Presence of a resident species aids invader evolution
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.469 | 0.265 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".