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| #' plot-level annual phenological signal | ||
| #' species-specific annual signals are weighted by their basal area at the plot level | ||
| #' | ||
| #' @param data junglerhythms data file | ||
| #' @param census_plot yangambi census data at plot level | ||
| #' @param total_basal_area_plot total basal area of a plot | ||
| #' @param species_name list of species | ||
| #' @param pheno only one phenophase | ||
| #' @param minimum_siteyears species not included if fewer observation-years overall (across all individuals) | ||
| #' @export | ||
| #' @return dataframe | ||
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| standlevel_phen_plotlevel <- function( | ||
| data = data, | ||
| census_plot = census_plot, | ||
| total_basal_area_plot = total_basal_area_plot, | ||
| species_list_dorm = dorm_sp1, | ||
| species_list_turn = turn_sp1, | ||
| minimum_siteyears = minimum_siteyears | ||
| ){ | ||
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| #----------------------------------------------------------------------- | ||
| #-- each phenophase is done seperately | ||
| #-- because different species can be sourced per phenophase | ||
| #-- based on group classifications | ||
| #----------------------------------------------------------------------- | ||
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| #----------------------------------------------------------------------- | ||
| #------------ Leaf turnover ------------------------------------------- | ||
| #----------------------------------------------------------------------- | ||
| if(!is_empty(species_list_turn)){ | ||
| timelines_turn <- data %>% | ||
| filter(phenophase == "leaf_turnover", | ||
| species_full %in% species_list_turn, | ||
| !is.na(value)) | ||
| # for each species, get summaries of each week (makes full year per species) | ||
| data_LT <- timelines_turn %>% | ||
| group_by(species_full, week) %>% | ||
| dplyr::summarise(mean_week = mean(value), | ||
| total_week = length(value)) | ||
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| # filter out species with too few total observation years across individuals | ||
| # to have a meaningfull average year (set by minimum_siteyears) | ||
| data_LT <- data_LT %>% | ||
| filter(total_week >= minimum_siteyears) | ||
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| # add species-level basal area (across the 5-ha plots) | ||
| data_LT_plot <- inner_join(data_LT, census_plot, by = c("species_full")) | ||
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| final_LT_plot <- data_LT_plot %>% | ||
| group_by(Plot, week) %>% | ||
| dplyr::summarise(ss = sum(mean_week*basal_area_plot, na.rm = TRUE)) | ||
| final_LT_plot <- inner_join(final_LT_plot, total_basal_area_plot, by = c("Plot")) | ||
| final_LT_plot <- final_LT_plot %>% | ||
| mutate(ss_turn = ss/total_basal_area_plot*100) | ||
| final_LT_plot <- final_LT_plot %>% | ||
| dplyr::select(Plot, week, ss_turn) | ||
| } else { | ||
| final_LT_plot <- data.frame(Plot = rep(c("Inventory 20","Inventory 21","Inventory 23"),48), | ||
| week = rep(c(1:48),2), | ||
| ss_turn = NA) | ||
| } | ||
| #----------------------------------------------------------------------- | ||
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| #----------------------------------------------------------------------- | ||
| #------------ Leaf dormancy ------------------------------------------- | ||
| #----------------------------------------------------------------------- | ||
| if(!is_empty(species_list_dorm)){ | ||
| timelines_dorm <- data %>% | ||
| filter(phenophase == "leaf_dormancy", | ||
| species_full %in% species_list_dorm, | ||
| !is.na(value)) | ||
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| data_LD <- timelines_dorm %>% | ||
| group_by(species_full, week) %>% | ||
| dplyr::summarise(mean_week = mean(value), | ||
| total_week = length(value)) | ||
| data_LD <- data_LD %>% | ||
| filter(total_week >= minimum_siteyears) | ||
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| data_LD_plot <- inner_join(data_LD, census_plot, by = c("species_full")) | ||
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| final_LD_plot <- data_LD_plot %>% | ||
| group_by(Plot, week) %>% | ||
| dplyr::summarise(ss = sum(mean_week*basal_area_plot, na.rm = TRUE)) | ||
| final_LD_plot <- inner_join(final_LD_plot, total_basal_area_plot, by = c("Plot")) | ||
| final_LD_plot <- final_LD_plot %>% | ||
| mutate(ss_dorm = ss/total_basal_area_plot*100) | ||
| final_LD_plot <- final_LD_plot %>% | ||
| dplyr::select(Plot, week, ss_dorm) | ||
| } else { | ||
| final_LD_plot <- data.frame(Plot = rep(c("Inventory 20","Inventory 21","Inventory 23"),48), | ||
| week = rep(c(1:48),2), | ||
| ss_dorm = NA) | ||
| } | ||
| #----------------------------------------------------------------------- | ||
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| # #----------------------------------------------------------------------- | ||
| # #------------ Leaf flushing ------------------------------------------- | ||
| # #----------------------------------------------------------------------- | ||
| # if(!is_empty(species_list_dorm)) { | ||
| # #------ | ||
| # # onset of flushing is first calculated as first week end of dormancy events | ||
| # # full timeline for flushing is first created for each individual | ||
| # #------ | ||
| # # just to avoid confusion, work with new name | ||
| # timelines_flush <- timelines_dorm | ||
| # # new column 'flushing_date' to merge with later on | ||
| # timelines_flush$flushing_date <- paste(timelines_flush$year, timelines_flush$week, sep = "-") | ||
| # | ||
| # # function event_length gets | ||
| # # for each individual of the requested species | ||
| # # start/end year&week of each event | ||
| # flushing_timing <- event_length(data = timelines_flush, | ||
| # species_name = species_list_dorm, | ||
| # pheno = "leaf_dormancy") | ||
| # # individuals without events give NA in event_length, so remove | ||
| # flushing_timing <- flushing_timing %>% | ||
| # filter(!is.na(year_start)) | ||
| # | ||
| # # date of onset flushing = week after end dormancy event | ||
| # flushing_timing$flushing_date <- paste(flushing_timing$year_end, flushing_timing$week_end +1, sep = "-") | ||
| # | ||
| # flushing_timing <- flushing_timing %>% | ||
| # select(species_full, | ||
| # id, | ||
| # flushing_date) | ||
| # flushing_timing$flushing_value <- 1 | ||
| # | ||
| # # merge using flushing_date for each id/species | ||
| # # flushing_value is | ||
| # data_flush <- merge(timelines_flush, flushing_timing, by = c("species_full","id","flushing_date"), all.x = TRUE) | ||
| # data_flush$flushing_value <- ifelse(is.na(data_flush$flushing_value),"0", data_flush$flushing_value) | ||
| # # if value for dormancy was NA, year was not observed (full timelines were needed for using 'event_length') | ||
| # # so remove year for flushing | ||
| # data_flush$flushing_value <- ifelse(is.na(data_flush$value),NA, data_flush$flushing_value) | ||
| # data_flush$flushing_value <- as.numeric(data_flush$flushing_value) | ||
| # | ||
| # #------ | ||
| # ## now continue the same as done for turnover and dormancy | ||
| # #------ | ||
| # data_flush <- data_flush %>% | ||
| # group_by(species_full, week) %>% | ||
| # dplyr::summarise(mean_week = mean(flushing_value, na.rm = TRUE), | ||
| # total_week = length(flushing_value)) | ||
| # | ||
| # data_flush <- data_flush %>% | ||
| # filter(total_week >= minimum_siteyears) | ||
| # | ||
| # data_flush_plot <- inner_join(data_flush, census_plot, by = c("species_full")) | ||
| # | ||
| # final_flush_plot <- data_flush_plot %>% | ||
| # group_by(Plot, week) %>% | ||
| # dplyr::summarise(ss = sum(mean_week*basal_area_plot, na.rm = TRUE)) | ||
| # final_flush_plot <- inner_join(final_flush_plot, total_basal_area_plot, by = c("Plot")) | ||
| # final_flush_plot <- final_flush_plot %>% | ||
| # mutate(ss_flush = ss/total_basal_area_plot*100) | ||
| # final_flush_plot <- final_flush_plot %>% | ||
| # dplyr::select(Plot, week, ss_flush) | ||
| # } else { | ||
| # final_flush_plot <- data.frame(Plot = rep(c("Inventory 20","Inventory 23"),48), | ||
| # week = rep(c(1:48),2), | ||
| # ss_flush = NA) | ||
| # } | ||
| # #----------------------------------------------------------------------- | ||
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| #----------------------------------------------------------------------- | ||
| #------ output dataframe --------------------------------------------- | ||
| #----------------------------------------------------------------------- | ||
| # # add full_range to make sure all plots are included, even if a species is not present in the plot | ||
| # combined <- data.frame(Plot = rep(c("Inventory 20","Inventory 23"),48), | ||
| # week = rep(c(1:48),2), | ||
| # full_range = NA) | ||
| # combined <- merge(combined, final_LT_plot, by = c("Plot","week"), all.x = TRUE) | ||
| # combined <- merge(combined, final_LD_plot, by = c("Plot","week"), all.x = TRUE) | ||
| # combined <- merge(combined, final_flush_plot, by = c("Plot","week"), all.x = TRUE) | ||
| # # both dormancy and turnover represent main period of senescence | ||
| # combined$ss_senescence <- combined$ss_turn + combined$ss_dorm | ||
| # combined <- combined %>% | ||
| # dplyr::select(!full_range) | ||
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| combined <- left_join(final_LD_plot,final_LT_plot) | ||
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| return(combined) | ||
| } |
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