Skip to content

Latest commit

 

History

History
69 lines (53 loc) · 4.36 KB

README.md

File metadata and controls

69 lines (53 loc) · 4.36 KB
Raw

Code repo for "The evolution of thermal performance curves in fungi farmed by attine ant mutualists in aboveground or belowground microclimates"

This repository contains code for the publication "The evolution of thermal performance curves in fungi farmed by attine ant mutualists in aboveground or belowground microclimates" by Oscar Hess, Tiem van der Deure, Mille Bolander, Caio Leal Dutra, and Jonathan Z. Shik.

In this paper, we analyze the performance of fungus symbionts cultivated by six fungus-farming ants across a temperature gradient.

Corresponding author information

Name: Jonathan Shik
Institution: University of Copenhagen
Address: Universitetsparken 15, 2100 Copenhagen
Email: [email protected]

Usage notes:

These datasets were generated at the University of Copenhagen based on attine ant colonies (Atta colombica, Acromyrmex echinatior, Paratrachymyrmex cornetzi, Cyphomyrmex costatus, Mycocepurus smithii, Apterostigma dentigerum) collected at the Smithsonian Tropical Research Institute in Panama (Soberenia Park).

Description of data and code

This archive contains all data and code to reproduce all statistical analyses and produce raw versions of the figures, as described in the Methods and reported in the Results of the associated paper.

Datasets included:

  1. data.csv
  2. metadata.csv
  3. alignment.fasta
  4. sequences.fasta

data.csv:

This file contains results of the laboratory in vitro experiment with fungi grown at different temperatures

  • Number of variables: 8
  • Number of cases/rows: 358
  • Variable List:
    • species: The attine ant species from which the fungus was isolated
    • colony: Colony IDs representing individual colonies (biological replicates) from which the fungal sample was isolated
    • temperature: the temperature treatment (degrees C) at which the Petri dish was incubated (10, 15, 20, 25, 30)
    • area_d0: the area of the fungus (cm^2) measured on Day 0
    • area_d7: the area of the fungus (cm^2) measured on Day 7
    • area_d14: the area of the fungus (cm^2) measured on Day 14
    • area_d21: the area of the fungus (cm^2) measured on Day 21
    • area_d28: the area of the fungus (cm^2) measured on Day 28

metadata.csv:

This file contains metadata for data.csv.

  • Number of variables: 4
  • Number of cases/rows: 19
  • Variable List:
    • species: The attine ant species from which the fungus was isolated
    • colony: Colony IDs representing individual colonies (biological replicates) from which the fungal sample was isolated
    • Ecology: Whether the fungus is farmed aboveground or belowground (Categorical variable)

alignment.fasta:

Aligned ITS sequences for the barcoding analyses and the tree in Figure 1 of the publication

  • Number of colonies sequenced: 20

sequences.fasta:

Raw ITS sequences for the barcoding analyses and the tree in Figure 1 of the publication

  • Number of colonies sequenced: 20

Reproducibility

Phylogenetic trees

The shell script was run using MAFFT v7.490 (available as a Tar file here) and IQ-TREE v 1.6.12 (available from GitHub here)

After installing the packages, run the script phylotree.sh in the shell to generate the tree. The script uses sequences.fasta and alignment.fasta.

Data analysis

All data analysis was done in Julia v.1.10. We recommend using juliaup to install this version of Julia.

After installing Julia and cloning this repository, start Julia in the directory that contains this code. To replicate the environment that was used to run the analysis, type ] to enter pkg mode, then activate . to activate the local environment, and then instantiate to download the packages specified in Manifest.toml.

The julia script 01_sampling.jl generates and saves the posterior distributions. The figures and tables in the paper are generated in the script 02_sampling.jl. These scripts load data from data.csv and metadata.csv, as well as helper functions and additional code in data.jl, bayesian_model.jl, and response_functions.

Note that the sampling script makes heavy use of multi-threading to speed up computations. It is highly recommended to start julia with multiple threads. See the official manual for how to do this.