Fishery resources of the United States of America

The term “fishery resources” is used in this book with a broad application. It includes the populations of the fishes and other organisms useful to men, the environment that makes life possible for them, the industry that exploits and utilizes them, and our knowledge about them by which we can conserve their productivity. This book aims to survey the present status of all these aspects of those fishery resources that are used or are available for use by United States anglers and commercial fishermen. It is planned primarily for the Congress, at its request, with the idea of giving to busy people, in condensed fashion, a perspective on its subject. (pdf contains 142 pages)

Survey of El Nino 1957-58 in its relation to tropical pacific meteorology

ENGLISH: The survey aims at demonstrating the close relationship between anomalies of sea temperature observed along the tropical Pacific coast of the Americas and those observed in the oceanwide tropical belt. The survey also covers the variations, from 1952 to the present, of the trade-wind circulations which prove to be responsible for the major part of the anomalies in sea surface temperature. Finally, the thermal feedback effects of the oceanic anomalies upon the large-scale circulation of the atmosphere are treated in a preliminary fashion. SPANISH: El estudio trata de demostrar la estrecha relación que existe entre las anomalías observadas de la temperatura del mar a lo largo de la costa tropical de las Américas y las observadas en la faja tropical de todo el océano. El estudio incluye también las variaciones, desde 1952 hasta el presente, de la circulación de los vientos alisios que demuestra ser responsable por la mayor parte de las anomalías de temperatura de la superficie del mar. Finalmente los efectos termales de las anomalías oceánicas sobre la circulación en gran escala de la atmósfera son tratados en forma preliminar. (PDF contains 62 pages.)

The design, construction, operation, and maintenance of the Replicated Modular Estuarine Mesocosm

Perhaps the most difficult job of the ecotoxicologist is extrapolating data calculated from laboratory experiments with high precision and accuracy into the real world of highly-dynamics aquatic environments. The establishment of baseline laboratory toxicity testing data for individual compounds and ecologically important and field studies serve as a precursor to ecosystem level studies needed for ecological risk assessment. The first stage in the field portion of risk assessment is the determination of actual environmental concentrations of the contaminant being studied and matching those concentrations with laboratory toxicity tests. Risk estimates can be produced via risk quotients that would determine the probability that adverse effects may occur. In this first stage of risk assessment, environmental realism is often not achieved. This is due, in part, to the fact that single-species laboratory toxicity tests, while highly controlled, do not account for the complex interactions (Chemical, physical, and biological) that take place in the natural environment. By controlling as many variables in the laboratory as possible, an experiment can be produced in such a fashion that real effects from a compound can be determined for a particular test organism. This type of approach obviously makes comparison with real world data most difficult. Conversely, field oriented studies fall short in the interpretation of ecological risk assessment because of low statistical power, lack of adequate replicaiton, and the enormous amount of time and money needed to perform such studies. Unlike a controlled laboratory bioassay, many other stressors other than the chemical compound in question affect organisms in the environment. These stressors range from natural occurrences (such as changes in temperature, salinity, and community interactions) to other confounding anthropogenic inputs. Therefore, an improved aquatic toxicity test that will enhance environmental realism and increase the accuracy of future ecotoxicological risk assessments is needed.

Wave forecasts associated with hurricane landfalls in the vicinity of Cape Lookout, North Carolina

Hurricanes can cause extensive damage to the coastline and coastal communities due to wind-generated waves and storm surge. While extensive modeling efforts have been conducted regarding storm surge, there is far less information about the effects of waves on these communities and ecosystems as storms make landfall. This report describes a preliminary use of NCCOS’ WEMo (Wave Exposure Model; Fonseca and Malhotra 2010) to compute the wind wave exposure within an area of approximately 25 miles radius from Beaufort, North Carolina for estuarine waters encompassing Bogue Sound, Back Sound and Core Sound during three hurricane landfall scenarios. The wind wave heights and energy of a site was a computation based on wind speed, direction, fetch and local bathymetry. We used our local area (Beaufort, North Carolina) as a test bed for this product because it is frequently impacted by hurricanes and we had confidence in the bathymetry data. Our test bed conditions were based on two recent Hurricanes that strongly affected this area. First, we used hurricane Isabel which made landfall near Beaufort in September 2003. Two hurricane simulations were run first by passing hurricane Isabel along its actual path (east of Beaufort) and second by passing the same storm to the west of Beaufort to show the potential effect of the reversed wind field. We then simulated impacts by a hurricane (Ophelia) with a different landfall track, which occurred in September of 2005. The simulations produced a geographic description of wave heights revealing the changing wind and wave exposure of the region as a consequence of landfall location and storm intensity. This highly conservative simulation (water levels were that of low tide) revealed that many inhabited and developed shorelines would receive wind waves for prolonged periods of time at heights far above that found during even the top few percent of non-hurricane events. The simulations also provided a sense for how rapidly conditions could transition from moderate to highly threatening; wave heights were shown to far exceed normal conditions often long before the main body of the storm arrived and importantly, at many locations that could impede and endanger late-fleeing vessels seeking safe harbor. When joined with other factors, such as storm surge and event duration, we anticipate that the WEMo forecasting tool will have significant use by local emergency agencies and the public to anticipate the relative exposure of their property arising as a function of storm location and may also be used by resource managers to examine the effects of storms in a quantitative fashion on local living marine resources.

Growth, mortality, and hatchdate distributions of larval and juvenile spotted seatrout (Cynoscion nebulosus) in Florida Bay, Everglades National Park

Life history aspects of larval and, mainly, juvenile spotted seatrout (Cynoscion nebulosus) were studied in Florida Bay, Everglades National Park, Florida. Collections were made in 1994−97, although the majority of juveniles were collected in 1995. The main objective was to obtain life history data to eventually develop a spatially explicit model and provide baseline data to understand how Everglades restoration plans (i.e. increased freshwater flows) could influence spotted seatrout vital rates. Growth of larvae and juveniles (<80 mm SL) was best described by the equation loge standard length = –1.31 + 1.2162 (loge age). Growth in length of juveniles (12–80 mm SL) was best described by the equation standard length = –7.50 + 0.8417 (age). Growth in wet weight of juveniles (15–69 mm SL) was best described by the equation loge wet-weight = –4.44 + 0.0748 (age). There were no significant differences in juvenile growth in length of spotted seatrout in 1995 between three geographical subdivisions of Florida Bay: central, western, and waters adjacent to the Gulf of Mexico. We found a significant difference in wet-weight for one of six cohorts categorized by month of hatchdate in 1995, and a significant difference in length for another cohort. Juveniles (i.e. survivors) used to calculate weekly hatchdate distributions during 1995 had estimated spawning times that were cyclical and protracted, and there was no correlation between spawning and moon phase. Temperature influenced otolith increment widths during certain growth periods in 1995. There was no evidence of a relationship between otolith growth rate and temperature for the first 21 increments. For increments 22–60, otolith growth rates decreased with increasing age and the extent of the decrease depended strongly in a quadratic fashion on the temperature to which the fish was exposed. For temperatures at the lower and higher range, increment growth rates were highest. We suggest that this quadratic relationship might be influenced by an environmental factor other than temperature. There was insufficient information to obtain reliable inferences on the relationship of increment growth rate to salinity.

Reproduction, age and growth of the kelee shad, Hilsa kelee (Cuvier, 1829) (pisces: fam. Clupeidae) with informations on its fishery in Maputo Bay, Mozambique

Age, growth and reproduction of H. kelee were studied, and a brief description of its fishery in Maputo Bay (Mozambique) is given. Most material was collected from gill net fisheries during 1977-1980, but some was taken from shrimp trawlers operating in the same area during 1980-1981. Main spawning takes place during October-January with a peak in December. There is also some evidence that spawning takes place during June-July. The size at first maturity was approximately equals 14-15 cm. Ageing was carried out using primary growth rings in the otoliths and length-frequency analysis of fish caught by shrimp trawlers. Von Bertalanffy's growth equation parameters were determined. Males and females grew in similar fashion. There are seasonal trends in the catch composition of the gill net fishery, showing high values during April to September and low during October to December.