Embracing uncertainty in fisheries stock assessment using Bayesian hierarchical models
Overfishing and environmental changes impose high risks on the wellbeing of the world's fish stocks. It is commonly acknowledged that fisheries management should be risk averse, following the principles of the precautionary approach, but unfortunately the statistical stock assessment methods of...
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ftunivhelsihelda:oai:helda.helsinki.fi:10138/153255 2023-08-20T04:05:19+02:00 Embracing uncertainty in fisheries stock assessment using Bayesian hierarchical models Pulkkinen, Henni Sillanpää, Mikko University of Helsinki, Faculty of Biological and Environmental Sciences, Department of Environmental Sciences, Akvaattiset tieteet Natural Resources Institute Finland (Luke) Helsingin yliopisto, bio- ja ympäristötieteellinen tiedekunta, ympäristötieteiden laitos Helsingfors universitet, bio- och miljövetenskapliga fakulteten, miljövetenskapliga institutionen Mäntyniemi, Samu Corander, Jukka 2015-02-09T06:38:28Z application/pdf http://hdl.handle.net/10138/153255 eng eng Helsingin yliopisto Helsingfors universitet University of Helsinki URN:ISBN:978-951-51-0559-2 http://hdl.handle.net/10138/153255 URN:ISBN:978-951-51-0560-8 Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. Publikationen är skyddad av upphovsrätten. Den får läsas och skrivas ut för personligt bruk. Användning i kommersiellt syfte är förbjuden. kala- ja kalastusbiologia Text Doctoral dissertation (article-based) Artikkeliväitöskirja Artikelavhandling doctoralThesis 2015 ftunivhelsihelda 2023-07-28T06:34:24Z Overfishing and environmental changes impose high risks on the wellbeing of the world's fish stocks. It is commonly acknowledged that fisheries management should be risk averse, following the principles of the precautionary approach, but unfortunately the statistical stock assessment methods often lack the ability to estimate the uncertainties related to their results. Further challenges arise from the fact that stocks which are in the most desperate need of a stock assessment are often data poor and resources to gather new data from them are scarce. Bayesian statistical inference can be utilized to conduct stock assessments cost-efficiently since these methods provide a formal way to combine information from various sources including databases, literature and expert knowledge. Bayesian inference is essentially a learning process where existing information is combined into a prior distribution, which is further updated with the most recent data. The result, a posterior distribution, expresses the best available knowledge about the phenomenon, including the related uncertainty. Furthermore, Bayesian hierarchical models enable learning between similar units, for example, stocks of the same or related species. This thesis consists of four studies that use Bayesian hierarchical models to improve knowledge in the fisheries stock assessment. Correlations between biological parameters, arising from different life history strategies among species, are utilized in paper [I] so that a data rich set of length-weight parameters can reduce the uncertainty of length-fecundity parameters for a data poor species. In paper [II], similarities between stocks of Atlantic salmon are used to estimate the stock specific key parameters of eggs-to-smolts relationship. A predictive distribution of this key parameter is also estimated, and could be used as an informative prior in a subsequent study of another Atlantic salmon stock. In addition, a hierarchical model is built to study structural uncertainty, estimating posterior ... Doctoral or Postdoctoral Thesis Atlantic salmon Helsingfors Universitet: HELDA – Helsingin yliopiston digitaalinen arkisto |
institution |
Open Polar |
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Helsingfors Universitet: HELDA – Helsingin yliopiston digitaalinen arkisto |
op_collection_id |
ftunivhelsihelda |
language |
English |
topic |
kala- ja kalastusbiologia |
spellingShingle |
kala- ja kalastusbiologia Pulkkinen, Henni Embracing uncertainty in fisheries stock assessment using Bayesian hierarchical models |
topic_facet |
kala- ja kalastusbiologia |
description |
Overfishing and environmental changes impose high risks on the wellbeing of the world's fish stocks. It is commonly acknowledged that fisheries management should be risk averse, following the principles of the precautionary approach, but unfortunately the statistical stock assessment methods often lack the ability to estimate the uncertainties related to their results. Further challenges arise from the fact that stocks which are in the most desperate need of a stock assessment are often data poor and resources to gather new data from them are scarce. Bayesian statistical inference can be utilized to conduct stock assessments cost-efficiently since these methods provide a formal way to combine information from various sources including databases, literature and expert knowledge. Bayesian inference is essentially a learning process where existing information is combined into a prior distribution, which is further updated with the most recent data. The result, a posterior distribution, expresses the best available knowledge about the phenomenon, including the related uncertainty. Furthermore, Bayesian hierarchical models enable learning between similar units, for example, stocks of the same or related species. This thesis consists of four studies that use Bayesian hierarchical models to improve knowledge in the fisheries stock assessment. Correlations between biological parameters, arising from different life history strategies among species, are utilized in paper [I] so that a data rich set of length-weight parameters can reduce the uncertainty of length-fecundity parameters for a data poor species. In paper [II], similarities between stocks of Atlantic salmon are used to estimate the stock specific key parameters of eggs-to-smolts relationship. A predictive distribution of this key parameter is also estimated, and could be used as an informative prior in a subsequent study of another Atlantic salmon stock. In addition, a hierarchical model is built to study structural uncertainty, estimating posterior ... |
author2 |
Sillanpää, Mikko University of Helsinki, Faculty of Biological and Environmental Sciences, Department of Environmental Sciences, Akvaattiset tieteet Natural Resources Institute Finland (Luke) Helsingin yliopisto, bio- ja ympäristötieteellinen tiedekunta, ympäristötieteiden laitos Helsingfors universitet, bio- och miljövetenskapliga fakulteten, miljövetenskapliga institutionen Mäntyniemi, Samu Corander, Jukka |
format |
Doctoral or Postdoctoral Thesis |
author |
Pulkkinen, Henni |
author_facet |
Pulkkinen, Henni |
author_sort |
Pulkkinen, Henni |
title |
Embracing uncertainty in fisheries stock assessment using Bayesian hierarchical models |
title_short |
Embracing uncertainty in fisheries stock assessment using Bayesian hierarchical models |
title_full |
Embracing uncertainty in fisheries stock assessment using Bayesian hierarchical models |
title_fullStr |
Embracing uncertainty in fisheries stock assessment using Bayesian hierarchical models |
title_full_unstemmed |
Embracing uncertainty in fisheries stock assessment using Bayesian hierarchical models |
title_sort |
embracing uncertainty in fisheries stock assessment using bayesian hierarchical models |
publisher |
Helsingin yliopisto |
publishDate |
2015 |
url |
http://hdl.handle.net/10138/153255 |
genre |
Atlantic salmon |
genre_facet |
Atlantic salmon |
op_relation |
URN:ISBN:978-951-51-0559-2 http://hdl.handle.net/10138/153255 URN:ISBN:978-951-51-0560-8 |
op_rights |
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. Publikationen är skyddad av upphovsrätten. Den får läsas och skrivas ut för personligt bruk. Användning i kommersiellt syfte är förbjuden. |
_version_ |
1774715804402581504 |