Single mating
Anastasia
29 April 2021
Working Title
Female Avoidance behaviour vs Aggressive males
Description
Single mating with different male morph and female egg-laying checked over a lifetime
Hypothesis - Life History Trade-offs
Question: Are fighter males more harmful to females, than scrambler males?
Fighters present a higher sexual conflict scenario for females post-copulatory by boosting fecundity earlier, higher in total fecundity, and decreasing longevity
Scramblers present a higher sexual conflict scenario for females post-copulatory by boosting fecundity earlier, higher in total fecundity, and decreasing longevity
All opposite is true
No changes between morphs
Data
- trial = experimental trial (1 or 2)
- mate_type = male morph as fighter (F) or scrambler (S)
- day = first day of egg counting, starts at day 2 as this is 2 days from mating
- status = dead (1) or alive (2)
- egg_count = number of eggs laid by female
- rep = replicate vial
- indv = unique individual number
Fecundity Over Time - Overall
Graph - Overall
Note: day 0 is mating day 
Model - Quadratic regression
Does fecundity over time for females differ on whether they mated with a fighter or a scrambler?
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: egg_count ~ (day^2) * mate_type + (1 | indv)
## Data: fecund_egg
##
## REML criterion at convergence: 5952.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8497 -0.7262 -0.2215 0.5769 3.7265
##
## Random effects:
## Groups Name Variance Std.Dev.
## indv (Intercept) 90.02 9.488
## Residual 800.91 28.300
## Number of obs: 621, groups: indv, 70
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.88173 2.99194 179.83340 15.001 < 2e-16 ***
## day -0.79676 0.15172 498.33108 -5.251 2.24e-07 ***
## mate_typeS -0.91143 4.35154 178.23775 -0.209 0.834
## day:mate_typeS -0.01416 0.20946 516.13158 -0.068 0.946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day mt_tyS
## day -0.642
## mate_typeS -0.688 0.441
## day:mt_typS 0.465 -0.724 -0.639
## $emtrends
## mate_type day^2.trend SE df lower.CL upper.CL
## F -0.0258 0.00496 491 -0.0355 -0.016
## S -0.0262 0.00471 529 -0.0355 -0.017
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## F - S 0.000458 0.00684 509 0.067 0.9466
##
## Degrees-of-freedom method: kenward-rogerFirst Fecund
Graph
Model
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: egg_count ~ mate_type + day + (1 | trial)
## Data: first.fecund
##
## REML criterion at convergence: 527.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1914 -0.6517 -0.2490 0.4268 4.0540
##
## Random effects:
## Groups Name Variance Std.Dev.
## trial (Intercept) 0.00 0.000
## Residual 86.78 9.315
## Number of obs: 73, groups: trial, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.8866 1.8187 70.0000 8.185 8.32e-12 ***
## mate_typeS -0.3123 2.2476 70.0000 -0.139 0.88989
## day -0.2751 0.1026 70.0000 -2.682 0.00912 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) mt_tyS
## mate_typeS -0.399
## day -0.587 -0.224
## convergence code: 0
## boundary (singular) fit: see ?isSingular
## $emmeans
## mate_type emmean SE df lower.CL upper.CL
## F 11.4 1.50 3.21 6.80 16.0
## S 11.1 1.72 4.34 6.47 15.7
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## F - S 0.312 2.27 69.5 0.138 0.8909
##
## Degrees-of-freedom method: kenward-roger## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: day ~ mate_type + egg_count + (1 | trial)
## Data: first.fecund
##
## REML criterion at convergence: 541.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4956 -0.8135 -0.2049 0.5082 3.0517
##
## Random effects:
## Groups Name Variance Std.Dev.
## trial (Intercept) 0.0 0.00
## Residual 106.8 10.34
## Number of obs: 73, groups: trial, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.4733 2.2309 70.0000 6.488 1.06e-08 ***
## mate_typeS 4.3403 2.4397 70.0000 1.779 0.07957 .
## egg_count -0.3387 0.1263 70.0000 -2.682 0.00912 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) mt_tyS
## mate_typeS -0.549
## egg_count -0.681 0.086
## convergence code: 0
## boundary (singular) fit: see ?isSingular
## $emmeans
## mate_type emmean SE df lower.CL upper.CL
## F 10.7 1.65 3.05 5.45 15.9
## S 15.0 1.88 4.13 9.83 20.2
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## F - S -4.34 2.46 69.5 -1.764 0.0821
##
## Degrees-of-freedom method: kenward-rogerPeak Fecundity
Graph
Model
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: egg_count ~ mate_type + day + (1 | trial)
## Data: peak.fecund.single
##
## REML criterion at convergence: 675.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.54961 -0.51241 0.00699 0.70239 1.99080
##
## Random effects:
## Groups Name Variance Std.Dev.
## trial (Intercept) 156.3 12.5
## Residual 1095.7 33.1
## Number of obs: 70, groups: trial, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 82.82853 11.73342 2.12851 7.059 0.0165 *
## mate_typeS -5.10166 7.99101 66.08017 -0.638 0.5254
## day 0.03635 0.65784 66.05110 0.055 0.9561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) mt_tyS
## mate_typeS -0.262
## day -0.466 -0.113
## $emmeans
## mate_type emmean SE df lower.CL upper.CL
## F 83.2 10.4 1.31 6.54 160
## S 78.1 10.6 1.41 8.35 148
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## F - S 5.1 8 66.1 0.638 0.5259
##
## Degrees-of-freedom method: kenward-roger## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: day ~ mate_type + egg_count + (1 | trial)
## Data: peak.fecund.single
##
## REML criterion at convergence: 452
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2415 -0.5862 -0.1316 0.4825 6.0908
##
## Random effects:
## Groups Name Variance Std.Dev.
## trial (Intercept) 0.00 0.000
## Residual 37.85 6.152
## Number of obs: 70, groups: trial, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.055507 2.096774 67.000000 3.842 0.000274 ***
## mate_typeS 1.415769 1.475644 67.000000 0.959 0.340794
## egg_count 0.003208 0.021922 67.000000 0.146 0.884079
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) mt_tyS
## mate_typeS -0.383
## egg_count -0.876 0.059
## convergence code: 0
## boundary (singular) fit: see ?isSingular
## $emmeans
## mate_type emmean SE df lower.CL upper.CL
## F 8.32 1.02 2.84 4.97 11.7
## S 9.73 1.13 3.24 6.28 13.2
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## F - S -1.42 1.5 66.8 -0.945 0.3480
##
## Degrees-of-freedom method: kenward-rogerTo Peak
Graph
Model
Does the rate at which fighters reach peak fecundity differ from the rate of scramblers?
The rate of reaching peak fecundity significantly differs between fighters and scramblers
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: egg_count ~ day * mate_type + (1 | indv)
## Data: to_peak_egg
##
## REML criterion at convergence: 2594.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0833 -0.6832 -0.0154 0.5479 3.2024
##
## Random effects:
## Groups Name Variance Std.Dev.
## indv (Intercept) 245.4 15.67
## Residual 693.9 26.34
## Number of obs: 272, groups: indv, 69
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.9107 5.2953 229.6742 1.494 0.137
## day 7.5955 0.7357 250.0918 10.324 < 2e-16 ***
## mate_typeS 33.9600 6.8550 185.6286 4.954 1.63e-06 ***
## day:mate_typeS -6.7435 0.8086 255.4912 -8.340 4.66e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day mt_tyS
## day -0.749
## mate_typeS -0.772 0.579
## day:mt_typS 0.682 -0.910 -0.662
## $emtrends
## mate_type day.trend SE df lower.CL upper.CL
## F 7.595 0.739 245 6.141 9.05
## S 0.852 0.339 268 0.184 1.52
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## F - S 6.74 0.813 252 8.297 <.0001
##
## Degrees-of-freedom method: kenward-rogerFrom Peak
Graph
Model
Does the rate at which fighters die from peak fecundity differ from the rate of scramblers?
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: egg_count ~ day * mate_type + (1 | indv)
## Data: from_peak_egg
##
## REML criterion at convergence: 4011.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6347 -0.7406 -0.1791 0.5406 3.2703
##
## Random effects:
## Groups Name Variance Std.Dev.
## indv (Intercept) 113.5 10.65
## Residual 841.0 29.00
## Number of obs: 416, groups: indv, 68
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.7265 4.1497 192.6586 13.188 < 2e-16 ***
## day -1.2149 0.1898 301.7284 -6.402 5.86e-10 ***
## mate_typeS 8.6013 6.4082 141.8983 1.342 0.182
## day:mate_typeS -0.4122 0.2801 180.1311 -1.472 0.143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day mt_tyS
## day -0.748
## mate_typeS -0.648 0.484
## day:mt_typS 0.507 -0.678 -0.774
## $emtrends
## mate_type day.trend SE df lower.CL upper.CL
## F -1.21 0.192 315 -1.59 -0.836
## S -1.63 0.209 132 -2.04 -1.214
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## F - S 0.412 0.284 198 1.451 0.1482
##
## Degrees-of-freedom method: kenward-rogerTotal Fecundity
Graph
Model
Does total fecundity differ between females mated with scramblers or fighters?
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: total ~ mate_type + (1 | trial)
## Data: total.fecund
##
## REML criterion at convergence: 876.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.98237 -0.71989 -0.07264 0.85126 2.55591
##
## Random effects:
## Groups Name Variance Std.Dev.
## trial (Intercept) 5628 75.02
## Residual 24349 156.04
## Number of obs: 69, groups: trial, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 273.092 59.113 1.209 4.620 0.103
## mate_typeS -10.682 37.650 66.039 -0.284 0.778
##
## Correlation of Fixed Effects:
## (Intr)
## mate_typeS -0.302
## $emmeans
## mate_type emmean SE df lower.CL upper.CL
## F 273 59.1 1.21 -231 777
## S 262 59.8 1.26 -212 737
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## F - S 10.7 37.7 66 0.284 0.7776
##
## Degrees-of-freedom method: kenward-rogerSurvival Graph
Model - Cox Model (egg count as covariate)
Is survival predicted by fecundity over time and does it differ between mate types?
While survival probability does not significantly differ between mate types (fighter or scrambler) the boost in fecundity does significantly decrease survivorship for females.
## Cox mixed-effects model fit by maximum likelihood
## Data: longevity
## events, n = 70, 70
## Iterations= 2 16
## NULL Integrated Fitted
## Log-likelihood -230.439 -221.8047 -221.7796
##
## Chisq df p AIC BIC
## Integrated loglik 17.27 4.00 0.0017139 9.27 0.27
## Penalized loglik 17.32 3.02 0.0006239 11.27 4.47
##
## Model: Surv(day) ~ mate_type * egg_count + (1 | indv)
## Fixed coefficients
## coef exp(coef) se(coef) z p
## mate_typeS -0.22215139 0.8007941 0.26734576 -0.83 0.4100
## egg_count 0.05239071 1.0537874 0.01782293 2.94 0.0033
## mate_typeS:egg_count 0.00699503 1.0070196 0.02120873 0.33 0.7400
##
## Random effects
## Group Variable Std Dev Variance
## indv Intercept 0.0199928009 0.0003997121## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## mate_type emmean SE df asymp.LCL asymp.UCL
## F 0.0863 0.115 Inf -0.139 0.312
## S -0.0990 0.129 Inf -0.352 0.154
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## F - S 0.185 0.244 Inf 0.760 0.4475
##
## Results are given on the log (not the response) scale.## chisq df p
## mate_type 0.101 4.00 1.00
## egg_count 3.202 3.02 0.37
## mate_type:egg_count 4.502 NA NA
## GLOBAL 5.339 7.02 0.62