Save data to reproduce building of Plant Functional Groups

This script is designed to gather all data and parameters used to build a set of Plant Functional Groups.

SAVE_FATE.step1_PFG(
  name.dataset,
  mat.observations,
  rules.selectDominant = c(doRuleA = NA, rule.A1 = NA, rule.A2_quantile = NA, doRuleB =
    NA, rule.B1_percentage = NA, rule.B1_number = NA, rule.B2 = NA, doRuleC = NA),
  mat.traits,
  mat.overlap = NA,
  rules.speciesDistance = c(opt.maxPercent.NA = NA, opt.maxPercent.similarSpecies = NA,
    opt.min.sd = NA),
  mat.species.DIST,
  clust.evaluation = NA,
  no.clusters,
  determ.all,
  mat.traits.PFG
)

Arguments

name.dataset

a string corresponding to the name to give to archive folder

mat.observations

a data.frame with at least 3 columns :
sites, species, abund (and optionally, habitat) (see PRE_FATE.selectDominant)

rules.selectDominant

(optional) default NA.
A vector containing all the parameter values given to the PRE_FATE.selectDominant function, if used (doRuleA, rule.A1, rule.A2_quantile, doRuleB, rule.B1_percentage, rule.B1_number, rule.B2, doRuleC).

mat.traits

a data.frame with at least 3 columns : species, GROUP, ... (one column for each functional trait)
(see PRE_FATE.speciesDistance)

mat.overlap

(optional) default NA.
Otherwise, two options :

a data.frame with 2 columns : species, raster
a dissimilarity structure representing the niche overlap between each pair of species.
It can be a dist object, a niolap object, or simply a matrix.

(see PRE_FATE.speciesDistance)

rules.speciesDistance

(optional) default NA.
A vector containing all the parameter values given to the PRE_FATE.speciesDistance function, if used
( opt.maxPercent.NA, opt.maxPercent.similarSpecies, opt.min.sd).

mat.species.DIST

a dist object, or a list of dist objects (one for each GROUP value), corresponding to the distance between each pair of species.
Such an object can be obtained with the PRE_FATE.speciesDistance function.

clust.evaluation

(optional) default NA.
A data.frame with 4 columns :
GROUP, no.clusters, variable, value.
Such an object can be obtained with the PRE_FATE.speciesClustering_step1 function.

no.clusters

an integer, or a vector of integer (one for each GROUP value), with the number of clusters to be kept (see PRE_FATE.speciesClustering_step2)

determ.all

a data.frame with 6 or 10 columns :
PFG, GROUP, ID.cluster, species, ID.species, DETERMINANT
(and optionally, sp.mean.dist, allSp.mean, allSp.min, allSp.max).
Such an object can be obtained with the PRE_FATE.speciesClustering_step2 function.

mat.traits.PFG

a data.frame with at least 3 columns : PFG, no.species, ... (one column for each functional trait, computed as the mean (for numeric traits) or the median (for categorical traits) of the values of the determinant species of this PFG).
Such an object can be obtained with the PRE_FATE.speciesClustering_step3 function.

Value

A list containing all the elements given to the function and checked :

name.dataset

name of the dataset

mat.observations

(see PRE_FATE.selectDominant)

sites: name of sampling site
(x, y): coordinates of sampling site
species: name of the concerned species
abund: abundance of the concerned species
(habitat): habitat of sampling site

rules.selectDominant

a vector containing values for the parameters doRuleA, rule.A1, rule.A2_quantile, doRuleB, rule.B1_percentage, rule.B1_number, rule.B2, doRuleC (see PRE_FATE.selectDominant)

mat.traits

(see PRE_FATE.speciesDistance)

species: name of the concerned species
GROUP: name of the concerned data subset
...: one column for each functional trait

mat.overlap

a dist object corresponding to the distance between each pair of species in terms of niche overlap (see PRE_FATE.speciesDistance)

rules.speciesDistance

a vector containing values for the parameters opt.maxPercent.NA, opt.maxPercent.similarSpecies, opt.min.sd (see PRE_FATE.speciesDistance)

mat.species.DIST

a dist object corresponding to the distance between each pair of species, or a list of dist objects, one for each GROUP value (see PRE_FATE.speciesDistance)

clust.evaluation

(see PRE_FATE.speciesClustering_step1)

GROUP: name of data subset
no.clusters: number of clusters used for the clustering
variable: evaluation metrics' name
value: value of evaluation metric

no.clusters

number of clusters to be kept for each data subset

determ.all

(see PRE_FATE.speciesClustering_step2)

PFG: ID of the plant functional group (GROUP + ID.cluster)
GROUP: name of data subset
ID.cluster: cluster number
species: name of species
ID.species: species number in each PFG
DETERMINANT: TRUE if determinant species, FALSE otherwise
(sp.mean.dist): species mean distance to other species of the same PFG
(allSp.mean): \(mean(\text{sp.mean.dist})\) within the PFG
(allSp.min): \(mean(\text{sp.mean.dist}) - 1.64 * sd(\text{sp.mean.dist})\) within the PFG
(allSp.max): \(mean(\text{sp.mean.dist}) + 1.64 * sd(\text{sp.mean.dist})\) within the PFG

mat.traits.PFG

(see PRE_FATE.speciesClustering_step3)

PFG: name of the concerned functional group
no.species: number of species in the concerned PFG
...: one column for each functional trait

The information is written in FATE_dataset_[name.dataset]_step1_PFG.RData file.

Author

Maya Guéguen

Examples


## Load example data

Save data to reproduce building of Plant Functional Groups

Arguments

Value

See also

Author

Examples