Biodiversity affects ecosystem function, and how this relationship will change in a warming world is a major and well-examined question in ecology. Yet, it remains understudied for pico-phytoplankton communities, which contribute to carbon cycles and aquatic food webs year-round. Observational studies show a link between phytoplankton community diversity and ecosystem stability, but there is only scarce causal or empirical evidence. Here, we sampled phytoplankton communities from two geographically related regions with distinct thermal and biological properties in the Southern Baltic Sea, and carried out a series of dilution/regrowth experiments across three assay temperatures. This allowed us to investigate the effects of loss of rare taxa and establish causal links in natural communities between species richness and several ecologically relevant traits (e.g. size, biomass production, and oxygen production), depending on sampling location and assay temperature. We found that the samples’ bio-geographical origin determined whether and how functional redundancy changed as a function of temperature for all traits under investigation. Samples obtained from the slightly warmer and more thermally variable regions showed overall high functional redundancy. Samples from the slightly cooler, less variable, stations showed little functional redundancy, i.e. function decreased when species were lost from the community. The differences between regions were more pronounced at elevated assay temperatures. Our results imply that the importance of rare species and the amount of species required to maintain ecosystem function even under short-term warming may differ drastically even within geographically closely related regions of the same ecosystem. 

Detailed methods are provided in the SI document available through Biology Letters. If any unclarities remain, the authors (Duyi Zhong, Luisa Listmann, Maria-Elisabetta Santelia, C-Elisa Schaum) will provide more details upon request. 

Here, we provide some information on the individual files. We are not uploading raw flow cytometry data (but present a representative exported file, plus cytograms) - those data are available from the authors upon reasonable request. 

File "all_slopes_combined - pooledforstations.csv" contains the data that Figure 02 in the main text is based on. Temp is the assay temperature in degrees C, Region refers to the sampling region,statID refers to the station (replicates are pooled on the station level),slope is the slope,sd_slope is the standard deviation of that slope, and trait is the trait under investigation.

File "20200615_flowphenotypes,csv" contains an example of flow cytometry data, where individual events (corresponding to the cellsmL column) have been summarised to yield a mean and sd for SSC, FL2,FL3, and FL4.  

File "growthrates_inocculation_real.csv" contains the growth rates and the inocculation cell numbers with their deviations. CellsmL_day0 is the number of cells measured at inocculation, SD_cellt0 is the ±1SD of this measurement. Growthrate and SD_growth are the growth rate and ±1SD of a sample. Region refers to the sampling region, repID to the replicate number within region, Dilution is the dilution and assaytemp is the assay temperature in degrees C.

File "20200217_alldata_metab_biod" contains the non-log transformed metabolic rates. Region, AssayTemp, repID, Dilution are as above. Columns 11-13 have the NP,R, and GP data per cell per hour (µmol O2). NP_real takes into account that during culture, the experimental units were exposed to a 12/12 light:dark cycle, so that they would have been photosynthesising for 12, but respiring for 24 hours. 

 File "20200606_timetoK.csv" contains the time points at which carrying capacity was reached. Region is the sampling region, Temp is the assay temperature in degrees C, Dilution is the level of dilution, StatID refers to the station number, K_reached_at_day is the day at which the baranyi model predicts that K is reached given the shape of the growth curve.

File "sizebeads_before_service.png" shows the distribution of size beads on forward and side scatter before the instrument was serviced. 

File "sizebeads_after_service.png"shows the distribution of size beads on forward and side scatter before the instrument was serviced. 

File "20200715_results_decomposition.docx" provides detailed information on the output of the decomposition analysis carried out in order to estimate variability in the two basins under investigation

File "biod_biom_justplot.R" is the R script used for plotting the richness/function plots, and also contains representative code for the mixed models (all other mixed models follow the same scheme)

File "Duyi_loss_of_biomass_biodiversity" is the R script to fit growth curves (baranyi) to data in order to obtain µmax and K values 

A step by step guide by Props et al (2016) on how to use the pheno-flow package to estimate  diversity from flow cytometric data can be found here https://github.com/rprops/Phenoflow_package. 

The cytograms have all necessary information in the title.