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compute_CWM(comm, traits)

Community-weighted mean (CWM) traits for each community. Abundance-weighted average of trait values across species present.

functional diversitycommunity

Args:comm — community × speciestraits — species × traits (numeric)

compute_CWM <- function(comm, traits) {
  sp <- intersect(colnames(comm), rownames(traits))
  comm   <- comm[, sp, drop = FALSE]
  traits <- traits[sp, , drop = FALSE]
  
  # Row-normalise community (relative abundances)
  comm_rel <- comm / rowSums(comm)
  
  # CWM = comm_rel %*% traits
  cwm <- as.matrix(comm_rel) %*% as.matrix(traits)
  as.data.frame(cwm)
}
# Example
comm   <- matrix(c(1,2,0,1,3,0,1,1), nrow=2, dimnames=list(c("A","B"), c("sp1","sp2","sp3","sp4")))
traits <- matrix(rnorm(12), nrow=4, dimnames=list(c("sp1","sp2","sp3","sp4"), c("t1","t2","t3")))
compute_CWM(comm, traits)
# ── Example ──────────────────────────────────────────────────────
# Build a toy community matrix from iris
set.seed(1)
comm   <- matrix(rbinom(30, 1, 0.6), nrow = 3, ncol = 10,
                 dimnames = list(c("site1","site2","site3"),
                                 paste0("sp", 1:10)))
traits <- as.data.frame(matrix(rnorm(40), 10, 4,
                               dimnames = list(paste0("sp", 1:10),
                                               c("t1","t2","t3","t4"))))
compute_CWM(comm, traits)