olazlaure / natpop2021

output
html_document keep_md true

GENERAL INFORMATION

1. Description

This research compendium describes how to analyze fecundity, egg-to-adult viability and oviposition preference data from an experiment conducted in D. suzukii in 2018, in South of France (CBGP, Montpellier, France). here.

The analyses of this research compendium are published in Evolution Letters: "Rapid and transient evolution of local adaptation to seasonal host fruits in an invasive pest fly"
✍️ Article DOI: 10.1002/evl3.304

2. Author information and main investigators

Corresponding Investigator and first author:
	Name: Laure Olazcuaga
	Institution: CBGP, INRAE, France and Colorado State University, USA
	Email: [email protected]
	ORCID: 0000-0001-9100-1305

Co-investigator and last author: 
	Name: Nicolas Rode
	Institution: CBGP, INRAE, France
	Email: [email protected]
	ORCID: 0000-0002-1121-4202

For the complete list of authors of the manuscript, see the manuscript.

3. Date and Geopgraphic location of data collection

📅 2018.

📍 South of France.

The exact date and location data is here: 📋 TableS6.

4. Funding sources that supported the collection of the data

Data collection was supported by the Languedoc-Roussillon region (France) through the European Union program FEDER FSE IEJ 2014-2020 (project CPADROL), the INRAE scientific department SPE (AAP-SPE 2016), the National Science Foundation (DEB-0949619), the USDA Agriculture and Food Research Initiative award (2014-67013-21594), Hatch project 1012868, the French Agropolis Fondation (LabEx Agro–Montpellier) through the AAP “International Mobility” (CfP 2015-02), the French programme investissement d'avenir, and the LabEx CEMEB through the AAP "invited scientist 2016", the CeMEB LabEx/University of Montpellier (ANR-10-LABX-04-01) and the INRAE scientific department SPE (AAP-SPE 2021PestAdapt).

5. Recommended citation for this dataset:

✍️ Olazcuaga et al. (2022), Data from: Analysis Nat Pop 2021, Dryad, Dataset

CONTENTS

The 🔨 dev_history.R file allow to rebuild our research compendium from scratch for maximum reproducibility.

The 📂 figures directory contains the figures generated during the analyses.

The 📂 R directory contains the functions used in the analyses.

The 📂 man directory contains the documentation for the functions.

The 📂 data directory contains the data sets used in the analyses.

Data et file overview

• 📊 Data_Pref : Dataset containing oviposition preference data..

• 📊 Data_Perf : Dataset containing offsrping performance and oviposition stimulation data.

• 📊 TableS6 : Database containing location and sampling date for each population.

• 📋 Readme_data : A detailed description of all the dataset.

The 📂 reports directory contains the .Rmd files used to build each part of the analyses and produce the final figures. They also have rendered versions and .html suitable for reading in a web browser.

Markdown versions

• 📋 Analysis_main_text : All the analyses that can be found in the main text.

• 📋 Simuls_powertest_nongenet : Simulations of fitness data with different values of SA plastic.

• 📋 Simuls_fitness : Simulations of fitness data.

• 📋 Supplements_WT3 : All the analyses of the WT3 data.

• 📋 Supplements_realdata : All the analyses with real data.

• 📋 Supplements_MotherKnowsBest : All the Mother knows best analyses.

• 📋 Supplements_acp : All the acp analyses.

• 📋 Supplements_correlation_generation : All the analyses of correlation between generations.

HTML versions

• 📋 Analysis_main_text : All the analyses that can be found in the main text.

• 📋 Simuls_powertest_nongenet : Simulations of fitness data with different values of SA plastic.

• 📋 Simuls_fitness : Simulations of fitness data.

• 📋 Supplements_WT3 : All the analyses of the WT3 data.

• 📋 Supplements_realdata : All the analyses with real data.

• 📋 Supplements_MotherKnowsBest : All the Mother knows best analyses.

• 📋 Supplements_acp : All the acp analyses.

• 📋 Supplements_correlation_generation : All the analyses of correlation between generations.

HOW TO RUN IT?

This research compendium has been developed using the statistical programming language R. To work with the compendium, you will need installed on your computer the R software itself and optionally RStudio Desktop.

You can download the compendium by cloning this repository:

• open the .Rproj file in RStudio

• open scripts .Rmd in reports folder and run it to produce all the analyses and associated reports.

• launch the README.html to be able to explore the contents on your web browser