Grazing strategies determine the size composition of phytoplankton in eutrophic lakes

##########################
##### 'SimData_yr10.nc' #####
##########################

The netCDF datafile, 'SimData_yr10.nc', stores the simulation data for the main text, Figure 4 and Figure 5. The data file comprises of a multidimensional dataset with different dimensions, namely, phytoplankton, zooplankton, and nutrient i.e., 'Phy', 'Zoo', 'Nut'(name used in the file). In each variable, multidimensional arrays are used to store the simulation data from different nutrient conditions, different mixing frequencies, and different grazing strategies. 

# To extract the data, one could use the netCDF4 library in python (https://unidata.github.io/netcdf4-python/)

import netCDF4    

out = netCDF4.Dataset('workingdirectory/filename.nc')  # output file
sol_phy = out.variables['Phy'][:]
sol_zoo = out.variables['Zoo'][:]
sol_nut = out.variables['Nut'][:]

# The shape of sol_phy, sol_zoo, and sol_nut correspond to the below descriptions.

The coordinates (dimensions) of the 'Phy' dataset are as below: 
'time': '365 days',
'Ps': '150 phytoplankton size classes ranged from 1 to 100µmESD',
'nut': '3 different nutrient levels are considered: Oligotrophic (1µmol N/L); Eutrophic (15µmol N/L); Hypertrophic (50µmol N/L)',
'mix': '3 different mixing regimes are considered: Constant (No mixing during the year); Medium (4 mixing/year); High (12 mixing/year)',
'GrazStrtgy': '4 different combinations of grazing strategies are considered.

The coordinates (dimensions) of the 'Zoo' dataset are as below: 
'time': '365 days',
'Zs': '2 zooplankton size classes at 5 and 200µm ESD',
'nut': '3 different nutrient levels are considered: Oligotrophic (1µmol N/L); Eutrophic (15µmol N/L); Hypertrophic (50µmol N/L)',
'mix': '3 different mixing regimes are considered: Constant (No mixing during the year); Medium (4 mixing/year); High (12 mixing/year)',
'GrazStrtgy': '4 different combinations of grazing strategies are considered.

The coordinates (dimensions) of the 'Nut' dataset are as below: 
'time': '365 days',
'nut': '3 different nutrient levels are considered: Oligotrophic (1µmol N/L); Eutrophic (15µmol N/L); Hypertrophic (50µmol N/L)',
'mix': '3 different mixing regimes are considered: Constant (No mixing during the year); Medium (4 mixing/year); High (12 mixing/year)',
'GrazStrtgy': '4 different combinations of grazing strategies are considered.

These data are all extracted from year 10, when the system has reached steady state.

############################
##### 'SuppData4_yr10.nc' #####
############################

This netCDF datafile, 'SuppData4_yr10.nc', shows the simulation data for the sensitivity analyses on zooplankton community structure, hence the supplementary figure 4 of the article. The datafile comprises of a multidimensional dataset with different dimensions, namely, 2grazer, 4grazers, 6grazers and 8grazers (same name used in the file). The sensitivity analyses was conducted for a subset of environmental conditions: the combination of medium mixing frequency and eutrophic or hypertrophic condition.
In each variable, multidimensional arrays are used to store the simulation data from 2 different nutrient conditions and different grazing strategies. 

The coordinates (dimensions) of the above mentioned dataset (i.e., 2grazer, 4grazers, 6grazers and 8grazers) are as below: 
'time': '365 days',
'Ps': '150 phytoplankton size classes ranged from 1 to 100µmESD',
'nut': '2 different nutrient levels are considered: Eutrophic (15µmol N/L); Hypertrophic (50µmol N/L)',
'mix': 'Medium (4 mixing/year)',
'GrazStrtgy': '4 different combinations of grazing strategies are considered'.

These data are all collected from year 10, when the system has reached steady state.

############################
##### 'SuppData5_yr10.nc' #####
############################

This netCDF datafile, 'SuppData5_yr10.nc', supports the simulation data for the sensitivity analyses on various slopes (+/-50%) of allometric relationships for phytoplankton maxmimum growth rate and zooplankton maximum ingestion rate, hence the supplementary figure 5 of the article. The datafile comprises of a multidimensional dataset with different dimensions, namely, mumax and imax (same name used in the file). The sensitivity analyses was conducted for a subset of environmental conditions: the combination of medium mixing frequency and eutrophic or hypertrophic condition.
In each variable, multidimensional arrays are used to store the simulation data from 2 different nutrient conditions and different grazing strategies. 

The coordinates (dimensions) of the 'mumax' are as below: 
'time': '365 days',
'Ps': '150 phytoplankton size classes ranged from 1 to 100µmESD',
'nut': '2 different nutrient levels are considered: Eutrophic (15µmol N/L); Hypertrophic (50µmol N/L)',
'mix': 'Medium (4 mixing/year)',
'GrazStrtgy': '4 different combinations of grazing strategies are considered,
'mu_slope': '+/-50% of the parameter alpha_mumax'.

The coordinates (dimensions) of the 'imax' are as below: 
'time': '365 days',
'Ps': '150 phytoplankton size classes ranged from 1 to 100µmESD',
'nut': '2 different nutrient levels are considered: Eutrophic (15µmol N/L); Hypertrophic (50µmol N/L)',
'mix': 'Medium (4 mixing/year)',
'GrazStrtgy': '4 different combinations of grazing strategies are considered,
'mu_slope': '+/-50% of the parameter alpha_imax'.

These data are all collected from year 10, when the system has reached steady state.

###############################################
##### 'LWST_13p_syn.csv' and 'nSSI_13p_syn.csv'#####
###############################################

The forcing data of temperature and irradiance, 'LWST_13p_syn.csv' and 'nSSI_13p_syn.csv' respectively, store the weather data in global lakes of different latitudes from Layden et al. (2015). The compilation was done over a period of 20 years (1991–2011).

Layden A., Merchant C. & MacCallum S. (2015). Global climatology of surface water temperatures of large lakes by remote sensing: Global climatology of Lake Surface Water Temperatures. International Journal of Climatology 35, 4464–4479. https://doi.org/10.1002/joc.4299. 

We chose the data from 40 degree N and digitalised the data in monthly-basis (Figure 4 in Layden et al., 2015).