01_DATA_load_and_clean — Step 6

Compute community-level Trait Probability Densities (TPDs) for each functional dimension (M, L, D, MLD) across DGGS hexagons. Additional 4D calculations for MLD and D spaces.

TPD spatial

Outputs: Birds_TPDs_sdggs7_MLD/M/L/D.rds Birds_TPDs_sdggs7_MLD_4D/D_4D.rds

sitesdggs7 <- readRDS("data/processed/sitesdggs7.RDS")

PCA_file <- c("data/processed/PCA_Birds_MLD.rds",
              "data/processed/PCA_Birds_M.rds",
              "data/processed/PCA_Birds_L.rds",
              "data/processed/PCA_Birds_D.rds")
type <- c("MLD", "M", "L", "D")

for (i in seq_along(type)) {
  PCA <- readRDS(PCA_file[i])
  TPDs_compute(TraitsPCA   = PCA$PCoA$vectors[, c(1, 2)],
               sitesdggs7,
               savePath    = sprintf("data/processed/Birds_TPDs_sdggs7_%s.rds", type[i]),
               sampleComms = 400, alphaUse = 0.95, gridSize = 100)
}
# Additional 4D (axes 1 & 4) for MLD and D
for (i in c(1, 4)) {
  PCA <- readRDS(PCA_file[i])
  TPDs_compute_large(TraitsPCA   = PCA$PCoA$vectors[, c(1, 4)],
                     sitesdggs7,
                     savePath    = sprintf("data/processed/Birds_TPDs_sdggs7_%s_4D.rds", type[i]),
                     sampleComms = 400, alphaUse = 0.95, gridSize = 20)
}