# Load dependencies
import numpy as np
import pandas as pd
import sys
sys.path.insert(0,'../../../statistics_helper/')
from excel_utils import *
To estimate the total biomass of humans, we rely on estimates of the total human population from the UN World Population Prospects of 2017 (File - 'Total Population - Both Sexes'). We use the estimate for the total human population in 2015
#Load data from the UN
data = pd.read_excel('humans_data.xlsx',index_col=0,skiprows=16)
# Use data from 2015, multiply by 1000 because data is given in thousands
tot_human_pop = data.loc[1,'2015']*1e3
print('The UN estimate for the total human population is ≈%.1e' %tot_human_pop)
We use an estimate for the average body mass of humans of ≈50 kg from Hern. We convert the average body weight to carbon mass assuming 70% water content and 50% carbon out of the dry weight:
wet_to_c = 0.15
human_cc = 5e4*wet_to_c
We estimate the total biomass of humans by multiplying the total number of humans by the average carbon content of a single human:
best_estimate = tot_human_pop*human_cc
print('Our best estimate for the total biomass of humans is ≈%.2f Gt C' %(best_estimate/1e15))
# Feed results to the chordate biomass data
old_results = pd.read_excel('../../animal_biomass_estimate.xlsx',index_col=0)
result = old_results.copy()
result.loc['Humans',(['Biomass [Gt C]','Uncertainty'])] = (best_estimate/1e15,None)
result.to_excel('../../animal_biomass_estimate.xlsx')
# Feed results to Table 1 & Fig. 1
update_results(sheet='Table1 & Fig1',
row=('Animals','Humans'),
col='Biomass [Gt C]',
values=best_estimate/1e15,
path='../../../results.xlsx')
# Feed results to Table S1
update_results(sheet='Table S1',
row=('Animals','Humans'),
col='Number of individuals',
values=tot_human_pop,
path='../../../results.xlsx')