Influence of the Estimator Selection in Scalloped Hammerhead Shark Stock Assessment

In natural sciences, frequentist paradigm has led statistical practice; however, Bayesian approach has been gaining strength in the last decades. Our study assessed the scalloped hammerhead shark population on the western North Atlantic Ocean using Bayesian methods. This approach allowed incorporate diverse types of errors in the surplus production model and compare the influences of different statistical estimators on the values of the key parameters (r, growth rate; K carrying capacity; depletion, FMSY , fishing levels that would sustain maximum yield; and NMSY, abundance at maximum sustainable yield). Furthermore, we considered multi-levelpriors due to the variety of results on the population growth rate of this species. Our research showed that estimator selection influences the results of the surplus production model and therefore, the value of the target management points. Based on key parameter estimates with uncertainty and Deviance Information Criterion, we suggest that state-space Bayesian models be used for assessing scalloped hammerhead shark or other fish stocks with poor data available. This study found the population was overfished and suffering overfishing. Therefore, based on our research and that there was very low evidence of recovery according with the last data available, we suggest prohibition of fishing for this species because: (1) it is highly depleted (14% of its initial population), (2) the fishery status is very unstable over time, (3) it has a low reproductive rate contributing to a higher risk of overexploitation, and (4) the easiness of misidentification among different hammerhead sharks (smooth, great, scalloped and cryptic species).

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Fisheries and Wildlife Sciences

Department dc:contributor.department

Fisheries and Wildlife Sciences

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2014

Author dc:creator

• Ballesta Artero, Irene Maria

Chair dc:contributor.committeechair

• Jiao, Yan

Committee members dc:contributor.committeemember

• Orth, Donald J.
• Siegried, Kate Andrews

• Sphyrna lewini
• logistic production model
• types of error
• likelihood
• Bayesian approach

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:2141

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/24819