Relevant bibliographies by topics / Fish stock assessment – Mathematical models

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Academic literature on the topic 'Fish stock assessment – Mathematical models'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Fish stock assessment – Mathematical models"

1

Schnute, Jon T., and Laura J. Richards. "Use and abuse of fishery models." Canadian Journal of Fisheries and Aquatic Sciences 58, no. 1 (2001): 10–17. http://dx.doi.org/10.1139/f00-150.

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Recent failures of important fish stocks give mathematical models a poor reputation as tools for fishery management. This paper examines the role of models in fish stock assessment and identifies reasons why they can fail. Starting with laws of arithmetic, models attempt to relate observed data to unknown quantities, such as the stock biomass and abundance. Typically, the number of unknowns greatly exceeds the number of observations, and models must impose hypothetical constraints to give useful estimates. We use the word "fishmetic" (rhymes with arithmetic) to represent uncertainty in the con
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2

Millar, Colin P., Ernesto Jardim, Finlay Scott, Giacomo Chato Osio, Iago Mosqueira, and Nekane Alzorriz. "Model averaging to streamline the stock assessment process." ICES Journal of Marine Science 72, no. 1 (2014): 93–98. http://dx.doi.org/10.1093/icesjms/fsu043.

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Abstract The current fish stock assessment process in Europe can be very resource- and time-intensive. The scientists involved require a very particular set of skills, acquired over their career, drawing from biology, ecology, statistics, mathematical modelling, oceanography, fishery policy, and computing. There is a particular focus on producing a single “best” stock assessment model, but as fishery science advances, there are clear needs to address a range of hypotheses and uncertainties, from large-scale issues such as climate change to specific ones, such as high observation error on young
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Orlowski, Andrzej. "Experimental verification of the acoustic characteristics of the clupeoid diel cycle in the Baltic." ICES Journal of Marine Science 62, no. 6 (2005): 1180–90. http://dx.doi.org/10.1016/j.icesjms.2005.02.013.

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Abstract This paper describes the results of a short-term experiment using measurements of sv from the diel, spatial, clupeoid distribution in the southern Baltic. The aim of the experiment was to verify fish-behaviour characteristics measured over the period from 1995 to 2001. It was also intended to estimate the dynamics of fish behaviour over one separate diel cycle. The studies were based on a 24 h continuous integration of fish echoes using an EY500 echosounder at 38 kHz. Measurements were carried out by RV “Baltica” travelling along the sides of a square of 4 nautical miles at a constant
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Xiao, Yongshun. "A general theory of fish stock assessment models." Ecological Modelling 128, no. 2-3 (2000): 165–80. http://dx.doi.org/10.1016/s0304-3800(00)00199-x.

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5

Vasilyev, D. A. "Diagnostics of the results of models application for fish stock assessment." Problems of fisheries 16, no. 4 (2015): 460–64. http://dx.doi.org/10.36038/0234-2774-2015-16-4-460-464.

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Albertsen, Christoffer Moesgaard, Anders Nielsen, and Uffe Høgsbro Thygesen. "Connecting single-stock assessment models through correlated survival." ICES Journal of Marine Science 75, no. 1 (2017): 235–44. http://dx.doi.org/10.1093/icesjms/fsx114.

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Abstract Fisheries management is mainly conducted via single-stock assessment models assuming that fish stocks do not interact, except through assumed natural mortalities. Currently, the main alternative is complex ecosystem models which require extensive data, are difficult to calibrate, and have long run times. We propose a simple alternative. In three case studies each with two stocks, we improve the single-stock models, as measured by Akaike information criterion, by adding correlation in the cohort survival. To limit the number of parameters, the correlations are parameterized through the
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7

Xiao, Yongshun. "Two simple approaches to use of production models in fish stock assessment." Fisheries Research 34, no. 1 (1998): 77–86. http://dx.doi.org/10.1016/s0165-7836(97)00064-7.

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8

Schnute, Jon T., and Ray Hilborn. "Analysis of Contradictory Data Sources in Fish Stock Assessment." Canadian Journal of Fisheries and Aquatic Sciences 50, no. 9 (1993): 1916–23. http://dx.doi.org/10.1139/f93-214.

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Fisheries stock assessments sometimes prove, in retrospect, to be wrong. Errors may be due to poor model assumptions or to data that do not reflect the biological process of interest. We develop a method that formally admits the possibility of such errors. Likelihood functions derived from this method indicate greater uncertainty in parameter values than conventional likelihoods, whose derivations presume that models correctly describe the observed data. The problem of uncertainty is particularly acute when more than one data source is available and different data sets provide contradictory pa
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9

Kuparinen, Anna, Samu Mäntyniemi, Jeffrey A. Hutchings, and Sakari Kuikka. "Increasing biological realism of fisheries stock assessment: towards hierarchical Bayesian methods." Environmental Reviews 20, no. 2 (2012): 135–51. http://dx.doi.org/10.1139/a2012-006.

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Excessively high rates of fishing mortality have led to rapid declines of several commercially important fish stocks. To harvest fish stocks sustainably, fisheries management requires accurate information about population dynamics, but the generation of this information, known as fisheries stock assessment, traditionally relies on conservative and rather narrowly data-driven modelling approaches. To improve the information available for fisheries management, there is a demand to increase the biological realism of stock-assessment practices and to better incorporate the available biological kno
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Deroba, J. J., D. S. Butterworth, R. D. Methot, et al. "Simulation testing the robustness of stock assessment models to error: some results from the ICES strategic initiative on stock assessment methods." ICES Journal of Marine Science 72, no. 1 (2014): 19–30. http://dx.doi.org/10.1093/icesjms/fst237.

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Abstract The World Conference on Stock Assessment Methods (July 2013) included a workshop on testing assessment methods through simulations. The exercise was made up of two steps applied to datasets from 14 representative fish stocks from around the world. Step 1 involved applying stock assessments to datasets with varying degrees of effort dedicated to optimizing fit. Step 2 was applied to a subset of the stocks and involved characteristics of given model fits being used to generate pseudo-data with error. These pseudo-data were then provided to assessment modellers and fits to the pseudo-dat
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Dissertations / Theses on the topic "Fish stock assessment – Mathematical models"

1

Northrop, Amanda Rosalind. "Importance of various data sources in deterministic stock assessment models." Thesis, Rhodes University, 2008. http://hdl.handle.net/10962/d1002811.

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In fisheries, advice for the management of fish populations is based upon management quantities that are estimated by stock assessment models. Fisheries stock assessment is a process in which data collected from a fish population are used to generate a model which enables the effects of fishing on a stock to be quantified. This study determined the effects of various data sources, assumptions, error scenarios and sample sizes on the accuracy with which the age-structured production model and the Schaefer model (assessment models) were able to estimate key management quantities for a fish resou
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Caneco, Bruno. "Modelling catch sampling uncertainty in fisheries stock assessment : the Atlantic-Iberian sardine case." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4474.

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The statistical assessment of harvested fish populations, such as the Atlantic-Iberian sardine (AIS) stock, needs to deal with uncertainties inherent in fisheries systems. Uncertainties arising from sampling errors and stochasticity in stock dynamics must be incorporated in stock assessment models so that management decisions are based on realistic evaluation of the uncertainty about the status of the stock. The main goal of this study is to develop a stock assessment framework that accounts for some of the uncertainties associated with the AIS stock that are currently not integrated into stoc
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Errigo, Michael. "Structured Errors in Modeling Fishery Population Dynamics and in Stock Assessment." Fogler Library, University of Maine, 2008. http://www.library.umaine.edu/theses/pdf/ErrigoM2008.pdf.

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Hill, Evelyn June. "Applying statistical and syntactic pattern recognition techniques to the detection of fish in digital images." University of Western Australia. School of Mathematics and Statistics, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0070.

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This study is an attempt to simulate aspects of human visual perception by automating the detection of specific types of objects in digital images. The success of the methods attempted here was measured by how well results of experiments corresponded to what a typical human’s assessment of the data might be. The subject of the study was images of live fish taken underwater by digital video or digital still cameras. It is desirable to be able to automate the processing of such data for efficient stock assessment for fisheries management. In this study some well known statistical pattern classif
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Ernst, Billy. "An investigation on length-based models used in quantitative population modeling /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/5281.

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McCafferty, James Ross. "An assessment of inland fisheries in South Africa using fisheries-dependent and fisheries-independent data sources." Thesis, Rhodes University, 2012. http://hdl.handle.net/10962/d1005072.

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The role of inland fisheries as contributors to local and national economies in developing African countries is well documented. In South Africa, there is increasing interest in inland fisheries as vehicles for achieving national policy objectives including food security, livelihoods provision, poverty alleviation and economic development but there is surprisingly little literature on the history, current status, and potential of inland fishery resources. This lack of knowledge constrains the development of management strategies for ensuring the biological sustainability of these resources and
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Yin, Yanshui. "Sensitivity of the stock synthesis assessment model : a simulation approach." Thesis, 2001. http://hdl.handle.net/1957/28564.

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Stock assessments for many U.S. Pacific coast groundfish stocks are developed using the catch-at-age method known as Stock Synthesis. In this work a simulation package was developed and used to evaluate the sensitivity of the Stock Synthesis program. More specifically, the evaluation focused on the impacts of input data errors and stock characteristics on the accuracy and precision of Synthesis estimates. Factors examined included the length of the time series of data, the rate of natural mortality, the shape of the fishery and survey selectivity curves, the trend in the rate of fishing mortal
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Helu, Siosaia Langitoto. "Statistical model selection criteria and their application to the Stock Synthesis assessment program." Thesis, 1998. http://hdl.handle.net/1957/33648.

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Statistical modeling has evolved around building increasingly more complex models, even though it is common knowledge among statisticians that an optimal model size usually exists for any given data set. Having overly complex models leads to imprecise parameter estimates and tends to increase the subjective role of the modeler, which can distort the perceived characteristics of the system under investigation. One approach for controlling the tendency of contemporary models to increase in complexity and subjectivity is to use model selection criteria that account for these factors. The initial
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McLeod, Kristin. "Risk analysis of a flatfish stock complex : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Mathematics at Massey University." 2010. http://hdl.handle.net/10179/1533.

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The New Zealand Ministry of Fisheries relies on fishery assessments to determine suitable catch quotas for exploited fisheries. Currently, 628 fish stocks are managed in New Zealand using the Quote Management System, which includes the 8 com- mercial flatfish species caught within the Exclusive Economic Zone. These eight species of flatfish, which includes four species of flounder, two species of sole, brill and turbot, are currently managed using a combined catch quota. Since these eight species are managed using a common catch quota, there is concern that some of the individual species may b
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Books on the topic "Fish stock assessment – Mathematical models"

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International Symposium on Fishery Stock Assessment Models for the 21st Century (1997 Anchorage, Alaska). Fishery stock assessment models: Proceedings of the International Symposium on Fishery Stock Assessment Models for the 21st Century, October 8-11, 1997, Anchorage, Alaska. University of Alaska Sea Grant College Program, 1998.

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Cooper, Andrew B. A guide to fisheries stock assessment: From data to recommendations. University of New Hampshire, Sea Grant College Program, 2006.

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Johnston, T. L. (Terry L. ). A numeric scoring matrix for use in the rapid assessment of extinction vulnerability of marine fish species in the NAFO regions 4VWX, 5Zc. Fisheries and Oceans Canada, 2004.

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Pella, J. Search algorithms for computing stock composition of a mixture from traits of individuals by maximum likelihood / by J. Pella, M. Masuda, and S. Nelson. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Alaska Fisheries Science Center, 1996.

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Guðmundsson, Guðmundur. Time series models of fishing mortality rates. Science Institute, University of Iceland, 1987.

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International Council for the Exploration of the Sea. Working Group on Methods of Fish Stock Assessments. Report of the Working Group on Methods of Fish Stock Assessments. International Council for the Exploration of the Sea, 1985.

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Workshop on Incorporating Uncertainty into Fishery Models (1999 Jekyll Island, Ga.). Incorporating uncertainty into fishery models: Proceedings of the Workshop Incorporating Uncertainty into Fishery Models held at Jekyll Island, Georgia, USA, 28-29 June 1999. American Fisheries Society, 2002.

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International Council for the Exploration of the Sea. Working Group on Methods of Fish Stock Assessments. Report of the Working Group on Methods of Fish Stock Assessments: St. John's, Newfoundland, Canada, 20-27 June 1991. International Council for the Exploration of the Sea, 1995.

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Weng, Kevin C. M. Analysis of the fisheries for two pelagic Carangids in Hawaii. University of Hawaii, Joint Institute for Marine and Atmospheric Research, 2000.

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Contor, Craig R. Assessment of COWFISH for predicting trout populations in grazed watersheds of the Intermountain West. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1991.

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Book chapters on the topic "Fish stock assessment – Mathematical models"

1

Pope, John G. "Stock Assessment Models and Predictions of Catch and Biomass." In Fish Reproductive Biology. John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118752739.ch7.

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Schnute, JT, LJ Richards, and N. Olsen. "Statistics, Software, and Fish Stock Assessment." In Fishery Stock Assessment Models. Alaska Sea Grant, University of Alaska Fairbanks, 1998. http://dx.doi.org/10.4027/fsam.1998.09.

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Booth, AJ. "Spatial Analysis of Fish Distribution and Abundance Patterns: A GIS Approach." In Fishery Stock Assessment Models. Alaska Sea Grant, University of Alaska Fairbanks, 1998. http://dx.doi.org/10.4027/fsam.1998.39.

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Heifetz, j., and TJ Quinn II. "Dynamics of a Migratory Fish Population with Applications to the Management of Sablefish." In Fishery Stock Assessment Models. Alaska Sea Grant, University of Alaska Fairbanks, 1998. http://dx.doi.org/10.4027/fsam.1998.12.

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Gascuel, D., G. Fontenelle, C. Maguer, and A. Biseau. "Consistency of Stock Assessment and Evaluation of Fisheries Management for European Fish Stocks, 1983-1995." In Fishery Stock Assessment Models. Alaska Sea Grant, University of Alaska Fairbanks, 1998. http://dx.doi.org/10.4027/fsam.1998.51.

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Vasconcellos, M., and T. Pitcher. "Harvest Control for Schooling Fish Stocks under Cyclic Oceanographic Regimes: A Case for Precaution and Gathering Auxiliary Information." In Fishery Stock Assessment Models. Alaska Sea Grant, University of Alaska Fairbanks, 1998. http://dx.doi.org/10.4027/fsam.1998.47.

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"Incorporating Uncertainty into Fishery Models." In Incorporating Uncertainty into Fishery Models, edited by Gregg T. Waugh and Susan Shipman. American Fisheries Society, 2002. http://dx.doi.org/10.47886/9781888569315.ch3.

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<em>Abstract.—</em> This paper focuses on greater amberjack management in the South Atlantic region, with an examination of the deficiencies in data collection and resulting impacts on the amberjack stock assessment and management decisions. This is a case study of uncertainty leading to more conservative, risk-averse management. Amberjack management in the South Atlantic is illustrative of the debate of whether you do what you can with what you have versus not doing anything until you have good information. Ultimately one has to ask what are the consequences to resource management. While National Standard #2 says you must base your decisions on the best scientific information, scientific information entails more than just the stock assessment results. It entails other biological information as well as social and economic information. The South Atlantic Fishery Management Council went as far as we could without opening ourselves up to a legal challenge. We attempted to balance concern for the resource, Florida’s desire to be more conservative, skepticism about the data, and the fishermen’s desire to fish.
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"Incorporating Uncertainty into Fishery Models." In Incorporating Uncertainty into Fishery Models, edited by Gregg T. Waugh and Susan Shipman. American Fisheries Society, 2002. http://dx.doi.org/10.47886/9781888569315.ch3.

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<em>Abstract.—</em> This paper focuses on greater amberjack management in the South Atlantic region, with an examination of the deficiencies in data collection and resulting impacts on the amberjack stock assessment and management decisions. This is a case study of uncertainty leading to more conservative, risk-averse management. Amberjack management in the South Atlantic is illustrative of the debate of whether you do what you can with what you have versus not doing anything until you have good information. Ultimately one has to ask what are the consequences to resource management. While National Standard #2 says you must base your decisions on the best scientific information, scientific information entails more than just the stock assessment results. It entails other biological information as well as social and economic information. The South Atlantic Fishery Management Council went as far as we could without opening ourselves up to a legal challenge. We attempted to balance concern for the resource, Florida’s desire to be more conservative, skepticism about the data, and the fishermen’s desire to fish.
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"Grenadiers of the World Oceans: Biology, Stock Assessment, and Fisheries." In Grenadiers of the World Oceans: Biology, Stock Assessment, and Fisheries, edited by N. J. King and I. G. Priede. American Fisheries Society, 2008. http://dx.doi.org/10.47886/9781934874004.ch11.

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<em>Abstract.</em>—The abyssal grenadier <em>Coryphaenoides armatus </em>(Hector 1875), family Macrouridae, is a benthopelagic deep-sea species with a depth range of 282–5,180 m, with most observations and captures at depths of 2,500 m and deeper. It is one of the most abundant grenadier species in the world’s oceans but is absent from the Mediterranean Sea, Arctic Ocean, Indian Ocean north of 45°S, and has not yet been recorded from the Southern Ocean south of the Antarctic Polar Front. Typical total lengths range from 20 to 80 cm and the maximum recorded is 102 cm. Dietary items include mesopelagic fishes and cephalopods, and benthic items such as crustaceans and bivalves. The abyssal grenadier is well documented as an opportunistic scavenger and this propensity has been used to attract individuals to bait within view of submersibles and underwater camera systems to make <em>in situ </em>observations of behavior. Baited cameras have been in use to observe deep-sea scavengers since the late 1960s, with the first confirmed observation of the abyssal grenadier in 1971. More recently, baited photographic autonomous landers have been used to gain data on respiration rates, size frequency, and swimming velocity. In addition, models have been developed and refined to estimate local densities and to quantify the number of fish present at a food fall and their staying time at bait. This ultimately means that baited-camera-derived abundance estimates can be produced, and that the numbers of scavenging grenadiers and how long they remain at a small food fall can be linked to the productivity of the overlying surface waters.
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"Grenadiers of the World Oceans: Biology, Stock Assessment, and Fisheries." In Grenadiers of the World Oceans: Biology, Stock Assessment, and Fisheries, edited by N. J. King and I. G. Priede. American Fisheries Society, 2008. http://dx.doi.org/10.47886/9781934874004.ch11.

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<em>Abstract.</em>—The abyssal grenadier <em>Coryphaenoides armatus </em>(Hector 1875), family Macrouridae, is a benthopelagic deep-sea species with a depth range of 282–5,180 m, with most observations and captures at depths of 2,500 m and deeper. It is one of the most abundant grenadier species in the world’s oceans but is absent from the Mediterranean Sea, Arctic Ocean, Indian Ocean north of 45°S, and has not yet been recorded from the Southern Ocean south of the Antarctic Polar Front. Typical total lengths range from 20 to 80 cm and the maximum recorded is 102 cm. Dietary items include mesopelagic fishes and cephalopods, and benthic items such as crustaceans and bivalves. The abyssal grenadier is well documented as an opportunistic scavenger and this propensity has been used to attract individuals to bait within view of submersibles and underwater camera systems to make <em>in situ </em>observations of behavior. Baited cameras have been in use to observe deep-sea scavengers since the late 1960s, with the first confirmed observation of the abyssal grenadier in 1971. More recently, baited photographic autonomous landers have been used to gain data on respiration rates, size frequency, and swimming velocity. In addition, models have been developed and refined to estimate local densities and to quantify the number of fish present at a food fall and their staying time at bait. This ultimately means that baited-camera-derived abundance estimates can be produced, and that the numbers of scavenging grenadiers and how long they remain at a small food fall can be linked to the productivity of the overlying surface waters.
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