Temporal and Density-dependent Variability as Source for Uncertainty in Fish Population Dynamics
Considering the uncertainties about model parameters and structure, observations, and the resulting predictions is crucial when managing natural resources. This thesis is compiled of four research articles that deal with uncertainties and change in marine fish populations. In all the articles, the B...
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ftunivhelsihelda:oai:helda.helsinki.fi:10138/233848 2023-08-20T04:05:12+02:00 Temporal and Density-dependent Variability as Source for Uncertainty in Fish Population Dynamics Perälä, Tommi Minto, Cóilín University of Helsinki, Faculty of Biological and Environmental Sciences, Department of Environmental Sciences Helsingin yliopisto, bio- ja ympäristötieteellinen tiedekunta, ympäristötieteiden laitos Helsingfors universitet, bio- och miljövetenskapliga fakulteten, miljövetenskapliga institutionen Kuparinen, Anna Mäntyniemi, Samu 2018-03-29T11:17:18Z application/pdf http://hdl.handle.net/10138/233848 eng eng Helsingin yliopisto Helsingfors universitet University of Helsinki URN:ISBN:978-951-51-4197-2 Helsinki: Unigrafia, 2018 http://hdl.handle.net/10138/233848 URN:ISBN:978-951-51-4198-9 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. Text Doctoral dissertation (article-based) Artikkeliväitöskirja Artikelavhandling doctoralThesis 2018 ftunivhelsihelda 2023-07-28T06:32:03Z Considering the uncertainties about model parameters and structure, observations, and the resulting predictions is crucial when managing natural resources. This thesis is compiled of four research articles that deal with uncertainties and change in marine fish populations. In all the articles, the Bayesian statistical modelling framework is adopted to account for various types of uncertainty. When modelling complex systems, one must recognize that all models are only approximations of reality and are based on the limited understanding about the system at that moment and on previously observed behaviour. Even if a model explains the recent behaviour and predicts how the system will respond in the near future, there is always a possibility that the system changes thus rendering the old model useless. Approaches to respond to sudden changes in the system’s dynamics are vital for successful resource management. Fish populations' renewal ability is mainly determined by their reproductive success. Thus, it is of utmost importance to be able to understand and model the reproductive dynamics of marine fish populations. In this thesis, fish reproduction is studied using models that link together the number of new individuals entering the adult population (recruits) and the size of the reproducing component of the population (spawning stock), namely, the stock-recruitment relationship. The focus in this work is on temporal and density-dependent variability in the stock-recruitment relationship. The temporal variability is studied using Bayesian change-point detection methods applied to detecting changes in the per capita reproductive output of four Atlantic cod populations by analysing recruit-per-spawner ratios, and in the parameters of the stock-recruitment relationship of four fish species in the southern Gulf of St. Lawrence. In this work, novel Bayesian methods are utilized to improve the handling of uncertainties about the parameters, the timings of the changes, and in short term predictions. This thesis presents ... Doctoral or Postdoctoral Thesis atlantic cod 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 |
description |
Considering the uncertainties about model parameters and structure, observations, and the resulting predictions is crucial when managing natural resources. This thesis is compiled of four research articles that deal with uncertainties and change in marine fish populations. In all the articles, the Bayesian statistical modelling framework is adopted to account for various types of uncertainty. When modelling complex systems, one must recognize that all models are only approximations of reality and are based on the limited understanding about the system at that moment and on previously observed behaviour. Even if a model explains the recent behaviour and predicts how the system will respond in the near future, there is always a possibility that the system changes thus rendering the old model useless. Approaches to respond to sudden changes in the system’s dynamics are vital for successful resource management. Fish populations' renewal ability is mainly determined by their reproductive success. Thus, it is of utmost importance to be able to understand and model the reproductive dynamics of marine fish populations. In this thesis, fish reproduction is studied using models that link together the number of new individuals entering the adult population (recruits) and the size of the reproducing component of the population (spawning stock), namely, the stock-recruitment relationship. The focus in this work is on temporal and density-dependent variability in the stock-recruitment relationship. The temporal variability is studied using Bayesian change-point detection methods applied to detecting changes in the per capita reproductive output of four Atlantic cod populations by analysing recruit-per-spawner ratios, and in the parameters of the stock-recruitment relationship of four fish species in the southern Gulf of St. Lawrence. In this work, novel Bayesian methods are utilized to improve the handling of uncertainties about the parameters, the timings of the changes, and in short term predictions. This thesis presents ... |
author2 |
Minto, Cóilín University of Helsinki, Faculty of Biological and Environmental Sciences, Department of Environmental Sciences Helsingin yliopisto, bio- ja ympäristötieteellinen tiedekunta, ympäristötieteiden laitos Helsingfors universitet, bio- och miljövetenskapliga fakulteten, miljövetenskapliga institutionen Kuparinen, Anna Mäntyniemi, Samu |
format |
Doctoral or Postdoctoral Thesis |
author |
Perälä, Tommi |
spellingShingle |
Perälä, Tommi Temporal and Density-dependent Variability as Source for Uncertainty in Fish Population Dynamics |
author_facet |
Perälä, Tommi |
author_sort |
Perälä, Tommi |
title |
Temporal and Density-dependent Variability as Source for Uncertainty in Fish Population Dynamics |
title_short |
Temporal and Density-dependent Variability as Source for Uncertainty in Fish Population Dynamics |
title_full |
Temporal and Density-dependent Variability as Source for Uncertainty in Fish Population Dynamics |
title_fullStr |
Temporal and Density-dependent Variability as Source for Uncertainty in Fish Population Dynamics |
title_full_unstemmed |
Temporal and Density-dependent Variability as Source for Uncertainty in Fish Population Dynamics |
title_sort |
temporal and density-dependent variability as source for uncertainty in fish population dynamics |
publisher |
Helsingin yliopisto |
publishDate |
2018 |
url |
http://hdl.handle.net/10138/233848 |
genre |
atlantic cod |
genre_facet |
atlantic cod |
op_relation |
URN:ISBN:978-951-51-4197-2 Helsinki: Unigrafia, 2018 http://hdl.handle.net/10138/233848 URN:ISBN:978-951-51-4198-9 |
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_ |
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