Size harmonizing planktonic Foraminifera number concentrations in the water column
Creators
- 1. Aix-Marseille Université, CNRS, IRD, INRAE, CEREGE, Aix-en-Provence, France
- 2. MARUM, Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany
- 3. NIOZ, Royal Netherlands Institute for Sea Research, Department of Ocean Systems, Texel, The Netherlands
- 4. Universitat Autònoma de Barcelona, ICTA and Dept. of Geography, Spain
- 5. Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland
- 6. Fondation pour la recherche sur la biodiversité (FRB-CESAB), Montpellier, France
- 7. Tohoku University Museum, Tohoku University, Japan
- 8. LPG-BIAF, UMR-CNRS 6112, University of Angers, France
- 9. Université Littoral Côte d'Opale, Univ. Lille, CNRS, UMR 8187, LOG, Laboratoire d'Océanologie et de Géosciences, France
- 10. Department of Climate Geochemistry, Max Planck Institute for Chemistry, Mainz, Germany
Description
Data and R code for " Size harmonizing planktonic Foraminifera number concentrations in the water column " by Sonia Chaabane, Thibault de Garidel-Thoron, Xavier Giraud, Julie Meilland, Geert-Jan A. Brummer, Lukas Jonkers, P. Graham Mortyn, Mattia Greco, Nicolas Casajus, Olivier Sulpis, Michal Kucera, Azumi Kuroyanagi, Hélène Howa, Gregory Beaugrand, Ralf Schiebel
The codes serve to generate the new normalization approach in order to not estimate number concentration of planktonic Foraminifera (ind/m3) in the finer and larger size fractions than the collection size fraction range. We use data from the FORCIS database that have been collected from the global ocean at different depths over the past century. We find a general cumulative distribution over the size fractions that can be described using a Michaelis Menten function. This yields multiplication factors by which one fraction can be normalized to any other size fraction equal to or larger than 100 µm. The resulting size normalization model was tested with data from various depths and compared to an earlier size normalization solution.
Scripts written by Sonia Chaabane
DATA SOURCES
* FORCIS database
https://doi.org/10.5281/zenodo.7390791
DATA
1. data_raw_from_excel.RDS
CODES
1. Prepare_data.R: Read the data and prepare it for the analyses
2. Calculate_f&Shalf.R: Analyse the data and build the model
3. Model_validation.R: compare the actual vs. estimated number concentration
4. Berger_number_concentration_estimations.R: compare the actual vs. estimated number concentration using Berger 1969 correction scheme
5. Cross validation.R: Application of the FORCIS number concentration-size correction scheme on independent dataset
6. Modelfunctions.R
Files
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