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Fourth-corner analysis

Source: R/traits.R
trait_fourth_corner.Rd

Tests every trait x environment association with the fourth-corner method (Legendre et al. 1997; Dray and Legendre 2008) and the max test (model 6, ter Braak et al. 2012), using ade4::fourthcorner(), and returns a tidy table.

Usage

trait_fourth_corner(
  data,
  traits,
  env_vars,
  site = site,
  species = species,
  abundance = NULL,
  cols = tidyselect::everything(),
  permutations = 999,
  adjust = "fdr"
)

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.

adjust

Method for adjusting p-values across all tests, passed to stats::p.adjust(); "none" for no adjustment.

Value

A tibble with trait, variable, the test statistic (Pearson r for two numeric variables, otherwise as in ade4), p.value and p.adj.

References

Dray, S. and Legendre, P. (2008). Testing the species traits-environment relationships: the fourth-corner problem revisited. Ecology, 89, 3400-3412. doi:10.1890/08-0349.1

Examples

aravo <- data_aravo()
set.seed(1)
trait_fourth_corner(aravo$community, aravo$traits, abundance = cover,
                 cols = c(Height, SLA), env_vars = c(Snow, PhysD),
                 permutations = 199)
#> # A tibble: 4 x 5
#>   trait  variable statistic p.value  p.adj
#>   <chr>  <chr>        <dbl>   <dbl>  <dbl>
#> 1 Height Snow        -0.272   0.01  0.0133
#> 2 Height PhysD        0.113   0.055 0.055 
#> 3 SLA    Snow         0.481   0.005 0.01  
#> 4 SLA    PhysD       -0.276   0.005 0.01