README: Scripts and data for: Integrating different facets of diversity into food web models: how adaptation among and within functional groups shape ecosystem functioning

https://doi.org/10.5061/dryad.ttdz08m4x

Note that biomasses are expressed in mg C m-3 and traits are unitless.

Tasks performed by the python and C scripts

Generating time series and computing ecosystem functions and properties

To generate biomass and trait time series of a particular food web, we executed the file get_timeseries_ecosystem_functions.py. This Python script performed several tasks: (a) setting the parametrisation of the model, (b) calling and executing the file get_timeseries_ecosystem_functions (produced by compiling get_timeseries_ecosystem_functions.c; cf details later), which solved the model's equations and generated the biomass and trait time series by using the parametrisation defined in python, and (c) computing the temporal means and the coefficient of variation of the ecosystem functions and properties.

For each parametrisation, a certain number of randomly-initialised simulations were run (n_duplicates) and the previous tasks (a,b and c) were performed for each simulation. To distinguish between different simulations of different parametrisations, we then used an ID number composed of 7 digits: the first digit refers to the number of prey (between 1 and 5), the second digit refers to the number of predators (between 1 and 5), the third digit indicates whether adaptation within prey functional groups is enabled (1) or disabled (0), the fourth digit indicates whether adaptation within prey functional groups is enabled (1) or disabled (0), the last four digits correspond to the number of the simulation of a given parametrisation (if n_duplicates=200, this number varies between 1 and 200). This ID number was used to name the timeseries files "timeseries_{ID_sim}.txt", where ID_sim is the ID number of the simulation. Thus, "timeseries_5310054.txt" was the biomass and trait timeseries of simulation 54 corresponding to a food web with 5 prey functional groups and 3 predator functional groups where adaptation within prey groups was enabled and adaptation within predator groups was disabled.

For each parametrisation, we gathered the temporal means and the coefficient of variation of the ecosystem functions and properties of each associated simulation into a single numpy dataframe and saved it into a .npy format. We named the file "outputs_nduplicates{x}_ {ID_param}.npy", where x is the total number of simulations run (e.g. 200) and ID_param is composed of 4 digits: the first digit refers to the number of prey (between 1 and 5), the second digit refers to the number of predators (between 1 and 5), the third digit indicates whether adaptation within prey functional groups is enabled (1) or disabled (0), the fourth digit indicates whether adaptation within prey functional groups is enabled (1) or disabled (0).

Analysing results and producing figures

We analysed the results and produced figures using the python script data_analyses_and_plots.py.

Folder and files structure

In the main folder you can find all the python and C scripts and two subfolders data and figures.

Folders structure

The subfolder data is divided into subsubfolders named "vP{x}vH{y}", where {x} and {y} refers to the values of vP and vH multiplied by 100. vP and vH are the parameters determining the speed of trait of adaptation of prey and predators, respectively (cf. detailed information in the associated article). Typically, we simulated four scenarios where vP=0.06 and vH=0.02 ("vP6_vH2"), vP=0.6 and vH=0.06 ("vP60_vH6") , vP=0.6 and vH=0.2 ("vP60_vH20") or vP=0.6 and vH=0.6 ("vP60_vH60"). In each subfolder, there are the timeseries files of each simulation and parametrisation ( "timeseries{ID_sim}.txt"), and the .npy files gathering the temporal means and the coefficient of variation of the ecosystem functions and properties of each associated simulation for a given parametrisation ("outputs_nduplicates{x}_ {ID_param}.npy").

The subfolder figure is also divided into four subsubfolders following the naming "vP{x}_vH{y}", and contains all the figures generated with the script data_analyses_and_plots.py.

Files structure

The number of columns in the data files generated with the python and C scripts depend on the number of prey functional groups (nP) and the number of predator functional groups (nH).

The timeseries files ( "timeseries_{ID_sim}.txt") present by column order: the timestep, solver crash (0 if no crash or -1 otherwise), nP, nH, adaptation within prey functional groups (boolean 0: disabled and 1: enabled; named wP), adaptation within predator functional groups (boolean 0: disabled and 1: enabled; named wH), ID_sim, the biomasses of each nP and nH groups, (across nP+nH columns), the trait values of each nP and nH groups (across nP+nH columns).

The .npy files ("outputs_nduplicates{x}_ {ID_param}.npy") present by column order: nP, nH, wP, wH, ID_sim, the biomasses of each nP and nH groups, the trait values of each nP and nH groups at the initialisation (across nP+nH columns), the minimal biomasses reached by each nP and nH groups once the system settled on its attractor (across nP+nH columns), the minimal trait values reached by each nP and nH groups once the system settled on its attractor (across nP+nH columns), the maximal biomasses reached by each nP and nH groups once the system settled on its attractor (across nP+nH columns), the maximal trait values reached by each nP and nH groups once the system settled on its attractor (across nP+nH columns), the temporal mean of the realised number of prey groups, the CV of the realised number of prey groups, the temporal mean of total prey biomass, the CV of total prey biomass, the synchrony of prey biomass dynamics, the temporal mean of prey production, the CV of prey production, the temporal mean of the biomass-weighted mean prey edibility at the trophic level, the CV of the biomass-weighted mean prey edibility at the trophic level, the temporal mean of the realised number of predator groups, the CV of the realised number predators, the temporal mean of total predator biomass, the CV of total predator biomass, the synchrony of predator biomass dynamics, the temporal mean of predator production, the CV of predator production, the temporal mean of the biomass-weighted mean predator selectivity at the trophic level, the CV of the biomass-weighted mean predator selectivity at the trophic level, the temporal mean of the relative top-down control of the prey trophic level, the CV of the relative top-down control of the prey trophic level, the temporal mean of the preference of the least selective predator for the least edible prey, the CV of the preference of the least selective predator for the least edible prey.

Software versions and packages

Before executing any C or python script, we first compiled the C script get_timeseries_ecosystem_functions.c by using the following command in the terminal: gcc -o get_timeseries_ecosystem_functions get_timeseries_ecosystem_functions.c -lm -lgsl -lgslcblas -lsundials_cvode -lsundials_nvecserial. We notably used the SUNDIALS CVODE solver 5.7.0 to solve numerically the system of ordinary differential equations in C. We also used several packages in Python 3.10 among which NumPy, Pandas, and Matplotlib.