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MASTER'S THESIS DEFENSE - WILCOX, GARRETT

  • Horn Point Laboratory 2020 Horns Point Road, AREL Lecture Hall Cambridge, MD, 21613 United States (map)

Name: Garrett Wilcox
Date: 07/22/2026
Time (EST/EDT): 01:00 pm
Location: AREL Lecture Hall
Remote Access: mailto:mees@umd.edu

Committee Chair: Dr. Elizabeth North
Committee Members: Dr. Elizabeth North Dr. Louis Plough Dr. Matthew Gray

Title: Genetic Analysis and Tracking of Heritage Among Oyster Spat Populations Within the Manokin River

Abstract: Because oysters are a vital part of many coastal ecosystems, oyster restoration has been undertaken around the world, especially along the Atlantic and Gulf coasts of North America. Despite the successes and metrics related to oyster restoration, the proportion of oysters on a restoration site that are from a hatchery-raised cohort and how this proportion changes over time are not known. The objective of this project was to genetically differentiate wild and hatchery-reared oysters, and to track parentage of oysters on a restoration reef over time. Oyster spat from the LOLA broodstock line known for disease resistance were bred from select parents and planted on a restoration site within the Manokin River oyster sanctuary in Maryland. Spat were collected and tissues were sampled before deployment and then 6, 12, and 18 months after deployment. Results indicate that there was a distinct genetic difference between the wild and LOLA populations based on a SNP panel (~3457 markers). LOLA and wild oysters were able to be differentiated with an average population assignment accuracy of 0.994 on a scale from 0 to 1. Of the 781 oysters collected from the Manokin restoration site, 62% were within 3 m of the track-line of the planting vessel. Of these oysters, 47% were identified as LOLA. The growth rate of these LOLA oysters was 2.2 mm per month. In addition to providing new information for restoration, this technique could lay the groundwork for analyzing larval spillover from marine protected areas (MPAs) utilizing a parentage-based genetic approach.