rtcc2: Clustering signal along an environmental gradient

In RTCC: Detecting Trait Clustering in Environmental Gradients

Description

For a given trait, this function determines whether the observed trait clustering/overdispersion on the metacommunity is linked to an environmental gradient. For this, it sequentially remove samples in decreasing order of the environmental variable and computes at each step the remaining metacommunity h-index. This index is based on the percentage of samples on a metacommunity presenting significant trait clustering/overdispersion.

Usage

rtcc2(
  table1,
  table2,
  table3,
  species_abundances,
  trait_col_number,
  min_env_col,
  max_env_col,
  env_var_col,
  h_iteration,
  repetitions,
  model
)

Arguments

table1	A data frame containing organisms names on the first column and its trait values on the consecutive ones. It also has to contain two columns with the maximum and the minimum values of the tested environmental variable where the organisms have been observed.
table2	A presence-absence observations table with the organisms names on the first column and the sample names as consecutive colnames.
table3	A dataframe containing sample names on the first column and environmental parameters on the consecutive ones.
species_abundances	A vector containing the relative abundance of the organisms on the whole data set on the same order as appear on Table 1.
trait_col_number	Table 1 column number of the tested trait.
min_env_col	Table 1 column number indicating the minimum value of the environmental variable were each organism has been observed.
max_env_col	Table 1 column number indicating the maximum value of the environmental variable were each organism has been observed.
env_var_col	Table 2 column number indicating the tested environmental variable.
h_iteration	Number of h-index calculations for computing a confidence interval.
repetitions	Number of simulated synthetic communities distributions.
model	Model selection. All models build synthetic communities based on the organisms richness of the observed communities. - Model 1: organism are selected randomly from the global pool. - Model 2: organism are selected randomly with a probability based on its relative abundance on the global pool. - Model 3: organism are selected randomly, but only those whose environmental range includes the value of the simulated community are elegible. - Model 4: organism are selected randomly, but only those whose environmental range includes the value of the simulated community are elegible and the selection probability is based on its relative abundance on the global pool.

Value

The function returns a dataframe with the maximum of the environmental variable on the remaining metacommunity after the sequential removal, h-index calculation for each environmental value, and its confidence standard deviation.

Examples

data(group_information)
data(table_presence_absence)
data(metadata)
rtcc2(group_information, table_presence_absence, metadata, group_information$sums,
9, 12, 13, 2, 100, 100, model = 1)

RTCC documentation built on July 1, 2020, 7:06 p.m.