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_targets.R
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/
_targets.R
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source("R/packages.R")
source("R/functions.R")
# Define workflow plan
tar_plan(
# Load data ----
# - microclimate data
tar_file(climate_file, "data/nitta_2020/moorea_climate.csv"),
climate = read_csv(climate_file),
# - site data
tar_file(moorea_sites_file, "data/nitta_2017/sites.csv"),
moorea_sites = read_csv(
moorea_sites_file,
col_types = "cnnn") %>%
# remove sites on Tahiti (Mt. Aorai)
filter(str_detect(site, "Aorai", negate = TRUE)),
# - growth habit
tar_file(filmy_habit_file, "data/filmy_growth_habit.csv"),
filmy_habit = read_csv(filmy_habit_file),
# - vector of filmy fern species
filmy_species = filmy_habit$species,
# - phylogenetic tree (time tree, lacks BS values at nodes)
tar_file(filmy_phy_file, "data/nitta_2017/treepl_Moorea_Tahiti.tre"),
filmy_phy_no_bs = ape::read.tree(filmy_phy_file) %>%
# trim to only filmy ferns
ape::keep.tip(filmy_species),
# - phylogenetic tree (ML tree with BS values at nodes)
tar_file(filmy_phy_bs_file, "data/nitta_2017/RAxML_bipartitions.all_broad.reduced"),
filmy_phy_bs = ape::read.tree(filmy_phy_bs_file) %>%
# trim to only filmy ferns
ape::keep.tip(filmy_phy_no_bs$tip.label),
# - transfer bootstrap values from ML tree to time tree
filmy_phy = transfer_bs(filmy_phy_bs, filmy_phy_no_bs),
# - desiccation tolerance yields
tar_file(filmy_dt_file, "data/filmy_dt.csv"),
filmy_dt = load_filmy_dt(filmy_dt_file),
# - desiccation tolerance chamber temp and RH
tar_file(filmy_dt_chamber_file, "data/filmy_dt_chamber.csv"),
filmy_dt_chamber = load_filmy_dt_chamber(filmy_dt_chamber_file),
# - community data
tar_file(community_matrix_raw_file, "data/nitta_2017/all_plots.csv"),
community_matrix_raw = read_csv(community_matrix_raw_file),
# - light responses
tar_file(light_data_file, "data/filmy_light_curves.csv"),
light_data_all = load_filmy_lc(light_data_file),
# remove outliers, sporos measured in field
light_data = filter_light_data(light_data_all),
# - specimen data
tar_file(specimens_raw_file, "data/fern_specimens.csv"),
specimens_raw = read_csv(specimens_raw_file),
# - gametophyte DT times entered manually (2012 only)
tar_file(gameto_times_2012_file, "data/2012_gameto_dt_times.csv"),
gameto_time_summary_2012 = load_gameto_time_summary_2012(gameto_times_2012_file),
# - sporophyte DT times entered manually (2012 only)
tar_file(sporo_dt_times_file, "data/2012_sporo_dt_times.csv"),
sporo_dt_times = load_sporo_dt_times(sporo_dt_times_file),
# Process data ----
# - subset collection data to just filmy ferns on Moorea
filmy_specimens = tidy_filmy_specimens(
specimens_raw = specimens_raw,
filmy_species = filmy_species),
# - calculate recovery during DT test per individual
recovery_indiv = filmy_dt %>%
# Only include samples that should be presented in the main MS
filter(section == "main") %>%
# Filter out individuals with low pre-treatment yields
filter(!is.na(yield_pre), yield_pre > .4) %>%
calculate_recovery(),
# - calculate water content during DT test per individual
rel_water_indiv = calculate_indiv_water(filmy_dt),
# - calculate mean maximum daily VPD per datalogger
mean_vpd = calculate_mean_max_vpd(climate),
# - calculate mean VPD per species for gametophytes
mean_vpd_gameto = calculate_mean_vpd_gameto(
mean_vpd = mean_vpd,
filmy_specimens = filmy_specimens,
moorea_sites = moorea_sites,
filmy_species = filmy_species
),
# - calculate mean VPD per species for sporophytes
mean_vpd_sporo = calculate_mean_vpd_sporo(
community_matrix_raw = community_matrix_raw,
mean_vpd = mean_vpd,
traits = filmy_habit,
moorea_sites = moorea_sites,
filmy_species = filmy_species
),
# - determine range of gametophytes beyond sporophytes
range_comparison = analyze_dist_pattern(
community_matrix_raw = community_matrix_raw,
filmy_species = filmy_species,
filmy_specimens = filmy_specimens,
phy = filmy_phy,
moorea_sites = moorea_sites
),
# - combine environmental and range data
env_range_data = purrr::reduce(
list(
mean_vpd_gameto,
mean_vpd_sporo,
range_comparison),
full_join,
by = "species"
) %>% assert(is_uniq, species),
# - fit light curve models to data
light_models = fit_lc_model(light_data),
# - extract fitted data points
filmy_lc_model_fitted_data = extract_fitted_lc_data(light_models),
# - extract model parameters:
# ETR at 95% of estimated max value and PAR at that value
filmy_lc_model_params = extract_lc_model_params(light_models),
# - calculate mean light curve parameters by species and generation
light_species_means = calculate_mean_light(filmy_lc_model_params),
# - calculate mean DT recovery by species and generation
recovery_species_means = calculate_mean_recovery(recovery_indiv),
# - calculate relative water content by species (sporophytes only)
rel_water_species_means = calculate_mean_water(rel_water_indiv),
# - combine the species means into a single dataframe
combined_species_means = combine_mean_phys_traits(
recovery_species_means = recovery_species_means,
light_species_means = light_species_means
),
# - make table of gametophyte DT treatment batches (groups)
gameto_indiv_times = prepare_gameto_dt_groups(filmy_dt, gameto_time_summary_2012, filmy_specimens),
# t-test ----
# Perform two-sided t-test on DT and light responses across generations
t_test_results = run_t_test(
filmy_lc_model_params = filmy_lc_model_params,
recovery_indiv = recovery_indiv),
# Phylogenetic signal ----
phylosig = analyze_phylosig_by_generation(
combined_species_means = combined_species_means,
phy = filmy_phy,
traits_select = c("recovery_mean", "etr_mean", "par_mean")
),
# GLMMS ----
# (Generalized Linear Mixed Models)
# Uses phylogeny for DT only
glmms = run_glmm(
combined_species_means = combined_species_means,
traits = filmy_habit,
phy = filmy_phy),
# Get DIC for each GLMM
glmm_summary = tidy_glmms(glmms),
# Get parameters for best GLMMs only
glmm_params = tidy_best_glmm_params(glmms),
# PGLS ----
# (Phylogenetic Generalized Least Squares)
# Run PGLS for VPD and range of gameto beyond sporo vs. desiccation tolerance
env_range_recover_data = combine_env_env_range_recover(
combined_species_means = combined_species_means,
env_range_data = env_range_data
),
env_range_dt_model = run_pgls(
env_range_recover_data = env_range_recover_data,
phy = filmy_phy),
env_range_dt_model_summary = map_df(env_range_dt_model, tidy_pgls, .id = "model"),
# Render manuscript ----
# Track bibliography files
tar_file(refs, "ms/references.bib"),
tar_file(refs_other, "ms/references_other.yaml"),
# Track docx style
tar_file(word_style, "ms/journal-of-plant-research.docx"),
# Render data README
tar_render(
data_readme,
"ms/data_readme.Rmd",
output_dir = here::here("results/data_readme")
),
# Render MS:
# - pdf for preprint
tar_render(
preprint_pdf,
"ms/manuscript.Rmd",
output_dir = here::here("results/ms"),
output_file = "moorea_filmies_preprint.pdf",
params = list(output_type = "preprint")),
# - pdf for conversion to docx
tar_render(
manuscript_pdf,
"ms/manuscript.Rmd",
output_dir = here::here("results/ms"),
params = list(output_type = "ms")
),
# - docx for submission
# FIXME: some fixes need to be made manually for MS submission
# - remove extra space from bib entries
# - add missing table header rows
ms_docx = latex2docx(
latex = "results/ms/manuscript.tex",
docx = "results/ms/manuscript.docx",
template = word_style,
lua_filter = "ms/pagebreak.lua",
wd = here::here("results"),
depends = manuscript_pdf
),
# Render SI: figures (ESM 1) for submission
tar_render(
si_pdf,
"ms/si.Rmd",
output_file = "ESM_1.pdf",
output_dir = here::here("results/si")
),
# Render SI: figures (ESM 1) for pre-print
tar_render(
si_pdf_bioarxiv,
"ms/si.Rmd",
output_file = "ESM_1_preprint.pdf",
params = list(output_type = "preprint"),
output_dir = here::here("results/si")
),
# Render SI: tables (ESM 2) for sumbission
tar_file(
gameto_indiv_times_table_ms,
write_gameto_times(gameto_indiv_times, ms_type = "ms", "results/si/ESM_2.csv")),
# Render SI: tables (ESM 2) for pre-print
tar_file(
gameto_indiv_times_table_preprint,
write_gameto_times(gameto_indiv_times, ms_type = "preprint", "results/si/ESM_2_preprint.csv"))
)