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Test CWM-environment relationships with the max test

Source: R/traits.R
trait_cwm_test.Rd

Relating community-weighted means to site variables with an ordinary test inflates Type I error, because CWMs of different sites share the same species traits (Zeleny 2018; Peres-Neto et al. 2017). The max test (ter Braak et al. 2012) combines a row-based permutation (shuffle sites) with a column-based permutation (shuffle traits among species) and reports the larger p-value. It controls Type I error whether the null hypothesis concerns the environment or the traits.

Usage

trait_cwm_test(
  data,
  traits,
  env_vars,
  site = site,
  species = species,
  abundance = NULL,
  cols = tidyselect::everything(),
  permutations = 999
)

Arguments

data

A data frame in long format, with one row per record.

traits

A data frame of traits, one row per species, with a column whose name matches the species column of data.

env_vars

<tidy-select> Numeric site variables in data (constant within sites) to test.

site, species

<tidy-select> The column holding site identifiers and the column holding species names.

abundance

<tidy-select> The column holding counts. If NULL (the default), a column called abundance is used when present, otherwise every row counts as one individual.

cols

<tidy-select> Trait columns of traits.

permutations

Number of permutations.

Value

A tibble with one row per numeric trait x variable: trait, variable, Pearson r between CWM and the variable, p_row, p_col and p_max.

References

Zeleny, D. (2018). Which results of the standard test for community-weighted mean approach are too optimistic? Journal of Vegetation Science, 29, 953-966. doi:10.1111/jvs.12688

ter Braak, C. J. F., Cormont, A. and Dray, S. (2012). Improved testing of species traits-environment relationships in the fourth-corner problem. Ecology, 93, 1525-1526. doi:10.1890/12-0126.1

Examples

aravo <- data_aravo()
set.seed(1)
trait_cwm_test(aravo$community, aravo$traits, abundance = cover,
            cols = c(Height, SLA), env_vars = Snow, permutations = 199)
#> # A tibble: 2 x 6
#>   trait  variable      r p_row p_col p_max
#>   <chr>  <chr>     <dbl> <dbl> <dbl> <dbl>
#> 1 Height Snow     -0.737 0.005 0.025 0.025
#> 2 SLA    Snow      0.864 0.005 0.005 0.005