A Catch-at-Length Analysis that Incorporates a Stochastic Model of Growth
01 Jan 1990-Canadian Journal of Fisheries and Aquatic Sciences (NRC Research Press Ottawa, Canada)-Vol. 47, Iss: 1, pp 184-198
TL;DR: A length-structured population model, which incorporates von Bertalanffy growth, is used to describe changes in population abundance over time and the parameter estimates of Pacific cod obtained from this algorithm were comparable with the values that were originally used to simulate the data.
Abstract: A length-structured population model, which incorporates von Bertalanffy growth, is used to describe changes in population abundance over time. The model is incorporated into a catch-at-length algo...
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Book•
01 Jan 1992
TL;DR: Quantitative fisheries stock assessment as mentioned in this paper, Quantitative fishery stock assessment: Quantitative fishes stock assessment, Quantitative fish stock assessment and stock assessment in the field of fishery management.
Abstract: Quantitative fisheries stock assessment : , Quantitative fisheries stock assessment : , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
1,581 citations
TL;DR: This work has shown clear trends in the decline in the total biomass of tropical tuna recorded in the wild over the past 50 years, and these trends are likely to be driven by warming oceans and declining productivity inducers.
Abstract: Andre E. Punt1,2*, TzuChuan Huang1, and Mark N. Maunder3,4 School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195-5020, USA CSIRO Wealth from Oceans Flagship, GPO Box 1538, Hobart, TAS 7001, Australia Quantitative Resource Assessment LLC, San Diego, CA 92129, USA Alternative address: Inter-American Tropical Tuna Commission, 8604 La Jolla Shores Drive, La Jolla, CA 92037-1508, USA *Corresponding Author: tel: +1 206 221 6319; fax: +1 206 685 7471; e-mail: aepunt@uw.edu
108 citations
Additional excerpts
...The distribution for growth increment has variously been assumed to be gamma (Sullivan et al., 1990), or normal (Punt et al....
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TL;DR: The first real efforts occurred in the period 1900 1920 with the work of Baranov and the formation of the International Council for the Exploration of the Sea (ICES) and the establishment of the science occurred between 1920 1960 with multi-species modeling, age-and size-structure dynamics, and production models.
Abstract: . I trace the development of fisheries models (i.e., fish population dynamics models of species subject to fisheries) to the 21st century. The first real efforts occurred in the period 1900 1920 with the work of Baranov (the “Grandfather” of fisheries population dynamics) and the formation of the International Council for the Exploration of the Sea (ICES). The establishment of the science occurred between 1920 1960 with multi-species modeling, age- and size-structure dynamics, and production models. Fundamental work during this time was done by Ricker (the “Father” of fisheries population dynamics), Beverton and Holt (the “Prophets” of fisheries population dynamics), Chapman, Dickie, DeLury, Graham, Gulland, Leslie, Lotka and Volterra, Russell, Schaefer, and Thompson. During this time, most of the workwas deterministic and mathematical. Between 1960 and 1980, statistical methodology evolved greatly but was separate from mathematical advances for the most part. The development of statistical principles for the estimation of animal abundance was further enhanced by Arnason, Buckland, Burnham and Anderson and White, Cormack, Eberhardt, Jolly, Manly, Pollock, Ricker, Robson, and Seber, among others. Fisheries models evolved in a deterministic setting, with advances in age-structured models (Gulland, Pope, Doubleday), surplus production models (Pella, Tomlin-son, Schnute, Fletcher, Hilborn), growth models, bioeconomic models (C. Clark) and management control models (Hilborn, Walters). The period 1980 2000 was the Golden Age. The integration between mathematics and statistics occurred when likelihood and least squares techniques were formally combined with mathematical models of population change. The number of fisheries modelers grew exponentially during this time, resulting in a concomitant increase in publications. A major advance in the 1990s has been the development of Bayesian and time series methods, which have allowed explicit specification of uncertainty. Currently, theory allows realistic modeling of age- and size-structured populations, migratory populations and harvesting strategies. These models routinely incorporate measurement error, process error (stochasticity) and time variation. But data needs often overwhelm the performance of models, and greater demands are being placed on models to answer complex questions. There has been poor communication between fisheries and ecological modelers, between fisheries researchers and statisticians, and among fisheries researchers in different geographic locales. Future models will need to deal better with habitat and spatial concerns, genetics, multispecies interactions, environmental factors, effects of harvesting on the ecosystem, model misspecification and so-cioeconomic concerns. Meta-analysis, retrospective analysis and operating models are some modern approaches for dealing with uncertainty and providing for sustainable fisheries. However, I fear that current attacks on single-species models and management may result in rejection of these advances and an attempt to substitute a less scientific approach.
98 citations
TL;DR: This critical review argues that several methods for the estimation and prediction of numbers-at-age, fishing mortality coefficients F, and recruitment for a stock of fish are too hard to explain to customers and do not pay enough attention to weaknesses in the supporting data, assumptions and theory.
Abstract: This critical review argues that several methods for the estimation and prediction of numbers-at-age, fishing mortality coefficients F, and recruitment for a stock of fish are too hard to explain to customers (the fishing industry, managers, etc.) and do not pay enough attention to weaknesses in the supporting data, assumptions and theory. The review is linked to North Sea demersal stocks. First, weaknesses in the various types of data used in North Sea assessments are summarized, i.e. total landings, discards, commercial and research vessel abundance indices, age-length keys and natural mortality (M). A list of features that an ideal assessment should have is put forward as a basis for comparing different methods. The importance of independence and weighting when combining different types of data in an assessment is stressed. Assessment methods considered are Virtual Population Analysis, ad hoc tuning, extended survivors analysis (XSA), year-class curves, catch-at-age modelling, and state-space models fitted by Kalman filter or Bayesian methods. Year-class curves (not to be confused with ‘catch-curves’) are the favoured method because of their applicability to data sets separately, their visual appeal, simple statistical basis, minimal assumptions, the availability of confidence limits, and the ease with which estimates can be combined from different data sets after separate analyses. They do not estimate absolute stock numbers or F but neither do other methods unless M is accurately known, as is seldom true.
96 citations
TL;DR: In this article, a state-space representation of a length-structured population under commercial harvest is described and a Kalman filter is used to develop the conditional likelihood equation needed for estimating the underlying system parameters.
Abstract: A state-space representation of a length-structured population under commercial harvest is described and a Kalman filter is used to develop the conditional likelihood equation needed for estimating the underlying system parameters. The state of the system is characterized using conventional fisheries theory with commercial harvest representing the observations taken on the population. The conditional likelihood framework embedded in the Kalman filter facilitates the incorporation of both system stochasticity as well as observation error in the development of the overall likelihood equation. Within this framework a maximum likelihood approach is used to estimate population parameters while taking into account both sources of error.
94 citations
Cites background from "A Catch-at-Length Analysis that Inc..."
...A detailed description of these equations and their derivation is given elsewhere (Sullivan et al., 1989; Sullivan,, unpublished Ph.D. dissertation, University of Washington, Seattle, 1988)....
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References
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23,905 citations
TL;DR: The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results and properties of the variance equation are of great interest in the theory of adaptive systems.
Abstract: A nonlinear differential equation of the Riccati type is derived for the covariance matrix of the optimal filtering error. The solution of this \"variance equation\" completely specifies the optimal filter for either finite or infinite smoothing intervals and stationary or nonstationary statistics. The variance equation is closely related to the Hamiltonian (canonical) differential equations of the calculus of variations. Analytic solutions are available in some cases. The significance of the variance equation is illustrated by examples which duplicate, simplify, or extend earlier results in this field. The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results. In several examples, the estimation problem and its dual are discussed side-by-side. Properties of the variance equation are of great interest in the theory of adaptive systems. Some aspects of this are considered briefly.
6,152 citations
Book•
01 Jan 1977
TL;DR: In this paper, the authors take into serious consideration the further development of regression computer programs that are efficient, accurate, and considered an important part of statistical research, and provide up-to-date accounts of computational methods and algorithms currently in use without getting entrenched in minor computing details.
Abstract: Description: Regression analysis is an often used tool in the statistician's toolbox. This new edition takes into serious consideration the furthering development of regression computer programs that are efficient, accurate, and considered an important part of statistical research. The book provides up-to-date accounts of computational methods and algorithms currently in use without getting entrenched in minor computing details.
2,811 citations
TL;DR: A general theory for analyzing catch at age data for a fishery is presented and seems to be the first to address itself properly to the stochastic nature of the errors in the observed catch atAge data.
Abstract: We present a general theory for analyzing catch at age data for a fishery. This theory seems to be the first to address itself properly to the stochastic nature of the errors in the observed catch ...
438 citations
TL;DR: The use of catch-at-age data for estimating population abundance, productivity, and year-class abundance was examined in this article, where various published models and new models were compared.
Abstract: We examined the use of catch-at-age data for estimating population abundance, productivity, and year-class abundance. A review section is included where various published models and our new models ...
390 citations