Untrawlable Bottom in Shrimp Statistical Zones of the Northwest Gulf of Mexico

The Gulf of Mexico Fisheries Management Council tasked the National Marine Fisheries Service with determining the extent, if any, of loss oft rawlable bottom in the Gulf of Mexico based upon fishing industry concerns. There are approximately 31 million hectares in the 21 shrimp statistical zones in the Gulf, approximately 23 million hectares of waters that are <35 fathoms (where most shrimp trawling effort occurs), and approximately 11 million hectares in zones 10-21, <35f athoms, which were examined. There are 31,338 known hangs, snags, artificial reefs, hazards to navigation, oil rigs, and similar obstructions which cause trawling to be unfeasible in these zones. There are several refuge (i.e. untrawlable) areas associated with the Alabama Artificial Reefs. Conservatively assuming 1 hectare for each known obstruction, coupled with the known area of each refuge, the estimate of total untrawlable bottom in zones 10-21 less than 35 fathoms in the Gulf is 185,953 hectares, or roughly 1.7% of this total trawlable area. Sensitivity analysis demonstrated the robustness of this assumption, with a range of 0.3-4.3% possible. In specific shrimp zones, untrawlable area is much less than 1% except in zones 10 (26%) and 11(2.5%), both of which possess a refuge. Other than the implementation periods of these refugia, no temporal trends were detectable with respect to the amount of untrawlable bottom.

A statistical investigation of fish prices in Nigeria

The main objective of this study is to describe and characterize the behaviour of fish prices in Nigeria. Drawing upon aspects of the data from a nationwide fish survey in 1980/81 and on various secondary data, the study analyses the pattern of fish price movement and makes projections of fish prices in Nigeria till 2002 A.D. It is concluded that unless efforts are directed at stemming inflation in fish prices, prices paid by fish consumers in Nigeria will be more than doubled within the next two decades

Fisheries statistical bulletin, Kainji Lake, northern Nigeria, 1997

The bulletin presents summary tables and charts on levels of fishing activity, fishing effort, yields and economic values of yields for the fisheries of Kainji Lake, Nigeria for the year 1997. Frame survey data and fishing gear measurements are also included. (PDF contains 34 pages)

Fisheries statistical bulletin, Kainji Lake, Nigeria, 1998

A tabulated summary is presented of the main fisheries data collected to date (1998) by the Nigerian-German Kainji Lake Fisheries Promotion Project, together with a current overview of the fishery. The data are given under the following sections: 1) Fishing localities and types; 2) Frame survey data; 3) Number of licensed fishermen by state; 4) Mesh size distribution; 5) Fishing net characteristics; 6) Fish yield; 7) Total annual fishing effort by gear type; 8) Total annual value of fish landed by gear type; 9) Graphs of effort and CPUE by gear type. (PDF contains 36 pages)

Fisheries statistical bulletin, Kainji Lake, Nigeria, 1999

A tabulated summary is presented of the main Lake Kainji fisheries data collected to date (1999) by the Nigerian-German Kainji Lake Fisheries Promotion Project, together with a current overview of the fishery. The data are given under the following sections: 1) Fishing localities and types; 2) Frame survey data; 3) Number of licensed fishermen by state; 4) Mesh size distribution; 5) Fishing net characteristics; 6) Fish yield; 7) Average monthly CPUE by gear type; 8)Average monthly fishing activity by gear type; 9) Total annual fishing effort by gear type; 10) Total annual value of fish landed by gear type; 11) Trends of the total yield by gear type. (PDF contains 34 pages)

Some statistical aspects of the long-term gill net monitoring programme for pike Esox lucius in Windermere (English Lake District)

For more than 55 years, data have been collected on the population of pike Esox lucius in Windermere, first by the Freshwater Biological Association (FBA) and, since 1989, by the Institute of Freshwater Ecology (IFE) of the NERC Centre for Ecology and Hydrology. The aim of this article is to explore some methodological and statistical issues associated with the precision of pike gill net catches and catch-per-unit-effort (CPUE) data, further to those examined by Bagenal (1972) and especially in the light of the current deployment within the Windermere long-term sampling programme. Specifically, consideration is given to the precision of catch estimates from gill netting, including the effects of sampling different locations, the effectiveness of sampling for distinguishing between years, and the effects of changing fishing effort.

On statistical treatment of the results of parallel trails with special reference to fishery research

Parallel trials form a most important part of the technique of scientific experimentation. Such trials may be divided into two; categories. In the first the results are comparable measurements of one kind or another. In the second the data consist of records of the number of times a certain 'event' has occurred in the two sets of trials compared. Only trials of the second category are dealt with here. In this paper all the reliable methods of testing for significance the results of parallel trials of a certain type with special reference to fishery research are described fully. Some sections relate to exact, others to approximate tests. The only advantage in the use of the latter lies in the fact that they are often the more expeditious. Apart from this it is always preferable to use exact methods.

A-SCALA: an age-structured statistical catch-at-length analysis for assessing tuna stocks in the eastern tropical Pacific Ocean

English: We describe an age-structured statistical catch-at-length analysis (A-SCALA) based on the MULTIFAN-CL model of Fournier et al. (1998). The analysis is applied independently to both the yellowfin and the bigeye tuna populations of the eastern Pacific Ocean (EPO). We model the populations from 1975 to 1999, based on quarterly time steps. Only a single stock for each species is assumed for each analysis, but multiple fisheries that are spatially separate are modeled to allow for spatial differences in catchability and selectivity. The analysis allows for error in the effort-fishing mortality relationship, temporal trends in catchability, temporal variation in recruitment, relationships between the environment and recruitment and between the environment and catchability, and differences in selectivity and catchability among fisheries. The model is fit to total catch data and proportional catch-at-length data conditioned on effort. The A-SCALA method is a statistical approach, and therefore recognizes that the data collected from the fishery do not perfectly represent the population. Also, there is uncertainty in our knowledge about the dynamics of the system and uncertainty about how the observed data relate to the real population. The use of likelihood functions allow us to model the uncertainty in the data collected from the population, and the inclusion of estimable process error allows us to model the uncertainties in the dynamics of the system. The statistical approach allows for the calculation of confidence intervals and the testing of hypotheses. We use a Bayesian version of the maximum likelihood framework that includes distributional constraints on temporal variation in recruitment, the effort-fishing mortality relationship, and catchability. Curvature penalties for selectivity parameters and penalties on extreme fishing mortality rates are also included in the objective function. The mode of the joint posterior distribution is used as an estimate of the model parameters. Confidence intervals are calculated using the normal approximation method. It should be noted that the estimation method includes constraints and priors and therefore the confidence intervals are different from traditionally calculated confidence intervals. Management reference points are calculated, and forward projections are carried out to provide advice for making management decisions for the yellowfin and bigeye populations. Spanish: Describimos un análisis estadístico de captura a talla estructurado por edad, A-SCALA (del inglés age-structured statistical catch-at-length analysis), basado en el modelo MULTIFAN- CL de Fournier et al. (1998). Se aplica el análisis independientemente a las poblaciones de atunes aleta amarilla y patudo del Océano Pacífico oriental (OPO). Modelamos las poblaciones de 1975 a 1999, en pasos trimestrales. Se supone solamente una sola población para cada especie para cada análisis, pero se modelan pesquerías múltiples espacialmente separadas para tomar en cuenta diferencias espaciales en la capturabilidad y selectividad. El análisis toma en cuenta error en la relación esfuerzo-mortalidad por pesca, tendencias temporales en la capturabilidad, variación temporal en el reclutamiento, relaciones entre el medio ambiente y el reclutamiento y entre el medio ambiente y la capturabilidad, y diferencias en selectividad y capturabilidad entre pesquerías. Se ajusta el modelo a datos de captura total y a datos de captura a talla proporcional condicionados sobre esfuerzo. El método A-SCALA es un enfoque estadístico, y reconoce por lo tanto que los datos obtenidos de la pesca no representan la población perfectamente. Además, hay incertidumbre en nuestros conocimientos de la dinámica del sistema e incertidumbre sobre la relación entre los datos observados y la población real. El uso de funciones de verosimilitud nos permite modelar la incertidumbre en los datos obtenidos de la población, y la inclusión de un error de proceso estimable nos permite modelar las incertidumbres en la dinámica del sistema. El enfoque estadístico permite calcular intervalos de confianza y comprobar hipótesis. Usamos una versión bayesiana del marco de verosimilitud máxima que incluye constreñimientos distribucionales sobre la variación temporal en el reclutamiento, la relación esfuerzo-mortalidad por pesca, y la capturabilidad. Se incluyen también en la función objetivo penalidades por curvatura para los parámetros de selectividad y penalidades por tasas extremas de mortalidad por pesca. Se usa la moda de la distribución posterior conjunta como estimación de los parámetros del modelo. Se calculan los intervalos de confianza usando el método de aproximación normal. Cabe destacar que el método de estimación incluye constreñimientos y distribuciones previas y por lo tanto los intervalos de confianza son diferentes de los intervalos de confianza calculados de forma tradicional. Se calculan puntos de referencia para el ordenamiento, y se realizan proyecciones a futuro para asesorar la toma de decisiones para el ordenamiento de las poblaciones de aleta amarilla y patudo.

Statistical inference about the relative efficiency of a new survey protocol, based on paired-tow survey calibration data

Paired-tow calibration studies provide information on changes in survey catchability that may occur because of some necessary change in protocols (e.g., change in vessel or vessel gear) in a fish stock survey. This information is important to ensure the continuity of annual time-series of survey indices of stock size that provide the basis for fish stock assessments. There are several statistical models used to analyze the paired-catch data from calibration studies. Our main contributions are results from simulation experiments designed to measure the accuracy of statistical inferences derived from some of these models. Our results show that a model commonly used to analyze calibration data can provide unreliable statistical results when there is between-tow spatial variation in the stock densities at each paired-tow site. However, a generalized linear mixed-effects model gave very reliable results over a wide range of spatial variations in densities and we recommend it for the analysis of paired-tow survey calibration data. This conclusion also applies if there is between-tow variation in catchability.

Statistical analyses to support guidelines for marine avian sampling: Final report. [Digital Supplements A-G attached]

Interest in development of offshore renewable energy facilities has led to a need for high-quality, statistically robust information on marine wildlife distributions. A practical approach is described to estimate the amount of sampling effort required to have sufficient statistical power to identify species specific “hotspots” and “coldspots” of marine bird abundance and occurrence in an offshore environment divided into discrete spatial units (e.g., lease blocks), where “hotspots” and “coldspots” are defined relative to a reference (e.g., regional) mean abundance and/or occurrence probability for each species of interest. For example, a location with average abundance or occurrence that is three times larger the mean (3x effect size) could be defined as a “hotspot,” and a location that is three times smaller than the mean (1/3x effect size) as a “coldspot.” The choice of the effect size used to define hot and coldspots will generally depend on a combination of ecological and regulatory considerations. A method is also developed for testing the statistical significance of possible hotspots and coldspots. Both methods are illustrated with historical seabird survey data from the USGS Avian Compendium Database.