library(tidyverse)Initial values and assumptions
#Percentage of carbon (C) per fresh mass
cperw = 0.2 * 0.52
##1. Correct for the expected difference between CO2 evolved and O2 consumed using a respiratory quotient (RQ) of 0.95
##Klekowski 1972 (doi: 10.1163/187529272x00665) reports a aO2 value of 1.40 nl O2 per hour. This can be converted to a aCO2 value of 2.43 using:
##aCO2 = mass.CO2 * (44 x 10^9)
a.O2 = 1.4
V.CO2 = 0.95 * a.O2
##2. Calculate the number of moles (m) of CO2
##We assume a temperature of 15 degrees Celcius.
t = 15
m = V.CO2 / (0.0821 * (273 + t) * 1E9)
##3. Convert from aO2 to aCO2
a.CO2 = m * (44 * 1E9)
##4. Convert to daily rate, scale to relative mass of C in CO2. Divide by 1000 to convert from ng to ug.
a.CO2.daily = a.CO2 * 24 * (12/44) / 1000Data source: For adult biomass and carbon budget calculations we use data from Nemaplex (http://nemaplex.ucdavis.edu/) and Mulder and Vonk 2011, doi:10.2307/23034835. For juveniles, we use data from Mulder and Vonk 2011.
# Nemaplex data
setwd("~/Work/ETH/Projects/Nematodes/Calculations")
Nemaplex_data_raw <- read.csv("Family Ecophysiology qParameters.csv", stringsAsFactors = FALSE, fileEncoding = "latin1")
# Mulder 2011 data
mulder_2011 <- read.csv("Mulder2011.csv")
mulder_data <- mulder_2011 %>%
select(trophic.group, Length, Width, Juv_Adult)
# Biome abundances
Biome_Abundances <- read.csv("20180827_Biome_Abundances.csv")
# Lowest estimates
numbers_min <- read.csv("20180912_Biome_Abundances_Minimum.csv") %>%
select(-Total_Number) %>%
gather(key, value, -Biome) %>%
rename(trophic.group = key) %>%
rename(Total.number = value)
# Highest estimates
numbers_max <- read.csv("20180912_Biome_Abundances_Maximum.csv") %>%
select(-Total_Number) %>%
gather(key, value, -Biome) %>%
rename(trophic.group = key) %>%
rename(Total.number = value)
adult_data_1 <- Nemaplex_data_raw %>%
select(cp.value, feeding.code, Length.micm, Width.micm) %>%
mutate(feeding.code = replace(feeding.code, feeding.code == "p", "Predators")) %>%
mutate(feeding.code = replace(feeding.code, feeding.code == "o", "Omnivores")) %>%
mutate(feeding.code = replace(feeding.code, feeding.code == "b", "Bacterivores")) %>%
mutate(feeding.code = replace(feeding.code, feeding.code == "h", "Herbivores")) %>%
mutate(feeding.code = replace(feeding.code, feeding.code == "f", "Fungivores")) %>%
filter(feeding.code != "e") %>%
filter(feeding.code != "d") %>%
filter(feeding.code != "") %>%
rename(trophic.group = feeding.code) %>%
rename(Length = Length.micm) %>%
rename(Width = Width.micm)
cp.values <- adult_data_1 %>% group_by(trophic.group) %>% summarise(cp.value = mean(cp.value))
mulder_data$trophic.group <- substr(mulder_data$trophic.group,1,4)
adult_data_2 <- mulder_data %>%
mutate(trophic.group = replace(trophic.group, trophic.group == "bact", "Bacterivores")) %>%
mutate(trophic.group = replace(trophic.group, trophic.group == "fung", "Fungivores")) %>%
mutate(trophic.group = replace(trophic.group, trophic.group == "herb", "Herbivores")) %>%
mutate(trophic.group = replace(trophic.group, trophic.group == "omni", "Omnivores")) %>%
mutate(trophic.group = replace(trophic.group, trophic.group == "pred", "Predators")) %>%
filter(Juv_Adult == "adult")
adult_data_2 <- adult_data_2 %>% select(-Juv_Adult)
adult_data_2 <- merge(cp.values, adult_data_2[1:3], by = "trophic.group")
adult_data <- rbind(adult_data_1, adult_data_2)#mass as function of length and width: W = (L ∙D^2)/(1.6 ∙ 10^6 )
adult_data$mass <- with(adult_data,(Length * Width * Width)/(1.6*1E6))
#Respiration rate in ug C per day as funciton of mass and aC02. b-value is set to 0.75
adult_data$Respiration.rate <- with(adult_data,(a.CO2.daily * ((adult_data$mass)^0.75)))
#Production rate in ug C per day as function of mass and cp value
adult_data$Production.rate <- with(adult_data, (cperw* ((adult_data$mass)/(12 * adult_data$cp.value))))
#Daily carbon budget in ug C; sum of respiration rate and production rate
adult_data$Carbon.budget <- with(adult_data, (adult_data$Respiration.rate + adult_data$Production.rate))
summary_adult <- adult_data %>%
group_by(trophic.group) %>%
summarise(n.measurements = n(),
mean.mass.ug = mean(mass),
mean.daily.respiration.rate.ug = mean(Respiration.rate),
mean.daily.production.rate.ug = mean(Production.rate),
mean.daily.carbon.budget.ug = mean(Carbon.budget)) %>%
na.omit()
summary_adult## # A tibble: 5 x 6
## trophic.group n.measurements mean.mass.ug mean.daily.resp…
## <chr> <int> <dbl> <dbl>
## 1 Bacterivores 3645 0.824 0.0106
## 2 Fungivores 390 0.361 0.00674
## 3 Herbivores 4386 1.94 0.0160
## 4 Omnivores 507 7.27 0.0567
## 5 Predators 569 4.29 0.0441
## # … with 2 more variables: mean.daily.production.rate.ug <dbl>,
## # mean.daily.carbon.budget.ug <dbl>juv_data <- mulder_2011 %>%
select(Taxonomy, trophic.group, Length, Width, Juv_Adult)
juv_data$trophic.group <- substr(juv_data$trophic.group,1,4)
juv_data <- juv_data %>%
mutate(trophic.group = replace(trophic.group, trophic.group == "bact", "Bacterivores")) %>%
mutate(trophic.group = replace(trophic.group, trophic.group == "fung", "Fungivores")) %>%
mutate(trophic.group = replace(trophic.group, trophic.group == "herb", "Herbivores")) %>%
mutate(trophic.group = replace(trophic.group, trophic.group == "omni", "Omnivores")) %>%
mutate(trophic.group = replace(trophic.group, trophic.group == "pred", "Predators")) %>%
filter(Juv_Adult == "juveniles")
cp.values <- adult_data %>% group_by(trophic.group) %>% summarise(cp.value = mean(cp.value))
juv_data<- merge(cp.values, juv_data[1:5], by = "trophic.group")
#Mass as function of length and width: W = (L x D^2)/(1.6 x 10^6 )
juv_data$Mass <- with(juv_data, (Length * Width * Width)/(1.6*1E6))
#Respiration rate in ug C per day as function of mass. aCO2 is as calculated above b-value assumed at 0.75
juv_data$Respiration.rate <- with(juv_data,(a.CO2.daily * ((juv_data$Mass)^0.75)))
#Production rate in ug C per day as function of mass and cp value
juv_data$Production.rate <- with(juv_data, (cperw* ((juv_data$Mass)/(12 * juv_data$cp.value))))
#Daily carbon budget in ug C; sum of respiration rate and production rate
juv_data$Carbon.budget <- with(juv_data, (juv_data$Respiration.rate + juv_data$Production.rate))
summary_juv <- juv_data %>%
group_by(trophic.group) %>%
summarise(n.measurements = n(),
mean.mass.ug = mean(Mass),
mean.daily.respiration.rate.ug = mean(Respiration.rate),
mean.daily.production.rate.ug = mean(Production.rate),
mean.daily.carbon.budget.ug = mean(Carbon.budget)) %>%
na.omit()
summary_juv## # A tibble: 5 x 6
## trophic.group n.measurements mean.mass.ug mean.daily.resp…
## <chr> <int> <dbl> <dbl>
## 1 Bacterivores 12275 0.156 0.00365
## 2 Fungivores 1193 0.0595 0.00180
## 3 Herbivores 8477 0.122 0.00299
## 4 Omnivores 770 0.453 0.00772
## 5 Predators 516 1.22 0.0163
## # … with 2 more variables: mean.daily.production.rate.ug <dbl>,
## # mean.daily.carbon.budget.ug <dbl>The numbers per biome are outputted from the geospatial models.
#Import calculated totals of individuals
numbers <- Biome_Abundances %>%
select(-Total_Number) %>%
gather(key, value, -Biome) %>%
rename(trophic.group = key) %>%
rename(Total.number = value)
#Total number of individuals, per trophic group
numbers_tot <- numbers %>%
group_by(trophic.group) %>%
summarise(tot.numb.indiv = sum(Total.number))
#Total number of individuals, per trophic group, per biome
numbers_biome <- numbers%>% group_by(trophic.group, Biome) %>% summarise(tot.numb.indiv = sum(Total.number)) #Total number of individuals, per trophic group
numbers_tot_print <- numbers_tot %>%
rename("Trophic group" = 1) %>%
rename("Number of individuals" = 2) %>%
bind_rows(summarise_all(., funs(if(is.numeric(.)) sum(.) else "Total")))## Warning: funs() is soft deprecated as of dplyr 0.8.0
## please use list() instead
##
## # Before:
## funs(name = f(.)
##
## # After:
## list(name = ~f(.))
## This warning is displayed once per session.numbers_tot_print## # A tibble: 6 x 2
## `Trophic group` `Number of individuals`
## <chr> <dbl>
## 1 Bacterivores 192471000000000000000
## 2 Fungivores 63764000000000000000
## 3 Herbivores 125001000000000000000
## 4 Omnivores 39411000000000000000
## 5 Predators 19734300000000000000
## 6 Total 440381299999999983616#Relative abundance per biome
abundances <- Biome_Abundances %>%
mutate(Biome = replace(Biome, Biome == 1, "Tropical Moist Forests")) %>%
mutate(Biome = replace(Biome, Biome == 2, "Tropical Dry Forest")) %>%
mutate(Biome = replace(Biome, Biome == 3, "Tropical Coniferous Forests")) %>%
mutate(Biome = replace(Biome, Biome == 4, "Temperate Broadleaf Forests")) %>%
mutate(Biome = replace(Biome, Biome == 5, "Temperate Conifer Forests")) %>%
mutate(Biome = replace(Biome, Biome == 6, "Boreal Forests")) %>%
mutate(Biome = replace(Biome, Biome == 7, "Tropical Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 8, "Temperate Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 9, "Flooded Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 10, "Montane Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 11, "Tundra")) %>%
mutate(Biome = replace(Biome, Biome == 12, "Mediterranean Forests")) %>%
mutate(Biome = replace(Biome, Biome == 13, "Deserts")) %>%
mutate(Biome = replace(Biome, Biome == 14, "Mangroves"))
abundances_biome <- abundances %>% select(-Total_Number)
abundances_biome[,2:6] <- prop.table(as.matrix(abundances_biome[,2:6]), 1)
abundances_biome[,2:6] <- round(select_if(abundances_biome[,2:6], is.numeric), 3)
abundances_biome## Biome Bacterivores Fungivores Herbivores
## 1 Tropical Moist Forests 0.443 0.120 0.249
## 2 Tropical Dry Forest 0.447 0.124 0.285
## 3 Tropical Coniferous Forests 0.423 0.135 0.235
## 4 Temperate Broadleaf Forests 0.445 0.153 0.248
## 5 Temperate Conifer Forests 0.390 0.201 0.239
## 6 Boreal Forests 0.446 0.158 0.293
## 7 Tropical Grasslands 0.371 0.140 0.354
## 8 Temperate Grasslands 0.478 0.138 0.278
## 9 Flooded Grasslands 0.410 0.141 0.308
## 10 Montane Grasslands 0.395 0.166 0.260
## 11 Tundra 0.462 0.130 0.303
## 12 Mediterranean Forests 0.435 0.155 0.263
## 13 Deserts 0.423 0.127 0.296
## 14 Mangroves 0.443 0.137 0.221
## Omnivores Predators
## 1 0.116 0.071
## 2 0.096 0.047
## 3 0.127 0.080
## 4 0.108 0.045
## 5 0.114 0.057
## 6 0.074 0.029
## 7 0.094 0.041
## 8 0.078 0.027
## 9 0.095 0.045
## 10 0.119 0.060
## 11 0.071 0.033
## 12 0.098 0.049
## 13 0.079 0.075
## 14 0.127 0.072abundances_tot <- abundances %>% select(Biome, Total_Number)
abundances_tot[,2] <- prop.table(as.matrix(abundances_tot[,2]), 2)
abundances_tot[,2] <- round(abundances_tot[,2], 3)
abundances_tot## Biome Total_Number
## 1 Tropical Moist Forests 0.103
## 2 Tropical Dry Forest 0.016
## 3 Tropical Coniferous Forests 0.005
## 4 Temperate Broadleaf Forests 0.124
## 5 Temperate Conifer Forests 0.042
## 6 Boreal Forests 0.250
## 7 Tropical Grasslands 0.081
## 8 Temperate Grasslands 0.079
## 9 Flooded Grasslands 0.005
## 10 Montane Grasslands 0.042
## 11 Tundra 0.138
## 12 Mediterranean Forests 0.014
## 13 Deserts 0.098
## 14 Mangroves 0.002rel.abundance.trophic <- format(round(prop.table(data.frame(as.list(apply(subset(abundances, select= c(-Biome, -Total_Number)), 2, sum)))), 3), nsmall = 3)
rel.abundance.trophic## Bacterivores Fungivores Herbivores Omnivores Predators
## 1 0.437 0.145 0.284 0.089 0.045abundances_final <- rbind(rel.abundance.trophic)
write.csv(abundances_final, "TableS5_rel_abundance_biome.csv")#Adults
summary_tot_adult <- merge(summary_adult, numbers_tot, by = "trophic.group")
#Total biomass in Gt: mean mass per trophic group x individuals
summary_tot_adult$total.biomass.Gt <- with(summary_tot_adult, (summary_tot_adult$mean.mass.ug * summary_tot_adult$tot.numb.indiv)/1E21)
#Total biomass in Gt C: biomass x 20% fresh mass x carbon content 52% of dry mass
summary_tot_adult$total.Cbiomass.Gt <- with(summary_tot_adult, (summary_tot_adult$total.biomass.Gt * cperw))
#Total monthly respiration in Gt C: daily rate x individuals x 31 days
summary_tot_adult$total.mon.resp.Gt <- with(summary_tot_adult, (summary_tot_adult$mean.daily.respiration.rate.ug * summary_tot_adult$tot.numb.indiv * 31)/1E21)
#Total monthly production in Gt C: daily rate x individuals x 31 days
summary_tot_adult$total.mon.prod.Gt <- with(summary_tot_adult, (summary_tot_adult$mean.daily.production.rate.ug * summary_tot_adult$tot.numb.indiv * 31)/1E21)
#Total monthly carbon budget in Gt C: sum of respiration + production
summary_tot_adult$total.C.budget.Gt <- with(summary_tot_adult, (summary_tot_adult$total.mon.prod.Gt + summary_tot_adult$total.mon.resp.Gt))
#Final output
summary_tot_adult <- summary_tot_adult %>%
select(trophic.group, tot.numb.indiv, total.biomass.Gt, total.Cbiomass.Gt, total.mon.resp.Gt, total.mon.prod.Gt, total.C.budget.Gt) %>%
rename("Trophic group" = 1) %>%
rename("Computed individuals" = 2) %>%
rename("Fresh biomass (Mt)" = 3) %>%
rename("Biomass (Mt C)" = 4) %>%
rename("Monthly respiration (Mt C)" = 5) %>%
rename("Monthly production (Mt C)" = 6) %>%
rename("Monthly carbon budget (Mt C)" = 7) %>%
bind_rows(summarise_all(., funs(if(is.numeric(.)) sum(.) else "Total")))
summary_tot_adult[,3:7] <- summary_tot_adult[,3:7] * 1000
summary_tot_adult[,-1] <- round(select_if(summary_tot_adult, is.numeric), 2)
summary_tot_adult[,2] <- formatC(summary_tot_adult[,2], format = "e", digits = 2)
summary_tot_adult## Trophic group Computed individuals Fresh biomass (Mt) Biomass (Mt C)
## 1 Bacterivores 1.92e+20 158.60 16.49
## 2 Fungivores 6.38e+19 23.02 2.39
## 3 Herbivores 1.25e+20 242.48 25.22
## 4 Omnivores 3.94e+19 286.69 29.82
## 5 Predators 1.97e+19 84.75 8.81
## 6 Total 4.40e+20 795.54 82.74
## Monthly respiration (Mt C) Monthly production (Mt C)
## 1 63.04 31.19
## 2 13.33 2.18
## 3 62.14 20.31
## 4 69.26 17.56
## 5 26.99 6.07
## 6 234.76 77.32
## Monthly carbon budget (Mt C)
## 1 94.23
## 2 15.51
## 3 82.45
## 4 86.83
## 5 33.06
## 6 312.08#Juveniles
summary_tot_juv <- merge(summary_juv, numbers_tot, by = "trophic.group")
#Total biomass in Gt: mean mass per trophic group x individuals
summary_tot_juv$total.biomass.Gt <- with(summary_tot_juv, (summary_tot_juv$mean.mass.ug * summary_tot_juv$tot.numb.indiv)/1E21)
#Total biomass in Gt C: biomass x 20% fresh mass x carbon content 52% of dry mass
summary_tot_juv$total.Cbiomass.Gt <- with(summary_tot_juv, (summary_tot_juv$total.biomass.Gt * cperw))
#Total monthly respiration in Gt C: daily rate x individuals x 31 days
summary_tot_juv$total.mon.resp.Gt <- with(summary_tot_juv, (summary_tot_juv$mean.daily.respiration.rate.ug * summary_tot_juv$tot.numb.indiv * 31)/1E21)
#Total monthly production in Gt C: daily rate x individuals x 31 days
summary_tot_juv$total.mon.prod.Gt <- with(summary_tot_juv, (summary_tot_juv$mean.daily.production.rate.ug * summary_tot_juv$tot.numb.indiv * 31)/1E21)
# #Total monthly carbon budget in Gt C: sum of respiration + production
summary_tot_juv$total.C.budget.Gt <- with(summary_tot_juv, (summary_tot_juv$total.mon.prod.Gt + summary_tot_juv$total.mon.resp.Gt))
summary_tot_juv <- summary_tot_juv %>%
select(trophic.group, tot.numb.indiv, total.biomass.Gt, total.Cbiomass.Gt, total.mon.resp.Gt, total.mon.prod.Gt, total.C.budget.Gt) %>%
rename("Trophic group" = 1) %>%
rename("Computed individuals" = 2) %>%
rename("Fresh biomass (Mt)" = 3) %>%
rename("Biomass (Mt C)" = 4) %>%
rename("Monthly respiration (Mt C)" = 5) %>%
rename("Monthly production (Mt C)" = 6) %>%
rename("Carbon budget (Mt C)" = 7) %>%
bind_rows(summarise_all(., funs(if(is.numeric(.)) sum(.) else "Total")))
summary_tot_juv[,3:7] <- summary_tot_juv[,3:7] * 1000
summary_tot_juv[,-1] <- round(select_if(summary_tot_juv, is.numeric), 2)
summary_tot_juv[,2] <- formatC(summary_tot_juv[,2], format = "e", digits = 2)# Final table of biomass and carbon budget
#Assuming a relative proportion of 30% adults and 70% juveniles
adult_frac <- 0.30
juv_frac <- 0.70
#Multiply adult and juvenile summaries by relative proportions
summary_tot_final <- summary_tot_juv[,1:2]
summary_tot_final[3:7] <- ((adult_frac * summary_tot_adult[3:7]) + (juv_frac * summary_tot_juv[3:7]))
summary_tot_final[3:7] <- round(select_if(summary_tot_final[3:7], is.numeric), 2)
summary_tot_final## Trophic group Computed individuals Fresh biomass (Mt) Biomass (Mt C)
## 1 Bacterivores 1.92e+20 68.57 7.13
## 2 Fungivores 6.38e+19 9.56 0.99
## 3 Herbivores 1.25e+20 83.41 8.67
## 4 Omnivores 3.94e+19 98.50 10.25
## 5 Predators 1.97e+19 42.25 4.39
## 6 Total 4.40e+20 302.30 31.44
## Monthly respiration (Mt C) Monthly production (Mt C)
## 1 34.16 12.22
## 2 6.49 0.91
## 3 26.74 7.01
## 4 27.38 6.08
## 5 15.05 3.00
## 6 109.82 29.24
## Monthly carbon budget (Mt C)
## 1 46.39
## 2 7.40
## 3 33.75
## 4 33.46
## 5 18.06
## 6 139.06#Write to file
setwd("~/Work/ETH/Projects/Nematodes/Calculations")
write.csv(summary_tot_final, "Table1_carbon_budget_2.csv")#to calculate the spread of the data, we take the minimum and maximum predicted values across all ensembled models
numbers_tot_min <- numbers_min %>%
group_by(trophic.group) %>%
summarise(tot.numb.indiv = sum(Total.number))
numbers_tot_max <- numbers_max %>%
group_by(trophic.group) %>%
summarise(tot.numb.indiv = sum(Total.number))
numbers_tot_min_print <- numbers_tot_min %>%
rename("Trophic group" = 1) %>%
rename("Number of individuals" = 2) %>%
bind_rows(summarise_all(., funs(if(is.numeric(.)) sum(.) else "Total")))
numbers_tot_max_print <- numbers_tot_max %>%
rename("Trophic group" = 1) %>%
rename("Number of individuals" = 2) %>%
bind_rows(summarise_all(., funs(if(is.numeric(.)) sum(.) else "Total")))
variation <- numbers_tot_print[,1]
#variation$avg <- (((numbers_tot_max_print$"Number of individuals" - numbers_tot_print$"Number of individuals") + (numbers_tot_print$"Number of individuals" - numbers_tot_min_print$"Number of individuals"))/2)
variation$mean.min <- (numbers_tot_print$"Number of individuals" - numbers_tot_min_print$"Number of individuals")
#variation$max.mean <- (numbers_tot_max_print$"Number of individuals" - numbers_tot_print$"Number of individuals")
#Adults
summary_tot_min_adult <- merge(summary_adult, numbers_tot_min, by = "trophic.group")
#Total biomass in Gt: mean mass per trophic group x individuals
summary_tot_min_adult$total.biomass.Gt <- with(summary_tot_min_adult, (summary_tot_min_adult$mean.mass.ug * summary_tot_min_adult$tot.numb.indiv)/1E21)
#Total biomass in Gt C: biomass x 20% fresh mass x carbon content 52% of dry mass
summary_tot_min_adult$total.Cbiomass.Gt <- with(summary_tot_min_adult, (summary_tot_min_adult$total.biomass.Gt * cperw))
#Total monthly respiration in Gt C: daily rate x individuals x 31 days
summary_tot_min_adult$total.mon.resp.Gt <- with(summary_tot_min_adult, (summary_tot_min_adult$mean.daily.respiration.rate.ug * summary_tot_min_adult$tot.numb.indiv * 31)/1E21)
#Total monthly production in Gt C: daily rate x individuals x 31 days
summary_tot_min_adult$total.mon.prod.Gt <- with(summary_tot_min_adult, (summary_tot_min_adult$mean.daily.production.rate.ug * summary_tot_min_adult$tot.numb.indiv * 31)/1E21)
#Total monthly carbon budget in Gt C: sum of respiration + production
summary_tot_min_adult$total.C.budget.Gt <- with(summary_tot_min_adult, (summary_tot_min_adult$total.mon.prod.Gt + summary_tot_min_adult$total.mon.resp.Gt))
#Final output
summary_tot_min_adult <- summary_tot_min_adult %>%
select(trophic.group, tot.numb.indiv, total.biomass.Gt, total.Cbiomass.Gt, total.mon.resp.Gt, total.mon.prod.Gt, total.C.budget.Gt) %>%
rename("Trophic group" = 1) %>%
rename("Computed individuals" = 2) %>%
rename("Fresh biomass (Mt)" = 3) %>%
rename("Biomass (Mt C)" = 4) %>%
rename("Monthly respiration (Mt C)" = 5) %>%
rename("Monthly production (Mt C)" = 6) %>%
rename("Monthly carbon budget (Mt C)" = 7) %>%
bind_rows(summarise_all(., funs(if(is.numeric(.)) sum(.) else "Total")))
summary_tot_min_adult[3:7] <- summary_tot_min_adult[3:7] * 1000
summary_tot_min_adult[,-1] <- round(select_if(summary_tot_min_adult, is.numeric), 3)
summary_tot_min_adult[,2] <- formatC(summary_tot_min_adult[,2], format = "e", digits = 3)
#Juveniles
summary_tot_min_juv <- merge(summary_juv, numbers_tot_min, by = "trophic.group")
#Total biomass in Gt: mean mass per trophic group x individuals
summary_tot_min_juv$total.biomass.Gt <- with(summary_tot_min_juv, (summary_tot_min_juv$mean.mass.ug * summary_tot_min_juv$tot.numb.indiv)/1E21)
#Total biomass in Gt C: biomass x 20% fresh mass x carbon content 52% of dry mass
summary_tot_min_juv$total.Cbiomass.Gt <- with(summary_tot_min_juv, (summary_tot_min_juv$total.biomass.Gt * cperw))
#Total monthly respiration in Gt C: daily rate x individuals x 31 days
summary_tot_min_juv$total.mon.resp.Gt <- with(summary_tot_min_juv, (summary_tot_min_juv$mean.daily.respiration.rate.ug * summary_tot_min_juv$tot.numb.indiv * 31)/1E21)
#Total monthly production in Gt C: daily rate x individuals x 31 days
summary_tot_min_juv$total.mon.prod.Gt <- with(summary_tot_min_juv, (summary_tot_min_juv$mean.daily.production.rate.ug * summary_tot_min_juv$tot.numb.indiv * 31)/1E21)
# #Total monthly carbon budget in Gt C: sum of respiration + production
summary_tot_min_juv$total.C.budget.Gt <- with(summary_tot_min_juv, (summary_tot_min_juv$total.mon.prod.Gt + summary_tot_min_juv$total.mon.resp.Gt))
summary_tot_min_juv <- summary_tot_min_juv %>%
select(trophic.group, tot.numb.indiv, total.biomass.Gt, total.Cbiomass.Gt, total.mon.resp.Gt, total.mon.prod.Gt, total.C.budget.Gt) %>%
rename("Trophic group" = 1) %>%
rename("Computed individuals" = 2) %>%
rename("Fresh biomass (Mt)" = 3) %>%
rename("Biomass (Mt C)" = 4) %>%
rename("Monthly respiration (Mt C)" = 5) %>%
rename("Monthly production (Mt C)" = 6) %>%
rename("Carbon budget (Mt C)" = 7) %>%
bind_rows(summarise_all(., funs(if(is.numeric(.)) sum(.) else "Total")))
summary_tot_min_juv[3:7] <- summary_tot_min_juv[3:7] * 1000
summary_tot_min_juv[,-1] <- round(select_if(summary_tot_min_juv, is.numeric), 3)
summary_tot_min_juv[,2] <- formatC(summary_tot_min_juv[,2], format = "e", digits = 3)
#Multiply adult and juvenile summaries by relative proportions
summary_tot_min_final <- summary_tot_min_juv[,1:2]
summary_tot_min_final[3:7] <- ((adult_frac * summary_tot_min_adult[3:7]) + (juv_frac * summary_tot_min_juv[3:7]))
variation <- variation %>%
rename("Trophic group" = 1) %>%
rename("Number ±" = 2)
variation$'Fresh biomass ±' <- summary_tot_final$`Fresh biomass (Mt)` - summary_tot_min_final$`Fresh biomass (Mt)`
variation$"Biomass C ±" <- summary_tot_final$`Biomass (Mt C)` - summary_tot_min_final$`Biomass (Mt C)`
variation$"Respiration ±" <- summary_tot_final$`Monthly respiration (Mt C)` - summary_tot_min_final$`Monthly respiration (Mt C)`
variation$"Production ±" <- summary_tot_final$`Monthly production (Mt C)` - summary_tot_min_final$`Monthly production (Mt C)`
variation$"Budget ±" <- summary_tot_final$`Monthly carbon budget (Mt C)` - summary_tot_min_final$`Monthly carbon budget (Mt C)`
variation## # A tibble: 6 x 7
## `Trophic group` `Number ±` `Fresh biomass … `Biomass C ±` `Respiration ±`
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Bacterivores 2.08e19 7.42 0.770 3.69
## 2 Fungivores 6.46e18 0.969 0.0966 0.661
## 3 Herbivores 1.14e19 7.59 0.785 2.43
## 4 Omnivores 4.56e18 11.4 1.19 3.17
## 5 Predators 3.08e18 6.59 0.682 2.35
## 6 Total 4.63e19 34.0 3.54 12.3
## # … with 2 more variables: `Production ±` <dbl>, `Budget ±` <dbl>write.csv(variation, "variation.csv")#Biomass per biome
#Total biomass per biome, per trophic group
#adult
summary_biome_adult <- merge(summary_adult, numbers_biome, by = "trophic.group")
summary_biome_adult$total.mon.resp.Gt <- with(summary_biome_adult, (summary_biome_adult$mean.daily.respiration.rate.ug * summary_biome_adult$tot.numb.indiv * 31)/1E21)
summary_biome_adult$total.biomass.Gt <- with(summary_biome_adult, (summary_biome_adult$mean.mass.ug * summary_biome_adult$tot.numb.indiv)/1E21)
summary_biome_adult$total.biomass.C.Gt <- with(summary_biome_adult, (summary_biome_adult$total.biomass.Gt * cperw * 1000))
summary_biome_adult$total.mon.prod.Gt <- with(summary_biome_adult, (summary_biome_adult$mean.daily.production.rate.ug * summary_biome_adult$tot.numb.indiv * 31)/1E21)
summary_biome_adult$total.C.budget.Gt <- with(summary_biome_adult, (summary_biome_adult$total.mon.prod.Gt + summary_biome_adult$total.mon.resp.Gt))
biomesum_adult <- summary_biome_adult %>% group_by(Biome, trophic.group) %>% summarise(sum(total.biomass.C.Gt)) %>%
spread(key = trophic.group, value = 'sum(total.biomass.C.Gt)')
biomesum_adult <- transform(biomesum_adult, Total.biomass=rowSums(subset(biomesum_adult, select= -Biome))) %>%
mutate(Biome = replace(Biome, Biome == 1, "Tropical Moist Forests")) %>%
mutate(Biome = replace(Biome, Biome == 2, "Tropical Dry Forest")) %>%
mutate(Biome = replace(Biome, Biome == 3, "Tropical Coniferous Forests")) %>%
mutate(Biome = replace(Biome, Biome == 4, "Temperate Broadleaf Forests")) %>%
mutate(Biome = replace(Biome, Biome == 5, "Temperate Conifer Forests")) %>%
mutate(Biome = replace(Biome, Biome == 6, "Boreal Forests")) %>%
mutate(Biome = replace(Biome, Biome == 7, "Tropical Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 8, "Temperate Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 9, "Flooded Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 10, "Montane Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 11, "Tundra")) %>%
mutate(Biome = replace(Biome, Biome == 12, "Mediterranean Forests")) %>%
mutate(Biome = replace(Biome, Biome == 13, "Deserts")) %>%
mutate(Biome = replace(Biome, Biome == 14, "Mangroves")) %>%
mutate(Biome = replace(Biome, Biome == 105, "Total"))
# juveniles
summary_biome_juv <- merge(summary_juv, numbers_biome, by = "trophic.group")
summary_biome_juv$total.mon.resp.Gt <- with(summary_biome_juv, (summary_biome_juv$mean.daily.respiration.rate.ug * summary_biome_juv$tot.numb.indiv * 31)/1E21)
summary_biome_juv$total.biomass.Gt <- with(summary_biome_juv, (summary_biome_juv$mean.mass.ug * summary_biome_juv$tot.numb.indiv)/1E21)
summary_biome_juv$total.biomass.C.Gt <- with(summary_biome_juv, (summary_biome_juv$total.biomass.Gt * cperw * 1000))
summary_biome_juv$total.mon.prod.Gt <- with(summary_biome_juv, (summary_biome_juv$mean.daily.production.rate.ug * summary_biome_juv$tot.numb.indiv * 31)/1E21)
summary_biome_juv$total.C.budget.Gt <- with(summary_biome_juv, (summary_biome_juv$total.mon.prod.Gt + summary_biome_juv$total.mon.resp.Gt))
biomesum_juv <- summary_biome_juv %>% group_by(Biome, trophic.group) %>% summarise(sum(total.biomass.C.Gt)) %>%
spread(key = trophic.group, value = 'sum(total.biomass.C.Gt)')
biomesum_juv <- transform(biomesum_juv, Total.biomass=rowSums(subset(biomesum_juv, select= -Biome))) %>%
mutate(Biome = replace(Biome, Biome == 1, "Tropical Moist Forests")) %>%
mutate(Biome = replace(Biome, Biome == 2, "Tropical Dry Forest")) %>%
mutate(Biome = replace(Biome, Biome == 3, "Tropical Coniferous Forests")) %>%
mutate(Biome = replace(Biome, Biome == 4, "Temperate Broadleaf Forests")) %>%
mutate(Biome = replace(Biome, Biome == 5, "Temperate Conifer Forests")) %>%
mutate(Biome = replace(Biome, Biome == 6, "Boreal Forests")) %>%
mutate(Biome = replace(Biome, Biome == 7, "Tropical Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 8, "Temperate Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 9, "Flooded Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 10, "Montane Grasslands")) %>%
mutate(Biome = replace(Biome, Biome == 11, "Tundra")) %>%
mutate(Biome = replace(Biome, Biome == 12, "Mediterranean Forests")) %>%
mutate(Biome = replace(Biome, Biome == 13, "Deserts")) %>%
mutate(Biome = replace(Biome, Biome == 14, "Mangroves")) %>%
mutate(Biome = replace(Biome, Biome == 105, "Total"))
biomesum <- biomesum_adult[,1, drop = FALSE]
biomesum[2:7] <- ((adult_frac * biomesum_adult[2:7]) + (juv_frac * biomesum_juv[2:7]))
biomesum_print <- biomesum %>%
bind_rows(summarise_all(., funs(if(is.numeric(.)) sum(.) else "Total"))) %>%
rename("Bacterivores (Mt C)" = 2) %>%
rename("Fungivores (Mt C)" = 3) %>%
rename("Herbivores (Mt C)" = 4) %>%
rename("Omnivores (Mt C)" = 5) %>%
rename("Predators (Mt C)" = 6) %>%
rename("Total biomass (Mt C)" = 7)
biomesum_print[-1] <- format(round(biomesum_print[-1], 3), nsmall = 5)
biomesum_print## Biome Bacterivores (Mt C) Fungivores (Mt C)
## 1 Tropical Moist Forests 0.74500 0.08500
## 2 Tropical Dry Forest 0.11700 0.01400
## 3 Tropical Coniferous Forests 0.03600 0.00500
## 4 Temperate Broadleaf Forests 0.90400 0.13100
## 5 Temperate Conifer Forests 0.26500 0.05700
## 6 Boreal Forests 1.81500 0.27000
## 7 Tropical Grasslands 0.48900 0.07800
## 8 Temperate Grasslands 0.61900 0.07500
## 9 Flooded Grasslands 0.03500 0.00500
## 10 Montane Grasslands 0.27300 0.04800
## 11 Tundra 1.03700 0.12300
## 12 Mediterranean Forests 0.10200 0.01500
## 13 Deserts 0.67800 0.08600
## 14 Mangroves 0.01500 0.00200
## 15 Total 7.13100 0.99400
## Herbivores (Mt C) Omnivores (Mt C) Predators (Mt C)
## 1 0.78400 1.37200 0.71900
## 2 0.13900 0.17600 0.07400
## 3 0.03700 0.07600 0.04100
## 4 0.94400 1.54100 0.55200
## 5 0.30400 0.54600 0.23200
## 6 2.23500 2.11600 0.71000
## 7 0.87400 0.86600 0.32300
## 8 0.67500 0.71000 0.21400
## 9 0.05000 0.05800 0.02400
## 10 0.33700 0.58000 0.25200
## 11 1.27700 1.12000 0.44800
## 12 0.11600 0.16200 0.06900
## 13 0.88800 0.89200 0.72400
## 14 0.01400 0.03000 0.01500
## 15 8.67500 10.24400 4.39500
## Total biomass (Mt C)
## 1 3.70500
## 2 0.51900
## 3 0.19400
## 4 4.07200
## 5 1.40400
## 6 7.14600
## 7 2.63000
## 8 2.29200
## 9 0.17200
## 10 1.49000
## 11 4.00600
## 12 0.46500
## 13 3.26700
## 14 0.07600
## 15 31.43900write.csv(biomesum_print, "TableS6_biomass.csv")#Relative biomass of each trophic group
#Relative biomass of each trophic group
rel.biomass.trophic <- format(round(prop.table(data.frame(as.list(apply(subset(biomesum, select= c(-Biome, -Total.biomass)), 2, sum)))), 3), nsmall = 3)
rel.biomass.trophic## Bacterivores Fungivores Herbivores Omnivores Predators
## 1 0.227 0.032 0.276 0.326 0.140#Relative biomass of each trophic group per biome
#Relative biomass of each trophic group per biome
rel.biomass.biome <- as.data.frame(prop.table(as.matrix(biomesum[2:6]),1))
rel.biomass.biome <- cbind(biomesum$Biome, format(round(rel.biomass.biome, 3), nsmall = 3)) %>%
rename(Biome = 1)
rel.biomass.trophic$Biome <- "Total"
rel.biomass <- rbind(rel.biomass.biome,rel.biomass.trophic)#relative biomass per biome
rel.biomass.sum <- as.data.frame(prop.table(as.matrix(subset(biomesum, select = Total.biomass))),1) %>%
bind_rows(summarise_all(., funs(if(is.numeric(.)) sum(.) else "Total"))) %>%
rename("Across trophic groups - fraction of total biomass" = 1)
rel.biomass.final <- cbind(rel.biomass, format(round(rel.biomass.sum, 3), nsmall = 3)) %>%
rename("Bacterivores - fraction of total" = 2) %>%
rename("Fungivores - fraction of total" = 3) %>%
rename("Herbivores - fraction of total" = 4) %>%
rename("Omnivores - fraction of total" = 5) %>%
rename("Predators - fraction of total" = 6)
rel.biomass.final## Biome Bacterivores - fraction of total
## 1 Tropical Moist Forests 0.201
## 2 Tropical Dry Forest 0.225
## 3 Tropical Coniferous Forests 0.184
## 4 Temperate Broadleaf Forests 0.222
## 5 Temperate Conifer Forests 0.189
## 6 Boreal Forests 0.254
## 7 Tropical Grasslands 0.186
## 8 Temperate Grasslands 0.270
## 9 Flooded Grasslands 0.206
## 10 Montane Grasslands 0.183
## 11 Tundra 0.259
## 12 Mediterranean Forests 0.220
## 13 Deserts 0.208
## 14 Mangroves 0.198
## 15 Total 0.227
## Fungivores - fraction of total Herbivores - fraction of total
## 1 0.023 0.212
## 2 0.026 0.269
## 3 0.025 0.192
## 4 0.032 0.232
## 5 0.041 0.217
## 6 0.038 0.313
## 7 0.030 0.333
## 8 0.033 0.294
## 9 0.030 0.290
## 10 0.032 0.226
## 11 0.031 0.319
## 12 0.033 0.249
## 13 0.026 0.272
## 14 0.026 0.185
## 15 0.032 0.276
## Omnivores - fraction of total Predators - fraction of total
## 1 0.370 0.194
## 2 0.338 0.142
## 3 0.390 0.209
## 4 0.379 0.136
## 5 0.389 0.165
## 6 0.296 0.099
## 7 0.329 0.123
## 8 0.310 0.093
## 9 0.337 0.137
## 10 0.389 0.169
## 11 0.280 0.112
## 12 0.349 0.149
## 13 0.273 0.222
## 14 0.399 0.193
## 15 0.326 0.140
## Across trophic groups - fraction of total biomass
## 1 0.118
## 2 0.017
## 3 0.006
## 4 0.130
## 5 0.045
## 6 0.227
## 7 0.084
## 8 0.073
## 9 0.005
## 10 0.047
## 11 0.127
## 12 0.015
## 13 0.104
## 14 0.002
## 15 1.000write.csv(rel.biomass.final, "TableS7_rel_biomass.csv")#Comparing nematodes to humans
#total nematode biomass (freshweight) in Gt:
tot_nema_biomass <- summary_tot_final$`Fresh biomass (Gt)`[6]
#Human biomass is calculated using assumptions from Bar-On et al. 2018 PNAS
#human population from 2015 UN data
human_pop <- 7383008.82 * 1000 #https://esa.un.org/unpd/wpp/Download/Standard/Population/
#human population versus nematode population in billions
cat("For each human on Earth,", format(round((numbers_tot_print$`Number of individuals`[6]/human_pop)/1E9, 1), nsmall = 1), "billion nematodes are present.")## For each human on Earth, 59.6 billion nematodes are present.#Average human weight assumed at 50 kg by Hern et al. 1999 https://link.springer.com/article/10.1023/A:1022153110536
human_weight <- 50
human_biomass <- (human_pop * human_weight)/1E12
#nematode biomass as fraction of human biomass
cat("Total soil nematode biomass represents", format(round((tot_nema_biomass/human_biomass*100), 1)),"% of total human biomass")## Total soil nematode biomass represents % of total human biomass