Proximate and elemental composition and energy content of mesopelagic crustaceans from the Eastern Gulf of Mexico

The proximate and elemental chemical compositions of 25 species of pelagic decapod and mysid crustaceans collected from the eastern Gulf of Mexico (27°N; 86°W, 1984 to 1989) was examined. Water level ranged from 63 to 95% and increased slightly with species' increased depth of occurrence. Protein levels were generally high (1.5 to 18.3% wet wt, WW; 27.6 to 62.4% ash-free dry wt, AFDW) and comprised the primary organic component in the majority of species. Protein, both as % WW and % AFDW, decreased with increased depth of occurrence. In contrast to protein, lipid levels were low (0.5 to 8.9% WW; 5.7 to 60.9% AFDW), and increased with increased depth of occurrence. Carbon and nitrogen best mirrored measured lipid and protein levels when considered as non-protein carbon and non-chitin nitrogen, respectively. C:N ratios increased with increased depth, consistent with changes in protein and lipid with depth. Because of their compositional attributes, resident Gulf of Mexico species have a low total wet weight energy content relative to species from more productive regions. Energy content showed no significant trend with depth. Vertical migration patterns were distinctly different between shallow-and deep-living gulf species and these differences were largely responsible for the relationships observed between composition and depth. In migrating species, the protein and nitrogen content were higher, the lipid and carbon contents and C:N ratio lower, than in non-migrating species. Three deep-living species of the genus Acanthephyra were found to be compositionally atypical, resembling shallow, migrating types rather than other deep-living, non-migratory species.     

Cost-effective selection of sampling frequencies for regulatory water quality monitoring

A dynamic programming code was formulated for the purpose of assigning sampling frequencies throughout a regulatory water quality monitoring network in order to optimize the statistical performance of the network while operating within a fixed budgetary constraint. The statistical objective is to achieve the greatest possible station to station uniformity in confidence interval widths about annual geometric means of the measured water quality variables and to keep the average confidence width reasonably small. The objective function is the sum (over several selected variables and all stations) of the normalized positive deviations of the predicted confidence interval widths from preselected design confidence interval widths. The code was designed to account for the effects of deterministic seasonal variation and serial correlation of the water quality observations by incorporating the results of the time series analysis of historical quality data. The economic constraint ensures that the annual operating cost of the system, including direct costs of travel and laboratory analysis, will not exceed the allowable budget. As an example situation, the dynamic programming code was used to assign sampling frequencies to the nine stations in Illinois from which historical quality data had been obtained and analyzed. Using five design quality constituents and representative travel and laboratory costs, an “optimal” design was produced. The optimal design achieved a 10% improvement in uniformity (standard deviation) of confidence interval widths when compared to a more traditional design based on the same budget and using identical sampling frequencies at every station.     

A note on natural production functions

Two techniques are presented for estimation of natural animal populations, both of which may incorporate the effect of pollutants on populations. Both techniques assume specific underlying population dynamics which may not be applicable to certain species or ecosystems. However, both techniques allow for testing the hypothesis that the population dynamics specified is applicable. The techniques are used to criticize two recent empirical investigations of fisheries.