crest.get_modern_data: Extract distributions from the database

In mchevalier2/crestr: A Probabilistic Approach to Reconstruct Past Climates Using Palaeoecological Datasets

Extract distributions from the database

Description

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

Usage

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,
  climateWithObs = FALSE,
  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.
climateWithObs	A boolean to indicate whether all climate values from the calibration dataset be included (FALSE, default) or only the climate values that corresponds to proxy observations (TRUE). Only useful in if the climate space weighting is activated.
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

## Not run: 
  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)

## End(Not run)

mchevalier2/crestr documentation built on Feb. 14, 2025, 6:31 p.m.