Dataset: Thermal growth for Skeletonema species as analyzed in Anderson and Rynearson, 2020

ValidatedRelease Date:2020-02-03Final no updates expectedDOI: 10.1575/1912/bco-dmo.774996.1Version 1 (2019-08-12)Dataset Type:experimental

Principal Investigator: Tatiana A. Rynearson (University of Rhode Island)

Contact: Stephanie I. Anderson (University of Rhode Island)

BCO-DMO Data Manager: Nancy Copley (Woods Hole Oceanographic Institution)


Program: Dimensions of Biodiversity (Dimensions of Biodiversity)

Project: Dimensions: Collaborative Research: Genetic, functional and phylogenetic diversity determines marine phytoplankton community responses to changing temperature and nutrients (Phytoplankton Community Responses)


Abstract

Thermal growth rates for 24 strains representing 5 species from the diatom genus Skeletonema, as analyzed in Anderson and Rynearson, 2020. Strains were grown at temperatures ranging from -2 to 36C to assess how inter- and intraspecific thermal trait variability could explain diatom community dynamics.

This dataset includes experimental thermal growth measurements from five Skeletonema species. Strains were collected at Narragansett Bay, Rhode Island and obtained from the National Center for Marine Algae and Microbiota (NCMA/CCMP), and grown at varying temperatures.


Related Datasets

No Related Datasets

Related Publications

Results

Anderson, S. I., & Rynearson, T. A. (2020). Variability approaching the thermal limits can drive diatom community dynamics. Limnology and Oceanography, 65(9), 1961–1973. Portico. https://doi.org/10.1002/lno.11430
Methods

Boyd, P. W., Rynearson, T. A., Armstrong, E. A., Fu, F., Hayashi, K., Hu, Z., … Thomas, M. K. (2013). Marine Phytoplankton Temperature versus Growth Responses from Polar to Tropical Waters – Outcome of a Scientific Community-Wide Study. PLoS ONE, 8(5), e63091. doi:10.1371/journal.pone.0063091
Methods

Gotelli, N. J. (1995). A Primer of Ecology, 206 p.
Software

R Core Team (n.d.) R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria).