Testing approaches to determine relative stock abundance priors when setting catch recommendations using data-limited methods

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Testing approaches to determine relative stock abundance priors when setting catch recommendations using data-limited methods. / Chrysafi, Anna; Cope, Jason M.

In: FISHERIES RESEARCH, Vol. 219, 105343, 01.11.2019.

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@article{26b4d2fecdd54f8199f86396c10f7767,
title = "Testing approaches to determine relative stock abundance priors when setting catch recommendations using data-limited methods",
abstract = "Data-limited methods for managing stocks have expanded greatly over the last decade due to the necessity of quantitatively assessing exploited populations with limited information. A special category of such approaches is based on stock reduction analysis. These “catch-only” methods provide a way to handle low data availability, but also require as an input relative stock status (e.g., current biomass/initial biomass), a difficult to determine value that leads to large sensitivity in method output and performance. Published methods have been developed to devise informative priors for this quantity, but have not been evaluated together with the assessment methods. Here, relative stock abundance priors derived from elicited expert knowledge, vulnerability analysis and catch trends are compared to the common assumption of a stock being at B40{\%} (40{\%} of the initial biomass). The performance of each prior source is evaluated both in the degree of bias in estimating stock status and in the estimation procedure of catches for ten data-rich stocks with six stock assessment models that require stock abundance input. The results from both performance metrics show that these alternative sources can provide more informative priors than assuming current biomass equals B40{\%}, with priors elicited from stock assessment experts performing best. Finally, based on the findings of this work and the data requirements to construct a stock abundance prior, we make recommendations on how to navigate the options for devising a relative stock status prior.",
keywords = "Data-limited, Expert knowledge, Fisheries management, Stock abundance, Stock assessment",
author = "Anna Chrysafi and Cope, {Jason M.}",
year = "2019",
month = "11",
day = "1",
doi = "10.1016/j.fishres.2019.105343",
language = "English",
volume = "219",
journal = "FISHERIES RESEARCH",
issn = "0165-7836",
publisher = "Elsevier",

}

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TY - JOUR

T1 - Testing approaches to determine relative stock abundance priors when setting catch recommendations using data-limited methods

AU - Chrysafi, Anna

AU - Cope, Jason M.

PY - 2019/11/1

Y1 - 2019/11/1

N2 - Data-limited methods for managing stocks have expanded greatly over the last decade due to the necessity of quantitatively assessing exploited populations with limited information. A special category of such approaches is based on stock reduction analysis. These “catch-only” methods provide a way to handle low data availability, but also require as an input relative stock status (e.g., current biomass/initial biomass), a difficult to determine value that leads to large sensitivity in method output and performance. Published methods have been developed to devise informative priors for this quantity, but have not been evaluated together with the assessment methods. Here, relative stock abundance priors derived from elicited expert knowledge, vulnerability analysis and catch trends are compared to the common assumption of a stock being at B40% (40% of the initial biomass). The performance of each prior source is evaluated both in the degree of bias in estimating stock status and in the estimation procedure of catches for ten data-rich stocks with six stock assessment models that require stock abundance input. The results from both performance metrics show that these alternative sources can provide more informative priors than assuming current biomass equals B40%, with priors elicited from stock assessment experts performing best. Finally, based on the findings of this work and the data requirements to construct a stock abundance prior, we make recommendations on how to navigate the options for devising a relative stock status prior.

AB - Data-limited methods for managing stocks have expanded greatly over the last decade due to the necessity of quantitatively assessing exploited populations with limited information. A special category of such approaches is based on stock reduction analysis. These “catch-only” methods provide a way to handle low data availability, but also require as an input relative stock status (e.g., current biomass/initial biomass), a difficult to determine value that leads to large sensitivity in method output and performance. Published methods have been developed to devise informative priors for this quantity, but have not been evaluated together with the assessment methods. Here, relative stock abundance priors derived from elicited expert knowledge, vulnerability analysis and catch trends are compared to the common assumption of a stock being at B40% (40% of the initial biomass). The performance of each prior source is evaluated both in the degree of bias in estimating stock status and in the estimation procedure of catches for ten data-rich stocks with six stock assessment models that require stock abundance input. The results from both performance metrics show that these alternative sources can provide more informative priors than assuming current biomass equals B40%, with priors elicited from stock assessment experts performing best. Finally, based on the findings of this work and the data requirements to construct a stock abundance prior, we make recommendations on how to navigate the options for devising a relative stock status prior.

KW - Data-limited

KW - Expert knowledge

KW - Fisheries management

KW - Stock abundance

KW - Stock assessment

UR - http://www.scopus.com/inward/record.url?scp=85070894967&partnerID=8YFLogxK

U2 - 10.1016/j.fishres.2019.105343

DO - 10.1016/j.fishres.2019.105343

M3 - Article

VL - 219

JO - FISHERIES RESEARCH

JF - FISHERIES RESEARCH

SN - 0165-7836

M1 - 105343

ER -

ID: 36533313