metricTester (version 1.3.6)

sesField: Calculate a species' standardized trait field

Description

Calculate the null-model standardized effect size of a species' trait field.

Usage

sesField(field.input, metrics, nulls, randomizations, regional.abundance,
  distances.among, cores = "seq")

Arguments

field.input

Prepped field.input object.

metrics

Optional. If not provided, defines the metrics as all of those in defineMetrics. If only a subset of those metrics is desired, then metrics should take the form of a character vector corresponding to named functions from defineMetrics. The available metrics can be determined by running names(defineMetrics()).

nulls

Optional. If not provided, defines the nulls as all of those in defineNulls. If only a subset of those is desired, then nulls should take the form of a character vector corresponding to named functions from defineNulls. The available nulls can be determined by running names(defineNulls()).

randomizations

The number of times the input CDM should be randomized and the metrics calculated across it.

regional.abundance

A character vector in the form "s1, s1, s1, s2, s2, s3, etc". Optional, will be generated from the input CDM if not provided.

distances.among

A symmetric distance matrix, summarizing the distances among all quadrats from the cdm. For use with the dispersal null.

cores

This function can run in parallel. In order to do so, the user must specify the desired number of cores to utilize. The default is "seq", which runs the calculations sequentially.

Value

Data frame of standardized effect sizes of species' trait fields. Table includes the observed trait field, the mean and standard deviation of the species' trait field after randomization with the chosen null model, and the resulting species-specific standardized effect size.

Details

The trait distance matrix should be symmetrical and "complete". See example. Currently only non-abundance-weighted mean pairwise and interspecific abundance-weighted mean pairwise phylogenetic distances are implemented. The only null models that are currently implemented are the richness and dispersal nulls. The function could be improved by tapping into any of the metrics and nulls defined in defineMetrics and defineNulls.

References

Miller, Wagner, Harmon & Ricklefs. In review. Radiating despite a lack of character: closely related, morphologically similar, co-occurring honeyeaters have diverged ecologically.

Examples

# NOT RUN {
#simulate tree with birth-death process
tree <- geiger::sim.bdtree(b=0.1, d=0, stop="taxa", n=50)

sim.abundances <- round(rlnorm(5000, meanlog=2, sdlog=1)) + 1

cdm <- simulateComm(tree, richness.vector=10:25, abundances=sim.abundances)

#in this example, occasionally some species are not in the CDM, so prune the tree
#accordingly so as not to throw any errors
tree <- drop.tip(tree, setdiff(tree$tip.label, colnames(cdm)))

prepped <- prepFieldData(tree=tree, picante.cdm=cdm)

results <- sesField(prepped, randomizations=3,
metrics="NAW_MPD", nulls="richness")
# }