Updated electivity figures

Added RII analysis and revised electivity figure and results table

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.Rproj.user/EAEB7255/sources/s-A74A5E15/9E5C163F-contents → .Rproj.user/EAEB7255/sources/per/t/9E5C163F-contents

@@ -23,6 +23,10 @@ greenwood_under <- read.csv("./raw data/Greenwood_Understory_Variables_SF_edits.
 hist(asin(sqrt(plot_data$All/100)))
 hist(logit(plot_data$All/100 + min(plot_data[plot_data$All != 0, "All"])))
 hist(log(plot_data$All/100))
+plot((plot_data$All/100) ~ plot_data$Tree_cover)
+plot(asin(sqrt(plot_data$All/100)) ~ plot_data$Tree_cover)
+plot(asin(sqrt(plot_data$All/100)) ~ plot_data$Delta_pdc)
+plot(asin(sqrt(plot_data$All/100)) ~ plot_data$cwd_normal_cum)
 
 hist(asin(sqrt(plot_data$Cheatgrass/100)))
 hist(logit(plot_data$Cheatgrass/100 + min(plot_data[plot_data$Cheatgrass != 0, "Cheatgrass"])))
@@ -54,11 +58,11 @@ plot(plot_data$Cheatgrass ~ plot_data$cwd_normal_cum)
 all_plot <- lmer(asin(sqrt(All/100)) ~ scale(Tree_cover) + scale(Delta_pdc)*scale(cwd_normal_cum) +
                           scale(AWC) + (1|Cluster), data = plot_data)
 
-all_plot <- lmer(logit(All/100) ~ scale(Tree_cover) + scale(Delta_pdc)*scale(cwd_normal_cum) +
-                          scale(AWC) + (1|Cluster), data = plot_data)
-
-summary(lm(asin(sqrt(All/100)) ~ scale(Tree_cover) + scale(Delta_pdc)*scale(cwd_normal_cum) +
-             scale(AWC), data = plot_data))
+# all_plot <- lmer(logit(All/100) ~ scale(Tree_cover) + scale(Delta_pdc)*scale(cwd_normal_cum) +
+#                           scale(AWC) + (1|Cluster), data = plot_data)
+# 
+# summary(lm(asin(sqrt(All/100)) ~ scale(Tree_cover) + scale(Delta_pdc)*scale(cwd_normal_cum) +
+#              scale(AWC), data = plot_data))
 
 summary(all_plot, ddf = "Kenward-Roger")
 r.squaredGLMM(all_plot)
@@ -136,7 +140,7 @@ coefplot2(cheatgrass_plot, add = TRUE, spacing = 10)
 
 
 #-----------------------------------------------
-# Comparison of greenwood and 2015
+# Comparison between 2005 and 2015 samples
 #-----------------------------------------------
 compare <- join(plot_data, greenwood_under, by = "Plot", type = "inner")
 
@@ -154,8 +158,9 @@ cor.test(compare$Cheatgrass - compare$Cheatgrass.Cover, compare$cwd_normal_cum,
          alternative = "two.sided", exact = FALSE)
 
 #linear model of log change ~ delta pdc and cwd
-compare$log_cg_change <- log(I(compare$Cheatgrass/100 - compare$Cheatgrass.Cover/100 - min(compare$Cheatgrass/100 - compare$Cheatgrass.Cover/100) + .01))
-cg_change_lm <- lm(log_cg_change ~ Delta_pdc*cwd_normal_cum, data= compare)
+# compare <- compare[-10, ] #outlier to remove?
+
+cg_change_lm <- lm(I((Cheatgrass - Cheatgrass.Cover)/100) ~ Delta_pdc*cwd_normal_cum, data= compare)
 
 summary(cg_change_lm)
 plot(cg_change_lm)
@@ -168,7 +173,6 @@ mean(compare$log_cg_change[-c(5,10)])
 (compare$log_cg_change[c(5)] - 1.72) / .18
 (compare$log_cg_change[c(10)] - 1.72) / .18
 
-
 summary(cg_change_lm)
 plot(cg_change_lm)
 plot(allEffects(cg_change_lm, partial.residuals = TRUE))
@@ -183,8 +187,9 @@ mean(compare$Pgrass-compare$Perrenial.Grass.Cover)
 t.test(compare$Pgrass, compare$Perrenial.Grass.Cover, paired = TRUE, alternative="less")
 wilcox.test(compare$Pgrass, compare$Perrenial.Grass.Cover, paired = TRUE, alternative="less")
 
-pg_change_lm <- lm(Pgrass - Perrenial.Grass.Cover ~ Delta_pdc*cwd_normal_cum, data= compare)
+pg_change_lm <- lm(Pgrass - Perrenial.Grass.Cover ~ cwd_normal_cum, data= compare)
 summary(pg_change_lm)
+plot(allEffects(pg_change_lm))
 
 ## Perr forb
 plot(I(compare$Aforb + compare$Pforb) ~ compare$Forb.Cover)

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.Rproj.user/EAEB7255/sources/s-A74A5E15/AD22B2CF-contents → .Rproj.user/EAEB7255/sources/per/t/BA4339-contents

@@ -6,7 +6,7 @@ library(multcompView)
 
 set.seed(16091315)
 
-daub <- read.csv("./raw data/daub_cover.csv", stringsAsFactors = FALSE)
+daub <- read.csv("./Raw data/daub_cover.csv", stringsAsFactors = FALSE)
 
 #some data proofing
   daub <- daub[(daub$Transect %in% c("N", "E", "S", "W")), ]
@@ -158,7 +158,7 @@ for(i in 1:ntypes){
 names(elect_results) <- types
 
 
-for(type in types){
+for(type in types){ # calculate electivity for each functional type
 
   for (i in 1:length(plots_to_use)){
 
@@ -189,16 +189,18 @@ for(type in types){
                           (cov[cov$Group.1 == "Live",2] + prev[which(names(prev) == "Live")]),
                         Inter = (cov[cov$Group.1 == "Inter",2] - prev[which(names(prev) == "Inter")]) / 
                           (cov[cov$Group.1 == "Inter",2] + prev[which(names(prev) == "Inter")]))
-    elect$DL = elect$Dead - elect$Live
-    elect$DI = elect$Dead - elect$Inter
-    elect$LI = elect$Live - elect$Inter
+    elect$DL = (cov[cov$Group.1 == "Dead",2] - cov[cov$Group.1 == "Live",2]) / (cov[cov$Group.1 == "Dead",2] + cov[cov$Group.1 == "Live",2])
+    elect$DI = (cov[cov$Group.1 == "Dead",2] - cov[cov$Group.1 == "Inter",2]) / (cov[cov$Group.1 == "Dead",2] + cov[cov$Group.1 == "Inter",2])
+    elect$LI = (cov[cov$Group.1 == "Live",2] - cov[cov$Group.1 == "Inter",2]) / (cov[cov$Group.1 == "Live",2] + cov[cov$Group.1 == "Inter",2])
 
     elect_results[[type]] <- rbind(elect_results[[type]], elect)
   }
 }
 
 saveRDS(elect_results, paste0("./outputs/results_elect_", n_plots, "dead.rds"))
 # elect_results <- readRDS("./outputs/results_elect_1dead.rds")
+
+
 #------------------------------------------------------------------------------
 #Calculate monte carlo means
 #------------------------------------------------------------------------------
@@ -260,9 +262,9 @@ for(type in types){
                             (cov[cov$Group.1 == "Live",2] + prev[which(names(prev) == "Live")]),
                           Inter = (cov[cov$Group.1 == "Inter",2] - prev[which(names(prev) == "Inter")]) / 
                             (cov[cov$Group.1 == "Inter",2] + prev[which(names(prev) == "Inter")]))
-      elect$DL = elect$Dead - elect$Live
-      elect$DI = elect$Dead - elect$Inter
-      elect$LI = elect$Live - elect$Inter
+      elect$DL = (cov[cov$Group.1 == "Dead",2] - cov[cov$Group.1 == "Live",2]) / (cov[cov$Group.1 == "Dead",2] + cov[cov$Group.1 == "Live",2])
+      elect$DI = (cov[cov$Group.1 == "Dead",2] - cov[cov$Group.1 == "Inter",2]) / (cov[cov$Group.1 == "Dead",2] + cov[cov$Group.1 == "Inter",2])
+      elect$LI = (cov[cov$Group.1 == "Live",2] - cov[cov$Group.1 == "Inter",2]) / (cov[cov$Group.1 == "Live",2] + cov[cov$Group.1 == "Inter",2])
 
       elect_rand <- rbind(elect_rand, elect)
     }
@@ -311,17 +313,35 @@ results_boots[[i]][2, ] <- pvals
 saveRDS(results_boots, paste0("./outputs/results_boots_", n_plots, "dead.rds"))
 
 # results_boots <- readRDS("./outputs/results_boots_1dead.rds")
+
+
+#-----------------------------------------------------------------------------------------------
+# Summary table
+#-----------------------------------------------------------------------------------------------
+summary <- as.data.frame(matrix(nrow = 12, ncol = 8))
+for(i in 1:4){
+  for(j in 1:6){
+    summary[3*i - 2, j+2] <- results_boots[[i]][[j]][[1]]
+    summary[3*i - 1, j+2] <- results_boots[[i]][[j]][[1]] - mean(elect_means[[i]][[j + 1]])
+    summary[3*i, j+2] <- results_boots[[i]][[j]][[2]]
+  }
+}
+
+summary[, 2] <- rep(c("Empirical", "Difference", "p-val"), times = 4)
+summary[, 1] <- rep(c("Perennial grass", "Cheatgrass", "Perennial forb", "Shrub"), each = 3)
+names(summary) <- c("FT", "var", "Dead", "Live", "Inter", "D-L", "D-I", "L-I")
+write.csv(summary, "./outputs/electivity summary table.csv")
 #-----------------------------------------------------------------------------------------------
 # Plot of electivity for each FT
 #-----------------------------------------------------------------------------------------------
 
-png(filename="electivity.png", 
+png(filename="./outputs/electivity.png", 
     type="cairo",
     units="in", 
     width = 4, 
     height=4, 
     pointsize=10, 
-    res=160)
+    res=600)
 
 par(mfrow = c(2,2),
     mar = c(2,1,1,2),
@@ -330,18 +350,23 @@ par(mfrow = c(2,2),
 for(i in 1:ntypes){
 
   melt_elect <- melt(elect_results[[i]][, 2:4])
-  plot(melt_elect$value ~ I(as.numeric(melt_elect$variable)+ runif(nrow(melt_elect), -.1, .1 )),
+  plot(NA,
        ylim = c(-1,1),
        xlim = c(.7, 3.3),
-       pch = 21,
-       bg = "grey",
        xaxt = "n")
   pvals <- results_boots[[i]][2, ]
   abline(h = 0)
 
+  vioplot(elect_means[[i]][[2]], add = TRUE, col = addTrans("blue", 30), drawRect = FALSE)
+  vioplot(elect_means[[i]][[3]], at = 2, add = TRUE, col = addTrans("blue", 30), drawRect = FALSE)
+  vioplot(elect_means[[i]][[4]], at = 3, add = TRUE, col = addTrans("blue", 30), drawRect = FALSE)
+
+  points(melt_elect$value ~ I(as.numeric(melt_elect$variable)+ runif(nrow(melt_elect), -.1, .1 )),
+         pch = 21,
+         bg = "grey")
   means <- aggregate(melt_elect$value, by = list(melt_elect$variable), FUN = function(x){mean(x, na.rm = TRUE)})
 
-  segments(x0 = c(0.9, 1.9, 2.9), y0 = means$x, x1 = c(1.1, 2.1, 3.1), lwd = 3)
+  segments(x0 = c(0.85, 1.85, 2.85), y0 = means$x, x1 = c(1.15, 2.15, 3.15), lwd = 3)
 
 
   # segments(x0 = 1, x1 = 2, y0 = melt_elect[melt_elect$variable == "Dead", "value"],
@@ -355,18 +380,21 @@ for(i in 1:ntypes){
   }
 
   if(i %in% c(3,4)){
-  axis(1, at = c(1,2,3), labels =  c("Dead", "Live", "Interspace"))
+  axis(1, at = c(1,2,3), labels =  c("Dead", "Live", "Inter"))
   }
 
-  text(x = c(1,2,3), y = 0.9, labels = pvals[2:4])
+  # text(x = c(1,2,3), y = 0.9, labels = pvals[4:6])
   mtext(text = types[i], outer = FALSE, side = 3, line = 0.3)
   mtext(text = paste0("(", letters[i], ")"), outer = FALSE, side = 3, at = 0.5, line = 0.3)
-  mtext(text = "Electivity", outer = TRUE, side = 2, line = 1.5)
+  mtext(text = "Ivlev's E", outer = TRUE, side = 2, line = 1.5)
 
+
 
 }
 
 dev.off()
+
+
 # 
 # #-----------------------------------------------------------------------------------------------
 # # For whole study area

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