東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Development and Evaluation of Spatia...

Nehemiah, Samara,

Linked to FindBook

Google Book

Amazon

博客來

Development and Evaluation of Spatially-Explicit Population Models for Estimating the Abundance of Chesapeake Bay Fishes /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Development and Evaluation of Spatially-Explicit Population Models for Estimating the Abundance of Chesapeake Bay Fishes // Samara Nehemiah.
Author:	Nehemiah, Samara,
Description:	1 electronic resource (295 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
Contained By:	Dissertations Abstracts International86-07B.
Subject:	Environmental science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31635632
ISBN:	9798302838094

Development and Evaluation of Spatially-Explicit Population Models for Estimating the Abundance of Chesapeake Bay Fishes /
Nehemiah, Samara,

Development and Evaluation of Spatially-Explicit Population Models for Estimating the Abundance of Chesapeake Bay Fishes /Samara Nehemiah. - 1 electronic resource (295 pages)

Source: Dissertations Abstracts International, Volume: 86-07, Section: B.

Although fish populations typically experience spatially varying abundance and fishing mortality, stock assessments that inform management decisions commonly model a population that is assumed to be well-mixed with homogenous mortality rates. When assumptions about population mixing are not met, these models can result in biased estimates. Spatial population estimates are particularly beneficial to the Chesapeake Bay because this region faces unique challenges as a result of climate change and fishing pressure. However, use of spatial population models for fisheries management relies on models that can provide more accurate estimates of biological parameters than non-spatial models. Objectives for this research were to 1) develop and implement a multi-stock, spatially-explicit population model for Striped Bass (Morone saxatilis) to estimate abundance and fishing mortality in the Chesapeake Bay and along the Atlantic coast; 2) assess the performance of spatially-explicit models compared to spatially-implicit models (i.e., fleets-as-areas) to estimate abundance, determine how improved data quality (e.g., stock composition) affects model performance, and determine the effect of aging error on model accuracy; and 3) determine how spatial model performance is affected by potential changes in population dynamics resulting from climate change (e.g., time-varying natural mortality). The population model was a two-stock model with two sub-annual time-steps and two regions with stock and age-specific occupancy probabilities representing movement into and out of the Chesapeake Bay. Fishing mortality was estimated to be higher in the Ocean than the Chesapeake Bay, and abundance increased during 1982-2004 for both stocks before declining slightly until 2017. Simulations were conducted to test the ability of models to estimate abundance and fishing mortality under alternative scenarios of data availability and quality. Spatially-explicit estimates were approximately unbiased when they closely matched the assumptions of the data generating model. Models that ignored potential aging bias in datasets resulted in highly biased estimates of abundance and fishing mortality. Although the performance of all models degraded under most climate change scenarios, spatially-explicit models produced the most accurate model estimates compared to fleets-as-areas models. This research highlights the potential benefits of implementing spatially-explicit population models for Striped Bass and ecologically valuable fish species in the Chesapeake Bay.

English

ISBN: 9798302838094Subjects--Topical Terms:

677245
Environmental science.
Subjects--Index Terms:

Chesapeake Bay

Development and Evaluation of Spatially-Explicit Population Models for Estimating the Abundance of Chesapeake Bay Fishes /
LDR:04105nmm a22004333i 4500 001 2429252
005 20250430093608.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798302838094
035 $a (MiAaPQD)AAI31635632
035 $a AAI31635632
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Nehemiah, Samara, $e author. $0 (orcid)0009-0005-4258-4379 $3 3816991
245 1 0 $a Development and Evaluation of Spatially-Explicit Population Models for Estimating the Abundance of Chesapeake Bay Fishes / $c Samara Nehemiah.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (295 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
500 $a Advisors: Wilberg, Michael J. Committee members: Secor, David H.; Nesslage, Genevieve; Schueller, Amy M.; Latour, Robert J.
502 $b Ph.D. $c University of Maryland, College Park $d 2024.
520 $a Although fish populations typically experience spatially varying abundance and fishing mortality, stock assessments that inform management decisions commonly model a population that is assumed to be well-mixed with homogenous mortality rates. When assumptions about population mixing are not met, these models can result in biased estimates. Spatial population estimates are particularly beneficial to the Chesapeake Bay because this region faces unique challenges as a result of climate change and fishing pressure. However, use of spatial population models for fisheries management relies on models that can provide more accurate estimates of biological parameters than non-spatial models. Objectives for this research were to 1) develop and implement a multi-stock, spatially-explicit population model for Striped Bass (Morone saxatilis) to estimate abundance and fishing mortality in the Chesapeake Bay and along the Atlantic coast; 2) assess the performance of spatially-explicit models compared to spatially-implicit models (i.e., fleets-as-areas) to estimate abundance, determine how improved data quality (e.g., stock composition) affects model performance, and determine the effect of aging error on model accuracy; and 3) determine how spatial model performance is affected by potential changes in population dynamics resulting from climate change (e.g., time-varying natural mortality). The population model was a two-stock model with two sub-annual time-steps and two regions with stock and age-specific occupancy probabilities representing movement into and out of the Chesapeake Bay. Fishing mortality was estimated to be higher in the Ocean than the Chesapeake Bay, and abundance increased during 1982-2004 for both stocks before declining slightly until 2017. Simulations were conducted to test the ability of models to estimate abundance and fishing mortality under alternative scenarios of data availability and quality. Spatially-explicit estimates were approximately unbiased when they closely matched the assumptions of the data generating model. Models that ignored potential aging bias in datasets resulted in highly biased estimates of abundance and fishing mortality. Although the performance of all models degraded under most climate change scenarios, spatially-explicit models produced the most accurate model estimates compared to fleets-as-areas models. This research highlights the potential benefits of implementing spatially-explicit population models for Striped Bass and ecologically valuable fish species in the Chesapeake Bay.
546 $a English
590 $a School code: 0117
650 4 $a Environmental science. $3 677245
650 4 $a Natural resource management. $3 589570
650 4 $a Aquatic sciences. $3 3174300
653 $a Chesapeake Bay
653 $a Fisheries management
653 $a Morone saxatilis
653 $a Population dynamics
653 $a Stock assessment
690 $a 0768
690 $a 0528
690 $a 0792
710 2 $a University of Maryland, College Park. $b Marine-Estuarine-Environmental Sciences. $e degree granting institution. $3 3816829
720 1 $a Wilberg, Michael J. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-07B.
790 $a 0117
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31635632