Dataset: Temperature/light data collected using Onset HOBO Pendant data loggers at two sites in Massachusetts, USA in 2019

ValidatedFinal no updates expectedDOI: 10.26008/1912/bco-dmo.846963.1Version 1 (2021-04-01)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 included temperature/light data collected using Onset HOBO Pendant data loggers at two sites in Massachusetts, USA in 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).

We deployed three Onset HOBO Pendant® Waterproof Temperature/Light Data Loggers in both the shallow and deep zone at each site. 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). The shallow and deep zones were defined as being along the respective edges of the eelgrass beds. The exact depths of the zones varied from bed to bed.

Each logger was changed once over the course of the dataset. Loggers were deployed on May 14 and swapped out on June 26 at Curlew Beach and deployed on May 15 and swapped out on June 27 at West Beach.


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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

Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Usinglme4. Journal of Statistical Software, 67(1). doi:10.18637/jss.v067.i01
Methods

Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software, 82(13). doi:10.18637/jss.v082.i13
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/