H3) Post-fire plant community GPP is more sensitive to changes in water availability. This hypothesis emerges from research showing drought reduces growth more in young forests than older forests (Anderson‐Teixeira et al., 2013).
#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 unit coversion
realtower$GPP <- (realtower$GPP*1800*12/1000000*-1)
#Column for weekly GPP sum in gC/m2/week
realtower <- realtower %>%
group_by(year, month, week) %>%
mutate(weeklyGPPsum = sum(GPP, na.rm = TRUE)) %>%
mutate(weeklyRECOsum = sum(RECO, na.rm = TRUE)) %>%
mutate(weeklyNEEsum = sum(NEE, na.rm = TRUE))
#Making new df by Selecting only one weeklysumGPP for each week
weekly <- distinct(realtower, weeklyGPPsum,weeklyRECOsum,weeklyNEEsum, year, .keep_all = TRUE)
#Creating weekly change in GPP in weekly df
weekly$weeklyGPPdifference <- weekly$weeklyGPPsum-lag(weekly$weeklyGPPsum)
weekly$weeklyRECOdifference <- weekly$weeklyRECOsum-lag(weekly$weeklyRECOsum)
weekly$weeklyNEEdifference <- weekly$weeklyNEEsum-lag(weekly$weeklyNEEsum)
#Plots used in determining what threshhold of change in weekly GPP is for determining beginning of time of year when there is a lot of GPP and when GPP drops off.
#Plot of prefire weeklyGPPsum
p <- weekly %>% filter(prepost=="Pre") %>% ggplot(data = .) + geom_line(aes(x=doy, y=weeklyGPPsum, group =1)) + geom_point(aes(x=doy, y=weeklyGPPsum, group =1)) + scale_x_discrete(breaks=c("070101","080101","090101","100101","110101","120101","130101","140101","150101","160101","170101","180101","190101"), labels=c("2007","2008", "2009","2010", "2011","2012","2013","2014","2015","2016","2017","2018","2019")) + labs(title = "Prefire Weekly GPP sum", y="gC/m2/week", x="prefire years")
p
# #Plot of postfire weeklyGPPsum
p <- weekly %>% filter(prepost=="Post") %>% ggplot(data = .) + geom_line(aes(x=doy, y=weeklyGPPsum, group =1)) + geom_point(aes(x=doy, y=weeklyGPPsum, group =1)) + scale_x_discrete(breaks=c("070101","080101","090101","100101","110101","120101","130101","140101","150101","160101","170101","180101","190101"), labels=c("2007","2008", "2009","2010", "2011","2012","2013","2014","2015","2016","2017","2018","2019")) + labs(title = "Postfire Weekly GPP sum", y="gC/m2/week", x="postfire years")
p
#Density of weeklyGPPsum observations
ggplot(weekly, aes(x=weeklyGPPsum, color=year)) + geom_density() + facet_wrap(~year) + labs(title = "Density of weekly GPP sum values")
ggplot(weekly, aes(x=weeklyRECOsum, color=year)) + geom_density() + facet_wrap(~year) + labs(title = "Density of weekly RECO sum values")
ggplot(weekly, aes(x=weeklyNEEsum, color=year)) + geom_density() + facet_wrap(~year) + labs(title = "Density of weekly NEE sum values")
#Density of observations for changes in GPP
p <- ggplot(weekly, aes(x=weeklyGPPdifference, fill = year)) + geom_density() + labs(title = "Density distribution of change in weekly GPP by year", x="gC/m2/week")
p
#Combined Density of observations for changes in GPP
ggplot(weekly, aes(x=weeklyGPPdifference)) + geom_density() + labs(title = "Density distribution of change in weekly GPP, all years combined", x="gC/m2/week") + geom_vline(xintercept = -5.2) + geom_vline(xintercept = 6.5)
# Cutoff values are -5.2 and
#Plot of yearly change in GPP and weeklyGPPsum
weekly %>% filter(year=="07") %>% ggplot(., aes(x=doy, group=1)) + geom_line(aes(y=weeklyGPPsum)) + geom_line(aes(y=weeklyGPPdifference, color = "red"))+ geom_point(aes(y=weeklyGPPsum)) + geom_point(aes(y=weeklyGPPdifference, color = "red")) + facet_wrap(~year, scales = "free_x")
weekly %>% filter(year=="08") %>% ggplot(., aes(x=doy, group=1)) + geom_line(aes(y=weeklyGPPsum)) + geom_line(aes(y=weeklyGPPdifference, color = "red"))+ geom_point(aes(y=weeklyGPPsum)) + geom_point(aes(y=weeklyGPPdifference, color = "red")) + facet_wrap(~year, scales = "free_x")
weekly %>% filter(year=="09") %>% ggplot(., aes(x=doy, group=1)) + geom_line(aes(y=weeklyGPPsum)) + geom_line(aes(y=weeklyGPPdifference, color = "red"))+ geom_point(aes(y=weeklyGPPsum)) + geom_point(aes(y=weeklyGPPdifference, color = "red")) + facet_wrap(~year, scales = "free_x")
weekly %>% filter(year=="10") %>% ggplot(., aes(x=doy, group=1)) + geom_line(aes(y=weeklyGPPsum)) + geom_line(aes(y=weeklyGPPdifference, color = "red"))+ geom_point(aes(y=weeklyGPPsum)) + geom_point(aes(y=weeklyGPPdifference, color = "red")) + facet_wrap(~year, scales = "free_x")
weekly %>% filter(year=="11") %>% ggplot(., aes(x=doy, group=1)) + geom_line(aes(y=weeklyGPPsum)) + geom_line(aes(y=weeklyGPPdifference, color = "red"))+ geom_point(aes(y=weeklyGPPsum)) + geom_point(aes(y=weeklyGPPdifference, color = "red")) + facet_wrap(~year, scales = "free_x")
weekly %>% filter(year=="12") %>% ggplot(., aes(x=doy, group=1)) + geom_line(aes(y=weeklyGPPsum)) + geom_line(aes(y=weeklyGPPdifference, color = "red"))+ geom_point(aes(y=weeklyGPPsum)) + geom_point(aes(y=weeklyGPPdifference, color = "red")) + facet_wrap(~year, scales = "free_x")
weekly %>% filter(year=="14") %>% ggplot(., aes(x=doy, group=1)) + geom_line(aes(y=weeklyGPPsum)) + geom_line(aes(y=weeklyGPPdifference, color = "red"))+ geom_point(aes(y=weeklyGPPsum)) + geom_point(aes(y=weeklyGPPdifference, color = "red")) + facet_wrap(~year, scales = "free_x")
weekly %>% filter(year=="15") %>% ggplot(., aes(x=doy, group=1)) + geom_line(aes(y=weeklyGPPsum)) + geom_line(aes(y=weeklyGPPdifference, color = "red"))+ geom_point(aes(y=weeklyGPPsum)) + geom_point(aes(y=weeklyGPPdifference, color = "red")) + facet_wrap(~year, scales = "free_x")
weekly %>% filter(year=="16") %>% ggplot(., aes(x=doy, group=1)) + geom_line(aes(y=weeklyGPPsum)) + geom_line(aes(y=weeklyGPPdifference, color = "red"))+ geom_point(aes(y=weeklyGPPsum)) + geom_point(aes(y=weeklyGPPdifference, color = "red")) + facet_wrap(~year, scales = "free_x")
weekly %>% filter(year=="17") %>% ggplot(., aes(x=doy, group=1)) + geom_line(aes(y=weeklyGPPsum)) + geom_line(aes(y=weeklyGPPdifference, color = "red"))+ geom_point(aes(y=weeklyGPPsum)) + geom_point(aes(y=weeklyGPPdifference, color = "red")) + facet_wrap(~year, scales = "free_x")
weekly %>% filter(year=="18") %>% ggplot(., aes(x=doy, group=1)) + geom_line(aes(y=weeklyGPPsum)) + geom_line(aes(y=weeklyGPPdifference, color = "red"))+ geom_point(aes(y=weeklyGPPsum)) + geom_point(aes(y=weeklyGPPdifference, color = "red")) + facet_wrap(~year, scales = "free_x")
