Predictability of thermal fluctuations influences functional traits of a cosmopolitan marine diatom - Gill, Raissa; Collins, Sinead; Argyle, Phoebe; Larsson, Michaela; Fleck, Robert; Doblin, Martina (Dryad, 2021)
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.
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
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.2021.2581Data File List:File Name:
Gill_et_al_2022_AdriftSimulation.csvDescription: Data exported from
simulations in Adrift, including raw data only (used in R notebook)Date of
file creation: 2019File Name:
Gill_et_al_2022_AdriftSimulation.xlsxDescription: Data exported from
simulations in Adrift, including raw data and summary statisticsDate of
file creation: 2019File Name:
Gill_et_al_2022_MainExperiment.csvDescription: Data exported from flow
cytometry analysis for the main experiment, including final data only
(used in R notebook)Date of file creation: 2019File Name:
Gill_et_al_2022_MainExperiment.xlsxDescription: Data exported from flow
cytometry analysis for the main experiment, including raw and final
dataDate of file creation: 2019File Name:
Gill_et_al_2022_PlatePilot.xlsxDescription: Data from pilot study used to
assess potential for between-plate effectsDate of file creation: 2019File
Name: Gill_et_al_2022_RegimeSchematic.xlsxDescription: Data used to create
the thermal regime schematic in supplementary figure 1Date of file
creation: 2019 File Name: Gill_et_al_2022_RosExperiment.xlsxDescription:
Data from reactive oxygen species experiment used to assess physiological
stressDate 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.RmdDescription: R Notebook used to plot and
analyse the dataDate of file creation: 2020
Keywords: FOS: Biological sciences