FDindexes: Wrapper to Space and Dendrogram Based Functional Diversity...
In ibartomeus/fundiv: tools to calculate several functional trait diversity indexes

Description Usage Arguments Value Examples

Calculate functional trait diversity for a set of communities using FD_dendro and dbFD and returns a single dataframe

FDindexes(S, A, w = NA, Distance.method = "gower", ord = c("podani",
  "metric"), Cluster.method = c(ward = "ward", single = "single", complete =
  "complete", UPGMA = "average", UPGMC = "centroid", WPGMC = "median", WPGMA =
  "mcquitty"), calc.FRic = TRUE, stand.x = TRUE, corr = c("sqrt",
  "cailliez", "lingoes", "none"), stand.FRic = FALSE, m = "max",
  stand.FD = FALSE, scale.RaoQ = FALSE, Weigthedby = c("biomasCarabids",
  "biomasBees", "biomassValue"), biomassValue = rep(1, nrow(S)),
  op_qhull = "FA")

`S`	matrix or data frame of functional traits. Traits can be numeric, ordered, or factor. NAs are tolerated.
`A`	matrix containing the abundances of the species in S (or presence-absence, i.e. 0 or 1). Rows are sites and species are columns. NA not tolerated. The number of species (columns) in A must match the number of species (rows) in S. In addition, the species labels in A and S must be identical and in the same order.
`w`	vector listing the weights for the traits in x. Can be missing, in which case all traits have equal weights.
`Distance.method`	metric to calculate the species distance matrix. Only Gower is fully implemented.
`ord`	character string specifying the method to be used for ordinal traits (i.e. ordered). "podani" refers to Eqs. 2a-b of Podani (1999), while "metric" refers to his Eq. 3. See gowdis for more details.
`Cluster.method`	Distance method used to produce the tree. UPGMA="average" is usually giving th ebest results (Podani et al. 2011)
`stand.x`	ogical; if all traits are numeric, should they be standardized to mean 0 and unit variance? If not all traits are numeric, Gower's (1971) standardization by the range is automatically used; see gowdis for more details.
`stand.FRic`	logical; should FRic be standardized by the ‘global’ FRic that include all species, so that FRic is constrained between 0 and 1?
`m`	the number of PCoA axes to keep as ‘traits’ for calculating FRic (when FRic is measured as the convex hull volume) and FDiv. Options are: any integer >1, "min" (maximum number of traits that allows the s >= 2^t condition to be met, where s is the number of species and t the number of traits), or "max" (maximum number of axes that allows the s > t condition to be met). See ‘dbFD’ details section.
`stand.FD`	logical; should FD be standardized by the max FD, so that FD is constrained between 0 and 1?
`scale.RaoQ`	logical; should Rao's Q be scaled by its maximal value over all frequency distributions? See divc.
`Weigthedby`	character string indicating weighted by biomass should be done on 'biomassValue' or corrected first for Carabids or bees.
`biomassValue`	numerical vector with body weigh (or length) values for each species in the same order as species are provided. It can also be a matrix or data frame with one mass value for each community and species (both communities and species arranged like in A). Default is 1, implying no weightening
`op_qhull`	access the options for calculating the volume of the convex hull. If a long scary warning appears mentioning qhull read convhullngeometry help page. default in FD is= "FA", but sometimes is needed to add stuff.
`character`	string specifying the correction method to use when the species-by-species distance matrix cannot be represented in a Euclidean space. Options are "sqrt", "cailliez", "lingoes", or "none". Can be abbreviated. Default is "sqrt". See ‘details’ section.

comm vector with the name of the community

n_sp vector listing the number of species for each community

n_tr vector listing the number of traits used

FDpg vector listing FDpg (petchey and gaston) for each community

FDw vector listing FD weighthed by species relative abundances in each community

FDwcomm vector listing FD weighthed by species abundances across all communities

qual.FD vector repeating the quality of the dendogram representation. clustering performance is assessed by the correlation with the cophenetic distance

FDw_bm FDw vector listing FD weighthed by species relative biomass in each community

FDwcomm_bm vector listing FD weighthed by species biomass across all communities

sing.sp vector listing the number of functionally singular species in each community. If all species are functionally different, sing.sp will be identical to nbsp.

qual.FRic quality of the reduced-space representation required to compute FRic and FDiv.

Frich vector listing the FRic of each community

Fdis vector listing the Fdis of each community

Fdis_bm vector listing the Fdis weighted by biomass of each community

Feve vector listing the Feve of each community

Feve_bm vector listing the Feve weighted by biomass of each community

Fdiv vector listing the Fdiv of each community

Fdiv_bm vector listing the Fdiv weighted by biomass of each community

RaoQ vector listing the RaoQ of each community

RaoQ_bm vector listing the RaoQ weighted by biomass of each community

Seve vector listing the species eveness (pielous J') of each community

Shannon vector listing the diversitit Shannon index of each community

Abund vector listing the Total abundance of each community

TotalBiomass vector listing the total biomass of each community

EvenessBiomass vector listing the eveness of biomass distribution of each community

ShannonBiomass vector listing the Shannon of the diversity weighted by biomass of each community. I am not sure this one makes much sense.

ex1 <- FDindexes(dummy$trait, A = dummy$abun, Distance.method= "gower", ord= "podani",
                 Cluster.method= "average", corr= "cailliez", Weigthedby = "biomassValue",
                 biomassValue = c(1.2, 2.3, 0.6, 1.0, 3.4, 0.2, 1.6, 2.2))
ex1