################################################################################################################################################################# # --------------------------------------------------------------------------------------------------------------------------------------------------------------# # Supplementary R code for: # # Exploration behavior differs between Darwin’s finch species and predicts territory defense and hatching success # # Published in Behavioral Ecology and Sociobiology # # Andrew C. Katsis [1,2*], Diane Colombelli-Négrel [1], Çağlar Akçay [3,4], Lauren K. Common [1,2], Jefferson García-Loor [2] & Sonia Kleindorfer [1,2] # # 1. College of Science and Engineering, Flinders University, Adelaide, Australia # # 2. Konrad Lorenz Research Center for Behavior and Cognition and Department of Behavioral and Cognitive Biology, University of Vienna, Vienna, Austria # # 3. Department of Psychology, Koç University, Istanbul, Turkey # # 4. School of Life Sciences, Anglia Ruskin University, Cambridge, United Kingdom # # * corresponding author: andrewckatsis@gmail.com # # --------------------------------------------------------------------------------------------------------------------------------------------------------------# ################################################################################################################################################################# # R packages required for data analysis and figure creation library(effects) #version 4.2-2 library(car) #version 3.1-1 library(emmeans) #version 1.8.5 library(multcomp) #version 1.4-23 library(lattice) #version 0.20-45 library(gridExtra) #version 2.3 library(grid) #base package library(ggpubr) #version 0.6.0 library(lmtest) #version 0.9-40 library(MuMIn) #version 1.47.5 library(gtools) #version 3.9.4 library(gdata) #version 2.18.0.1 library(ggplot2) #version 3.4.1 library(plyr) #version 1.8.8 library(dplyr) #version 1.1.1 library(plotrix) #version 3.8-2 library(conflicted) #version 1.2.0 library(ggbeeswarm) #version 0.7.1 library(glmmTMB) #version 1.1.6 library(PCAtest) #version 0.0.1 conflict_prefer("rename", "plyr") # Import dataset # Import supplementary data file (sheet: "FinchBehaviorDataset"), naming the dataset "DarwinsFinchBehavior" # Check number of rows in dataset nrow(DarwinsFinchBehavior) #275 ##################################################### ########## PREPARING BEHAVIORAL VARIABLES ########## ##################################################### # ----------------------------------------------------------------------------------- # # ------------- Create separate data sets for each behavioral variable -------------- # # ----------------------------------------------------------------------------------- # # Only trials where boldness was measured was measured DarwinsFinchBoldness <- subset(DarwinsFinchBehavior, MeasuredBoldness == "Y") nrow(DarwinsFinchBoldness) #275 # Only trials where exploration was measured DarwinsFinchExploration <- subset(DarwinsFinchBehavior, MeasuredExploration == "Y") nrow(DarwinsFinchExploration) #162 # Only trials where aggressiveness was measured DarwinsFinchAggressiveness <- subset(DarwinsFinchBehavior, MeasuredAggressiveness == "Y") nrow(DarwinsFinchAggressiveness) # 161 # ----------------------------------------------------------------------------------- # # ------------------------ Make behavioral variables numeric ------------------------ # # ----------------------------------------------------------------------------------- # # Make 'unique sector visits' numeric DarwinsFinchExploration$Exploration_UniqueSectorVisits <- as.numeric(as.character(DarwinsFinchExploration$Exploration_UniqueSectorVisits)) # Make 'total sector visits' numeric DarwinsFinchExploration$Exploration_SectorVisits <- as.numeric(as.character(DarwinsFinchExploration$Exploration_SectorVisits)) # ----------------------------------------------------------------------------------- # # --- Remove cactus finches (CF) and hybrid tree finches (HTF) from all datasets ---- # # ----------------------------------------------------------------------------------- # # Remove CF and HTF from exploration dataset nrow(DarwinsFinchExploration) #162 DarwinsFinchExplorationMain <- subset(DarwinsFinchExploration, Species!="CF" & Species!="Hybrid TF") nrow(DarwinsFinchExplorationMain) #152 # Remove CF and HTF from aggressiveness dataset nrow(DarwinsFinchAggressiveness) #161 DarwinsFinchAggressivenessMain <- subset(DarwinsFinchAggressiveness, Species!="CF" & Species!="Hybrid TF") nrow(DarwinsFinchAggressivenessMain) #151 # Remove CF and HTF from boldness dataset nrow(DarwinsFinchBoldness) #275 DarwinsFinchBoldnessMain <- subset(DarwinsFinchBoldness, Species!="CF" & Species!="Hybrid TF") nrow(DarwinsFinchBoldnessMain) #262 # ------------------------------------------------------ # # ------------ Create PC_Exploration variable----------- # # ------------------------------------------------------ # # Confirm significant correlation between 'total sector visits' and 'unique sector visits' cor.test(DarwinsFinchExplorationMain$Exploration_SectorVisits, DarwinsFinchExplorationMain$Exploration_UniqueSectorVisits, method="spearman", exact=FALSE) # Use PCA to combine 'total sector visits' and 'unique sector visits', retain only one principal component (PC_Exploration) local({ .PC <- princomp(~ Exploration_SectorVisits + Exploration_UniqueSectorVisits, cor=TRUE, data=DarwinsFinchExplorationMain) cat("\nComponent loadings:\n") print(unclass(loadings(.PC))) cat("\nComponent variances:\n") print(.PC$sd^2) cat("\n") print(summary(.PC)) screeplot(.PC) DarwinsFinchExplorationMain <<- within(DarwinsFinchExplorationMain, { PC_Exploration <- .PC$scores[,1] }) }) ############################# ########## RESULTS ########## ############################# #################################################################################### # ---------------------------------------------------------------------------------# # Part A: Between-species differences in boldness, exploration, and aggressiveness # # ---------------------------------------------------------------------------------# #################################################################################### # ----------------------------------------------------------------------------------- # # ------------------------- Species differences in boldness ------------------------- # # ----------------------------------------------------------------------------------- # # Conduct ANOVAs to test whether 'Species' significantly predicts behavioral variable # If 'Species' is significant, conduct post hoc pairwise comparisons between Darwin's finch species nrow(DarwinsFinchBoldnessMain) #262 DarwinsFinchBoldnessMain$Species = relevel(DarwinsFinchBoldnessMain$Species, ref="SGF") ### Processing response, zero-inflated Poisson GLM Model.1a <- glmmTMB(Boldness_ProcessingResponse ~ Sex + Species + Boldness_HandlerID, ziformula = ~1, , data = DarwinsFinchBoldnessMain, family=poisson) Anova(Model.1a, type = 2) multi <- emmeans(Model.1a, ~ Species) pairs(multi, adjust="holm") # posthoc pairwise comparisons ### Back-test response, zero-inflated Poisson GLM Model.1b <- glmmTMB(Boldness_BackTestResponse ~ Sex + Species + Boldness_HandlerID, ziformula= ~1, data = DarwinsFinchBoldnessMain, family=poisson) summary(Model.1b) Anova(Model.1b, type = 2) multi <- emmeans(Model.1b, ~ Species) pairs(multi, adjust="holm") # posthoc pairwise comparisons # ----------------------------------------------------------------------------------- # # ------------------------ Species differences in exploration ----------------------- # # ----------------------------------------------------------------------------------- # # Conduct ANOVAs to test whether 'Species' significantly predicts behavioral variable # If 'Species' is significant, conduct post hoc pairwise comparisons between Darwin's finch species ### PC_Exploration, linear model Model.2a <- lm(PC_Exploration ~ Sex + Species, data=DarwinsFinchExplorationMain) Anova(Model.2a, type=2) #ANOVA multi <- emmeans(Model.2a, ~ Species) pairs(multi, adjust="holm") ###### For supplementary material: Species differences in 'floor use' # Floor use, binomial GLM Model.2b <- glm(Exploration_FloorUse ~ Sex + Species, data=DarwinsFinchExplorationMain, family=binomial(link="logit")) Anova(Model.2b, type=2) multi <- emmeans(Model.2b, ~ Species) pairs(multi, adjust="holm") ###### For supplementary material: Species differences in 'total sector visits' only # Cube-root-transform total sector visits to normalise model residuals DarwinsFinchExplorationMain$Exploration_SectorVisitsCR <- (DarwinsFinchExplorationMain$Exploration_SectorVisits)^(1/3) Model.2c <- lm(Exploration_SectorVisitsCR ~ Sex + Species, data=DarwinsFinchExplorationMain) Anova(Model.2c, type=2) multi <- emmeans(Model.2c, ~ Species) pairs(multi, adjust="holm") # ----------------------------------------------------------------------------------- # # ---------------------- Species differences in aggressiveness ---------------------- # # ----------------------------------------------------------------------------------- # # Conduct ANOVAs to test whether 'Species' significantly predicts behavioral variable # 'Species' is not significant, so we do not conduct post hoc pairwise comparisons # Make 'Time Near Mirror' a numerical variable DarwinsFinchAggressivenessMain$Aggressiveness_TimeNearMirror <- as.numeric(as.character(DarwinsFinchAggressivenessMain$Aggressiveness_TimeNearMirror)) Model.3a <- glm(Aggressiveness_AttackedMirror ~ Sex + Species, data=DarwinsFinchAggressivenessMain, family=binomial(link="logit")) Anova(Model.3a, type=2) # No significant effect of species or sex on aggressiveness (attacked mirror) Model.3b <- glmmTMB(Aggressiveness_TimeNearMirror^0.5 ~ Sex + Species, ziformula = ~Species, data = DarwinsFinchAggressivenessMain, family=gaussian) Anova(Model.3b, type=2) # No significant effect of species or sex on aggressiveness (time near mirror, square-root-transformed) # ---------------------------------------------------------------------------------------------------------# # ---------------------------------------------------------------------------------------------------------# # Part A: For supplementary material: Between-site differences in boldness, exploration and aggressiveness # # ---------------------------------------------------------------------------------------------------------# # ---------------------------------------------------------------------------------------------------------# # ----------------------------------------------------------------------------------- # # ---------------------- Comparing boldness between study sites --------------------- # # ----------------------------------------------------------------------------------- # # -------------------------------------------------- # # ---- Study site differences in boldness (SGF) ---- # # -------------------------------------------------- # # Create dataset with only SGF nrow(DarwinsFinchBoldnessMain) #262 DarwinsFinchBoldnessMainOnlySGF <- subset (DarwinsFinchBoldnessMain, Species=="SGF") nrow(DarwinsFinchBoldnessMainOnlySGF) #152 #### PROCESSING RESPONSE Model.4a <- glmmTMB(Boldness_ProcessingResponse ~ Site + Boldness_HandlerID, ziformula = ~1, data = DarwinsFinchBoldnessMainOnlySGF, family=poisson) Anova(Model.4a, type = 2) #### BACK-TEST RESPONSE Model.4b <- glmmTMB(Boldness_BackTestResponse ~ Site + Boldness_HandlerID, ziformula = ~1, data = DarwinsFinchBoldnessMainOnlySGF, family=poisson) Anova(Model.4b, type = 2) # -------------------------------------------------- # # ---- Study site differences in boldness (STF) ---- # # -------------------------------------------------- # # Create dataset with only STF nrow(DarwinsFinchBoldnessMain) #262 DarwinsFinchBoldnessMainOnlySTF <- subset (DarwinsFinchBoldnessMain, Species=="STF") nrow(DarwinsFinchBoldnessMainOnlySTF) #61 #### PROCESSING RESPONSE Model.5a <- glmmTMB(Boldness_ProcessingResponse ~ Site + Boldness_HandlerID, ziformula= ~1, data =DarwinsFinchBoldnessMainOnlySTF, family=poisson) Anova(Model.5a , type = 2) #### BACK-TEST RESPONSE Model.5b <- glmmTMB(Boldness_BackTestResponse ~ Site + Boldness_HandlerID, ziformula = ~1, data = DarwinsFinchBoldnessMainOnlySTF, family=poisson) Anova(Model.5b, type = 2) multi <- emmeans(Model.5b, ~ Site) pairs(multi, adjust="holm") # posthoc pairwise comparisons # -------------------------------------------------- # # ---- Study site differences in boldness (MTF) ---- # # -------------------------------------------------- # # Create dataset with only MTF nrow(DarwinsFinchBoldnessMain) #262 DarwinsFinchBoldnessMainOnlyMTF <- subset (DarwinsFinchBoldnessMain, Species=="MTF") nrow(DarwinsFinchBoldnessMainOnlyMTF) #24 #### PROCESSING RESPONSE Model.6a <- glmmTMB(Boldness_ProcessingResponse ~ Site + Boldness_HandlerID, ziformula = ~1, data = DarwinsFinchBoldnessMainOnlyMTF, family=poisson) Anova(Model.6a, type = 2) #### BACK-TEST RESPONSE Model.6b <- glmmTMB(Boldness_BackTestResponse ~ Site + Boldness_HandlerID, ziformula = ~1, data = DarwinsFinchBoldnessMainOnlyMTF, family=poisson) Anova(Model.6b, type = 2) multi <- emmeans(Model.6b, ~ Site) pairs(multi, adjust="holm") # posthoc pairwise comparisons # ----------------------------------------------------------------------------------- # # -------------------- Comparing exploration between study sites -------------------- # # ----------------------------------------------------------------------------------- # # ----------------------------------------------------- # # ---- Study site differences in exploration (SGF) ---- # # ----------------------------------------------------- # # Create dataset with only SGF nrow(DarwinsFinchExplorationMain) #152 DarwinsFinchExplorationMainOnlySGF <- subset (DarwinsFinchExplorationMain, Species=="SGF") nrow(DarwinsFinchExplorationMainOnlySGF) #75 Model.7 <- lm(PC_Exploration ~ Site, data=DarwinsFinchExplorationMainOnlySGF) AICc(Model.7) Anova(Model.7, type=2) # ----------------------------------------------------- # # ---- Study site differences in exploration (STF) ---- # # ----------------------------------------------------- # # Create dataset with only STF nrow(DarwinsFinchExplorationMain) #152 DarwinsFinchExplorationMainOnlySTF <- subset (DarwinsFinchExplorationMain, Species=="STF") nrow(DarwinsFinchExplorationMainOnlySTF) #46 Model.8 <- lm(PC_Exploration ~ Site, data=DarwinsFinchExplorationMainOnlySTF) AICc(Model.8) Anova(Model.8, type=2) multi <- emmeans(Model.8, ~ Site) pairs(multi, adjust="holm") # ----------------------------------------------------- # # ---- Study site differences in exploration (MTF) ---- # # ----------------------------------------------------- # # Create dataset with only MTF nrow(DarwinsFinchExplorationMain) #152 DarwinsFinchExplorationMainOnlyMTF <- subset (DarwinsFinchExplorationMain, Species=="MTF") nrow(DarwinsFinchExplorationMainOnlyMTF) #21 Model.9 <- lm(PC_Exploration ~ Site, data=DarwinsFinchExplorationMainOnlyMTF) AICc(Model.9) Anova(Model.9, type=2) multi <- emmeans(Model.9, ~ Site) pairs(multi, adjust="holm") # ----------------------------------------------------------------------------------- # # --------- Comparing aggressiveness (time near mirror) between study sites --------- # # ----------------------------------------------------------------------------------- # # -------------------------------------------------------- # # ---- Study site differences in aggressiveness (SGF) ---- # # -------------------------------------------------------- # # Create dataset with only SGF nrow(DarwinsFinchAggressivenessMain) #151 DarwinsFinchAggressivenessMainOnlySGF <- subset (DarwinsFinchAggressivenessMain, Species=="SGF") nrow(DarwinsFinchAggressivenessMainOnlySGF) #75 Model.10 <- glmmTMB(Aggressiveness_TimeNearMirror ~ Site, ziformula = ~1, data = DarwinsFinchAggressivenessMainOnlySGF, family=gaussian) Anova(Model.10, type = 2) # -------------------------------------------------------- # # ---- Study site differences in aggressiveness (STF) ---- # # -------------------------------------------------------- # # Create dataset with only STF nrow(DarwinsFinchAggressivenessMain) #151 DarwinsFinchAggressivenessMainOnlySTF <- subset (DarwinsFinchAggressivenessMain, Species=="STF") nrow(DarwinsFinchAggressivenessMainOnlySTF) #45 Model.11 <- glmmTMB(Aggressiveness_TimeNearMirror ~ Site, ziformula = ~1, data = DarwinsFinchAggressivenessMainOnlySTF, family=gaussian) Anova(Model.11, type = 2) # -------------------------------------------------------- # # ---- Study site differences in aggressiveness (MTF) ---- # # -------------------------------------------------------- # # Create dataset with only MTF nrow(DarwinsFinchAggressivenessMain) #151 DarwinsFinchAggressivenessMainOnlyMTF <- subset (DarwinsFinchAggressivenessMain, Species=="MTF") nrow(DarwinsFinchAggressivenessMainOnlyMTF) #21 Model.12 <- glmmTMB(Aggressiveness_TimeNearMirror ~ Site, ziformula = ~1, data = DarwinsFinchAggressivenessMainOnlyMTF, family=gaussian) Anova(Model.12, type = 2) # ----------------------------------------------------------------------------# # ----------------------------------------------------------------------------# # Part A: For supplementary material: Correlations between behavioral traits # # ----------------------------------------------------------------------------# # ----------------------------------------------------------------------------# # --------------------------------------------------------------------------------------------- # # ---- Correlation between boldness variables (back-test response and processing response) ---- # # --------------------------------------------------------------------------------------------- # nrow(DarwinsFinchBoldnessMain) #262 # Spearman's tie-adjusted rank correlation cor.test(DarwinsFinchBoldnessMain$Boldness_ProcessingResponse, DarwinsFinchBoldnessMain$Boldness_BackTestResponse, method="spearman", exact=FALSE) # --------------------------------------------------------------------------------------------- # # ---- Relationship between aggressiveness variables (attacked mirror and time near mirror) --- # # --------------------------------------------------------------------------------------------- # # Subset that only includes birds with mirror stimulation test nrow(DarwinsFinchExplorationMain) #152 DarwinsFinchExplorationMainWithMirror <- subset (DarwinsFinchExplorationMain, MeasuredAggressiveness!="N") nrow(DarwinsFinchExplorationMainWithMirror) #151 # Make 'Time Near Mirror' as numerical variable DarwinsFinchExplorationMainWithMirror$Aggressiveness_TimeNearMirror <- as.numeric(as.character(DarwinsFinchExplorationMainWithMirror$Aggressiveness_TimeNearMirror)) # Time near mirror vs Attacked mirror wilcox.test(Aggressiveness_TimeNearMirror ~ Aggressiveness_AttackedMirror, data = DarwinsFinchExplorationMainWithMirror) # --------------------------------------------------------------------------------------------- # # -------------------- Relationships between exploration and aggressiveness ------------------- # # --------------------------------------------------------------------------------------------- # # --------------------------------------------------------------------------------------- # # -------------------------- PC_Exploration vs Attacked mirror -------------------------- # # --------------------------------------------------------------------------------------- # # All species combined wilcox.test(PC_Exploration ~ Aggressiveness_AttackedMirror, data = DarwinsFinchExplorationMainWithMirror) # Create new datasets for each species DarwinsFinchExplorationMainWithMirror_SGF <- subset(DarwinsFinchExplorationMainWithMirror, Species == "SGF") nrow(DarwinsFinchExplorationMainWithMirror_SGF) # 75 DarwinsFinchExplorationMainWithMirror_MGF <- subset(DarwinsFinchExplorationMainWithMirror, Species == "MGF") nrow(DarwinsFinchExplorationMainWithMirror_MGF) # 10 DarwinsFinchExplorationMainWithMirror_STF <- subset(DarwinsFinchExplorationMainWithMirror, Species == "STF") nrow(DarwinsFinchExplorationMainWithMirror_STF) # 45 DarwinsFinchExplorationMainWithMirror_MTF <- subset(DarwinsFinchExplorationMainWithMirror, Species == "MTF") nrow(DarwinsFinchExplorationMainWithMirror_MTF) # 21 # Separate tests per species wilcox.test(PC_Exploration ~ Aggressiveness_AttackedMirror, data = DarwinsFinchExplorationMainWithMirror_SGF) wilcox.test(PC_Exploration ~ Aggressiveness_AttackedMirror, data = DarwinsFinchExplorationMainWithMirror_MGF) wilcox.test(PC_Exploration ~ Aggressiveness_AttackedMirror, data = DarwinsFinchExplorationMainWithMirror_STF, exact = FALSE) wilcox.test(PC_Exploration ~ Aggressiveness_AttackedMirror, data = DarwinsFinchExplorationMainWithMirror_MTF, exact = FALSE) # --------------------------------------------------------------------------------------- # # -------------------------- PC_Exploration vs Time Near Mirror ------------------------- # # --------------------------------------------------------------------------------------- # # All species combined # Comparing model fit with and without 'Time near mirror' as a quadratic term # PC_Exploration vs Time Near Mirror (linear term) Model.13a <- lm(PC_Exploration ~ Aggressiveness_TimeNearMirror, DarwinsFinchExplorationMainWithMirror) Anova(Model.13a) # PC_Exploration vs Time Near Mirror (quadratic term) Model.13b <- lm(PC_Exploration ~ Aggressiveness_TimeNearMirror + I(Aggressiveness_TimeNearMirror^2), DarwinsFinchExplorationMainWithMirror) Anova(Model.13b) # Akaike's Information Criteria (AIC) for linear and quadratic models AIC(Model.13a, Model.13b) #Model 13a = 512 , Model 13b = 495 # Model.13b (with quadratic term) does a better job explaining our data # Separate tests per species # SGF only # PC_Exploration and Time Near Mirror (quadratic term) Model.14a <- lm(PC_Exploration ~ Aggressiveness_TimeNearMirror, DarwinsFinchExplorationMainWithMirror_SGF) Anova(Model.14a) Model.14b <- lm(PC_Exploration ~ Aggressiveness_TimeNearMirror + I(Aggressiveness_TimeNearMirror^2), DarwinsFinchExplorationMainWithMirror_SGF) Anova(Model.14b) AIC(Model.14a, Model.14b) #Quadratic term does improve model fit # MGF only Model.15a <- lm(PC_Exploration ~ Aggressiveness_TimeNearMirror, DarwinsFinchExplorationMainWithMirror_MGF) Anova(Model.15a) Model.15b <- lm(PC_Exploration ~ Aggressiveness_TimeNearMirror + I(Aggressiveness_TimeNearMirror^2), DarwinsFinchExplorationMainWithMirror_MGF) Anova(Model.15b) AIC(Model.15a, Model.15b) #Quadratic term doesn't improve model fit, so we measure correlation using a Spearman's correlation test cor.test(DarwinsFinchExplorationMainWithMirror_MGF$PC_Exploration, DarwinsFinchExplorationMainWithMirror_MGF$Aggressiveness_TimeNearMirror, method="spearman") # STF only Model.16a <- lm(PC_Exploration ~ Aggressiveness_TimeNearMirror, DarwinsFinchExplorationMainWithMirror_STF) Anova(Model.16a) Model.16b <- lm(PC_Exploration ~ Aggressiveness_TimeNearMirror + I(Aggressiveness_TimeNearMirror^2), DarwinsFinchExplorationMainWithMirror_STF) Anova(Model.16b) AIC(Model.16a, Model.16b) #Quadratic term does improve model fit # MTF only Model.17a <- lm(PC_Exploration ~ Aggressiveness_TimeNearMirror, DarwinsFinchExplorationMainWithMirror_MTF) Anova(Model.17a) Model.17b <- lm(PC_Exploration ~ Aggressiveness_TimeNearMirror + I(Aggressiveness_TimeNearMirror^2), DarwinsFinchExplorationMainWithMirror_MTF) Anova(Model.17b) AIC(Model.17a, Model.17b) #Quadratic term doesn't improve model fit, so we measure correlation using a Spearman's correlation test cor.test(DarwinsFinchExplorationMainWithMirror_MTF$PC_Exploration, DarwinsFinchExplorationMainWithMirror_MTF$Aggressiveness_TimeNearMirror, method="spearman", exact=FALSE) # --------------------------------------------------------------------------------------- # # -------------------- Relationships between exploration and boldness ------------------- # # --------------------------------------------------------------------------------------- # # Create separate datasets for each species DarwinsFinchExplorationMain_SGF <- subset(DarwinsFinchExplorationMain, Species == "SGF") nrow(DarwinsFinchExplorationMain_SGF) # 75 DarwinsFinchExplorationMain_MGF <- subset(DarwinsFinchExplorationMain, Species == "MGF") nrow(DarwinsFinchExplorationMain_MGF) # 10 DarwinsFinchExplorationMain_STF <- subset(DarwinsFinchExplorationMain, Species == "STF") nrow(DarwinsFinchExplorationMain_STF) # 46 DarwinsFinchExplorationMain_MTF <- subset(DarwinsFinchExplorationMain, Species == "MTF") nrow(DarwinsFinchExplorationMain_MTF) # 21 # ------------------------------------------------------------------------------------------ # # -------------------------- PC_Exploration vs Back-test response -------------------------- # # ------------------------------------------------------------------------------------------ # # All species combined cor.test(DarwinsFinchExplorationMain$Boldness_BackTestResponse, DarwinsFinchExplorationMain$PC_Exploration, method="spearman", exact=FALSE) # Separate tests per species cor.test(DarwinsFinchExplorationMain_SGF$Boldness_BackTestResponse, DarwinsFinchExplorationMain_SGF$PC_Exploration, method="spearman", exact=FALSE) cor.test(DarwinsFinchExplorationMain_MGF$Boldness_BackTestResponse, DarwinsFinchExplorationMain_MGF$PC_Exploration, method="spearman", exact=FALSE) cor.test(DarwinsFinchExplorationMain_STF$Boldness_BackTestResponse, DarwinsFinchExplorationMain_STF$PC_Exploration, method="spearman", exact=FALSE) cor.test(DarwinsFinchExplorationMain_MTF$Boldness_BackTestResponse, DarwinsFinchExplorationMain_MTF$PC_Exploration, method="spearman", exact=FALSE) # ------------------------------------------------------------------------------------------- # # -------------------------- PC_Exploration vs Processing response -------------------------- # # ------------------------------------------------------------------------------------------- # # All species combined cor.test(DarwinsFinchExplorationMain$Boldness_ProcessingResponse, DarwinsFinchExplorationMain$PC_Exploration, method="spearman", exact=FALSE) # Separate tests per species cor.test(DarwinsFinchExplorationMain_SGF$Boldness_ProcessingResponse, DarwinsFinchExplorationMain_SGF$PC_Exploration, method="spearman", exact=FALSE) cor.test(DarwinsFinchExplorationMain_MGF$Boldness_ProcessingResponse, DarwinsFinchExplorationMain_MGF$PC_Exploration, method="spearman", exact=FALSE) cor.test(DarwinsFinchExplorationMain_STF$Boldness_ProcessingResponse, DarwinsFinchExplorationMain_STF$PC_Exploration, method="spearman", exact=FALSE) cor.test(DarwinsFinchExplorationMain_MTF$Boldness_ProcessingResponse, DarwinsFinchExplorationMain_MTF$PC_Exploration, method="spearman", exact=FALSE) #####################################################################################3 # -----------------------------------------------------------------------------------# # Part B: Relationship between behavioral traits and territory defense in the wild # # -----------------------------------------------------------------------------------# ###################################################################################### # Only include birds with successful playback trial nrow(DarwinsFinchBoldnessMain) #262 DarwinsFinchBoldnessMainPlayback <- subset(DarwinsFinchBoldnessMain, MeasuredTerritoryDefense == "Y") nrow(DarwinsFinchBoldnessMainPlayback) #48 # ------------------------------------------------------------ # # ------------- Creation of PC_Playback variable ------------- # # ------------------------------------------------------------ # # Make playback trial response variables numeric DarwinsFinchBoldnessMainPlayback$Playback_Crosses <- as.numeric(as.character(DarwinsFinchBoldnessMainPlayback$Playback_Crosses)) DarwinsFinchBoldnessMainPlayback$Playback_Flights <- as.numeric(as.character(DarwinsFinchBoldnessMainPlayback$Playback_Flights)) DarwinsFinchBoldnessMainPlayback$Playback_Latency <- as.numeric(as.character(DarwinsFinchBoldnessMainPlayback$Playback_Latency)) DarwinsFinchBoldnessMainPlayback$Playback_MinDist <- as.numeric(as.character(DarwinsFinchBoldnessMainPlayback$Playback_MinDist)) DarwinsFinchBoldnessMainPlayback$Playback_TimeWithin1m <- as.numeric(as.character(DarwinsFinchBoldnessMainPlayback$Playback_TimeWithin1m)) DarwinsFinchBoldnessMainPlayback$Playback_TimeWithin5m <- as.numeric(as.character(DarwinsFinchBoldnessMainPlayback$Playback_TimeWithin5m)) DarwinsFinchBoldnessMainPlayback$Playback_TotalVocalisations <- as.numeric(as.character(DarwinsFinchBoldnessMainPlayback$Playback_TotalVocalisations)) # Principal component analysis (PCA) combining seven playback response variables local({ .PC <- princomp(~ Playback_Crosses + Playback_Flights + Playback_Latency + Playback_MinDist + Playback_TimeWithin1m + Playback_TimeWithin5m + Playback_TotalVocalisations, cor=TRUE, data=DarwinsFinchBoldnessMainPlayback) cat("\nComponent loadings:\n") print(unclass(loadings(.PC))) cat("\nComponent variances:\n") print(.PC$sd^2) cat("\n") print(summary(.PC)) screeplot(.PC) }) # Create data subset that only includes the seven playback response variables DarwinsFinchBoldnessMainPlaybackPCA <- dplyr::select(DarwinsFinchBoldnessMainPlayback, c('Playback_Crosses' , 'Playback_Flights' , 'Playback_Latency' , 'Playback_MinDist' , 'Playback_TimeWithin1m' , 'Playback_TimeWithin5m' , 'Playback_TotalVocalisations')) # Test whether PCA is biologically meaningful for this dataset, using package 'PCAtest' # Calculate the ψ and φ statistics of the PCA, the distinctness of the first two principal components (eigenvalues), and the significance of the loadings of each variable on these principal components # Number of bootstrapping iterations = 1000, number of permutations = 1000, significance threshold = 0.05 result <- PCAtest( DarwinsFinchBoldnessMainPlaybackPCA, nperm = 1000, nboot = 1000, alpha = 0.05, indload = TRUE, varcorr = TRUE, counter = FALSE, plot = TRUE ) # Significant ψ and φ statistics indicate that PCA is biologically meaningful for this dataset because data have a non-random correlational structure # Only the first principal component (eigenvalue) is significantly distinct, later principal components are not # Therefore, we only analyse PC1 # We also exclude 'Number of vocalisations' from the PCA because it doesn't load significantly on PC1 # Principal component analysis (PCA) combining six playback response variables, after excluding 'number of vocalisations': PC1 is retained as 'PC_Playback' local({ .PC <- princomp(~ Playback_Crosses + Playback_Flights + Playback_Latency + Playback_MinDist + Playback_TimeWithin1m + Playback_TimeWithin5m, cor=TRUE, data=DarwinsFinchBoldnessMainPlayback) cat("\nComponent loadings:\n") print(unclass(loadings(.PC))) cat("\nComponent variances:\n") print(.PC$sd^2) cat("\n") print(summary(.PC)) screeplot(.PC) DarwinsFinchBoldnessMainPlayback <<- within(DarwinsFinchBoldnessMainPlayback, { PC_Playback <- .PC$scores[,1] }) }) # ------------------------------------------------------------------------------- # # ------------- Relationship between boldness and territory defense ------------- # # ------------------------------------------------------------------------------- # # Set SGF as reference category DarwinsFinchBoldnessMainPlayback$Species = relevel(DarwinsFinchBoldnessMainPlayback$Species, ref="SGF") DarwinsFinchBoldnessMainPlayback$Site = relevel(DarwinsFinchBoldnessMainPlayback$Site, ref="Cerro Pajas") # Processing response vs PC_Playback Model.18a <- lm(PC_Playback ~ Species + Site + Boldness_ProcessingResponse + Boldness_HandlerID, data=DarwinsFinchBoldnessMainPlayback) Anova(Model.18a, type=2) # Back-test response vs PC_Playback Model.18b <- lm(PC_Playback ~ Species + Site + Boldness_BackTestResponse + Boldness_HandlerID, data=DarwinsFinchBoldnessMainPlayback) Anova(Model.18b, type=2) # ---------------------------------------------------------------------------------- # # ------------- Relationship between exploration and territory defense ------------- # # ---------------------------------------------------------------------------------- # nrow(DarwinsFinchExplorationMain) #152 DarwinsFinchExplorationMainPlayback <- subset(DarwinsFinchExplorationMain, MeasuredTerritoryDefense == "Y") nrow(DarwinsFinchExplorationMainPlayback) #45 #### Import PC_Playback values from dataset DarwinsFinchBoldnessMainPlayback DarwinsFinchExplorationMainPlayback <- left_join(DarwinsFinchExplorationMainPlayback, DarwinsFinchBoldnessMainPlayback %>% dplyr::select(BirdID, PC_Playback), by = "BirdID") # PC_Exploration vs PC_Playback Model.19 <- lm(PC_Playback ~ Species + Site + PC_Exploration, data=DarwinsFinchExplorationMainPlayback) Anova(Model.19, type=2) # ------------------------------------------------------------------------------------- # # ------------- Relationship between aggressiveness and territory defense ------------- # # ------------------------------------------------------------------------------------- # # Make 'Time near mirror' a numeric variable DarwinsFinchExplorationMainPlayback$Aggressiveness_TimeNearMirror <- as.numeric(as.character(DarwinsFinchExplorationMainPlayback$Aggressiveness_TimeNearMirror)) # Aggressiveness (time near mirror) vs PC_Playback Model.20a <- lm(PC_Playback ~ Species + Site + Aggressiveness_TimeNearMirror, data=DarwinsFinchExplorationMainPlayback) Anova(Model.20a, type=2) # Aggressiveness (attacked mirror) vs PC_Playback Model.20b <- lm(PC_Playback ~ Species + Site + Aggressiveness_AttackedMirror, data=DarwinsFinchExplorationMainPlayback) Anova(Model.20b, type=2) ######################################################### # ------------------------------------------------------# # Part C: Relationship between exploration and fitness # # ------------------------------------------------------# ######################################################### # Males only nrow(DarwinsFinchExplorationMain) #152 DarwinsFinchExplorationMainMalesOnly <- subset(DarwinsFinchExplorationMain, Sex == "M") nrow(DarwinsFinchExplorationMainMalesOnly) #125 # Only birds with known pairing status nrow(DarwinsFinchExplorationMainMalesOnly) #125 DarwinsFinchExplorationMainMalesOnlyPairingStatus <- subset(DarwinsFinchExplorationMainMalesOnly, PairingStatus != "na") nrow(DarwinsFinchExplorationMainMalesOnlyPairingStatus) #49 # ---------------------------------------------------------------------------------------- # # ------------- Relationship between PC_Exploration and male pairing success ------------- # # ---------------------------------------------------------------------------------------- # # With Species*PC_Exploration interaction Model.21b <- glm(PairingStatus ~ Species*PC_Exploration, data=DarwinsFinchExplorationMainMalesOnlyPairingStatus, family=binomial) Anova(Model.21b, type=3) #Interaction not significant # Without Species*PC_Exploration interaction Model.21a <- glm(PairingStatus ~ Species + PC_Exploration, data=DarwinsFinchExplorationMainMalesOnlyPairingStatus, family=binomial) Anova(Model.21a, type=2) #P = 0.067 (visual inspection suggests that non-significant trend is driven by SGF, but Species*PC_Exploration interaction is not significant, see above) # Compare fit of model with and without interaction AIC(Model.21b, Model.21a) #models with and without interaction term are not significantly different (AIC 70.0 vs 68.8) # ------------------------------------------------------------------------------------ # # ------------- Relationship between PC_Exploration and hatching success ------------- # # ------------------------------------------------------------------------------------ # # Limit dataset to paired birds with known nesting outcomes nrow(DarwinsFinchExplorationMainMalesOnlyPairingStatus) #49 DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired <- subset(DarwinsFinchExplorationMainMalesOnlyPairingStatus, PairingStatus == "1" & Hatched != "U") nrow(DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired) #26 # With Species*PC_Exploration interaction Model.22a <- glm(Hatched ~ Species*PC_Exploration, data=DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired, family=binomial) Anova(Model.22a, type=2) # Without Species*PC_Exploration interaction Model.22b <- glm(Hatched ~ Species + PC_Exploration, data=DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired, family=binomial) Anova(Model.22b, type=2) # Compare fit of model with and without interaction AIC(Model.22b, Model.22a) #model without interaction term has lower AIC (36.0 vs 39.3) # ------------------------------------------------------------------------------------ # # ------------- Relationship between PC_Exploration and fledging success ------------- # # ------------------------------------------------------------------------------------ # # With Species*PC_Exploration interaction Model.23a <- glm(Fledged ~ Species*PC_Exploration, data=DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired, family=binomial) Anova(Model.23a, type=3) # Without Species*PC_Exploration interaction Model.23b <- glm(Fledged ~ Species + PC_Exploration, data=DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired, family=binomial) Anova(Model.23b, type=3) # Compare fit of model with and without interaction AIC(Model.23b, Model.23a) #model without interaction term has lower AIC (32.0 vs 35.9) ########################### ########################### ######### FIGURES ######### ########################### ########################### ###################################################################### #### Figure 2: Graph comparing exploration by species (4 species) #### ###################################################################### DarwinsFinchExplorationMain$Species <- factor(DarwinsFinchExplorationMain$Species,levels = c("SGF", "MGF", "STF", "MTF")) #+++++++++++++++++++++++++ # Function to calculate the mean and the standard deviation # for each group #+++++++++++++++++++++++++ # data : a data frame # varname : the name of a column containing the variable #to be summarized # groupnames : vector of column names to be used as # grouping variables data_summary <- function(data, varname, groupnames){ require(plyr) summary_func <- function(x, col){ c(mean = mean(x[[col]], na.rm=TRUE), SEM = ((sd(x[[col]]))/sqrt(length(x[[col]]))), na.rm=TRUE) } data_sum<-ddply(data, groupnames, .fun=summary_func, varname) data_sum <- rename(data_sum, c("mean" = varname)) return(data_sum) } df3 <- data_summary(DarwinsFinchExplorationMain, varname="PC_Exploration", groupnames=c("Species")) df3$Species=as.factor(df3$Species) head(df3) mean_data <- group_by(DarwinsFinchExplorationMain, Species) %>% plyr::summarise(PC_Exploration = mean(PC_Exploration, na.rm = TRUE)) Dots <- ggplot(DarwinsFinchExplorationMain, aes(x=factor(Species), y=PC_Exploration)) + geom_beeswarm(size = 3, shape = 21, fill = "gray", cex = 2.3) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=16), axis.title.y = element_text(colour="black", size=16)) + labs(x="Species", y="PC_Exploration") + theme(axis.text.x= element_text(colour="black", size=16), axis.text.y= element_text(colour="black", size=16), legend.title=element_text(size=15), legend.text=element_text(size=15), legend.key.size = unit(3,"line")) + coord_cartesian(ylim=c(-2, 6)) + scale_y_continuous(breaks=seq(-2, 6, 1)) + annotate("text", x=2, y=5.45, label= "italic(P) < 0.001", size=3, parse=TRUE) + annotate("text", x=2.5, y=4.45, label= "italic(P) == 0.007", size=3, parse=TRUE) + annotate("text", x=2.5, y=5.95, label= "italic(P) < 0.001", size=3, parse=TRUE) + annotate("text", x=3, y=4.95, label= "italic(P) == 0.034", size=3, parse=TRUE) + annotate("segment", x = 1, xend = 3, y = 5.25, yend = 5.25) + annotate("segment", x = 2, xend = 3, y = 4.25, yend = 4.25) + annotate("segment", x = 1, xend = 4, y = 5.75, yend = 5.75) + annotate("segment", x = 2, xend = 4, y = 4.75, yend = 4.75) A4 <- Dots + geom_point(data=df3, aes(x = Species, y = PC_Exploration), shape=16, size=3, colour="red") + geom_errorbar(data=df3, aes(x = Species, ymin=PC_Exploration-SEM, ymax=PC_Exploration+SEM), width=.20, linewidth=1.2, colour="red") #Save plot ggsave(file="Figure2_ExplorationBySpecies.pdf", dpi = 200, width = 15, height = 15, units = c("cm")) ################################################################### #### Figure 3: Exploration vs territory defense (PC_Playback) ##### ################################################################### ggplot(DarwinsFinchExplorationMainPlayback, aes(x=PC_Exploration, y=PC_Playback)) + geom_smooth(method = "lm", se=FALSE, aes(color=Species, linetype=Species), lwd=1.2) + geom_jitter(aes(color=Species, fill=Species, shape=Species), color="black", size=4, width=0, height=0, stroke=0.7) + scale_shape_manual(breaks = c("SGF", "STF", "MTF"), values=c(21:23), labels = c('Small ground finch', 'Small tree finch', 'Medium tree finch')) + scale_fill_manual(breaks = c("SGF", "STF", "MTF"), values=c("black", "grey75", "grey40"), labels = c('Small ground finch', 'Small tree finch', 'Medium tree finch')) + scale_color_manual(breaks = c("SGF", "STF", "MTF"), values=c("black", "grey75", "grey40"), labels = c('Small ground finch', 'Small tree finch', 'Medium tree finch')) + scale_linetype_manual(breaks = c("SGF", "STF", "MTF"), values=c("solid", "dashed", "dotted"), labels = c('Small ground finch', 'Small tree finch', 'Medium tree finch')) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=20), axis.title.y = element_text(colour="black", size=20)) + labs(x="Exploration\n(PC_Exploration)", y="Response to playback\n(PC_Playback)") + theme(axis.text.x= element_text(colour="black", size=20), axis.text.y= element_text(colour="black", size=20), legend.text=element_text(size=16), legend.key.width = unit(4,"line"), legend.key.height = unit(2,"line"), legend.key= element_rect(fill = NA, color = "NA"), legend.box.background = element_rect(colour = "black", linewidth=2.5), legend.margin = margin(-0.2, 0.2, 0, 0, "cm"), legend.title=element_blank()) + coord_cartesian(xlim=c(-2,3)) + scale_x_continuous(breaks=seq(-2, 3, 1)) + coord_cartesian(ylim=c(-5, 4)) + scale_y_continuous(breaks=seq(-4, 4, 2)) + guides(color = guide_legend(keyheight=0.5), default.unit="cm") #Save plot ggsave(file="Figure3_ExplorationAndTerritoryDefense.pdf", dpi = 200, width = 25, height = 15, units = c("cm")) ################################################### #### Figure 4: Exploration vs breeding success #### ################################################### # ------------------------------------------------------------------ # # ------------- Fig. 4a: Exploration vs pairing status ------------- # # ------------------------------------------------------------------ # #set order of species and fitness categories FinchSpecies <- c("Small ground finch","Small tree finch","Medium tree finch") names(FinchSpecies) <- c("SGF", "STF", "MTF") level_order <- c('MTF','STF','SGF') level_order2 <- c('yes','no') DarwinsFinchExplorationMainMalesOnlyPairingStatus$PairingStatusNames <- DarwinsFinchExplorationMainMalesOnlyPairingStatus$PairingStatus levels(DarwinsFinchExplorationMainMalesOnlyPairingStatus$PairingStatusNames) levels(DarwinsFinchExplorationMainMalesOnlyPairingStatus$PairingStatusNames) <- c("no", "yes", "na", "U") F1 <- ggplot(DarwinsFinchExplorationMainMalesOnlyPairingStatus, aes(x=PC_Exploration, y=factor(Species, level = level_order), group=factor(PairingStatusNames, level=level_order2))) + geom_jitter(position=position_dodge(0.5), size = 3.5, shape = 21, aes(fill = PairingStatusNames), stroke=1.05) + scale_fill_manual(values=c("darkgrey", "white")) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=18), axis.title.y = element_text(colour="black", size=18)) + labs(x="Male exploration\n(PC_Exploration)", y="Species") + theme(axis.text.x= element_text(colour="black", size=14), axis.text.y= element_text(colour="black", size=16), legend.text=element_text(size=16), legend.position="top", legend.title=element_text(size=16), legend.box.background = element_rect(colour = "black", linewidth=1.5), legend.key=element_blank()) + guides(fill=guide_legend(title="Paired:")) # -------------------------------------------------------------------- # # ------------- Fig. 4b: Exploration vs hatching success ------------- # # -------------------------------------------------------------------- # DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired$HatchedNames <- DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired$Hatched levels(DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired$HatchedNames) levels(DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired$HatchedNames) <- c("no", "yes", "na", "U") F2 <- ggplot(DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired, aes(x=PC_Exploration, y=factor(Species, level = level_order), group=factor(HatchedNames, level=level_order2))) + geom_jitter(position=position_dodge(0.5), size = 3.5, shape = 21, aes(fill = HatchedNames), stroke=1.05) + scale_fill_manual(values=c("darkgrey", "white")) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=18), axis.title.y = element_text(colour="black", size=18)) + labs(x="Male exploration\n(PC_Exploration)", y="") + theme(axis.text.x= element_text(colour="black", size=14), axis.text.y= element_text(colour="black", size=16), legend.text=element_text(size=16), legend.position="top", legend.title=element_text(size=16), legend.box.background = element_rect(colour = "black", linewidth=1.5),legend.key=element_blank()) + guides(fill=guide_legend(title="Hatched offspring:")) # -------------------------------------------------------------------- # # ------------- Fig. 4c: Exploration vs fledging success ------------- # # -------------------------------------------------------------------- # DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired$FledgedNames <- DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired$Fledged levels(DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired$FledgedNames) levels(DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired$FledgedNames) <- c("no", "yes", "na", "U") F3 <- ggplot(DarwinsFinchExplorationMainMalesOnlyPairingStatusOnlyPaired, aes(x=PC_Exploration, y=factor(Species, level = level_order), group=factor(FledgedNames, level=level_order2))) + geom_jitter(position=position_dodge(0.5), size = 3.5, shape = 21, aes(fill = FledgedNames), stroke=1.05) + scale_fill_manual(values=c("darkgrey", "white")) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=18), axis.title.y = element_text(colour="black", size=18)) + labs(x="Male exploration\n(PC_Exploration)", y="") + theme(axis.text.x= element_text(colour="black", size=14), axis.text.y= element_text(colour="black", size=16), legend.text=element_text(size=16), legend.position="top", legend.title=element_text(size=16), legend.box.background = element_rect(colour = "black", linewidth=1.5), legend.key=element_blank()) + guides(fill=guide_legend(title="Fledged offspring:")) # ------------------------------------------------------------ # # ------------- Combine 3 panels into one figure ------------- # # ------------------------------------------------------------ # Panel1 <- ggarrange(F1, F2, F3, ncol=3, nrow=1, common.legend = FALSE, widths = c(1.05,1,1)) Panel2<- annotate_figure(Panel1, top = textGrob("(a)", x = unit(0, "npc"), y = unit(0.1, "npc"), just=c("left"), gp=gpar(col="black", font=3, fontsize=20))) Panel3<- annotate_figure(Panel2, top = textGrob("(b)", x = unit(0.37, "npc"), y = unit(-1, "npc"), just=c("left"), gp=gpar(col="black", font=3, fontsize=20))) Panel4<- annotate_figure(Panel3, bottom = textGrob("(c)", x = unit(0.7, "npc"), y = unit(17.8, "npc"), just=c("left"), gp=gpar(col="black", font=3, fontsize=20))) #Save plot ggsave(file="Figure4_ExplorationAndFitness.pdf", dpi = 200, width = 37, height = 15, units = c("cm")) ######################################### ######################################### ######### SUPPLEMENTARY FIGURES ######### ######################################### ######################################### ################################################################# #### Figure S1: Histogram for boldness (processing response) #### ################################################################# df1 <- DarwinsFinchBoldnessMain %>% group_by(Species) %>% dplyr::summarise(Boldness_ProcessingResponse = mean(Boldness_ProcessingResponse)) ggplot(DarwinsFinchBoldnessMain, aes(x=Boldness_ProcessingResponse)) + geom_histogram(binwidth=1, fill="dark grey", colour="black") + facet_wrap(Species ~ ., labeller=as_labeller(c(`SGF` = "(a) Small ground finch", `STF` = "(d) Small tree finch",`MGF` = "(b) Medium ground finch", `MTF` = "(c) Medium tree finch"))) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=15), axis.title.y = element_text(colour="black", size=15)) + labs(x="processing response", y="count") + theme(axis.text.x= element_text(colour="black", size=16), axis.text.y= element_text(colour="black", size=15), legend.title=element_text(size=15), legend.text=element_text(size=15), legend.key.size = unit(3,"line")) + theme(strip.background = element_blank(), strip.text.x = element_text(size = 13), strip.text = element_text(face = "italic")) + coord_cartesian(xlim=c(-0.5, 5.5)) + scale_x_continuous(breaks=seq(0, 5, 1)) + geom_vline(data = df1, mapping = aes(xintercept = Boldness_ProcessingResponse), linetype="dotted", linewidth=1, colour="red") #Save plot ggsave(file="FigureS1_ProcessingResponseHistogram.pdf", dpi = 200, width = 15, height = 15, units = c("cm")) ################################################################ #### Figure S2: Histogram for boldness (back-test response) #### ################################################################ df2 <- DarwinsFinchBoldnessMain %>% group_by(Species) %>% dplyr::summarise(Boldness_BackTestResponse = mean(Boldness_BackTestResponse)) ggplot(DarwinsFinchBoldnessMain, aes(x=Boldness_BackTestResponse)) + geom_histogram(binwidth=1, fill="dark grey", colour="black") + facet_wrap(Species ~ ., labeller=as_labeller(c(`SGF` = "(a) Small ground finch", `STF` = "(d) Small tree finch",`MGF` = "(b) Medium ground finch", `MTF` = "(c) Medium tree finch"))) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=15), axis.title.y = element_text(colour="black", size=15)) + labs(x="back-test response", y="count") + theme(axis.text.x= element_text(colour="black", size=16), axis.text.y= element_text(colour="black", size=16), legend.title=element_text(size=15), legend.text=element_text(size=15), legend.key.size = unit(3,"line")) + theme(strip.background = element_blank(), strip.text.x = element_text(size = 13), strip.text = element_text(face = "italic")) + coord_cartesian(xlim=c(-0.5, 8.5)) + scale_x_continuous(breaks=seq(0, 8, 1)) + geom_vline(data = df2, mapping = aes(xintercept = Boldness_BackTestResponse), linetype="dotted", linewidth=1, colour="red") #Save plot ggsave(file="FigureS2_BackTestResponseHistogram.pdf", dpi = 200, width = 15, height = 15, units = c("cm")) ####################################################################### #### Figure S3: Graph comparing exploration by species (6 species) #### ####################################################################### # Re-do PCA to include cactus finches and hybrid tree finches local({ .PC <- princomp(~ Exploration_SectorVisits + Exploration_UniqueSectorVisits, cor=TRUE, data=DarwinsFinchExploration) cat("\nComponent loadings:\n") print(unclass(loadings(.PC))) cat("\nComponent variances:\n") print(.PC$sd^2) cat("\n") print(summary(.PC)) screeplot(.PC) DarwinsFinchExploration <<- within(DarwinsFinchExploration, { PC_Exploration6Species <- .PC$scores[,1] }) }) DarwinsFinchExploration$Species <- factor(DarwinsFinchExploration$Species,levels = c("SGF", "MGF", "CF", "STF", "MTF", "Hybrid TF")) #+++++++++++++++++++++++++ # Function to calculate the mean and the standard deviation # for each group #+++++++++++++++++++++++++ # data : a data frame # varname : the name of a column containing the variable #to be summarized # groupnames : vector of column names to be used as # grouping variables data_summary <- function(data, varname, groupnames){ require(plyr) summary_func <- function(x, col){ c(mean = mean(x[[col]], na.rm=TRUE), SEM = ((sd(x[[col]]))/sqrt(length(x[[col]]))), na.rm=TRUE) } data_sum<-ddply(data, groupnames, .fun=summary_func, varname) data_sum <- rename(data_sum, c("mean" = varname)) return(data_sum) } df3 <- data_summary(DarwinsFinchExploration, varname="PC_Exploration6Species", groupnames=c("Species")) df3$Species=as.factor(df3$Species) head(df3) mean_data <- group_by(DarwinsFinchExploration, Species) %>% plyr::summarise(PC_Exploration6Species = mean(PC_Exploration6Species, na.rm = TRUE)) Dots <- ggplot(DarwinsFinchExploration, aes(x=factor(Species), y=PC_Exploration6Species)) + geom_beeswarm(size = 3, shape = 21, fill = "gray", cex = 1.6) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=16), axis.title.y = element_text(colour="black", size=16)) + labs(x="Species", y="PC_Exploration") + theme(axis.text.x= element_text(colour="black", size=16), axis.text.y= element_text(colour="black", size=16), legend.title=element_text(size=15), legend.text=element_text(size=15), legend.key.size = unit(3,"line")) + coord_cartesian(ylim=c(-2, 4.2)) + scale_y_continuous(breaks=seq(-2, 4, 1)) A4 <- Dots + geom_point(data=df3, aes(x = Species, y = PC_Exploration6Species), shape=16, size=3, colour="red") + geom_errorbar(data=df3, aes(x = Species, ymin=PC_Exploration6Species-SEM, ymax=PC_Exploration6Species+SEM), width=.20, linewidth=1.2, colour="red") #Save plot ggsave(file="FigureS3_ExplorationBySpeciesAll6.pdf", dpi = 200, width = 20, height = 15, units = c("cm")) ############################################################# #### Figure S4: comparing exploration across study sites #### ############################################################# HighlandSpecies2 <- c("Small ground finch","Medium ground finch","Small tree finch","Medium tree finch") names(HighlandSpecies2) <- c("SGF", "MGF", "STF", "MTF") DarwinsFinchExplorationMain$Site = relevel(DarwinsFinchExplorationMain$Site, ref="Puerto Velazco Ibarra") #+++++++++++++++++++++++++ # Function to calculate the mean and the standard deviation # for each group #+++++++++++++++++++++++++ # data : a data frame # varname : the name of a column containing the variable #to be summarized # groupnames : vector of column names to be used as # grouping variables data_summary <- function(data, varname, groupnames){ require(plyr) summary_func <- function(x, col){ c(mean = mean(x[[col]], na.rm=TRUE), SEM = ((sd(x[[col]]))/sqrt(length(x[[col]]))), na.rm=TRUE) } data_sum<-ddply(data, groupnames, .fun=summary_func, varname) data_sum <- rename(data_sum, c("mean" = varname)) return(data_sum) } df3 <- data_summary(DarwinsFinchExplorationMain, varname="PC_Exploration", groupnames=c("Site", "Species")) df3$Site=as.factor(df3$Site) head(df3) mean_data <- group_by(DarwinsFinchExplorationMain, Site, Species) %>% plyr::summarise(PC_Exploration = mean(PC_Exploration, na.rm = TRUE)) Dots <- ggplot(DarwinsFinchExplorationMain, aes(x=factor(Site), y=PC_Exploration, group=Species)) + geom_beeswarm(shape=21, fill="grey", cex=3.5, size=2, stat = "identity") + scale_colour_manual(values=c("red", "blue"), name="Location", breaks=c("Asilo de la Paz", "Cerro Pajas", "Puerto Velasco Ibarra"), labels=c("Asilo de la Paz", "Cerro Pajas", "Puerto Velasco Ibarra")) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=16), axis.title.y = element_text(colour="black", size=14)) + labs(x="", y="PC_Exploration") + theme(axis.text.x= element_text(colour="black", size=12), axis.text.y= element_text(colour="black", size=16), legend.title=element_text(size=15), legend.text=element_text(size=15), legend.key.size = unit(3,"line"), panel.spacing = unit(0, "lines"), strip.background = element_blank(), strip.placement = "outside", strip.text.x = element_text(size = 14, colour = "black")) + coord_cartesian(ylim=c(-2, 4.2)) + scale_x_discrete(labels = c('Puerto\nVelasco\nIbarra','Asilo de\nla Paz','Cerro\nPajas')) + facet_wrap(factor(Species, levels=c('SGF','MGF','STF','MTF'), labels = c('Small ground finch','Medium ground finch','Small tree finch','Medium tree finch')) ~ . , strip.position = "bottom", scales = "fixed", labeller = labeller(Species = HighlandSpecies2), nrow = 1) + geom_vline(aes(xintercept=3.5), linetype="dotted", linewidth=0.5) dat_text <- data.frame( label = c("", "", "P = 0.040", "P = 0.003"), Species = c("SGF", "MGF", "STF", "MTF")) data.segm<-data.frame(x=2,y=3.95,xend=3,yend=3.95,Species="STF") data.segm2<-data.frame(x=2,y=3.95,xend=3,yend=3.95,Species="MTF") A1 <- Dots + geom_point(data=df3, aes(x = Site, y = PC_Exploration), shape=16, size=3, colour="red", position=position_dodge(1)) + geom_errorbar(data=df3, aes(x = Site, ymin=PC_Exploration-SEM, ymax=PC_Exploration+SEM), width=.25, linewidth=1, colour="red", position=position_dodge(1)) + geom_segment(data=data.segm, aes(x=x,y=y,yend=yend,xend=xend),inherit.aes=FALSE, linewidth = 0.7) + geom_segment(data=data.segm2, aes(x=x,y=y,yend=yend,xend=xend),inherit.aes=FALSE, linewidth = 0.7) + geom_text(data = dat_text, mapping = aes(x = -Inf, y = -Inf, label = label), hjust = -1.85, vjust = -29.1) #Save plot ggsave(file="FigureS4_ExplorationAcrossSites.pdf", dpi = 200, width = 25, height = 12, units = c("cm")) ###################################################################################### #### Figure S5: Four panels showing relationships between behavioral variables ###### ###################################################################################### # ---------------------------------------------------------------------------------------- # # ------------- Figure S5a: PC_Exploration vs Boldness (processing response) ------------- # # ---------------------------------------------------------------------------------------- # S5_A1 <- ggplot(DarwinsFinchExplorationMain, aes(x=Boldness_ProcessingResponse, y=PC_Exploration)) + geom_beeswarm(size = 3, shape = 21, fill = "gray", cex=1.9) + geom_smooth(method = "lm", se=FALSE) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=18), axis.title.y = element_text(colour="black", size=18)) + labs(x="Processing response\n", y="\nPC_Exploration") + theme(axis.text.x= element_text(colour="black", size=18), axis.text.y= element_text(colour="black", size=18), legend.title=element_text(size=16), legend.text=element_text(size=16), legend.key.size = unit(3,"line")) + coord_cartesian(xlim=c(0, 5)) + scale_x_continuous(breaks=seq(0, 5, 1)) + coord_cartesian(ylim=c(-2, 4.3)) + scale_y_continuous(breaks=seq(-2, 4, 1)) # --------------------------------------------------------------------------------------- # # ------------- Figure S5a: PC_Exploration vs Boldness (back test response) ------------- # # --------------------------------------------------------------------------------------- # S5_A2 <- ggplot(DarwinsFinchExplorationMain, aes(x = Boldness_BackTestResponse, y=PC_Exploration)) + geom_beeswarm(size = 3, shape = 21, fill = "gray", cex=1.9) + #geom_smooth(method = "lm", se=FALSE) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=18), axis.title.y = element_text(colour="black", size=18)) + labs(x="Back-test response\n", y="\nPC_Exploration") + theme(axis.text.x= element_text(colour="black", size=18), axis.text.y= element_text(colour="black", size=18), legend.title=element_text(size=16), legend.text=element_text(size=16), legend.key.size = unit(3,"line")) + coord_cartesian(xlim=c(0, 8)) + scale_x_continuous(breaks=seq(0, 8, 1)) + coord_cartesian(ylim=c(-2, 4.3)) + scale_y_continuous(breaks=seq(-2, 4, 1)) # ------------------------------------------------------------------------------------------ # # ------------- Figure S5c: PC_Exploration vs Aggressiveness (attacked mirror) ------------- # # ------------------------------------------------------------------------------------------ # #+++++++++++++++++++++++++ # Function to calculate the mean and the standard deviation # for each group #+++++++++++++++++++++++++ # data : a data frame # varname : the name of a column containing the variable #to be summarized # groupnames : vector of column names to be used as # grouping variables data_summary <- function(data, varname, groupnames){ require(plyr) summary_func <- function(x, col){ c(mean = mean(x[[col]], na.rm=TRUE), SEM = ((sd(x[[col]]))/sqrt(length(x[[col]]))), na.rm=TRUE) } data_sum<-ddply(data, groupnames, .fun=summary_func, varname) data_sum <- rename(data_sum, c("mean" = varname)) return(data_sum) } df3 <- data_summary(DarwinsFinchExplorationMainWithMirror, varname="PC_Exploration", groupnames=c("Aggressiveness_AttackedMirror")) df3$Aggressiveness=as.factor(df3$Aggressiveness_AttackedMirror) head(df3) mean_data <- group_by(DarwinsFinchExplorationMainWithMirror, Aggressiveness_AttackedMirror) %>% plyr::summarise(PC_Exploration = mean(PC_Exploration, na.rm = TRUE)) Dots <- ggplot(DarwinsFinchExplorationMainWithMirror, aes(x=factor(Aggressiveness_AttackedMirror), y=PC_Exploration)) + geom_beeswarm(size = 3, shape = 21, fill = "gray", cex = 2) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=18), axis.title.y = element_text(colour="black", size=18)) + labs(x="Attacked mirror\n", y="\nPC_Exploration") + theme(axis.text.x= element_text(colour="black", size=18), axis.text.y= element_text(colour="black", size=18), legend.title=element_text(size=16), legend.text=element_text(size=16), legend.key.size = unit(3,"line")) + coord_cartesian(ylim=c(-2, 5)) + scale_y_continuous(breaks=seq(-2, 4, 1)) + scale_x_discrete(labels = c('No', 'Yes')) + annotate("text", x=1.5, y=4.95, label = "italic(P) == 0.018", size=5, parse=TRUE) + annotate("segment", x = 1, xend = 2, y = 4.65, yend = 4.65) + annotate("segment", x = 1, xend = 1, y = 4.65, yend = 4.55) + annotate("segment", x = 2, xend = 2, y = 4.65, yend = 4.55) S5_A3 <- Dots + geom_point(data=df3, aes(x = Aggressiveness_AttackedMirror, y = PC_Exploration), shape=16, size=3, colour="red") + geom_errorbar(data=df3, aes(x = Aggressiveness_AttackedMirror, ymin=PC_Exploration-SEM, ymax=PC_Exploration+SEM), width=.10, linewidth=1.2, colour="red") # ------------------------------------------------------------------------------------------- # # ------------- Figure S5d: PC_Exploration vs Aggressiveness (time near mirror) ------------- # # ------------------------------------------------------------------------------------------- # S5_A4 <- ggplot(DarwinsFinchExplorationMainWithMirror, aes(x=Aggressiveness_TimeNearMirror, y=PC_Exploration)) + geom_smooth(method = "lm", formula = y ~ x + I(x^2), se=FALSE, lwd=1.2) + geom_jitter(shape=21, color="black", size=4, width=0, height=0, stroke=0.7) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=20), axis.title.y = element_text(colour="black", size=20)) + labs(x="Time near mirror", y="PC_Exploration") + theme(axis.text.x= element_text(colour="black", size=20), axis.text.y= element_text(colour="black", size=20), legend.text=element_text(size=16), legend.key.width = unit(4,"line"), legend.key.height = unit(2,"line"), legend.key= element_rect(fill = NA, color = "NA"), legend.box.background = element_rect(colour = "black", linewidth=2.5), legend.margin = margin(-0.2, 0.2, 0, 0, "cm"), legend.title=element_blank()) + coord_cartesian(xlim=c(0,180)) + scale_x_continuous(breaks=seq(0, 180, 30)) + coord_cartesian(ylim=c(-2,4.3)) + scale_y_continuous(breaks=seq(-2, 4, 1)) # ------------------------------------------------------------ # # ------------- Combine 4 panels into one figure ------------- # # ------------------------------------------------------------ # Panel1 <- ggarrange(S5_A1,S5_A2,NULL,NULL,S5_A3,S5_A4, ncol=2, nrow=3, common.legend = FALSE, legend="right", heights = c(1,0.10,1,0.1,1)) Panel2<- annotate_figure(Panel1, top = textGrob("(a) Exploration vs boldness (processing)", x = unit(0, "npc"), y = unit(1.05, "npc"), just=c("left"), gp=gpar(col="black", font=3, fontsize=20))) Panel3<- annotate_figure(Panel2, top = textGrob("(b) Exploration vs boldness (back test)", x = unit(0.50, "npc"), y = unit(0.05, "npc"), just=c("left"), gp=gpar(col="black", font=3, fontsize=20)), fig.lab.pos="top.right") Panel4<- annotate_figure(Panel3, bottom = textGrob("(c) Exploration vs aggressiveness (attacked mirror)", x = unit(0, "npc"), y = unit(17.2, "npc"), just=c("left","top"), gp=gpar(col="black", font=3, fontsize=20))) Panel5<- annotate_figure(Panel4, bottom = textGrob("(d) Exploration vs aggressiveness (time near mirror)", x = unit(0.50, "npc"), y = unit(17.5, "npc"), just=c("left","bottom"), gp=gpar(col="black", font=3, fontsize=20))) ggsave(file="FigureS5_TraitCorrelations.pdf", dpi = 200, width = 35, height = 27, units = c("cm")) ############################################################################################# #### Figure S6: Graph comparing exploration (total sector visits) by species (4 species) #### ############################################################################################# # set order of species categories DarwinsFinchExplorationMain$Species <- factor(DarwinsFinchExplorationMain$Species,levels = c("SGF", "MGF", "STF", "MTF")) #+++++++++++++++++++++++++ # Function to calculate the mean and the standard deviation # for each group #+++++++++++++++++++++++++ # data : a data frame # varname : the name of a column containing the variable #to be summarized # groupnames : vector of column names to be used as # grouping variables data_summary <- function(data, varname, groupnames){ require(plyr) summary_func <- function(x, col){ c(mean = mean(x[[col]], na.rm=TRUE), SEM = ((sd(x[[col]]))/sqrt(length(x[[col]]))), na.rm=TRUE) } data_sum<-ddply(data, groupnames, .fun=summary_func, varname) data_sum <- rename(data_sum, c("mean" = varname)) return(data_sum) } df3 <- data_summary(DarwinsFinchExplorationMain, varname="Exploration_SectorVisitsCR", groupnames=c("Species")) df3$Species=as.factor(df3$Species) head(df3) mean_data <- group_by(DarwinsFinchExplorationMain, Species) %>% plyr::summarise(Exploration_SectorVisitsCR = mean(Exploration_SectorVisitsCR, na.rm = TRUE)) Dots <- ggplot(DarwinsFinchExplorationMain, aes(x=factor(Species), y=Exploration_SectorVisitsCR)) + geom_beeswarm(size = 3, shape = 21, fill = "gray", cex = 2.3) + theme(panel.border = element_blank(), panel.background = element_rect(fill="white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_text(colour="black", size=16), axis.title.y = element_text(colour="black", size=16)) + labs(x="Species", y="Total sector visits\n(cube-root)") + theme(axis.text.x= element_text(colour="black", size=16), axis.text.y= element_text(colour="black", size=16), legend.title=element_text(size=15), legend.text=element_text(size=15), legend.key.size = unit(3,"line")) + coord_cartesian(ylim=c(0, 7.5)) + scale_y_continuous(breaks=seq(0, 7, 1)) + annotate("text", x=2, y=6.90, label= "italic(P) < 0.001", size=3, parse=TRUE) + annotate("text", x=2.5, y=6.40, label= "italic(P) == 0.044", size=3, parse=TRUE) + annotate("text", x=2.5, y=7.40, label= "italic(P) < 0.001", size=3, parse=TRUE) + annotate("segment", x = 1, xend = 3, y = 6.75, yend = 6.75) + annotate("segment", x = 2, xend = 3, y = 6.25, yend = 6.25) + annotate("segment", x = 1, xend = 4, y = 7.25, yend = 7.25) Dots + geom_point(data=df3, aes(x = Species, y = Exploration_SectorVisitsCR), shape=16, size=3, colour="red") + geom_errorbar(data=df3, aes(x = Species, ymin=Exploration_SectorVisitsCR-SEM, ymax=Exploration_SectorVisitsCR+SEM), width=.20, linewidth=1.2, colour="red") #Save plot ggsave(file="FigureS6_ExplorationSectorVisitsBySpecies.pdf", dpi = 200, width = 15, height = 15, units = c("cm"))