Assessing abundance of populations with limited data: Lessons learned from data-poor fisheries stock assessment

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Assessing abundance of populations with limited data: Lessons learned from data-poor fisheries stock assessment. / Chrysafi, Anna; Kuparinen, Anna.

In: ENVIRONMENTAL REVIEWS, Vol. 24, No. 1, 2016, p. 25-38.

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@article{567dd7b2f6744b1b944ebf7963de3ab4,
title = "Assessing abundance of populations with limited data: Lessons learned from data-poor fisheries stock assessment",
abstract = "stimation of population abundances in the absence of good observational data are notoriously difficult, yet urgently needed for biodiversity conservation and sustainable use of natural resources. In the field of fisheries research, management regulations have long demanded population abundance estimates even if data available are sparse, leading to the development of a range of fish stock assessment methods designed for data-poor populations. Here, we present methods developed within the context of fisheries research that can be applied to conduct population abundance estimations when facing data-limitations. We begin the review from the less data-demanding approaches and continue with more data-intensive ones. We discuss the advantages and caveats of these approaches, the challenges and management implications associated with data-poor stock assessments, and we propose the implementation of the Bayesian hierarchical framework as the most promising avenue for future development and improvement of the current practices.",
keywords = "Bayesian statistics, conservation, data-poor stock assessment, small populations, sustainable harvesting",
author = "Anna Chrysafi and Anna Kuparinen",
year = "2016",
doi = "10.1139/er-2015-0044",
language = "English",
volume = "24",
pages = "25--38",
journal = "ENVIRONMENTAL REVIEWS",
issn = "1181-8700",
publisher = "National Research Council of Canada",
number = "1",

}

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TY - JOUR

T1 - Assessing abundance of populations with limited data: Lessons learned from data-poor fisheries stock assessment

AU - Chrysafi, Anna

AU - Kuparinen, Anna

PY - 2016

Y1 - 2016

N2 - stimation of population abundances in the absence of good observational data are notoriously difficult, yet urgently needed for biodiversity conservation and sustainable use of natural resources. In the field of fisheries research, management regulations have long demanded population abundance estimates even if data available are sparse, leading to the development of a range of fish stock assessment methods designed for data-poor populations. Here, we present methods developed within the context of fisheries research that can be applied to conduct population abundance estimations when facing data-limitations. We begin the review from the less data-demanding approaches and continue with more data-intensive ones. We discuss the advantages and caveats of these approaches, the challenges and management implications associated with data-poor stock assessments, and we propose the implementation of the Bayesian hierarchical framework as the most promising avenue for future development and improvement of the current practices.

AB - stimation of population abundances in the absence of good observational data are notoriously difficult, yet urgently needed for biodiversity conservation and sustainable use of natural resources. In the field of fisheries research, management regulations have long demanded population abundance estimates even if data available are sparse, leading to the development of a range of fish stock assessment methods designed for data-poor populations. Here, we present methods developed within the context of fisheries research that can be applied to conduct population abundance estimations when facing data-limitations. We begin the review from the less data-demanding approaches and continue with more data-intensive ones. We discuss the advantages and caveats of these approaches, the challenges and management implications associated with data-poor stock assessments, and we propose the implementation of the Bayesian hierarchical framework as the most promising avenue for future development and improvement of the current practices.

KW - Bayesian statistics

KW - conservation

KW - data-poor stock assessment

KW - small populations

KW - sustainable harvesting

U2 - 10.1139/er-2015-0044

DO - 10.1139/er-2015-0044

M3 - Review Article

VL - 24

SP - 25

EP - 38

JO - ENVIRONMENTAL REVIEWS

JF - ENVIRONMENTAL REVIEWS

SN - 1181-8700

IS - 1

ER -

ID: 34643036