Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Supplemental Files
• Related Datasets
• Parameters
• Instruments
• Project Information
• Funding

Field transect surveys

Field surveys of eelgrass meadow sites were conducted at mid-summer low tides at field sites along the west coast of North America in the U.S. and Canada.  Samples and data were collected within the intertidal area of 32 eelgrass meadows distributed in six regions (Alaska, British Columbia, Washington, Oregon, California -Bodega Bay, and California -San Diego). Surveys were conducted between late June and early August in 2019, 2020, and 2021 by teams from six institutions.

For each site, three 20 meter transects were laid parallel to the shore at the shoreward (upper edge) of continuous eelgrass, and three lower (intertidal) 20 meter transects were laid at least 4 meters closer to the water.  Along each transect, individual third-rank eelgrass leaves were collected at meters 1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, and 19, and full eelgrass shoots were collected at meters 4, 8, 12, 16, and 20.  Leaf and shoot samples were transported in individual containers on ice to the laboratory for immediate processing. 

Transect locations were recorded using a hand-held GPS (exact model varied between field locations). Salinity was measured at the time of sampling using a refractometer. Temperature loggers (HOBO MX 2201 and UA-001-64, Onset, Bourne, MA) were deployed at each eelgrass meadow site to provide a continuous record of in situ temperature.  For HOBO data, see https://www.bco-dmo.org/dataset/877355 and Related Datasets section below.

Laboratory (Imaging and Disease Metrics)

In the lab, eelgrass blades were cleaned and prepared for imaging to capture disease metrics. Cleaned blades were imaged at high resolution (600 dpi) using an Epson Perfection V550 scanner. Shoot morphology measurements (sheath length, number of leaves, canopy height) were taken by hand in the laboratory (see Shoot Metrics, https://www.bco-dmo.org/dataset/878857).  The third-rank leaf from each shoot was analyzed for epiphyte load and grazing scars.  

Third-rank leaves were further analyzed for disease metrics through imaging.  Cleaned leaves were placed between sheets of acetate and imaged at high resolution (600 dpi) using an Epson Perfection V550 scanner. The high-resolution images were saved in TIFF format and then processed using a program developed by the authors. The Eelgrass Lesion Image Segmentation Application (EeLISA) uses machine learning to identify healthy and diseased eelgrass tissue and outputs the following metrics:

• disease prevalence (presence or absence of disease on a given leaf)
• disease lesion area (absolute size of wasting disease lesions), and
• disease severity (proportion of leaf area damaged by disease).

~ For details on the development, testing, and training of EeLISA, see Rappazzo et al. (2021).
~ For methodology details, see Aoki et al. (2022)
~ Additional details for the field surveys are available in the Eelgrass Disease Project Handbook.
~ For 16S rRNA amplicon sequencing of eelgrass associated bacteria, refer to NCBI BioProject PRJNA802566 in the Related Datasets section below.  

BCO-DMO Processing:
- Imported data from source file "meter_level_disease_metrics.csv" into the BCO-DMO data system. Data file imported using missing data identifier "NA".
- Converted date to year-month-day format
- Imported data from source file "combined_site_metadata.csv".  Converted coordinates to decimal degrees, made LocationNames consistent.
- Joined this data with the combined site metadata
- Added conventional header with dataset name, PI name, version date.
- Modified parameter (column) names to conform with BCO-DMO naming conventions.
- For the column 'Broken Tip', all lowercase y/n were changed to uppercase Y/N to be consistent
- Put site metadata as supplemental document with defined field names

Parameters/Fields for Supplemental Files
(The following parameter descriptions are for the Supplemental File titled "Eelgrass study site metadata".  For this dataset's fields, please see the heading "Parameters" below).   

• SampleCollectionDate:  Date when samples were collected in the field
• Region: Two-letter identifier for the geographic region where the sample was collected (AK=Alaska, BC=British Columbia, WA=Washington, OR=Oregon, BB=Bodega Bay in California, SD=San Diego in California)
• SiteCode: One-letter identifier for site within a geographic region where the sample was collected (A, B, C, D, E, F)
• LocationName: Full name of each sampling site (eelgrass meadow) where samples were taken
• TidalHeight: Single letter indicating the tidal height at which samples were collected.  U = upper tidal height; L = lower tidal height
• Transect: Integer indicating the transect at which samples were collected.  Upper transects = 1, 2, 3; Lower transects = 4, 5, 6.
• SampleProcessingDate: Date when samples were processed in the lab
• Depth: Depth relative to MLLW (mean lower low water)
• Salinity: Salinity of surface water at the time of sampling (point measurement made with refractometer or probe)
• LocationComments: Comments describing location including changes between sampling year
• TransectBeginDecimalLatitude: Latitudinal coordinate for the beginning (meter 0) of the transect
• TransectBeginDecimalLongitude: Longitudinal coordinate for the beginning (meter 0) of the transect
• TransectEndDecimalLatitude: Latitudinal coordinate for the end (meter 20) of the transect
• TransectEndDecimalLongitude: Longitudinal coordinate for the end (meter 20) of the transect
• Year: Year in which samples were collected

This project is part of the Marine Global Earth Observatory (MarineGEO), directed by the Smithsonian’s Tennenbaum Marine Observatories Network (TMON); a global network of partners focused on understanding how coastal marine ecosystems work—and how to keep them working https://marinegeo.si.edu/

NSF Abstract:

Pathogens may be unrecognized key species in many ecosystems, causing massive impacts on other species and habitats despite the microscopic size of disease-causing organisms. Yet the triggers to disease epidemics likely involve complex interactions among changing environmental conditions and associated biological communities. In the ocean, understanding disease outbreaks has been hindered by inadequate knowledge of how these various influences interact to determine susceptibility and resilience to disease. This project integrates research in community and disease ecology with microbial genomics, geospatial analysis, and state-of-the-art computational approaches toward an unprecedented understanding of the causes and consequences of wasting disease in eelgrass, an important vegetation type supporting coastal and estuarine ecosystems throughout the northern hemisphere. The research advances frontiers in understanding the growing but poorly appreciated threat of marine diseases, how disease ecology interacts with environmental change, and its consequences for the extensive ecosystems and coastal communities that depend on eelgrass, across 23 degrees of latitude along the Pacific coast of North America. The research will inform better management of threatened seagrass ecosystems, which provide important services including fisheries habitat, erosion control, carbon storage, and capture of nutrient runoff. The research will foster integrative approaches in the next generation, including high school students, undergraduates, graduate students, and postdocs working on the project, and each investigator's institution will work to recruit participants from under-represented groups. Best practices developed under this award, including the Eelisa disease app and drone mapping, will be disseminated for broader surveillance of seagrass disease and coastal habitat quality by both professional and citizen scientists in coordination with the Global Ocean Observing System's (GOOS) develpoment of seagrass extent as an Essential Ocean Variable.

The triggers to marine disease epidemics are likely complex, and progress in understanding them has been hindered by a poor understanding of the multifaceted ecological context of the host-disease interaction. This project's overarching goal is to disentangle the web of direct and indirect interactions by which changing climate mediates prevalence of eelgrass wasting disease, and its consequences for threatened but important eelgrass ecosystems. The centerpiece is a comparative, cross-scale survey of eelgrass community composition, microbiome, and disease prevalence along thermal gradients of latitude and exposure to the ocean, providing the first coast-wide picture of disease dynamics in response to environmental change. In situ sampling will be linked to dynamics of eelgrass at landscape scales using unmanned aerial systems (drones) to quantify high-resolution changes in eelgrass extent and habitat quality. Experiments will test how the diverse biological community mediates impacts of the pathogen on eelgrass ecosystems.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.