblages and 23 site groups. A species assemblage consisted of species caught together, whereas a site group consisted of fishing trips or trawl locations that tended to have the same species assemblages. At the scale of these datasets, 97% of all site groups were significantly segregated by depth.

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Red snapper (Lutjanus campechanus) in the United States waters of the Gulf of Mexico (GOM) has been considered a single unit stock since management of the species began in 1991. The validity of this assumption is essential to management decisions because measures of growth can differ for nonmixing populations. We examined growth rates, size-at-age, and length and weight information of red snapper collected from the recreational harvests of Alabama (n=2010), Louisiana (n=1905), and Texas (n =1277) from 1999 to 2001. Ages were obtained from 5035 otolith sections and ranged from one to 45 years. Fork length, total weight, and age-frequency distributions differed significantly among all states; Texas, however, had a much higher proportion of smaller, younger fish. All red snapper showed rapid growth until about age 10 years, after which growth slowed considerably. Von Bertalanffy growth models of both mean fork length and mean total weight-at-age predicted significantly smaller fish at age from Texas, whereas no differences were found between Alabama and Louisiana models. Texas red snapper were also shown to differ significantly from both Alabama and Louisiana red snapper in regressions of mean weight at age. Demographic variation in growth rates may indicate the existence of separate management units of red snapper in the GOM. Our data indicate that the red snapper inhabiting the waters off Texas are reaching smaller maximum sizes at a faster rate and have a consistently smaller total weight at age than those collected from Louisiana and Alabama waters. Whether these differences are environmentally induced or are the result of genetic divergence remains to be determined, but they should be considered for future management regulations.

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The red drum (Sciaenops ocellatus) is a popular gamefish found throughout the coastal waters of the Gulf of Mexico and along the eastern seaboard as far north as Massachusetts. Juvenile red drum grow extremely rapidly, especially during the warmer months, but adults grow very little. In fact, the change in growth with age is so abrupt that the standard von Bertalanffy curve has proven inadequateâ the predicted lengths of younger fish are generally too large and the predicted lengths of older fish too small (see Beckman et al., 1988; Murphy and Taylor, 1990).

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EXTRACT (SEE PDF FOR FULL ABSTRACT): Since 1986, the U.S. Geological Survey and National Weather Service have operated a warning system for debris flows triggered by severe rainstorms in the San Francisco Bay region. The NWS tracks storm systems as they approach the region, forecasts precipitation, and observes rainfall with a network of radiotelemetered rain gauges (ALERT). The USGS also monitors ALERT data and compares the observed and forecast rainfall to thresholds for debris-flow initiation. Both groups jointly assess debris-flow hazards and issue public advisories when rainfall conditions reach or approach critical levels.

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EXTRACT (SEE PDF FOR FULL ABSTRACT): As part of a study of climatic influences on landslide initiation, a statistical analysis of long-term (>40 years) records of daily rainfall from 24 Pacific coastal stations, from San Diego to Cape Flattery, disclosed an unexpected result - the square root of the daily rainfall closely approximates a normal distribution function. ... This paper illustrates the use of the square-root-normal distribution to analyze variations in precipitation along the mainland United States Pacific Coast with examples of orographic enhancement, rain shadows, and increase in precipitation frequency with geographic latitude.

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We define a copula process which describes the dependencies between arbitrarily many random variables independently of their marginal distributions. As an example, we develop a stochastic volatility model, Gaussian Copula Process Volatility (GCPV), to predict the latent standard deviations of a sequence of random variables. To make predictions we use Bayesian inference, with the Laplace approximation, and with Markov chain Monte Carlo as an alternative. We find both methods comparable. We also find our model can outperform GARCH on simulated and financial data. And unlike GARCH, GCPV can easily handle missing data, incorporate covariates other than time, and model a rich class of covariance structures.

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We introduce a stochastic process with Wishart marginals: the generalised Wishart process (GWP). It is a collection of positive semi-definite random matrices indexed by any arbitrary dependent variable. We use it to model dynamic (e.g. time varying) covariance matrices. Unlike existing models, it can capture a diverse class of covariance structures, it can easily handle missing data, the dependent variable can readily include covariates other than time, and it scales well with dimension; there is no need for free parameters, and optional parameters are easy to interpret. We describe how to construct the GWP, introduce general procedures for inference and predictions, and show that it outperforms its main competitor, multivariate GARCH, even on financial data that especially suits GARCH. We also show how to predict the mean of a multivariate process while accounting for dynamic correlations.