02.03.00.biome-species-list.R

library("tidyverse")
library("terra")

# load TRY trait data
load("02.data/Traits_CWMs_sPlot3.RData")
load("02.data/PhyloTree_sPlot3.0.Rdata")


species <- sPlot.traits |>
  filter(gsub(" ", "_", Species) %in% sPlot.phylo.tree$scenario.3$tip.label) |>
  dplyr::select(
    Species,
    SLA_mean,
    PlantHeight_mean,
    SpecificRootLength_mean
  ) |>
  drop_na()

load("02.data/header_sPlot3.0.RData")
load("02.Data/DT_sPlot3.0.RData")

## filter DT by species present in traits and phylo

DT <- DT2 |>
  filter(Species %in% species$Species)

## read Floristic - Kingdoms


kings <- vect("02.data/shapefiles/kindgoms.shape.shp")

data <- header %>%
  filter(!is.na(Longitude))

exf <- terra::extract(kings, data.frame(
  x = data$Longitude,
  y = data$Latitude
))

saveRDS(exf, "kings.exf.RDS")

exf <- readRDS("kings.exf.RDS")

table(exf$cluster)
sum(is.na(exf$cluster))

dff <- cbind(data, exf$cluster)

dff %>%
  filter(is.na(`exf$cluster`)) %>%
  pull(Country) %>%
  table()

dff %>%
  filter(!is.na(`exf$cluster`)) %>%
  rename("Cluster" = "exf$cluster") %>%
  pull(Cluster) %>%
  table()

dff %>%
  filter(`exf$cluster` == 1) %>%
  pull(Country) %>%
  table()

countryx <- dff %>%
  group_by(`exf$cluster`, Country) %>%
  slice_tail() %>%
  dplyr::select(`exf$cluster`, Country)

naa <- dff %>%
  filter(is.na(`exf$cluster`))

clust <- dff %>%
  filter(!is.na(`exf$cluster`))

library(FNN)

matches <- knnx.index(clust[, c("Longitude", "Latitude")], naa[, c("Longitude", "Latitude")], k = 1)

naa$match.row <- matches[, 1]

oout <- naa %>%
  dplyr::select(-`exf$cluster`) %>%
  mutate(match.row = as.character(match.row)) %>%
  left_join(
    clust %>%
      dplyr::select(Cluster = `exf$cluster`) %>%
      rownames_to_column("match.row"),
    by = "match.row"
  ) %>%
  dplyr::select(-match.row) %>%
  rbind(clust %>%
    rename("Cluster" = "exf$cluster"))

oout[, c("Country", "Cluster")] %>% table()

saveRDS(oout, "02.data/phylo-kingdom-data.RDS")

ott <- DT |>
  group_by(PlotObservationID, Species) |>
  summarise(Abundance_sum = sum(Abundance, na.rm = TRUE)) |>
  left_join(
    oout |>
      dplyr::select(PlotObservationID, Phyl.clust = Cluster),
    by = "PlotObservationID"
  ) |>
  group_by(Phyl.clust, Species) |>
  summarise(
    Abundance_sum = sum(Abundance_sum, na.rm = TRUE),
    Species_count = n()
  )

saveRDS(ott, "02.Data/species-list-phyl.RDS")


## biomes
sum(is.na(header$sBiome))

DTT <- DT |>
  group_by(PlotObservationID, Species) |>
  summarise(Abundance_sum = sum(Abundance, na.rm = TRUE)) |>
  left_join(
    header |>
      dplyr::select(PlotObservationID, Biome = sBiome),
    by = "PlotObservationID"
  ) |>
  group_by(Biome, Species) |>
  summarise(
    Abundance_sum = sum(Abundance_sum, na.rm = TRUE),
    Species_count = n()
  )

DTTT <- DTT |>
  filter(!is.na(Biome))

saveRDS(DTTT, "02.Data/species-list-biome.RDS")

## number of of ferns, lycopods and/or gymnosperms per biome

ferns <- c(
  "Anemiaceae",
  "Aspleniaceae",
  "Athyriaceae",
  "Blechnaceae",
  "Cibotiaceae",
  "Culcitaceae",
  "Cyatheaceae",
  "Cystodiaceae",
  "Cystopteridaceae",
  "Davalliaceae",
  "Dennstaedtiaceae",
  "Desmophlebiaceae",
  "Dicksoniaceae",
  "Didymochlaenaceae",
  "Diplaziopsidaceae",
  "Dipteridaceae",
  "Dryopteridaceae",
  "Equisetaceae",
  "Gleicheniaceae",
  "Hemidictyaceae",
  "Hymenophyllaceae",
  "Hypodematiaceae",
  "Lindsaeaceae",
  "Lomariopsidaceae",
  "Lonchitidaceae",
  "Loxsomataceae",
  "Lygodiaceae",
  "Marattiaceae",
  "Marsileaceae",
  "Matoniaceae",
  "Metaxyaceae",
  "Nephrolepidaceae",
  "Oleandraceae",
  "Onocleaceae",
  "Ophioglossaceae",
  "Osmundaceae",
  "Plagiogyriaceae",
  "Polypodiaceae",
  "Psilotaceae",
  "Pteridaceae",
  "Rhachidosoraceae",
  "Saccolomataceae",
  "Salviniaceae",
  "Schizaeaceae",
  "Tectariaceae",
  "Thelypteridaceae",
  "Thyrsopteridaceae",
  "Woodsiaceae"
)

gymno <- c(
  "Araucariaceae",
  "Boweniaceae",
  "Cephalotaxaceae",
  "Cupressaceae",
  "Cycadaceae",
  "Ephedraceae",
  "Ginkgoaceae",
  "Gnetaceae",
  "Pinaceae",
  "Podocarpaceae",
  "Taxaceae",
  "Taxodiaceae",
  "Welwitschiaceae",
  "Zamiaceae"
)

lyco <- c(
  "Lycopodiaceae",
  "Isoetaceae",
  "Selaginellaceae"
)

st <- left_join(DTTT %>% as.data.frame(), tree.species,
  by = c("Species" = "species")
)

st.out <- st %>%
  mutate(devision = ifelse(family %in% ferns, "Fern",
    ifelse(family %in% gymno, "Gymnosperm",
      ifelse(family %in% lyco, "Lycopods", "Other")
    )
  )) %>%
  group_by(Biome) %>%
  mutate(n_biome = n()) %>%
  ungroup() %>%
  group_by(Biome, devision) %>%
  summarise(
    n = n(),
    n_biome = mean(n_biome)
  ) %>%
  mutate(rel = n / n_biome * 100) %>%
  pivot_wider(id_cols = Biome, names_from = devision, values_from = rel)

DT22 <- DT %>%
  left_join(tree.species, by = c("Species" = "species")) %>%
  mutate(Devision = ifelse(family %in% ferns, "Fern",
    ifelse(family %in% gymno, "Gymnosperm",
      ifelse(family %in% lyco, "Lycopods", "Other")
    )
  )) %>%
  group_by(PlotObservationID, Devision) %>%
  summarise(n = n())

DT222 <- DT22 %>%
  left_join(DT22 %>%
    group_by(PlotObservationID) %>%
    summarise(nn = sum(n))) %>%
  mutate(perc.n = n / nn * 100) %>%
  pivot_wider(id_cols = PlotObservationID, names_from = Devision, values_from = perc.n, values_fill = 0)

saveRDS(DT222, "02.data/perc-fern-sPlot.RDS")