paracou <- read_tsv("data/inventories/guyafor.tsv") %>%
filter(Forest == "Paracou") %>%
mutate(plot = as.numeric(Plot)) %>%
filter(!is.na(plot)) %>%
filter(Plot %in% c(1, 6, 11, 13:14)) %>%
filter(!is.na(Xutm), !is.na(Yutm)) %>%
filter(CensusYear %in% 2003:2013) %>%
mutate(dbh = CircCorr/pi, year = CensusYear, tree = idTree) %>%
filter(dbh >= 10) %>%
filter(CodeAlive == 1) %>%
select(plot, idTree, year, dbh)
par_adr <- filter(paracou, year == 2003) %>%
rename(dbh_initial = dbh) %>%
select(-year) %>%
full_join(filter(paracou, year == 2013) %>%
rename(dbh_final = dbh) %>%
select(-year)) %>%
mutate(dbh_final = ifelse(is.na(dbh_final), 0, dbh_final)) %>%
mutate(dbh_class = cut(dbh_initial,
breaks = c(seq(10, 155, by = 5)),
labels = c(seq(10, 150, by = 5)))) %>%
mutate(dbh_class = as.numeric(as.character(dbh_class))) %>%
group_by(plot, dbh_class) %>%
mutate(n_tot = n()) %>%
filter(dbh_final <= dbh_initial) %>%
filter(dbh_initial <= 101) %>%
group_by(plot, dbh_class, n_tot) %>%
summarise(n = n()) %>%
mutate(adr = n/n_tot/10*100) %>%
mutate(type = "obs", site = "Paracou")
sim_adr_p <- lapply(1:10, function(i)
read_tsv(paste0("results/eval/Paracou_R", i,
"/Paracou_R", i, "_0_initial_pattern.txt")) %>%
mutate(dbh_initial = dbh*100) %>%
select(s_name, col, row, dbh_initial) %>%
filter(dbh_initial > 10) %>%
left_join(read_tsv(paste0("results/eval/Paracou_R", i,
"/Paracou_R", i, "_0_final_pattern.txt")) %>%
mutate(dbh_final = dbh*100) %>%
select(s_name, col, row, dbh_final) %>%
filter(dbh_final > 10)) %>%
mutate(dbh_final = ifelse(is.na(dbh_final), 0, dbh_final)) %>%
mutate(dbh_class = cut(dbh_initial,
breaks = c(seq(10, 155, by = 5)),
labels = c(seq(10, 150, by = 5)))) %>%
mutate(dbh_class = as.numeric(as.character(dbh_class))) %>%
group_by(dbh_class) %>%
mutate(n_tot = n()) %>%
filter(dbh_final <= dbh_initial) %>%
filter(dbh_initial <= 101) %>%
group_by(dbh_class, n_tot) %>%
summarise(n = n()) %>%
mutate(adr = n/n_tot/10*100) %>%
mutate(type = "sim", site = "Paracou") %>%
mutate(plot = i)) %>%
bind_rows()
sim_adr_t <- lapply(1:10, function(i)
read_tsv(paste0("results/eval/Tapajos_R", i,
"/Tapajos_R", i, "_0_initial_pattern.txt")) %>%
mutate(dbh_initial = dbh*100) %>%
select(s_name, col, row, dbh_initial) %>%
filter(dbh_initial > 10) %>%
left_join(read_tsv(paste0("results/eval/Tapajos_R", i,
"/Tapajos_R", i, "_0_final_pattern.txt")) %>%
mutate(dbh_final = dbh*100) %>%
select(s_name, col, row, dbh_final) %>%
filter(dbh_final > 10)) %>%
mutate(dbh_final = ifelse(is.na(dbh_final), 0, dbh_final)) %>%
mutate(dbh_class = cut(dbh_initial,
breaks = c(seq(10, 155, by = 5)),
labels = c(seq(10, 150, by = 5)))) %>%
mutate(dbh_class = as.numeric(as.character(dbh_class))) %>%
group_by(dbh_class) %>%
mutate(n_tot = n()) %>%
filter(dbh_final <= dbh_initial) %>%
filter(dbh_initial <= 101) %>%
group_by(dbh_class, n_tot) %>%
summarise(n = n()) %>%
mutate(adr = n/n_tot/10*100) %>%
mutate(type = "sim", site = "Tapajos") %>%
mutate(plot = i)) %>%
bind_rows()
g_adr <- bind_rows(par_adr, sim_adr_p, sim_adr_t) %>%
group_by(site, type, dbh_class) %>%
summarise(adr_ll = quantile(adr, .05),
adr_m = median(adr),
adr_hh = quantile(adr, .95)) %>%
ggplot(aes(dbh_class, adr_m, col = type)) +
geom_ribbon(aes(ymin = adr_ll, ymax = adr_hh, fill = type), col = NA, alpha = 0.2) +
geom_line() +
theme_bw() +
scale_y_log10() +
xlab("DBH [cm]") +
ylab(expression("Death Rate [%"~yr^{-~1}~"]")) +
scale_color_manual("", values = as.vector(cols[c("obs", "sim")]),
labels = c("Inventory", "TROLL")) +
scale_fill_manual("", values = as.vector(cols[c("obs", "sim")]),
labels = c("Inventory", "TROLL")) +
facet_wrap(~ site) +
xlim(10, 100)
g_adr
