The European whitefish, Coregonus lavaretus (L.), under conditions of technogenic pollution in the Kostomuksha tailing pond

The population structure and biological parameters of the European whitefish, Coregonus lavaretus (L.), have been analyzed in a technogenically polluted water body. The biochemical state of the whitefish and the fauna and morphology of helminths parasitizing them have been assessed. It is shown that the species composition of the fish, plankton, benthos, and parasite faunas of this water body are depleted and the morphological parameters of common fish parasites are altered. It is noted that, in addition to adaptive rearrangements in biochemical metabolism, the whitefish from the transformed water body are characterized by a reduction in the level of protein synthesis in muscles and the rate of aerobic energy metabolism in the liver and muscles.     

Operational decisions in response to a performance-based regulation to reduce organic waste impacts near Atlantic salmon farms in British Columbia,Canada

Nutrient enrichment of the seafloor (benthos) has been a primary subject of investigation since the beginning of commercial salmon farming. Waste feed and feces passing through the containment nets can create zones of organic enrichment to the benthos in the vicinity of the pen cages. Early studies of salmon farming in British Columbia Canada (500–1200 metric ton production farms) showed that these effects varied greatly and were reduced where conditions of adequate water depth and strong tidal currents were present. More recent studies of farms producing in excess of 2000 tons of fish have documented measurable organic benthic impacts.The implementation of a performance-based waste regulation in British Columbia in 2002 has caused operational changes to achieve and maintain compliance. This paper describes field monitoring data for four representative farm operations including the waste impact levels prior to the implementation of regulatory standards and the effect of adaptive management actions taken to reduce organic enrichment such as modified feed delivery, relatively small position changes to the cages or reducing site production to achieve continuing compliance.Monitoring over several full production cycles at selected farms indicated that sediment impacts at maximum production levels have decreased while maintaining, and in some cases increasing overall production of salmon.     

Quantification and reduction of erroneous differences between images in remote sensing

The reliability of image data, along with the difficulty of accurately comparing images acquired at different times or from different sensors, is a generic problem in remote sensing. Measurement repeatability errors occur frequently and can substantially reduce the system’s ability to reliably quantify real spectral and spatial change in a target. This paper outlines methodologies for quantifying and reducing erroneous differences between monochrome and multispectral or hyperspectral images. [Each of these image types is acquired by an instrument that collects light photons across a variable range of the electromagnetic spectrum (often referred to as an image band). The hyperspectral image is often referred to as a hyperspectral cube with XY spatial dimensions and many Z bands (spectral demotions)]. In this paper, we specifically discuss the Pixel Block Transform (PBT), the Spectral Averaging Transform (SAT), and the Wavelet Transform (WAVEL). We briefly address sensor fusion. Results indicate that the PBT is a powerful cross-noise and repeatability error reducing tool, applicable to monochrome, multispectral, and hyperspectral images. The SAT is as powerful as the PBT in reducing error but is suitable only for hyperspectral imagery. WAVEL can reduce some of the finer scale noise, but it is not as powerful in reducing cross-noise as PBT or SAT and it requires some trial and error for selecting the appropriate wavelet function. It is important that those involved in developing statistically sound relationships between remotely sensed imagery and other data sources understand the problems and their solutions to prevent wasting time or developing relationships that are statistically insignificant or unstable, or that lead to faulty conclusions.     

Incorporating Uncertainty into Management Models for Marine Mammals

Abstract: Good management models and good models for understanding biology differ in basic philosophy. Management models must facilitate management decisions despite large amounts of uncertainty about the managed populations. Such models must be based on parameters that can be estimated readily, must explicitly account for uncertainty, and should be simple to understand and implement. In contrast, biological models are designed to elucidate the workings of biology and should not be constrained by management concerns. We illustrate the need to incorporate uncertainty in management models by reviewing the inadequacy of using standard biological models to manage marine mammals in the United States. Past management was based on a simple model that, although it may have represented population dynamics adequately, failed as a management tool because the parameter that triggered management action was extremely difficult to estimate for the majority of populations. Uncertainty in parameter estimation resulted in few conservation actions. We describe a recently adopted management scheme that incorporates uncertainty and its resulting implementation. The approach used in this simple management scheme, which was tested by using simulation models, incorporates uncertainty and mandates monitoring abundance and human-caused mortality. Although the entire scheme may be suitable for application to some terrestrial and marine problems, two features are broadly applicable: the incorporation of uncertainty through simulations of management and the use of quantitative management criteria to translate verbal objectives into levels of acceptable risk.