# Calibration data for CREST

#### Manuel Chevalier

#### 2021-12-03

## Source of the calibration data

A multiproxy calibration dataset to estimate PDFs from a global collection of geolocalised presence-only data (hereafter proxy distributions) was first presented in [1]. These data were obtained from the Global Biodiversity Information Facility (GBIF) database, an online collection of geolocalised observations of biological entities. The calibration dataset (hereafter gbif4crest) contains the species distributions of six common palaeoecological fossil: the five taxa presented in the original version of the dataset — plants [2-12] for fossil pollen and macrofossils, chironomids [13], beetles [14], diatoms [15] and foraminifera [16] – to which rodents [17] were recently added (Fig. 1).

Fig. 1 Data density of the six climate proxies available in the gbif4crest calibration database. The total number of unique species occurrences (N) is indicated for each proxy. The maps are based on the ‘Equal Earth’ map projection to better account for the relative sizes of the different continents.

The coordinates of all the presence records of these six common palaeoecological fossil proxies were upscaled at a spatial resolution of 0.25 x 0.25° (hereafter QDGC for Quarter-Degree Grid Cell) and subsequently associated with terrestrial and oceanic environmental variables at the same resolution [18-24] (see details in Table 1). The QDGC spatial resolution is an empirical trade-off between numerous factors, including the resolution of the presence data, the quality of the data or the spatial representativity of the studied proxy. However, this tradeoff may be suboptimal in some situations and for that reason `crestr` can also be used with the raw GBIF data and even alternative calibration datasets.

In its current version (V2), the gbif4crest calibration dataset contains about 25.3 millions unique presence data for the six proxies. Unfortunately, the density of data available varies strongly between proxies and regions (Fig. 1). Plant data dominate the calibration dataset (>22 million unique occurrences) and allow for a use of `crestr` across all landmasses where vegetation currently grows. For the five other proxies, the datasets are still incomplete in many regions, which restricts the use of `crestr` (e.g. chironomids). However, these datasets are regularly updated by GBIF. For example, the first version of the gbif4crest dataset released in 2018 contained about 17.5 million QDGC entries, but the new version presented here contains about 25.3 million entries (~44% increase). The range of ‘reconstructible’ areas is thus rapidly broadening (see for instance the coverage of Russia by plant data compared to the first version of the gbif4crest dataset [1].

Table 1 List of terrestrial and marine variables available in the gbif4crest database. Each one can be selected in crestr using its associated code. List of abbreviations: (Temp.) Temperature, (Precip.) Precipitation, (SST) Sea Surface Temperature, (SSS) Sea Surface Salinity.

Code | Full name | Source
--- | --- | ---
bio1 | Mean Annual Temp. (°C) | [18]
bio2 | Mean Diurnal Range (°C) | [18]
bio3 | Isothermality (x100) | [18]
bio4 | Temp. Seasonality (standard deviation x100) (°C) | [18]
bio5 | Max Temp. of the Warmest Month (°C) | [18]
bio6 | Min Temp. of the Coldest Month (°C) | [18]
bio7 | Temp. Annual Range (°C) | [18]
bio8 | Mean Temp. of the Wettest Quarter (°C) | [18]
bio9 | Mean Temp. of the Driest Quarter (°C) | [18]
bio10 | Mean Temp. of the Warmest Quarter (°C) | [18]
bio11 | Mean Temp. of the Coldest Quarter (°C) | [18]
bio12 | Annual precip. (mm) | [18]
bio13 | Precip. of the Wettest Month (mm) | [18]
bio14 | Precip. of the Driest Month (mm) | [18]
bio15 | Precip. Seasonality (Coefficient of Variation) (mm) | [18]
bio16 | Precip. of the Wettest Quarter (mm) | [18]
bio17 | Precip. of the Driest Quarter (mm) | [18]
bio18 | Precip. of the Warmest Quarter (mm) | [18]
bio19 | Precip. of the Coldest Quarter (mm) | [18]
ai | Aridity Index (unitless) | [19]
sst_ann | Mean Annual SST (°C) | [20]
sst_jfm | Mean Winter SST (°C) | [20]
sst_amj | Mean Spring SST (°C) | [20]
sst_jas | Mean Summer SST (°C) | [20]
sst_ond | Mean Fall SST (°C) | [20]
sss_ann | Mean Annual SSS (PSU) | [21]
sss_jfm | Mean Winter SSS (PSU) | [21]
sss_amj | Mean Spring SSS (PSU) | [21]
sss_jas | Mean Summer SSS (PSU) | [21]
sss_ond | Mean Fall SSS (PSU) | [21]
diss_oxy | Dissolved Oxygen Concentration (mol/L) | [22]
nitrate | Nitrate Concentration (mol/L) | [23]
phosphate | Phosphate Concentration (mol/L) | [23]
silicate | Silicate Concentration (mol/L) | [23]
icec_ann | Mean Annual Sea Ice Concentration (%) | [24]
icec_jfm | Mean Winter Sea Ice Concentration (%) | [24]
icec_amj | Mean Spring Sea Ice Concentration (%) | [24]
icec_jas | Mean Summer Ice Concentration (%) | [24]
icec_ond | Mean Fall Sea Ice Concentration (%) | [24]

## Processing and storage of the calibration data

All these data were curated in a relational database to ensure the consistency of the data (Fig. 2). The gbif4crest database is composed of three main types of data: taxonomic data (`TAXA` table on Fig. 2), distribution data (`DISTRIB` and `DISTRIB_QDGC` tables) and diverse geopolitical, climatological and environmental data (`DATA_QDGC` table). Its structure is slightly different compared to the first version with a grouping of all the distinct QDGC tables in a unique `DATA_QDGC` table to enable a faster extraction of the data. Additional environmental and geographical descriptors were also added to characterise each grid cell and enable a more refined data selection. These include elevation and elevation variability [25], the country (www.naturalearthdata.com) or ocean (www.marineregions.org) names, as well as different levels of ecological classification for the terrestrial [26] and marine [27] realms. The first and last date of observation are also now included, along with the type of observation, as reported by GBIF (see `DISTRIB_QDGC` table on Fig. 2). Finally, the `DATA` table was entirely recalculated using a new protocol that better accounts for coastal margins so that climate values at some locations are expected to be slightly different from the first version of the gbif4crest dataset.

FiG. 2 Structure of the gbif4crest PostgreSQL database. By default, the package extracts data from the TAXA, DISTRIB-QDGC and DATA-QDGC tables. The DISTRIB table contains the raw occurrence data and can be used to process the data at a different spatial resolution for example.

Due to its large size (about 15 Gb), this database is not downloaded when installing the package, but it can be assessed in different ways. First, the data are stored in an open-access, cloud-based PostgreSQL database that can be dynamically accessed via `crestr`. This is the recommended option, as users without any a priori SQL knowledge can benefit from the package’s interface to automatically query the database simply by providing study-specific parameters, such as the name of the taxa or boundaries for the study area, to import all the necessary data in the correct format to the R environment. Second, advanced users can also directly query the database to extract and curate data from the `DISTRIB` or `DISTRIB_QDGC` tables using the dbRequest() function, and subsequently associate these data with climate variables. Finally, the complete gbif4crest calibration dataset can also be downloaded as a SQLite3 portable database file from here.

## References

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