BeeBDC (version 1.3.1)

richnessEstimateR: Estimate country, continental, and global species richnesses

Description

Takes an output dataset from richnessPrepR() to estimate species richness using iChao and iNEXT (hill numbers) for countries, continents, and/or the entire globe.

Usage

richnessEstimateR(
  data = NULL,
  sampleSize = 10000,
  countrySamples = 1,
  continentSamples = 1,
  globalSamples = 1,
  countriesToExclude = NULL,
  mc.cores = 1,
  k = 10,
  filterToRecordedCountries = TRUE,
  outPath = tempdir(),
  fileName = "continentSampled.pdf"
)

Value

Outputs an R file with four tables ("Summary", "SiteOutput", "ContinentOutput", and "GlobalOutput"; depending on the number required). The summary table shows the Median overall estimates, while the remaining three shows the outputs from each iteration (useful for plotting, see relevant vignette). Some figures may also be saved to the selected outPath.

Arguments

data

an RData file created using the richnessPrepR() function.

sampleSize

Numeric. The size of the sample randomly drawn from the provided curve. See curveFunction. Default = 10000.

countrySamples

Numeric. The number of times to sample the country species richness for both iChao and iNEXT. If equal to zero (0), then this will not be analysed. Default = 5.

continentSamples

Numeric. The number of times to sample the continent species richness for both iChao and iNEXT. If equal to zero (0), then this will not be analysed. Default = 5.

globalSamples

Numeric. The number of times to sample the global species richness for both iChao and iNEXT. If equal to zero (0), then this will not be analysed. Default = 5.

countriesToExclude

Character vector. You may decide to excluse some countries if they are being problematic or their sample sizes are too small. Default = NULL.

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.

k

Numeric. For iChao; 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.

filterToRecordedCountries

Logical. If TRUE, the checklist will be filtered to the countries Where occurrence records were found. Default = TRUE. Change at your own peril.

outPath

A directory as character. Directory where to save output figure. Default = tempdir().

fileName

A character vector with file name for the output figure, ending with '.pdf'. Default = "continentSampled.pdf".

See Also

countryHarmoniseR() to harmonise country names based on a short list; richnessPrepR() to produce the input data and for the required column names; as well as ChaoWrapper() and iNEXTwrapper() for the parallelised implementation of SpadeR and iNEXT functions.

Examples

if (FALSE) {

  # Use the example data 
data(beesCountrySubset)

  # First, 
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()
)

 exampleEstimate <- richnessEstimateR(
   data = estimateDataExample,
   sampleSize = 10000,
   countrySamples = 1,
   continentSamples = 1,
   globalSamples = 1,
   filterToRecordedCountries = TRUE,
   mc.cores = 1,
   # Directory where to save files
   outPath = tempdir(),
   fileName = "Sampled.pdf"
 )
 
} # END dontrun