Storyline C

Plot arrows

dat_phenophase_time_summ <- dat_phenophase_time_clean %>%
  summ_phenophase_time(group_vars = c("site", "canopy", "heat", "heat_name", "water", "water_name", "species", "phenophase", "year")) # ignore block and plot

dat_climate_spring <- summ_climate_season(dat_climate_daily, date_start = "Mar 15", date_end = "May 15", rainfall = 1, group_vars = c("site", "canopy", "heat", "heat_name", "water", "water_name", "year"))

dat_climate_fall <- summ_climate_season(dat_climate_daily, date_start = "Jun 15", date_end = "Sep 15", rainfall = 0, group_vars = c("site", "canopy", "heat", "heat_name", "water", "water_name", "year"))

Note that some climatic data not available in 2009.

dat_climate_spring %>% filter(site == "cfc", canopy == "open", year == "2009")

## # A tibble: 2 × 10
##   site  canopy heat  heat_name water water_name  year  temp  mois  prcp
##   <chr> <fct>  <fct> <fct>     <fct> <fct>      <dbl> <dbl> <dbl> <dbl>
## 1 cfc   open   +     +1.7 °C   _     ambient     2009  5.73 0.200     0
## 2 cfc   open   ++    +3.4 °C   _     ambient     2009  6.30 0.200     0

plot_trend(
  dat_phenophase = dat_phenophase_time_summ %>%
    filter(phenophase == "budbreak") %>%
    filter(species == "pinba"),
  dat_climate = dat_climate_spring
)

plot_trend(
  dat_phenophase = dat_phenophase_time_summ %>%
    filter(phenophase == "mostleaf") %>%
    filter(species == "poptr"),
  dat_climate = dat_climate_spring,
  var = "heat"
)

plot_trend(
  dat_phenophase = dat_phenophase_time_summ %>%
    filter(phenophase == "senescence") %>%
    filter(species == "acesa"),
  dat_climate = dat_climate_fall,
  var = "heat"
)

plot_trend(
  dat_phenophase = dat_phenophase_time_summ %>%
    filter(phenophase == "leafdrop") %>%
    filter(species == "pinst"),
  dat_climate = dat_climate_fall,
  var = "heat"
)

Interaction model

df_plot_ambient <- summ_treatment(dat_phenophase) %>%
  filter(heat_name == "ambient", water_name == "ambient") %>%
  distinct(site, canopy, block, plot)

dat_climate_spring <- summ_climate_season(dat_climate_daily, date_start = "Mar 15", date_end = "May 15", rainfall = 1)
dat_climate_spring_ambient <- dat_climate_spring %>%
  inner_join(df_plot_ambient, by = c("site", "canopy", "block", "plot")) %>%
  group_by(site, canopy, block, year) %>%
  summarise(
    temp = mean(temp),
    mois = mean(mois)
  ) %>%
  ungroup() %>%
  mutate(
    temp_std = scale(temp) %>% as.numeric(),
    mois_std = scale(mois) %>% as.numeric()
  )
temp_sd_spring <- dat_climate_spring_ambient %>%
  pull(temp) %>%
  sd()

dat_climate_fall <- summ_climate_season(dat_climate_daily, date_start = "Jun 15", date_end = "Sep 15", rainfall = 0)
dat_climate_fall_ambient <- dat_climate_fall %>%
  inner_join(df_plot_ambient, by = c("site", "canopy", "block", "plot")) %>%
  group_by(site, canopy, block, year) %>%
  summarise(
    temp = mean(temp),
    mois = mean(mois)
  ) %>%
  ungroup() %>%
  mutate(
    temp_std = scale(temp) %>% as.numeric(),
    mois_std = scale(mois) %>% as.numeric()
  )
temp_sd_fall <- dat_climate_fall_ambient %>%
  pull(temp) %>%
  sd()

dat_phenophase_time_cov <- dat_phenophase_time_clean %>%
  tidy_treatment_code() %>%
  left_join(dat_climate_spring_ambient, by = c("site", "canopy", "block", "year")) %>%
  group_by(species) %>%
  mutate(group = site %>% factor() %>% as.integer()) %>% # use site as random effects, not site-year
  ungroup()

test_phenophase_lme(data = dat_phenophase_time_cov, path = "alldata/intermediate/phenophase/bg/", season = "spring", chains = 1, iter = 4000, version = 3, num_cores = 5) # v3 with interactions
tidy_stanfit_all(season = "spring", path = "alldata/intermediate/phenophase/bg/")
calc_bayes_derived(path = "alldata/intermediate/phenophase/bg/", season = "spring", type = "phenophase", random = F, num_cores = 20)

dat_phenophase_time_cov <- dat_phenophase_time_clean %>%
  tidy_treatment_code() %>%
  left_join(dat_climate_fall_ambient, by = c("site", "canopy", "block", "year")) %>%
  group_by(species) %>%
  mutate(group = site %>% factor() %>% as.integer()) %>% # use site as random effects, not site-year
  ungroup()

test_phenophase_lme(data = dat_phenophase_time_cov, path = "alldata/intermediate/phenophase/bg/", season = "fall", chains = 1, iter = 4000, version = 3, num_cores = 5) # v3 with interactions
tidy_stanfit_all(season = "fall", path = "alldata/intermediate/phenophase/bg/")
calc_bayes_derived(path = "alldata/intermediate/phenophase/bg/", season = "fall", type = "phenophase", random = F, num_cores = 20)

Plot comparisons

df_lme_spring <- read_bayes_all(path = "alldata/intermediate/phenophase/bg/", season = "spring", full_factorial = T, derived = T, tidy_mcmc = T)
df_compare_trends_spring <- test_compare_trends(df_lme_spring, temp_sd = temp_sd_spring) %>%
  mutate(season = "spring") %>%
  tidy_phenophase_name(season = "spring")

df_lme_fall <- read_bayes_all(path = "alldata/intermediate/phenophase/bg/", season = "fall", full_factorial = T, derived = T, tidy_mcmc = T)
df_compare_trends_fall <- test_compare_trends(df_lme_fall, temp_sd = temp_sd_fall) %>%
  mutate(season = "fall") %>%
  tidy_phenophase_name(season = "fall")

df_compare_trends <- bind_rows(df_compare_trends_spring, df_compare_trends_fall) %>%
  mutate(season = factor(season, levels = c("spring", "fall"))) %>%
  tidy_species_name()

plot_trend_compare(df_compare_trends %>% filter(response == "start", season == "spring"), species_label = T)

plot_trend_compare(df_compare_trends %>% filter(response == "end", season == "spring"), species_label = T)

plot_trend_compare(df_compare_trends %>% filter(response == "start", season == "fall"), species_label = T)

plot_trend_compare(df_compare_trends %>% filter(response == "end", season == "fall"), species_label = T)