f_eve: Functional evenness

In alexology/biomonitoR: biomonitoring indices and metrics

Functional evenness

Description

\Sexpr[results=rd, stage=render]{ lifecycle::badge("maturing") }

This function calculates functional evenness index.

Usage

f_eve(
  x,
  trait_db = NULL,
  tax_lev = "Taxa",
  type = NULL,
  traitSel = FALSE,
  col_blocks = NULL,
  nbdim = 2,
  distance = "gower",
  zerodist_rm = FALSE,
  correction = "none",
  traceB = FALSE,
  set_param = NULL
)

Arguments

x	Result of aggregate_taxa().
trait_db	A trait dataset. Can be a data.frame ot a dist object. Taxonomic level of the traits dataset must match those of the taxonomic dataset. No automatic check is done by the function.
tax_lev	Character string giving the taxonomic level used to retrieve trait information. Possible levels are Taxa, Species, Genus, Family as returned by aggregate_taxa().
type	The type of variables speciefied in trait_db. Must be one of F, fuzzy, or C, continuous. If more control is needed please consider to provide trait_db as a dist object. It works only when trait_db is a data.frame, otherwise ingored.
traitSel	Select traits interactively.
col_blocks	A vector that contains the number of modalities for each trait. Not needed when euclidean distance is used.
nbdim	Number of dimensions for the multidimensional functional spaces. We suggest to keep nbdim as low as possible. By default biomonitoR set the number of dimensions to 2. Select auto if you want the automated selection approach according to Maire et al. (2015).
distance	To be used to compute functional distances, euclidean or gower. Default to gower. See details.
zerodist_rm	If TRUE aggregates taxa with the same traits.
correction	Correction methods for negative eigenvalues, can be one of none, lingoes, cailliez, sqrt and quasi. Ignored when type is set to C.
traceB	If TRUE returns a list as specified in details.
set_param	A list of parameters for fine tuning the calculations. max_nbdim set the maximum number of dimension for evaluating the quality of the functional space. prec can be Qt or QJ, please refere to the convhulln documentation for more information. Deafault to QJ, less accurate but less prone to errors. tol a tolerance threshold for zero, see the function is.euclid, lingoes and cailliez from the ade4 for more details. Default to 1e-07. If cor.zero is TRUE, zero distances are not modified. See functions is.euclid, lingoes and cailliez from the ade4 for more details. Default to TRUE. The gower distance refers to the mixed-variables coefficient of distance of Pavoine et al. (2009) as implemented in the ade4 package. This distance is meant to be used with fuzzy data.

Details

Functional evenness represents a facet of functional diversity for a community with species distributed in a multidimensional functional space. The metric is based on the minimum spanning tree which links all the species in the multidimensional functional space. Then it quantifies the regularity with which species abundances are distributed along the spanning tree.

Functional evenness values are strictly positive and constrained between 0 and 1. The higher they are, the higher the component of functional diversity they quantify is. The measure quantifies the regularity with which the functional space is filled by species, weighted by their abundance and it is independent of species richness. See formulas and more information in Mason et al. (2005) and Villeger et al. (2008).

Value

a vector with fuzzy functional richness results.

1. results Results of f_eve().

2. traits A data.frame containing the traits used for the calculations.

3. taxa A data.frame conaining the taxa used for the calculations.

4. nbdim Number of dimensions used after calculating the quality of functional spaces according to Maire et al. (2015).

5. correction The type of correction used.

6. NA_detection A data.frame containing taxa on the first column and the corresponding trais with NAs on the second column.

7. duplicated_traits If present, list the taxa with the same traits.

References

Mason, N. W., Mouillot, D., Lee, W. G., and Wilson, J. B. (2005). Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos, 111(1), 112-118.

Pavoine, S., Vallet, J., Dufour, A. B., Gachet, S., & Daniel, H. (2009). On the challenge of treating various types of variables: application for improving the measurement of functional diversity. Oikos, 118(3), 391-402.

Villeger, S., Mason, N. W., & Mouillot, D. (2008). New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology, 89(8), 2290-2301.

See Also

aggregatoR

Examples

data(macro_ex)

data_bio <- as_biomonitor(macro_ex)
data_agr <- aggregate_taxa(data_bio)
data_ts <- assign_traits(data_agr)
# averaging
data_ts_av <- average_traits(data_ts)

col_blocks <- c(8, 7, 3, 9, 4, 3, 6, 2, 5, 3, 9, 8, 8, 5, 7, 5, 4, 4, 2, 3, 8)

f_eve(data_agr, trait_db = data_ts_av, type = "F", col_blocks = col_blocks)
f_eve(data_agr,
  trait_db = data_ts_av, type = "F", col_blocks = col_blocks,
  nbdim = 10, correction = "cailliez"
)

library(ade4)

rownames(data_ts_av) <- data_ts_av$Taxa
traits_prep <- prep.fuzzy(data_ts_av[, -1], col.blocks = col_blocks)

traits_dist <- ktab.list.df(list(traits_prep))
traits_dist <- dist.ktab(traits_dist, type = "F")

f_eve(data_agr, trait_db = traits_dist)

alexology/biomonitoR documentation built on April 7, 2024, 10:15 a.m.