library(lme4) library(lmerTest) #to get p values library(lsmeans) library(rptR) ##Sprint speed data conversion to get all sprint speed data velocity over 50 cm in cm/s #convert John's sprint speed data from m/s to cm/s by multiplying velocities by 100 to match Kali's data #Convert Kali sprint speed from 25cm to 50 cm to match John's #1-Calculated TIME to go each of the 4 25-cm sections of racetrack using T=25 cm/ velocity data #2-Added times of consecutive 25-cm sections (i.e, 1 & 2, 2 & 3, 3 & 4) together to get time taken to go 50 cm #3-Cacluated velocities of 50-cm sections using combined times calculated in step 2 using V=50 cm/ combined Times (V=D/T) ##Change variables to factors and Numeric## J.ID=as.factor(R_DAILY_GROWTH_NEG_REMOVED_Full$J.ID) GEN=as.factor(R_DAILY_GROWTH_NEG_REMOVED_Full$GEN) CONDITION=as.numeric(R_DAILY_GROWTH_NEG_REMOVED_Full$CONDITION) Vmax.30.COM=as.numeric(R_DAILY_GROWTH_NEG_REMOVED_Full$Vmax.30.COM) Tsel=as.numeric(R_DAILY_GROWTH_NEG_REMOVED_Full$Tsel) NO.TRANS=as.numeric(R_DAILY_GROWTH_NEG_REMOVED_Full$NO.TRANS) TIME.GOAL=as.numeric(R_DAILY_GROWTH_NEG_REMOVED_Full$TIME.GOAL) INSPECT=as.numeric(R_DAILY_GROWTH_NEG_REMOVED_Full$INSPECT) TIME.SOCIAL=as.numeric(R_DAILY_GROWTH_NEG_REMOVED_Full$TIME.SOCIAL) TIME.ASOCIAL=as.numeric(R_DAILY_GROWTH_NEG_REMOVED_Full$TIME.ASOCIAL) NO.SOC.TRANS=as.numeric(R_DAILY_GROWTH_NEG_REMOVED_Full$NO.SOC.TRANS) NO.DAYS.BTWN.REPEAT=as.numeric(R_DAILY_GROWTH_NEG_REMOVED_Full$NO.DAYS.BTWN.REPEAT) DAILY.GROWTH.RATE=as.numeric(R_DAILY_GROWTH_NEG_REMOVED_Full$DAILY.GROWTH.RATE) J.ID=as.factor(R_No_Repeat_Stack_Full$J.ID) GEN=as.factor(R_No_Repeat_Stack_Full$GEN) CONDITION=as.numeric(R_No_Repeat_Stack_Full$CONDITION) Vmax.30.COM=as.numeric(R_No_Repeat_Stack_Full$Vmax.30.COM) Tsel=as.numeric(R_No_Repeat_Stack_Full$Tsel) NO.DAYS.BTWN.REPEAT_Vmax=as.numeric(R_No_Repeat_Stack_Full$NO.DAYS.BTWN.REPEAT_Vmax) NO.DAYS.BTWN.REPEAT_Tsel=as.numeric(R_No_Repeat_Stack_Full$NO.DAYS.BTWN.REPEAT_Tsel) NO.TRANS=as.numeric(R_No_Repeat_Stack_Full$NO.TRANS) TIME.GOAL=as.numeric(R_No_Repeat_Stack_Full$TIME.GOAL) INSPECT=as.numeric(R_No_Repeat_Stack_Full$INSPECT) TIME.SOCIAL=as.numeric(R_No_Repeat_Stack_Full$TIME.SOCIAL) TIME.ASOCIAL=as.numeric(R_No_Repeat_Stack_Full$TIME.ASOCIAL) NO.SOC.TRANS=as.numeric(R_No_Repeat_Stack_Full$NO.SOC.TRANS) GOAL=as.numeric(R_No_Repeat_Stack_Full$GOAL) GROWTH.RATE=as.numeric(R_No_Repeat_Stack_Full$GROWTH.RATE) RES.GROWTH.RATE=as.numeric(R_No_Repeat_Stack_Full$RES.GROWTH.RATE) JUV.GROWTH.RATE=as.numeric(R_No_Repeat_Stack_Full$JUV.GROWTH.RATE) GEN=as.factor(R_No_Repeat_Column_Full_MeanPop$GEN) CONDITION.MEAN=as.numeric(R_No_Repeat_Column_Full_MeanPop$CONDITION.MEAN) Vmax.30.COM.MEAN=as.numeric(R_No_Repeat_Column_Full_MeanPop$Vmax.30.COM.MEAN) NO.TRANS.MEAN=as.numeric(R_No_Repeat_Column_Full_MeanPop$NO.TRANS.MEAN) TIME.GOAL.MEANL=as.numeric(R_No_Repeat_Column_Full_MeanPop$TIME.GOAL.MEAN) TIME.SOCIAL.MEAN=as.numeric(R_No_Repeat_Column_Full_MeanPop$TIME.SOCIAL.MEAN) SVL.MEAN=as.numeric(R_No_Repeat_Column_Full_MeanPop$SVL.MEAN) MASS.MEAN=as.numeric(R_No_Repeat_Column_Full_MeanPop$MASS.MEAN) DAILY.GROWTH.RATE=as.numeric(R_No_Repeat_Column_Full_MeanPop$DAILY.GROWTH.RATE) J.ID=as.factor(R_No_Repeat_Stack_F1$J.ID) CONDITION=as.numeric(R_No_Repeat_Stack_F1$CONDITION) Vmax.30.COM=as.numeric(R_No_Repeat_Stack_F1$Vmax.30.COM) Tsel=as.numeric(R_No_Repeat_Stack_F1$Tsel) NO.TRANS=as.numeric(R_No_Repeat_Stack_F1$NO.TRANS) TIME.GOAL=as.numeric(R_No_Repeat_Stack_F1$TIME.GOAL) INSPECT=as.numeric(R_No_Repeat_Stack_F1$INSPECT) TIME.SOCIAL=as.numeric(R_No_Repeat_Stack_F1$TIME.SOCIAL) TIME.ASOCIAL=as.numeric(R_No_Repeat_Stack_F1$TIME.ASOCIAL) NO.SOC.TRANS=as.numeric(R_No_Repeat_Stack_F1$NO.SOC.TRANS) GROWTH.RATE=as.numeric(R_No_Repeat_Stack_F1$GROWTH.RATE) RES.GROWTH.RATE=as.numeric(R_No_Repeat_Stack_F1$RES.GROWTH.RATE) JUV.GROWTH.RATE=as.numeric(R_No_Repeat_Stack_F1$JUV.GROWTH.RATE) MOTHER.ID=as.factor(R_No_Repeat_Stack_F1$MOTHER.ID) DAILY.GROWTH.RATE=as.numeric(R_No_Repeat_Stack_F1$DAILY.GROWTH.RATE) J.ID=as.factor(R_No_Repeat_Stack_F0$J.ID) CONDITION=as.numeric(R_No_Repeat_Stack_F0$CONDITION) Vmax.30.COM=as.numeric(R_No_Repeat_Stack_F0$Vmax.30.COM) Tsel=as.numeric(R_No_Repeat_Stack_F0$Tsel) NO.DAYS.BTWN.REPEAT_Vmax=as.numeric(R_No_Repeat_Stack_F0$NO.DAYS.BTWN.REPEAT_Vmax) NO.DAYS.BTWN.REPEAT_Tsel=as.numeric(R_No_Repeat_Stack_F0$NO.DAYS.BTWN.REPEAT_Tsel) NO.TRANS=as.numeric(R_No_Repeat_Stack_F0$NO.TRANS) TIME.GOAL=as.numeric(R_No_Repeat_Stack_F0$TIME.GOAL) INSPECT=as.numeric(R_No_Repeat_Stack_F0$INSPECT) TIME.SOCIAL=as.numeric(R_No_Repeat_Stack_F0$TIME.SOCIAL) TIME.ASOCIAL=as.numeric(R_No_Repeat_Stack_F0$TIME.ASOCIAL) NO.SOC.TRANS=as.numeric(R_No_Repeat_Stack_F0$NO.SOC.TRANS) GROWTH.RATE=as.numeric(R_No_Repeat_Stack_F0$GROWTH.RATE) DAILY.GROWTH.RATE=as.numeric(R_POLS_F1$DAILY.GROWTH.RATE) GROWTH.RATE=as.numeric(R_POLS_F1$GROWTH.RATE) NO.TRANS=as.numeric(R_POLS_F1$NO.TRANS) Vmax.30.COM=as.numeric(R_POLS_F1$Vmax.30.COM) MOTHER.ID=as.factor(R_POLS_F1$MOTHER.ID) DAILY.GROWTH.RATE=as.numeric(R_POLS_F0$DAILY.GROWTH.RATE) MEAN.MASS=as.numeric(R_POLS_F0$MEAN.MASS) MEAN.SVL=as.numeric(R_POLS_F0$MEAN.SVL) MEAN.No.TRANS.COM=as.numeric(R_POLS_F0$MEAN.NO.TRANS) J.ID=as.factor(R_POLS_F1_J_A_Growth$J.ID) JUV.ADULT.GROWTH.RATE=as.numeric(R_POLS_F1_J_A_Growth$JUV.ADULT.GROWTH.RATE) NO.TRANS=as.numeric(R_POLS_F1_J_A_Growth$NO.TRANS) Vmax.30.COM=as.numeric(R_POLS_F1_J_A_Growth$Vmax.30.COM) INC.TEMP=as.factor(R_POLS_F1_J_A_Growth$INC.TEMP) MASS=as.numeric(R_POLS_F1_J_A_Growth$MASS) DAILY.GROWTH.RATE=as.numeric(R_POLS_F1corr$DAILY.GROWTH.RATE) GROWTH.RATE=as.numeric(R_POLS_F1corr$GROWTH.RATE) NO.TRANS=as.numeric(R_No_POLS_F1corr$NO.TRANS) Vmax.30.COM=as.numeric(R_POLS_F1corr$Vmax.30.COM) SVL=as.numeric(R_No_Repeat_Column_F1$SVL) CONDITION=as.numeric(R_No_Repeat_Column_F1$CONDITION) Vmax.30.COM=as.numeric(R_No_Repeat_Column_F1$Vmax.30.COM) Tsel=as.numeric(R_No_Repeat_Column_F1$Tsel) NO.TRANS=as.numeric(R_No_Repeat_Column_F1$NO.TRANS) TIME.GOAL=as.numeric(R_No_Repeat_Column_F1$TIME.GOAL) INSPECT=as.numeric(R_No_Repeat_Column_F1$INSPECT) TIME.SOCIAL=as.numeric(R_No_Repeat_Column_F1$TIME.SOCIAL) TIME.ASOCIAL=as.numeric(R_No_Repeat_Column_F1$TIME.ASOCIAL) NO.SOC.TRANS=as.numeric(R_No_Repeat_Column_F1$NO.SOC.TRANS) GOAL=as.numeric(R_No_Repeat_Column_F1$GOAL) CONDITION2=as.numeric(R_No_Repeat_Column_F1$CONDITION2) Vmax.30.COM2=as.numeric(R_No_Repeat_Column_F1$Vmax.30.COM2) Tsel2=as.numeric(R_No_Repeat_Column_F1$Tsel2) NO.TRANS2=as.numeric(R_No_Repeat_Column_F1$NO.TRANS2) TIME.GOAL2=as.numeric(R_No_Repeat_Column_F1$TIME.GOAL2) INSPECT2=as.numeric(R_No_Repeat_Column_F1$INSPECT2) TIME.SOCIAL2=as.numeric(R_No_Repeat_Column_F1$TIME.SOCIAL2) TIME.ASOCIAL2=as.numeric(R_No_Repeat_Column_F1$TIME.ASOCIAL2) NO.SOC.TRANS2=as.numeric(R_No_Repeat_Column_F1$NO.SOC.TRANS2) GOAL2=as.numeric(R_No_Repeat_Column_F1$GOAL2) GROWTH.RATE=as.numeric(R_No_Repeat_Column_F1$GROWTH.RATE) RES.GROWTH.RATE=as.numeric(R_No_Repeat_Column_F1$RES.GROWTH.RATE) JUV.GROWTH.RATE=as.numeric(R_No_Repeat_Column_F1$JUV.GROWTH.RATE) #SUMMARY STATISTICS BY POP by(R_No_Repeat_Stack_Full, R_No_Repeat_Stack_Full$GEN, summary) by(R_DAILY_GROWTH_NEG_REMOVED_Full, R_DAILY_GROWTH_NEG_REMOVED_Full$GEN, summary) ##TEST FOR EFFECTS OF INCUBATION TEMP FOR CAPTIVE POP# inc.model=lmer(Tsel ~ INC.TEMP + SVL + INC.TEMP:SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit)#REML False because only one random effect (also set to FALSE if random effects nested and data is balanced) #no effect inc.model1=lmer(Vmax.30.COM ~ INC.TEMP + SVL + INC.TEMP:SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) #no effect inc.model3=lmer(NO.TRANS ~ INC.TEMP + SVL + INC.TEMP:SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) #no effect inc.model4=lmer(log(TIME.GOAL) ~ INC.TEMP + SVL + INC.TEMP:SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) #no effect inc.model6=lmer(TIME.SOCIAL ~ INC.TEMP + SVL + INC.TEMP:SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) #no effect inc.model7=lm(DAILY.GROWTH.RATE ~ INC.TEMP, R_No_Repeat_Stack_F1, na.action=na.omit) #no effect ##TEST FOR EFFECTS OF MOTHER ID# mum.model=lmer(Tsel ~ MOTHER.ID + SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) #no effect mum.model1=lmer(Vmax.30.COM ~ MOTHER.ID + SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) #no effect mum.model3=lmer(NO.TRANS ~ MOTHER.ID + SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) #no effect mum.model4=lmer(log(TIME.GOAL) ~ MOTHER.ID + SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) #no effect mum.model6=lmer(TIME.SOCIAL ~ MOTHER.ID + SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) #no effect mum.model7=lm(GROWTH.RATE ~ MOTHER.ID, R_No_Repeat_Stack_F1, na.action=na.omit) #no effect ##INFLUENCE OF CAPTIVITY ON TRAITS; POPULATION DIFFERENCES IN TRAIT MEANS; AND REPEATABILITIES #GROWTH RATE pop.gr.modela=lm(DAILY.GROWTH.RATE ~ GEN + MASS, R_DAILY_GROWTH_NEG_REMOVED_Full, na.action=na.omit) summary(pop.gr.modela) # Estimate Std. Error t value Pr(>|t|) #(Intercept) 0.020877 0.002640 7.908 1.30e-08 *** #GENFO -0.005560 0.001092 -5.092 2.15e-05 *** #MASS -0.012263 0.002721 -4.506 0.000107 *** lsmeans::lsmeans(pop.gr.modela, pairwise~GEN) #$lsmeans #GEN lsmean SE df lower.CL upper.CL #F1 0.00914 0.000618 28 0.00787 0.01040 #FO 0.00358 0.000897 28 0.00174 0.00541 #$contrasts #contrast estimate SE df t.ratio p.value #F1 - FO 0.00556 0.00109 28 5.092 <.0001 CAPTIVE POPULATIONS GROWING FASTER #BODY CONDITION popdif=lmer(CONDITION ~ GEN + (1|J.ID),REML = FALSE, R_No_Repeat_Stack_Full, na.action=na.omit) summary(popdif) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 0.0000000 0.00000 #Residual 0.0001089 0.01044 #Number of obs: 85, groups: J.ID, 43 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 0.0382310 0.0016106 85.0000000 23.74 <2e-16 *** #GENF1 -0.0002937 0.0022644 85.0000000 -0.13 0.897 NO EFFECT OF POPULATION ON BODY CONDITION lsmeans::lsmeans(popdif, pairwise~GEN) #$lsmeans #GEN lsmean SE df lower.CL upper.CL #F0 0.0382 0.00163 42.2 0.0349 0.0415 #F1 0.0379 0.00161 43.2 0.0347 0.0412 #$contrasts #contrast estimate SE df t.ratio p.value #F0 - F1 0.000294 0.00229 42.7 0.128 0.8986 rpt.pop.condition<-rpt(CONDITION ~ GEN + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_Full, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.pop.condition) # R SE 2.5% 97.5% P_permut LRT_P # 0 0.0929 0 0.301 NA 1 #SVL popdif1a=lmer(SVL ~ GEN + (1|J.ID),REML = FALSE, R_No_Repeat_Stack_Full, na.action=na.omit) summary(popdif1a) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 0.000 0.000 #Residual 7.858 2.803 #Number of obs: 85, groups: J.ID, 43 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 39.3771 0.4325 85.0000 91.038 < 2e-16 *** #GENF1 -3.2144 0.6081 85.0000 -5.286 9.55e-07 *** VARIATION IN SVL BTWN POPS lsmeans::lsmeans(popdif1a, pairwise~GEN) #$lsmeans #GEN lsmean SE df lower.CL upper.CL #F0 39.4 0.438 42.2 38.5 40.3 #F1 36.2 0.433 43.2 35.3 37.0 #$contrasts #contrast estimate SE df t.ratio p.value #F0 - F1 3.21 0.616 42.7 5.222 <.0001 WILD POPULATION LARGER THAN CAPTIVE #THERMAL PREFERENCES popdif1=lmer(Tsel ~ GEN + SVL + NO.DAYS.BTWN.REPEAT_Tsel + (1|J.ID), REML = FALSE, R_No_Repeat_Stack_Full, na.action=na.omit) summary(popdif1) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 0.3022 0.5497 #Residual 4.2996 2.0736 #Number of obs: 67, groups: J.ID, 34 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 26.72416 3.71788 53.09710 7.188 2.23e-09 *** #GENF1 5.47629 0.65087 48.46713 8.414 4.88e-11 *** #SVL -0.03718 0.09184 51.71664 -0.405 0.68727 #NO.DAYS.BTWN.REPEAT_Tsel 0.17567 0.06258 34.40338 2.807 0.00818 ** lsmeans::lsmeans(popdif1, pairwise~GEN) #GEN lsmean SE df lower.CL upper.CL #F0 26.1 0.523 47.7 25.0 27.1 #F1 31.5 0.375 43.9 30.8 32.3 #contrast estimate SE df t.ratio p.value #F0 - F1 -5.48 0.683 53.2 -8.016 <.0001 rpt.popmodel1=rpt(Tsel ~ GEN + SVL + NO.DAYS.BTWN.REPEAT_Tsel + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_Full, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.popmodel1) # R SE 2.5% 97.5% P_permut LRT_P #0.104 0.142 0 0.47 NA 0.353 #SPRINT SPEED popdif2a=lmer(Vmax.30.COM ~ GEN + SVL + NO.DAYS.BTWN.REPEAT_Vmax + (1|J.ID),REML = FALSE, R_No_Repeat_Stack_Full, na.action=na.omit) summary(popdif2a) #Random effects: #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 19.58 4.425 #Residual 55.54 7.452 #Number of obs: 84, groups: J.ID, 43 #Fixed effects: #Estimate Std. Error df t value Pr(>|t|) #(Intercept) 39.66535 14.35749 72.32774 2.763 0.00726 ** #GENF1 0.47558 6.39797 63.43219 0.074 0.94098 POPULATIONS DID NOT DIFFER IN VMAX #SVL -0.10964 0.31211 60.14396 -0.351 0.72660 #NO.DAYS.BTWN.REPEAT_Vmax 0.01873 0.01106 59.19493 1.693 0.09566 lsmeans::lsmeans(popdif2a, pairwise~GEN) #GEN lsmean SE df lower.CL upper.CL #F0 40.5 3.55 66.5 33.4 47.6 #F1 40.9 3.43 62.7 34.1 47.8 #contrast estimate SE df t.ratio p.value #F0 - F1 -0.476 6.63 66.9 -0.072 0.9430 rpt.popmodel2<-rpt(Vmax.30.COM ~ GEN + SVL + NO.DAYS.BTWN.REPEAT_Vmax + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_Full, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.popmodel2) # R SE 2.5% 97.5% P_permut LRT_P #0.284 0.141 0.00796 0.565 NA 0.045 VMAX NOT REPEATABLE #ACTIVITY popdif3a=lmer(NO.TRANS ~ GEN + SVL + (1|J.ID),REML = FALSE, R_No_Repeat_Stack_Full, na.action=na.omit) summary(popdif3a) #Random effects: #Groups Name Variance Std.Dev. # J.ID (Intercept) 1715.2 41.41 #Residual 934.1 30.56 #Number of obs: 85, groups: J.ID, 43 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 134.0148 53.9143 52.8306 2.486 0.016129 * #GENF1 -60.0298 14.9203 50.3525 -4.023 0.000193 *** POP DIFFERENCES IN ACTIVITY #SVL 0.4223 1.3445 49.5444 0.314 0.754778 lsmeans::lsmeans(popdif3a, pairwise~GEN) #$lsmeans #F0 150.0 10.7 48.5 128.5 171 #F1 89.9 10.5 49.1 68.9 111 #$contrasts #contrast estimate SE df t.ratio p.value #F0 - F1 60 15.3 52.5 3.927 0.0003 WILD POP HIGHER ACTIVITY LEVELS rpt.popmodel3<-rpt(NO.TRANS ~ GEN + SVL + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_Full, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.popmodel3) # R SE 2.5% 97.5% P_permut LRT_P # 0.655 0.0921 0.464 0.805 NA 0 ACTIVITY REPEATABLE #EXPLORATION popdif4a=lmer(TIME.GOAL ~ GEN + SVL + (1|J.ID),REML = FALSE, R_No_Repeat_Stack_Full, na.action=na.omit) summary(popdif4a) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 46466 215.6 #Residual 171375 414.0 #Number of obs: 85, groups: J.ID, 43 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 1777.448 670.041 63.978 2.653 0.0101 * #GENF1 -58.357 123.938 57.330 -0.471 0.6395 #SVL -3.616 16.896 62.734 -0.214 0.8312 NO DIFFERENCE IN EXPLORATION BTWN POPS lsmeans::lsmeans(popdif4a, pairwise~GEN) #$lsmeans #GEN lsmean SE df lower.CL upper.CL #F0 1641 86.2 52.8 1468 1814 #F1 1583 85.0 53.6 1412 1753 #$contrasts #contrast estimate SE df t.ratio p.value #F0 - F1 58.4 127 60.6 0.458 0.6484 rpt.popmodel4<-rpt(sqrt(TIME.GOAL) ~ GEN + SVL + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_Full, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.popmodel4) # R SE 2.5% 97.5% P_permut LRT_P #0.153 0.13 0 0.456 NA 0.192 EXPLORATION NOT REPEATABLE #SOCIAL BEHAVIOUR popdif5a=lmer(TIME.SOCIAL ~ GEN + SVL + (1|J.ID),REML = FALSE, R_No_Repeat_Stack_Full, na.action=na.omit) summary(popdif5a) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 0 0.0 #Residual 137051 370.2 #Number of obs: 85, groups: J.ID, 43 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 427.104 566.952 85.000 0.753 0.453 #GENF1 -13.017 92.577 85.000 -0.141 0.889 #SVL 7.349 14.325 85.000 0.513 0.609 lsmeans::lsmeans(popdif5a, pairwise~GEN) #lsmeans #GEN lsmean SE df lower.CL upper.CL #F0 705 63.0 52.5 578 831 #F1 692 62.2 53.3 567 816 #contrasts #contrast estimate SE df t.ratio p.value #F0 - F1 13 94.8 61.1 0.137 0.8912 rpt.popmodel5a<-rpt(TIME.SOCIAL ~ GEN + SVL + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_Full, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.popmodel5a) # R SE 2.5% 97.5% P_permut LRT_P #0.000837 0.101 0 0.335 NA 1 ##WILD POPULATION REPEATABLTIES WILD=lmer(Tsel ~ SVL + NO.DAYS.BTWN.REPEAT_Tsel + (1|J.ID), R_No_Repeat_Stack_F0, REML=FALSE, na.action=na.omit) summary(WILD) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 0.008191 0.09051 #Residual 5.948213 2.43890 #Number of obs: 24, groups: J.ID, 12 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 16.42620 10.07992 23.85916 1.630 0.1163 #SVL 0.21986 0.25117 23.84532 0.875 0.3901 #NO.DAYS.BTWN.REPEAT_Tsel 0.20055 0.07252 13.46345 2.766 0.0156 * rpt.wild=rpt(Tsel ~ SVL + NO.DAYS.BTWN.REPEAT_Tsel + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F0, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.wild) # R SE 2.5% 97.5% P_permut LRT_P #0.0899 0.214 0 0.679 NA 0.498 WILD1=lmer(CONDITION ~ (1|J.ID), R_No_Repeat_Stack_F0, REML=FALSE, na.action=na.omit) summary(WILD1) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 0.000e+00 0.000000 #Residual 9.831e-05 0.009915 #Number of obs: 42, groups: J.ID, 21 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 0.03823 0.00153 42.00000 24.99 <2e-16 *** rpt.wild1=rpt(CONDITION ~ (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F0, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.wild1) #R SE 2.5% 97.5% P_permut LRT_P #0 0.122 0 0.398 NA 1 CONDITION NOT REPEATABLE FOR WILD POP WILD2=lmer(Vmax.30.COM ~ SVL + NO.DAYS.BTWN.REPEAT_Vmax + (1|J.ID), R_No_Repeat_Stack_F0, REML=FALSE, na.action=na.omit) summary(WILD2) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 6.62 2.573 #Residual 64.30 8.019 #Number of obs: 41, groups: J.ID, 21 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 3.53894 24.39132 40.02696 0.145 0.8854 #SVL 0.74916 0.56867 39.37246 1.317 0.1953 #NO.DAYS.BTWN.REPEAT_Vmax 0.02331 0.01053 30.81359 2.213 0.0344 * rpt.WILD2<-rpt(Vmax.30.COM ~ SVL + NO.DAYS.BTWN.REPEAT_Vmax + NO.DAYS.BTWN.REPEAT_Vmax + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F0, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.WILD2) # R SE 2.5% 97.5% P_permut LRT_P #0.128 0.176 0 0.584 NA 0.336 VMAX NOT REPEATABLE IN WILD POP WILD4=lmer(NO.TRANS ~ SVL + (1|J.ID), R_No_Repeat_Stack_F0, REML=FALSE, na.action=na.omit) summary(WILD4) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 1373.2 37.06 #Residual 765.6 27.67 #Number of obs: 42, groups: J.ID, 21 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 77.237 87.617 28.262 0.882 0.385 #SVL 1.864 2.213 27.768 0.842 0.407 rpt.WILD4<-rpt(NO.TRANS ~ SVL + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F0, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.WILD4) # R SE 2.5% 97.5% P_permut LRT_P #0.646 0.134 0.34 0.856 NA 0 ACTIVITY NOT REPEATABLE WILD5=lmer(sqrt(TIME.GOAL) ~ SVL + (1|J.ID), R_No_Repeat_Stack_F0, REML=FALSE, na.action=na.omit) summary(WILD5) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 13.36 3.655 #Residual 32.67 5.716 #Number of obs: 42, groups: J.ID, 21 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 47.0249 16.3640 35.7715 2.874 0.00679 ** #SVL -0.1818 0.4145 35.5747 -0.439 0.66351 rpt.WILD5<-rpt(sqrt(TIME.GOAL) ~ SVL + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F0, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.WILD5) # R SE 2.5% 97.5% P_permut LRT_P #0.304 0.184 0 0.658 NA 0.095 EXPLORATION NOT REPEATABLE IN WILD POP WILD7=lmer(TIME.SOCIAL ~ SVL + (1|J.ID), R_No_Repeat_Stack_F0, REML=FALSE, na.action=na.omit) summary(WILD7) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 16514 128.5 #Residual 142802 377.9 #Number of obs: 42, groups: J.ID, 21 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) -439.78 1012.47 39.84 -0.434 0.666 #SVL 29.36 25.66 39.75 1.144 0.259 rpt.WILD7<-rpt(TIME.SOCIAL ~ SVL + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F0, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.WILD7) # R SE 2.5% 97.5% P_permut LRT_P #0.122 0.169 0 0.567 NA 0.318 ##CAPTIVE POPULATION REPEATABILITIES lab8=lmer(JUV.ADULT.GROWTH.RATE ~ MASS + (1|J.ID), R_POLS_F1_J_A_Growth, REML=FALSE, na.action=na.omit) summary(lab8) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 0.2972 0.5452 #Residual 0.2595 0.5094 #Number of obs: 43, groups: J.ID, 22 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 4.7745 0.2633 36.4907 18.13 < 2e-16 *** #MASS -2.3263 0.1595 21.8368 -14.58 9.79e-13 *** rpt.growth.rate<-rpt(JUV.ADULT.GROWTH.RATE ~ MASS + (1|J.ID), grname = c("J.ID"), data = R_POLS_F1_J_A_Growth, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.growth.rate) # R SE 2.5% 97.5% P_permut LRT_P #0.548 0.151 0.209 0.788 NA 0.009 GROWTH RATE REPEATABLE IN CAPTIVE POP lab=lmer(CONDITION ~ (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) summary (lab) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 0.0000000 0.00000 #Residual 0.0001193 0.01092 #Number of obs: 43, groups: J.ID, 22 #Fixed effects: #Estimate Std. Error df t value Pr(>|t|) #(Intercept) 0.037937 0.001666 43.000000 22.77 <2e-16 *** rpt.lab=rpt(CONDITION ~ (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F1, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.lab) #R SE 2.5% 97.5% P_permut LRT_P #0 0.124 0 0.421 NA 1 CONDITION NOT REPEATABLE lab1=lmer(Tsel ~ SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) summary(lab1) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 0.4368 0.6609 #Residual 3.2390 1.7997 #Number of obs: 43, groups: J.ID, 22 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 34.92125 3.21861 30.46827 10.850 5.47e-12 *** #SVL -0.09297 0.08859 30.05970 -1.049 0.302 rpt.lab1=rpt(Tsel ~ SVL + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F1, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.lab1) # R SE 2.5% 97.5% P_permut LRT_P #0.126 0.165 0 0.547 NA 0.29 lab2=lmer(Vmax.30.COM ~ SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) summary(lab2) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 36.56 6.046 #Residual 34.12 5.841 #Number of obs: 43, groups: J.ID, 22 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 60.0329 11.0887 25.5760 5.414 1.19e-05 *** #SVL -0.6576 0.3036 24.6670 -2.166 0.0402 * rpt.lab2<-rpt(Vmax.30.COM ~ SVL + MASS + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F1, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.lab2) # R SE 2.5% 97.5% P_permut LRT_P #0.508 0.161 0.143 0.784 NA 0.006 SPRINT SPEED REPEATABLE IN CAPTIVE POP lab4=lmer(NO.TRANS ~ SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) summary(lab4) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 2023 44.98 #Residual 1090 33.02 #Number of obs: 43, groups: J.ID, 22 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 96.3999 63.7924 24.9352 1.511 0.143 #SVL -0.1976 1.7385 23.6342 -0.114 0.910 rpt.lab4<-rpt(NO.TRANS ~ SVL + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F1, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.lab4) # R SE 2.5% 97.5% P_permut LRT_P #0.651 0.124 0.372 0.851 NA 0 ACTIVITY REPEATABLE IN CAPTIVE POP lab5=lmer(sqrt(TIME.GOAL) ~ SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) summary(lab5) #Random effects: #Groups Name Variance Std.Dev. #J.ID (Intercept) 6.972 2.640 #Residual 93.614 9.675 #Number of obs: 43, groups: J.ID, 22 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 41.26345 17.14279 31.07542 2.407 0.0222 * #SVL -0.07466 0.47203 30.69934 -0.158 0.8754 rpt.lab5<-rpt(sqrt(TIME.GOAL) ~ SVL + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F1, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.lab5) # R SE 2.5% 97.5% P_permut LRT_P #0.0778 0.164 0 0.538 NA 0.375 EXPLORATION NOT REPEATABLE IN CAPTIVE POP lab7=lmer(TIME.SOCIAL ~ SVL + (1|J.ID), R_No_Repeat_Stack_F1, REML=FALSE, na.action=na.omit) summary(lab7) #Random effects: # Groups Name Variance Std.Dev. #J.ID (Intercept) 0 0.0 #Residual 111681 334.2 #Number of obs: 43, groups: J.ID, 22 #Fixed effects: # Estimate Std. Error df t value Pr(>|t|) #(Intercept) 822.880 583.769 43.000 1.410 0.166 #SVL -3.956 16.081 43.000 -0.246 0.807 rpt.lab7<-rpt(TIME.SOCIAL ~ SVL + (1|J.ID), grname = c("J.ID"), data = R_No_Repeat_Stack_F1, datatype = "Gaussian", nboot = 1000, npermut = 0) summary(rpt.lab7) # R SE 2.5% 97.5% P_permut LRT_P # 0 0.131 0 0.421 NA 1