About the Project
In the lower Chesapeake Bay, Virginia, warmer water temperatures in recent years have resulted in large scale diebacks of eelgrass meadows (Zostera marina). In contrast, many eelgrass populations in Back Sound, North Carolina appear to be more resilient to warming water temperatures. Understanding the drivers of these regional differences in eelgrass resilience could help more effectively restore eelgrass meadows in a changing climate. With a network of the intended users from reserves, state agencies, and Chesapeake Bay nonprofits, this project compared resiliency traits of eelgrass populations in Virginia and North Carolina by conducting reciprocal restoration trials and genomic sequencing. The project results indicate the importance of seed sources in potential future eelgrass restoration, in addition to site selection.
About this Resource
This resource provides a comprehensive, script-based workflow designed for users to apply to their own Zostera marina (eelgrass) genomics data . It includes detailed R code and command-line steps for conducting principal component analysis (PCA), pairwise FST calculations, SNP pruning, admixture modeling, and landscape genomics analyses (e.g., LFMM and OutFLANK) to detect signatures of selection and population structure. The workflow also incorporates functional annotation of candidate genes and gene ontology enrichment using TopGO, as well as spatial visualization of genetic patterns.
This resource is intended for researchers and practitioners with experience in R and population genomics who are investigating adaptive variation, restoration planning, or conservation genetics in seagrass systems. It is particularly suited for researchers working with Zostera marina or other non- model organisms who aim to assess genetic structure, detect signals of local adaptation, or inform seed sourcing strategies for restoration. The resource assumes familiarity with genomic data formats (e.g., VCF, GDS), bioinformatic tools, and statistical methods used in landscape genomics and gene- environment association studies.