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Predictability of thermal fluctuations influences functional traits of a cosmopolitan marine diatom

Citation

Gill, Raissa et al. (2022), Predictability of thermal fluctuations influences functional traits of a cosmopolitan marine diatom, Dryad, Dataset, https://doi.org/10.5061/dryad.pnvx0k6p2

Abstract

Evolutionary theory predicts that organismal plasticity should evolve in environments that fluctuate regularly. However, in environments that fluctuate less predictably, plasticity may be constrained because environmental cues become less reliable for expressing the optimum phenotype. Here we examine how the predictability of +5 °C temperature fluctuations impacts the phenotype of the marine diatom Thalassiosira pseudonana. Thermal regimes were informed by temperatures experienced by microbes in an ocean simulation, and featured regular or irregular temporal sequences of fluctuations that induced mild physiological stress. Physiological traits (growth, cell size, complexity, pigmentation) were quantified at the individual cell level using flow cytometry. Changes in cellular complexity emerged as the first impact of predictability after only 8-11 days, followed by deleterious impacts on growth on days 13-16. Specifically, cells with a history of irregular fluctuation exposure exhibited a 50% reduction in growth compared with the stable reference environment, while growth was 3-18 times higher when fluctuations were regular. We observed no evidence of heat hardening (increasingly positive growth) with recurrent fluctuations. This study demonstrates that unpredictable temperature fluctuations impact this cosmopolitan diatom under ecologically-relevant time frames, suggesting shifts in environmental stochasticity under a changing climate could have widespread consequences amongst ocean primary producers.

Methods

Description of methods for data collection:

Adrift (adrift-project.com): Simulating passive particle movement through the uppermost surface layer of a three-dimensional numeric ocean model of the Eastern Australian Current in 2017.

Flow cytometry: Quantifying cell abundance and traits of Thalassiosira pseudonana populations across experimental thermal regimes (regular fluctuations 'R-1114' and 'R-2224', irregular fluctuations 'I-1124' and 'I-2114', stable temperature 'Control'), and pilot study.

Probe carboxy-H2DCFDA and microplate reader: Quantifying fluorescence of Thalassiosira pseudonana populations induced by production of reactive oxygen species during an experimental temperature fluctuation.

Description of methods for data processing:

To examine how T. pseudonana responded to each temperature fluctuation within regimes, growth rate (cell divisions per hour) and proportional changes in median cell size (estimated by forward scatter; FSC), complexity (side scatter; SSC), and chlorophyll-a (Chl-A) per cell, were calculated from trait values preceding and succeeding each fluctuation.

Growth rates were calculated using the following equation: Growth rate (h-1) = (ln⁡(N)-ln⁡(N0)) / (t-t0), where N0 and N denote the cell abundance at 10:00 and 20:00, respectively, and t0 and t denote time in hours (t0 = 0 and t = 10 hours, respectively).

Functional traits were calculated using the following equation: Functional trait (proportional change) = (Trait-Trait0) / Trait0, where Trait0 and Trait denote the population trait values at 10:00 and 20:00, respectively. Similar to growth rate, functional traits reflect a rate of change over the fluctuation period to assess how traits shift irrespective of their preceding values.

To standardise responses, all traits were normalised by subtracting the mean stable temperature control (Control) value of each day from individual observed values for each fluctuating regime (R-1114, R-2224, I-1124, I-2114); a positive/negative relative trait value indicates the trait is higher/lower compared to the mean Control condition.

Please see the research article associated with this Dryad Data for more detailed information on the methodology and experimental design: https://doi.org/10.1098/rspb.2021.2581

Usage Notes

Please read the README.txt file uploaded with this Dryad Data to use this dataset, and see the associated research article for more detailed information on the methodology and experimental design: https://doi.org/10.1098/rspb.2021.2581

Data File List:
File Name: Gill_et_al_2022_AdriftSimulation.csv
Description: Data exported from simulations in Adrift, including raw data only (used in R notebook)
Date of file creation: 2019

File Name: Gill_et_al_2022_AdriftSimulation.xlsx
Description: Data exported from simulations in Adrift, including raw data and summary statistics
Date of file creation: 2019

File Name: Gill_et_al_2022_MainExperiment.csv
Description: Data exported from flow cytometry analysis for the main experiment, including final data only (used in R notebook)
Date of file creation: 2019

File Name: Gill_et_al_2022_MainExperiment.xlsx
Description: Data exported from flow cytometry analysis for the main experiment, including raw and final data
Date of file creation: 2019

File Name: Gill_et_al_2022_PlatePilot.xlsx
Description: Data from pilot study used to assess potential for between-plate effects
Date of file creation: 2019

File Name: Gill_et_al_2022_RegimeSchematic.xlsx
Description: Data used to create the thermal regime schematic in supplementary figure 1
Date of file creation: 2019

File Name: Gill_et_al_2022_RosExperiment.xlsx
Description: Data from reactive oxygen species experiment used to assess physiological stress
Date of file creation: 2020

Missing values in the datasets (n/a) generally indicate non-applicable cells, with a few exceptions due to human error during experimental sampling

Software File List:
Software Name: Gill_et_al_2022_RNotebook.Rmd
Description: R Notebook used to plot and analyse the data
Date of file creation: 2020

Funding

Australian Research Council, Award: DP180100054

Department of Industry, Innovation and Science, Australian Government, Award: CSG56336