Dataset: Seed counts from a survey of the shallow and deep zones at four different sites in Massachusetts, USA in 2019

Final no updates expectedDOI: 10.26008/1912/bco-dmo.847088.1Version 1 (2021-03-31)Dataset Type:Other Field Results

Principal Investigator: A. Randall Hughes (Northeastern University)

BCO-DMO Data Manager: Shannon Rauch (Woods Hole Oceanographic Institution)


Project: RUI: Collaborative Research: Trait differentiation and local adaptation to depth within meadows of the foundation seagrass Zostera marina (ZosMarLA)


Abstract

This dataset includes seed counts from a survey of the shallow and deep zones at four different sites in Massachusetts, USA in 2019. The four sites were West Beach in Beverly (N 42.55921, W 70.80578), Curlew Beach in Nahant (N 42.42009, W 70.91553), Lynch Park in Beverly (N 42.54488, W 70.85842), and Niles Beach in Gloucester (N 42.59711, W 70.65592).

We conducted surveys of four different eelgrass beds in Massachusetts in mid-August of 2019. The four sites were West Beach in Beverly (N 42.55921, W 70.80578), Curlew Beach in Nahant (N 42.42009, W 70.91553), Lynch Park in Beverly (N 42.54488, W 70.85842), and Niles Beach in Gloucester (N 42.59711, W 70.65592). Surveys were done in both the shallow and deep zone. These zones were defined as being along the respective edges of the eelgrass beds. The exact depths of the zones varied from bed to bed.

During the surveys, we collected flowering shoots from within 0.0625 m^2 quadrats every 2 m along a 30 m transect (that would be extended for each quadrat that had no eelgrass). Any flowering shoots found within the quadrats were taken and their seeds counted. This led to there being up to 25 flowering shoots per depth per site.


Related Datasets

No Related Datasets

Related Publications

Results

Von Staats, D. A., Hanley, T. C., Hays, C. G., Madden, S. R., Sotka, E. E., & Hughes, A. R. (2020). Intra-Meadow Variation in Seagrass Flowering Phenology Across Depths. Estuaries and Coasts, 44(2), 325–338. doi:10.1007/s12237-020-00814-0
Methods

Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (4th ed., Ser. Statistics and computing). Springer. URL: http://www.stats.ox.ac.uk/pub/MASS4
Software

R Core Team (2019). R: A language and environment for statistical computing. R v3.6.0. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/