################## ### REANALISYS ### ### 01 May 2021 ## ################## rm(list = ls()) sample(1:.Machine$integer.max, 1) # This command had generated the value 1487175758 to be used as set.seed(1487175758) to get reproducible results ################ ### PACKAGES ### ################ library(vegan) library(plyr) library(iNEXT) library(ggplot2) library(gridExtra) library(reshape2) library(eulerr) ############# ### DADOS ### ############# # importa a matriz de dados baseada nos dados brutos matrix = read.csv("mammalData.csv") # importa as coordenadas das trilhas coordinates = read.csv("siteCoordinates.csv", row.names = 1) # exclui a especie Didelphis imperfecta matrix$Didelphis_imperfecta = NULL # renomeia o municipio Porto_Grande para Tartarugalzinho, pois ambos constituem um par de sitios (i.e. uma das quatro area de estudo) matrix$municipio[matrix$municipio == "Porto_Grande"] = "Tartarugalzinho" matrix = droplevels(matrix) # combina (soma) os dados de segmentos em trilhas trilhas = aggregate(matrix[7:ncol(matrix)], by = list(municipio = matrix$municipio, ambiente = matrix$ambiente, trilha = matrix$trilha2, locationID = matrix$trilha3), FUN = sum) # seleciona somente trilhas (locationID) com coordenadas geograficas trilhas$analisar = match(trilhas$locationID, rownames(coordinates), nomatch = 0) trilhas = subset(trilhas, analisar != 0) # nomeia as linhas rownames(trilhas) = trilhas$locationID # exclui as colunas "locationID" e "analisar" trilhas$locationID = NULL trilhas$analisar = NULL # separa os dados por area de estudo alm = subset(trilhas, municipio == "Almeirim") alm = droplevels(alm) moj = subset(trilhas, municipio == "Moju") moj = droplevels(moj) par = subset(trilhas, municipio == "Paragominas") par = droplevels(par) tar = subset(trilhas, municipio == "Tartarugalzinho") tar = droplevels(tar) ############### ### RIQUEZA ### ############### # PREPARACAO DOS DADOS # combina (soma) os dados de trilhas em ambientes # Almeirim alm.ambientes = aggregate(alm[5:ncol(alm)], by = list(ambiente = alm$ambiente), FUN = sum) rownames(alm.ambientes) = alm.ambientes$ambiente alm.ambientes = alm.ambientes[-1] # Moju moj.ambientes = aggregate(moj[5:ncol(moj)], by = list(ambiente = moj$ambiente), FUN = sum) rownames(moj.ambientes) = moj.ambientes$ambiente moj.ambientes = moj.ambientes[-1] # Paragominas par.ambientes = aggregate(par[5:ncol(par)], by = list(ambiente = par$ambiente), FUN = sum) rownames(par.ambientes) = par.ambientes$ambiente par.ambientes = par.ambientes[-1] # Tartarugalzinho + Porto_Grande tar.ambientes = aggregate(tar[5:ncol(tar)], by = list(ambiente = tar$ambiente), FUN = sum) rownames(tar.ambientes) = tar.ambientes$ambiente tar.ambientes = tar.ambientes[-1] # Adequa os dados no formato a ser utilizado na funcao iNEXT # Almeirim alm.raref = alm.ambientes[colSums(alm.ambientes) > 0] alm.raref = as.data.frame(t(alm.raref)) alm.raref$relict = ifelse(alm.raref$PF != 0, alm.raref$SF, 0) # Moju moj.raref = moj.ambientes[colSums(moj.ambientes) > 0] moj.raref = as.data.frame(t(moj.raref)) moj.raref$relict = ifelse(moj.raref$PF != 0, moj.raref$OP, 0) # Paragominas par.raref = par.ambientes[colSums(par.ambientes) > 0] par.raref = as.data.frame(t(par.raref)) par.raref$relict = ifelse(par.raref$PF != 0, par.raref$LF, 0) # Tartarugalzinho + Porto_Grande tar.raref = tar.ambientes[colSums(tar.ambientes) > 0] tar.raref = as.data.frame(t(tar.raref)) tar.raref$relict = ifelse(tar.raref$PF != 0, tar.raref$EP, 0) #################################### ### Numero rarefeito de especies ### #################################### # Almeirim set.seed(1487175758) alm.inext = iNEXT(alm.raref, q = 0, datatype = "abundance", size = NULL, endpoint = max(colSums(alm.raref)), knots = max(colSums(alm.raref)), se = TRUE, conf = 0.95, nboot = 1000) alm.richness = rbind(alm.inext$iNextEst$PF, alm.inext$iNextEst$SF, alm.inext$iNextEst$relict) alm.richness$habitat = c(rep("a.Primary forest", nrow(alm.inext$iNextEst$PF)), rep("b.Secondary forest (total species)", nrow(alm.inext$iNextEst$SF)), rep("c.Secondary forest (primary forest species)", nrow(alm.inext$iNextEst$relict))) alm.richness = alm.richness[, c(1, 2, 4, 5, 6, 7, 10)] # Moju set.seed(1487175758) moj.inext = iNEXT(moj.raref, q = 0, datatype = "abundance", size = NULL, endpoint = max(colSums(moj.raref)), knots = max(colSums(moj.raref)), se = TRUE, conf = 0.95, nboot = 1000) moj.richness = rbind(moj.inext$iNextEst$PF, moj.inext$iNextEst$OP, moj.inext$iNextEst$relict) moj.richness$habitat = c(rep("a.Primary forest", nrow(moj.inext$iNextEst$PF)), rep("b.Oil palm plantation (total species)", nrow(moj.inext$iNextEst$OP)), rep("c.Oil palm plantation (primary forest species)", nrow(moj.inext$iNextEst$relict))) moj.richness = moj.richness[, c(1, 2, 4, 5, 6, 7, 10)] # Paragominas set.seed(1487175758) par.inext = iNEXT(par.raref, q = 0, datatype = "abundance", size = NULL, endpoint = max(colSums(par.raref)), knots = max(colSums(par.raref)), se = TRUE, conf = 0.95, nboot = 1000) par.richness = rbind(par.inext$iNextEst$PF, par.inext$iNextEst$LF, par.inext$iNextEst$relict) par.richness$habitat = c(rep("a.Primary forest", nrow(par.inext$iNextEst$PF)), rep("b.Logged forest (total species)", nrow(par.inext$iNextEst$LF)), rep("c.Logged forest (primary forest species)", nrow(par.inext$iNextEst$relict))) par.richness = par.richness[, c(1, 2, 4, 5, 6, 7, 10)] # Tartarugalzinho + Porto_Grande set.seed(1487175758) tar.inext = iNEXT(tar.raref, q = 0, datatype = "abundance", size = NULL, endpoint = max(colSums(tar.raref)), knots = max(colSums(tar.raref)), se = TRUE, conf = 0.95, nboot = 1000) tar.richness = rbind(tar.inext$iNextEst$PF, tar.inext$iNextEst$EP, tar.inext$iNextEst$relict) tar.richness$habitat = c(rep("a.Primary forest", nrow(tar.inext$iNextEst$PF)), rep("b.Eucalypt plantation (total species)", nrow(tar.inext$iNextEst$EP)), rep("c.Eucalypt plantation (primary forest species)", nrow(tar.inext$iNextEst$relict))) tar.richness = tar.richness[, c(1, 2, 4, 5, 6, 7, 10)] ################################### ### Numero estimado de especies ### ################################### # Almeirim set.seed(1487175758) almD = estimateD(alm.raref, datatype = "abundance", base = "coverage", level = NULL, conf = 0.95) almD = subset(almD, order == 0) # Moju set.seed(1487175758) mojD = estimateD(moj.raref, datatype = "abundance", base = "coverage", level = NULL, conf = 0.95) mojD = subset(mojD, order == 0)[c(2, 1, 3), ] # Paragominas set.seed(1487175758) parD = estimateD(par.raref, datatype = "abundance", base = "coverage", level = NULL, conf = 0.95) parD = subset(parD, order == 0)[c(2, 1, 3), ] # Tartarugalzinho + Porto_Grande set.seed(1487175758) tarD = estimateD(tar.raref, datatype = "abundance", base = "coverage", level = NULL, conf = 0.95) tarD = subset(tarD, order == 0)[c(2, 1, 3), ] # Use sample coverage of 0.905 rather than 0.886 # 0.905 is closer to 0.910 than 0.886 tarD[2, ] = c("EP", 20, "interpolated", 0, 0.905, 7.905, 6.079, 9.731) tarD$m = as.numeric(tarD$m) tarD$qD = as.numeric(tarD$qD) tarD$qD.LCL = as.numeric(tarD$qD.LCL) tarD$qD.UCL = as.numeric(tarD$qD.UCL) ################################################################# ### Species richness difference in relation to Primary forest ### ################################################################# almDiff = -(1 - (almD[, 6:8] / almD$qD[almD$site == "PF"])) * 100 mojDiff = -(1 - (mojD[, 6:8] / mojD$qD[mojD$site == "PF"])) * 100 parDiff = -(1 - (parD[, 6:8] / parD$qD[parD$site == "PF"])) * 100 tarDiff = -(1 - (tarD[, 6:8] / tarD$qD[tarD$site == "PF"])) * 100 richnessDiff = rbind(almDiff, mojDiff, parDiff, tarDiff) richnessDiff$site = rep(c("a.alm", "b.moj", "c.par", "d.tar"), each = 3) richnessDiff$habitat = c(unique(alm.richness$habitat), unique(moj.richness$habitat), unique(par.richness$habitat), unique(tar.richness$habitat)) richnessDiff$pairs = c(4, 4.5, 4.5, # Secondaty forest 10, 10.5, 10.5, # Oil palm plantation 1, 1.5, 1.5, # Logged forest 7, 7.5, 7.5) # Eucalypt plantation richnessDiff$m = c(almD$m, mojD$m, parD$m, tarD$m) ##################################### ### Graficos: curvas de rarefacao ### ##################################### ##################################################### # Almerim: Dados (adiciona pontos e barras de erro) # ##################################################### alm.richness = rbind(alm.richness, c(almD$m[1], "added", almD$qD[1], almD$qD.LCL[1], almD$qD.UCL[1], almD$SC[1], "a.Primary forest"), c(almD$m[2], "added", almD$qD[2], almD$qD.LCL[2], almD$qD.UCL[2], almD$SC[2], "b.Secondary forest (total species)"), c(almD$m[3], "added", almD$qD[3], almD$qD.LCL[3], almD$qD.UCL[3], almD$SC[3], "c.Secondary forest (primary forest species)")) alm.richness$m = as.numeric(alm.richness$m) alm.richness$qD = as.numeric(alm.richness$qD) alm.richness$qD.LCL = as.numeric(alm.richness$qD.LCL) alm.richness$qD.UCL = as.numeric(alm.richness$qD.UCL) alm.richness$SC = as.numeric(alm.richness$SC) rbind(head(alm.richness), tail(alm.richness)) #################### # Almerim: Grafico # #################### alm.raref.graph = ggplot(data = subset(alm.richness, method != "extrapolated" & method != "added"), aes(x = m, y = qD, shape = habitat, fill = habitat, linetype = habitat)) + labs(x = "Number of records", y = "", shape = NULL, fill = NULL, linetype = NULL) + scale_x_continuous(limits = c(1, NA), breaks = c(1, 50, 100, 150)) + scale_y_continuous(limits = c(1, NA), breaks = c(1, 5, 10, 15)) + scale_shape_manual(values = c(21, 21, 24), labels = c("Primary forest", "Secondary forest (total species)", "Secondary forest (primary forest species)")) + scale_fill_manual(values = c("grey", "#34a853", "#34a853"), labels = c("Primary forest", "Secondary forest (total species)", "Secondary forest (primary forest species)")) + scale_linetype_manual(values = c("solid", "dashed", "dotted")) + geom_line(colour = "black", size = 1) + geom_errorbar(data = subset(alm.richness, method == "added"), aes(x = m, ymin = qD.LCL, ymax = qD.UCL), width = 4.166666667, linetype = "solid") + geom_point(data = subset(alm.richness, method == "added"), size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.95, 0.05), legend.justification = c(0.95, 0.05)) + guides(linetype = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(b)") #ggsave(alm.raref.graph, # filename = "./new_figures/Fig3b.png", dpi = 600, # width = 20, height = 20, units = "cm") ################################################## # Moju: Dados (adiciona pontos e barras de erro) # ################################################## moj.richness = rbind(moj.richness, c(mojD$m[1], "added", mojD$qD[1], mojD$qD.LCL[1], mojD$qD.UCL[1], mojD$SC[1], "a.Primary forest"), c(mojD$m[2], "added", mojD$qD[2], mojD$qD.LCL[2], mojD$qD.UCL[2], mojD$SC[2], "b.Oil palm plantation (total species)"), c(mojD$m[3], "added", mojD$qD[3], mojD$qD.LCL[3], mojD$qD.UCL[3], mojD$SC[3], "c.Oil palm plantation (primary forest species)")) moj.richness$m = as.numeric(moj.richness$m) moj.richness$qD = as.numeric(moj.richness$qD) moj.richness$qD.LCL = as.numeric(moj.richness$qD.LCL) moj.richness$qD.UCL = as.numeric(moj.richness$qD.UCL) moj.richness$SC = as.numeric(moj.richness$SC) rbind(head(moj.richness), tail(moj.richness)) ################# # Moju: Grafico # ################# moj.raref.graph = ggplot(data = subset(moj.richness, method != "extrapolated" & method != "added"), aes(x = m, y = qD, shape = habitat, fill = habitat, linetype = habitat)) + labs(x = "Number of records", y = "", shape = NULL, fill = NULL, linetype = NULL) + scale_x_continuous(limits = c(1, NA), breaks = c(1, 250, 500, 750, 1000)) + scale_y_continuous(limits = c(1, NA), breaks = c(1, 10, 20, 30)) + scale_shape_manual(values = c(21, 21, 24), labels = c("Primary forest", "Oil palm plantation (total species)", "Oil palm plantation (primary forest species)")) + scale_fill_manual(values = c("grey", "#ea4335", "#ea4335"), labels = c("Primary forest", "Oil palm plantation (total species)", "Oil palm plantation (primary forest species)")) + scale_linetype_manual(values = c("solid", "dashed", "dotted")) + geom_line(colour = "black", size = 1) + geom_errorbar(data = subset(moj.richness, method == "added"), aes(x = m, ymin = qD.LCL, ymax = qD.UCL), width = 28.97222222, linetype = "solid") + geom_point(data = subset(moj.richness, method == "added"), size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.95, 0.05), legend.justification = c(0.95, 0.05)) + guides(linetype = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(d)") #ggsave(moj.raref.graph, # filename = "./new_figures/Fig3d.png", dpi = 600, # width = 20, height = 20, units = "cm") ######################################################### # Paragominas: Dados (adiciona pontos e barras de erro) # ######################################################### par.richness = rbind(par.richness, c(parD$m[1], "added", parD$qD[1], parD$qD.LCL[1], parD$qD.UCL[1], parD$SC[1], "a.Primary forest"), c(parD$m[2], "added", parD$qD[2], parD$qD.LCL[2], parD$qD.UCL[2], parD$SC[2], "b.Logged forest (total species)"), c(parD$m[3], "added", parD$qD[3], parD$qD.LCL[3], parD$qD.UCL[3], parD$SC[3], "c.Logged forest (primary forest species)")) par.richness$m = as.numeric(par.richness$m) par.richness$qD = as.numeric(par.richness$qD) par.richness$qD.LCL = as.numeric(par.richness$qD.LCL) par.richness$qD.UCL = as.numeric(par.richness$qD.UCL) par.richness$SC = as.numeric(par.richness$SC) rbind(head(par.richness), tail(par.richness)) ######################## # Paragominas: Grafico # ######################## par.raref.graph = ggplot(data = subset(par.richness, method != "extrapolated" & method != "added"), aes(x = m, y = qD, shape = habitat, fill = habitat, linetype = habitat)) + labs(x = "Number of records", y = "Number of species", shape = NULL, fill = NULL, linetype = NULL) + scale_x_continuous(limits = c(1, NA), breaks = c(1, 500, 1000, 1500, 2000)) + scale_y_continuous(limits = c(1, NA), breaks = c(1, 5, 10, 15, 20)) + scale_shape_manual(values = c(21, 21, 24), labels = c("Primary forest", "Logged forest (total species)", "Logged forest (primary forest species)")) + scale_fill_manual(values = c("grey", "#4285f4", "#4285f4"), labels = c("Primary forest", "Logged forest (total species)", "Logged forest (primary forest species)")) + scale_linetype_manual(values = c("solid", "dashed", "dotted")) + geom_line(colour = "black", size = 1) + geom_errorbar(data = subset(par.richness, method == "added"), aes(x = m, ymin = qD.LCL, ymax = qD.UCL), width = 56.52777778, linetype = "solid") + geom_point(data = subset(par.richness, method == "added"), size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.95, 0.05), legend.justification = c(0.95, 0.05)) + guides(linetype = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(a)") #ggsave(par.raref.graph, # filename = "./new_figures/Fig3a.png", dpi = 600, # width = 20, height = 20, units = "cm") ############################################################# # Tartarugalzinho: Dados (adiciona pontos e barras de erro) # ############################################################# tar.richness = rbind(tar.richness, c(tarD$m[1], "added", tarD$qD[1], tarD$qD.LCL[1], tarD$qD.UCL[1], tarD$SC[1], "a.Primary forest"), c(tarD$m[2], "added", tarD$qD[2], tarD$qD.LCL[2], tarD$qD.UCL[2], tarD$SC[2], "b.Eucalypt plantation (total species)"), c(tarD$m[3], "added", tarD$qD[3], tarD$qD.LCL[3], tarD$qD.UCL[3], tarD$SC[3], "c.Eucalypt plantation (primary forest species)")) tar.richness$m = as.numeric(tar.richness$m) tar.richness$qD = as.numeric(tar.richness$qD) tar.richness$qD.LCL = as.numeric(tar.richness$qD.LCL) tar.richness$qD.UCL = as.numeric(tar.richness$qD.UCL) tar.richness$SC = as.numeric(tar.richness$SC) rbind(head(tar.richness), tail(tar.richness)) ############################ # Tartarugalzinho: Grafico # ############################ tar.raref.graph = ggplot(data = subset(tar.richness, method != "extrapolated" & method != "added"), aes(x = m, y = qD, shape = habitat, fill = habitat, linetype = habitat)) + labs(x = "Number of records", y = "", shape = NULL, fill = NULL, linetype = NULL) + scale_x_continuous(limits = c(1, NA), breaks = c(1, 20, 40, 60)) + scale_y_continuous(limits = c(1, NA), breaks = c(1, 5, 10, 15)) + scale_shape_manual(values = c(21, 21, 24), labels = c("Primary forest", "Eucalypt plantation (total species)", "Eucalypt plantation (primary forest species)")) + scale_fill_manual(values = c("grey", "#fbbc04", "#fbbc04"), labels = c("Primary forest", "Eucalypt plantation (total species)", "Eucalypt plantation (primary forest species)")) + scale_linetype_manual(values = c("solid", "dashed", "dotted")) + geom_line(colour = "black", size = 1) + geom_errorbar(data = subset(tar.richness, method == "added"), aes(x = m, ymin = qD.LCL, ymax = qD.UCL), width = 2, linetype = "solid") + geom_point(data = subset(tar.richness, method == "added"), size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.95, 0.05), legend.justification = c(0.95, 0.05)) + guides(linetype = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(c)") #ggsave(tar.raref.graph, # filename = "./new_figures/Fig3c.png", dpi = 600, # width = 20, height = 20, units = "cm") ############################################ ### Graph of species richness difference ### ### in relation to Primary forest ########## ############################################ # Total species graphRichnessAllDiff = ggplot(data = richnessDiff[-which(grepl("c\\.", richnessDiff$habitat)), ], aes(x = pairs, y = qD, fill = habitat)) + labs(x = NULL, y = "Change in relation to primary forest (%)") + ggtitle("Total species") + scale_x_continuous(limits = c(0, 11.5), breaks = c(1.25, 4.25, 7.25, 10.25), labels = c("Logged\nforest", "Secondary\nforest", "Eucalypt\nplantation", "Oil palm\nplantation")) + scale_y_continuous(limits = c(-108.745285, 108.745285), breaks = seq(-100, 100, 25)) + scale_fill_manual(values = c("grey", "#fbbc04", # amarelo "#4285f4", # azul, "#ea4335", # vermelho "#34a853")) + # verde geom_hline(yintercept = 0, colour = "black", size = 0.5, linetype = "dashed") + geom_errorbar(aes(ymin = qD.LCL, ymax = qD.UCL, x = pairs), width = 0.2) + geom_point(shape = 21, colour = "black", size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold")) + guides(fill = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(a)") #ggsave(graphRichnessAllDiff, # filename = "./new_figures/Fig4a.png", # w = 20, h = 20, units = "cm", dpi = 600) # Primary forest species held graphRichnessRelictDiff = ggplot(data = richnessDiff[-which(grepl("b\\.", richnessDiff$habitat)), ], aes(x = pairs, y = qD, fill = habitat, shape = habitat)) + labs(x = NULL, y = "") + ggtitle("Primary forest species") + scale_x_continuous(limits = c(0, 11.5), breaks = c(1.25, 4.25, 7.25, 10.25), labels = c("Logged\nforest", "Secondary\nforest", "Eucalypt\nplantation", "Oil palm\nplantation")) + scale_y_continuous(limits = c(-108.745285, 108.745285), breaks = seq(-100, 100, 25)) + scale_fill_manual(values = c("grey", "#fbbc04", # amarelo "#4285f4", # azul, "#ea4335", # vermelho "#34a853")) + # verde scale_shape_manual(values = c(21, 24, 24, 24, 24)) + geom_hline(yintercept = 0, colour = "black", size = 0.5, linetype = "dashed") + geom_errorbar(aes(ymin = qD.LCL, ymax = qD.UCL, x = pairs), width = 0.2) + geom_point(colour = "black", size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold")) + guides(fill = FALSE, shape = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(b)") #ggsave(graphRichnessRelictDiff, # filename = "./new_figures/Fig4b.png", # w = 20, h = 20, units = "cm", dpi = 600) ################## ### ABUNDANCIA ### ################## # transforma avistamentos em taxa de avistamento a cada 10 km # "/ 1000" transformar metros em km e "* 10" transforma a taxa de 'individuos a cada 1 km' para 'individuos a cada 10 km' # remove especies nao registradas nos sitios alm.spp.tx = (alm[5:ncol(alm)] / (alm$metros_percorridos / 1000)) * 10 alm.spp.tx = alm.spp.tx[colSums(alm.spp.tx) > 0] moj.spp.tx = (moj[5:ncol(moj)] / (moj$metros_percorridos / 1000)) * 10 moj.spp.tx = moj.spp.tx[colSums(moj.spp.tx) > 0] par.spp.tx = (par[5:ncol(par)] / (par$metros_percorridos / 1000)) * 10 par.spp.tx = par.spp.tx[colSums(par.spp.tx) > 0] tar.spp.tx = (tar[5:ncol(tar)] / (tar$metros_percorridos / 1000)) * 10 tar.spp.tx = tar.spp.tx[colSums(tar.spp.tx) > 0] ############################ ### TESTE T - ABUNDANCIA ### ############################ # Calcula a abundancia das trilhas # Corresponde a soma das taxas de avistamento das especies a cada 10 km alm.abund.PF = rowSums(alm.spp.tx[grepl("PF", rownames(alm.spp.tx)), ]) alm.abund.SF = rowSums(alm.spp.tx[!grepl("PF", rownames(alm.spp.tx)), ]) moj.abund.PF = rowSums(moj.spp.tx[grepl("PF", rownames(moj.spp.tx)), ]) moj.abund.OP = rowSums(moj.spp.tx[!grepl("PF", rownames(moj.spp.tx)), ]) par.abund.PF = rowSums(par.spp.tx[grepl("PF", rownames(par.spp.tx)), ]) par.abund.LF = rowSums(par.spp.tx[!grepl("PF", rownames(par.spp.tx)), ]) tar.abund.PF = rowSums(tar.spp.tx[grepl("PF", rownames(tar.spp.tx)), ]) tar.abund.EP = rowSums(tar.spp.tx[!grepl("PF", rownames(tar.spp.tx)), ]) # t-test t.test(alm.abund.PF, alm.abund.SF) # no difference t.test(moj.abund.PF, moj.abund.OP) # there is difference t.test(par.abund.PF, par.abund.LF, paired = TRUE) # no difference t.test(tar.abund.PF, tar.abund.EP) # there is difference ################################################################## ### Species abundance difference in relation to Primary forest ### ################################################################## # Almerim (PF vs. SF) alm.abund.SF.mean = -(1 - mean(alm.abund.SF) / mean(alm.abund.PF)) * 100 alm.abund.SF.lowerCI = -(1 - t.test(alm.abund.SF)$conf.int[1] / mean(alm.abund.PF)) * 100 alm.abund.SF.upperCI = -(1 - t.test(alm.abund.SF)$conf.int[2] / mean(alm.abund.PF)) * 100 alm.abund.PF.mean = -(1 - mean(alm.abund.PF) / mean(alm.abund.PF)) * 100 alm.abund.PF.lowerCI = -(1 - t.test(alm.abund.PF)$conf.int[1] / mean(alm.abund.PF)) * 100 alm.abund.PF.upperCI = -(1 - t.test(alm.abund.PF)$conf.int[2] / mean(alm.abund.PF)) * 100 # Moju (PF vs. OP) moj.abund.OP.mean = -(1 - mean(moj.abund.OP) / mean(moj.abund.PF)) * 100 moj.abund.OP.lowerCI = -(1 - t.test(moj.abund.OP)$conf.int[1] / mean(moj.abund.PF)) * 100 moj.abund.OP.upperCI = -(1 - t.test(moj.abund.OP)$conf.int[2] / mean(moj.abund.PF)) * 100 moj.abund.PF.mean = -(1 - mean(moj.abund.PF) / mean(moj.abund.PF)) * 100 moj.abund.PF.lowerCI = -(1 - t.test(moj.abund.PF)$conf.int[1] / mean(moj.abund.PF)) * 100 moj.abund.PF.upperCI = -(1 - t.test(moj.abund.PF)$conf.int[2] / mean(moj.abund.PF)) * 100 # Paragominas (PF vs. LF) par.abund.LF.mean = -(1 - mean(par.abund.LF) / mean(par.abund.PF)) * 100 par.abund.LF.lowerCI = -(1 - t.test(par.abund.LF)$conf.int[1] / mean(par.abund.PF)) * 100 par.abund.LF.upperCI = -(1 - t.test(par.abund.LF)$conf.int[2] / mean(par.abund.PF)) * 100 par.abund.PF.mean = -(1 - mean(par.abund.PF) / mean(par.abund.PF)) * 100 par.abund.PF.lowerCI = -(1 - t.test(par.abund.PF)$conf.int[1] / mean(par.abund.PF)) * 100 par.abund.PF.upperCI = -(1 - t.test(par.abund.PF)$conf.int[2] / mean(par.abund.PF)) * 100 # Tartarugalzinho + Porto_Grande (PF vs. EP) tar.abund.EP.mean = -(1 - mean(tar.abund.EP) / mean(tar.abund.PF)) * 100 tar.abund.EP.lowerCI = -(1 - t.test(tar.abund.EP)$conf.int[1] / mean(tar.abund.PF)) * 100 tar.abund.EP.upperCI = -(1 - t.test(tar.abund.EP)$conf.int[2] / mean(tar.abund.PF)) * 100 tar.abund.PF.mean = -(1 - mean(tar.abund.PF) / mean(tar.abund.PF)) * 100 tar.abund.PF.lowerCI = -(1 - t.test(tar.abund.PF)$conf.int[1] / mean(tar.abund.PF)) * 100 tar.abund.PF.upperCI = -(1 - t.test(tar.abund.PF)$conf.int[2] / mean(tar.abund.PF)) * 100 ####################################################### ### combina os resultados do t-test para abundancia ### ####################################################### ttest.abund = data.frame(metric = rep("abundance", 8), mean = c(alm.abund.PF.mean, alm.abund.SF.mean, moj.abund.PF.mean, moj.abund.OP.mean, par.abund.PF.mean, par.abund.LF.mean, tar.abund.PF.mean, tar.abund.EP.mean), lowerCI = c(alm.abund.PF.lowerCI, alm.abund.SF.lowerCI, moj.abund.PF.lowerCI, moj.abund.OP.lowerCI, par.abund.PF.lowerCI, par.abund.LF.lowerCI, tar.abund.PF.lowerCI, tar.abund.EP.lowerCI), upperCI = c(alm.abund.PF.upperCI, alm.abund.SF.upperCI, moj.abund.PF.upperCI, moj.abund.OP.upperCI, par.abund.PF.upperCI, par.abund.LF.upperCI, tar.abund.PF.upperCI, tar.abund.EP.upperCI), test = rep("ttest", 8), habitat = c(4, 4.5, # Secondaty forest 10, 10.5, # Oil palm plantation 1, 1.5, # Logged forest 7, 7.5)) # Eucalypt plantation ############################ ### Graficos: abundancia ### ############################ # preparacao dos dados alm.tx = data.frame(tx = rowSums(alm.spp.tx), habitat = c(rep("a.Primary forest", 5), rep("b.Secondary forest", 5))) moj.tx = data.frame(tx = rowSums(moj.spp.tx), habitat = c(rep("b.Oil palm plantation", 8), rep("a.Primary forest", 8))) par.tx = data.frame(tx = rowSums(par.spp.tx), habitat = c(rep("b.Logged forest", 21), rep("a.Primary forest", 21))) tar.tx = data.frame(tx = rowSums(tar.spp.tx), habitat = c(rep("b.Eucalypt plantation", 4), rep("a.Primary forest", 6))) # Almerim alm.abund.graph = ggplot(data = alm.tx[order(alm.tx$tx), ], aes(x = habitat, y = tx, fill = habitat)) + labs(x = NULL, y = "") + scale_x_discrete(labels = c("Primary\nforest", "Secondary\nforest")) + scale_y_continuous(breaks = seq(3, 12, 3)) + scale_fill_manual(values = c("grey", "#34a853")) + geom_boxplot(width = 0.5, outlier.shape = NA) + geom_point(shape = 21, colour = "black", size = 3, alpha = 0.5, position = position_jitter(width = 0.15, height = 0)) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.05, 0.95), legend.justification = c(0.05, 0.95)) + guides(fill = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(f)") #ggsave(alm.abund.graph, # filename = "./new_figures/Fig3f.png", dpi = 600, # width = 20, height = 20, units = "cm") # Moju moj.abund.graph = ggplot(data = moj.tx[order(moj.tx$tx), ], aes(x = habitat, y = tx, fill = habitat)) + labs(x = NULL, y = "") + scale_x_discrete(labels = c("Primary\nforest", "Oil palm\nplantation")) + scale_fill_manual(values = c("grey", "#ea4335")) + geom_boxplot(width = 0.5, outlier.shape = NA) + geom_point(shape = 21, colour = "black", size = 3, alpha = 0.5, position = position_jitter(width = 0.15, height = 0)) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.05, 0.95), legend.justification = c(0.05, 0.95)) + guides(fill = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(h)") #ggsave(moj.abund.graph, # filename = "./new_figures/Fig3h.png", dpi = 600, # width = 20, height = 20, units = "cm") # Paragominas par.abund.graph = ggplot(data = par.tx[order(par.tx$tx), ], aes(x = habitat, y = tx, fill = habitat)) + labs(x = NULL, y = "Records per 10 km walked") + scale_x_discrete(labels = c("Primary\nforest", "Logged\nforest")) + scale_y_continuous(breaks = seq(10, 60, 10)) + scale_fill_manual(values = c("grey", "#4285f4")) + geom_boxplot(width = 0.5, outlier.shape = NA) + geom_point(shape = 21, colour = "black", size = 3, alpha = 0.5, position = position_jitter(width = 0.15, height = 0)) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.05, 0.95), legend.justification = c(0.05, 0.95)) + guides(fill = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(e)") #ggsave(par.abund.graph, # filename = "./new_figures/fig3e.png", dpi = 600, # width = 20, height = 20, units = "cm") # Tartarugalzinho + Porto_Grande tar.abund.graph = ggplot(data = tar.tx[order(tar.tx$tx), ], aes(x = habitat, y = tx, fill = habitat)) + labs(x = NULL, y = "") + scale_x_discrete(labels = c("Primary\nforest", "Eucalypt\nplantation")) + scale_fill_manual(values = c("grey", "#fbbc04")) + geom_boxplot(width = 0.5, outlier.shape = NA) + geom_point(shape = 21, colour = "black", size = 3, alpha = 0.5, position = position_jitter(width = 0.15, height = 0)) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.05, 0.95), legend.justification = c(0.05, 0.95)) + guides(fill = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(g)") #ggsave(tar.abund.graph, # filename = "./new_figures/Fig3g.png", dpi = 600, # width = 20, height = 20, units = "cm") ############################## ### COMPOSICAO DE ESPECIES ### ############################## # NMDS # Almeirim # Standardise trail-by-species abundance matrix alm.spp.std = alm[names(alm.spp.tx)] / rowSums(alm[names(alm.spp.tx)]) set.seed(1487175758) alm.nmds = metaMDS(alm.spp.std, distance = "bray", binary = FALSE, k = 2, trymax = 1000, autotransform = FALSE) alm.nmds.xy = data.frame(alm.nmds$points) alm.nmds.xy$habitat = c(rep("a.Primary forest", 5), rep("b.Secondary forest", 5)) # Moju # Standardise trail-by-species abundance matrix moj.spp.std = moj[names(moj.spp.tx)] / rowSums(moj[names(moj.spp.tx)]) set.seed(1487175758) moj.nmds = metaMDS(moj.spp.std, distance = "bray", binary = FALSE, k = 2, trymax = 1000, autotransform = FALSE) moj.nmds.xy = data.frame(moj.nmds$points) moj.nmds.xy$habitat = c(rep("d.Oil palm plantation", 8), rep("a.Primary forest", 8)) # Paragominas # Standardise trail-by-species abundance matrix par.spp.std = par[names(par.spp.tx)] / rowSums(par[names(par.spp.tx)]) set.seed(1487175758) par.nmds = metaMDS(par.spp.std, distance = "bray", binary = FALSE, k = 2, trymax = 1000, autotransform = FALSE) par.nmds.xy = data.frame(par.nmds$points) par.nmds.xy$habitat = c(rep("b.Logged forest", 21), rep("a.Primary forest", 21)) # Tartarugalzinho + Porto_Grande # Standardise trail-by-species abundance matrix tar.spp.std = tar[names(tar.spp.tx)] / rowSums(tar[names(tar.spp.tx)]) set.seed(1487175758) tar.nmds = metaMDS(tar.spp.std, distance = "bray", binary = FALSE, k = 2, trymax = 1000, autotransform = FALSE) tar.nmds.xy = data.frame(tar.nmds$points) tar.nmds.xy$habitat = c(rep("b.Eucalypt plantation", 4), rep("a.Primary forest", 6)) ################# ### PERMANOVA ### ################# # Almerim set.seed(1487175758) alm.adonis = adonis(alm.spp.std ~ alm.nmds.xy$habitat, permutations = 1000, method = "bray") # There is difference # Moju set.seed(1487175758) moj.adonis = adonis(moj.spp.std ~ moj.nmds.xy$habitat, permutations = 1000, method = "bray") # There is difference # Paragominas set.seed(1487175758) par.adonis = adonis(par.spp.std ~ par.nmds.xy$habitat, permutations = 1000, method = "bray") # No difference # Tartarugalzinho + Porto_Grande set.seed(1487175758) tar.adonis = adonis(tar.spp.std ~ tar.nmds.xy$habitat, permutations = 1000, method = "bray") # There is difference ############################ ### Graficos: composicao ### ############################ # Code for the polygons find_hull = function(alm.nmds.xy) alm.nmds.xy[chull(alm.nmds.xy$MDS1, alm.nmds.xy$MDS2), ] # Almerim alm.hulls = ddply(alm.nmds.xy, "habitat", find_hull) alm.nmds.graph = ggplot(data = alm.nmds.xy[order(alm.nmds.xy$MDS1, alm.nmds.xy$MDS2), ], aes(x = MDS1, y = MDS2, fill = habitat)) + labs(x = "NMDS axis 1", y = "") + scale_fill_manual(values = c("grey", "#33a02c")) + geom_polygon(data = alm.hulls, aes(colour = NULL), alpha = 0.2) + geom_point(shape = 21, colour = "black", size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + guides(fill = FALSE) + annotate("text", x = min(alm.nmds.xy$MDS1), y = min(alm.nmds.xy$MDS2), hjust = 0, vjust = 0, size = 5, label = paste("Stress: ", round(alm.nmds$stress, 3), 0, sep = "")) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(j)") #ggsave(alm.nmds.graph, # filename = "./new_figures/Fig3j.png", dpi = 600, # width = 20, height = 20, units = "cm") # Moju moj.hulls = ddply(moj.nmds.xy, "habitat", find_hull) moj.nmds.graph = ggplot(data = moj.nmds.xy[order(moj.nmds.xy$MDS1, moj.nmds.xy$MDS2), ], aes(x = MDS1, y = MDS2, fill = habitat)) + labs(x = "NMDS axis 1", y = "") + scale_fill_manual(values = c("grey", "#ea4335")) + geom_polygon(data = moj.hulls, aes(colour = NULL), alpha = 0.2) + geom_point(shape = 21, colour = "black", size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + guides(fill = FALSE) + annotate("text", x = min(moj.nmds.xy$MDS1), y = min(moj.nmds.xy$MDS2), hjust = 0, vjust = 0, size = 5, label = paste("Stress:", round(moj.nmds$stress, 3))) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(l)") #ggsave(moj.nmds.graph, # filename = "./new_figures/Fig3l.png", dpi = 600, # width = 20, height = 20, units = "cm") # Paragominas par.hulls = ddply(par.nmds.xy, "habitat", find_hull) par.nmds.graph = ggplot(data = par.nmds.xy[order(par.nmds.xy$MDS1, par.nmds.xy$MDS2), ], aes(x = MDS1, y = MDS2, fill = habitat)) + labs(x = "NMDS axis 1", y = "NMDS axis 2") + scale_fill_manual(values = c("grey", "#4285f4")) + geom_polygon(data = par.hulls, aes(colour = NULL), alpha = 0.2) + geom_point(shape = 21, colour = "black", size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + guides(fill = FALSE) + annotate("text", x = min(par.nmds.xy$MDS1), y = min(par.nmds.xy$MDS2), hjust = 0, vjust = 0, size = 5, label = paste("Stress:", round(par.nmds$stress, 3))) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(i)") #ggsave(par.nmds.graph, # filename = "./new_figures/Fig3i.png", dpi = 600, # width = 20, height = 20, units = "cm") # Tartarugalzinho + Porto_Grande tar.hulls = ddply(tar.nmds.xy, "habitat", find_hull) tar.nmds.graph = ggplot(data = tar.nmds.xy[order(tar.nmds.xy$MDS1, tar.nmds.xy$MDS2), ], aes(x = MDS1, y = MDS2, fill = habitat)) + labs(x = "NMDS axis 1", y = "") + scale_fill_manual(values = c("grey", "#fbbc04")) + geom_polygon(data = tar.hulls, aes(colour = NULL), alpha = 0.2) + geom_point(shape = 21, colour = "black", size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + guides(fill = FALSE) + annotate("text", x = min(tar.nmds.xy$MDS1), y = min(tar.nmds.xy$MDS2), hjust = 0, vjust = 0, size = 5, label = paste("Stress:", round(tar.nmds$stress, 3))) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(k)") #ggsave(tar.nmds.graph, # filename = "./new_figures/Fig3k.png", dpi = 600, # width = 20, height = 20, units = "cm") ################################# ### INTEGRIDADE DA COMUNIDADE ### ################################# # calcula o community integrity index para dados de abundancia relativa (padronizados pela soma das linhas) das trilhas de cada regiao # Almerim # mean Bray-Curtis distance among control (primary forest) sites alm.bray.PF = as.matrix(vegdist(alm.spp.std, method = "bray", binary = FALSE))[1:5, 1:5] alm.int.PF = mean(1 - apply(alm.bray.PF, 2, mean)) # scaled Bray-Curtis index among 'control and treatment sites' and 'control and control' alm.bray.all = as.matrix(vegdist(alm.spp.std, method = "bray", binary = FALSE))[1:5, ] alm.int.all = ((1 - apply(alm.bray.all, 2, mean)) / alm.int.PF) * 100 alm.int.all = data.frame(alm.int = alm.int.all, habitat = c(rep("a.Primary forest", 5), rep("b.Secondary forest", 5))) # Data for t-test alm.int.PF = subset(alm.int.all, habitat == "a.Primary forest")[, 1] alm.int.SF = subset(alm.int.all, habitat != "a.Primary forest")[, 1] # Moju moj.bray.OP = as.matrix(vegdist(moj.spp.std, method = "bray", binary = FALSE))[9:16, 9:16] moj.int.OP = mean(1 - apply(moj.bray.OP, 2, mean)) moj.bray.all = as.matrix(vegdist(moj.spp.std, method = "bray", binary = FALSE))[9:16, ] moj.int.all = ((1 - apply(moj.bray.all, 2, mean)) / moj.int.OP) * 100 moj.int.all = data.frame(moj.int = moj.int.all, habitat = c(rep("d.Oil palm plantation", 8), rep("a.Primary forest", 8))) moj.int.PF = subset(moj.int.all, habitat == "a.Primary forest")[, 1] moj.int.OP = subset(moj.int.all, habitat != "a.Primary forest")[, 1] # Paragominas par.bray.LF = as.matrix(vegdist(par.spp.std, method = "bray", binary = FALSE))[22:42, 22:42] par.int.LF = mean(1 - apply(par.bray.LF, 2, mean)) par.bray.all = as.matrix(vegdist(par.spp.std, method = "bray", binary = FALSE))[22:42, ] par.int.all = ((1 - apply(par.bray.all, 2, mean)) / par.int.LF) * 100 par.int.all = data.frame(par.int = par.int.all, habitat = c(rep("b.Logged forest", 21), rep("a.Primary forest", 21))) par.int.PF = subset(par.int.all, habitat == "a.Primary forest")[, 1] par.int.LF = subset(par.int.all, habitat != "a.Primary forest")[, 1] # Tartarugalzinho + Porto_Grande tar.bray.EP = as.matrix(vegdist(tar.spp.std, method = "bray", binary = FALSE))[5:10, 5:10] tar.int.EP = mean(1 - apply(tar.bray.EP, 2, mean)) tar.bray.all = as.matrix(vegdist(tar.spp.std, method = "bray", binary = FALSE))[5:10, ] tar.int.all = ((1 - apply(tar.bray.all, 2, mean)) / tar.int.EP) * 100 tar.int.all = data.frame(tar.int = tar.int.all, habitat = c(rep("b.Eucalypt plantation", 4), rep("a.Primary forest", 6))) tar.int.PF = subset(tar.int.all, habitat == "a.Primary forest")[, 1] tar.int.EP = subset(tar.int.all, habitat != "a.Primary forest")[, 1] # t-test t.test(alm.int.PF, alm.int.SF) t.test(moj.int.PF, moj.int.OP) t.test(par.int.PF, par.int.LF) t.test(tar.int.PF, tar.int.EP) #################################################################### ### Community integrity difference in relation to Primary forest ### #################################################################### # Almerim (PF vs. SF) alm.int.SF.mean = -(1 - mean(alm.int.SF) / mean(alm.int.PF)) * 100 alm.int.SF.lowerCI = -(1 - t.test(alm.int.SF)$conf.int[1] / mean(alm.int.PF)) * 100 alm.int.SF.upperCI = -(1 - t.test(alm.int.SF)$conf.int[2] / mean(alm.int.PF)) * 100 alm.int.PF.mean = -(1 - mean(alm.int.PF) / mean(alm.int.PF)) * 100 alm.int.PF.lowerCI = -(1 - t.test(alm.int.PF)$conf.int[1] / mean(alm.int.PF)) * 100 alm.int.PF.upperCI = -(1 - t.test(alm.int.PF)$conf.int[2] / mean(alm.int.PF)) * 100 # Moju (PF vs. OP) moj.int.OP.mean = -(1 - mean(moj.int.OP) / mean(moj.int.PF)) * 100 moj.int.OP.lowerCI = -(1 - t.test(moj.int.OP)$conf.int[1] / mean(moj.int.PF)) * 100 moj.int.OP.upperCI = -(1 - t.test(moj.int.OP)$conf.int[2] / mean(moj.int.PF)) * 100 moj.int.PF.mean = -(1 - mean(moj.int.PF) / mean(moj.int.PF)) * 100 moj.int.PF.lowerCI = -(1 - t.test(moj.int.PF)$conf.int[1] / mean(moj.int.PF)) * 100 moj.int.PF.upperCI = -(1 - t.test(moj.int.PF)$conf.int[2] / mean(moj.int.PF)) * 100 # Paragominas (PF vs. LF) par.int.LF.mean = -(1 - mean(par.int.LF) / mean(par.int.PF)) * 100 par.int.LF.lowerCI = -(1 - t.test(par.int.LF)$conf.int[1] / mean(par.int.PF)) * 100 par.int.LF.upperCI = -(1 - t.test(par.int.LF)$conf.int[2] / mean(par.int.PF)) * 100 par.int.PF.mean = -(1 - mean(par.int.PF) / mean(par.int.PF)) * 100 par.int.PF.lowerCI = -(1 - t.test(par.int.PF)$conf.int[1] / mean(par.int.PF)) * 100 par.int.PF.upperCI = -(1 - t.test(par.int.PF)$conf.int[2] / mean(par.int.PF)) * 100 # Tartarugalzinho + Porto_Grande (PF vs. EP) tar.int.EP.mean = -(1 - mean(tar.int.EP) / mean(tar.int.PF)) * 100 tar.int.EP.lowerCI = -(1 - t.test(tar.int.EP)$conf.int[1] / mean(tar.int.PF)) * 100 tar.int.EP.upperCI = -(1 - t.test(tar.int.EP)$conf.int[2] / mean(tar.int.PF)) * 100 tar.int.PF.mean = -(1 - mean(tar.int.PF) / mean(tar.int.PF)) * 100 tar.int.PF.lowerCI = -(1 - t.test(tar.int.PF)$conf.int[1] / mean(tar.int.PF)) * 100 tar.int.PF.upperCI = -(1 - t.test(tar.int.PF)$conf.int[2] / mean(tar.int.PF)) * 100 ################################################################ ### combina os resultados do t-test para community integrity ### ################################################################ ttest.int = data.frame(metric = rep("integrity", 8), mean = c(alm.int.PF.mean, alm.int.SF.mean, moj.int.PF.mean, moj.int.OP.mean, par.int.PF.mean, par.int.LF.mean, tar.int.PF.mean, tar.int.EP.mean), lowerCI = c(alm.int.PF.lowerCI, alm.int.SF.lowerCI, moj.int.PF.lowerCI, moj.int.OP.lowerCI, par.int.PF.lowerCI, par.int.LF.lowerCI, tar.int.PF.lowerCI, tar.int.EP.lowerCI), upperCI = c(alm.int.PF.upperCI, alm.int.SF.upperCI, moj.int.PF.upperCI, moj.int.OP.upperCI, par.int.PF.upperCI, par.int.LF.upperCI, tar.int.PF.upperCI, tar.int.EP.upperCI), test = rep("ttest", 8), habitat = c(4, 4.5, # Secondaty forest 10, 10.5, # Oil palm plantation 1, 1.5, # Logged forest 7, 7.5)) # Eucalypt plantation ttest.int ############################# ### Graficos: integridade ### ############################# # Almerim alm.int.graph = ggplot(data = alm.int.all[order(alm.int.all$alm.int), ], aes(x = habitat, y = alm.int, fill = habitat)) + labs(x = NULL, y = "") + scale_x_discrete(labels = c("Primary\nforest", "Secondary\nforest")) + scale_y_continuous(limits = c(50, NA), breaks = c(50, 75, 100)) + scale_fill_manual(values = c("grey", "#33a02c")) + geom_boxplot(width = 0.5, outlier.shape = NA) + geom_point(shape = 21, colour = "black", size = 3, alpha = 0.5, position = position_jitter(width = 0.15, height = 0)) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.05, 0.95), legend.justification = c(0.05, 0.95)) + guides(fill = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(n)") #ggsave(alm.int.graph, # filename = "./new_figures/Fig3n.png", dpi = 600, # width = 20, height = 20, units = "cm") # Moju moj.int.graph = ggplot(data = moj.int.all[order(moj.int.all$moj.int), ], aes(x = habitat, y = moj.int, fill = habitat)) + labs(x = NULL, y = "") + scale_x_discrete(labels = c("Primary\nforest", "Oil palm\nplantation")) + scale_y_continuous(breaks = c(20, 60, 100)) + scale_fill_manual(values = c("grey", "#ea4335")) + geom_boxplot(width = 0.5, outlier.shape = NA) + geom_point(shape = 21, colour = "black", size = 3, alpha = 0.5, position = position_jitter(width = 0.15, height = 0)) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.05, 0.95), legend.justification = c(0.05, 0.95)) + guides(fill = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(p)") #ggsave(moj.int.graph, # filename = "./new_figures/Fig3p.png", dpi = 600, # width = 20, height = 20, units = "cm") # Paragominas par.int.graph = ggplot(data = par.int.all[order(par.int.all$par.int), ], aes(x = habitat, y = par.int, fill = habitat)) + labs(x = NULL, y = "Community integrity (%)") + scale_x_discrete(labels = c("Primary\nforest", "Logged\nforest")) + scale_y_continuous(breaks = c(60, 80, 100)) + scale_fill_manual(values = c("grey", "#4285f4")) + geom_boxplot(width = 0.5, outlier.shape = NA) + geom_point(shape = 21, colour = "black", size = 3, alpha = 0.5, position = position_jitter(width = 0.15, height = 0)) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.05, 0.95), legend.justification = c(0.05, 0.95)) + guides(fill = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(m)") #ggsave(par.int.graph, # filename = "./new_figures/Fig3m.png", dpi = 600, # width = 20, height = 20, units = "cm") # Tartarugalzinho + Porto_Grande tar.int.graph = ggplot(data = tar.int.all[order(tar.int.all$tar.int), ], aes(x = habitat, y = tar.int, fill = habitat)) + labs(x = NULL, y = "") + scale_x_discrete(labels = c("Primary\nforest", "Eucalypt\nplantation")) + scale_y_continuous(breaks = c(50, 75, 100)) + scale_fill_manual(values = c("grey", "#fbbc04")) + geom_boxplot(width = 0.5, outlier.shape = NA) + geom_point(shape = 21, colour = "black", size = 3, alpha = 0.5, position = position_jitter(width = 0.15, height = 0)) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.text = element_text(size = 14), legend.position = c(0.05, 0.95), legend.justification = c(0.05, 0.95)) + guides(fill = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(o)") #ggsave(tar.int.graph, # filename = "./new_figures/Fig3o.png", dpi = 600, # width = 20, height = 20, units = "cm") ############################################# ### combina todos os graficos (par a par) ### ############################################# ggsave(grid.arrange(par.raref.graph, alm.raref.graph, tar.raref.graph, moj.raref.graph, par.abund.graph, alm.abund.graph, tar.abund.graph, moj.abund.graph, par.nmds.graph, alm.nmds.graph, tar.nmds.graph, moj.nmds.graph, par.int.graph, alm.int.graph, tar.int.graph, moj.int.graph, ncol = 4), filename = "./new_figures/Fig3.pdf", width = 15.5*4, height = 15.5*4, units = "cm") ################################################################# ### combina os graficos de diferenca percentual de abundancia ### ################################################################# # Abundance abund.graph = ggplot(data = ttest.abund, aes(x = habitat, y = mean, fill = as.factor(habitat))) + labs(x = NULL, y = "") + ggtitle("Abundance") + scale_x_continuous(limits = c(0, 11.5), breaks = c(1.25, 4.25, 7.25, 10.25), labels = c("Logged\nforest", "Secondary\nforest", "Eucalypt\nplantation", "Oil palm\nplantation")) + scale_y_continuous(limits = c(-108.745285, 108.745285), breaks = seq(-100, 100, 25)) + scale_fill_manual(values = c("grey", "#4285f4", # azul "grey", "#34a853", # verde "grey", "#fbbc04", # amarelo "grey", "#ea4335")) + # vermelho geom_hline(yintercept = 0, colour = "black", size = 0.5, linetype = "dashed") + geom_errorbar(aes(ymin = lowerCI, ymax = upperCI, x = habitat), width = 0.2) + geom_point(shape = 21, colour = "black", size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold")) + guides(fill = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(c)") #ggsave(abund.graph, # filename = "./new_figures/Fig4c.png", dpi = 600, # w = 20, h = 20, units = "cm") ########################################################################## ### combina os graficos de diferenca percentual de community integrity ### ########################################################################## # Community integrity int.graph = ggplot(data = ttest.int, aes(x = habitat, y = mean, fill = as.factor(habitat))) + labs(x = NULL, y = "") + ggtitle("Community integrity") + scale_x_continuous(limits = c(0, 11.5), breaks = c(1.25, 4.25, 7.25, 10.25), labels = c("Logged\nforest", "Secondary\nforest", "Eucalypt\nplantation", "Oil palm\nplantation")) + scale_y_continuous(limits = c(-108.745285, 108.745285), breaks = seq(-100, 100, 25)) + scale_fill_manual(values = c("grey", "#4285f4", # azul "grey", "#34a853", # verde "grey", "#fbbc04", # amarelo "grey", "#ea4335")) + # vermelho geom_hline(yintercept = 0, colour = "black", size = 0.5, linetype = "dashed") + geom_errorbar(aes(ymin = lowerCI, ymax = upperCI, x = habitat), width = 0.2) + geom_point(shape = 21, colour = "black", size = 5) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold")) + guides(fill = FALSE) + annotate("text", x = -Inf, y = Inf, hjust = -0.5, vjust = 2, size = 10, label = "(d)") #ggsave(int.graph, # filename = "./new_figures/Fig4d.png", dpi = 600, # w = 20, h = 20, units = "cm") ######################################### ### combina todos os graficos (geral) ### ######################################### ggsave(grid.arrange(graphRichnessAllDiff, graphRichnessRelictDiff, abund.graph, int.graph, ncol = 4), filename = "./new_figures/Fig4.pdf", width = 15.5*4, height = 15.5, units = "cm") ################ ### GGPONCHO ### ################ intactness = trilhas$ambiente intactness[intactness == "PF"] = 1 intactness[intactness == "LF"] = 2 intactness[intactness == "SF"] = 3 intactness[intactness == "EP"] = 4 intactness[intactness == "OP"] = 5 intactness = as.numeric(intactness) #poncho(trilhas[5:ncol(trilhas)], as.numeric(intactness), # forcePA = TRUE, col = as.numeric(intactness)) poncho = trilhas[5:ncol(trilhas)] poncho$intactness = as.numeric(intactness) poncho = poncho[order(poncho$intactness), ] poncho$order = 1:nrow(poncho) poncho.pa = decostand(poncho[1:46], method = "pa") colnames(poncho.pa) = gsub("_", " ", colnames(poncho.pa)) spp.order = names(poncho.pa[order(poncho$intactness), order(colSums(poncho.pa * poncho$intactness) / colSums(poncho.pa))]) poncho.pa = poncho.pa[spp.order] longData = melt(t(poncho.pa)) longData = longData[longData$value != 0, ] longData$Var3 = factor(as.factor(substring(longData$Var2, 1, 2)), levels(as.factor(substring(longData$Var2, 1, 2)))[c(4, 2, 5, 1, 3)]) ggponcho = ggplot(data = longData, aes(x = Var2, y = Var1, fill = Var3)) + labs(x = "Transect code", y = NULL, fill = NULL) + geom_tile(colour = "black") + scale_x_discrete(labels = as.factor(1:length(levels(longData$Var2)))) + scale_fill_manual(values = c("grey", "#4285f4", "#34a853", "#fbbc04", "#ea4335"), labels = c("Primary forest", "Logged forest", "Secondary forest", "Eucalypt plantation", "Oil palm plantation")) + theme_minimal(base_size = 20) + theme(panel.grid = element_line(size = 0.1), axis.title = element_text(colour = "black", size = 14), axis.text.x = element_text(colour = "black", size = 8), axis.text.y = element_text(colour = "black", face = "italic", size = 10), axis.ticks = element_blank()) + theme(legend.text = element_text(size = 12)) + theme(legend.position = "bottom") ggsave(ggponcho, filename = "./new_figures/Fig5.pdf", h = 20, w = 78/46*20, units = "cm") ###################### ### EULER DIAGRAMS ### ###################### # Almerim alm.spp.pf = alm[1:5, 5:ncol(alm)][colSums(alm[1:5, 5:ncol(alm)]) > 0] alm.spp.sf = alm[6:10, 5:ncol(alm)][colSums(alm[6:10, 5:ncol(alm)]) > 0] length(colnames(alm.spp.pf)) - length(table(match(colnames(alm.spp.pf), colnames(alm.spp.sf)))) length(colnames(alm.spp.sf)) - length(table(match(colnames(alm.spp.sf), colnames(alm.spp.pf)))) length(table(match(colnames(alm.spp.pf), colnames(alm.spp.sf)))) alm.euler = plot(euler(c(A = 5, B = 4, "A&B" = 10)), legend = NULL, quantities = list(cex = 4), fills = list(fill = c("grey", "#33a02c"))) # Moju moj.spp.pf = moj[9:16, 5:ncol(moj)][colSums(moj[9:16, 5:ncol(moj)]) > 0] moj.spp.op = moj[1:8, 5:ncol(moj)][colSums(moj[1:8, 5:ncol(moj)]) > 0] length(colnames(moj.spp.pf)) - length(table(match(colnames(moj.spp.pf), colnames(moj.spp.op)))) length(colnames(moj.spp.op)) - length(table(match(colnames(moj.spp.op), colnames(moj.spp.pf)))) length(table(match(colnames(moj.spp.pf), colnames(moj.spp.op)))) moj.euler = plot(euler(c(A = 18, B = 2, "A&B" = 11)), legend = NULL, quantities = list(cex = 4), fills = list(fill = c("grey", "#ea4335"))) # Paragominas par.spp.pf = par[22:42, 5:ncol(par)][colSums(par[22:42, 5:ncol(par)]) > 0] par.spp.lf = par[1:22, 5:ncol(par)][colSums(par[1:22, 5:ncol(par)]) > 0] length(colnames(par.spp.pf)) - length(table(match(colnames(par.spp.pf), colnames(par.spp.lf)))) length(colnames(par.spp.lf)) - length(table(match(colnames(par.spp.lf), colnames(par.spp.pf)))) length(table(match(colnames(par.spp.pf), colnames(par.spp.lf)))) par.euler = plot(euler(c(A = 4, B = 5, "A&B" = 15)), legend = NULL, quantities = list(cex = 4), fills = list(fill = c("grey", "#4285f4"))) # Tartarugalzinho + Porto_Grande tar.spp.pf = tar[5:10, 5:ncol(tar)][colSums(tar[5:10, 5:ncol(tar)]) > 0] tar.spp.ep = tar[1:4, 5:ncol(tar)][colSums(tar[1:4, 5:ncol(tar)]) > 0] length(colnames(tar.spp.pf)) - length(table(match(colnames(tar.spp.pf), colnames(tar.spp.ep)))) length(colnames(tar.spp.ep)) - length(table(match(colnames(tar.spp.ep), colnames(tar.spp.pf)))) length(table(match(colnames(tar.spp.pf), colnames(tar.spp.ep)))) tar.euler = plot(euler(c(A = 8, B = 1, "A&B" = 7)), legend = NULL, quantities = list(cex = 4), fills = list(fill = c("grey", "#fbbc04"))) ########################################### ### PROPORTIONAL OCCURRENCE PER HABITAT ### ########################################### # Almerim # prepara os dados alm.prop = alm.spp.tx[colSums(alm.spp.tx) > 0] alm.prop = as.data.frame(aggregate(alm.prop, by = list(habitat = substring(rownames(alm.spp.tx), 1, 2)), sum)) alm.prop = as.data.frame(t(alm.prop[2:ncol(alm.prop)]) / rowSums(t(alm.prop[2:ncol(alm.prop)]))) alm.prop.levels = rownames(alm.prop[order(-alm.prop$V1, alm.prop$V2), ]) alm.prop = t(alm.prop) rownames(alm.prop) = unique(substring(rownames(alm.spp.tx), 1, 2)) alm.prop = melt(alm.prop) alm.prop$Var2 = factor(alm.prop$Var2, levels = alm.prop.levels) alm.records = colSums(alm[names(alm.spp.tx)]) # grafico alm.prop.graph = ggplot(data = alm.prop[order(alm.prop$Var1), ], aes(x = Var2, y = value * 100, fill = Var1)) + labs(x = NULL, y = "Records per 10 km walked (%)", fill = NULL) + scale_x_discrete(labels = unique(gsub("_", " ", paste0(alm.prop$Var2, " (", alm.records[match(alm.prop$Var2, names(alm.records))], ")")))) + scale_fill_manual(values = c("grey", "#34a853"), labels = c("Primary forest", "Secondary forest")) + geom_bar(stat = "identity") + theme_minimal(base_size = 20) + theme(panel.grid = element_blank(), axis.text.x = element_text(colour = "black"), axis.text.y = element_text(colour = "black", face = "italic"), axis.ticks = element_line(colour = "black", size = 0.5), axis.ticks.y = element_blank()) + theme(legend.position = "bottom") #ggsave(alm.prop.graph + coord_flip(), filename = "./new_figures/alm.prop.pdf", width = 25, height = 20, units = "cm") # Moju # prepara os dados moj.prop = moj.spp.tx[colSums(moj.spp.tx) > 0] moj.prop = as.data.frame(aggregate(moj.prop, by = list(habitat = substring(rownames(moj.spp.tx), 1, 2)), sum)) moj.prop = as.data.frame(t(moj.prop[2:ncol(moj.prop)]) / rowSums(t(moj.prop[2:ncol(moj.prop)]))) moj.prop.levels = rownames(moj.prop[order(moj.prop$V1, -moj.prop$V2), ]) moj.prop = t(moj.prop) rownames(moj.prop) = unique(substring(rownames(moj.spp.tx), 1, 2)) moj.prop = melt(moj.prop) moj.prop$Var1 = factor(moj.prop$Var1, levels = c("PF", "OP")) moj.prop$Var2 = factor(moj.prop$Var2, levels = moj.prop.levels) # grafico moj.prop.graph = ggplot(data = moj.prop[order(moj.prop$Var1), ], aes(x = Var2, y = value * 100, fill = Var1)) + labs(x = NULL, y = "Records per 10 km walked (%)", fill = NULL) + scale_x_discrete(labels = gsub("_", " ", levels(moj.prop$Var2))) + scale_fill_manual(values = c("grey", "#ea4335"), labels = c("Primary forest", "Oil palm plantation")) + geom_bar(stat = "identity") + theme_minimal(base_size = 20) + theme(panel.grid = element_blank(), axis.text.x = element_text(colour = "black", #face = "italic", #angle = 90, hjust = 1, vjust = 0.5, margin = margin(-10, 0, 0, 0)), axis.text.y = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5), axis.ticks.x = element_blank()) + theme(legend.position = "bottom") #ggsave(moj.prop.graph, filename = "moj.prop.pdf", width = 20, height = 20, units = "cm") # Paragominas # prepara os dados par.prop = par.spp.tx[colSums(par.spp.tx) > 0] par.prop = as.data.frame(aggregate(par.prop, by = list(habitat = substring(rownames(par.spp.tx), 1, 2)), sum)) par.prop = as.data.frame(t(par.prop[2:ncol(par.prop)]) / rowSums(t(par.prop[2:ncol(par.prop)]))) par.prop.levels = rownames(par.prop[order(par.prop$V1, -par.prop$V2), ]) par.prop = t(par.prop) rownames(par.prop) = unique(substring(rownames(par.spp.tx), 1, 2)) par.prop = melt(par.prop) par.prop$Var1 = factor(par.prop$Var1, levels = c("PF", "LF")) par.prop$Var2 = factor(par.prop$Var2, levels = par.prop.levels) # grafico par.prop.graph = ggplot(data = par.prop[order(par.prop$Var1), ], aes(x = Var2, y = value * 100, fill = Var1)) + labs(x = NULL, y = "Records per 10 km walked (%)", fill = NULL) + scale_x_discrete(labels = gsub("_", " ", levels(par.prop$Var2))) + scale_fill_manual(values = c("grey", "#4285f4"), labels = c("Primary forest", "Logged forest")) + geom_bar(stat = "identity") + theme_minimal(base_size = 20) + theme(panel.grid = element_blank(), axis.text.x = element_text(colour = "black", #face = "italic", #angle = 90, hjust = 1, vjust = 0.5, margin = margin(-10, 0, 0, 0)), axis.text.y = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5), axis.ticks.x = element_blank()) + theme(legend.position = "bottom") #ggsave(par.prop.graph, filename = "par.prop.pdf", width = 20, height = 20, units = "cm") # Tartarugalzinho + Porto_Grande # prepara os dados tar.prop = tar.spp.tx[colSums(tar.spp.tx) > 0] tar.prop = as.data.frame(aggregate(tar.prop, by = list(habitat = substring(rownames(tar.spp.tx), 1, 2)), sum)) tar.prop = as.data.frame(t(tar.prop[2:ncol(tar.prop)]) / rowSums(t(tar.prop[2:ncol(tar.prop)]))) tar.prop.levels = rownames(tar.prop[order(tar.prop$V1, -tar.prop$V2), ]) tar.prop = t(tar.prop) rownames(tar.prop) = unique(substring(rownames(tar.spp.tx), 1, 2)) tar.prop = melt(tar.prop) tar.prop$Var1 = factor(tar.prop$Var1, levels = c("PF", "EP")) tar.prop$Var2 = factor(tar.prop$Var2, levels = tar.prop.levels) # grafico tar.prop.graph = ggplot(data = tar.prop[order(tar.prop$Var1), ], aes(x = Var2, y = value * 100, fill = Var1)) + labs(x = NULL, y = "Records per 10 km walked (%)", fill = NULL) + scale_x_discrete(labels = gsub("_", " ", levels(tar.prop$Var2))) + scale_fill_manual(values = c("grey", "#fbbc04"), labels = c("Primary forest", "Eucalypt plantation")) + geom_bar(stat = "identity") + theme_minimal(base_size = 20) + theme(panel.grid = element_blank(), axis.text.x = element_text(colour = "black", #face = "italic", #angle = 90, hjust = 1, vjust = 0.5, margin = margin(-10, 0, 0, 0)), axis.text.y = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5), axis.ticks.x = element_blank()) + theme(legend.position = "bottom") #ggsave(tar.prop.graph, filename = "tar.prop.pdf", width = 20, height = 20, units = "cm") ggsave(grid.arrange(par.euler, par.prop.graph, alm.euler, alm.prop.graph, tar.euler, tar.prop.graph, moj.euler, moj.prop.graph, ncol = 2), filename = "./new_figures/Fig2.pdf", w = 20 * 4, h = 20 * 4, units = "cm") #################### ### ELTON TRAITS ### #################### elton = read.table("https://ndownloader.figshare.com/files/5631084", header = TRUE, sep = "\t") speciesPaula = colnames(trilhas[5:ncol(trilhas)]) speciesPaula = gsub("_", " ", speciesPaula) # Species not found in Elton Traits database speciesPaula[is.na(elton$Scientific[match(speciesPaula, elton$Scientific)])] # Change species name to match with those in Elton Traits database speciesPaula[speciesPaula == "Mazama nemorivaga"] = "Mazama gouazoubira" speciesPaula[speciesPaula == "Microsciurus sp."] = "Sciurus aestuans" speciesPaula[speciesPaula == "Saguinus ursulus"] = "Saguinus niger" speciesPaula[speciesPaula == "Saimiri collinsi"] = "Saimiri sciureus" speciesPaula[speciesPaula == "Sapajus apella"] = "Cebus apella" speciesPaula[speciesPaula == "Sapajus libidinosus"] = "Cebus libidinosus" # Subset Elton Traits database eltonSubset = cbind(elton[match(speciesPaula, elton$Scientific), ], Scientific.Paula = colnames(trilhas[5:ncol(trilhas)])) # Calculation of the trophic level dietWeight = data.frame(diet = colnames(eltonSubset[4:13]), weight = c(4, 5, 5, 5, 5, 5, 2, 2, 3, 1)) eltonSubset$Trophic.Level = (eltonSubset[, 4] * dietWeight[1, 2] + eltonSubset[, 5] * dietWeight[2, 2] + eltonSubset[, 6] * dietWeight[3, 2] + eltonSubset[, 7] * dietWeight[4, 2] + eltonSubset[, 8] * dietWeight[5, 2] + eltonSubset[, 9] * dietWeight[6, 2] + eltonSubset[, 10] * dietWeight[7, 2] + eltonSubset[, 11] * dietWeight[8, 2] + eltonSubset[, 12] * dietWeight[9, 2] + eltonSubset[, 13] * dietWeight[10, 2])/100 # Select only relevant columns eltonSubset = eltonSubset[c(2, 27, 16, 28, 24)] # Add a column with a vulnerability index eltonSubset$Vulnerability.Index = NA # See the object head(eltonSubset) ########################### ### VULNERABILITY INDEX ### ########################### spp = list() for (i in 5:ncol(trilhas)) { sp = tapply(trilhas[, i], paste(trilhas$municipio, trilhas$ambiente), sum) sp = as.data.frame(cbind(sp[which(grepl("PF", rownames(sp)) == "FALSE")], sp[which(grepl("PF", rownames(sp)) == "TRUE")])) colnames(sp) = c("Treatment", "Control") rownames(sp) = c("SF", "OP", "LF", "EP") sp$Vulnerability.Index = log((sp$Treatment+1) / (sp$Control+1)) sp$LandUseType = rownames(sp) sp$Species = colnames(trilhas)[i] rownames(sp) = NULL sp = sp[c(2, 4, 1, 3), ] spp[[i]] = sp } spp = do.call(rbind, spp) head(spp) eltonSubset = rbind(eltonSubset, eltonSubset, eltonSubset, eltonSubset) eltonSubset$LandUseType = rep(c("Oil palm plantation", "Eucalypt plantation", "Secondary forest", "Logged forest"), each = 46) eltonSubset$LandUseType = factor(eltonSubset$LandUseType, levels = c("Logged forest", "Secondary forest", "Eucalypt plantation", "Oil palm plantation")) eltonSubset$Vulnerability.Index = c(subset(spp, LandUseType == "OP")$Vulnerability.Index, subset(spp, LandUseType == "EP")$Vulnerability.Index, subset(spp, LandUseType == "SF")$Vulnerability.Index, subset(spp, LandUseType == "LF")$Vulnerability.Index) head(eltonSubset) eltonSubset$ForStrat.Value = factor(eltonSubset$ForStrat.Value, levels = c("G", "S", "Ar")) # Graph traits.plot = ggplot(data = eltonSubset[order(eltonSubset$Vulnerability.Index), ], aes(x = -Vulnerability.Index, y = ForStrat.Value, size = BodyMass.Value/1000, fill = -Vulnerability.Index)) + labs(x = "Vulnerability index", y = "Foraging stratum", size = "Body mass (kg)", fill = "Vulnerability index") + geom_vline(xintercept = 0, size = 0.5, colour = "grey", linetype = "dashed") + geom_point(shape = 21, colour = "black", alpha = 0.5) + scale_x_continuous(limits = c(-4, 6)) + scale_y_discrete(labels = c("Ground", "Scansorial", "Arboreal")) + scale_fill_viridis_c() + scale_size_continuous(range = c(3, 14)) + theme_bw(base_size = 20) + theme(panel.grid = element_blank(), panel.border = element_rect(colour = "black", size = 0.5), axis.title = element_text(colour = "black"), axis.text = element_text(colour = "black"), axis.ticks = element_line(colour = "black", size = 0.5)) + theme(legend.title = element_text(size = 14), legend.text = element_text(size = 12)) + facet_wrap(. ~ LandUseType) traits.plot ggsave(traits.plot, filename = "./new_figures/Fig6.pdf", width = 30, height = 20, units = "cm") ### Table S1 transect.code = read.csv("./new_figures/!transect_code.csv") colnames(transect.code)[2] = "habitat_transect" transect.code$transect = substring(transect.code$habitat_transect, 4) transect.code$municipality = trilhas$municipio[match( transect.code$habitat_transect, rownames(trilhas))] transect.code$decimalLatitude = coordinates$decimalLatitude[match( transect.code$habitat_transect, rownames(coordinates))] transect.code$decimalLongitude = coordinates$decimalLongitude[match( transect.code$habitat_transect, rownames(coordinates))] transect.code$effort = trilhas$metros_percorridos[match( transect.code$habitat_transect, rownames(trilhas))] # Salva a Table S1 write.csv(transect.code, "./new_figures/tables1.csv", row.names = FALSE) ### Table S3 tables2 = aggregate(trilhas[-c(1:4)], by = list(paste(trilhas$municipio, trilhas$ambiente, sep = "_")), FUN = sum) row.names(tables2) = tables2$Group.1 tables2$Group.1 = NULL tables2 = as.data.frame(t(tables2)) tables2$relative.abundance = round(rowSums(tables2)/sum(tables2)*100, 2) # Salva a Table S2 write.csv(tables2, "./new_figures/tables2.csv")