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README FILE
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This readme file was generated on 05-11-2024 
V1. updated on 17-04-2025

It must be used as a guide to understand the procedures the authors followed to pre-treat and to generate data used in the manuscript entitled "Late Miocene greening of the Peruvian Desert", by Ochoa et al., 2025

It is meant to complement and to be read along the Methods section of the main article, as well as the Supplementary Data.
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GENERAL INFORMATION

1. Tittle of Dataset: SOM AGL Datafiles owned by Ochoa et al., 
Description:  Supplementary  Online Material associated to the manuscript "Late Miocene greening of the Peruvian Desert"

2. Autorship:
Code authorship: Dayenari Caballero

3.Journal:
Nature Communications Earth & Environment
editor: Dr. Alice Drinkwater


DESCRIPTION

1. Dataset language: Excel, R script,  PDF, Word file

2. Abstract:
The response of the hyperarid Pacific coast of South America to greenhouse warming remains highly uncertain but has profound implications for the future habitability of the region. Here, we present the first paleobotanical study documenting the Peruvian Desert's response to the Late Miocene greenhouse climate, when radiative forcing was comparable to 21st century projections. Fossil leaves, wood, and pollen from the Pisco Formation lagerstätte indicate a diverse dry forest biome where an absolute desert is observed today. Rainfall at 8-6 Ma is estimated to have been three times higher than today, possibly due to a warmer Eastern Tropical Pacific Ocean or to more frequent El Niño events. Our results suggest that the impacts of tropical climate dynamical changes on ecosystems of the Pacific South American coast under greenhouse warming conditions may outweigh those of heat and evaporation, potentially enabling vegetation expansion, biome transformations, and enhanced habitability in this region. 


3. Keywords:
Atacama Desert, Pisco Basin, desert greening, aridity, South American Arid Diagonal

4. Date of data collection:
01/2021 to 11/2024

5. Date of dataset publication:
17/04/2025

6. Funding:
This project has received funding from:
- European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101034371 (DO)
- CONCYTEC-PROCIENCIA research grant awarded to DO (No. PE501078563-2022-PROCIENCIA) 
- Programa de incorporación de investigadores awarded to JO and MC (Grant No. E034-2019-02-FONDECYT-BM).
- Ministerio de Ciencia, Innovación y Universidades (Spain) PICTURE to JAF (Project PID2021-128322NB-I00).

7. Geographic location/s of data collection:
Coastal Peruvian Desert (15.5ºS; 74.8ºW)
Aguada de Lomas Locality, Sacaco, Arequipa, Peru


ACCESS INFORMATION

1. Dataset Creative Commons License: 
CC BY-NC-ND 4.0

2. Dataset DOI: https://gredos.usal.es/handle/10366/160491

3. Related publication: 
Ochoa et al., accepted in Nature Communications Earth & Environment, under the title Late Miocene greening of the Peruvian Desert


METHODOLOGICAL INFORMATION
1. Description of methods used for collection-generation of data:

- Geological fieldwork, sampling
- Zircon U-Pb geochronology dating
- Palynological counting, diversity and compositional analyses
- Fossil wood identification and physiognomy 
- Leaf identification and physiognomy analysis
- Mean Annual Precipitation (MAP) estimates based on leaf physiognomic characteristics
- MAP based on the climatic tolerance of coexistent Nearest Living Relatives (NLR) of identified palynofloral elements
- Estimation of climatic envelopes of modern biomes


FILE OVERVIEW
1. File List:
Supplementary Data So. Word File - Supplementary file with extended methodological details
Supplementary Data S1. Excel File - U-Pb calculated weighted mean ages (LA-MC-ICP-MS data)
Supplementary Data S2. Excel File - Palynological data from the East Pisco Basin, with botanical affinities assigned for NLR and Co-existence (Co-Ex) analyses
Supplementary Data S3. R script for diversity analyses - Source Code
Supplementary Data S4. Excel File - Wood taxonomical measurements
Supplementary Data. Excel File - Leaf physiognomy, vein density measurements, leaf-derived sizes, and regression models for predicting log(MAP) based on leaf size
Supplementary Data S6. Excel File - Georeferenced occurrences of modern taxa used as NLR
Supplementary Data S7. PDF- Probability density function of modern species, comparing MAT vs MAP
Supplementary Data S8. PDF - Probability density function of Holocene and Miocene samples, comparing MAT vs MAP
Supplementary Data S9. R script for defining climatic envelopes of modern biomes and NLR-CoEx analyses - Source Code- Source Code
Supplementary Data S10. Modern distribution of selected key species across South America

Source code written for R.
R Core Team (2024). R: A language and environment for statistical
  computing. R Foundation for Statistical Computing, Vienna, Austria.
  URL https://www.R-project.org/.