Supporting data - Predicting plant water availability from phytolith assemblages: an experimental approach for archaeological reconstructions in drylands

Supporting data for the publication in Vegetation Histoy and Archaeobotany DOI https://doi.org/10.1007/s00334-024-01012-9

Title: Predicting plant water availability from phytolith assemblages: an experimental approach for archaeological reconstructions in drylands 

Authors: D'Agostini Francesca, Ruiz Pérez Javier, Madella Marco, Vadez Vincent, Lancelotti Carla

This repository contains all the supplementary information cited in the manuscript. 

Contents:

File S1: Excel file with the dataset of the experimental cultivations (both 2019 and 2020). This file includes the landraces tested, experimental layout, physiological data (including weekly weights, transpiration rate, biomass values, water added and flowering time), soil elemental composition, and associated metadata.

File S2:  Excel file with the phytolith counting data from the 2019 and 2020 experiments. The file includes the raw counting data, morphotype concentrations, ratios, and associated metadata.

File S3: R code that includes all the scripts used for statistics and data visualisation.

Figure S1: weekly transpiration trend of crops grown in a) 2019 and b) 2020.

Figure S2: boxplots and linear regressions of the normalised phytolith concentration and ratio of sensitive to fixed morphotypes against total water transpired for sorghum, separated by species and sample plant parts.

Figure S3: boxplots and linear regressions of the normalised phytolith concentration and ratio of sensitive to fixed morphotypes against total water transpired for finger millet, separated by species and sample plant parts.

Figure S4: boxplots and linear regressions (in relation to total water transpired) of the normalised phytolith concentration and ratio of sensitive to fixed morphotypes for pearl millet, sorted per species and plant part of deposition.

Figure S5: logistic regression curves of ACUTE BULBOSUS against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species.

Figure S6: logistic regression curves of BULLIFORM FLABELLATE against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species. 

Figure S7: logistic regression curves of BULLIFORM (sum of BLOCKY and BULLIFORM FLABELLATE) against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species.

Figure S8: logistic regression curves of CROSS against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species.

Figure S9: logistic regression curves of ELONGATE SINUATE clavate against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species. 

Figure S10: logistic regression curves of ELONGATE DENTATE against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species. 

Figure S11: logistic regression curves of ELONGATE ENTIRE against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species. 

Figure S12: logistic regression curves of ELONGATE SINUATE against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species. 

Figure S13: logistic regression curves of POLYLOBATE against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species.

Figure S14: logistic regression curves of RONDEL against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species.

Figure S15: logistic regression curves of SADDLE against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species.

Figure S16: logistic regression curves of STOMA against well-watered treatment for both experimental runs, using all plant parts analysed and divided per species.