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Data and code from: Phytoplankton thermal responses adapt in the absence of hard thermodynamic constraints

Citation

Kontopoulos, Dimitrios - Georgios et al. (2020), Data and code from: Phytoplankton thermal responses adapt in the absence of hard thermodynamic constraints, v4, Dryad, Dataset, https://doi.org/10.5061/dryad.63xsj3tzv

Abstract

To better predict how populations and communities respond to climatic temperature variation, it is necessary to understand how the shape of the response of fitness-related rates to temperature evolves (the thermal performance curve). Currently, there is disagreement about the extent to which the evolution of thermal performance curves is constrained. One school of thought has argued for the prevalence of thermodynamic constraints through enzyme kinetics, whereas another argues that adaptation can—at least partly—overcome such constraints. To shed further light on this debate, we perform a phylogenetic meta-analysis of the thermal performance curves of growth rate of phytoplankton—a globally important functional group—, controlling for environmental effects (habitat type and thermal regime). We find that thermodynamic constraints have a minor influence on the shape of the curve. In particular, we detect a very weak increase of maximum performance with the temperature at which the curve peaks, suggesting a weak "hotter-is-better" constraint. Also, instead of a constant thermal sensitivity of growth across species, as might be expected from strong constraints, we find that all aspects of the thermal performance curve evolve along the phylogeny. Our results suggest that phytoplankton thermal performance curves adapt to thermal environments largely in the absence of hard thermodynamic constraints.

Usage Notes

  CONTENTS OF THIS DATASET

Data files:

   1) growth_rate_dataset.csv
        Phytoplankton growth rate vs temperature measurements, extracted
        from the literature.

   2) final_calibrated_tree.phy
        The relative time-calibrated phylogeny reconstructed for this 
        study.

   3) dataset_for_TPC_MCMCglmms.csv
        Reliable TPC parameter estimates (along with measures of 
        uncertainty), latitude/longitude values, and habitat (marine or 
        freshwater) for all phytoplankton strains of this study.

   4) dataset_for_marine_TPC_MCMCglmms.csv
        As above, but only for marine strains. Besides lat/lon values, 
        this dataset also includes temperature data (from Lagrangian 
        simulations and from the NOAA OISST dataset) for each isolation 
        location.

   5) dataset_of_B_0_B_pk_and_cell_volume.csv
        B_0 and B_pk estimates for all phytoplankton along with cell 
        volume measurements in units of cubic meters.

Source code:

   1) TPC_fitting.py
        Python 2 script for fitting the 4-parameter variant of the 
        Sharpe-Schoolfield model to the data in growth_rate_dataset.csv.

   2) Lagrangian_trajectory_simulation.py
        Python 2 script for simulating backwards-in-time Lagrangian 
        trajectories of drifting particles (marine phytoplankton) from a
        list of latide/longitude locations.

   3) run_TPC_MCMCglmms.R
        R script for fitting regression models to the entire TPC dataset
        with all TPC parameters forming a combined response.

   4) run_marine_TPC_MCMCglmms.R
        R script for fitting regression models to the TPC dataset of 
        marine phytoplankton only.

   5) run_B_0_or_B_pk_against_cell_volume_MCMCglmms.R
        R script for estimating the influence of cell volume on B_0 or 
        B_pk values.
 

Funding

Natural Environment Research Council, Award: NE/L002515/1

Natural Environment Research Council, Award: NE/M004740/1

Natural Environment Research Council, Award: NE/M003205/1

References