UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

Browse

Filters

20231

Settings

Author: search.filters.author.Rodriguez, Lauren KellySubject: search.filters.subject.Genetics

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Assessing vertebrate biodiversity across the Chesapeake Bay using environmental DNA metabarcoding
    (2023) Rodriguez, Lauren Kelly; Bailey, Helen; Woodland, Ryan J; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Through the collection and sequencing of trace genomic evidence from environmental samples (e.g., water, air, and soil), environmental DNA (eDNA) metabarcoding can detect a range of vertebrates. Despite the dynamic characteristics of estuarine environments, which often hinder the persistence of genomic material, this project successfully employed metabarcoding to assess the distribution of vertebrates in the Chesapeake Bay. Primarily, the study evaluated the effects of using various eDNA sampling, laboratory, and post-hoc analysis techniques when investigating species presence and biodiversity of an area. This study also identified spatially-explicit fish communities along salinity gradients as described by a Generalized Additive Mixed Model (GAMM) and a Permutational Multivariate Analysis of Variance (PERMANOVA). Community compositions were similar to previous findings by traditional trawling and seining methods. This research supports the usefulness of eDNA metabarcoding to assess species presence across spatiotemporal extents, making it a promising tool for future biomonitoring efforts in the Chesapeake Bay.