Environmental Concerns in Water Pricing Policy: an Application of Data Envelopment Analysis (DEA)

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Spatial and seasonal abundance of sand seatrout (Cynoscion arenarius) and silver seatrout (C. nothus) off the coast of Texas, determined with twenty years of data (1987–200

Sand seatrout (Cynoscion arenarius) and silver seatrout (C. nothus) are both found within the immediate offshore areas of the Gulf of Mexico, especially around Texas; however information is limited on how much distributional overlap really occurs between these species. In order to investigate spatial and seasonal differences between species, we analyzed twenty years of bay and offshore trawl data collected by biologists of the Coastal Fisheries Division, Texas Parks and Wildlife Department. Sand seatrout and silver seatrout were distributed differently among offshore sampling areas, and salinity and water depth appeared to correlate with their distribution. Additionally, within the northernmost sampling area of the gulf waters, water depth correlated significantly with the presence of silver seatrout, which were found at deeper depths than sand seatrout. There was also an overall significant decrease in silver seatrout abundance during the summer season, when temperatures were at their highest, and this decrease may have indicated a migration farther offshore. Sand seatrout abundance had an inverse relationship with salinity and water depth offshore. In addition, sand seatrout abundance was highest in bays with direct passes to the gulf and correlated with corresponding abundance in offshore areas. These data highlight the seasonal and spatial differences in abundance between sand and silver seatrout and relate these differences to the hydrological and geological features found along the Texas coastline.

On preservation of data

This paper presents, for three aquatic research projects, the type of data that were collected, and the reasons why these data eventually became lost or inaccessible. A strategy for countering such data loss is proposed.

The Cheshire Meres an analysis of data and a prioritisation of sites

This is a report on the Analysis of Data and a Prioritisation of Sites at the Cheshire Meres by the Institute of Freshwater Ecology. The report addresses data collected by the Agency for 24 basin sites in Cheshire. At least two samples were collected from each site, though not simultaneously. Sites were visited in May/June and in November. The determinands are standard and they included: water, temperature, conductivity, pH, DO, fractional white light penetration, TSS, chlorophyll, TP, ortho-phosphate, nitrate-, nitrite-, ammonium and silicate. Though concentrations were often higher than for other lakes in the region, rather exceeding criteria for classification as eutrophic lakes, the results confirmed that the series of lakes is, naturally, highly eutrophic and nothing in the present data differs so far from expectation that is persuasive that the ecosystems are reacting adversely to environmental stress. The data set is review and summarised, site-by-site, in an appendix. The grounds for prioritisation are discussed. Whether or not this preferred prioritised option is adopted, the Agency is recommended to review the way it carries out monitoring. The determinands and the sampling frequency need to be geared to the information that is required.

Integration of data for nowcasting of harmful algal blooms

Harmful algal blooms (HABs) are a significant and potentially expanding problem around the world. Resource management and public health protection require sufficient information to reduce the impacts of HABs by response strategies and through warnings and advisories. To be effective, these programs can best be served by an integration of improved detection methods with both evolving monitoring systems and new communications capabilities. Data sets are typically collected from a variety of sources, these can be considered as several types: point data, such as water samples; transects, such as from shipboard continuous sampling; and synoptic, such as from satellite imagery. Generation of a field of the HAB distribution requires all of these sampling approaches. This means that the data sets need to be interpreted and analyzed with each other to create the field or distribution of the HAB. The HAB field is also a necessary input into models that forecast blooms. Several systems have developed strategies that demonstrate these approaches. These range from data sets collected at key sites, such as swimming beaches, to automated collection systems, to integration of interpreted satellite data. Improved data collection, particularly in speed and cost, will be one of the advances of the next few years. Methods to improve creation of the HAB field from the variety of data types will be necessary for routine nowcasting and forecasting of HABs.

The western Indian Ocean tuna fishery from 1980 to 1985: a summary of data collected by coastal states

Longliner and purse-seiner catch/effort statistics for tuna fisheries in the western Indian Ocean collected by Mozambique, Seychelles and Somalia are summarized. Although the data are not considered sufficient to indicate trends for the western Indian Ocean as a whole, an examination of data from the Seychelles EEZ shows that catch rates for yellowfin tuna declined consistently from 1982 to 1985, to about half their former levels. The data were processed by the FAO/Indo Pacific Tuna Development and Management Programme, Colombo, Sri Lanka.

On the problem of nonadditivity in two-way analysis of variance of data for comparing the efficiency of fishing gears

To bring out the relative efficiency of various types of fishing gears, in the analysis of catch data, a combination of Tukey's test, consequent transformation and graphical analysis for outlier elimination has been introduced, which can be advantageously used for applying ANOVA techniques, Application of these procedures to actual sets of data showed that nonadditivity in the data was caused by either the presence of outliers, or the absence of a suitable transformation or both. As a corollary, the concurrent model: X sub(ij) = µ + α sub(i) + β sub(j) + λ α sub(i) β sub(j) + E sub(ij) adequately fits the data.