BeeBDC (version 1.3.1)

iNEXTwrapper: Parallel estimation of species richness in a community using iNEXT

Description

A wrapper for iNEXT::iNEXT() to interpolate and extrapolate Hill numbers with order q (rarify species richness). The wrapper has the ability to estimate species richness for multiple sites (or countries) at once and to do this using multiple cores.

Usage

iNEXTwrapper(
  data = NULL,
  variableColumn = "groupVariable",
  valueColumn = "n",
  q = 0,
  datatype = "abundance",
  conf = 0.95,
  se = TRUE,
  nboot = 50,
  size = NULL,
  endpoint = NULL,
  knots = 40,
  mc.cores = 1
)

Value

Returns a list containing two tibbles. The first is a tibble that concatenates the outputs from the basic data and rare species information in columns per input variable (column). The second is a tibble that concatenates the various species richness estimates, with input variables in chunks of rows. Additionally a console output will list the variables (columns) that lacked sufficient data to be analysed.

Arguments

data

A data frame or tibble. A data frame containing "abundance"-type data per variable (population, country, species...) in columns.

variableColumn

Character. The column to be used to group the data. Probably "country" or "site". Default = "groupVariable".

valueColumn

Character. The column containing the count data. Defualt = "n".

q

a number or vector specifying the richness order(s) of Hill numbers.

datatype

data type of input data: individual-based abundance data (datatype = "abundance"), sampling-unit-based incidence frequencies data (datatype = "incidence_freq") or species by sampling-units incidence matrix (datatype = "incidence_raw").

conf

a positive number < 1 specifying the level of confidence interval; default is 0.95.

se

a logical variable to calculate the bootstrap standard error and conf confidence interval.

nboot

an integer specifying the number of replications; default is 50.

size

an integer vector of sample sizes (number of individuals or sampling units) for which richness estimates will be computed. If NULL, then richness estimates will be computed for those sample sizes determined by the specified/default endpoint and knots.

endpoint

an integer specifying the sample size that is the endpoint for rarefaction/extrapolation. If NULL, then endpoint = double the reference sample size.

knots

an integer specifying the number of equally-spaced knots (say K, default is 40) between size 1 and the endpoint; each knot represents a particular sample size for which richness estimate will be calculated. If the endpoint is smaller than the reference sample size, then iNEXT() computes only the rarefaction esimates for approximately K evenly spaced knots. If the endpoint is larger than the reference sample size, then iNEXT() computes rarefaction estimates for approximately K/2 evenly spaced knots between sample size 1 and the reference sample size, and computes extrapolation estimates for approximately K/2 evenly spaced knots between the reference sample size and the endpoint.

mc.cores

Numeric. If > 1, the function will run in parallel using mclapply using the number of cores specified. If = 1 then it will be run using a serial loop. NOTE: Windows machines must use a value of 1 (see ?parallel::mclapply). Additionally, be aware that each thread can use large chunks of memory. Default = 1.

See Also

iNEXT::iNEXT()

Examples

if (FALSE) {
  # Read in some example data and use [BeeBDC::richnessPrepR()] to create the example input data
 #' data(beesCountrySubset)

estimateDataExample <- BeeBDC::richnessPrepR(
  data = beesCountrySubset,
  # Download the taxonomy
  taxonomyFile = BeeBDC::beesTaxonomy(),
  # Download the checklist
  checklistFile = BeeBDC::beesChecklist(),
  curveFunction = function(x) (228.7531 * x * x^-log(12.1593)),
  sampleSize = 10000,
  countryColumn = "country_suggested",
  limitGlobal = NULL,
  outPath = tempdir()
)

  # In this function we can directly feed in estimateDataExample$site_speciesCounts 
 iNextOut <- iNEXTwrapper(data = estimateDataExample$site_speciesCounts,
                          variableColumn = "country_suggested",
                          valueColumn = "n",
                          q = 0,
                          datatype = "abundance",
                          conf = 0.95,
                          se = TRUE,
                          nboot = 50,
                          size = NULL,
                          endpoint = NULL,
                          knots = 40,
                          mc.cores = 1)
}