In [1]:
# Load dependencies
import pandas as pd
import sys
sys.path.insert(0,'../statistics_helper/')
from CI_helper import *
pd.options.display.float_format = '{:,.0e}'.format
from excel_utils import *

Estimating the biomass of phages

Our estimate of the total biomass of phages relies upon the estimates for the total number of phages and the carbon content of a single phage which we derived in the relevant sections

These are our best estimates for the different parameters required for the estimate, along with the associated uncertainties:

In [2]:
# Load estimates for the total number of phages and for the carbon cont
estimate = pd.read_excel('phage_biomass_estimate.xlsx')
estimate
Out[2]:
Parameter Value Units Uncertainty
0 Carbon content of a single phage 2e-17 g C per individual 2e+00
1 Total number of phages 1e+31 Number of individuals 1e+01

In order to estimate the total biomass of phages, we multiply our estimate of the total number of phages by our estimate of the carbon content of a single phage.

In [3]:
best_estimate = estimate['Value'].prod()

print('Our best estimate for the total biomass of phages is %.1f Gt C' %(best_estimate/1e15))

Our best estimate for the total biomass of phages is 0.2 Gt C

We propagate the uncertainties associated with each of the parameters to project the uncertainty of our estimate of the total biomass of phages:

In [4]:
mul_CI = CI_prod_prop(estimate['Uncertainty'])

print('Our best projection for the uncertainty associated with our estiamte of the biomass of phages is %.1f-fold' %mul_CI)

Our best projection for the uncertainty associated with our estiamte of the biomass of phages is 15.0-fold

Due to the scarcity of data on the different parameters used to estimate the total biomass of phages, we use a higher uncertainty projection of ≈20-fold.

In [5]:
mul_CI = 20

# Feed results to Table 1 & Fig. 1
update_results(sheet='Table1 & Fig1', 
               row=('Viruses','Viruses'), 
               col=['Biomass [Gt C]', 'Uncertainty','Total uncertainty'],
               values=[best_estimate/1e15,mul_CI,mul_CI],
               path='../results.xlsx')


# Feed results to Table S1
update_results(sheet='Table S1', 
               row=('Viruses','Viruses'), 
               col=['Number of individuals'],
               values=estimate.loc[1,'Value'],
               path='../results.xlsx')

# Calculate non-deep subsurface virus biomass
phage_num = pd.read_excel('phage_num/phage_num_estimate.xlsx')
non_deep_phage = phage_num.loc[[0,2],'Value'].sum()*estimate.loc[0,'Value']

# Feed results to Fig S1
update_results(sheet='FigS1', 
               row=('Viruses','Viruses'), 
               col=['Biomass [Gt C]'],
               values=non_deep_phage/1e15,
               path='../results.xlsx')

/usr/lib/python3/dist-packages/ipykernel/ipkernel.py:196: PerformanceWarning: indexing past lexsort depth may impact performance.
  res = shell.run_cell(code, store_history=store_history, silent=silent)
/usr/lib/python3/dist-packages/IPython/core/interactiveshell.py:2717: PerformanceWarning: indexing past lexsort depth may impact performance.
  interactivity=interactivity, compiler=compiler, result=result)