Application of signal detection methods to fisheries management

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dc.contributor.advisor Rogan, Emer en
dc.contributor.advisor Kelly, Ciarán J. en
dc.contributor.advisor Codling, Edward A. en Pazhayamadom, Deepak George 2015-08-14T11:54:11Z 2014 2014
dc.identifier.citation Pazhayamadom, D. G. 2014. Application of signal detection methods to fisheries management. PhD Thesis, University College Cork. en
dc.identifier.endpage 255
dc.description.abstract The abundance of many commercially important fish stocks are declining and this has led to widespread concern on the performance of traditional approach in fisheries management. Quantitative models are used for obtaining estimates of population abundance and the management advice is based on annual harvest levels (TAC), where only a certain amount of catch is allowed from specific fish stocks. However, these models are data intensive and less useful when stocks have limited historical information. This study examined whether empirical stock indicators can be used to manage fisheries. The relationship between indicators and the underlying stock abundance is not direct and hence can be affected by disturbances that may account for both transient and persistent effects. Methods from Statistical Process Control (SPC) theory such as the Cumulative Sum (CUSUM) control charts are useful in classifying these effects and hence they can be used to trigger management response only when a significant impact occurs to the stock biomass. This thesis explores how empirical indicators along with CUSUM can be used for monitoring, assessment and management of fish stocks. I begin my thesis by exploring various age based catch indicators, to identify those which are potentially useful in tracking the state of fish stocks. The sensitivity and response of these indicators towards changes in Spawning Stock Biomass (SSB) showed that indicators based on age groups that are fully selected to the fishing gear or Large Fish Indicators (LFIs) are most useful and robust across the range of scenarios considered. The Decision-Interval (DI-CUSUM) and Self-Starting (SS-CUSUM) forms are the two types of control charts used in this study. In contrast to the DI-CUSUM, the SS-CUSUM can be initiated without specifying a target reference point (‘control mean’) to detect out-of-control (significant impact) situations. The sensitivity and specificity of SS-CUSUM showed that the performances are robust when LFIs are used. Once an out-of-control situation is detected, the next step is to determine how much shift has occurred in the underlying stock biomass. If an estimate of this shift is available, they can be used to update TAC by incorporation into Harvest Control Rules (HCRs). Various methods from Engineering Process Control (EPC) theory were tested to determine which method can measure the shift size in stock biomass with the highest accuracy. Results showed that methods based on Grubb’s harmonic rule gave reliable shift size estimates. The accuracy of these estimates can be improved by monitoring a combined indicator metric of stock-recruitment and LFI because this may account for impacts independent of fishing. The procedure of integrating both SPC and EPC is known as Statistical Process Adjustment (SPA). A HCR based on SPA was designed for DI-CUSUM and the scheme was successful in bringing out-of-control fish stocks back to its in-control state. The HCR was also tested using SS-CUSUM in the context of data poor fish stocks. Results showed that the scheme will be useful for sustaining the initial in-control state of the fish stock until more observations become available for quantitative assessments. en
dc.description.sponsorship Marine Institute (PHD/FS/07/004) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2013, Deepak George Pazhayamadom. en
dc.rights.uri en
dc.subject Fisheries en
dc.subject Indicator en
dc.subject CUSUM en
dc.subject Monitor en
dc.subject Process control en
dc.title Application of signal detection methods to fisheries management en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD (Science) en
dc.internal.availability Full text not available en Restricted to everyone for five years en 2020-08-12T11:54:11Z
dc.description.version Accepted Version
dc.contributor.funder Marine Institute en
dc.description.status Not peer reviewed en Biological, Earth & Environmental Sciences en
dc.check.reason This thesis is due for publication or the author is actively seeking to publish this material en
dc.check.opt-out No en
dc.thesis.opt-out false
dc.check.chapterOfThesis 4,6,7,8,9
dc.check.embargoformat Both hard copy thesis and e-thesis en

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© 2013, Deepak George Pazhayamadom. Except where otherwise noted, this item's license is described as © 2013, Deepak George Pazhayamadom.
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