Add files via upload

BC.csv

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+cond,weight(g),femur(mm)
+no_light,0.196,5.7
+no_light,0.145,5.5
+no_light,0.232,5.1
+no_light,0.185,5.05
+no_light,0.147,4.8
+no_light,0.108,4.55
+no_light,0.132,4.3
+no_light,0.132,4.7
+no_light,0.142,5.1
+no_light,0.142,5.5
+no_light,0.128,5.2
+no_light,0.131,5
+no_light,0.13,5.3
+no_light,0.115,4.5
+no_light,0.121,4.7
+light,0.209,5.15
+light,0.175,4.75
+light,0.196,5.05
+light,0.223,5.35
+light,0.244,5.6
+light,0.156,4.25
+light,0.176,4.45
+light,0.283,4.9
+light,0.18,4.85
+light,0.157,4.55
+light,0.211,5.25
+light,0.213,5.75
+light,0.181,4.7
+light,0.295,6
+light,0.15,4.6
+light,0.158,5.05
+light,0.147,4.1
+light,0.344,5.3
+light,0.178,5.3
+light,0.202,4.6
+light,0.188,5
+light,0.27,5.5
+light,0.189,5.55

README.txt

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+This data is from August 13-23 of 2018 on the Saint-Symphorien suspension bridge over the Loire River in Tours, France (47.399158° N, 0.692519° E). This 350m pedestrian bridge consists of 324 individual metal side railing panels within which orb-weaving spiders (Family Araneidae) constructed webs. Bridge panels had an alternating lighting pattern; every other panel was lit from above by a blue fluorescent tube light, which was absent from the other panels. These two conditions acted as a natural block-on block-off design within I measured spider abundance, web-dimensions, prey capture success, and body condition. In artificially lit conditions, spiders caught more prey with smaller webs, and had higher body condition. However, there were fewer spiders with active webs in those lit areas. 
+
+BC.csv contains data on individual Larinioides sclopetarius body size (femur length in mm) and weight (in g). Body condition can be calculated with the attached R script.
+TOURS.csv contains data on spider abundance in bridge panels. This is the number of spiders which had active orb webs in each panel (either lit or unlit).
+webs.csv contains data on spider web dimensions and prey capture success. These web dimensions can be used to calculate vertical web asymmetry and total web catch area.
+
+Tours_Spider.R contains the scripts to read all three csv files above, analyze the data, and plot the data.

Spider_Light.R

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+
+
+
+# > sessionInfo()
+# R version 3.5.1 (2018-07-02)
+# Platform: x86_64-w64-mingw32/x64 (64-bit)
+# Running under: Windows >= 8 x64 (build 9200)
+# 
+# Matrix products: default
+# 
+# locale:
+#   [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
+#   [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
+#   [5] LC_TIME=English_United States.1252    
+# 
+# attached base packages:
+#   [1] grid      stats     graphics  grDevices utils     datasets  methods   base     
+# 
+# other attached packages:
+#   [1] cowplot_0.9.3 ggpubr_0.1.8  magrittr_1.5  ggplot2_3.0.0 DHARMa_0.2.4  glmmTMB_0.2.3 dplyr_0.8.1  
+
+
+##### Load Libraries: ####
+library(dplyr)
+library(glmmTMB)
+library(DHARMa)
+library("ggpubr")
+library(grid)
+library(cowplot)
+
+#### LOAD DATA ####
+
+### Abundance
+TOURS<-read.csv("TOURS.csv")
+TOURS$Light[TOURS$Light=="0"]<-"No light"
+TOURS$Light[TOURS$Light=="1"]<-"Light"
+
+### WEBS
+WEB<-read.csv("webs.csv")
+WEB$Area=pi*(WEB$Dv/2)*(WEB$Dh/2)-(pi*(WEB$Dfz/2)^2) # calculate web catch area
+WEB$Asy<-(WEB$Rt-(WEB$Dv-WEB$Rt))/WEB$Dv # calculate web asymmetry
+WEB$Prey[which(WEB$Prey=="NaN")]<-NA
+
+### Body Condition
+SP<-read.csv("BC.csv")
+SP$Wt<-SP$weight.g. ### Wt is measured in grams (g) # clean up names
+SP$Fe<-SP$femur.mm. ### Fe is measured in milimeter (mm) # clean up names
+SP$cond<-as.character(SP$cond)
+SP$cond[which(SP$cond=="no_light")]<-("No Light") # clean up names
+SP$cond[which(SP$cond=="light")]<-("Light") # clean up names
+SP$cond<-as.factor(SP$cond)
+
+# 
+# 
+# 
+
+### Abundance data ####
+
+## Summary Statistics for ABUNDANCE
+Abun<-group_by(TOURS, Light) %>%
+  dplyr::summarise(
+    count = n(),
+    total = sum(Spiders),
+    mean = mean(Spiders, na.rm=TRUE),
+    SD = sd(Spiders, na.rm=TRUE),
+    median = median(Spiders, na.rm = TRUE),
+    IQR = IQR(Spiders, na.rm = TRUE),
+    min_range = min(Spiders, na.rm=TRUE),
+    max_range = max(Spiders, na.rm=TRUE)
+      )
+
+Abun
+
+## Statistical testing of abundance data:
+
+# plot(density(TOURS$Spiders)) # look at distribution
+# var(TOURS$Spiders)/mean(TOURS$Spiders) # overdispersed
+
+mod1<-glmmTMB(Spiders ~ Light, family=nbinom1(link="log"), data=TOURS)
+# plot(simulateResiduals(mod1)) # check residuals = good fit
+
+# check model output:
+summary(mod1)
+
+#95% confidence interval
+# exp(0.32024+1.96*0.09572)  # Upper 95% CI
+# exp(0.32024-1.96*0.09572)  # Lower 95% CI
+# exp(0.32024) # Parameter estimate
+
+
+
+### WEB MEASUREMENTS ####
+
+### WEB AREA ###
+
+## summary stats
+Web<-group_by(WEB, Condition) %>%
+  dplyr::summarise(
+    count = n(),
+    median = median(Area, na.rm = TRUE),
+    mean = mean(Area, na.rm=TRUE),
+    SD = sd(Area, na.rm=TRUE)
+  )
+Web
+
+# plot(density(WEB$Area)) # look at distribution
+# var(WEB$Area)/mean(WEB$Area) # Overdispersed?
+
+## Statistical testing
+mod2<-glm(Area~Condition,family=Gamma(link="log"),data=WEB)
+# plot(simulateResiduals(mod2)) # check residual plots
+
+summary(mod2)
+
+# exp(0.43308) # parameter estimate
+# exp(0.43308+ 1.96*0.12917) # Upper 95% CI
+# exp(0.43308- 1.96*0.12917) # Lower 95% CI
+
+
+
+### VERTICAL WEB ASYMMETRY ###
+
+# plot(density(WEB$Asy)) # look at distribution
+
+WEB$Condition<-relevel(WEB$Condition,"No Light")
+mod3<-lm(Asy ~ Condition + Area, data=WEB)
+
+# qqnorm(resid(mod3)) # check residual plots
+# qqline(resid(mod3)) # check residual plots
+
+summary(mod3)
+confint(mod3)
+
+## summary stats VWA
+group_by(WEB, Condition) %>%
+  summarise(
+    count = n(),
+    median = median(Asy, na.rm = TRUE),
+    mean = mean(Asy, na.rm=TRUE),
+    SD = sd(Asy, na.rm=TRUE)
+  )
+
+
+
+
+### PREY CAPTURE ####
+plot(density(PREY$Prey)) # plot data distribution
+PREY<-WEB[!is.na(WEB$Prey),] # remove NA to look at dispersion
+var(PREY$Prey)/mean(PREY$Prey) # overdispersed
+
+mod4<-glmmTMB(Prey ~  Condition+Area, data=PREY, family=nbinom2(link="log"))
+# plot(simulateResiduals(mod4)) # check residual plots
+
+summary(mod4) 
+
+# exp(1.9376593) # parameter estimate
+# exp(1.9376593+1.96*0.1894091) # Upper 95% CI
+# exp(1.9376593-1.96*0.1894091) # Lower 95% CI
+
+## Prey capture summary statistics
+group_by(WEB,Condition) %>% 
+  summarize(
+    count=length(which(!is.na(Prey))),
+    mean=mean(Prey,na.rm=TRUE),
+    SD = sd(Prey, na.rm=TRUE))
+
+
+
+
+## Body Condition ####
+
+## Summary stats for body condition
+group_by(SP, cond) %>% 
+  summarise(
+    count = n(),
+    meanWt=mean(Wt),
+    meanFe=mean(Fe)
+  )
+
+## Generate residuals for Body Condition (following Jakob et al. 1996)
+plot(Wt~Fe, data=SP)
+
+Bod.lm = lm(Wt~Fe, data=SP) # run lm for residuals
+# plot(Bod.lm) # visualize
+
+SP$res<-resid(Bod.lm) # get residuals into original dataset
+
+## Summary stats for Residuals
+Res<-group_by(SP, cond) %>% 
+  dplyr::summarise(
+    count = n(),
+    mean=mean(res),
+    SD = sd(res),
+    median = median(res, na.rm = TRUE),
+    IQR = IQR(res, na.rm = TRUE),
+    min_range = min(res, na.rm=TRUE),
+    max_range = max(res, na.rm=TRUE)
+  ) 
+
+# Res #View summary stats
+
+# plot(density(SP$res)) # look at distribution
+
+lm1<-lm(res ~ cond, data=SP)
+# qqnorm(resid(lm1))  # check residual plots
+# qqline(resid(lm1))
+
+summary(lm1)
+confint(lm1)
+
+lm2<-lm(Fe ~ cond, data=SP)
+# qqnorm(resid(lm2)) # check residual plots
+# qqline(resid(lm2))
+
+summary(lm2)
+confint(lm2)
+
+# Summary table of body size (Femur length)
+group_by(SP, cond) %>% 
+  dplyr::summarise(
+    count = n(),
+    mean=mean(Fe),
+    SD = sd(Fe),
+    median = median(Fe, na.rm = TRUE),
+    IQR = IQR(Fe, na.rm = TRUE),
+    min_range = min(Fe, na.rm=TRUE),
+    max_range = max(Fe, na.rm=TRUE)
+  ) 
+
+
+
+
+
+
+#### Graphical Visualization of Data ####
+
+## Figure 3
+### Visualization of abundance data 
+
+grob <- grobTree(textGrob("*", x=0.475,  y=0.9, hjust=0,
+                          gp=gpar(col="black", fontsize=36, fontface="bold")))
+
+A<-ggboxplot(data=TOURS, x = "Light", y = "Spiders", fill = "Light", palette = c("#00AFBB", "#FC4E07"),
+             ylab ="Spiders / \nbridge panel \n", xlab = "", legend="FALSE")+
+  theme(axis.text=element_text(size=12),
+        axis.title=element_text(size=14))+
+  scale_y_continuous(breaks=c(5,10,15,20,25))+
+  annotation_custom(grob)
+
+### Web Area
+
+B<-ggboxplot(data=WEB, x = "Condition", y = "Area", fill = "Condition", order=c("No Light","Light"),palette = c("#00AFBB", "#FC4E07"),
+             ylab = expression(paste('Web area ',(cm^2))), xlab = "", legend="FALSE")+
+  theme(axis.text=element_text(size=12),
+        axis.title=element_text(size=14))+
+  scale_y_continuous(breaks=c(250,500,750,1000,1250))+
+  annotation_custom(grob)
+
+
+### Body Condition plot 
+C<-ggboxplot(data=SP, x = "cond", y = "res", fill = "cond", order=c("No Light","Light"), 
+             palette = c("#00AFBB", "#FC4E07"),
+             ylab ="Body Condition\n(Residual Index) \n", xlab = "\nCondition", legend="FALSE")+
+  theme(axis.text=element_text(size=12),
+        axis.title=element_text(size=14))+
+  geom_hline(yintercept=0,linetype=2)+
+  scale_y_continuous(breaks=c(-0.05,0.00,0.05), limits = c(-.05,.05))+
+  annotation_custom(grob)
+
+grob <- grobTree(textGrob("N.S.", x=0.435,  y=0.9, hjust=0,
+                          gp=gpar(col="black", fontsize=28, fontface="bold")))
+
+SP$sFe<-scale(SP$Fe)
+D<-ggboxplot(data=SP, x = "cond", y = "sFe", fill = "cond", order=c("No Light","Light"), 
+             palette = c("#00AFBB", "#FC4E07"),
+             ylab ="Body Size\n(Scaled Femur length) \n", xlab = "\nCondition", legend="FALSE")+
+  theme(axis.text=element_text(size=12),
+        axis.title=element_text(size=14))+
+  geom_hline(yintercept=0,linetype=2)+
+  annotation_custom(grob)
+
+
+## Making final plot of Figure 3 - combining ggplots in a grid of 3 panels
+
+plot_grid(A +rremove("x.text"),B+rremove("x.text"),C,D, ncol=2,align="hv",labels = "AUTO",label_x=.1,label_y=1,hjust=-3, label_size=22)
+# 
+# 
+# 
+# 
+# 
+# 
+# 
+# 
+
+### Figure 4
+Asy1<-ggplot(WEB,aes(Area,Asy, color=Condition, fill=Condition))+
+  geom_point(size=2)+
+  labs(color="Legend text")+
+  geom_smooth(method='lm', se=F)+
+  xlab(expression(paste('\nWeb catch area ',(cm^2))))+
+  ylab("Vertical web asymmetry\n")+
+  guides(fill=FALSE)+
+  theme_classic()+
+  geom_hline(yintercept=0,linetype=2)+
+    theme(axis.text=element_text(size=12), 
+          axis.title=element_text(size=14),
+          legend.position = c(0.7, 0.7),
+          legend.text = element_text(size=14),
+          legend.title = element_blank())
+
+
+# 
+# 
+# 
+# 
+# 
+
+### Figure 5
+prey1<-ggplot(WEB,aes(Area,Prey, color=Condition, fill=Condition))+
+  geom_point(size=2)+
+  labs(color="Legend text")+
+  geom_smooth(method="lm", alpha=.1, formula=y~x, size=1.1,
+              fullrange=F, se=F)+
+  xlab(expression(paste('Web catch area ',(cm^2))))+
+  ylab("Number of captured prey per web\n")+
+  guides(fill=FALSE)+
+  theme_classic()+
+  scale_y_continuous(breaks=seq(0,100,by=10))+
+  theme(axis.text=element_text(size=14), 
+        axis.title=element_text(size=18),
+        legend.position = c(0.85, 0.8),
+        legend.text = element_text(size=20),
+        legend.title = element_blank())
+
+grob <- grobTree(textGrob("*", x=0.475,  y=0.9, hjust=0,
+                          gp=gpar(col="black", fontsize=36, fontface="bold")))
+
+prey2<-ggboxplot(data=WEB, x = "Condition", y = "Prey", fill = "Condition", order=c("No Light","Light"),palette = c("#00AFBB", "#FC4E07"),
+                ylab ="", xlab = "", legend="FALSE")+
+  scale_y_continuous(breaks=seq(0,100,by=10))+
+  theme(axis.text.x=element_text(size=12),
+        axis.title.x=element_text(size=14),
+        axis.text.y = element_blank(),
+        axis.title.y = element_blank())+
+  annotation_custom(grob)
+
+plot_grid(prey1,prey2, align="hv")