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) borders <- sf::st_as_sf(maps::map("world", plot = FALSE, fill = TRUE)) setwd("C:/Users/CMCC-ESM2/") gall16 <- read.table("cmass.out", sep="", header=T) gall16$Year <- as.POSIXct(paste(gall16$Year), format = "%Y") gall16 <- as.data.frame(gall16) All_yr1 <- gall16 %>% group_by(year=floor_date(Year, "year")) Per_yr1 <- All_yr1 %>% mutate(year=year(Year)) %>% group_by(Year, year) Per_yr1 <- Per_yr1 %>% filter(year < 2100) CM <- Per_yr1 %>% filter(year <= "1990") # CM <- Per_yr1 %>% filter(year >= "2071") # CM <- Per_yr1 %>% filter(year >= "2031" & year <= "2060") # CM <- Per_yr1 %>% filter(year >= "1991" & year <= "2020") Mean_Ab <- CM %>% group_by(Lon, Lat) %>% summarise(Ab_mean = mean(Abi_nor)) Mean_Ced <- CM %>% group_by(Lon, Lat) %>% summarise(Ced_mean = mean(Ced_lib)) Mean_BES <- CM %>% group_by(Lon, Lat) %>% summarise(BES_mean = mean(BES)) Mean_BP <- CM %>% group_by(Lon, Lat) %>% summarise(BP_mean = mean(Bet_pen)) Mean_BPU <- CM %>% group_by(Lon, Lat) %>% summarise(BPU_mean = mean(Bet_pub)) Mean_Car <- CM %>% group_by(Lon, Lat) %>% summarise(Car_mean = mean(Car_bet)) Mean_Cor <- CM %>% group_by(Lon, Lat) %>% summarise(Cor_mean = mean(Cor_ave)) Mean_Fag <- CM %>% group_by(Lon, Lat) %>% summarise(Fag_mean = mean(Fag_syl)) Mean_Fra <- CM %>% group_by(Lon, Lat) %>% summarise(Fra_mean = mean(Fra_exc)) Mean_Jun <- CM %>% group_by(Lon, Lat) %>% summarise(Jun_mean = mean(Jun_oxy)) Mean_MRS <- CM %>% group_by(Lon, Lat) %>% summarise(MRS_mean = mean(MRS)) Mean_PA <- CM %>% group_by(Lon, Lat) %>% summarise(PA = mean(Pic_abi)) Mean_AG <- CM %>% group_by(Lon, Lat) %>% summarise(PA = mean(Aln_glu)) Mean_PS <- CM %>% group_by(Lon, Lat) %>% summarise(PS_mean = mean(Pin_syl)) Mean_PN <- CM %>% group_by(Lon, Lat) %>% summarise(PN_mean = mean(Pin_nig)) Mean_PB <- CM %>% group_by(Lon, Lat) %>% summarise(PB_mean = mean(Pin_bru)) Mean_PH <- CM %>% group_by(Lon, Lat) %>% summarise(PH_mean = mean(Pin_hal)) Mean_Pop <- CM %>% group_by(Lon, Lat) %>% summarise(Pop_mean = mean(Pop_tre)) Mean_QC <- CM %>% group_by(Lon, Lat) %>% summarise(QC_mean = mean(Que_coc)) Mean_QI <- CM %>% group_by(Lon, Lat) %>% summarise(QI_mean = mean(Que_ile)) Mean_QP <- CM %>% group_by(Lon, Lat) %>% summarise(QP_mean = mean(Que_pub)) Mean_QR <- CM %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_rob)) Mean_QM <- CM %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_mac)) Mean_Til <- CM %>% group_by(Lon, Lat) %>% summarise(Til_mean = mean(Til_cor)) Mean_Ulm <- CM %>% group_by(Lon, Lat) %>% summarise(Ulm_mean = mean(Ulm_gla)) Mean_C3 <- CM %>% group_by(Lon, Lat) %>% summarise(C3_mean = mean(C3_gr)) CM_Species <- cbind.data.frame(Mean_Ab[3], 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]) colnames(CM_Species) <- 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") setwd("C:/Users/NorESM/") gall16 <- read.table("cmass.out", sep="", header=T) gall16$Year <- as.POSIXct(paste(gall16$Year), format = "%Y") gall16 <- as.data.frame(gall16) All_yr2 <- gall16 %>% group_by(year=floor_date(Year, "year")) Per_yr2 <- All_yr2 %>% mutate(year=year(Year)) %>% group_by(Year, year) Per_yr2 <- Per_yr2 %>% filter(year < 2100) Nor <- Per_yr2 %>% filter(year <= "1990") # Nor <- Per_yr2 %>% filter(year >= "2071") # Nor <- Per_yr2 %>% filter(year >= "2031" & year <= "2060") # Nor <- Per_yr2 %>% filter(year >= "1991" & year <= "2020") Mean_Ab <- Nor %>% group_by(Lon, Lat) %>% summarise(Ab_mean = mean(Abi_nor)) Mean_Ced <- Nor %>% group_by(Lon, Lat) %>% summarise(Ced_mean = mean(Ced_lib)) Mean_BES <- Nor %>% group_by(Lon, Lat) %>% summarise(BES_mean = mean(BES)) Mean_BP <- Nor %>% group_by(Lon, Lat) %>% summarise(BP_mean = mean(Bet_pen)) Mean_BPU <- Nor %>% group_by(Lon, Lat) %>% summarise(BPU_mean = mean(Bet_pub)) Mean_Car <- Nor %>% group_by(Lon, Lat) %>% summarise(Car_mean = mean(Car_bet)) Mean_Cor <- Nor %>% group_by(Lon, Lat) %>% summarise(Cor_mean = mean(Cor_ave)) Mean_Fag <- Nor %>% group_by(Lon, Lat) %>% summarise(Fag_mean = mean(Fag_syl)) Mean_Fra <- Nor %>% group_by(Lon, Lat) %>% summarise(Fra_mean = mean(Fra_exc)) Mean_Jun <- Nor %>% group_by(Lon, Lat) %>% summarise(Jun_mean = mean(Jun_oxy)) Mean_MRS <- Nor %>% group_by(Lon, Lat) %>% summarise(MRS_mean = mean(MRS)) Mean_PA <- Nor %>% group_by(Lon, Lat) %>% summarise(PA = mean(Pic_abi)) Mean_AG <- Nor %>% group_by(Lon, Lat) %>% summarise(PA = mean(Aln_glu)) Mean_PS <- Nor %>% group_by(Lon, Lat) %>% summarise(PS_mean = mean(Pin_syl)) Mean_PN <- Nor %>% group_by(Lon, Lat) %>% summarise(PN_mean = mean(Pin_nig)) Mean_PB <- Nor %>% group_by(Lon, Lat) %>% summarise(PB_mean = mean(Pin_bru)) Mean_PH <- Nor %>% group_by(Lon, Lat) %>% summarise(PH_mean = mean(Pin_hal)) Mean_Pop <- Nor %>% group_by(Lon, Lat) %>% summarise(Pop_mean = mean(Pop_tre)) Mean_QC <- Nor %>% group_by(Lon, Lat) %>% summarise(QC_mean = mean(Que_coc)) Mean_QI <- Nor %>% group_by(Lon, Lat) %>% summarise(QI_mean = mean(Que_ile)) Mean_QP <- Nor %>% group_by(Lon, Lat) %>% summarise(QP_mean = mean(Que_pub)) Mean_QR <- Nor %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_rob)) Mean_QM <- Nor %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_mac)) Mean_Til <- Nor %>% group_by(Lon, Lat) %>% summarise(Til_mean = mean(Til_cor)) Mean_Ulm <- Nor %>% group_by(Lon, Lat) %>% summarise(Ulm_mean = mean(Ulm_gla)) Mean_C3 <- Nor %>% group_by(Lon, Lat) %>% summarise(C3_mean = mean(C3_gr)) Nor_Species <- cbind.data.frame(Mean_Ab[3], 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]) colnames(Nor_Species) <- 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") setwd("C:/Users/EC-Earth/") gall16 <- read.table("cmass.out", sep="", header=T) gall16$Year <- as.POSIXct(paste(gall16$Year), format = "%Y") gall16 <- as.data.frame(gall16) All_yr3 <- gall16 %>% group_by(year=floor_date(Year, "year")) Per_yr3 <- All_yr3 %>% mutate(year=year(Year)) %>% group_by(Year, year) EE<- Per_yr3 %>% filter(year <= "1990") # EE<- Per_yr3 %>% filter(year >= "2071") # EE <- Per_yr3 %>% filter(year >= "2031" & year <= "2060") # EE <- Per_yr3 %>% filter(year >= "1991" & year <= "2020") Mean_Ab <- EE %>% group_by(Lon, Lat) %>% summarise(Ab_mean = mean(Abi_nor)) Mean_Ced <- EE %>% group_by(Lon, Lat) %>% summarise(Ced_mean = mean(Ced_lib)) Mean_BES <- EE %>% group_by(Lon, Lat) %>% summarise(BES_mean = mean(BES)) Mean_BP <- EE %>% group_by(Lon, Lat) %>% summarise(BP_mean = mean(Bet_pen)) Mean_BPU <- EE %>% group_by(Lon, Lat) %>% summarise(BPU_mean = mean(Bet_pub)) Mean_Car <- EE %>% group_by(Lon, Lat) %>% summarise(Car_mean = mean(Car_bet)) Mean_Cor <- EE %>% group_by(Lon, Lat) %>% summarise(Cor_mean = mean(Cor_ave)) Mean_Fag <- EE %>% group_by(Lon, Lat) %>% summarise(Fag_mean = mean(Fag_syl)) Mean_Fra <- EE %>% group_by(Lon, Lat) %>% summarise(Fra_mean = mean(Fra_exc)) Mean_Jun <- EE %>% group_by(Lon, Lat) %>% summarise(Jun_mean = mean(Jun_oxy)) Mean_MRS <- EE %>% group_by(Lon, Lat) %>% summarise(MRS_mean = mean(MRS)) Mean_PA <- EE %>% group_by(Lon, Lat) %>% summarise(PA = mean(Pic_abi)) Mean_AG <- EE %>% group_by(Lon, Lat) %>% summarise(PA = mean(Aln_glu)) Mean_PS <- EE %>% group_by(Lon, Lat) %>% summarise(PS_mean = mean(Pin_syl)) Mean_PN <- EE %>% group_by(Lon, Lat) %>% summarise(PN_mean = mean(Pin_nig)) Mean_PB <- EE %>% group_by(Lon, Lat) %>% summarise(PB_mean = mean(Pin_bru)) Mean_PH <- EE %>% group_by(Lon, Lat) %>% summarise(PH_mean = mean(Pin_hal)) Mean_Pop <- EE %>% group_by(Lon, Lat) %>% summarise(Pop_mean = mean(Pop_tre)) Mean_QC <- EE %>% group_by(Lon, Lat) %>% summarise(QC_mean = mean(Que_coc)) Mean_QI <- EE %>% group_by(Lon, Lat) %>% summarise(QI_mean = mean(Que_ile)) Mean_QP <- EE %>% group_by(Lon, Lat) %>% summarise(QP_mean = mean(Que_pub)) Mean_QR <- EE %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_rob)) Mean_QM <- EE %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_mac)) Mean_Til <- EE %>% group_by(Lon, Lat) %>% summarise(Til_mean = mean(Til_cor)) Mean_Ulm <- EE %>% group_by(Lon, Lat) %>% summarise(Ulm_mean = mean(Ulm_gla)) Mean_C3 <- EE %>% group_by(Lon, Lat) %>% summarise(C3_mean = mean(C3_gr)) EE_Species <- cbind.data.frame(Mean_Ab[3], 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]) colnames(EE_Species) <- 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") setwd("C:/Users/MPI/") gallfin <- read.table("cmass.out", sep="", header=T) gallfin$Year <- as.POSIXct(paste(gallfin$Year), format = "%Y") gallfin <- as.data.frame(gallfin) All_yr4 <- gallfin %>% group_by(year=floor_date(Year, "year")) Per_yr4 <- All_yr4 %>% mutate(year=year(Year)) %>% group_by(Year, year) Per_yr4 <- Per_yr4 %>% filter(year < 2100) MP <- Per_yr4 %>% filter(year <= "1990") # MP <- Per_yr4 %>% filter(year >= "2071") # MP <- Per_yr4 %>% filter(year >= "2031" & year <= "2060") # MP <- Per_yr4 %>% filter(year >= "1991" & year <= "2020") Mean_Ab <- MP %>% group_by(Lon, Lat) %>% summarise(Ab_mean = mean(Abi_nor)) Mean_Ced <- MP %>% group_by(Lon, Lat) %>% summarise(Ced_mean = mean(Ced_lib)) Mean_BES <- MP %>% group_by(Lon, Lat) %>% summarise(BES_mean = mean(BES)) Mean_BP <- MP %>% group_by(Lon, Lat) %>% summarise(BP_mean = mean(Bet_pen)) Mean_BPU <- MP %>% group_by(Lon, Lat) %>% summarise(BPU_mean = mean(Bet_pub)) Mean_Car <- MP %>% group_by(Lon, Lat) %>% summarise(Car_mean = mean(Car_bet)) Mean_Cor <- MP %>% group_by(Lon, Lat) %>% summarise(Cor_mean = mean(Cor_ave)) Mean_Fag <- MP %>% group_by(Lon, Lat) %>% summarise(Fag_mean = mean(Fag_syl)) Mean_Fra <- MP %>% group_by(Lon, Lat) %>% summarise(Fra_mean = mean(Fra_exc)) Mean_Jun <- MP %>% group_by(Lon, Lat) %>% summarise(Jun_mean = mean(Jun_oxy)) Mean_MRS <- MP %>% group_by(Lon, Lat) %>% summarise(MRS_mean = mean(MRS)) Mean_PA <- MP %>% group_by(Lon, Lat) %>% summarise(PA = mean(Pic_abi)) Mean_AG <- MP %>% group_by(Lon, Lat) %>% summarise(PA = mean(Aln_glu)) Mean_PS <- MP %>% group_by(Lon, Lat) %>% summarise(PS_mean = mean(Pin_syl)) Mean_PN <- MP %>% group_by(Lon, Lat) %>% summarise(PN_mean = mean(Pin_nig)) Mean_PB <- MP %>% group_by(Lon, Lat) %>% summarise(PB_mean = mean(Pin_bru)) Mean_PH <- MP %>% group_by(Lon, Lat) %>% summarise(PH_mean = mean(Pin_hal)) Mean_Pop <- MP %>% group_by(Lon, Lat) %>% summarise(Pop_mean = mean(Pop_tre)) Mean_QC <- MP %>% group_by(Lon, Lat) %>% summarise(QC_mean = mean(Que_coc)) Mean_QI <- MP %>% group_by(Lon, Lat) %>% summarise(QI_mean = mean(Que_ile)) Mean_QP <- MP %>% group_by(Lon, Lat) %>% summarise(QP_mean = mean(Que_pub)) Mean_QR <- MP %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_rob)) Mean_QM <- MP %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_mac)) Mean_Til <- MP %>% group_by(Lon, Lat) %>% summarise(Til_mean = mean(Til_cor)) Mean_Ulm <- MP %>% group_by(Lon, Lat) %>% summarise(Ulm_mean = mean(Ulm_gla)) Mean_C3 <- MP %>% group_by(Lon, Lat) %>% summarise(C3_mean = mean(C3_gr)) MP_Species <- cbind.data.frame(Mean_Ab[3], 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]) colnames(MP_Species) <- 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") setwd("C:/Users/INM-CM5/") gall16 <- read.table("cmass.out", sep="", header=T) gall16$Year <- as.POSIXct(paste(gall16$Year), format = "%Y") gall16 <- as.data.frame(gall16) All_yr5 <- gall16 %>% group_by(year=floor_date(Year, "year")) Per_yr5 <- All_yr5 %>% mutate(year=year(Year)) %>% group_by(Year, year) Per_yr5 <- Per_yr5 %>% filter(year < 2100) IN <- Per_yr5 %>% filter(year <= "1990") # IN <- Per_yr5 %>% filter(year >= "2071") # IN <- Per_yr5 %>% filter(year >= "2031" & year <= "2060") # IN <- Per_yr5 %>% filter(year >= "1991" & year <= "2020") Mean_Ab <- IN %>% group_by(Lon, Lat) %>% summarise(Ab_mean = mean(Abi_nor)) Mean_Ced <- IN %>% group_by(Lon, Lat) %>% summarise(Ced_mean = mean(Ced_lib)) Mean_BES <- IN %>% group_by(Lon, Lat) %>% summarise(BES_mean = mean(BES)) Mean_BP <- IN %>% group_by(Lon, Lat) %>% summarise(BP_mean = mean(Bet_pen)) Mean_BPU <- IN %>% group_by(Lon, Lat) %>% summarise(BPU_mean = mean(Bet_pub)) Mean_Car <- IN %>% group_by(Lon, Lat) %>% summarise(Car_mean = mean(Car_bet)) Mean_Cor <- IN %>% group_by(Lon, Lat) %>% summarise(Cor_mean = mean(Cor_ave)) Mean_Fag <- IN %>% group_by(Lon, Lat) %>% summarise(Fag_mean = mean(Fag_syl)) Mean_Fra <- IN %>% group_by(Lon, Lat) %>% summarise(Fra_mean = mean(Fra_exc)) Mean_Jun <- IN %>% group_by(Lon, Lat) %>% summarise(Jun_mean = mean(Jun_oxy)) Mean_MRS <- IN %>% group_by(Lon, Lat) %>% summarise(MRS_mean = mean(MRS)) Mean_PA <- IN %>% group_by(Lon, Lat) %>% summarise(PA = mean(Pic_abi)) Mean_AG <- IN %>% group_by(Lon, Lat) %>% summarise(PA = mean(Aln_glu)) Mean_PS <- IN %>% group_by(Lon, Lat) %>% summarise(PS_mean = mean(Pin_syl)) Mean_PN <- IN %>% group_by(Lon, Lat) %>% summarise(PN_mean = mean(Pin_nig)) Mean_PB <- IN %>% group_by(Lon, Lat) %>% summarise(PB_mean = mean(Pin_bru)) Mean_PH <- IN %>% group_by(Lon, Lat) %>% summarise(PH_mean = mean(Pin_hal)) Mean_Pop <- IN %>% group_by(Lon, Lat) %>% summarise(Pop_mean = mean(Pop_tre)) Mean_QC <- IN %>% group_by(Lon, Lat) %>% summarise(QC_mean = mean(Que_coc)) Mean_QI <- IN %>% group_by(Lon, Lat) %>% summarise(QI_mean = mean(Que_ile)) Mean_QP <- IN %>% group_by(Lon, Lat) %>% summarise(QP_mean = mean(Que_pub)) Mean_QR <- IN %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_rob)) Mean_QM <- IN %>% group_by(Lon, Lat) %>% summarise(QR_mean = mean(Que_mac)) Mean_Til <- IN %>% group_by(Lon, Lat) %>% summarise(Til_mean = mean(Til_cor)) Mean_Ulm <- IN %>% group_by(Lon, Lat) %>% summarise(Ulm_mean = mean(Ulm_gla)) Mean_C3 <- IN %>% group_by(Lon, Lat) %>% summarise(C3_mean = mean(C3_gr)) IN_Species <- cbind.data.frame(Mean_Ab[3], 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]) colnames(IN_Species) <- 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") Abies <- cbind.data.frame(IN_Species$`Abies spp.`, Nor_Species$`Abies spp.`, EE_Species$`Abies spp.`, CM_Species$`Abies spp.`, MP_Species$`Abies spp.`) Abies$row_median = rowMedians(as.matrix(Abies)) Cedrus <- cbind.data.frame(IN_Species$`C. libani`, Nor_Species$`C. libani`, EE_Species$`C. libani`, CM_Species$`C. libani`, MP_Species$`C. libani`) Cedrus$row_median = rowMedians(as.matrix(Cedrus)) BES <- cbind.data.frame(IN_Species$`Boreal shrub`, Nor_Species$`Boreal shrub`, EE_Species$`Boreal shrub`, CM_Species$`Boreal shrub`, MP_Species$`Boreal shrub`) BES$row_median = rowMedians(as.matrix(BES)) Bet_pen <- cbind.data.frame(IN_Species$`B. pendula`, Nor_Species$`B. pendula`, EE_Species$`B. pendula`, CM_Species$`B. pendula`, MP_Species$`B. pendula`) Bet_pen$row_median = rowMedians(as.matrix(Bet_pen)) Bet_pub <- cbind.data.frame(IN_Species$`B. pubescens`, Nor_Species$`B. pubescens`, EE_Species$`B. pubescens`, CM_Species$`B. pubescens`, MP_Species$`B. pubescens`) Bet_pub$row_median = rowMedians(as.matrix(Bet_pub)) Carpinus <- cbind.data.frame(IN_Species$`Carpinus spp.`, Nor_Species$`Carpinus spp.`, EE_Species$`Carpinus spp.`, CM_Species$`Carpinus spp.`, MP_Species$`Carpinus spp.`) Carpinus$row_median = rowMedians(as.matrix(Carpinus)) Corylus <- cbind.data.frame(IN_Species$`C. avellana`, Nor_Species$`C. avellana`, EE_Species$`C. avellana`, CM_Species$`C. avellana`, MP_Species$`C. avellana`) Corylus$row_median = rowMedians(as.matrix(Corylus)) Fagus <- cbind.data.frame(IN_Species$`Fagus spp.`, Nor_Species$`Fagus spp.`, EE_Species$`Fagus spp.`, CM_Species$`Fagus spp.`, MP_Species$`Fagus spp.`) Fagus$row_median = rowMedians(as.matrix(Fagus)) Frax <- cbind.data.frame(IN_Species$`F. excelsior`, Nor_Species$`F. excelsior`, EE_Species$`F. excelsior`, CM_Species$`F. excelsior`, MP_Species$`F. excelsior`) Frax$row_median = rowMedians(as.matrix(Frax)) Juniper <- cbind.data.frame(IN_Species$`J. oxycedrus`, Nor_Species$`J. oxycedrus`, EE_Species$`J. oxycedrus`, CM_Species$`J. oxycedrus`, MP_Species$`J. oxycedrus`) Juniper$row_median = rowMedians(as.matrix(Juniper)) MRS <- cbind.data.frame(IN_Species$`Med. shrub`, Nor_Species$`Med. shrub`, EE_Species$`Med. shrub`, CM_Species$`Med. shrub`, MP_Species$`Med. shrub`) MRS$row_median = rowMedians(as.matrix(MRS)) Picea <- cbind.data.frame(IN_Species$`Picea spp.`, Nor_Species$`Picea spp.`, EE_Species$`Picea spp.`, CM_Species$`Picea spp.`, MP_Species$`Picea spp.`) Picea$row_median = rowMedians(as.matrix(Picea)) Alnus <- cbind.data.frame(IN_Species$`A. glutinosa`, Nor_Species$`A. glutinosa`, EE_Species$`A. glutinosa`, CM_Species$`A. glutinosa`, MP_Species$`A. glutinosa`) Alnus$row_median = rowMedians(as.matrix(Alnus)) PS <- cbind.data.frame(IN_Species$`P. sylvestris`, Nor_Species$`P. sylvestris`, EE_Species$`P. sylvestris`, CM_Species$`P. sylvestris`, MP_Species$`P. sylvestris`) PS$row_median = rowMedians(as.matrix(PS)) PN <- cbind.data.frame(IN_Species$`P. nigra`, Nor_Species$`P. nigra`, EE_Species$`P. nigra`, CM_Species$`P. nigra`, MP_Species$`P. nigra`) PN$row_median = rowMedians(as.matrix(PN)) PH <- cbind.data.frame(IN_Species$`P. halepensis`, Nor_Species$`P. halepensis`, EE_Species$`P. halepensis`, CM_Species$`P. halepensis`, MP_Species$`P. halepensis`) PH$row_median = rowMedians(as.matrix(PH)) PB <- cbind.data.frame(IN_Species$`P. brutia`, Nor_Species$`P. brutia`, EE_Species$`P. brutia`, CM_Species$`P. brutia`, MP_Species$`P. brutia`) PB$row_median = rowMedians(as.matrix(PB)) Populus <- cbind.data.frame(IN_Species$`P. tremula`, Nor_Species$`P. tremula`, EE_Species$`P. tremula`, CM_Species$`P. tremula`, MP_Species$`P. tremula`) Populus$row_median = rowMedians(as.matrix(Populus)) QC <- cbind.data.frame(IN_Species$`Q. coccifera`, Nor_Species$`Q. coccifera`, EE_Species$`Q. coccifera`, CM_Species$`Q. coccifera`, MP_Species$`Q. coccifera`) QC$row_median = rowMedians(as.matrix(QC)) QI <- cbind.data.frame(IN_Species$`Q. ilex`, Nor_Species$`Q. ilex`, EE_Species$`Q. ilex`, CM_Species$`Q. ilex`, MP_Species$`Q. ilex`) QI$row_median = rowMedians(as.matrix(QI)) QP <- cbind.data.frame(IN_Species$`Q. pubescens`, Nor_Species$`Q. pubescens`, EE_Species$`Q. pubescens`, CM_Species$`Q. pubescens`, MP_Species$`Q. pubescens`) QP$row_median = rowMedians(as.matrix(QP)) QR <- cbind.data.frame(IN_Species$`Q. robur`, Nor_Species$`Q. robur`, EE_Species$`Q. robur`, CM_Species$`Q. robur`, MP_Species$`Q. robur`) QR$row_median = rowMedians(as.matrix(QR)) QM <- cbind.data.frame(IN_Species$`Q. macranthera`, Nor_Species$`Q. macranthera`, EE_Species$`Q. macranthera`, CM_Species$`Q. macranthera`, MP_Species$`Q. macranthera`) QM$row_median = rowMedians(as.matrix(QM)) Tilia <- cbind.data.frame(IN_Species$`T. cordata`, Nor_Species$`T. cordata`, EE_Species$`T. cordata`, CM_Species$`T. cordata`, MP_Species$`T. cordata`) Tilia$row_median = rowMedians(as.matrix(Tilia)) Ulmus <- cbind.data.frame(IN_Species$`U. glabra`, Nor_Species$`U. glabra`, EE_Species$`U. glabra`, CM_Species$`U. glabra`, MP_Species$`U. glabra`) Ulmus$row_median = rowMedians(as.matrix(Ulmus)) C3 <- cbind.data.frame(IN_Species$`Grass`, Nor_Species$`Grass`, EE_Species$`Grass`, CM_Species$`Grass`, MP_Species$`Grass`) C3$row_median = rowMedians(as.matrix(C3)) Ensemble <- cbind.data.frame(Mean_Ulm[1:2], Abies[6], Cedrus[6], BES[6], Bet_pen[6], Bet_pub[6], Carpinus[6], Corylus[6], Fagus[6], Frax[6], Juniper[6], MRS[6], Picea[6], Alnus[6], PS[6], PN[6], PB[6], PH[6], Populus[6], QC[6], QI[6], QP[6], QR[6], QM[6], Tilia[6], Ulmus[6], C3[6]) ################################################################################ BM_sp <- Ensemble[c(3:28)] #taking only the CMASS fields colnames(BM_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") BM_spm <- reshape2::melt(BM_sp) #reshaping the dataframe theme_set(theme_minimal()) BM_box <- ggplot(BM_spm, aes(x = variable, y = value, color = variable)) + geom_boxplot(fatten = 7.5)+ # fatten = 4 lwd=1 # labs(title =paste("1961-1990 mean biomass per species from ensemble median"))+ # labs(title =paste("1991-2020 mean biomass per species from ensemble median"))+ labs(title =paste("2031-2060 mean biomass per species from ensemble median"))+ # labs(title =paste("2071-2100 mean biomass per species from ensemble median"))+ 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(0,13.5))+ theme(legend.position="none", plot.title = element_text(size=25, face="bold"), axis.text.x=element_text(size=30, angle = -60, hjust = 0, vjust = 0), axis.text.y = element_text(size=30)) print(BM_box) setwd("C:/Users/ITU/Documents/DATA/GCMs/LPJ_GUESS runs/ENSEMBLE_local_AB/New_Dominance/") png(file=paste("Biomass_1961-1990.png"), width=1980, height=932) # png(file=paste("Biomass_1991-2020.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()