Presence of a resident species aids invader evolution
Notice bibliographique
Résumé
These are the script and data needed to replicate all figures and stats in Presence of a resident species aids invader evolution Authors: Josianne Lachapelle, Elvire Bestion, Eleanor E Jackson , C- Elisa Schaum (corresponding author) as accepted for publication with Limnology & Oceanography The word file "ZenodoSaltFresh_Extra" contains additional information on ROS tolerance and production in the strains used in this experiment. While we do not focus on the ROS data in the main manuscript, we think the additional information is worth sharing. The R file "R code for Presence of a resident species aids invader evolution.R" contains all code needed, and should run in all current versions of R and R studio (in mid 2022). Below, we describe in alphabetical order the content of the csv files. Should any raw raw data (e.g. flow cytometre files) be of interest, please get in touch with the corresponding author. biomass.cellsize.over.time.csv is used for stats and figures tracking changes in cell size and /or biomass throughout the experiment. clones_SAL only.csv contains the growth rates of invaders and coloniser species after the samples had been made clonal again after evolution in their selection and ancestral environments. This allows us to test how replicable a certain response is within a population. decomposed samples growth.csv has all data needed to analyse the growth of mixed culture samples that were turned into mono culture samples at the end of the selection experiment, and the 'relative growth' column compares growth rates of these 'decomposed' samples to samples that have always been in co culture and to growth rates of samples that have always been in mono -culture. decomposed short.csv looks at how quickly (or whether it happens quickly in the first place) samples develop any kind of dependence on the other species being present. fitness.assay.data.csv contains cell counts and weekly growth rates per well plate ID ("plateloc") of each unique bio replicate for each unique combination of biological and environmental treatment throughout the entire experiment. forestplot_salt.csv combines the phenotype data needed to create the forest plot in the main manuscript. growthNILE.csv is for analysing and plotting the relationship between (changes in) growth rate and (changes in) Nile Red fluorescence, which is a proxy for intracellular lipid content. means_transfer.csv is built from fitness.assay.data.csv and contains averages per week per unique treatment combination. NP decomposed.csv contains net photosynthesis data for samples evolved in mono and mixed culture, as well as for samples evolved in mixed culture, but decomposed back into mono cultures. sal_trial_OTchlamy.csv we ran a salinity trial to test the ranges of salinity to use. The data are in this spreadsheet. shoRtlong.csv contains growth rate data for each unique treatment combination in the short-term (beginning of the selection experiment or reciprocal assay at the end of the experiment) and in the long-term (end of selection experiment )to test whether long term responses can be predicted from short-term data sizegrowthROS_datasets_BOTH.csv This combines the size dataset with our dataset of ROS tolerance and ROS production to test for correlations between size and ROS production/sensitivity. temptrial_OTchlamy.csv was used to create temperature tolerance curves following a trial to determine the temperatures to use in the selection experiment trajecs_stable_gr.csv contains cell counts at the beginning, middle, and end of each batch cycle, allowing us to test whether growth remained exponential throughout the experiment.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,469 | 0,265 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».