SuessR (version 0.1.6)

SuessR.custom: Calculate Suess and Laws corrections for stable carbon isotope data from a custom region

Description

The SuessR.custom() function generates region-specific Suess, Laws, and net (Suess + Laws) corrections for δ¹³C data input by the user. The net correction is then used to calculate the corrected δ¹³C data, which are supplied in the output. This function is specifically for data from regions not currently built into SuessR (i.e., different from "Bering Sea", "Aleutian Islands", "Gulf of Alaska", and "Subpolar North Atlantic", as of Version 0.1.6). Or for scenarios in which users want to change the default parameters used by SuessR for the built-in regions (e.g., averge phytoplankton cell radius).

Usage

SuessR.custom(data, custom.region.data, correct.to = 1850)

Value

The output of this function is a data frame that includes the sample ID ('id'), year ('year'), uncorrected δ¹³C ('d13c.uncor'), Laws correction ('Laws.cor'), Suess Correction ('Suess.cor'), net correction ('net.cor'), and corrected δ¹³C ('d13c.cor') for each sample. The corrected δ¹³C data is equal to the uncorrected δ¹³C data plus the net correction. The units for all values are the standard 'per mil' used for δ¹³C data.

Arguments

data

A dataframe including sample ID, year of sample collection, uncorrected δ¹³C data, and region. This function is specifically for data from regions not currently built into SuessR. Columns must be named 'id', 'year', 'd13c', and 'region'.

custom.region.data

A data frame containing environmental data for the custom region from which the samples originated. Must contain columns titled 'year', 'region', 'r', 'sst', 'sss', 'CO2atm', 'up.con', and 'Cp'. See details for information on how to supply these parameters appropriately.

correct.to

The year to which the δ¹³C data will be corrected. Defaults to 1850, pre-Suess effect.

Details

The SuessR.custom() allows users to calculate and apply Suess and Laws corrections to δ¹³C data from marine organisms collected in a region not currently built into the SuessR package. In Version 0.1.6, the built-in regions are the Bering Sea ('Bering Sea'), the Aleutian archipelago ('Aleutian Islands'), and the Gulf of Alaska ('Gulf of Alaska'). Because the Suess and Laws corrections require region-specific environmental data from 1850-present, users must supply these data using the 'custom.region.data' argument. Once these data have been supplied, this function calculates the corrections exactly like the SuessR() function. See the built-in 'SuessR.reference.data' object for an example template for the data, as well as the references for detailed information on how to compile and supply the appropriate data to the SuessR.custom() function. After compiling these data, please consider sharing them with the package authors (ctclark 'at' alaska.edu) to be included as built-in regions in future version of this package.

As with the SuessR() function, SuessR.custom() allows users to correct their data to a specific year, using the 'correct.to' argument. This argument defaults to AD1850, which represents onset of the Industrial Revolution and the initiation of the large-scale combusion of fossil fuels that created the Suess Effect. Correcting data to 1850 allows them to be compared to data from any samples collected during or before 1850 (including archaeological samples), as well as to any other samples Suess corrected to the year 1850. Users examining contemporary datasets may wish to correct their data to another time year. For example, a user examining a dataset of δ¹³C data spanning the years 1970-2010 might choose to correct all their data back to the year 1970, to correct the older samples forward to 2010, or to correct all the samples to 1990, the middle of the time series. In any of these scenarios, corrected data would be comparable to one another. When choosing a value for 'correct.to' consideration should be given to the comparability of the user's data to existing data sets, clarity of presentation of results (i.e., clear statements of the year to which the data were corrected), and reproducibility of results (i.e., presentation of both uncorrected and corrected data so future users can repeat analyses or correct the raw data to another year).

Examples

example.region.data <- data.frame(year = seq(from = 1850, to = 2023, by = 1),
                                  region = rep("Example Region", 174),
                                  r = rep(5, 174),
                                  sst = seq(5.9, 6.6, 174),
                                  sss = seq(32.3, 32.7, 174),
                                  CO2atm = SuessR.reference.data$CO2atm[1:174],
                                  up.con = rep(0.014, 174),
                                  Cp = rep(0.41, 174))

example.custom.data <- data.frame(id = c("Sample 1", "Sample 2",
                                         "Sample 3", "Sample 4"),
                                  year = c(1850, 1900, 1950, 2000),
                                  d13c = c(-12, -12, -12, -12),
                                  region = rep("Example Region", 4))

SuessR.custom(data = example.custom.data,
              custom.region.data = example.region.data,
              correct.to = 1850)