library(tidyverse) library(lubridate) library(patchwork) library(RColorBrewer) #color schemes library(rio) library(rgdal) library(fields) library(maptools) library(sf) library(sp) library(raster) library(maps) library(metR) library(ggmap) library(data.table) library(geofacet) library(matrixStats) ############################################################################## # # Biomass calculated for individual GCM runs # ############################################################################## setwd("C:/Users/") #change direcory per GCM gall15 <- read.table("cmass.out", sep="", header=T) gall15$Year <- as.POSIXct(paste(gall15$Year), format = "%Y") gall15 <- as.data.frame(gall15) All_yr <- gall15 %>% group_by(year=floor_date(Year, "year")) Per_yr <- All_yr %>% mutate(year=year(Year)) %>% group_by(Year, year) dir.create("Biomass") setwd("C:/Users/Biomass/") ########################## TIMESLICES ########################################## # just un-commentout the respective period: BM1961_1990 <- Per_yrBM %>% filter(year <= "1990") BM2 <- BM1961_1990 # BM1991_2020 <- Per_yr %>% filter(year >= "1991" & year <= "2020") # BM2 <- BM1991_2020 # BM2031_2060 <- Per_yr %>% filter(year >= "2031" & year <= "2060") # BM2 <- BM2031_2060 # BM2071_2100 <- Per_yr %>% filter(year >= "2070") # BM2 <- BM2071_2100 Mean_Ab <- BM2 %>% group_by(Lon, Lat) %>% summarise(Ab_mean = mean(Abi_nor)) Mean_Ced <- BM2 %>% group_by(Lon, Lat) %>% summarise(Ced_mean = mean(Ced_lib)) Mean_BES <- BM2 %>% group_by(Lon, Lat) %>% summarise(BES_mean = mean(BES)) Mean_BP <- BM2 %>% group_by(Lon, Lat) %>% summarise(BP_mean = mean(Bet_pen)) Mean_BPU <- BM2 %>% group_by(Lon, Lat) %>% summarise(BPU_mean = mean(Bet_pub)) Mean_Car <- BM2 %>% group_by(Lon, Lat) %>% summarise(Car_mean = mean(Car_bet)) Mean_Cor <- BM2 %>% group_by(Lon, Lat) %>% summarise(Cor_mean = mean(Cor_ave)) Mean_Fag <- BM2 %>% group_by(Lon, Lat) %>% summarise(Fag_mean = mean(Fag_syl)) Mean_Fra <- BM2 %>% group_by(Lon, Lat) %>% summarise(Fra_mean = mean(Fra_exc)) Mean_Jun <- BM2 %>% group_by(Lon, Lat) %>% summarise(Jun_mean = mean(Jun_oxy)) Mean_MRS <- BM2 %>% group_by(Lon, Lat) %>% summarise(MRS_mean = mean(MRS)) Mean_PA <- BM2 %>% group_by(Lon, Lat) %>% summarise(PA = mean(Pic_abi)) Mean_AG <- BM2 %>% group_by(Lon, Lat) %>% summarise(PA = mean(Aln_glu)) Mean_PS <- BM2 %>% group_by(Lon, Lat) %>% summarise(PS_mean = mean(Pin_syl)) Mean_PN <- BM2 %>% group_by(Lon, Lat) %>% summarise(PN_mean = mean(Pin_nig)) Mean_PB <- BM2 %>% group_by(Lon, Lat) %>% summarise(PB_mean = mean(Pin_bru)) Mean_PH <- BM2 %>% group_by(Lon, Lat) %>% summarise(PH_mean = mean(Pin_hal)) Mean_Pop <- BM2 %>% group_by(Lon, Lat) %>% summarise(Pop_mean = mean(Pop_tre)) Mean_QC <- BM2 %>% group_by(Lon, Lat) %>% summarise(QC_mean = mean(Que_coc)) Mean_QI <- BM2 %>% group_by(Lon, Lat) %>% summarise(QI_mean = mean(Que_ile)) Mean_QP <- BM2 %>% group_by(Lon, Lat) %>% summarise(QP_mean = mean(Que_pub)) Mean_QR <- BM2 %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_rob)) Mean_QM <- BM2 %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_mac)) Mean_Til <- BM2 %>% group_by(Lon, Lat) %>% summarise(Til_mean = mean(Til_cor)) Mean_Ulm <- BM2 %>% group_by(Lon, Lat) %>% summarise(Ulm_mean = mean(Ulm_gla)) Mean_C3 <- BM2 %>% group_by(Lon, Lat) %>% summarise(C3_mean = mean(C3_gr)) Mean_Tot <- BM2 %>% group_by(Lon, Lat) %>% summarise(Tot_mean = mean(Total)) BM2mean <- cbind.data.frame(Mean_Ab, Mean_Ced[3], Mean_BES[3], Mean_BP[3], Mean_BPU[3], Mean_Car[3], Mean_Cor[3], Mean_Fag[3], Mean_Fra[3], Mean_Jun[3], Mean_MRS[3], Mean_PA[3], Mean_AG[3], Mean_PS[3], Mean_PN[3], Mean_PB[3], Mean_PH[3], Mean_Pop[3], Mean_QC[3], Mean_QI[3], Mean_QP[3], Mean_QR[3], Mean_QM[3], Mean_Til[3], Mean_Ulm[3], Mean_C3[3], Mean_Tot[3]) BM2_sp <- BM2mean[c(3:28)] #taking only the CMASS fields colnames(BM2_sp) <- c("Abies spp.", "C. libani", "Boreal shrub", "B. pendula", "B. pubescens", "Carpinus spp.", "C. avellana", "Fagus spp.","F. excelsior", "J. oxycedrus", "Med. shrub", "Picea spp.", "A. glutinosa", "P. sylvestris", "P. nigra", "P. brutia", "P. halepensis", "P. tremula", "Q. coccifera", "Q. ilex", "Q. pubescens", "Q. robur", "Q. macranthera", "T. cordata", "U. glabra", "Grass") BM2_spm <- reshape2::melt(BM2_sp) #reshaping the dataframe theme_set(theme_minimal()) BM_box <- ggplot(BM2_spm, aes(x = variable, y = value, color = variable)) + geom_boxplot()+ labs(title =paste("1961-1990 mean biomass per species"))+ # labs(title =paste("1991-2020 mean biomass per species"))+ # labs(title =paste("2031-2060 mean biomass per species"))+ # labs(title =paste("2071-2100 mean biomass per species"))+ scale_color_manual(values = c("Abies spp."= "#799c11","C. libani" = "#b7c50e","Boreal shrub" = "blue", "B. pendula"="red","B. pubescens"="steel blue", "Carpinus spp."="#90eb10", "C. avellana" = "#0cb8e8", "Fagus spp."="#134d19", "F. excelsior" = "#b595e6", "J. oxycedrus" = "#207852", "Med. shrub" = "#122da6", "Picea spp."="#e02b73", "A. glutinosa" = "#F87F65", "P. sylvestris"="#8d49cc", "P. nigra" = "#1c83ba", "P. brutia" = "grey", "P. halepensis"="#f772b0", "P. tremula"= "#b7eb34", "Q. coccifera"= "#e8550c", "Q. ilex"="#57c27e", "Q. pubescens" = "#e8940c", "Q. robur"="#DB6C05", "Q. macranthera" = "#fcba03", "T. cordata"= "#ad1320", "U. glabra" = "#611975", "Grass" = "#89dc69"))+ ylab("")+ xlab("")+ scale_y_continuous(limits=c(0,13))+ theme(legend.position="none", plot.title = element_text(size=25, face="bold"), axis.text.x=element_text(size=24, angle = -60, hjust = 0, vjust = 0), axis.text.y = element_text(size=24)) # print(BM_box) # png(file=paste("Biomass_1991-2020.png"), width=1980, height=932) png(file=paste("Biomass_1961-1990.png"), width=1980, height=932) # png(file=paste("Biomass_2031-2060.png"), width=1980, height=932) # png(file=paste("Biomass_2071_2100.png"), width=1980, height=932) print(BM_box) dev.off()