Species identification and authentication of some of the most important seafood products in Iranian markets using biotechnological methods based on DNA

This study describes the molecular identification of sixteen fish species present in processed products imported into Iran for human consumption. DNA barcoding using direct sequencing of about 650 bp of the mitochondrial Cytochrome Oxidase subunit I gene revealed incorrect labeling (31.25%). Substitution of fish species constitutes serious economic fraud, and our results increase concern regarding the trading of processed fish products in Iran from both health and conservation points of view.

Assessment of cobia, Rachycentron canadum, in the waters of the U.S. Gulf of Mexico

This assessment applies to cobia (Rachycentron canadum) located in the territorial waters of the U.S. Gulf of Mexico. Separation of the Gulf of Mexico and Atlantic Ocean is defined by the seaward extension of the Dade/Monroe county line in south Florida. Mixing of fish between the Atlantic and Gulf of Mexico occurs in the Florida Keys during winter months. Cobia annually migrate north in early spring in the Gulf to spawning grounds in the northern Gulf of Mexico, returning to the Florida Keys by winter. Catches of cobia in the Gulf of Mexico are dominated by recreational landings, accounting for nearly 90% of the total. Since 1980, the landings of cobia in the recreational fishery have remained fairly stable at around 400-600 mt with a slight peak of 1,014 mt in 1997. The recreational fishery was estimated to have landed 471 mt in 2000. The landings from the commercial fishery have shown a steady increase from 45 mt in 1980 to a peak of 120 mt in 1994, followed by a decline to 62 mt in 2000. The previous assessment of cobia occurred in 1996 using a virtual population analysis (VPA) model. For this analysis a surplus-production model (ASPIC) and a forward-projecting, age-structured population model programmed in the AD Model Builder (ADMB) software were applied to cobia data from the Gulf of Mexico. The primary data consisted of four catch-per-unit-effort (CPUE) indices derived from the Marine Recreational Fisheries Statistics Survey (MRFSS) (1981-1999), Southeast region headboat survey (1986-1999), Texas creel survey (1983-1999), and shrimp bycatch estimates (1980-1999). Length samples were available from the commercial (1983-2000) and recreational (1981-2000) fisheries. The ASPIC model applied to the cobia data provided unsatisfactory results. The ADMB model fit described the observed length composition data and fishery landings fairly well based on graphical examination of model residuals. The CPUE indices indicated some disagreement for various years, but the model fit an overall increasing trend from 1992-1997 for the MRFSS, headboat, and Texas creel indices. The shrimp bycatch CPUE was treated as a recruitment index in the model. The fit to these data followed an upward trend in recruitment from 1988-1997, but did not fit the 1994-1997 data points very well. This was likely the result of conflicting information from other data sources. Natural mortality (M) for cobia is unknown. As a result, a range of values for M from 0.2-0.4, based on longevity and growth parameters, were selected for use in the age-structured model. The choice of natural mortality appears to greatly influence the perceived status of the population. Population status as measured by spawning stock biomass in the last year relative to the value at maximum sustainable yield (SSB2000/SSBMSY), spawning stock biomass in the last year relative to virgin spawning stock biomass (SSB2000/S0), and static spawning stock biomass per recruit (SSBR) all indicate the population is either depleted, near MSY, or well above MSY depending on the choice of M. The variance estimates for these benchmarks are very large and in most cases ranges from depleted to very healthy status. The only statement that can be made with any degree of certainty about cobia in the Gulf of Mexico is that the population has increased since the 1980s. (PDF contains 61 pages)

Geographical distributions of effort and catches of tunas by purse-seine vessels in the Eastern Pacific Ocean during 1965-1998

ENGLISH: This report presents fine-scale spatial summaries of annual catch and effort information compiled by the IATTC staff. These data summaries are presented in a graphical format, and display only information collected from purse-seine vessels fishing in the eastern Pacific Ocean (EPO) during 1965-1998. Data collected from baitboat and longline vessels fishing in the EPO are not considered in this report. Equivalent data from Japanese longline vessels fishing in the EPO are presented by Uosaki and Bayliff(1999) for 1988-1992 and by' references cited in that publication for 1956-1987. SPANISH: Este informe presenta resúmenes espaciales a escala fina de información sobre captura y esfuerzo anuales compilada por el personal de la CIAT. Se presentan los resúmenes en formato gráfico, e incluyen solamente información tomada de barcos cerqueros pescando en el Océano Pacifico oriental (OPO) durante 1965-1998. No se consideran en el informe datos provenientes de barcos de camada y palangreros que pescan en el OPO. Se presentan datos equivalentes de barcos palangreros japoneses pescando en el OPO en Uosaki y Bayliff (1999) para 1988-1992 yen las referencias citadas en dicha publicación para 1956-1987. (PDF contains 104 pages.)

Limited resources? Let the habitat priority planner help!

In this time of scarce resources, coastal resource managers must find ways to prioritize conservation, land use, and restoration efforts. The Habitat Priority Planner (HPP) is a free geospatial tool created by the National Oceanic and Atmospheric Administration’s Coastal Services Center that has received wide praise for its ease of use and broad applicability to conservation strategic planning, restoration, climate change scenarios, and other natural resource management actions. Not a geographic information system (GIS) user? Don’t worry―this tool was designed to be used in a team setting. One intermediate-level GIS user can push the buttons to show quick results while a roomful of resource managers and stakeholders provide input criteria that determine the results. The Habitat Priority Planner is a toolbar for ESRI’s ArcGIS platform that is composed of three modules: Habitat Classification, Habitat Analysis, and Data Explorer. The tool calculates basic ecological statistics that are used to examine how habitats function within a landscape. The tool pre‐packages several common landscape metrics into a user‐friendly interface for intermediate GIS users. In addition, HPP allows the user to build queries interactively using a graphical interface for demonstrating criteria selections quickly in a visual manner that is useful in stakeholder interactions. Tool advocates and users include land trusts, conservation alliances, nonprofit organizations, and select National Estuarine Research Reserves and refuges of the U.S. Fish and Wildlife Service. Participants in this session will learn the basic requirements for HPP use and the multiple ways the HPP has been applied to geographies nationwide. (PDF contains 5 pages)

The distribution of Corixidae in relation to the water quality of British lakes: A monitoring model

This article is an attempt to devise a method of using certain species of Corixidae as a basis for the assessment of general water quality in lakes. An empirical graphical representation of the distribution of populations or communities of Corixidae in relation to conductivity, based mainly on English and Welsh lakes, is used as a predictive monitoring model to establish the "expected" normal community at a given conductivity, representing the total ionic concentration of the water body. A test sample from another lake of known conductivity is then compared with "expected" community. The "goodness of fit" is examined visually or by calculation of indices of similarity based on the relative proportions of the constituent species of each community. A computer programme has been devised for this purpose.

Biologie de Penaeus duorarum notialis en Côte d'Ivoire. 5 - Nouvelle étude de la croissance

Von Bertalanffy's growth curve parameters K, L∞ and t'o have been estimated for female Penaeus duorarum by modal progression analysis, using the "successive maximums method" of Gheno and Le Guen (1968) for the polymodal size frequency curves analysis and the Tomlinson and Abrahamson's least squares method for parameters computations. For the male the authors used an original method to get an age/length key. The parameters were calculated by Gulland's graphical method (1969).

PCR-SSCP and Isoelectric Focusing as Screening Methods for Identification of (Sub-) Tropical Fish Species

To ensure the authentication of fishery products lacking biological characters, rapid species identification methods are required. Two DNA- and protein-based methods, PCR-SSCP (polymerase chain reaction - single strand conformation polymorphism) of a 464 bp segment of the cytochrome b – gene and isoelectric focusing (IEF) of water-soluble proteins from fish fillets, were applied to identify fillets of (sub-) tropical fish species available on the European market. Among the samples analysed were two taxonomically identified species from the family Sciaenidae and one from Sphyraenidae. By comparison of DNA- and protein patterns of different samples, information about intra-species variability of patterns, and homogeneity of batches (e.g. fillet blocks or bags) can be obtained. PCR-SSCP and IEF may be useful for pre-checking of a large number of samples by food control laboratories. Zusammenfassung Zur Sicherstellung der Authentizität von Fischerei-Erzeugnissen ohne biologische Merkmale sind schnelle Verfahren zur Speziesidentifizierung hilfreich. Zwei Methoden der DNA- bzw. Protein-Analyse wurden eingesetzt, um Filets (sub-) tropischer Fischarten, die auf dem europäischen Markt angeboten werden, zu identifizieren. Bei diesen Methoden handelt es sich um die PCR-SSCP (Polymerase-Kettenreaktion – Einzelstrang-Konformationspolymorphismus) – Analyse der PCR-Produkte und die IEF (isoelektrische Fokussierung) der wasserlöslichen Fischmuskelproteine. Unter den untersuchten Proben waren zwei taxonomisch bestimmte Arten aus der Familie Sciaenidae und eine Spezies aus der Familie Sphyraenidae. Durch Vergleich der DNA- bzw. Proteinmuster lassen sich Informationen über die intra-spezifische Variabilität solcher Muster und die Einheitlichkeit von Partien (beispielsweise Filetblöcke oder Filetbeutel) gewinnen. PCR-SSCP und IEF können in Laboratorien der Lebensmittelüberwachung als Vortest gerade bei hohen Probenzahlen sinnvoll eingesetzt werden.

Length-weight relationship and relative condition factor of pond-reared mahseer, Tor putitora(Ham.)

The study deals with the length-weight relationship and relative condition factor (Kn) of mahseer, Tor putitora reared for 150 days in ponds. The logarithmic form of equation for the relationship was found to be logW = -1.727+2.875logL or W=O.Ol875U·875 • The graphical presentation of the parabolic and logarithmic forms showed respectively the curvilinear and linear relationships between length and weight of the fish. The mean value (±sd) of relative condition factor was found to be 0.95±0.12. The exponential value 'b' was found to be 2.96 and the coefficient of correlation 'r' was 0.965, which showed strong and highly correlated relationships between length and weight of the fish.

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.