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Software Used In Fisheries For Statistical Analysis

Smit Ramesh Lende

Department of aquaculture

College of Fisheries Veraval JAU Gujrat

Introduction:

Fish is an important source of protein and its harvest, handling, processing and distribution provide livelihood for millions of people as well as providing valuable foreign exchange earnings to the country. It is a highly perishable food, requires proper handling, processing and distribution, if it is to be utilized in a cost effective and efficient way. Today, fisheries are estimated to provide 16% of the world population's protein, and that figure is considerably elevated in some developing nations and in regions that depend heavily on the sea.

The world capture fisheries production in 2008 was about 90 million tonnes, with an estimated first-sale value of US$ 93.9 billion, of which about 80 million tones from marine waters and a record 10 million tonnes from inland waters. It has been relatively stable in the past decade, with the exception of marked fluctuations driven by catches of anchoveta – a species extremely susceptible to oceanographic conditions determined by the El Niño Southern Oscillation – in the Southeast Pacific. In 2008, China, Peru and Indonesia were the top capture fish producing countries. China remained by far the global leader with production of about 15 million tones (FAO-2010).

With its 8129 km long coastline, India has immense potential for developing and producing a variety of sea foods, highly sought after throughout the world, directly leading to a huge amount of export in this field. It forms more than 70% of sea food all over the world. This requirement has created a big fishing industry. The increased requirement of sea food all over the world and relatively easy breeding of certain varieties of fish, has led to creation of sea breeding farms in the shallow coastal areas of our country's eastern coast. Fish breeding is a new industry in our country.

The exploited fish populations and the fishery dependent on them are highly dynamic that require regular monitoring and periodical stock assessment. The resource evaluation is essential for evolving appropriate management strategies for rational exploitation and long-term sustenance of the exploited stocks. The validity and efficiency of the management formulations depend on the quality of database of which information on the catch and fishing effort is an important component. Thus, the data on catch and the expended effort besides other fishery-related statistics (including socio-economic status, infrastructure etc.) are essential for effective management of the fisheries. The compilation of accurate, relevant and timely data in a standard form that makes it comparable, and the considered analysis of this data, is essential to underpin the development and utilization of the fisheries sector. As per the FAO Technical Guidelines for Responsible Fisheries, "States should ensure that timely, complete and reliable statistics on catch and fishing effort are collected and maintained in accordance with applicable international standards and practices and in sufficient detail to allow sound statistical analysis. Such data should be updated regularly and verified through an appropriate system" (CCRF 7.4.4).

Fishery statistics are the primary means to measure the performance of a fishery within the social, economic, biological and environmental framework in which it is conducted. The collection of fishery data is based on a relatively small group of concepts and approaches, including most importantly the quantities harvested (catch), the related type and duration of fishing operations (fishing effort), the economic costs and returns of fishing and the distribution of these in time and space.

SPSS:

Analytics plays an increasingly important role in helping your organization achieve its objectives. The IBM SPSS Statistics family delivers the core capabilities needed for end-to-end analytics. To ensure that the most advanced techniques are available to a broader group of analysts and business users, we have made enhancements to the features and capabilities of IBM SPSS Statistics Base and its many specialized modules.

IBM SPSS Statistics 20 continues to increase accessibility to advanced analytics through improved tools, output, and ease-of-use features. This release focuses on increasing the analytic capabilities of the software through:

  • Mapping, which adds a geographic dimension to analysis and reporting

  • Improvements to existing procedures

  • Enhancements that increase analysts' productivity

 Highlights

  • New mapping capabilities - add a geographic dimension to analysis

  • Faster tabular outputs - allow viewing of results much faster

  • Improved Generalized Linear Mixed Models (GLMM) - can now be run with ordinal values

  • Updates to IBM® SPSS® AMOS™ scripting, enabling greater customization of complex models

  • Save large files within the sort procedure to save time and boost performance

  • Compression of temporary files within the sort procedure to save space and improve performance

Statistical Analysis with SAS/STAT® Software

Overview

From traditional statistical analysis of variance and predictive modeling to exact methods and statistical visualization techniques, SAS/STAT software is designed for both specialized and enterprisewide analytical needs. SAS/STAT software provides a complete, comprehensive set of tools that can meet the data analysis needs of the entire organization.

Benefits

  • Take advantage of all data in order to uncover new business opportunities and increase revenue. SAS/STAT software is designed to handle large data sets from disparate sources, enabling you to take advantage of all data that is available for your analyses. Analysts are freed to focus on analysis rather than data issues.

  • Move the scientific discovery process forward by applying the latest statistical techniques. Statistical procedures in SAS are constantly being updated to reflect the latest advances in statistical methodology, thus enabling you to go beyond the basics for more advanced statistical analyses. In addition, technical support is provided by experienced master's- and doctorate-level statisticians who provide a level of service not often found with other software vendors.

  • Achieve corporate and governmental compliance. SAS has more than 35 years of experience developing advanced statistical analysis software and a proven reputation for delivering superior, reliable results. With SAS/STAT software, you can produce repeatable code that is easily documented and verified for corporate and governmental compliance issues.

Features

Analysis of variance

  • Balanced and unbalanced designs.

  • Multivariate analysis of variance and repeated measurements.

  • Linear and nonlinear mixed models.

Mixed models

  • Linear mixed models.

  • Nonlinear mixed models.

  • Generalized linear mixed models.

Regression

  • Least squares regression with nine model selection techniques, including stepwise regression.

  • Diagnostic measures.

  • Robust regression; Loess regression.

  • Nonlinear regression and quadratic response surface models.

  • Partial least squares.

  • Quantile regression.

Categorical data analysis

  • Contingency tables and measures of association.

  • Logistic regression and log linear models; generalized linear models.

  • Bioassay analysis.

  • Generalized estimating equations.

  • Weighted least squares regression.

  • Exact methods.

  • Zero-inflated Poisson regression.

  • Zero-inflated negative binomial regression.

Bayesian analysis

  • Bayesian modeling and inference for generalized linear models, accelerated life failure models, Cox regression models and piecewise exponential models.

  • General procedure fits Bayesian models with arbitrary priors and likelihood functions.

Multivariate analysis

  • Factor analysis.

  • Principal components.

  • Canonical correlation and discriminate analysis.

  • Path analysis.

  • Structural equations.

Survival analysis

  • Comparison of survival distributions.

  • Accelerated failure time models.

  • Proportional hazards models.

Psychometric analysis

  • Multidimensional scaling.

  • Conjoint analysis with variable transformations.

  • Correspondence analysis.

Cluster analysis

  • Hierarchical clustering of multivariate data or distance data.

  • Disjoint clustering of large data sets.

  • Nonparametric clustering with hypothesis tests for the number of clusters.

Nonparametric analysis

  • Nonparametric analysis of variance. Exact probabilities computed for many nonparametric statistics.

  • Kruskal-Wallis, Wilcoxon-Mann-Whitney and Friedman tests.

  • Other rank tests for balanced or unbalanced one-way or two-way designs.

Survey data analysis

  • Sample selection.

  • Descriptive statistics and t-tests.

  • Linear and logistic regression.

  • Frequency table analysis.

  • Cox proportional hazards model.

Multiple imputation for missing values

  • Regression and propensity scoring for monotone missing patterns.

  • MCMC method for arbitrary missing patterns.

  • Combine results for statistically valid inferences.

Study planning

  • Power and Sample Size application provides interface for computation of sample sizes and characterization of power for t-tests, confidence intervals, linear models, tests of proportions and rank tests for survival analysis.

SYSTAT

SYSTAT is a statistics and statistical graphics software package, developed by Leland Wilkinson in the late 1970s, who was at the time an assistant professor of psychology at the University of Illinois at Chicago. Systat was incorporated in 1983 and grew to over 50 employees.

In 1995 SYSTAT was sold to SPSS Inc., who marketed the product to a scientific audience under the SPSS Science division. By 2002, SPSS had changed its focus to business analytics and decided to sell SYSTAT to Cranes Software in BangaloreIndia. Cranes formed Systat Software, Inc. to market and distribute SYSTAT in the US, and a number of other divisions for global distribution. The headquarters are in Chicago, Illinois.

By 2005, SYSTAT was in its eleventh version having a revamped codebase completely changed from Fortran into C++. Version 13 came out in 2009, with improvements in the user interface and several new features.

STATISTICA

STATISTICA is a statistics and analytics software package developed by StatSoft. STATISTICA provides data analysis, data management, data mining, and data visualization procedures. STATISTICA product categories include Enterprise (for use across a site or organization), Web-Based (for use with a server and web browser), Concurrent Network Desktop, and Single-User Desktop.

STATISTICA originally derives from a set of software packages and add-ons that were initially developed during the Mid 1980's by StatSoft. Following the 1986 release of CSS (Complete Statistical System) and the 1988 release of MacSS (Macintosh Statistical System), the first DOS version of STATISTICA (trademarked in capitals as STATISTICA) was released in 1991. In 1992, the Macintosh version of STATISTICA was released.

STATISTICA 5.0, was released in 1995 which automatically configured itself for new 32-bit Windows 95/NT or the older version of Windows (3.1) and featured a large number of new statistics and graphics procedures, a word-processor-style output editor of unlimited capacity (combining tables and graphs), and a built-in professional development environment that enabled the user to easily design new procedures (e.g., via the included, comprehensive STATISTICA Basic language) and integrate them with the STATISTICA system. STATISTICA 5.1 was released in 1996 followed by STATISTICA '97 and STATISTICA '98 editions.

In 2001, STATISTICA 6 was based on the COM architecture and high-end technologies (such as multithreading and support for distributed computing). STATISTICA 9 was released in 2009, supporting 32 bit and 64-bit computing. The most recent release of STATISTICA is STATISTICA 10 (release was announced in November 2010). This release features further performance optimizations for the 64-bit CPU architecture, as well as advanced multithreading technologies, integration with Microsoft SharepointMicrosoft Office 2010 and other applications, the ability to generate Java and C# code, and other GUI and kernel improvements.

Localized versions of STATISTICA (including the entire STATISTICA family of products) are available in Chinese (both Traditional and Simplified), Czech, English, French, German, Italian, Japanese, Polish, Russian, and Spanish. STATISTICA documentation is available in Arabic, Chinese, Czech, English, French, German, Hungarian, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, and other languages.

ARTFISH - Approaches, Rules and Techniques for Fisheries statistical monitoring

ARTFISH stands for Approaches, Rules and Techniques for Fisheries statistical monitoring. It has been developed as a standardized tool adaptable to most fisheries in the developing countries. Its design was driven by the need to provide users with robust, user-friendly and error-free approaches with computer software, and achieve the implementation of cost-effective fishery statistical systems with minimal external assistance.

Software components

The first ARTFISH system appeared in 1993 as an MS-DOS application. ARTFISH was re-written for Windows in 2000. Its present upgraded version was released in November 2007.

The ARTFISH outputs consist of documents, guidelines, manuals, case studies, training kits and computer software. Its components are ARTBASIC for handling sample data and producing monthly estimates and ARTSER for the integration of monthly estimates into annual databases. The basic variables involve catch, effort, CPUE, prices, values and average fish size. ARTFISH utilizes technology that increases user cognition and decreases training time. It also provides detailed statistical diagnostics on data quality and allows insights into the fisheries for which the data is being collected.

ARTBASIC 
It is used during the production phase of a data collection programme. Its purpose is to organize the system standards and classifications as well as primary sample data into standardized database structures and produce estimates of total effort, catch and values. The ARTBASIC generic approach operates within the context of a calendar month, a logical stratum and a specific boat/gear type. Its architecture provides for decentralized entry of data and production of estimates with multi-lingual capability.

ARTSER 
This component is used to integrate the monthly estimates produced by ARTBASIC into standardized tables showing monthly estimates of catch, CPUEs, effort, prices and values during a reference year. Its functions allow for flexible data extraction and grouping and provide users with reporting, exporting and graphical representation services. It also provides a multi-lingual capability for performing data mining and analysis.

Capacity building

The primary objective of ARTFISH is to improve the methodological and operational aspects of national fishery statistical programmes, by increasing national capacity in the sector of fishery statistical development. This involves:

  • Design of well-defined sample-based fishery surveys

  • Emphasis on increased cost-effectiveness

  • Achieving self-sustaining operations

  • Widening the utility of produced statistics

  • Adoption of reputable, robust and standardized survey methods and terminology

    Countries in which ARTFISH was implemented or presented

  • BEAM 1 and 2 - Bioeconomic modeling of artisanal and industrial sequential shrimp fisheries

    This document presents two software packages for bioeconomic modeling of artisanal and industrial sequential shrimp fisheries based on an age-structured Thompson and Bell (in Ricker, 1975) yield per recruit biological model and a simple input-output microeconomic model. BEAM1 gives its simulated results by age groups. BEAM2 gives them by standard commercial categories as used in the shrimp fishing industry.

    It contains a user guide for using BEAM1 and BEAM2 and an example of the application of BEAM1 to the shrimp fisheries of Suriname. The user interface of the program is like a spreadsheet which allows the users to simulate interactively various management options, changing the number of recruits, the size of the fishing fleets, the fishing regime (i.e., the age at first capture in the two sequential fisheries), and the economic parameters of the fleets and of the processing sector.

    The programs give their results in terms of biomass and catch, in numbers, weight and value. These results are given by simulation interval (i.e., by age groups in BEAM1 and commercial categories in BEAM2), by fleet (for a maximum of two fleets operating sequentially), and by fishery sector (capture and processing).

    The economic results refer to total costs and revenues, total and domestic value added, profits and losses, employment and foreign exchange earnings.

    This document presents two software packages for bioeconomic modeling of artisanal and industrial sequential shrimp fisheries based on an age-structured Thompson and Bell (in Ricker, 1975) yield per recruit biological model and a simple input-output microeconomic model. BEAM1 gives its simulated results by age groups. BEAM2 gives them by standard commercial categories as used in the shrimp fishing industry.

    It contains a user guide for using BEAM1 and BEAM2 and an example of the application of BEAM1 to the shrimp fisheries of Suriname. The user interface of the program is like a spreadsheet which allows the users to simulate interactively various management options, changing the number of recruits, the size of the fishing fleets, the fishing regime (i.e., the age at first capture in the two sequential fisheries), and the economic parameters of the fleets and of the processing sector.

    The programs give their results in terms of biomass and catch, in numbers, weight and value. These results are given by simulation interval (i.e., by age groups in BEAM1 and commercial categories in BEAM2), by fleet (for a maximum of two fleets operating sequentially), and by fishery sector (capture and processing).

    The economic results refer to total costs and revenues, total and domestic value added, profits and losses, employment and foreign exchange earnings.

    Key Features:

    BEAM1

    • Model Setup: Set Simulated lifespan and Fisheries sequence. Set Growth, Mortality and Value Parameters

    • Biological SubModel: Thompson and Bell Simulation of Catch, Biomass and Value by Age Group and Fishery for a given Recruitment

    • Economic SubModel: Input/Output Simulation of Economic Results: Investments, Costs, Returns, Value Added, etc., by Fishery

    • Exit to DOS

    BEAM2

    • Model Setup: Set Commercial Categories and Fisheries sequence. Set Growth, Mortality and Value Parameters

    • Biological SubModel: Thompson and Bell Simulation of Catch, Biomass and Value by Commercial Category and fishery for a given Recruitment

    • Economic SubModel: Input/Output Simulation of Economic Results: Investments, Costs, Returns, Value Added, etc., by Fishery

    • Exit to DOS

    System Requirements

    BEAM programmes can, in principle, be used on any IBM Personal Computer or 100% compatible machine under PCDOS or MSDOS (2.1 or higher version). The minimum hardware configuration required is: an IBM pc or 100% compatible computer with 256 KB RAM memory, one 360 KB 5.25" or 720 KB 3.5"disk drive, a monochrome or color Video Display Unit, and a standard keyboard.

    With this minimal configuration, the results are printed on the screen. The program can be run more comfortably with the following optional hardware: a second floppy disk, a printer (on LPT1) with 80 columns or more. When Using an enhanced keyboard(101/102 keys), arrows on the numeric pad must be used to move the cursor.

    BEAM3 - A Bio economic Simulation Model of Tropical Shrimp Fisheries Using Fixed or Random Recruitment

    BEAM3 (Cochet and Gilly, 1990) is a stochastic model that can handle up to four species (or two species by two sexes) and many fleets operating sequentially or simultaneously. The recruitment can be considered constant or variable. The model aims at determining the optimum size, in the long term, of the level of fishing capacity corresponding to the management goals if one accepts a given level of risk. It calculates the probability density functions of the outputs and their distribution among fleets and allows for the determination of the probabilities of occurrence of undesirable economic results. This model was tested on the French Guyana fisheries (2nd FAO/WECAF Workshop on Biological and Economic Modelling of the Shrimp Resources on the Guyana Brazil Shelf, Cayenne, French Guyana, May 1988).

    Key Features

    1. Creation or modification of parameter file.

    2. Simulation

    3. Graphical presentation

    System Requirements

    The software has been compiled using Microsoft Quick Basic and can run on any PC or PC compatible computer that has at least 640 KB RAM and ODS version 2.11 or later.

    The running time of a simulation depends on the number of fleets and species being considered and the type of the processor, e.g., 180286 or 180386. For example, with 4 species, 25 size classes asnd 3 fleets, each iteration lasts 1 second with an AT180286 microcomputer or 15 seconds with an XT18086 PC.

    Printing on a graphics printer can be done through a hard copy, if GRAPHICS utility has been defined.



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