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
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.
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 |
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.
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.
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).
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