mate vs fight
Anastasia Shavrova
2022-10-11
Notes
Packages
library("dplyr")##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary("ggplot2")
library(ggmosaic)
library("lme4")## Loading required package: Matrixlibrary("scales")
library("car")## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
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## recodelibrary("optimx")
library(emmeans)
library(ggsci)
library(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
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## lmer## The following object is masked from 'package:stats':
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## steplibrary(tidyr)##
## Attaching package: 'tidyr'## The following objects are masked from 'package:Matrix':
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## expand, pack, unpacklibrary(survival)
library(coxme)## Loading required package: bdsmatrix##
## Attaching package: 'bdsmatrix'## The following object is masked from 'package:base':
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## backsolvelibrary(ggpubr)
library(survminer)##
## Attaching package: 'survminer'## The following object is masked from 'package:survival':
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## myelomalibrary(viridis)## Loading required package: viridisLite##
## Attaching package: 'viridis'## The following object is masked from 'package:scales':
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## viridis_palmate_fight <- read.csv("mate.fight.csv", fileEncoding="UTF-8-BOM")
str(mate_fight)## 'data.frame': 324 obs. of 32 variables:
## $ grp : int 2 2 2 2 2 2 3 3 3 3 ...
## $ Experiment : int 1 1 1 1 1 1 1 1 1 1 ...
## $ morph : chr "F" "F" "F" "F" ...
## $ trmnt_type : chr "FF" "FF" "FF" "FF" ...
## $ competitor : chr "F" "F" "F" "F" ...
## $ trmt : int 75 73 73 74 74 75 2 3 3 1 ...
## $ mate : int 1 1 1 0 0 0 1 1 1 0 ...
## $ mate.order : int 1 1 2 0 0 0 1 1 2 0 ...
## $ times.mated : int 1 2 2 NA NA NA 1 2 2 NA ...
## $ time.mate : int 2426 646 1313 NA NA NA 918 909 2224 NA ...
## $ fight : int 1 1 1 1 1 1 0 1 1 0 ...
## $ morph.fight : chr "FF" "FF" "FF" "FF" ...
## $ fight.fight : chr "F" "F" "F" "F" ...
## $ comp.fight : chr "F" "F" "F" "F" ...
## $ time.fight : int 4530 2033 2033 2044 2044 4530 NA 909 909 NA ...
## $ mating.fight : int 1 1 1 0 0 1 NA 1 1 NA ...
## $ mate.rhodamine: int 0 1 0 NA NA NA 1 0 0 NA ...
## $ week1 : int 0 59 59 NA NA NA 0 116 116 NA ...
## $ week2 : int 0 106 106 NA NA NA 0 94 94 NA ...
## $ week3 : int 25 7 7 NA NA NA NA 38 38 NA ...
## $ week4 : int 55 NA NA NA NA NA NA NA NA NA ...
## $ week5 : int 20 NA NA NA NA NA NA NA NA NA ...
## $ week6 : int 0 NA NA NA NA NA NA NA NA NA ...
## $ week7 : int NA NA NA NA NA NA NA NA NA NA ...
## $ week8 : int NA NA NA NA NA NA NA NA NA NA ...
## $ week9 : int NA NA NA NA NA NA NA NA NA NA ...
## $ week10 : int NA NA NA NA NA NA NA NA NA NA ...
## $ week.11 : int NA NA NA NA NA NA NA NA NA NA ...
## $ week.12 : int NA NA NA NA NA NA NA NA NA NA ...
## $ long : int 42 21 21 14 14 42 14 21 21 14 ...
## $ total : int 100 172 172 NA NA NA 0 248 248 NA ...
## $ total.NA : int 100 172 172 0 0 0 0 248 248 0 ...Fighting
1. Does fighting precede mating?
If +ve then fighting first, if -ve then mating first
fight_over_mating <- mate_fight %>%
replace(is.na(.), 0) %>%
mutate(preceed = time.fight-time.mate) %>%
mutate(preced2 = case_when(preceed<=0 ~ "1", preceed>=0 ~"0")) %>%
mutate(preced3=as.numeric(preced2))
str(fight_over_mating)## 'data.frame': 324 obs. of 35 variables:
## $ grp : int 2 2 2 2 2 2 3 3 3 3 ...
## $ Experiment : int 1 1 1 1 1 1 1 1 1 1 ...
## $ morph : chr "F" "F" "F" "F" ...
## $ trmnt_type : chr "FF" "FF" "FF" "FF" ...
## $ competitor : chr "F" "F" "F" "F" ...
## $ trmt : int 75 73 73 74 74 75 2 3 3 1 ...
## $ mate : int 1 1 1 0 0 0 1 1 1 0 ...
## $ mate.order : num 1 1 2 0 0 0 1 1 2 0 ...
## $ times.mated : num 1 2 2 0 0 0 1 2 2 0 ...
## $ time.mate : num 2426 646 1313 0 0 ...
## $ fight : int 1 1 1 1 1 1 0 1 1 0 ...
## $ morph.fight : chr "FF" "FF" "FF" "FF" ...
## $ fight.fight : chr "F" "F" "F" "F" ...
## $ comp.fight : chr "F" "F" "F" "F" ...
## $ time.fight : num 4530 2033 2033 2044 2044 ...
## $ mating.fight : num 1 1 1 0 0 1 0 1 1 0 ...
## $ mate.rhodamine: num 0 1 0 0 0 0 1 0 0 0 ...
## $ week1 : num 0 59 59 0 0 0 0 116 116 0 ...
## $ week2 : num 0 106 106 0 0 0 0 94 94 0 ...
## $ week3 : num 25 7 7 0 0 0 0 38 38 0 ...
## $ week4 : num 55 0 0 0 0 0 0 0 0 0 ...
## $ week5 : num 20 0 0 0 0 0 0 0 0 0 ...
## $ week6 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ week7 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ week8 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ week9 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ week10 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ week.11 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ week.12 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ long : num 42 21 21 14 14 42 14 21 21 14 ...
## $ total : num 100 172 172 0 0 0 0 248 248 0 ...
## $ total.NA : int 100 172 172 0 0 0 0 248 248 0 ...
## $ preceed : num 2104 1387 720 2044 2044 ...
## $ preced2 : chr "0" "0" "0" "0" ...
## $ preced3 : num 0 0 0 0 0 0 1 1 1 1 ...Graph
ggplot(fight_over_mating, aes(trmnt_type,preceed, fill=trmnt_type)) + geom_boxplot() + theme_classic() + theme(legend.position="none")+ scale_fill_nejm() + ylab("Fighting-Mating") + scale_x_discrete(limits=c("FFF", "FFS", "FSS", "SSS", "SS", "FF", "FS")) + theme(legend.title=element_blank()) + xlab("Treatment Type")
Model
preceed_mod2 <- glmer(preced3~morph*competitor + (1|trmt), family="binomial", data=fight_over_mating)
Anova(preceed_mod2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: preced3
## Chisq Df Pr(>Chisq)
## morph 6.1306 1 0.01329 *
## competitor 0.9287 4 0.92041
## morph:competitor 8.3473 4 0.07965 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1preceed.grid <- ref_grid(preceed_mod2)
emmeans(regrid(preceed.grid), pairwise~morph|competitor)## $emmeans
## competitor = F:
## morph prob SE df asymp.LCL asymp.UCL
## F 0.441 0.0891 Inf 0.2668 0.616
## S 0.843 0.0871 Inf 0.6722 1.014
##
## competitor = FF:
## morph prob SE df asymp.LCL asymp.UCL
## F 0.520 0.0747 Inf 0.3731 0.666
## S 0.674 0.1152 Inf 0.4486 0.900
##
## competitor = FS:
## morph prob SE df asymp.LCL asymp.UCL
## F 0.588 0.0881 Inf 0.4149 0.760
## S 0.587 0.0881 Inf 0.4149 0.760
##
## competitor = S:
## morph prob SE df asymp.LCL asymp.UCL
## F 0.675 0.1153 Inf 0.4487 0.901
## S 0.646 0.0855 Inf 0.4781 0.813
##
## competitor = SS:
## morph prob SE df asymp.LCL asymp.UCL
## F 0.269 0.1082 Inf 0.0567 0.481
## S 0.674 0.0698 Inf 0.5375 0.811
##
## Confidence level used: 0.95
##
## $contrasts
## competitor = F:
## contrast estimate SE df z.ratio p.value
## F - S -0.401460 0.125 Inf -3.210 0.0013
##
## competitor = FF:
## contrast estimate SE df z.ratio p.value
## F - S -0.154783 0.137 Inf -1.128 0.2592
##
## competitor = FS:
## contrast estimate SE df z.ratio p.value
## F - S 0.000115 0.124 Inf 0.001 0.9993
##
## competitor = S:
## contrast estimate SE df z.ratio p.value
## F - S 0.028853 0.143 Inf 0.202 0.8399
##
## competitor = SS:
## contrast estimate SE df z.ratio p.value
## F - S -0.405474 0.130 Inf -3.125 0.0018preced.graph <- emmip(preceed.grid, morph~competitor, CIs=TRUE, type = "response", style = "factor", plotit = FALSE)
fight_likely <- ggplot(preced.graph, aes(x=competitor, y=yvar, col=morph)) + theme_classic() + geom_pointrange(aes(ymin=yvar-SE, ymax=yvar+SE), position="jitter") + scale_color_viridis(discrete=TRUE) + ylab("Likelihood to Fight First then Mate") + xlab("Rivals") + theme(legend.position = "none", text = element_text(size = 15)) + scale_x_discrete(limits=c("FF", "FS", "SS", "S", "F"))2. Does timing of fight differ between morph and competitor?
fight_time <- lm(time.fight~morph*competitor + trmt, contrast=list(morph=contr.sum, competitor=contr.sum), data=mate_fight) #need to have trmt in as covariate
Anova(fight_time, type=2)## Anova Table (Type II tests)
##
## Response: time.fight
## Sum Sq Df F value Pr(>F)
## morph 5815 1 0.0030 0.956543
## competitor 17957567 4 2.2991 0.060804 .
## trmt 18374413 1 9.4100 0.002505 **
## morph:competitor 24578151 4 3.1468 0.015789 *
## Residuals 337808221 173
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(fight_time, pairwise~competitor|morph)## $emmeans
## morph = F:
## competitor emmean SE df lower.CL upper.CL
## F 2235 361 173 1523 2947
## FF 2513 281 173 1959 3067
## FS 3014 356 173 2312 3717
## S 1241 533 173 190 2292
## SS 2150 378 173 1404 2896
##
## morph = S:
## competitor emmean SE df lower.CL upper.CL
## F 1241 533 173 190 2292
## FF 3329 477 173 2388 4271
## FS 2054 334 173 1396 2713
## S 1365 382 173 610 2119
## SS 2917 264 173 2396 3438
##
## Confidence level used: 0.95
##
## $contrasts
## morph = F:
## contrast estimate SE df t.ratio p.value
## F - FF -277.8 516 173 -0.539 0.9831
## F - FS -778.8 549 173 -1.420 0.6159
## F - S 994.6 577 173 1.724 0.4221
## F - SS 85.3 545 173 0.157 0.9999
## FF - FS -501.0 418 173 -1.198 0.7528
## FF - S 1272.4 647 173 1.968 0.2861
## FF - SS 363.1 453 173 0.802 0.9298
## FS - S 1773.4 673 173 2.633 0.0688
## FS - SS 864.1 507 173 1.705 0.4335
## S - SS -909.3 671 173 -1.356 0.6568
##
## morph = S:
## contrast estimate SE df t.ratio p.value
## F - FF -2088.3 743 173 -2.811 0.0433
## F - FS -813.5 657 173 -1.238 0.7289
## F - S -123.6 578 173 -0.214 0.9995
## F - SS -1676.0 625 173 -2.684 0.0605
## FF - FS 1274.7 566 173 2.254 0.1652
## FF - S 1964.6 650 173 3.021 0.0239
## FF - SS 412.3 527 173 0.782 0.9356
## FS - S 689.9 549 173 1.256 0.7181
## FS - SS -862.5 405 173 -2.131 0.2117
## S - SS -1552.4 510 173 -3.044 0.0224
##
## P value adjustment: tukey method for comparing a family of 5 estimatestimefight.graph <- emmip(fight_time, morph~competitor, CIs=TRUE, type = "response", style = "factor", plotit = FALSE)
timetofight <- ggplot(timefight.graph, aes(x=competitor, y=yvar, col=morph)) + theme_classic() + geom_pointrange(aes(ymin=yvar-SE, ymax=yvar+SE), position="jitter") + scale_color_viridis(discrete=TRUE, labels=c("Focal Fighter Male", "Focal Scrambler Male")) + ylab("Time to Initiate Fight (sec)") + xlab("Rivals") + theme(legend.position = "bottom",legend.title=element_blank(), text = element_text(size = 15))+ scale_x_discrete(limits=c("FF", "FS", "SS", "S", "F"))
ggarrange(fight_likely, timetofight, ncol=1, labels=c("A", "B"))
emmeans(fight_time, pairwise~competitor)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## competitor emmean SE df lower.CL upper.CL
## F 1738 352 173 1044 2432
## FF 2921 289 173 2350 3492
## FS 2534 254 173 2033 3036
## S 1303 362 173 588 2018
## SS 2534 236 173 2067 3000
##
## Results are averaged over the levels of: morph
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## F - FF -1183.05 506 173 -2.337 0.1382
## F - FS -796.18 478 173 -1.666 0.4580
## F - S 435.46 408 173 1.066 0.8235
## F - SS -795.35 458 173 -1.736 0.4149
## FF - FS 386.87 352 173 1.099 0.8068
## FF - S 1618.51 518 173 3.125 0.0175
## FF - SS 387.70 348 173 1.113 0.7993
## FS - S 1231.65 490 173 2.516 0.0919
## FS - SS 0.83 324 173 0.003 1.0000
## S - SS -1230.82 469 173 -2.622 0.0707
##
## Results are averaged over the levels of: morph
## P value adjustment: tukey method for comparing a family of 5 estimatesGraph
ggplot(mate_fight, aes(competitor,time.fight, fill=morph)) + geom_boxplot() + theme_classic() + scale_fill_nejm() + xlab("Competitor Morph")+ scale_x_discrete(limits=c("FF", "FS", "SS", "S", "F")) + ylab("Time of Fight") + theme(legend.position="bottom")## Warning: Removed 140 rows containing non-finite values (`stat_boxplot()`).
3. Who fights more depending on competitors?
who_fight <- glmer(fight~morph*competitor + (1|trmt), family="binomial", contrast=list(morph=contr.sum, competitor=contr.sum), data=mate_fight)
Anova(who_fight, type=2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: fight
## Chisq Df Pr(>Chisq)
## morph 0.5207 1 0.4706
## competitor 3.0125 4 0.5557
## morph:competitor 4.3307 4 0.3631fight.grid <- ref_grid(who_fight)
emmeans(regrid(fight.grid), pairwise~morph|competitor)## $emmeans
## competitor = F:
## morph prob SE df asymp.LCL asymp.UCL
## F 0.633 0.1001 Inf 0.437 0.829
## S 0.433 0.1377 Inf 0.163 0.703
##
## competitor = FF:
## morph prob SE df asymp.LCL asymp.UCL
## F 0.627 0.0851 Inf 0.460 0.793
## S 0.499 0.1375 Inf 0.229 0.768
##
## competitor = FS:
## morph prob SE df asymp.LCL asymp.UCL
## F 0.466 0.1034 Inf 0.263 0.669
## S 0.531 0.1031 Inf 0.329 0.733
##
## competitor = S:
## morph prob SE df asymp.LCL asymp.UCL
## F 0.433 0.1377 Inf 0.163 0.703
## S 0.633 0.1001 Inf 0.437 0.829
##
## competitor = SS:
## morph prob SE df asymp.LCL asymp.UCL
## F 0.806 0.0993 Inf 0.612 1.001
## S 0.607 0.0867 Inf 0.437 0.776
##
## Confidence level used: 0.95
##
## $contrasts
## competitor = F:
## contrast estimate SE df z.ratio p.value
## F - S 0.1998 0.171 Inf 1.170 0.2420
##
## competitor = FF:
## contrast estimate SE df z.ratio p.value
## F - S 0.1281 0.162 Inf 0.792 0.4285
##
## competitor = FS:
## contrast estimate SE df z.ratio p.value
## F - S -0.0651 0.146 Inf -0.445 0.6560
##
## competitor = S:
## contrast estimate SE df z.ratio p.value
## F - S -0.1997 0.171 Inf -1.170 0.2420
##
## competitor = SS:
## contrast estimate SE df z.ratio p.value
## F - S 0.1997 0.131 Inf 1.522 0.1279emmeans(regrid(fight.grid), pairwise~competitor|morph)## $emmeans
## morph = F:
## competitor prob SE df asymp.LCL asymp.UCL
## F 0.633 0.1001 Inf 0.437 0.829
## FF 0.627 0.0851 Inf 0.460 0.793
## FS 0.466 0.1034 Inf 0.263 0.669
## S 0.433 0.1377 Inf 0.163 0.703
## SS 0.806 0.0993 Inf 0.612 1.001
##
## morph = S:
## competitor prob SE df asymp.LCL asymp.UCL
## F 0.433 0.1377 Inf 0.163 0.703
## FF 0.499 0.1375 Inf 0.229 0.768
## FS 0.531 0.1031 Inf 0.329 0.733
## S 0.633 0.1001 Inf 0.437 0.829
## SS 0.607 0.0867 Inf 0.437 0.776
##
## Confidence level used: 0.95
##
## $contrasts
## morph = F:
## contrast estimate SE df z.ratio p.value
## F - FF 0.00618 0.131 Inf 0.047 1.0000
## F - FS 0.16696 0.144 Inf 1.157 0.7758
## F - S 0.19975 0.171 Inf 1.170 0.7686
## F - SS -0.17339 0.140 Inf -1.240 0.7277
## FF - FS 0.16078 0.134 Inf 1.198 0.7524
## FF - S 0.19357 0.162 Inf 1.194 0.7551
## FF - SS -0.17957 0.130 Inf -1.380 0.6403
## FS - S 0.03279 0.172 Inf 0.191 0.9997
## FS - SS -0.34035 0.144 Inf -2.367 0.1243
## S - SS -0.37314 0.170 Inf -2.191 0.1829
##
## morph = S:
## contrast estimate SE df z.ratio p.value
## F - FF -0.06547 0.195 Inf -0.337 0.9972
## F - FS -0.09785 0.172 Inf -0.569 0.9796
## F - S -0.19975 0.171 Inf -1.170 0.7686
## F - SS -0.17346 0.163 Inf -1.064 0.8249
## FF - FS -0.03238 0.172 Inf -0.188 0.9997
## FF - S -0.13428 0.170 Inf -0.789 0.9339
## FF - SS -0.10799 0.163 Inf -0.664 0.9640
## FS - S -0.10190 0.144 Inf -0.709 0.9544
## FS - SS -0.07561 0.135 Inf -0.561 0.9805
## S - SS 0.02629 0.132 Inf 0.199 0.9996
##
## P value adjustment: tukey method for comparing a family of 5 estimatesnum_fights <- mate_fight %>%
count(fight)
num_fight_trmnt <- mate_fight %>%
group_by(trmnt_type, morph.fight) %>%
count(fight)Mating
1. Likelihood of mating
mate_likely <- glm(mate~morph*competitor + trmt, family="binomial",contrast=list(morph=contr.sum, competitor=contr.sum), data=mate_fight)
Anova(mate_likely)## Analysis of Deviance Table (Type II tests)
##
## Response: mate
## LR Chisq Df Pr(>Chisq)
## morph 4.2987 1 0.03814 *
## competitor 1.8284 4 0.76728
## trmt 0.2885 1 0.59116
## morph:competitor 1.8498 4 0.76335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1likely.grid <- ref_grid(mate_likely)
emmeans(regrid(likely.grid), pairwise~morph)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## morph prob SE df asymp.LCL asymp.UCL
## F 0.394 0.0419 Inf 0.312 0.476
## S 0.519 0.0437 Inf 0.434 0.605
##
## Results are averaged over the levels of: competitor
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## F - S -0.125 0.0596 Inf -2.102 0.0356
##
## Results are averaged over the levels of: competitormany_mates <- mate_fight %>%
count(mate)
many_mates_trmnt <- mate_fight %>%
group_by(trmnt_type, morph) %>%
count(mate)2. Time to mate
timing_mating <- lm(time.mate~morph*competitor + trmt, data=mate_fight)
Anova(timing_mating, type=2)## Anova Table (Type II tests)
##
## Response: time.mate
## Sum Sq Df F value Pr(>F)
## morph 980279 1 0.2988 0.5855
## competitor 11639527 4 0.8871 0.4736
## trmt 3079223 1 0.9387 0.3343
## morph:competitor 7650745 4 0.5831 0.6754
## Residuals 442832358 135timemate.graph <- emmip(timing_mating, morph~competitor, CIs=TRUE, type = "response", style = "factor", plotit = FALSE)
ggplot(timemate.graph, aes(x=competitor, y=yvar, col=morph)) + theme_classic() + geom_pointrange(aes(ymin=yvar-SE, ymax=yvar+SE), position="jitter") + scale_color_viridis(discrete=TRUE, labels=c("Focal Fighter Male", "Focal Scrambler Male")) + ylab("Time to Initiate Mating (sec)") + xlab("Rivals") + theme(legend.position = "bottom",legend.title=element_blank(),text = element_text(size = 15)) + scale_x_discrete(limits=c("FF", "FS", "SS", "S", "F"))
Graph
ggplot(mate_fight, aes(competitor, time.mate, fill=morph)) + geom_boxplot() + theme_classic()+ scale_fill_nejm() + xlab("Competitor Morph")+ scale_x_discrete(limits=c("FF", "FS", "SS", "S", "F")) + ylab("Time of Mating") + theme(legend.position="bottom")## Warning: Removed 178 rows containing non-finite values (`stat_boxplot()`).
Male Fitness: total egg count and longevity
Total egg count/number of matings
- majority of fitness is 0
fit_mod <- lmer(fitness.NA~competitor*morph + (1|trmt) + mate.rhodamine, contrast=list(morph=contr.sum, competitor=contr.sum), data=fit)
Anova(fit_mod)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: fitness.NA
## Chisq Df Pr(>Chisq)
## competitor 3.3387 4 0.50282
## morph 2.7240 1 0.09885 .
## mate.rhodamine 0.0000 1 0.99778
## competitor:morph 3.3353 4 0.50336
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(fit_mod, pairwise~competitor|morph)## $emmeans
## morph = F:
## competitor emmean SE df lower.CL upper.CL
## F 58.7 16.2 96.1 26.419 90.9
## FF 38.3 15.6 96.0 7.378 69.3
## FS 26.2 13.7 96.5 -0.884 53.3
## S 45.0 15.6 96.8 13.981 76.0
## SS 41.8 14.2 99.1 13.679 69.9
##
## morph = S:
## competitor emmean SE df lower.CL upper.CL
## F 45.0 15.6 96.8 13.946 76.0
## FF 26.3 13.7 97.2 -0.900 53.4
## FS 42.0 14.1 96.5 14.019 69.9
## S 15.4 15.0 96.0 -14.442 45.2
## SS 17.0 13.3 95.9 -9.333 43.3
##
## Results are averaged over the levels of: mate.rhodamine
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## morph = F:
## contrast estimate SE df t.ratio p.value
## F - FF 20.31 22.5 96.0 0.902 0.8954
## F - FS 32.43 21.2 96.2 1.528 0.5468
## F - S 13.65 22.5 96.4 0.606 0.9739
## F - SS 16.85 21.6 97.3 0.782 0.9353
## FF - FS 12.12 20.7 96.2 0.584 0.9771
## FF - S -6.67 22.1 96.4 -0.302 0.9982
## FF - SS -3.46 21.1 97.4 -0.164 0.9998
## FS - S -18.78 20.8 96.6 -0.905 0.8944
## FS - SS -15.58 19.7 97.8 -0.791 0.9326
## S - SS 3.21 21.1 97.8 0.152 0.9999
##
## morph = S:
## contrast estimate SE df t.ratio p.value
## F - FF 18.72 20.8 96.9 0.901 0.8958
## F - FS 3.01 21.0 96.6 0.143 0.9999
## F - S 29.58 21.7 96.4 1.364 0.6519
## F - SS 28.00 20.5 96.4 1.366 0.6506
## FF - FS -15.70 19.6 96.8 -0.800 0.9300
## FF - S 10.86 20.3 96.5 0.535 0.9835
## FF - SS 9.28 19.0 96.5 0.487 0.9884
## FS - S 26.57 20.6 96.2 1.290 0.6979
## FS - SS 24.99 19.3 96.2 1.292 0.6966
## S - SS -1.58 20.0 96.0 -0.079 1.0000
##
## Results are averaged over the levels of: mate.rhodamine
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 5 estimatesemmeans(fit_mod, pairwise~morph)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## morph emmean SE df lower.CL upper.CL
## F 42.0 6.75 97.0 28.6 55.4
## S 29.1 6.43 96.7 16.4 41.9
##
## Results are averaged over the levels of: competitor, mate.rhodamine
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## F - S 12.9 6.07 98.7 2.124 0.0362
##
## Results are averaged over the levels of: competitor, mate.rhodamine
## Degrees-of-freedom method: kenward-rogermale_fit_graph <- emmip(fit_mod, morph~competitor, CIs=TRUE, type = "response", style = "factor", plotit = FALSE)
malefitnessgraph <- ggplot(male_fit_graph, aes(x=competitor, y=yvar, col=morph)) + theme_classic()+ geom_pointrange(aes(ymin=yvar-SE, ymax=yvar+SE), position="jitter") + scale_color_viridis(discrete=TRUE, labels=c("Focal Fighter Male", "Focal Scrambler Male")) + ylab("Male Fitness") + xlab("Rivals") + theme(legend.position = "bottom", legend.title=element_blank(),text = element_text(size = 15)) + scale_x_discrete(limits=c("FF", "FS", "SS", "S", "F"))Probability of 0
fit_prob <- glm(as.numeric(fitness_prob)~competitor*morph + mate.rhodamine, family="binomial", data=fit_zero)
Anova(fit_prob)## Analysis of Deviance Table (Type II tests)
##
## Response: as.numeric(fitness_prob)
## LR Chisq Df Pr(>Chisq)
## competitor 4.2764 4 0.3699
## morph 0.7598 1 0.3834
## mate.rhodamine 1.3856 1 0.2391
## competitor:morph 0.7761 4 0.9416fitprob.grid <- ref_grid(fit_prob)
emmeans(regrid(fitprob.grid), pairwise~competitor | morph)## $emmeans
## morph = F:
## competitor response SE df asymp.LCL asymp.UCL
## F 0.529 0.1380 Inf 0.2587 0.800
## FF 0.460 0.1201 Inf 0.2247 0.696
## FS 0.475 0.1339 Inf 0.2122 0.737
## S 0.400 0.1633 Inf 0.0800 0.720
## SS 0.630 0.2027 Inf 0.2325 1.027
##
## morph = S:
## competitor response SE df asymp.LCL asymp.UCL
## F 0.388 0.1636 Inf 0.0675 0.709
## FF 0.496 0.1728 Inf 0.1574 0.835
## FS 0.453 0.1204 Inf 0.2173 0.689
## S 0.243 0.0965 Inf 0.0534 0.432
## SS 0.510 0.0893 Inf 0.3354 0.685
##
## Results are averaged over the levels of: mate.rhodamine
## Confidence level used: 0.95
##
## $contrasts
## morph = F:
## contrast estimate SE df z.ratio p.value
## F - FF 0.0689 0.181 Inf 0.380 0.9956
## F - FS 0.0545 0.188 Inf 0.290 0.9985
## F - S 0.1290 0.208 Inf 0.622 0.9717
## F - SS -0.1006 0.240 Inf -0.419 0.9936
## FF - FS -0.0144 0.179 Inf -0.081 1.0000
## FF - S 0.0601 0.201 Inf 0.299 0.9983
## FF - SS -0.1696 0.234 Inf -0.723 0.9512
## FS - S 0.0745 0.207 Inf 0.359 0.9964
## FS - SS -0.1551 0.240 Inf -0.646 0.9674
## S - SS -0.2297 0.256 Inf -0.898 0.8977
##
## morph = S:
## contrast estimate SE df z.ratio p.value
## F - FF -0.1081 0.228 Inf -0.474 0.9897
## F - FS -0.0652 0.200 Inf -0.327 0.9975
## F - S 0.1455 0.186 Inf 0.782 0.9358
## F - SS -0.1224 0.185 Inf -0.661 0.9646
## FF - FS 0.0428 0.207 Inf 0.207 0.9996
## FF - S 0.2536 0.194 Inf 1.309 0.6856
## FF - SS -0.0143 0.193 Inf -0.074 1.0000
## FS - S 0.2107 0.153 Inf 1.380 0.6407
## FS - SS -0.0571 0.149 Inf -0.382 0.9955
## S - SS -0.2678 0.131 Inf -2.047 0.2437
##
## Results are averaged over the levels of: mate.rhodamine
## P value adjustment: tukey method for comparing a family of 5 estimatesFemale Fitness
Survival
Model
long_num <- mate_fight %>%
filter(long != "CHECK") %>%
mutate(num.long=as.numeric(long)) %>%
mutate(times.mated.NA= ifelse(is.na(times.mated), 0, times.mated))
surv_long <- coxme(Surv(num.long)~trmnt_type*times.mated.NA + total.NA +(1|trmt) + mate.rhodamine, data=long_num)
Anova(surv_long)## Analysis of Deviance Table (Type II tests)
##
## Response: Surv(num.long)
## Df Chisq Pr(>Chisq)
## trmnt_type 6 10.2396 0.1149
## times.mated.NA 1 0.0003 0.9853
## total.NA 1 6.4332 0.0112 *
## mate.rhodamine 1 0.0764 0.7822
## trmnt_type:times.mated.NA 6 2.5917 0.8581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1surv.grid <- ref_grid(surv_long)
emmeans(regrid(surv.grid), pairwise~trmnt_type)## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## trmnt_type response SE df asymp.LCL asymp.UCL
## FF 3.429 2.493 Inf -1.4572 8.31
## FFF 0.745 0.428 Inf -0.0945 1.58
## FFS 0.562 0.276 Inf 0.0220 1.10
## FS 0.609 0.330 Inf -0.0368 1.26
## FSS 1.196 0.572 Inf 0.0743 2.32
## SS 2.029 1.043 Inf -0.0151 4.07
## SSS 0.946 0.416 Inf 0.1312 1.76
##
## Results are averaged over the levels of: mate.rhodamine
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## FF - FFF 2.6837 2.560 Inf 1.048 0.9427
## FF - FFS 2.8667 2.528 Inf 1.134 0.9178
## FF - FS 2.8193 2.527 Inf 1.116 0.9236
## FF - FSS 2.2323 2.616 Inf 0.854 0.9791
## FF - SS 1.3998 2.789 Inf 0.502 0.9988
## FF - SSS 2.4827 2.569 Inf 0.966 0.9611
## FFF - FFS 0.1830 0.533 Inf 0.343 0.9999
## FFF - FS 0.1356 0.556 Inf 0.244 1.0000
## FFF - FSS -0.4514 0.763 Inf -0.591 0.9971
## FFF - SS -1.2839 1.185 Inf -1.084 0.9331
## FFF - SSS -0.2010 0.642 Inf -0.313 0.9999
## FFS - FS -0.0474 0.442 Inf -0.107 1.0000
## FFS - FSS -0.6344 0.690 Inf -0.919 0.9697
## FFS - SS -1.4669 1.108 Inf -1.324 0.8408
## FFS - SSS -0.3840 0.527 Inf -0.728 0.9909
## FS - FSS -0.5870 0.704 Inf -0.834 0.9815
## FS - SS -1.4195 1.117 Inf -1.271 0.8652
## FS - SSS -0.3366 0.576 Inf -0.584 0.9973
## FSS - SS -0.8325 1.270 Inf -0.655 0.9949
## FSS - SSS 0.2504 0.762 Inf 0.329 0.9999
## SS - SSS 1.0829 1.169 Inf 0.926 0.9685
##
## Results are averaged over the levels of: mate.rhodamine
## P value adjustment: tukey method for comparing a family of 7 estimatesPlot - PICK COLOURS
survfit <- survfit(Surv(num.long)~trmnt_type + (1|trmt), data=long_num)
ggsurvplot(survfit, data=long_num)
ggsurvplot(survfit, data=long_num,
legend.title="",
legend.labs=c("FF", "FFF", "FFS", "FS", "FSS", "SS", "SSS"),
xlab="Time in weeks",
conf.int=TRUE,
conf.int.style="step",
xlim =c(0,100),
legend = "bottom",
ggtheme = theme_pubr(),
palette=c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7"),
)
Fecundity
fem_fit <- mate_fight %>%
filter(total !="CHECK") %>%
mutate(total_num=as.numeric(total)) %>%
filter(mate == 1)
fem_fit_two <- mate_fight %>%
filter(total !="CHECK") %>%
mutate(total_num=as.numeric(total)) %>%
filter(mate == 1) %>%
filter(times.mated != "3")
mated_fem <- mate_fight %>%
group_by(trmt) %>%
filter(mate == "1") %>%
count(mate)
mated_fem_zero <- mate_fight %>%
filter(mate=="1") %>%
group_by(trmt) %>%
filter(total == 0) %>%
distinct(trmt, .keep_all=TRUE)Total Female Fecundity
fem_fecund <- glmer(total_num~trmnt_type*as.factor(times.mated) + (1|trmt) + mate.rhodamine, family="poisson",control = glmerControl(optimizer ='optimx', optCtrl=list(method='nlminb')), data=fem_fit)## fixed-effect model matrix is rank deficient so dropping 5 columns / coefficients## Warning in optimx.check(par, optcfg$ufn, optcfg$ugr, optcfg$uhess, lower, : Parameters or bounds appear to have different scalings.
## This can cause poor performance in optimization.
## It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.Anova(fem_fecund)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: total_num
## Chisq Df Pr(>Chisq)
## trmnt_type 7.7107 6 0.2601
## as.factor(times.mated) 2.3569 2 0.3077
## mate.rhodamine 0.0015 1 0.9692
## trmnt_type:as.factor(times.mated) 4.5688 7 0.7124fem_fecund_twice <- glmer(total_num~trmnt_type*as.factor(times.mated) + (1|trmt), family="poisson",control = glmerControl(optimizer ='optimx', optCtrl=list(method='nlminb')), data=fem_fit_two)
Anova(fem_fecund_twice)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: total_num
## Chisq Df Pr(>Chisq)
## trmnt_type 7.2790 6 0.2958
## as.factor(times.mated) 1.1043 1 0.2933
## trmnt_type:as.factor(times.mated) 4.1984 6 0.6498fecund.grid <- ref_grid(fem_fecund_twice)
emmeans(regrid(fecund.grid), pairwise~trmnt_type | times.mated)## $emmeans
## times.mated = 1:
## trmnt_type rate SE df asymp.LCL asymp.UCL
## FF 2.7985 4.7154 Inf -6.4436 12.0405
## FFF 0.3057 0.6223 Inf -0.9140 1.5255
## FFS 2.5200 4.0905 Inf -5.4973 10.5373
## FS 0.5129 1.0586 Inf -1.5619 2.5878
## FSS 4.9876 8.5097 Inf -11.6911 21.6664
## SS 0.0277 0.0471 Inf -0.0646 0.1200
## SSS 0.0357 0.0703 Inf -0.1021 0.1735
##
## times.mated = 2:
## trmnt_type rate SE df asymp.LCL asymp.UCL
## FF 1.2105 4.6543 Inf -7.9118 10.3327
## FFF 0.2051 0.5885 Inf -0.9482 1.3585
## FFS 0.0173 0.0437 Inf -0.0685 0.1030
## FS 0.2236 0.6100 Inf -0.9720 1.4193
## FSS 0.1538 0.3536 Inf -0.5393 0.8469
## SS 0.0123 0.0272 Inf -0.0411 0.0657
## SSS 0.4868 1.0137 Inf -1.5001 2.4737
##
## Confidence level used: 0.95
##
## $contrasts
## times.mated = 1:
## contrast estimate SE df z.ratio p.value
## FF - FFF 2.49275 4.7084 Inf 0.529 0.9984
## FF - FFS 0.27846 6.1326 Inf 0.045 1.0000
## FF - FS 2.28557 4.7672 Inf 0.479 0.9991
## FF - FSS -2.18914 9.5822 Inf -0.228 1.0000
## FF - SS 2.77076 4.7119 Inf 0.588 0.9972
## FF - SSS 2.76281 4.7103 Inf 0.587 0.9972
## FFF - FFS -2.21429 4.1020 Inf -0.540 0.9982
## FFF - FS -0.20718 1.1713 Inf -0.177 1.0000
## FFF - FSS -4.68190 8.4965 Inf -0.551 0.9980
## FFF - SS 0.27801 0.6179 Inf 0.450 0.9994
## FFF - SSS 0.27005 0.6170 Inf 0.438 0.9995
## FFS - FS 2.00711 4.1768 Inf 0.481 0.9991
## FFS - FSS -2.46761 9.3444 Inf -0.264 1.0000
## FFS - SS 2.49230 4.0880 Inf 0.610 0.9965
## FFS - SSS 2.48434 4.0869 Inf 0.608 0.9966
## FS - FSS -4.47472 8.5257 Inf -0.525 0.9985
## FS - SS 0.48519 1.0546 Inf 0.460 0.9993
## FS - SSS 0.47724 1.0534 Inf 0.453 0.9994
## FSS - SS 4.95991 8.5070 Inf 0.583 0.9973
## FSS - SSS 4.95195 8.5058 Inf 0.582 0.9973
## SS - SSS -0.00795 0.0791 Inf -0.101 1.0000
##
## times.mated = 2:
## contrast estimate SE df z.ratio p.value
## FF - FFF 1.00534 4.6752 Inf 0.215 1.0000
## FF - FFS 1.19320 4.6528 Inf 0.256 1.0000
## FF - FS 0.98683 4.6712 Inf 0.211 1.0000
## FF - FSS 1.05666 4.6527 Inf 0.227 1.0000
## FF - SS 1.19816 4.6532 Inf 0.257 1.0000
## FF - SSS 0.72362 4.7355 Inf 0.153 1.0000
## FFF - FFS 0.18787 0.5873 Inf 0.320 0.9999
## FFF - FS -0.01850 0.8209 Inf -0.023 1.0000
## FFF - FSS 0.05133 0.6650 Inf 0.077 1.0000
## FFF - SS 0.19282 0.5872 Inf 0.328 0.9999
## FFF - SSS -0.28172 1.1483 Inf -0.245 1.0000
## FFS - FS -0.20637 0.6078 Inf -0.340 0.9999
## FFS - FSS -0.13654 0.3520 Inf -0.388 0.9997
## FFS - SS 0.00496 0.0492 Inf 0.101 1.0000
## FFS - SSS -0.46959 1.0118 Inf -0.464 0.9993
## FS - FSS 0.06983 0.6752 Inf 0.103 1.0000
## FS - SS 0.21133 0.6080 Inf 0.348 0.9999
## FS - SSS -0.26322 1.1496 Inf -0.229 1.0000
## FSS - SS 0.14150 0.3517 Inf 0.402 0.9997
## FSS - SSS -0.33304 1.0495 Inf -0.317 0.9999
## SS - SSS -0.47454 1.0122 Inf -0.469 0.9992
##
## P value adjustment: tukey method for comparing a family of 7 estimatesfem_fecund_graph <- emmip(fem_fecund_twice, ~trmnt_type | times.mated, CIs=TRUE, style = "factor", type="response", plotit = FALSE)
femalefitnessgraph <- ggplot(fem_fecund_graph, aes(x=trmnt_type, y=yvar, col=as.factor(times.mated))) + theme_classic() + geom_pointrange(aes(ymin=yvar-SE, ymax=yvar+SE), position="jitter") + ylab("Female Fitness") + xlab("Treatment Type") + theme(legend.position = "bottom",legend.title=element_blank(), text = element_text(size = 15)) + scale_x_discrete(limits=c("FFF", "FFS", "FSS", "SSS", "SS", "FF")) + scale_color_d3(labels=c("Mated Once", "Mated Twice"))
ggarrange(malefitnessgraph, femalefitnessgraph, ncol=1, labels=c("A", "B"))## Warning: Removed 2 rows containing missing values (`geom_pointrange()`).
Graph
ggplot(fem_fit, aes(trmnt_type,total_num, fill=as.factor(times.mated))) + geom_boxplot() + theme_classic() + theme(legend.position="bottom") + scale_fill_nejm()
How many females mated but did not produce eggs?
fem_noegg <- mate_fight %>%
filter(total !="CHECK") %>%
mutate(total_num=as.numeric(total)) %>%
filter(mate == 1) %>%
filter(total == 0) %>%
group_by(trmnt_type, times.mated) %>%
count(times.mated)
fem_noegg## # A tibble: 15 × 3
## # Groups: trmnt_type, times.mated [15]
## trmnt_type times.mated n
## <chr> <int> <int>
## 1 FF 1 4
## 2 FF 2 2
## 3 FFF 1 5
## 4 FFF 2 4
## 5 FFS 1 3
## 6 FFS 2 6
## 7 FS 1 4
## 8 FS 2 6
## 9 FSS 1 3
## 10 FSS 2 8
## 11 SS 1 6
## 12 SS 2 8
## 13 SSS 1 5
## 14 SSS 2 6
## 15 SSS 3 3How many times females mated vs produced eggs?
fem_egg <- mate_fight %>%
filter(total !="CHECK") %>%
mutate(total_num=as.numeric(total)) %>%
filter(mate == 1) %>%
filter(total != 0) %>%
group_by(trmnt_type, times.mated) %>%
count(times.mated)
fem_egg## # A tibble: 16 × 3
## # Groups: trmnt_type, times.mated [16]
## trmnt_type times.mated n
## <chr> <int> <int>
## 1 FF 1 6
## 2 FF 2 2
## 3 FFF 1 4
## 4 FFF 2 4
## 5 FFS 1 7
## 6 FFS 2 2
## 7 FFS 3 3
## 8 FS 1 4
## 9 FS 2 4
## 10 FSS 1 6
## 11 FSS 2 6
## 12 SS 1 3
## 13 SS 2 2
## 14 SSS 1 2
## 15 SSS 2 8
## 16 SSS 3 6