Biology
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Item Investigating the Utility of Environmental DNA Analysis for the Monitoring and Management of Mid-Atlantic Alosine Fishes(2023) Fowler, Chelsea; Plough, Louis V; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Environmental DNA (eDNA) tools can address gaps in fish assessment data while reducing the cost and the impact of sampling on threatened anadromous alosine fishes in Chesapeake Bay. Here, I tested the ability of high-frequency eDNA sampling of river herring to predict fish abundances from sonar-based fish counts on the Choptank River and developed and validated novel species-specific eDNA assays for American and hickory shads. River herring eDNA concentrations from daily eDNA sampling were highly correlated to sonar-based fish counts (Spearman’s Rho = 0.84). This relationship informed a model that could accurately predict fish count from eDNA and relevant covariates (R2 = 0.88). The two new shad assays are highly specific and quantitative, and field testing validated detections in Delaware, Maryland, and North Carolina. This work provides a set of eDNA monitoring tools for the Mid-Atlantic alosines and highlights the capacity for eDNA data to generate quantitative metrics of fish abundance.Item Clustering metagenomic sequences with interpolated Markov models(2010-11-02) Kelley, David R; Salzberg, Steven LBackground: Sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods. Because the output from metagenomic sequencing is a large set of reads of unknown origin, clustering reads together that were sequenced from the same species is a crucial analysis step. Many effective approaches to this task rely on sequenced genomes in public databases, but these genomes are a highly biased sample that is not necessarily representative of environments interesting to many metagenomics projects. Results: We present SCIMM (Sequence Clustering with Interpolated Markov Models), an unsupervised sequence clustering method. SCIMM achieves greater clustering accuracy than previous unsupervised approaches. We examine the limitations of unsupervised learning on complex datasets, and suggest a hybrid of SCIMM and supervised learning method Phymm called PHYSCIMM that performs better when evolutionarily close training genomes are available. Conclusions: SCIMM and PHYSCIMM are highly accurate methods to cluster metagenomic sequences. SCIMM operates entirely unsupervised, making it ideal for environments containing mostly novel microbes. PHYSCIMM uses supervised learning to improve clustering in environments containing microbial strains from well-characterized genera. SCIMM and PHYSCIMM are available open source from http://www.cbcb.umd.edu/software/scimm.