1 Research Questions

1.1 Q1) How do pre and post-fire soil respiration, GPP and plant communities compare?

1.2 Q2) How did the fire change land-atmosphere C exchange sensitivity to environmental variables.

1.3 Q3) Why did this ecosystem return to a net carbon sink so fast?

2 Hypothesis

2.1 H1) Pre and post-fire NEE differences were driven by changes in GPP.

2.2 H2) Rapid return to net carbon sink was due to a highly favorable climate and conditions for rapid establishment and growth of fast-growing plants.

2.3 H3) Post-fire plant community GPP is more sensitive to changes in water availability.

#Converting units from umolCO2/m2/s to gC/m2/30min
realtower$NEE <- (realtower$NEE*1800*12/1000000)
realtower$RECO <- (realtower$RECO*1800*12/1000000)
#Making GPP negative in addition to negative

realtower$GPP <- (realtower$GPP*1800*12/1000000*-1)
#NEE daily df
dailyNEE <- aggregate(realtower$NEE, by=list(year=realtower$year, month=realtower$month, day=realtower$day),FUN=sum, na.rm=T)

dailyNEE$date <- paste0(dailyNEE$year, dailyNEE$month, dailyNEE$day)

dailyNEE <- filter(dailyNEE, year!="20")

dailyNEE <- dailyNEE %>% arrange(date)

dailyNEE$x <- ifelse(dailyNEE$year==13,NA,dailyNEE$x)
#GPP daily df
dailyGPP <- aggregate(realtower$GPP, by=list(year=realtower$year, month=realtower$month, day=realtower$day),FUN=sum, na.rm=T)

dailyGPP$date <- paste0(dailyGPP$year, dailyGPP$month, dailyGPP$day)

dailyGPP <- filter(dailyGPP, year!="20")

dailyGPP <- dailyGPP %>% arrange(date)

dailyGPP$x <- ifelse(dailyGPP$year==13,NA,dailyGPP$x)
#RECO daily df
dailyRECO <- aggregate(realtower$RECO, by=list(year=realtower$year, month=realtower$month, day=realtower$day),FUN=sum, na.rm=T)

dailyRECO$date <- paste0(dailyRECO$year, dailyRECO$month, dailyRECO$day)

dailyRECO <- filter(dailyRECO, year!="20")

dailyRECO <- dailyRECO %>% arrange(date)

dailyRECO$x <- ifelse(dailyRECO$year==13,NA,dailyRECO$x)

3 Daily Boxplots of Fluxes

# Daily Sum NEE in gC/m2/day boxplots
p <- ggplot(dailyNEE, aes(x=year, y=x)) + geom_boxplot() + labs(title="Distribution of NEE in gC/m2/day") 
p

# Daily Sum GPP in gC/m2/day boxplots
p <- ggplot(dailyGPP, aes(x=year, y=x)) + geom_boxplot() + labs(title="Distribution of GPP in gC/m2/day")
p

# Daily Sum RECO in gC/m2/day boxplots
p <- ggplot(dailyRECO, aes(x=year, y=x)) + geom_boxplot() + labs(title="Distribution of RECO in gC/m2/day")
p

# Daily Sum NEE in gC/m2/day boxplots
p <- ggplot(dailyNEE, aes(x=month, y=x)) + geom_boxplot() + labs(title="Distribution of NEE in gC/m2/day") + facet_wrap(~year)
p

# Daily Sum GPP in gC/m2/day boxplots
p <- ggplot(dailyGPP, aes(x=month, y=x)) + geom_boxplot() + labs(title="Distribution of GPP in gC/m2/day")+ facet_wrap(~year)
p

# Daily Sum RECO in gC/m2/day boxplots
p <- ggplot(dailyRECO, aes(x=month, y=x)) + geom_boxplot() + labs(title="Distribution of RECO in gC/m2/day")+ facet_wrap(~year)
p