BeeBDC (version 1.3.1)

ChaoWrapper: Parallel estimation of species richness in a community using iChao

Description

ChaoSpecies: Estimation of species richness in a single community based on five types of data: Type (1) abundance data (datatype="abundance"), Type (1A) abundance-frequency counts (datatype="abundance_freq_count"), Type (2) incidence-frequency data (datatype = "incidence_freq"), Type (2A) incidence-frequency counts (datatype="incidence_freq_count"), and Type (2B) incidence-raw data (datatype="incidence_raw"); see SpadeR-package details for data input formats.

Usage

ChaoWrapper(
  data = NULL,
  datatype = "abundance",
  k = 10,
  conf = 0.95,
  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.

datatype

Character. The type of input data, "abundance", "abundance_freq_count", "incidence_freq", "incidence_freq_count" or "incidence_raw". So far only tested with "abundance" data. Default = "abundace".

k

Numeric. the cut-off point (default = 10), which separates species into "abundant" and "rare" groups for abundance data for the estimator ACE; it separates species into "frequent" and "infrequent" groups for incidence data for the estimator ICE. Default = 10.

conf

Numeric. A positive number equal to or less than 1 specifying the level of confidence interval. Default = 0.95.

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

SpadeR::ChaoSpecies()

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()
)

  # Transform the data for input
inputTestData <- estimateDataExample$site_speciesCounts %>%
  dplyr::select(scientificName, country_suggested, n) %>%
  tidyr::pivot_wider(names_from = country_suggested,
                     values_from = n,
                     values_fill = 0) %>%
  # Create the rownames
tibble::column_to_rownames("scientificName") %>%
  dplyr::tibble()

iChaoOut <- ChaoWrapper(
data = inputTestData,
datatype = "abundance",
k = 10,
conf = 0.95,
mc.cores = 1)
}