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πŸ”πŸŒ± Data collected for group 3 during the 5th PFTC in Peru πŸŒ±πŸ”

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README Plant functional trait course 5 - group 3

Cusco/Wayqecha, Peru - March 2020

Group 3: Trait & taxonomic community response to fire and elevation

Contact: [email protected]

For repo access contact Tanya either on Slack or shoot her an email: [email protected]

Repository for a proposed manuscript focusing on the effects of fire and elevation on species composition and functional trait in the high Andean moist Puna grasslands. This repository is used to store code associated with the data analysis portion of the manuscript. The proposal can be found here

Downloading and 'cleaning' the data

This can be done by running the scripts/0_data_import.R file. This will then download the data as well as filter/clean the data so that we only have the data that we will be using for our analyses.

Cleaning/filtering the data

The scripts/0_data_import.R code will also filter out the the sites relevant for our analyses. As we are downloading the entire Puna dataset form osf we need to filter the data so that we have the following sites and treatments from the following years:

Site Treatment Dry Season Wet Season
QUE C 2019 2019
QUE NB 2020
TRE C 2019 2018, 2019, 2020
TRE NB 2019 2019, 2020
ACJ C 2019 2018, 2019, 2020
ACJ NB 2019 2020

Datasets to use

Given the Frankenstein nature of the dataset we have used the [traitstrap (see preprint here)](https: //doi.org/10.22541/au.162196147.76797968/v1) package to help smooth out some of the gaps in the data. A short vignette can be found here.

The workflow itself is in scripts/DA1_traitstrap.R if you are interested. The bootstrapped data can be found in data/processed as two different datasets. The traits_traitstrapped_raw.csv has trait values at the individual level (for the different treatment/sites/plot_id combos) and is 'similar' to the raw data downloaded to osf in terms of how it looks but of course the data are generated using the bootstrapping simulations from traitstrap. The other data file traits_traitstrapped_moments.csv has the moment summaries for the distributions for the different treatment/sites/plot_id combos. That is this dataset will give you the equivalent of the community weighted mean for example.

In summary: import and use the traits_traitstrapped_raw.csv dataset if working with individual trait-level questions and traits_traitstrapped_moments.csv when concerned with community-level work.

Script naming structure

Each subtask related to/needing a coding workflow should be contained within its own script and should be named starting with the subtask number and a brief descriptive name. Scripts should be placed in the scripts/ folder. This means we can keep track of each task separately.

Working on subtasks - using pull requests

Ideally each subtask should be on a new branch. This means that each subtask can be turned into a pull request (PR) allowing us to easily see the full commit history for that subtask and also allows subgroup members to request reviews/feedback from each other as well as have conversation threads. PRs can
initially be marked as drafts and once ready (i.e. completed) it can be marked as ready for review and then merged into the master branch.

branches should be named after the subtask code - same for the PR (although this can be a bit more comprehensive/descriptive).

Brief summaries of subtasks

DC5 - Allocation of Grimes CSR strategy for FT's

CSR scores were calculated for each individual based on trait values using StrateFY. This process is not automated so the output is saved in data/processed/ and has been appended to the original leaf traits dataset. it has also been integrated into scripts/0_data_import.R so calling that script will automatically 'add' the CSR traits to the traits df to your environment

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