DOI: 10.5061/dryad.63xsj3tzv
title: Data and code from: Phytoplankton thermal responses adapt in the absence of hard thermodynamic constraints

description: 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.
description:   CONTENTS OF THIS DATASET Data
 files:    1) growth_rate_dataset.csv<br>        
 Phytoplankton growth rate vs temperature measurements, extracted<br>
         from the literature.    2)
 final_calibrated_tree.phy<br>         The relative time-calibrated
 phylogeny reconstructed for this <br>         study.
    3) dataset_for_TPC_MCMCglmms.csv<br>         Reliable
 TPC parameter estimates (along with measures of <br>        
 uncertainty), latitude/longitude values, and habitat (marine or <br>
         freshwater) for all phytoplankton strains of this study.
    4) dataset_for_marine_TPC_MCMCglmms.csv<br>         As
 above, but only for marine strains. Besides lat/lon values, <br>    
     this dataset also includes temperature data (from
 Lagrangian <br>         simulations and from the NOAA OISST dataset)
 for each isolation <br>         location.    5)
 dataset_of_B_0_B_pk_and_cell_volume.csv<br>         B_0 and B_pk
 estimates for all phytoplankton along with cell <br>         volume
 measurements in units of cubic meters. Source
 code:    1) TPC_fitting.py<br>         Python 2
 script for fitting the 4-parameter variant of the <br>        
 Sharpe-Schoolfield model to the data in growth_rate_dataset.csv.
    2) Lagrangian_trajectory_simulation.py<br>        
 Python 2 script for simulating backwards-in-time Lagrangian <br>    
     trajectories of drifting particles (marine phytoplankton) from
 a<br>         list of latide/longitude locations.
    3) run_TPC_MCMCglmms.R<br>         R script for fitting
 regression models to the entire TPC dataset<br>         with all TPC
 parameters forming a combined response.    4)
 run_marine_TPC_MCMCglmms.R<br>         R script for fitting
 regression models to the TPC dataset of <br>         marine
 phytoplankton only.    5)
 run_B_0_or_B_pk_against_cell_volume_MCMCglmms.R<br>         R script
 for estimating the influence of cell volume on B_0 or <br>        
 B_pk values.<br>