Lithology and palynology of cave floor sediment cores from Wakulla Spring, Wakulla County, Florida

Five short bottom sediment cores taken in Wakulla Spring Wakulla County, Florida, were described lithologically and sampled for palynological study. Four of the cores were recoveredfrom sediments at the spring cave entrance (130 feet water depth). One core was taken in a fossil vertebrate bone bed, 280 feet distance into the main spring cave at a water depth of 240 feet. Sediments in the cores are composed of alternating intervals of quartz sand and calcilitite, containing freshwater diatoms, freshwater mollusk shells and plant remains. The predominant pollen present in all cores consists of a periporate variety typical of the herb families Chenopodiaceae and Amaranthaceae. Arboreal flora, typical of the area surrounding the spring today, represent a very low percentage of thle pollen assemblage in the cores. Clustered Chenopod-Amaranth type pollen observed in one core suggest minimal transport prior to deposition, and indicate that the bottom sediments in the cave may be essentially In situ. An absence of exotic flora suggests a Quaternary age for the sediments. (PDF contains 11 pages.)

Measurements of resistance and reactance in fish with the use of bioelectrical impedance analysis: sources of error

New technologies can be riddled with unforeseen sources of error, jeopardizing the validity and application of their advancement. Bioelectrical impedance analysis (BIA) is a new technology in fisheries research that is capable of estimating proximate composition, condition, and energy content in fish quickly, cheaply, and (after calibration) without the need to sacrifice fish. Before BIA can be widely accepted in fisheries science, it is necessary to identify sources of error and determine a means to minimize potential errors with this analysis. We conducted controlled laboratory experiments to identify sources of errors within BIA measurements. We concluded that electrode needle location, procedure deviations, user experience, time after death, and temperature can affect resistance and reactance measurements. Sensitivity analyses showed that errors in predictive estimates of composition can be large (>50%) when these errors are experienced. Adherence to a strict protocol can help avoid these sources of error and provide BIA estimates that are both accurate and precise in a field or laboratory setting.

Harry Hess and sea-floor spreading

Harry Hess's hypothesis of sea-floor spreading brought together his long-standing interests in island arcs, oceanic topography, and the oceanic crust. The one unique feature of Hess's hypothesis was the origin of the oceanic crust as a hydration rind on the top of the mantle -- an idea that was not well received, even by the early converts to sea-floor spreading. Hess never changed his mind on this issue, and his stubbornness illuminates the logic of his discovery. Published and archival records show that 1) Hess became convinced the oceanic crust was a hydration rind as early as mid 1958, when he was still a fixist, 2) he devised sea-floor spreading in 1960 to reconcile the hydration-rind model with the newly discovered, high heat flow at oceanic ridge crests, and 3) Hess's new mobilist solution did the least amount of violence to his older fixist solution.

The measurement error in marine survey catches: the bottom trawl case

We have formulated a model for analyzing the measurement error in marine survey abundance estimates by using data from parallel surveys (trawl haul or acoustic measurement). The measurement error is defined as the component of the variability that cannot be explained by covariates such as temperature, depth, bottom type, etc. The method presented is general, but we concentrate on bottom trawl catches of cod (Gadus morhua). Catches of cod from 10 parallel trawling experiments in the Barents Sea with a total of 130 paired hauls were used to estimate the measurement error in trawl hauls. Based on the experimental data, the measurement error is fairly constant in size on the logarithmic scale and is independent of location, time, and fish density. Compared with the total variability of the winter and autumn surveys in the Barents Sea, the measurement error is small (approximately 2–5%, on the log scale, in terms of variance of catch per towed distance). Thus, the cod catch rate is a fairly precise measure of fish density at a given site at a given time.