Manipulating the gbif4crest calibration data

Manuel Chevalier

2025-02-13

Note 1: If you have your own calibration data, this vignette is of little interest to you. Refer to the documentation of crest.set_modern_data().

Note 2: The calibration dataset comes now in multiple versions (v2 and v3 on Jan. 2025). The data structure in the two versions being the same, the content of this page applies to both equally.

Downloading calibration dataset

crestr comes with a companion global calibration dataset for several proxies (see details in other vignettes andd the dedicated webpage). This dataset can be downloaded from [here]. Alternatively, you can also download it directly from R using the dbDownload() function.

A quick illustration

Let’s begin by loading crestr.

library(crestr)

In this example, we will work with a reduced dataset, composed of three pollen taxa:

PSE
#>   Level     Family     Genus Species ProxyName
#> 1     1  Ericaceae      <NA>    <NA> Ericaceae
#> 2     2 Asteraceae Artemisia    <NA> Artemisia
#> 3     2   Oleaceae      Olea    <NA>      Olea

Using the gbif4crest database

Independently of how you downloaded the calibration data, you will use the crest.get_modern_data() if you want to use the gbif4crest data. You will need to note the location of the dataset on your computer and provide that physical address to the function, as follow:

reconstr <- crest.get_modern_data(  pse = PSE,
                                    taxaType = 1,
                                    climate = c("bio1", "bio12"),
                                    # The full path to the local database
                                    # Or place the database in the working
                                    #   directory.
                                    dbname = "path/to/gbif4crest_03.sqlite3",
                                    verbose = TRUE
)

This is it! You have used the gbif4crest calibration dataset to estimate the response of Ericaceae, artemisia and Olea. Please note that since no geographical constraints were provided, the fitted responses englobe all species worldwide. These curves have, therefore, little meaning. You always need to tailor the extent of your calibration dataset to your study region!

Note: The local calibration database file can be saved in your working directory or anywhere else and renamed in any way you want. The file extension ‘.sqlite3’ is, however, important and cannot be modified.

Local subset of the calibration dataset

If the database is too big for our computer and you want to save space, you can subset it after downloading it. This way, you do not need to keep a large file on your computer and you only need an active internet connection to create the subset. Such subsets can be created using the dbSubset() function.

dbSubset( taxaType = 1,
          xmn = 0, xmx = 50, ymn = -45, ymx = 0,
          dbname = "path/to/gbif4crest_03.sqlite3",
          out = 'myDatabaseSubset.sqlite3',
          verbose = TRUE
      )

This will create a local snapshot of the database in your working directory (unless you give it a different path with out). You can then use this restricted dataset with crest.get_modern_data() and all other functions as if it was the “full” dataset. This restricted database is only 45Mb and the storage problem is therefore avoided.

reconstr <- crest.get_modern_data(  pse = PSE,
                                    taxaType = 1,
                                    climate = c("bio1", "bio12"),
                                    # The full path to the local database
                                    # Or place the database in the working
                                    #   directory.
                                    dbname = "myDatabaseSubset.sqlite3",
                                    verbose = TRUE
)