Exploration

This section present the analyses mainly based on joint modelling of genus diversity of order 1.

Small and big trees diversity

Code

library(tidyverse)
library(entropart)
read_harmonized <- function(file) {
  cols <- c(
    ScientificName = NA_character_,
    VernName = NA_character_,
    Family = NA_character_,
    Genus = NA_character_,
    Species = NA_character_
  )
  read_csv(file,
    locale = readr::locale(encoding = "latin1"),
    col_types = cols()
  ) %>%
    add_column(!!!cols[!names(cols) %in% names(.)]) %>%
    mutate(raw_taxonomy = paste(
      ScientificName, "-", VernName, "-",
      Family, Genus, Species
    )) %>%
    mutate(file = gsub("../inventories/data/raw_data/", "", file))
}
jaccard <- function(a, b) {
    intersection = length(intersect(a, b))
    union = length(a) + length(b) - intersection
    return (intersection/union)
}

Code

t <- "../inventories/data/raw_data/harmonized_data_paracou-temoin_v1.csv" %>% 
  read_harmonized() %>% 
  left_join(read_tsv("../species/outputs/taxonomy_v6.tsv") %>% 
              select(contains("cleaned") | "raw_taxonomy") %>% 
              unique())
dbh_median <- median(t$Diameter)

t %>% 
  rename_all(tolower) %>% 
  mutate(group = ifelse(diameter < dbh_median, "lower", "upper")) %>% 
  select(year, plot, group, scientific_cleaned) %>% 
  unique() %>% 
  na.omit() %>% 
  nest(data = scientific_cleaned) %>% 
  pivot_wider(names_from = group, values_from = data) %>% 
  rowwise() %>% 
  mutate(jaccard = jaccard(unname(unlist(lower)), 
                           unname(unlist(upper)))) %>% 
  ggplot(aes(year, jaccard, col = as.character(plot))) +
  geom_line() +
  theme_bw() +
  ggtitle("Paracou control diversity between small and big diameter")

Code

t <- "../inventories/data/raw_data/harmonized_data_paracou-temoin_v1.csv" %>% 
  read_harmonized() %>% 
  left_join(read_tsv("../species/outputs/taxonomy_v6.tsv") %>% 
              select(contains("cleaned") | "raw_taxonomy") %>% 
              unique())
dbh_median <- median(t$Diameter)

t2 <- t %>% 
  rename_all(tolower) %>% 
  mutate(group = ifelse(diameter < dbh_median, "lower", "upper")) %>% 
  select(year, plot, group, scientific_cleaned) %>% 
  na.omit() %>% 
  group_by(year, plot, group, scientific_cleaned) %>% 
  summarise(abundance = n()) %>% 
  pivot_wider(names_from = group, values_from = abundance) %>% 
  mutate_all(~ifelse(is.na(.),0,.)) %>% 
  nest(data = c(scientific_cleaned, lower, upper))

t2 %>% 
  rowwise() %>% 
  mutate(mc = list(MetaCommunity(column_to_rownames(data, "scientific_cleaned")))) %>% 
  mutate(beta = BetaDiversity(mc, Correction = "None")$Total) %>% 
  ggplot(aes(year, beta, col = as.character(plot))) +
  geom_line() +
  theme_bw() +
  ggtitle("Paracou control diversity between small and big diameter")

Code

t <- "../inventories/data/raw_data/harmonized_data_paracou-t3_v1.csv" %>% 
  read_harmonized() %>% 
  left_join(read_tsv("../species/outputs/taxonomy_v6.tsv") %>% 
              select(contains("cleaned") | "raw_taxonomy") %>% 
              unique())
dbh_median <- median(t$Diameter)

t2 <- t %>% 
  rename_all(tolower) %>% 
  mutate(group = ifelse(diameter < dbh_median, "lower", "upper")) %>% 
  select(year, plot, group, scientific_cleaned) %>% 
  na.omit() %>% 
  group_by(year, plot, group, scientific_cleaned) %>% 
  summarise(abundance = n()) %>% 
  pivot_wider(names_from = group, values_from = abundance) %>% 
  mutate_all(~ifelse(is.na(.),0,.)) %>% 
  nest(data = c(scientific_cleaned, lower, upper))

t2 %>% 
  rowwise() %>% 
  mutate(mc = list(MetaCommunity(column_to_rownames(data, "scientific_cleaned")))) %>% 
  mutate(beta = BetaDiversity(mc, Correction = "None")$Total) %>% 
  ggplot(aes(year, beta, col = as.character(plot))) +
  geom_line() +
  theme_bw() +
  ggtitle("Paracou exploited diversity between small and big diameter")

Diameter distribution

Code

t <- "../inventories/data/raw_data/harmonized_data_paracou-temoin_v1.csv" %>% 
  read_harmonized() %>% 
  left_join(read_tsv("../species/outputs/taxonomy_v6.tsv") %>% 
              select(contains("cleaned") | "raw_taxonomy") %>% 
              unique())
t %>% 
  rename_all(tolower) %>% 
  group_by(year, plot) %>% 
  summarise(mean_log = exp(mean(log(diameter))),
            median_log = exp(median(log(diameter))),
            sd_log = sd(log(diameter))) %>% 
  gather(variable, value, -year, -plot) %>% 
  ggplot(aes(year, value, col = as.character(plot))) +
  geom_line() +
  theme_bw() +
  facet_wrap(~ variable, scales = "free_y", nrow = 3) +
  ggtitle("Paracou control mean diameter")

Code

t <- "../inventories/data/raw_data/harmonized_data_paracou-t3_v1.csv" %>% 
  read_harmonized() %>% 
  left_join(read_tsv("../species/outputs/taxonomy_v6.tsv") %>% 
              select(contains("cleaned") | "raw_taxonomy") %>% 
              unique())
t %>% 
  rename_all(tolower) %>% 
  group_by(year, plot) %>% 
  summarise(mean_log = exp(mean(log(diameter))),
            median_log = exp(median(log(diameter))),
            sd_log = sd(log(diameter))) %>% 
  gather(variable, value, -year, -plot) %>% 
  ggplot(aes(year, value, col = as.character(plot))) +
  geom_line() +
  theme_bw() +
  facet_wrap(~ variable, scales = "free_y", nrow = 3) +
  ggtitle("Paracou control mean diameter")

Code

read_csv("../modelling/data/raw_data/aggregated_data_v9.csv") %>% 
  filter(variable == "dbh_median") %>% 
  ggplot(aes(Year, value, col = as.character(Plot))) +
  geom_line() +
  theme_bw() +
  facet_wrap(~ Site, scales = "free") +
  scale_color_discrete(guide = "none") +
  ggtitle("DBH median across sites, year and plots.")

Wood density

Code

read_csv("../modelling/data/raw_data/aggregated_data_v9.csv") %>% 
  filter(variable == "WD_cwm_ba") %>% 
  ggplot(aes(Year, value, col = as.character(Plot))) +
  geom_line() +
  theme_bw() +
  facet_wrap(~ Site, scales = "free") +
  scale_color_discrete(guide = "none") +
  ggtitle("DBH median across sites, year and plots.")