Dataset: Vegetative and flowering density of Zostera marina determined from from weekly-biweekly surveys in shallow and deep zones at two sites in Massachusetts, USA in 2019

Final no updates expectedDOI: 10.26008/1912/bco-dmo.847023.1Version 1 (2021-03-30)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 vegetative and flowering density of Zostera marina determined from from weekly-biweekly surveys in shallow and deep zones at two sites in Massachusetts, USA in 2019. Eleven surveys of two different eelgrass beds were conducted every 1-2 weeks starting June 4th and ending August 27th during the summer of 2019. The two sites were West Beach in Beverly (N 42.55921, W 70.80578) and Curlew Beach in Nahant (N 42.42009, W 70.91553).

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We conducted eleven surveys of two different eelgrass beds in Massachusetts every 1-2 weeks starting June 4th and ending August 27th during the summer of 2019. The two sites were West Beach in Beverly (N 42.55921, W 70.80578) and Curlew Beach in Nahant (N 42.42009, W 70.91553). Each survey consisted of a 20 m transect being laid out parallel to shore 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 each survey we counted the number of both flowering and vegetative shoots in a 0.25 m^2 quadrat every 2 meters along the transect. If a quadrat had no shoots, an additional quadrat was added at the end of the transect. A few weeks we sampled more than 10 quadrats per depth.


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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/