Disturbance Treatments: length and pools

Objective

What happens when the stroage C pool is disturbanced for mulitple time steps. With only the C storage pool distrubed it turns out that there is little to no change, we think that is because the disturbance has no long term effect. Talking with Jackie Mathes we tried out a distrubance for a full year and a month.

library(ggplot2)
library(data.table)
library(magrittr)
library(purrr)
BASE_DIR <- here::here()
THEME    <- theme_bw()
# Modify the ed results, add columns with the disturbance severity and disturbance length, this information 
# will be used in the plots. 
ed_rslts <- readRDS(file.path(BASE_DIR, 'ED-outputs', 'disturbance_long.rds'))
ed_rslts <- map(ed_rslts, function(x){
  
  x$length   <- gsub(pattern = 'harvest_0_|harvest_45_|harvest_65_|harvest_85_', replacement = "", x = x$scn)
  x$severity <- gsub(pattern = '1month|_|1yr|1day|harvest', replacement = "", x = x$scn)
  
  return(x)
  
})

Disturbance Length

In these plots only the storage C pool was disturbed (harvested). The ED runs vary in the length of time the disturabnce treatment was applied to, 1 day, 1 month, and 1 year.

NPP_values <- ed_rslts$NPP[year <= 2021, list(value = sum(value)), by = c("year", "variable", "description", "scn", "length", "severity")]
ggplot(data = NPP_values) + 
  geom_line(aes(year, value, color = length)) + 
 facet_wrap("severity") +
  THEME + 
  labs(title = 'NPP per plot', y = 'kgC/m2/yr')

NEP_values <- ed_rslts$NEP[year <= 2021, list(value = sum(value)), by = c("year", "variable", "description", "scn", "length", "severity")]

ggplot(data = NEP_values) + 
  geom_line(aes(year, value, color = length)) + 
  facet_wrap("severity") +
  THEME + 
  labs(title = 'NEP per plot', y = 'kgC/m2/yr')

Do the 1 day results differ from one another?

Impacted pools

Here we applied a 1 day disturbance to the above pools (all of the carbon pools expect the below-ground biomass), all four of the disturbance pools, and only the storage C pool.

# Modify the ed results, add columns with the disturbance severity and disturbance length, this information 
# will be used in the plots. 
ed_rslts <- readRDS(file.path(BASE_DIR, 'ED-outputs', 'disturbance_treatments.rds'))
ed_rslts <- map(ed_rslts, function(x){
  
  x <- x[grepl(pattern = '1day', x = scn), ]
  x$scn <- gsub(pattern = 'uniform|unifrom', replacement = 'uniform', x = x$scn)
  x$length   <- gsub(pattern = 'harvest_0_|harvest_45_|harvest_65_|harvest_85_|uniform|_above', replacement = "", x = x$scn)
  x$severity <- gsub(pattern = '1month|_|1yr|1day|harvest|uniform|above', replacement = "", x = x$scn)
  x$pools <- gsub(pattern = 'harvest_0_|harvest_45_|harvest_65_|harvest_85_|1day|_', replacement = "", x = x$scn)
  x$pools <- ifelse(x$pools == "", "storage C", x$pools)
    
    return(x)
  
})

NPP_values <- ed_rslts$NPP[year <= 2021, list(value = sum(value)), by = c("year", "variable", "description", "scn", "length", "severity", "pools")]
ggplot(data = NPP_values) + 
  geom_line(aes(year, value, color = pools)) + 
 facet_wrap("severity") +
  THEME + 
  labs(title = 'NPP per plot', y = 'kgC/m2/yr')

Observations & Conclusions

1. ED’s response to the 1 day treatment is minor verges on trivial
2. ED has a large response to the 45 and 65% 1 month treatments but exhbits some resillience, the 85% 1 month treatment leads to ecological collapse
3. All of the 1 year treaments lead to ecological collapse
4. With the above treatment (the below group C is left alone) it is intresting to me that the GPP and NEP is higher in the above treatment group, I was somewhat expecting the biomass left below ground to be respired leading to a lower NEP than compared to the uniformly harvested treatment.