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Developing an in-season predictor of...

Vara, Mary Janine.

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Developing an in-season predictor of commercial landings for quota monitoring in the U. S. virgin islands.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Developing an in-season predictor of commercial landings for quota monitoring in the U. S. virgin islands./
Author:	Vara, Mary Janine.
Description:	103 p.
Notes:	Source: Masters Abstracts International, Volume: 52-06.
Contained By:	Masters Abstracts International52-06(E).
Subject:	Agriculture, Fisheries and Aquaculture. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1554378
ISBN:	9781303847752

Developing an in-season predictor of commercial landings for quota monitoring in the U. S. virgin islands.
Vara, Mary Janine.

Developing an in-season predictor of commercial landings for quota monitoring in the U. S. virgin islands. - 103 p.

Source: Masters Abstracts International, Volume: 52-06.

Thesis (M.S.)--University of South Florida, 2014.

This item must not be sold to any third party vendors.

The lack of timely reporting of commercial fisheries landings interferes with effective management of fisheries in United States Virgin Islands (USVI). Federal law requires that landings be limited to prevent annual catch limits (ACLs) from being exceeded. Previous attempts to predict total landings have used historic data from prior fishing seasons to predict future landings rather than leveraging available in-season data to provide a more real-time prediction of landings. This study presents an in-season model that predicts total landings using partial reports from the current fishing year. This estimate of total landings, including error bounds around that estimate, can then be compared to the ACL established for the species to estimate potential deviations from the allowable landings and adjust effort accordingly. The performance of the model was tested in a retrospective analysis on historical commercial landings data. Differences between predicted and observed fishing year landings by defined cut-off dates were used to identify reasonable deadlines for fishery managers to begin making reliable predictions on total annual landings. On average, predictions can be made with less than 9% error with at least four months of partial data, and with less than 5% error with at least seven months of partial data. This model's in-season predictions should be useful to managers to prevent ACL overages, and to guide fishers in their application of effort within and among components of the fishery, for example, to shift effort from one fishery management unit to another in response to excessive landings.

ISBN: 9781303847752Subjects--Topical Terms:

1020913
Agriculture, Fisheries and Aquaculture.

Developing an in-season predictor of commercial landings for quota monitoring in the U. S. virgin islands.
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035 $a AAI1554378
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100 1 $a Vara, Mary Janine. $3 3169747
245 1 0 $a Developing an in-season predictor of commercial landings for quota monitoring in the U. S. virgin islands.
300 $a 103 p.
500 $a Source: Masters Abstracts International, Volume: 52-06.
500 $a Adviser: Mark E. Luther.
502 $a Thesis (M.S.)--University of South Florida, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a The lack of timely reporting of commercial fisheries landings interferes with effective management of fisheries in United States Virgin Islands (USVI). Federal law requires that landings be limited to prevent annual catch limits (ACLs) from being exceeded. Previous attempts to predict total landings have used historic data from prior fishing seasons to predict future landings rather than leveraging available in-season data to provide a more real-time prediction of landings. This study presents an in-season model that predicts total landings using partial reports from the current fishing year. This estimate of total landings, including error bounds around that estimate, can then be compared to the ACL established for the species to estimate potential deviations from the allowable landings and adjust effort accordingly. The performance of the model was tested in a retrospective analysis on historical commercial landings data. Differences between predicted and observed fishing year landings by defined cut-off dates were used to identify reasonable deadlines for fishery managers to begin making reliable predictions on total annual landings. On average, predictions can be made with less than 9% error with at least four months of partial data, and with less than 5% error with at least seven months of partial data. This model's in-season predictions should be useful to managers to prevent ACL overages, and to guide fishers in their application of effort within and among components of the fishery, for example, to shift effort from one fishery management unit to another in response to excessive landings.
590 $a School code: 0206.
650 4 $a Agriculture, Fisheries and Aquaculture. $3 1020913
650 4 $a Caribbean Studies. $3 1670141
690 $a 0792
690 $a 0432
710 2 $a University of South Florida. $b Marine Science. $3 1683799
773 0 $t Masters Abstracts International $g 52-06(E).
790 $a 0206
791 $a M.S.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1554378