Extract distributions from the database

This function will extract the distributions of all the species composing each taxon and return them as a list.

crest.get_modern_data(
  pse,
  taxaType,
  climate,
  df = NA,
  ai.sqrt = FALSE,
  xmn = NA,
  xmx = NA,
  ymn = NA,
  ymx = NA,
  continents = NA,
  countries = NA,
  basins = NA,
  sectors = NA,
  realms = NA,
  biomes = NA,
  ecoregions = NA,
  minGridCells = 20,
  elev_min = NA,
  elev_max = NA,
  elev_range = NA,
  year_min = 1900,
  year_max = 2021,
  nodate = TRUE,
  type_of_obs = c(1, 2, 3, 8, 9),
  selectedTaxa = NA,
  site_info = c(NA, NA),
  site_name = NA,
  dbname = "gbif4crest_02",
  verbose = TRUE
)

Arguments

pse

A pollen-Species equivalency table. See createPSE for details.

taxaType

A numerical index (between 1 and 6) to define the type of palaeoproxy used: 1 for plants, 2 for beetles, 3 for chironomids, 4 for foraminifers, 5 for diatoms and 6 for rodents. The example dataset uses taxaType=0 (pseudo-data). Default is 1.

climate

A vector of the climate variables to extract. See accClimateVariables for the list of accepted values.

df

A data frame containing the data to reconstruct (counts, percentages or presence/absence data).

ai.sqrt

A boolean to indicate whether ai values should be square-root transformed (default FALSE).

xmn, xmx, ymn, ymx

The coordinates defining the study area.

continents

A vector of the continent names defining the study area.

countries

A vector of the country names defining the study area.

basins

A vector of the ocean names defining the study area.

sectors

A vector of the marine sector names defining the study area.

realms

A vector of the studied botanical realms defining the study area.

biomes

A vector of the studied botanical biomes defining the study area.

ecoregions

A vector of the studied botanical ecoregions defining the study area.

minGridCells

The minimum number of unique presence data necessary to estimate a species' climate response. Default is 20.

elev_min, elev_max

Parameters to only selected grid cells with an elevation higher than elev_min or lower than elev_max (default is 'NA ).

elev_range

Parameters discard the grid cell with a high elevation range (default is NA).

year_min, year_max

The oldest and youngest occurrences accepted (default is 1900-2021).

nodate

A boolean to accept occurrences without a date (can overlap with occurrences with a date; default TRUE).

type_of_obs

The type of observation to use in the study. 1: human observation, 2: observation, 3: preserved specimen, 4: living specimen, 5: fossil specimen, 6: material sample, 7: machine observation, 8: literature, 9: unknown (Default c(1, 2, 3, 8, 9))

selectedTaxa

A data frame assigns which taxa should be used for each variable (1 if the taxon should be used, 0 otherwise). The colnames should be the climate variables' names and the rownames the taxa names. Default is 1 for all taxa and all variables.

site_info

A vector containing the coordinates of the study site. Default c(NA, NA).

site_name

The name of the dataset (default NA).

dbname

The name of the database. Default is 'gbif4crest_02' and data will be extracted from the online database. The SQLite3 version of the database can also be used here by providing the complete path to a file ending by .sqlite3, e.g. /path/to/file/gbif4crest_02.sqlite3

verbose

A boolean to print non-essential comments on the terminal (default TRUE).

Value

A crestObj object containing the spatial distributions.

See also

The SQLite3 database can be downloaded from https://figshare.com/articles/dataset/GBIF_for_CREST_database/6743207.

Examples

if (FALSE) {
  data(crest_ex_pse)
  data(crest_ex_selection)
  data(crest_ex)
  x <- crest.get_modern_data( df = crest_ex,
    pse = crest_ex_pse, taxaType = 0,
    climate = c("bio1", "bio12"),
    selectedTaxa = crest_ex_selection, dbname = "crest_example",
    verbose = FALSE
  )
  x
  lapply(x$modelling$distributions, head)
}