DOI: 10.5061/dryad.nvx0k6f17
title: Going with the flow? Relative importance of riverine hydrologic connectivity versus tidal influence for spatial structure of genetic diversity and relatedness in a foundational submersed aquatic plant

description: Genetic connectivity in rivers is generally high and levels of genotypic
 and genetic diversity of riverine species are expected to accumulate in
 downstream locations. Genetic structure of marine and estuarine species is
 less predictable even though hydrologic connectivity is expected to be
 relatively high. These observations have been generated across different
 species and locations such that understanding effects hydrologic
 connectivity relative to tidal versus non-tidal environments in the same
 river remains incomplete. To control for species and location, we
 quantified diversity in 941 samples of Vallisneria americana Michx.
 (Hydrocharitaceae) collected from 36 sites along the species’ entire
 distribution in the tidal and non-tidal Potomac River of Maryland,
 Virginia, and the District of Columbia. Using ten microsatellite loci, we
 found 508 unique multilocus genotypes (MLGs), 36 of which were found
 multiple times across the riverscape, accounting for over 53% of the
 genotyped shoots. We found some evidence supporting connectivity
 throughout the river and stronger evidence that tidal regime drives
 genotypic and genetic structure within V. americana. Extensive clonality,
 including two MLGs spanning 230 and 152 river km, limits diversity in the
 non-tidal reaches and contrasts with very little evidence of clonal
 expansion (e.g., asexual reproduction) in non-tidal reaches. Genetic
 differentiation, structure, and pairwise relatedness of sampled shoots and
 MLGs also differed by tidal regime with the non-tidal Potomac having
 higher levels of relatedness and lower levels of genetic diversity. The
 differences in spatial distribution of genetic diversity suggest very
 different outlooks for V. americana adaptation and acclimation to current
 and future perturbations across tidal and non-tidal regions of the
 Potomac, which lead to different recommendations for restoration of the
 same species in the same river.
description: Leaves were collected in the field and DNA was extracted. 
 Microsatellites were amplified using PCR and detected on an Applied
 Biosystems Incorporated 3730 DNA analyzer.  Microsatellite alleles were
 called using GeneMapper and checked manually for accuracy.  Multilocus
 genotypes were identified using the program Genodive and the poppr package
 in the R statistical environment as described in the
 publication.
description: # Genetic data for *Vallisneria americana* in the Potomac River, USA
 [Access this dataset on Dryad](https://doi.org/10.5061/dryad.nvx0k6f17)
 These data were used to understand the relative roles of hydrologic
 connectivity and tidal regime on the genetic diversity of *Vallisneria
 americana* in the Potomac River in Maryland, Washington, DC; and Virginia,
 USA collected in 2007, 2011, and 2013. ## Description of the data and file
 structure The zip files all contain data folders that were used to
 complete the analyses for the manuscript titled, Going with the flow?
 Relative importance of riverine hydrologic connectivity versus tidal
 influence for spatial structure of genetic diversity & relatedness in
 a foundational submersed aquatic plant. The files are needed for the R
 project Potomac.Manuscript.2024.RProject-v0.2
 ([doi:10.5281/zenodo.14787930](doi:10.5281/zenodo.14787930)) to work. The
 zip files should be unzipped and the directories in those files should be
 placed at the top level of the project directory. ### data.zip The
 data.zip file contains three csv files. The csv file
 **allpotomac.microsatellite.data.csv** includes the microsatellite
 genotype data and associated information about each sample that form the
 basis of the analyses. These data were generated from samples collected in
 the field. **IDName:** The identifier code for the sample **NewPop:** The
 code for the sampling location **OrderPop:** A numeric code for sites with
 higher values downstream that is used to order sites when plotting.
 **Full.Population.Name:** The full name of the sampling location.
 **Year.Collected:** The year the sample was collected from the field.
 **Clone.ID.2018:** The identifier code for the multilocus genotype of each
 sample. **Tide:** Indicates whether sample same from a tidal or nontidal
 environment. **TideCode:** A code required by the R package related to
 denote groups ? AA = nontidal and AB = tidal samples. **Collector:** Names
 of individuals: involved with collecting material from the field.
 **Longitude and Latitude:** Sampling coordinates collected in the field
 with a handheld Garmin ETrex GPS unit. **X and Y:** Sampling coordinates
 in Universal Transverse Mercator (UTM) coordinate eastings and northings
 in UTM Zone 18. Columns **aagx030-m16** are the microsatellite loci
 formatted with the two alleles at each locus separated by a colon. NA
 values in those columns represent missing data where allele calls could
 not be made. This format for alleles is used by adegenet to create the
 genind objects that are the basis of most of the analyses. The remaining
 columns **aagx030.1-m16.2** Provide the same genotype data but the alleles
 are given in two separate columns. This data format is required by the R
 package related. The data in the remaining csv files were derived from the
 observations and data in allpotomac.microsatellite.data.csv The
 **mlg.count.and.freq.by.pop.csv** File contains the count of the number of
 samples in each site that were identified as one of eight large multilocus
 genotypes (MLGs). **NewPop:** The code for the sampling location **Columns
 starting with MLG_:** Counts of samples assigned to that MLG
 **Number_of_Stems:** The total number of samples collected from the
 sampling location. **Columns starting with FR_:** Relative frequency of
 each MLG (count/number of stems). The csv file
 **PR.Pop.Centroids.UTMs.csv** Includes the centroid locations of each of
 the sampling locations calculated as the mean latitude and longitude and
 the mean UTM coordinates of all samples at each site. **NewPop:** The code
 for the sampling location. **OrderPop:** A numeric code for sites with
 higher values downstream that is used to order sites when plotting.
 **Tide:** Indicates whether sample same from a tidal or nontidal
 environment. **Year.Collected:** The year the site was sampled.
 **Longitude and Latitude:** Centroid of the sampling coordinates for all
 samples at each site. If the centroid fell outside of water it was moved
 to the same position along the river but within water. **X and Y:** The
 Universal Transverse Mercator (UTM) coordinate of the sample centroid
 eastings and northings in UTM Zone 18. **Source:** Indicates if the site
 was included in Lloyd et al. 2011 or if the data are new in this analysis.
 **RiverKm:** Distance in kilometers from the mouth of the Potomac River.
 This information is used to assess correlation of genetic diversity with
 position along the course of the river. ### shapefiles.zip
 **CB.PR.Digitized.MN.2024.shp** is the polygon representation of water
 extent for the Chesapeake Bay and Potomac River that was hand digitized
 based on aerial imagery. The variable **Water** has the following values
 that denote different tributaries of major rivers or the main stem of the
 Chesapeake Bay: "Chesapeake", "Shenandoah",
 "Cacapon", "South Branch", "Patterson
 Creek", and "Potomac". The data are in the geodetic
 coordinate reference system (CRS), NAD83 (EPSG 4269). ### raw\_rasters.zip
 **potomac10m.tif** was created in and exported from ArcMap 10.2. The
 raster is created from the shapefile CB.PR.Digitized.MN.2024.shp by
 selecting only the Potomac River portion of the shapefile and projecting
 into UTM Zone 18, NAD83 before converting to raster. ###
 processed\_rasters.zip **processed_rasters.zip** Contains two tif files
 that are raster representations of the Potomac River with 10 m cell size
 projected in UTM Zone 18, NAD83. **Potomac10m_trimmed.tif** was treated to
 ensure that the tif created by ArcMap had no extraneous rows or columns
 beyond the area of interest. **Potomac10m_corrected.tif** modified
 Potomac10m_trimmed.tif to ensure that all sample and site centroids were
 included in cells coded as water. Given the loss of precision converting
 polygons to raster cells, points close to shorelines sometimes are not
 coded being in water. This correction is needed to keep cost distance
 analysis from failing. ### cost\_surfaces.zip **cost_surfaces.zip**
 contains two transition matrices created using the r package gdistance and
 saved to rds files that are readable in the R statistical environment
 (**tr1.Potomac.Transition.10m.rds** and
 **tr1c.Potomac.Transition.10m.rds**). The transition matrices are both
 based on the rasters of the extent of the Potomac River. The
 **tr1c.Potomac.Transition.10m.rds** version is corrected for diagonal
 movement and north south distortion.? ## Sharing/Access information The
 data can be accessed in the R Project for this manuscript that is archived
 on Zenodo ([doi:10.5281/zenodo.14787930](doi:10.5281/zenodo.14787930))
 along with the R code that uses all these data sets. ## Code/Software The
 data were analyzed using various packages within the R statistical
 environment. The R code is organized to run within a project that is
 available at [doi:10.5281/zenodo.14787930](doi:10.5281/zenodo.14787930).
 Scripts are located within the code folder of the project and are numbered
 in the order they need to be run. Each script contains information on
 dependencies on other scripts and data sources, and installs and loads all
 required packages. ## Usage notes The csv files that can be opened by any
 text editor most other programs and coding languages used for data
 analysis. Shapefiles are a common format for vector-based geographic
 information system (GIS) data developed by the company esri. They can be
 opened and used in any GIS software and in R or Python. A shapefile
 consists of multiple file types beyond the .shp (specifically, .cpg, .dbf,
 .prj, .sbn, and .sbx). The user only interacts directly with the .shp file
 but the other files need to be in the same directory. Raster files
 represent geographic information as a grid of cells with values
 representing features of interest. The rasters in this repository are
 provided as tif files. They can be read by any image processor, GIS
 software, R, or Python. The transition matrices are .tif files, but they
 are of a specific format that is used within the R statistical environment
 using the R package gdistance.