Warming disrupts infection patterns and primarily accelerates Ostreococcus-virus infection dynamics in the Baltic Sea

These are the R Scripts and data files needed to run the statistics and visualizations in

Warming disrupts infection patterns and primarily accelerates Ostreococcus-virus infection dynamics in the Baltic Sea

Carina Peters*¹, C.-Elisa Schaum¹, Luisa Listmann¹

¹Institute of Marine Ecosystem and Fisheries Science, University of Hamburg, Hamburg, Germany

*Corresponding author: carina.peters@uni-hamburg.de

1. R Script: PlaqueGrowthFit 18-24C.R
Datasets needed: PlaqueSizeTracking_18C.csv, PlaqueSizeTracking_20-24C.csv

Datasets produced in this code for further analysis: slopes_modelsummary_alltemps_goodR2_0.7R2.csv, slopes_modelsummary18_goodR2_0.7.csv

Used for linear fits and extracting slopes for each plaque from the cross infection at 18°C and the warmed infection at 20,22 and 24°C. As not all plaques were measurable, not all infections have diameter measurements. In addition, some were difficult to measure due to being, e.g.  faint or irregular, therefore only fits with an R² of 0.7 or bigger were included in further analysis. 

2. R Script: Network Analysis statistics and null models.R
Datasets needed: Infection_Matrix.csv, optional to avoid run time: Q_nulls_results.rds

Datasets produced in this code for further visualization: mod_object_Jan2026.rds

Used to run the network analysis of binary infection data at 18°C for all host virus combinations.

3.  R Script:Network Analysis Visualization.R
Datasets needed: Infection_Matrix.csv, Infection_Matrix_NA.csv, mod_object_Jan2026.rds, slopes_modelsummary18_goodR2_0.7.csv

Used to visualize the modular infection network with an overlay of the fitted plaque-growth rates at 18°C.

 4.  R Script: PlaqueSize_Stats.R
Datasets needed: slopes_modelsummary18_goodR2_0.7.csv, Ostreo_data.csv, Virus_data.csv, Host+Virus+Cluster_Info

Used for statistical analysis of differences in extracted plaque growth rates at 18°C between/within different clusters, host groups or virus groups.

5.  R Script: DOI_Stats.R
Datasets needed: Day of Infection Table.csv, Ostreo_data.csv, Virus_data.csv, Host+Virus+Cluster_Info

Used for statistical analysis of differences in observed first visible day of infection (DoI) between/within different clusters, host groups or virus groups.

6.  R Script: GAM_InfectedHost_Warming_new.R
Datasets needed: slopes_modelsummary_alltemps_goodR2_0.7R2.csv, Ostreo_data.csv, Virus_data.csv, Host+Virus+Cluster_Info

Used to fit a GAM model of the plaque growth rates across temperatures for each group of viruses originating from the same host for each infected host of the warmed cross infection. Additionally this also includes the visualization of the predicted GAM fits.

Datasets for visualization: Mumax_perStrain_withzeros.csv, rTPC_modelfits_new_17042026.csv

These two datasets contain the observed maximum growth rates and the predicted maximum growth rates of the Ostreococcus strains used in this study at different temperatures from a previous experiment (https://doi.org/10.64898/2026.01.13.698997) to visualize the plaque-growth rates together with thermal performance curves of the uninfected host. 

7.  R Script: Phylogenetic Tree Virus.R
Datasets needed: Tree_labels.csv,Phylotree_Viruses_polB_13.02.2026.nwk

Additionally provided: Fasta file PolBSequences_Virus_Fasta_13.02.2026.fasta if tree building wants to be replicated in the program of your choice.

Used to visualize the phylogenetic tree in R. The tree file was computed in Codon Code Aligner.

8.  R Script: PresenceAbsenceChanges_GLMM.R
Datasets needed: Binary_Infection_Success_Table.csv

Used for statistical analysis via GLMM of quantitative changes in presence/absence data on infections between the different temperature treatments.