DOI: 10.5061/dryad.pnvx0k6p2
title: Predictability of thermal fluctuations influences functional traits of a cosmopolitan marine diatom

description: 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: 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 <em>Thalassiosira
 pseudonana </em>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-H<sub>2</sub>DCFDA and microplate
 reader: Quantifying fluorescence of <em>Thalassiosira
 pseudonana </em>populations induced by production of reactive oxygen
 species during an experimental temperature fluctuation.
 Description of methods for data processing: To examine how <em>T.
 pseudonana</em> 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<sup>-1</sup>) =
 (ln⁡(N)-ln⁡(N<sub>0</sub>)) / (t-t<sub>0</sub>),
 where N<sub>0</sub> and N denote the cell abundance at 10:00
 and 20:00, respectively, and t<sub>0</sub> and t denote time
 in hours (t<sub>0</sub> = 0 and t = 10 hours,
 respectively). Functional traits were calculated
 using the following equation: Functional trait (proportional change) =
 (Trait-Trait<sub>0</sub>) / Trait<sub>0</sub>,
 where Trait<sub>0</sub> 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
description: 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<br><br>Data File
 List:<br>File Name:
 Gill_et_al_2022_AdriftSimulation.csv<br>Description: Data exported
 from simulations in Adrift, including raw data only (used in R
 notebook)<br>Date of file creation: 2019<br><br>File
 Name: Gill_et_al_2022_AdriftSimulation.xlsx<br>Description: Data
 exported from simulations in Adrift, including raw data and summary
 statistics<br>Date of file creation: 2019<br><br>File
 Name: Gill_et_al_2022_MainExperiment.csv<br>Description: Data
 exported from flow cytometry analysis for the main experiment, including
 final data only (used in R notebook)<br>Date of file creation:
 2019<br><br>File Name:
 Gill_et_al_2022_MainExperiment.xlsx<br>Description: Data exported
 from flow cytometry analysis for the main experiment, including raw and
 final data<br>Date of file creation: 2019<br><br>File
 Name: Gill_et_al_2022_PlatePilot.xlsx<br>Description: Data from
 pilot study used to assess potential for between-plate
 effects<br>Date of file creation: 2019<br><br>File Name:
 Gill_et_al_2022_RegimeSchematic.xlsx<br>Description: Data used to
 create the thermal regime schematic in supplementary figure
 1<br>Date of file creation: 2019 File Name:
 Gill_et_al_2022_RosExperiment.xlsx<br>Description: Data from
 reactive oxygen species experiment used to assess physiological
 stress<br>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:<br>Software Name:
 Gill_et_al_2022_RNotebook.Rmd<br>Description: R Notebook used to
 plot and analyse the data<br>Date of file creation: 2020