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Andre E Punt - One of the best experts on this subject based on the ideXlab platform.
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spatial structure theory estimation and application in Stock Assessment models
Fisheries Research, 2020Co-Authors: Steven X. Cadrin, Mark N Maunder, Andre E PuntAbstract:Abstract All fish populations and fisheries exhibit spatial structure, but accounting for the spatial patterns is commonly ignored in most Stock Assessments. The Center for the Advancement of Population Assessment Methodology (CAPAM) hosted a workshop on “Spatial Stock Assessment Models” (La Jolla, CA, USA; October 1-5, 2018), and this special volume includes twelve papers on various aspects of spatial Stock Assessments, including Stock identification, development and application of spatially-structured models, simulation studies, and management implications. The workshop and these contributions demonstrate that Stock Assessments require adequate representation of spatial scope and structure, and tag-integrated models generally perform best, but spatially-structured Stock Assessments pose several challenges, and remain rarely applied as the scientific basis for fishery management.
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essential features of the next generation integrated fisheries Stock Assessment package a perspective
Fisheries Research, 2020Co-Authors: Andre E Punt, Mark N Maunder, Alistair Dunn, Bjarki þor Elvarsson, John M Hampton, Simon D Hoyle, Richard D Methot, Anders NielsenAbstract:Abstract Integrated analysis (or integrated population modelling) methods have become the preferred approach for conducting Stock Assessments, and providing the basis for management advice for fish and invertebrate Stocks since the publication of a seminal paper by Fournier and Archibald in 1982. Methods to assess fish Stocks based on single-species, single-area, age-structured models are now standard, with the major debates associated with these models related to data choice, model configuration assumptions, and data weighting. However, the current generation of Stock Assessment packages is not addressing all of the needs of Stock Assessment analysts and managers. A major challenge for any next-generation Stock Assessment package is the set of extensions needed to assess Stocks that do not satisfy the ‘well-mixed single-Stock’ paradigm. In addition, the next-generation Stock Assessment package needs to: (a) be able to capture age and size/stage dynamics simultaneously yet computationally efficiently, (b) scale from data-rich to data-poor, (c) include some multi-species capability, and (d) more appropriately deal with temporal variation (e.g., random effects and state-space models). In relation to data, there is a need to ensure that the next-generation Stock Assessment package better handles tagging data (age-size/stage models may help in this regard), in particular, to be able to use close-kin mark-recapture data. Efficient methods are needed to share parameter priors among Stocks (satisfying the promise of the ‘Robin Hood’ paradigm). The next-generation Stock Assessment package needs to have associated appropriate training programs and documentation. Adoption of such a package will be facilitated by a data entry system that is well-documented, does not require specification of inputs that will not be used in an application, has an expert system to configure default settings based on best practices, and has associated code to automatically produce diagnostic statistics. Some technical challenges that have plagued Stock Assessment for decades warrant continued attention (at the theoretical and applied level) such as automatic data weighting and tuning, how to handle spatial and Stock structure, improved coding to facilitate application of state-of-the-art methods for quantifying uncertainty, and adoption of true state-space formulations to allow more parameters to be treated as random effects. Future needs for features cannot be anticipated, so the key design consideration for the next-generation Stock Assessment package is to be flexible and modifiable to meet the requirements of analysts and users.
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spatial Stock Assessment methods a viewpoint on current issues and assumptions
Fisheries Research, 2019Co-Authors: Andre E PuntAbstract:Abstract Spatial Stock Assessments have been developed to address violations of the dynamic pool assumption that the region to be assessed contains a single homogeneous Stock. The possibility of such violation is often evident in data that suggest different trends in abundance or catch / survey age- / size-structure among areas that cannot be explained simply by the fishing history among areas. Currently, most Stock Assessments account for spatial structure using the ‘areas-as-fleets’ approach in which fishery or survey selectivity and catchability are assumed to differ spatially. However, several simulation studies suggest that adopting spatial approaches to Stock Assessment will improve estimation performance compared to the areas-as-fleets approach or ignoring spatial structure when conducting Stock Assessments, although at the cost of a larger number of estimable parameters. Spatial approaches to Stock Assessment and the provision of management advice have been available since the 1950s. However, spatial Stock Assessments only became adopted for management purposes in the 1990s, with the widespread adoption of the “integrated approach” to Stock Assessment, which allowed the use of multiple data sets for parameter estimation. The number of spatial Stock Assessments is now increasing rapidly. This paper outlines some of the key decisions that need to be made when conducting a spatial Stock Assessment (number of areas, how to model recruitment, movement, growth and dispersal, and model parameterization).
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How many of Australia's Stock Assessments can be conducted using Stock Assessment packages?
Marine Policy, 2016Co-Authors: Catherine M. Dichmont, Roy Deng, Andre E PuntAbstract:Most of the Stock Assessments conducted in the USA and in New Zealand are based on packages that have been developed for generic use, are well documented, and have been tested using simulation. However, this is not the case for Assessments conducted in Australia and many other countries. This paper reviews all of the model-based Stock Assessments for Australian fisheries to evaluate how many of these Assessments could have been conducted using the publicly-available Stock Assessment packages used widely in the USA and New Zealand. The 76 model-based Assessments reflect 37% of the 2013 catch recorded in Australia's Status for Key Australian Fish Stocks Reports (or 34% of the total catch in 2013). All but 18 (or 24 if full rather than approximate age-size-structured models need to be used) of the Stock Assessments could have been conducted using Stock Assessment packages used in the United States and New Zealand. Adoption and use of packages for more Stocks in Australia should increase the likelihood that results are based on correctly-coded models whose estimation performance is widely understood, reduce the time needed to conduct Assessments, and speed up the peer-review process. The availability of training, manuals, and example data sets for Stock Assessment packages should partially address their additional complexity. Additional benefits, in terms of numbers of assessed Stocks, could occur if Australian Stock Assessment scientists develop a forum to collaborate and share methods. These results are applicable to many other jurisdictions that undertake Stock Assessments.
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can a spatially structured Stock Assessment address uncertainty due to closed areas a case study based on pink ling in australia
Fisheries Research, 2016Co-Authors: Andre E Punt, M Haddon, Richard L Little, Geoffrey N TuckAbstract:Spatial structure in biological characteristics and exploitation rates impact the performance of Stock Assessment methods used to estimate the status of fish Stocks relative to target and limit reference points. Spatially-structured Stock Assessment methods can reduce the bias and imprecision in the estimates of management-related model outputs. However, their performance has only recently been evaluated formally, in particular when some of the area fished is closed. In order to evaluate the effects of closed areas and spatial variation in growth and exploitation rate when estimating spawning biomass, a spatially-explicit operating model was developed to simulate spatial data, and five configurations of the Stock Assessment package Stock Synthesis (three of which were spatially structured) were applied. The bias in estimates of spawning Stock biomass associated with spatially-aggregated Assessment methods increases in the presence of closed areas while these biases can be reduced (or even eliminated) by applying appropriately constructed spatially-structured Stock Assessments. The performance of spatially-aggregated Assessments when estimating spawning Stock biomass is found to depend on the interactions among spatial variation in growth, in exploitation rate, and in knowledge of the spatial areas over which growth and exploitation rate are homogeneous.
Ray Hilborn - One of the best experts on this subject based on the ideXlab platform.
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measuring uncertainty in fisheries Stock Assessment the delta method bootstrap and mcmc
Fish and Fisheries, 2013Co-Authors: Arni Magnusson, Andre E Punt, Ray HilbornAbstract:Fisheries management depends on reliable quantification of uncertainty for decision-making. We evaluate which uncertainty method can be expected to perform best for fisheries Stock Assessment. The method should generate confidence intervals that are neither too narrow nor too wide, in order to cover the true value of estimated quantities with a probability matching the claimed confidence level. This simulation study compares the performance of the delta method, the bootstrap, and Markov chain Monte Carlo (MCMC). A statistical catch-at-age model is fitted to 1000 simulated datasets, with varying recruitment and observation noise. Six reference points are estimated, and confidence intervals are constructed across a range of significance levels. Overall, the delta method and MCMC performed considerably better than the bootstrap, and MCMC was the most reliable method in terms of worst-case performance, for our relatively data-rich scenario and catch-at-age model, which was not subject to substantial model misspecification. All three methods generated too narrow confidence intervals, underestimating the true uncertainty. Bias correction improved the bootstrap performance, but not enough to match the performance of the delta method and MCMC. We recommend using MCMC as the default method for quantifying uncertainty in fisheries Stock Assessment, although the delta method is the fastest to apply, and the bootstrap is useful to diagnose estimator bias.
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fisheries Stock Assessment and decision analysis the bayesian approach
Reviews in Fish Biology and Fisheries, 1997Co-Authors: Andre E Punt, Ray HilbornAbstract:The Bayesian approach to Stock Assessment determines the probabilities of alternative hypotheses using information for the Stock in question and from inferences for other Stocks/species. These probabilities are essential if the consequences of alternative management actions are to be evaluated through a decision analysis. Using the Bayesian approach to Stock Assessment and decision analysis it becomes possible to admit the full range of uncertainty and use the collective historical experience of fisheries science when estimating the consequences of proposed management actions. Recent advances in computing algorithms and power have allowed methods based on the Bayesian approach to be used even for fairly complex Stock Assessment models and to be within the reach of most Stock Assessment scientists. However, to avoid coming to ill-founded conclusions, care must be taken when selecting prior distributions. In particular, selection of priors designed to be noninformative with respect to quantities of interest to management is problematic. The arguments of the paper are illustrated using New Zealand's western Stock of hoki, Macruronus novaezelandiae (Merlucciidae) and the Bering--Chukchi--Beaufort Seas Stock of bowhead whales as examples
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Analysis of contradictory data sources in fish Stock Assessment
Canadian Journal of Fisheries and Aquatic Sciences, 1993Co-Authors: Jon T. Schnute, Ray HilbornAbstract: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 parameter estimates. Traditional methods of Stock Assessment involve weighted averages of the contradictory data, and these generally produce parameter estimates intermediate to those obtained from the data sets individually. We demonstrate that, when model or data errors are considered, the most likely parameter values are not intermediary to conflicting values; instead, they occur at one of the apparent extremes. We ...
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Current Trends in Including Risk and Uncertainty in Stock Assessment and Harvest Decisions
Canadian Journal of Fisheries and Aquatic Sciences, 1993Co-Authors: Ray Hilborn, Ellen K. Pikitch, Robert C. FrancisAbstract:Many fisheries management bodies are considering methods for explicit consideration of uncertainty and risk in harvest decisions. We propose a two-step process where, in the first step, a Stock Assessment group determines the possible biological and economic "states" of the fishery and evaluates the expected outcomes of different possible management actions for each possible "state." The evaluation may be summarized in a tabular form, or it can take the form of a computer model. In the second step, a decision-making body, consisting of government, public, and user-group representatives, examines the risks and benefits of each possible management action, given the possible "states" of the fishery, and formulates a decision. This proposal differs from many current Stock Assessment and management procedures in that (1) the Stock Assessment group makes no recommendations about catch levels, (2) the Stock Assessment group does not attempt to produce a "best" estimate of Stock condition, and (3) the decision gr...
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Role of Stock Assessment in Fisheries Management
Quantitative Fisheries Stock Assessment, 1992Co-Authors: Ray Hilborn, Carl J WaltersAbstract:Stock Assessment involves the use of various statistical and mathematical calculations to make quantitative predictions about the reactions of fish populations to alternative management choices. Two key words are critical in this thumbnail definition: quantitativeand choices. The basic concern of Stock Assessment is to go beyond the obvious qualitative predictions that any student of nature should be able to make about natural limits to production, risks of overfishing spawning populations, the need to allow fish to grow to a reasonable size before they are harvested, and so forth. Furthermore, it does not make sense to engage in the risky and often embarrassing business of quantitative prediction in settings where there are no management choices to be made in the first place, except perhaps as an aid to scientific thinking and hypothesis formulation
Mark N Maunder - One of the best experts on this subject based on the ideXlab platform.
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spatial structure theory estimation and application in Stock Assessment models
Fisheries Research, 2020Co-Authors: Steven X. Cadrin, Mark N Maunder, Andre E PuntAbstract:Abstract All fish populations and fisheries exhibit spatial structure, but accounting for the spatial patterns is commonly ignored in most Stock Assessments. The Center for the Advancement of Population Assessment Methodology (CAPAM) hosted a workshop on “Spatial Stock Assessment Models” (La Jolla, CA, USA; October 1-5, 2018), and this special volume includes twelve papers on various aspects of spatial Stock Assessments, including Stock identification, development and application of spatially-structured models, simulation studies, and management implications. The workshop and these contributions demonstrate that Stock Assessments require adequate representation of spatial scope and structure, and tag-integrated models generally perform best, but spatially-structured Stock Assessments pose several challenges, and remain rarely applied as the scientific basis for fishery management.
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essential features of the next generation integrated fisheries Stock Assessment package a perspective
Fisheries Research, 2020Co-Authors: Andre E Punt, Mark N Maunder, Alistair Dunn, Bjarki þor Elvarsson, John M Hampton, Simon D Hoyle, Richard D Methot, Anders NielsenAbstract:Abstract Integrated analysis (or integrated population modelling) methods have become the preferred approach for conducting Stock Assessments, and providing the basis for management advice for fish and invertebrate Stocks since the publication of a seminal paper by Fournier and Archibald in 1982. Methods to assess fish Stocks based on single-species, single-area, age-structured models are now standard, with the major debates associated with these models related to data choice, model configuration assumptions, and data weighting. However, the current generation of Stock Assessment packages is not addressing all of the needs of Stock Assessment analysts and managers. A major challenge for any next-generation Stock Assessment package is the set of extensions needed to assess Stocks that do not satisfy the ‘well-mixed single-Stock’ paradigm. In addition, the next-generation Stock Assessment package needs to: (a) be able to capture age and size/stage dynamics simultaneously yet computationally efficiently, (b) scale from data-rich to data-poor, (c) include some multi-species capability, and (d) more appropriately deal with temporal variation (e.g., random effects and state-space models). In relation to data, there is a need to ensure that the next-generation Stock Assessment package better handles tagging data (age-size/stage models may help in this regard), in particular, to be able to use close-kin mark-recapture data. Efficient methods are needed to share parameter priors among Stocks (satisfying the promise of the ‘Robin Hood’ paradigm). The next-generation Stock Assessment package needs to have associated appropriate training programs and documentation. Adoption of such a package will be facilitated by a data entry system that is well-documented, does not require specification of inputs that will not be used in an application, has an expert system to configure default settings based on best practices, and has associated code to automatically produce diagnostic statistics. Some technical challenges that have plagued Stock Assessment for decades warrant continued attention (at the theoretical and applied level) such as automatic data weighting and tuning, how to handle spatial and Stock structure, improved coding to facilitate application of state-of-the-art methods for quantifying uncertainty, and adoption of true state-space formulations to allow more parameters to be treated as random effects. Future needs for features cannot be anticipated, so the key design consideration for the next-generation Stock Assessment package is to be flexible and modifiable to meet the requirements of analysts and users.
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Modeling temporal variation in recruitment in fisheries Stock Assessment: A review of theory and practice
Fisheries Research, 2019Co-Authors: Mark N Maunder, James T. ThorsonAbstract:Abstract Recruitment is one of the most variable biological processes driving fisheries population dynamics and the characteristics and drivers of the variation differ among Stocks. Understanding, describing (modeling), estimating, and predicting (forecasting) this variation is essential to appropriate Stock Assessment and fisheries management. Most modern Stock Assessments estimate annual variation in recruitment, particularly those that include age or size composition data. Changes over time are often partitioned into those related to the spawning biomass due to density dependence (the Stock-recruitment relationship) and those related to other factors such as environmental conditions. Several methods have been used to model recruitment inside Stock Assessment models, and they differ by how recruitment is represented and how statistical inference is conducted. Virtual population analyses make few statistical assumptions about recruitment and instead calculate recruitment as the sum of the mortality-adjusted catches of a cohort while surplus production models generally imply a Stock-recruitment relationship in their production function. Integrated statistical Stock Assessment models have modelled recruitment using a Stock-recruitment relationship, autocorrelation, a function of covariates, regime shifts, or a combination of these. Statistical inference has typically been conducted using state-space (hierarchical) models, whether using Bayesian or maximum likelihood methods, or inferences are approximated using penalized likelihood approaches, which may not be statistically reliable. All these methods have specific issues that need to be addressed and there are tradeoffs in their use. Future temporal variation also needs to be considered when providing management advice. We review the theory and practice of modelling temporal variation in recruitment in fisheries Stock Assessment, provide advice on good practices, and recommend important research.
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using movement data from electronic tags in fisheries Stock Assessment a review of models technology and experimental design
Fisheries Research, 2015Co-Authors: Tim Sippel, Pierre Kleiber, Mark N Maunder, Simon D Hoyle, Paige J Eveson, Benjamin Galuardi, Felipe Carvalho, Vardis Tsontos, Alexandre Airesdasilva, Simon J NicolAbstract:Abstract Tag-recapture data have long been important data sources for fisheries management, with the capacity to inform abundance, mortality, growth and movement within Stock Assessments. Historically, this role has been fulfilled with low-tech conventional tags, but the relatively recent and rapid development of electronic tags has dramatically increased the potential to collect more high quality data. Stock Assessment models have also been evolving in power and complexity recently, with the ability to integrate multiple data sources into unified spatially explicit frameworks. However, electronic tag technologies and Stock Assessment models have developed largely independently, and frameworks for incorporating these valuable data in contemporary Stock Assessments are nascent, at best. Movement dynamics of large pelagic species have been problematic to resolve in modern Assessments, and electronic tags offer new opportunities to resolve some of these issues. Pragmatic ways of modeling movement are often not obvious, and basic research into discrete and continuous processes, for example, is ongoing. Experimental design of electronic tagging research has been driven mostly by ecological and biological questions, rather than optimized for Stock Assessment, and this is probably a complicating factor in integration of the data into Assessment models. A holistic overview of the current state of Assessment models, electronic tag technologies, and experimental design is provided here, with the aim to provide insight into how Stock Assessment and electronic tagging research can be conducted most effectively together.
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contemporary fisheries Stock Assessment many issues still remain
Ices Journal of Marine Science, 2015Co-Authors: Mark N Maunder, Kevin R PinerAbstract:Interpretation of data used in fisheries Assessment and management requires knowledge of population (e.g. growth, natural mortality, and recruitment), fisheries (e.g. selectivity), and sampling processes. Without this knowledge, assumptions need to be made, either implicitly or explicitly based on the methods used. Incorrect assumptions can have a substantial impact on Stock Assessment results and management advice. Unfortunately, there is a lack of understanding of these processes for most, if not all, Stocks and even for processes that have traditionally been assumed to be well understood (e.g. growth and selectivity). We use information content of typical fisheries data that is informative about absolute abundance to illustrate some of the main issues in fisheries Stock Assessment. We concentrate on information about absolute abundance from indices of relative abundance combined with catch, and age and length-composition data and how the information depends on knowledge of population, fishing, and sampling processes. We also illustrate two recently developed diagnostic methods that can be used to evaluate the absolute abundance information content of the data. Finally, we discuss some of the reasons for the slowness of progress in fisheries Stock Assessment.
Anders Nielsen - One of the best experts on this subject based on the ideXlab platform.
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essential features of the next generation integrated fisheries Stock Assessment package a perspective
Fisheries Research, 2020Co-Authors: Andre E Punt, Mark N Maunder, Alistair Dunn, Bjarki þor Elvarsson, John M Hampton, Simon D Hoyle, Richard D Methot, Anders NielsenAbstract:Abstract Integrated analysis (or integrated population modelling) methods have become the preferred approach for conducting Stock Assessments, and providing the basis for management advice for fish and invertebrate Stocks since the publication of a seminal paper by Fournier and Archibald in 1982. Methods to assess fish Stocks based on single-species, single-area, age-structured models are now standard, with the major debates associated with these models related to data choice, model configuration assumptions, and data weighting. However, the current generation of Stock Assessment packages is not addressing all of the needs of Stock Assessment analysts and managers. A major challenge for any next-generation Stock Assessment package is the set of extensions needed to assess Stocks that do not satisfy the ‘well-mixed single-Stock’ paradigm. In addition, the next-generation Stock Assessment package needs to: (a) be able to capture age and size/stage dynamics simultaneously yet computationally efficiently, (b) scale from data-rich to data-poor, (c) include some multi-species capability, and (d) more appropriately deal with temporal variation (e.g., random effects and state-space models). In relation to data, there is a need to ensure that the next-generation Stock Assessment package better handles tagging data (age-size/stage models may help in this regard), in particular, to be able to use close-kin mark-recapture data. Efficient methods are needed to share parameter priors among Stocks (satisfying the promise of the ‘Robin Hood’ paradigm). The next-generation Stock Assessment package needs to have associated appropriate training programs and documentation. Adoption of such a package will be facilitated by a data entry system that is well-documented, does not require specification of inputs that will not be used in an application, has an expert system to configure default settings based on best practices, and has associated code to automatically produce diagnostic statistics. Some technical challenges that have plagued Stock Assessment for decades warrant continued attention (at the theoretical and applied level) such as automatic data weighting and tuning, how to handle spatial and Stock structure, improved coding to facilitate application of state-of-the-art methods for quantifying uncertainty, and adoption of true state-space formulations to allow more parameters to be treated as random effects. Future needs for features cannot be anticipated, so the key design consideration for the next-generation Stock Assessment package is to be flexible and modifiable to meet the requirements of analysts and users.
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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, 2015Co-Authors: Jonathan J Deroba, Steven X. Cadrin, Richard D Methot, Anders Nielsen, Doug S Butterworth, J A A De Oliveira, Carmen Fernandez, Mark Dickeycollas, Christopher M Legault, James N. IanelliAbstract: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-data provided consistency checks within (self-tests) and among (cross-tests) Assessment models. Although trends in biomass were often similar across models, the scaling of absolute biomass was not consistent across models. Similar types of models tended to perform similarly (e.g. age based or production models). Self-testing and cross-testing of models are a useful diagnostic approach, and suggested that estimates in the most recent years of time-series were the least robust. Results from the simulation exercise provide a basis for guidance on future large-scale simulation experiments and demonstrate the need for strategic investments in the evaluation and development of Stock Assessment methods.
Sakari Kuikka - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of standard ICES Stock Assessment and Bayesian Stock Assessment in the light of uncertainty: North Sea herring as an example
2020Co-Authors: Samu Mantyniemi, Richard M. Hillary, Sakari KuikkaAbstract:The mainstream ICES Stock Assessment methods were evaluated in their conceptual ability to provide quantitative measures of uncertainty about variables of management interest. Probability statements generated about future of the Stock were found to be conceptually inconsistent with the statistical methods used. The Bayesian approach to statistical inference is recommended to substitute the current uncertainty methods in order to achieve conceptually consistent probability statements about the consequences of alternative management actions.
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incorporating stakeholders knowledge to Stock Assessment central baltic herring
Canadian Journal of Fisheries and Aquatic Sciences, 2013Co-Authors: Samu Mantyniemi, Paivi Elisabet Haapasaari, Sakari Kuikka, Raimo Parmanne, Maiju Lehtiniemi, Joni KaitarantaAbstract:We present a method by which the knowledge of stakeholders can be taken into account in Stock Assessment. The approach consists of a structured interview process followed by quantitative modelling of the answers. The outcome is a set of probability models, each describing the views of different stakeholders. Individual models are then merged to a large model by applying the techniques of Bayesian model averaging, and this model is conditioned on Stock Assessment data. As a result, the views of interviewed stakeholders have been taken into account and weighed based on how well their views are supported by the observed data. We applied this method to the Baltic Sea herring (Clupea harengus) Stock Assessment by interviewing six stakeholders and conditioning the resulting models on Stock Assessment data provided by the International Council for the Exploration of the Sea.
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increasing biological realism of fisheries Stock Assessment towards hierarchical bayesian methods
Environmental Reviews, 2012Co-Authors: Anna Kuparinen, Samu Mantyniemi, Jeffrey A Hutchings, Sakari KuikkaAbstract: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 de- mand to increase the biological realism of Stock-Assessment practices and to better incorporate the available biological knowledge and theory. Here, we explore the development of fisheries Stock-Assessment models with an aim to increasing their biological realism, and focus particular attention on the possibilities provided by the hierarchical Bayesian modelling framework and ways to develop this approach as a means of efficiently incorporating different sources of information to construct more biologically realistic Stock-Assessment models. The main message emerging from our review is that to be able to efficiently improve the biological realism of Stock-Assessment models, fisheries scientists must go beyond the tradi- tional Stock-Assessment data and explore the resources available in other fields of biological research, such as ecology, life- history theory and evolutionary biology, in addition to utilizing data available from other Stocks of the same or comparable species. The hierarchical Bayesian framework provides a way of formally integrating these sources of knowledge into the Stock-Assessment protocol and to accumulate information from multiple sources and over time. Resume : Des taux de mortalite excessifs de poissons ont conduit a des declins rapides de plusieurs Stocks de poissons com- merciaux importants. Afin de recolter de facon durable les Stocks de poissons, l'amenagement des pecheries necessite une in- formation precise sur la dynamique des populations, mais la generation de cette information, connue sous le nom d'evaluation des Stocks de poissons, repose traditionnellement sur la conservation plutot qu'a des approches de modelisation conduites a par- tir de donnees precises. Afin d'ameliorer l'information disponible pour l'amenagement des pecheries, on observe une demande croissante pour l'ameliorer du realisme biologique des pratiques d'evaluation des Stocks et pour mieux incorporer la connais- sance et la theorie biologique disponibles. Les auteurs examinent le developpement de modeles d'evaluation des Stocks de pois- sons avec l'objectif d'augmenter leur realisme biologique, et de porter une attention particuliere sur les possibilites provenant du cadre de modelisation bayesien ainsi que les facons de developper cette approche comme moyen d'incorporer efficacement differentes sources d'information permettant de construire des modeles plus realistes pour l'evaluation des Stocks. Le principal message emergeant de cette revue est a l'effet que pour arriver a ameliorer efficacement le realisme des modeles d'evaluation des Stocks, les specialistes des pecheries doivent aller au-dela des donnees traditionnelles d'evaluation des Stocks et explorer les ressources disponibles dans d'autres champs de recherche biologique, comme l'ecologie, la theorie du cycle vital et la biologie evolutive, en plus d'utiliser les donnees disponibles a partir d'autres Stocks de la meme ou d'especes comparables. Le cadre bayesien hierarchique fournit une facon d'integrer formellement ces sources de connaissances dans le protocole d'evaluation des Stocks et d'accumuler de l'information a partir d'autres sources avec le temps.
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incorporating stakeholders knowledge to Stock Assessment how
Unknown host publication, 2009Co-Authors: Samu Mantyniemi, Paivi Elisabet Haapasaari, Sakari KuikkaAbstract:In this paper we present a method by which the knowledge of stakeholders can be taken into account in Stock Assessment. The approach consists of a structured interview process followed by quantitative modelling of the answers. The outcome is a set of probability models, each describing the views of different stakeholder. Graphical representations of the models can be used to explore and communicate the differences and similarities between the views of stakeholders. Individual models are then merged to a large model by using the techniques of Bayesian model averaging, and this model is conditioned on Stock Assessment data. As result, the model can be used to give management advice where the views of interviewed stakeholders have been taken into account with a weight determined based on how well the views are supported by the observed data. The individual stakeholder models can also be analysed separately to see whether the different views imply different advice or not.
