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Accounting for Variability and Biase...

Rudd, Merrill B.

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Accounting for Variability and Biases in Data-Limited Fisheries Stock Assessment.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Accounting for Variability and Biases in Data-Limited Fisheries Stock Assessment./
Author:	Rudd, Merrill B.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	154 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
Subject:	Natural resource management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10606022
ISBN:	9780355355222

Accounting for Variability and Biases in Data-Limited Fisheries Stock Assessment.
Rudd, Merrill B.

Accounting for Variability and Biases in Data-Limited Fisheries Stock Assessment. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 154 p.

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)--University of Washington, 2017.

Many regions of the world have very few stocks assessed, often due to limited data quality or quantity or lack of trained scientists to apply and interpret stock assessments. These same areas with fewer assessments perform worse across fishery management attributes, including research capacity, management, enforcement, and socioeconomics. Some studies have used the limited data available to approximate the status of these "unassessed" stocks and find them to be declining compared to assessed stocks. Global assessments of "unassessed" stocks are informed by many attributes of the stock, one of which is the trend in reported catch to the U.N. Food and Agriculture Organization. These assessments assume that the catch data are accurate, at least in trend if not in magnitude. However, quantifying catch is a major challenge due to commonly misreported sectors such as discards, small-scale, recreational, and illegal, unreported, unregulated fisheries. One approach is to improve catch data by improving monitoring programs or conducting catch reconstructions. Another approach is to collect alternative data types to conduct stock assessments. Stocks that are not assessed because they have the lowest information or research capacity appear more likely to be unsustainable, and further research and management action is required to improve the status of the data- and research-capacity-limited fisheries.

ISBN: 9780355355222Subjects--Topical Terms:

589570
Natural resource management.

Accounting for Variability and Biases in Data-Limited Fisheries Stock Assessment.
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245 1 0 $a Accounting for Variability and Biases in Data-Limited Fisheries Stock Assessment.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 154 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Advisers: Trevor A. Branch; Ray Hilborn.
502 $a Thesis (Ph.D.)--University of Washington, 2017.
520 $a Many regions of the world have very few stocks assessed, often due to limited data quality or quantity or lack of trained scientists to apply and interpret stock assessments. These same areas with fewer assessments perform worse across fishery management attributes, including research capacity, management, enforcement, and socioeconomics. Some studies have used the limited data available to approximate the status of these "unassessed" stocks and find them to be declining compared to assessed stocks. Global assessments of "unassessed" stocks are informed by many attributes of the stock, one of which is the trend in reported catch to the U.N. Food and Agriculture Organization. These assessments assume that the catch data are accurate, at least in trend if not in magnitude. However, quantifying catch is a major challenge due to commonly misreported sectors such as discards, small-scale, recreational, and illegal, unreported, unregulated fisheries. One approach is to improve catch data by improving monitoring programs or conducting catch reconstructions. Another approach is to collect alternative data types to conduct stock assessments. Stocks that are not assessed because they have the lowest information or research capacity appear more likely to be unsustainable, and further research and management action is required to improve the status of the data- and research-capacity-limited fisheries.
520 $a This dissertation addresses issues of data quality in the monitoring process and data limitation in the assessment process. Chapter 1 addresses an issue of data quality in asking, "does unreported catch lead to overfishing?" We used simulation analysis to demonstrate that if catch is misreported at a constant rate, surplus production models can still estimate unbiased stock status and would avoid overfishing with effective management. If catch misreporting is increasing over time, stock assessments would recommend harvest limits that would lead to under-exploitation, while decreasing reporting rates lead to over-exploitation. This question is relevant to fisheries around the world dealing with uncertainties in catch reporting in their stock assessment, as well as current debates over the use of catch reconstructions.
520 $a In Chapter 2 stock assessments are examined for fisheries that only have length composition data and biological information, since it is often easier to collect length measurements than to quantify total catch. Length measurements from samples of the catch, referred to as length composition data, can be contrasted with expected length composition in an unfished state to reveal information about fishing mortality, recruitment, and selectivity. Most data-poor length-based stock assessment methods assume the population is in equilibrium, i.e. that fishing mortality and recruitment have not changed within one generation of the species. In this chapter we present a Length-based Integrated Mixed Effects (LIME) model that relaxes this equilibrium assumption and directly estimates variable fishing mortality and recruitment using the same data inputs as other length-based methods. Using simulation testing we demonstrated LIME performs best for life history types with a maximum age of less than 20 years, and is unbiased across a range of recruitment and fishing mortality patterns, provided individual growth parameters are known. LIME also has the capability of including multiple years of length data, abundance indices, and catch time series when available. LIME is a flexible new tool for stock assessments of fish usually caught as bycatch and other small-scale fisheries.
520 $a Chapter 3 applies LIME and the equilibrium-based Length-Based Spawning Potential Ratio (LB-SPR) methods to assess a medium-lived Costa Rican spotted rose snapper, Lutjanus guttatus, and short-lived Kenyan rabbitfish, Siganus sutor. LIME estimated the Costa Rican snapper fishery to be overfished in the most recent year of data after a period of full exploitation, whereas LB-SPR estimated more variability in stock status throughout the time series but the fishery was above the target reference point in the most recent year of data. LIME estimated the rabbitfish fishery to have undergone a period of overexploitation in the late 1990s and early 2000s, but fishing mortality has continually decreased over time resulted in a recovered stock in the most recent year of data. Alternatively, LB-SPR estimated the stock slightly less than the SPR target. Chapter 3 presented the first empirical LIME assessment and comparison with a commonly-used alternative method, and presented guidelines for future LIME applications.
590 $a School code: 0250.
650 4 $a Natural resource management. $3 589570
650 4 $a Statistics. $3 517247
650 4 $a Ecology. $3 516476
650 4 $a Aquatic sciences. $3 3174300
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710 2 $a University of Washington. $b Aquatic and Fishery Sciences. $3 2093042
773 0 $t Dissertation Abstracts International $g 79-04B(E).
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791 $a Ph.D.
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793 $a English
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