Cooperative interactions between invader and resident microbial community members weaken the negative diversity-invasion relationship
Creators: name: Vandermaesen, Johanna
name: Daly, Aisling J
name: Mawarda, Panji Cahya
name: Baetens, Jan M.
name: De Baets, Bernard
name: Boon, Nico
name: Springael, Dirk
Publisher: name: Zenodo
Year: 2024

description: These files contain the original data and code for the paper "Cooperative interactions between invader and resident microbial community members weaken the negative diversity-invasion relationship" published in Ecology Letters.

Vandermaesen, J., Daly, A. J., Mawarda, P. C., Baetens, J. M., De Baets, B., Boon, N., & Springael, D. (2024). Cooperative interactions between invader and resident microbial community members weaken the negative diversity‐invasion relationship. Ecology letters, 27(5), e14433. https://doi.org/10.1111/ele.14433

Cumulative BAM mineralization curves were determined as explained in the materials and methods section. Due to the large amount of SFI/MSH1 combinations to be tested, the experiment was done in two parts (I and II) with 5 minutes in between (both regarding start-up and measuring of the mineralization) but using the same pre-cultured inocula. The annotated raw data are attached as “Raw Data_mineralization curves.xlsx”. In order to derive kinetic parameter values, these cumulative mineralization curves were then copied (per part) to the .csv files “Part1_matlab_input.csv” and “Part2_matlab_input.csv” in which the first row represents the time points at which mineralization was evaluated and the following rows represent the corresponding cumulative BAM mineralization percentage for each microcosm, in the same order as in the “Raw Data_mineralization curves.xlsx” file. Subsequently, by running the matlab script “Gompertz_routine.m”, the kinetic BAM mineralization parameters were derived from the cumulative BAM mineralization curves, returning the .csv files “Part1_results.csv” and “Part2_results.csv” with the following columns:






Column nr



Parameter name



Explanation





1



A



total extent of mineralization after the exponential mineralization phase





2



μ



maximum mineralization rate constant





3



c



endogenous mineralization rate constant





4



λ



lag time





5



standard_error A



standard error of the total extent of mineralization after the exponential mineralization phase





6



standard_error μ



standard error of the maximum mineralization rate constant





7



standard_error c



standard error of the endogenous mineralization rate constant





8



standard_error λ



standard error of the lag time





9



SSE



sum of squares of residuals





10



exitflag



describes the reason why the algorithm stopped iterating, 2 = “change in x was smaller than the specified tolerance”





 

The rows each represent one microcosm, in the same order as in “Raw Data_mineralization curves.xlsx”.

 

Kinetic parameter values were copied from “Part1_results.csv” and “Part2_results.csv” to “Kinetic parameters.xlsx” and average values were calculated per four replicates. Results indicated in red were treated as outliers and discarded to calculate average values. These average values form the basis for all figures and tables presented in the manuscript.

 

In addition, the number of MSH1-cells surviving the competition phase (SMSH1) was determined by biomass extraction and selective plate counting. Raw data and processing to average values are attached as “Survival MSH1.xlsx”. The resulting average values form the basis for all figures and tables as presented in the manuscript.

 

-Data for Figure 1.xlsx: Data and calculation needed for making Figure 1

-Data for Figure 2.xlsx: Data and calculation needed for making Figure 2

-Data for Figure 3.xlsx: Data and calculation needed for making Figure 3

-Data for Table 1.xlsx: Data and calculation needed for making Table 1

-Data for Figure S2.1.xlsx: Data and calculation needed for making Figure S2.1

-Data for Figure S2.2.xlsx: Data and calculation needed for making Figure S2.2

-Data for Figure S3.1 S3.2 and S3.3.xlsx: Data and calculation needed for making Figure S3.1, S3.2, and S3.3

- `Gompertz_costftn` is a matlab code, needed for cost function that calculates the difference between the Gompertz model prediction and the experimental data. 

‘Gompertz_routine.m’ is a matlab code, designed to read the experimental data from Part 1_matlab_input.csv and Part 2_matlab_input.csv) to fit the Gompertz cost function to the data using nonlinear least squares optimization, calculate standard errors and confidence intervals for the estimated parameters, and save the results to Part1_results.csv and Part2_results.csv.

 - “matlabcode_explanation.docx” is the explanation of each step in those two matlab codes.

- R code contains the code to quantify the Pearson correlation, ANOVA, and tukey posthoc.

- df_correlation.csv, df_table1, df_figure3, an df_figures3 are the dataframe we used for statistical analyses in the R code and SPSS.