Table of Contents

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

Fatty acid profiles by biovolume of replicate populations of Thalassiosira pseudonana, selected at 16 and 31C for ~500 generations and assayed at 4 temperatures.

These data were published in O'Donnell and Litchman (2019) Evol. Appl. and can also be found in Supporting Information file eva12798-sup-0001-Supinfo.docx, Figures 1-5 of the main text, and on Dryad: https://datadryad.org/stash/dataset/doi:10.5061/dryad.pn04m84.

Details of the long-term selection experiment can be found in another dataset associated with this project, entitled “Temperature-growth rate curves for Thalassiosira pseudonana”, and in O’Donnell et al. (2018), Global Change Biology. After ~500 generations of experimental selection (5 replicate populations at 16°C and 5 at 31°C), we acclimated each replicate population for 10 generations at 20°C, then acclimated each at 10, 16, 26 and 31°C for an additional 10 generations. We then sub-cultured each acclimated population into triplicate batch cultures, starting with 100,000 cells in 40 ml of L1 marine culture medium in a 50 ml tissue culture flask. We grew all experimental cultures (2 selection temperatures x 5 replicate populations x 4 assay temperatures x 3 assay replicates = 120 total experimental units) to late log phase, estimating per-capita growth rates by placing each tissue culture flask in a spectrophotometer daily and measuring absorbance at 436 nm. We harvested 15 ml of culture for fatty acid analysis, retaining ~25 µl for enumeration on a CASY particle counter. The remaining volume was used for analyses not addressed here.

Cells were harvested by filtration through a 25mm Whatman GF/B filter. We extracted fatty acids from T. pseudonana cells using a solution of chloroform, methanol, and formic acid (10:20:1). We analyzed fatty acid profiles by performing fatty acid methyl ester (FAME) reactions on each GF/B filter sample (Wang and Benning 2011; adapted for algae by Boyle et al. 2012) and quantifying fatty acids using gas chromatography.

All calculations were done and data processed and analyzed using R statistical programming software, version 3.3.2. Cell volumes were estimated by measuring the length and width of T. pseudonana frustules under oil immersion at 1000x magnification, then using these measurements to calculate the volume of a cylinder (π x rˆ2 x h). We thusly measured 10 cells per sample (10 x 120 = 1200 total cells). Mean per-biovolume fatty acid contents were estimated using these volumes.

BCO-DMO Processing Notes:
- added conventional header with dataset name, PI name, version date
- modified parameter names to conform with BCO-DMO naming conventions
- removed duplicate mol_w3_bv column

NSF Award Abstract:
Photosynthetic marine microbes, phytoplankton, contribute half of global primary production, form the base of most aquatic food webs and are major players in global biogeochemical cycles. Understanding their community composition is important because it affects higher trophic levels, the cycling of energy and elements and is sensitive to global environmental change. This project will investigate how phytoplankton communities respond to two major global change stressors in aquatic systems: warming and changes in nutrient availability. The researchers will work in two marine systems with a long history of environmental monitoring, the temperate Narragansett Bay estuary in Rhode Island and a subtropical North Atlantic site near Bermuda. They will use field sampling and laboratory experiments with multiple species and varieties of phytoplankton to assess the diversity in their responses to different temperatures under high and low nutrient concentrations. If the diversity of responses is high within species, then that species may have a better chance to adapt to rising temperatures and persist in the future. Some species may already be able to grow at high temperatures; consequently, they may become more abundant as the ocean warms. The researchers will incorporate this response information in mathematical models to predict how phytoplankton assemblages would reorganize under future climate scenarios. Graduate students and postdoctoral associates will be trained in diverse scientific approaches and techniques such as shipboard sampling, laboratory experiments, genomic analyses and mathematical modeling. The results of the project will be incorporated into K-12 teaching, including an advanced placement environmental science class for underrepresented minorities in Los Angeles, data exercises for rural schools in Michigan and disseminated to the public through an environmental journalism institute based in Rhode Island.

Predicting how ecological communities will respond to a changing environment requires knowledge of genetic, phylogenetic and functional diversity within and across species. This project will investigate how the interaction of phylogenetic, genetic and functional diversity in thermal traits within and across a broad range of species determines the responses of marine phytoplankton communities to rising temperature and changing nutrient regimes. High genetic and functional diversity within a species may allow evolutionary adaptation of that species to warming. If the phylogenetic and functional diversity is higher across species, species sorting and ecological community reorganization is likely. Different marine sites may have a different balance of genetic and functional diversity within and across species and, thus, different contribution of evolutionary and ecological responses to changing climate. The research will be conducted at two long-term time series sites in the Atlantic Ocean, the Narragansett Bay Long-Term Plankton Time Series and the Bermuda Atlantic Time Series (BATS) station. The goal is to assess intra- and inter-specific genetic and functional diversity in thermal responses at contrasting nutrient concentrations for a representative range of species in communities at the two sites in different seasons, and use this information to parameterize eco-evolutionary models embedded into biogeochemical ocean models to predict responses of phytoplankton communities to projected rising temperatures under realistic nutrient conditions. Model predictions will be informed by and tested with field data, including the long-term data series available for both sites and in community temperature manipulation experiments. This project will provide novel information on existing intraspecific genetic and functional thermal diversity for many ecologically and biogeochemically important phytoplankton species, estimate generation of new genetic and functional diversity in evolution experiments, and develop and parameterize novel eco-evolutionary models interfaced with ocean biogeochemical models to predict future phytoplankton community structure. The project will also characterize the interaction of two major global change stressors, warming and changing nutrient concentrations, as they affect phytoplankton diversity at functional, genetic, and phylogenetic levels. In addition, the project will develop novel modeling methodology that will be broadly applicable to understanding how other types of complex ecological communities may adapt to a rapidly warming world.

(adapted from the NSF Synopsis of Program)
Dimensions of Biodiversity is a program solicitation from the NSF Directorate for Biological Sciences. FY 2010 was year one of the program.  [MORE from NSF]

The NSF Dimensions of Biodiversity program seeks to characterize biodiversity on Earth by using integrative, innovative approaches to fill rapidly the most substantial gaps in our understanding. The program will take a broad view of biodiversity, and in its initial phase will focus on the integration of genetic, taxonomic, and functional dimensions of biodiversity. Project investigators are encouraged to integrate these three dimensions to understand the interactions and feedbacks among them. While this focus complements several core NSF programs, it differs by requiring that multiple dimensions of biodiversity be addressed simultaneously, to understand the roles of biodiversity in critical ecological and evolutionary processes.