Appendices PhD thesis- Phytoliths as proxies for plant water availability. An experimental approach on selected C4 species and its archaeological application in drylands

Appendices forPhD thesis This repository contains all the supplementary information cited in the manuscript. Title:Phytoliths as proxies for plant water availability.An experimental approach on selected C4species and its archaeological application in drylands Author: Francesca D'Agostini Year: submitted on 21 October 2022 University Pompeu Fabra and Université de Montpellier Supervisors: Dr. Carla Lancelotti, Dr Vincent Vadez and Dr Marco Madella Contents: Chapter 5 File A5.1: the complete dataset of the experimental cultivation, including the experimental design for both seasons. File A5.2: the complete dataset of the phytolith analysis including morphometry and isotopes data. File A5.3: R scripts. File A5.3.1: the csv UTF8 (comma-delimited) files to run the code in R regarding phytoliths. File A5.3.2: the csv UTF8 (comma-delimited) files to run the code in R regarding bulliforms morphometry. File A5.3.3: the csv UTF8 (comma-delimited) files to run the code in R regarding elongates morphometry. File A5.3.4: the csv UTF8 (comma-delimited) files to run the code in R regarding stomata morphometry. File A5.3.5: the csv UTF8 (comma-delimited) files to run the code in R regarding Si isotopic analysis. File A5.3.6: the csv UTF8 (comma-delimited) files to run the code in R for the archaeological samples tested (chapter 7). File A5.3.7: the csv UTF8 (comma-delimited) files to run the code in R for modern samples used to built the model applied on archaeological samples (chapter 7). File A5.4: metadata. Chapter 6 Since it is a published article, the supplementary material is accessible open access in the article repository, referenced as in the text:https://doi.org/10.5281/zenodo.5497871 Chapter 7 Since it is an accepted article, the supplementary material is accessible open access in the article repository, referenced as in the text:https://doi.org/10.5281/zenodo.7120448 Chapter 8 Figure A8.1.1: boxplots for phytolith concentration for sorghum cultivated in 2020. Figure A8.1.2: linear regression plots for phytolith concentration (dependent variables) and total water transpired (independent variable) for sorghum cultivated in 2020. Figure A8.1.3: boxplots for ratio of sensitive to fixed morphotypes for sorghum cultivated in 2020. Figure A8.1.4: linear regression plots for ratio sensitive to fixed morphotypes (dependent variables) and total water transpired (independent variable) for sorghum cultivated in 2020. Figure A8.1.5: boxplots for phytolith concentration for finger millet cultivated in 2020 Figure A8.1.6: linear regression plots for phytolith concentration (dependent variables) and total water transpired (independent variable) for finger millet cultivated in 2020. Figure A8.1.7: boxplots for ratio of sensitive to fixed morphotypes for finger millet cultivated in 2020. Figure A8.1.8: linear regression plots for ratio sensitive to fixed morphotypes (dependent variables) and total water transpired (independent variable) for finger millet cultivated in 2020. Figure A8.1.9: boxplots for phytolith concentration for pearl millet cultivated in 2020. Figure A8.1.10: linear regression plots for phytolith concentration (dependent variables) and total water transpired (independent variable) for pearl millet cultivated in 2020. Figure A8.1.11: boxplots for ratio of sensitive to fixed morphotypes for pearl millet cultivated in 2020. Figure A8.1.12: linear regression plots for ratio sensitive to fixed morphotypes (dependent variables) and total water transpired (independent variable) for pearl millet cultivated in 2020. Figure A8.2.1: logistic regression plots for acute bulbosus arranged for the three species considered separate. Figure A8.2.2: logistic regression plots for bulliforms (general category sum of blockies and bulliforms flabellate) arranged for the three species considered separate. Figure A8.2.3: logistic regression plots for elongates clavate arranged for the three species considered separate. Figure A8.2.4: logistic regression plots for elongates dentate arranged for the three species considered separate. Figure A8.2.5: logistic regression plots for elongates entire arranged for the three species considered separate. Figure A8.2.6: logistic regression plots for elongate sinuate arranged for the three species considered separate. . Figure A8.2.7: logistic regression plots for crosses arranged for the three species considered separate. Figure A8.2.8: logistic regression plots for polylobates arranged for the three species considered separate. Figure A8.2.9: logistic regression plots for rondels arranged for the three species considered separate. Figure A8.2.10: logistic regression plots for saddles arranged for the three species considered separate. File A8.1: the complete dataset of the experimental cultivation, including the experimental design for both seasons. File A8.2: the complete dataset of the phytolith both row data, concentration and ratio of sensitive to fixed morphotypes used for the analysis of chapter 8. File A8.3: R scripts. File A8.3.1: csv UTF8 (comma-delimited) file to run the code in R. File A8.4: metadata. File A8.5: table of means and standard deviations of morphotypes concentration from plant samples grown in the experimental cultivation 2020. Chapter 9 Figure A9.1.1: linear regression plots of height (“He”) in relation with leaves biomass (“Leaves”) (independent variable) for bulliforms. Figure A9.1.2: linear regression plots of height (“He”) (dependent variable) in relation with transpiration efficiency (“TE”) (independent variable) for bulliforms. Figure A9.1.3:linear regression plots of height (“He”) dependent variable) in relation with total water added (“TWA”) (independent variable) for bulliforms. Figure A9.1.4: linear regression plots of height (“He”) (dependent variable) in relation with total water transpired (“TWU”) (independent variable) for bulliforms. Figure A9.1.5:linear regression plots of length of the beak (“LB”) (dependent variable) in relation with leaves biomass (“Leaves”) (independent variable) for bulliforms. Figure A9.1.6: linear regression plots of length of the beak (“LB”) (dependent variable) in relation with transpiration efficiency (“TE”) (independent variable) for bulliforms. Figure A9.1.7: linear regression plots of length of the beak (“LB”)(dependent variable) in relation with total water added (“TWA”) (independent variable) for bulliforms. Figure A9.1.8: linear regression plots of length of the beak (“LB”) (dependent variable) in relation with total water transpired (“TWU”) (independent variable) for bulliforms. Figure A9.1.9:linear regression plots of width (“Wi”) (dependent variable) in relation with leaves biomass (“Leaves”) (independent variable) for bulliforms. Figure A9.1.10: linear regression plots of width (“Wi”) (dependent variable) in relation with transpiration efficiency (“TE”) (independent variable) for bulliforms. Figure A9.1.11: linear regression plots of width (“Wi”) (dependent variable) in relation with total water added (“TWA”) (independent variable) for bulliforms. Figure A9.1.12: linear regression plots of width (“Wi”) (dependent variable) in relation with total water transpired (“TWU”) (independent variable) for bulliforms. Figure A9.2.1: density plot for subsidiary cells 1 size. Figure A9.2.2: density plot for subsidiary cells 2 size. Figure A9.3.1: density plot for bulliform height of the beak. Figure A9.3.2: density plot for bulliform length of the beak. Figure A9.3.3: density plot for bulliform number of peaks. Figure A9.3.4: density plot for bulliform length peak to peak. Figure A9.4.1: linear regression plot ofδ29Si (dependent variable) in relation to total water transpired (independent variable) for all the morphotypes considered together. Figure A9.4.2: linear regression plots ofδ29Si (dependent variable) in relation to total water transpired (independent variable) for all the morphotypes considered together. File A9.1: the complete dataset of the phytolith morphometry and isotopic data used for the analysis of chapter 9. File A9.2: R scripts. File A9.2.1: csv UTF8 (comma-delimited) files to run the code in R regarding bulliforms morphometry. File A9.2.2: csv UTF8 (comma-delimited) files to run the code in R regarding elongates morphometry. File A9.2.3: csv UTF8 (comma-delimited) files to run the code in R regarding stomata morphometry. File A9.2.4: csv UTF8 (comma-delimited) files to run the code in R regarding Si isotopic analysis. File A9.3: metadata. Abstract: This work investigated the relationship between phytoliths from finger millet, pearl millet and sorghum and the water conditions under which these species grow. The archaeological debate on the use of millets remains open and finding a proxy to help recognise their growing conditions is crucial for the reconstruction of their dispersal. In this research phytolith concentration, ratio of sensitive to fixed morphotypes, phytolith assemblage composition, morphometry, and Si isotopic composition of sensitive morphotypes were analysed as potential indicators of plant water availability. The results showed changes in both the assemblage composition, the bulliform dimensions and isotopic content, depending on the species. Intra- and inter-specific differences were highlighted. In view of the results a predictive model based on morphotype concentration was elaborated and applied to four case-studies of the Indus Valley Civilization, proving that phytoliths can be efficient proxies to assess the water condition of plant growth in the archaeological record. &nbsp