Working Title

Female Avoidance behaviour vs Aggressive males

Authors

Anastasia J. Shavrova, Bruno A. Buzatto, & Michael M. Kasumovic

Description

In the bulb mite, Rhizoglyphus echinopus, two male morphs are present: a fighter and a scrambler male. The females are typically polyamorous, and hte more mates she has the higher her fecundity. To potentially control this, the fighter morph male, if given the chance, will try to eliminate rival males by using his fighter legs to pierce and kill them. There is some evidence that suggests that if a female is mated with a scrambler male this boosts her fecundity, and on the contrary if she is mated with a fighter male. However, recent evidence suggests that in lines selected for fighters, if a female of the fighter lines is mated with a fighter male this boosts her fecundity, and her fecundity is decreased if she is mated with a scrambler male from the scrambler selected lines.

Hypotheses

Question 1: Are females more resistance to mate with a specific male morph?

Females prefer to mate with scrambler males over fighter males in terms of behavioural sexual conflict
Females prefer to mate with fighters due to post-copulatory lower fecundity boost
Females have no preference

Question 2: Are fighter males 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
There is no difference in peak fecundity or longevity between the two morphs

#Brief Methods Females were presented with either two males of the same morph, or two males of a different morph in opposite order, with 5 replicate mating vials for each treatment. Mating latency, lifetime fecundity, and female longevity were all observed.

Fecundity Over Time

Fecundity per female (egg laying and non-egg laying) in each treatment group over time until female death and most models are for for females who mated twice (i.e completed the behavioural trial).

General Data Description

trial = experimental trial (1-8)
mate_order = male morph mating order in either of 4 treatments: “FF”, “FS”, “SS”, “SF”
day = the day of egg laying starting at the mating day relates to female longevity
egg_count = the number of eggs the female laid
times_mated = how many times a female mated either 1 or 2 times
replicate = replicate vial within a treatment (1-5)
indv = unique identification number for female

Graph

Average between all individuals

Model

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(egg_count + 1) ~ day + I(day^2) * mate_order + (1 | indv)
##    Data: two.time.fert
## 
## REML criterion at convergence: 4585.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2610 -0.7177  0.1891  0.7493  3.1515 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  indv     (Intercept) 0.7599   0.8717  
##  Residual             2.1935   1.4810  
## Number of obs: 1200, groups:  indv, 125
## 
## Fixed effects:
##                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            2.363e+00  2.176e-01  2.221e+02  10.863  < 2e-16 ***
## day                   -2.222e-03  1.131e-02  1.146e+03  -0.196 0.844267    
## I(day^2)              -2.593e-04  1.545e-04  1.121e+03  -1.679 0.093460 .  
## mate_orderFS           1.903e-01  2.712e-01  1.456e+02   0.702 0.483940    
## mate_orderSF           3.698e-01  2.734e-01  1.365e+02   1.352 0.178501    
## mate_orderSS           2.189e-01  2.671e-01  1.422e+02   0.820 0.413678    
## I(day^2):mate_orderFS -3.449e-04  3.879e-04  1.180e+03  -0.889 0.374132    
## I(day^2):mate_orderSF -1.201e-03  3.168e-04  1.184e+03  -3.789 0.000159 ***
## I(day^2):mate_orderSS -1.137e-03  3.277e-04  1.184e+03  -3.469 0.000540 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    I(d^2) mt_rFS mt_rSF mt_rSS I(^2):_F I(^2):_SF
## day         -0.533                                                      
## I(day^2)     0.411 -0.893                                               
## mate_ordrFS -0.668  0.176 -0.104                                        
## mate_ordrSF -0.646  0.144 -0.076  0.482                                 
## mate_ordrSS -0.671  0.164 -0.093  0.497  0.487                          
## I(dy^2):_FS  0.272 -0.461  0.331 -0.318 -0.087 -0.097                   
## I(dy^2):_SF  0.254 -0.417  0.274 -0.099 -0.276 -0.095  0.231            
## I(dy^2):_SS  0.271 -0.450  0.307 -0.104 -0.089 -0.288  0.245    0.234

## Type III Analysis of Variance Table with Satterthwaite's method
##                     Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
## day                  0.085  0.0847     1 1146.35  0.0386  0.844267    
## I(day^2)            23.381 23.3812     1 1146.84 10.6594  0.001128 ** 
## mate_order           4.086  1.3619     3  147.42  0.6209  0.602549    
## I(day^2):mate_order 47.608 15.8694     3 1183.34  7.2348 8.222e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## $emtrends
##  mate_order (day)^2.trend       SE   df  lower.CL  upper.CL
##  FF             -0.000330 0.000232 1171 -0.000784  1.25e-04
##  FS             -0.000675 0.000340 1184 -0.001342 -7.24e-06
##  SF             -0.001530 0.000294 1185 -0.002107 -9.54e-04
##  SS             -0.001467 0.000294 1187 -0.002044 -8.90e-04
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate       SE   df t.ratio p.value
##  FF - FS   3.45e-04 0.000388 1180   0.888  0.8111
##  FF - SF   1.20e-03 0.000317 1184   3.784  0.0009
##  FF - SS   1.14e-03 0.000328 1184   3.464  0.0031
##  FS - SF   8.56e-04 0.000441 1184   1.940  0.2119
##  FS - SS   7.92e-04 0.000443 1185   1.789  0.2791
##  SF - SS  -6.36e-05 0.000400 1187  -0.159  0.9986
## 
## Note: contrasts are still on the 1(mu + 1) scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(egg_count + 1) ~ day + I(day^2) * mate_order + (1 | indv)
##    Data: allfert_nooutliers
## 
## REML criterion at convergence: 4461.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2387 -0.7670  0.2121  0.7421  3.0859 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  indv     (Intercept) 0.651    0.8069  
##  Residual             2.244    1.4978  
## Number of obs: 1164, groups:  indv, 122
## 
## Fixed effects:
##                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            2.549e+00  2.168e-01  2.260e+02  11.758  < 2e-16 ***
## day                   -5.427e-03  1.170e-02  1.120e+03  -0.464 0.642888    
## I(day^2)              -2.350e-04  1.583e-04  1.091e+03  -1.485 0.137952    
## mate_orderFS           2.687e-02  2.633e-01  1.433e+02   0.102 0.918865    
## mate_orderSF           2.059e-01  2.649e-01  1.335e+02   0.777 0.438268    
## mate_orderSS           1.373e-01  2.613e-01  1.397e+02   0.526 0.599965    
## I(day^2):mate_orderFS -3.149e-04  3.932e-04  1.150e+03  -0.801 0.423450    
## I(day^2):mate_orderSF -1.128e-03  3.207e-04  1.154e+03  -3.515 0.000456 ***
## I(day^2):mate_orderSS -1.122e-03  3.322e-04  1.153e+03  -3.376 0.000759 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    I(d^2) mt_rFS mt_rSF mt_rSS I(^2):_F I(^2):_SF
## day         -0.551                                                      
## I(day^2)     0.426 -0.897                                               
## mate_ordrFS -0.675  0.185 -0.110                                        
## mate_ordrSF -0.653  0.151 -0.079  0.497                                 
## mate_ordrSS -0.671  0.170 -0.096  0.507  0.498                          
## I(dy^2):_FS  0.288 -0.473  0.345 -0.334 -0.094 -0.103                   
## I(dy^2):_SF  0.270 -0.429  0.288 -0.107 -0.291 -0.101  0.242            
## I(dy^2):_SS  0.285 -0.459  0.318 -0.112 -0.096 -0.303  0.255    0.243

## Type III Analysis of Variance Table with Satterthwaite's method
##                     Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
## day                  0.483  0.4826     1 1120.03  0.2151 0.6428885    
## I(day^2)            20.097 20.0965     1 1120.30  8.9575 0.0028239 ** 
## mate_order           1.786  0.5952     3  145.64  0.2653 0.8503290    
## I(day^2):mate_order 43.717 14.5723     3 1152.63  6.4952 0.0002335 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## $emtrends
##  mate_order (day)^2.trend       SE   df  lower.CL  upper.CL
##  FF             -0.000407 0.000240 1144 -0.000877  6.32e-05
##  FS             -0.000722 0.000342 1153 -0.001394 -4.99e-05
##  SF             -0.001534 0.000296 1154 -0.002115 -9.53e-04
##  SS             -0.001528 0.000297 1155 -0.002112 -9.45e-04
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate       SE   df t.ratio p.value
##  FF - FS   3.15e-04 0.000394 1150   0.799  0.8547
##  FF - SF   1.13e-03 0.000321 1154   3.509  0.0026
##  FF - SS   1.12e-03 0.000333 1153   3.370  0.0043
##  FS - SF   8.13e-04 0.000444 1153   1.830  0.2596
##  FS - SS   8.07e-04 0.000446 1154   1.808  0.2700
##  SF - SS  -5.96e-06 0.000403 1155  -0.015  1.0000
## 
## Note: contrasts are still on the 1(mu + 1) scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

First Fecund

Does egg laying on the first day differ between treatment groups?

Model

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: egg_count ~ mate_order + day + (1 | trial)
##    Data: first.fecund.double
## 
## REML criterion at convergence: 1074
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2383 -0.5515 -0.1894  0.2515  5.5747 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  trial    (Intercept)  33.39    5.779  
##  Residual             228.88   15.129  
## Number of obs: 131, groups:  trial, 8
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   20.0307     4.0232  32.9014   4.979 1.98e-05 ***
## mate_orderFS   2.3158     3.7826 119.0484   0.612    0.542    
## mate_orderSF   4.1471     3.8688 118.4843   1.072    0.286    
## mate_orderSS  -0.1895     3.7444 118.4498  -0.051    0.960    
## day           -0.5472     0.1279 124.1422  -4.278 3.74e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mt_rFS mt_rSF mt_rSS
## mate_ordrFS -0.538                     
## mate_ordrSF -0.524  0.497              
## mate_ordrSS -0.527  0.513  0.500       
## day         -0.565  0.153  0.148  0.131

## $emmeans
##  mate_order emmean   SE   df lower.CL upper.CL
##  FF           12.2 3.35 22.7     5.28     19.2
##  FS           14.5 3.36 22.7     7.58     21.5
##  SF           16.4 3.47 25.4     9.23     23.5
##  SS           12.0 3.35 22.1     5.10     19.0
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE  df t.ratio p.value
##  FF - FS     -2.32 3.79 121  -0.611  0.9284
##  FF - SF     -4.15 3.87 120  -1.070  0.7082
##  FF - SS      0.19 3.75 120   0.051  1.0000
##  FS - SF     -1.83 3.85 120  -0.476  0.9642
##  FS - SS      2.51 3.72 120   0.674  0.9068
##  SF - SS      4.34 3.82 120   1.137  0.6676
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: day ~ mate_order + egg_count + (1 | trial)
##    Data: first.fecund.double
## 
## REML criterion at convergence: 963.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.49564 -0.75966 -0.08461  0.52654  2.59896 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  trial    (Intercept) 39.87    6.314   
##  Residual             90.14    9.494   
## Number of obs: 131, groups:  trial, 8
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   20.09149    2.82445  12.75540   7.113 8.77e-06 ***
## mate_orderFS  -3.01598    2.36751 119.86055  -1.274    0.205    
## mate_orderSF  -2.53128    2.43284 119.63083  -1.040    0.300    
## mate_orderSS  -2.91140    2.33878 119.67369  -1.245    0.216    
## egg_count     -0.24507    0.05254 120.56949  -4.664 8.08e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mt_rFS mt_rSF mt_rSS
## mate_ordrFS -0.386                     
## mate_ordrSF -0.366  0.494              
## mate_ordrSS -0.400  0.507  0.492       
## egg_count   -0.190 -0.110 -0.146 -0.047

## $emmeans
##  mate_order emmean   SE   df lower.CL upper.CL
##  FF           16.8 2.78 12.6    10.79     22.8
##  FS           13.8 2.79 12.8     7.75     19.8
##  SF           14.3 2.85 13.8     8.16     20.4
##  SS           13.9 2.79 12.7     7.86     19.9
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE  df t.ratio p.value
##  FF - FS     3.016 2.37 120   1.272  0.5825
##  FF - SF     2.531 2.44 120   1.039  0.7268
##  FF - SS     2.911 2.34 120   1.243  0.6007
##  FS - SF    -0.485 2.42 120  -0.200  0.9971
##  FS - SS    -0.105 2.34 119  -0.045  1.0000
##  SF - SS     0.380 2.41 119   0.158  0.9986
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

Model - no outliers

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: egg_count ~ mate_order + day + (1 | trial)
##    Data: first.fecund.double.noout
## 
## REML criterion at convergence: 1048.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2695 -0.5721 -0.1610  0.2974  5.6131 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  trial    (Intercept)  58.18    7.628  
##  Residual             222.13   14.904  
## Number of obs: 128, groups:  trial, 8
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   21.0939     4.4049  22.2219   4.789 8.58e-05 ***
## mate_orderFS   2.5138     3.7771 115.3476   0.666    0.507    
## mate_orderSF   4.2060     3.8589 114.8850   1.090    0.278    
## mate_orderSS   0.7357     3.7759 115.4873   0.195    0.846    
## day           -0.6218     0.1297 122.6746  -4.793 4.65e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mt_rFS mt_rSF mt_rSS
## mate_ordrFS -0.495                     
## mate_ordrSF -0.483  0.508              
## mate_ordrSS -0.474  0.522  0.507       
## day         -0.511  0.136  0.134  0.098

## $emmeans
##  mate_order emmean   SE   df lower.CL upper.CL
##  FF           12.3 3.81 17.0     4.29     20.4
##  FS           14.8 3.77 16.2     6.86     22.8
##  SF           16.5 3.86 17.7     8.42     24.7
##  SS           13.1 3.80 16.4     5.04     21.1
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE  df t.ratio p.value
##  FF - FS    -2.514 3.78 117  -0.665  0.9102
##  FF - SF    -4.206 3.86 117  -1.089  0.6970
##  FF - SS    -0.736 3.78 117  -0.195  0.9974
##  FS - SF    -1.692 3.79 117  -0.446  0.9702
##  FS - SS     1.778 3.70 117   0.481  0.9631
##  SF - SS     3.470 3.80 117   0.914  0.7976
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: day ~ mate_order + egg_count + (1 | trial)
##    Data: first.fecund.double.noout
## 
## REML criterion at convergence: 936.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.70863 -0.77925 -0.06761  0.50305  2.64984 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  trial    (Intercept) 54.36    7.373   
##  Residual             85.09    9.224   
## Number of obs: 128, groups:  trial, 8
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   19.98050    3.12763  11.00892   6.388 5.14e-05 ***
## mate_orderFS  -2.40480    2.33518 116.44862  -1.030    0.305    
## mate_orderSF  -2.02548    2.39523 116.19041  -0.846    0.399    
## mate_orderSS  -1.79851    2.33533 116.60670  -0.770    0.443    
## egg_count     -0.26458    0.05146 117.44462  -5.141 1.10e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mt_rFS mt_rSF mt_rSS
## mate_ordrFS -0.353                     
## mate_ordrSF -0.336  0.507              
## mate_ordrSS -0.361  0.520  0.504       
## egg_count   -0.169 -0.112 -0.146 -0.059

## $emmeans
##  mate_order emmean   SE   df lower.CL upper.CL
##  FF           16.4 3.09 11.2     9.64     23.2
##  FS           14.0 3.08 11.0     7.24     20.8
##  SF           14.4 3.12 11.7     7.57     21.2
##  SS           14.6 3.09 11.1     7.84     21.4
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE  df t.ratio p.value
##  FF - FS     2.405 2.34 117   1.029  0.7330
##  FF - SF     2.025 2.40 117   0.845  0.8329
##  FF - SS     1.799 2.34 117   0.769  0.8682
##  FS - SF    -0.379 2.35 116  -0.161  0.9985
##  FS - SS    -0.606 2.29 116  -0.265  0.9935
##  SF - SS    -0.227 2.36 116  -0.096  0.9997
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

Peak Fecundity

#Model

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: egg_count ~ mate_order + day + (1 | trial)
##    Data: peak.fecund
## 
## REML criterion at convergence: 1302
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8725 -0.7008 -0.0182  0.5083  3.3114 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  trial    (Intercept)  214.4   14.64   
##  Residual             1985.7   44.56   
## Number of obs: 127, groups:  trial, 8
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   83.15060   12.09753  48.88226   6.873 1.05e-08 ***
## mate_orderFS -10.53468   11.28118 117.42014  -0.934    0.352    
## mate_orderSF  -1.76243   11.77411 117.17883  -0.150    0.881    
## mate_orderSS  -7.49749   11.39385 117.12031  -0.658    0.512    
## day            0.05351    0.59676 101.40722   0.090    0.929    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mt_rFS mt_rSF mt_rSS
## mate_ordrFS -0.539                     
## mate_ordrSF -0.550  0.515              
## mate_ordrSS -0.582  0.538  0.525       
## day         -0.602  0.086  0.139  0.167

## $emmeans
##  mate_order emmean   SE   df lower.CL upper.CL
##  FF           83.7 9.76 29.7     63.8    103.6
##  FS           73.2 9.34 24.7     53.9     92.4
##  SF           81.9 9.84 30.4     61.8    102.0
##  SS           76.2 9.36 25.3     56.9     95.5
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE  df t.ratio p.value
##  FF - FS     10.53 11.3 117   0.932  0.7879
##  FF - SF      1.76 11.8 117   0.149  0.9988
##  FF - SS      7.50 11.4 116   0.657  0.9130
##  FS - SF     -8.77 11.4 117  -0.770  0.8678
##  FS - SS     -3.04 10.9 116  -0.278  0.9924
##  SF - SS      5.74 11.3 116   0.508  0.9571
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: day ~ mate_order + egg_count + (1 | trial)
##    Data: peak.fecund
## 
## REML criterion at convergence: 837.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5072 -0.5979 -0.2236  0.5531  3.4743 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  trial    (Intercept) 24.98    4.998   
##  Residual             39.47    6.282   
## Number of obs: 127, groups:  trial, 8
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)   11.787536   2.374435  17.070714   4.964 0.000117 ***
## mate_orderFS  -1.333779   1.597477 115.326964  -0.835 0.405485    
## mate_orderSF  -2.619141   1.647382 114.914542  -1.590 0.114609    
## mate_orderSS  -2.868894   1.592677 115.183383  -1.801 0.074271 .  
## egg_count      0.004597   0.013025 117.495852   0.353 0.724755    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mt_rFS mt_rSF mt_rSS
## mate_ordrFS -0.393                     
## mate_ordrSF -0.350  0.509              
## mate_ordrSS -0.384  0.538  0.516       
## egg_count   -0.459  0.095  0.024  0.075

## $emmeans
##  mate_order emmean   SE   df lower.CL upper.CL
##  FF          12.16 2.11 11.5     7.54     16.8
##  FS          10.83 2.08 10.9     6.24     15.4
##  SF           9.54 2.12 11.8     4.90     14.2
##  SS           9.29 2.08 10.9     4.70     13.9
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE  df t.ratio p.value
##  FF - FS      1.33 1.60 116   0.834  0.8381
##  FF - SF      2.62 1.65 115   1.589  0.3888
##  FF - SS      2.87 1.59 116   1.800  0.2786
##  FS - SF      1.29 1.61 116   0.799  0.8548
##  FS - SS      1.54 1.53 115   1.001  0.7492
##  SF - SS      0.25 1.60 115   0.157  0.9986
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

#Model - no outliers

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: egg_count ~ mate_order + day + (1 | trial)
##    Data: peak.fecund.noout
## 
## REML criterion at convergence: 1266.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9289 -0.6261 -0.0221  0.5394  3.3198 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  trial    (Intercept)  175.9   13.26   
##  Residual             1937.0   44.01   
## Number of obs: 124, groups:  trial, 8
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   84.7371    11.7802  50.9394   7.193 2.71e-09 ***
## mate_orderFS -14.4892    11.3044 114.8016  -1.282    0.203    
## mate_orderSF  -5.5542    11.7621 114.6067  -0.472    0.638    
## mate_orderSS  -9.0038    11.4704 114.6410  -0.785    0.434    
## day            0.3275     0.6065  95.1701   0.540    0.590    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mt_rFS mt_rSF mt_rSS
## mate_ordrFS -0.545                     
## mate_ordrSF -0.558  0.528              
## mate_ordrSS -0.583  0.547  0.532       
## day         -0.583  0.041  0.098  0.122

## $emmeans
##  mate_order emmean   SE   df lower.CL upper.CL
##  FF           87.9 9.63 34.2     68.4    107.5
##  FS           73.4 9.01 26.2     54.9     92.0
##  SF           82.4 9.50 32.6     63.0    101.7
##  SS           78.9 9.14 27.6     60.2     97.7
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE  df t.ratio p.value
##  FF - FS     14.49 11.3 114   1.278  0.5785
##  FF - SF      5.55 11.8 114   0.471  0.9652
##  FF - SS      9.00 11.5 114   0.783  0.8620
##  FS - SF     -8.93 11.3 115  -0.794  0.8570
##  FS - SS     -5.49 10.9 113  -0.505  0.9577
##  SF - SS      3.45 11.3 113   0.306  0.9900
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: day ~ mate_order + egg_count + (1 | trial)
##    Data: peak.fecund.noout
## 
## REML criterion at convergence: 808.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7884 -0.6237 -0.2433  0.5555  3.7344 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  trial    (Intercept) 28.31    5.321   
##  Residual             36.31    6.026   
## Number of obs: 124, groups:  trial, 8
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)   10.709681   2.474333  15.916737   4.328 0.000525 ***
## mate_orderFS  -0.464275   1.565016 111.972357  -0.297 0.767277    
## mate_orderSF  -1.869032   1.608618 111.680206  -1.162 0.247759    
## mate_orderSS  -1.793973   1.572145 112.036123  -1.141 0.256262    
## egg_count      0.008685   0.012810 113.567765   0.678 0.499176    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mt_rFS mt_rSF mt_rSS
## mate_ordrFS -0.395                     
## mate_ordrSF -0.354  0.528              
## mate_ordrSS -0.375  0.553  0.529       
## egg_count   -0.456  0.127  0.058  0.091

## $emmeans
##  mate_order emmean   SE   df lower.CL upper.CL
##  FF          11.43 2.20 11.0     6.58     16.3
##  FS          10.96 2.16 10.2     6.16     15.8
##  SF           9.56 2.20 10.8     4.72     14.4
##  SS           9.63 2.17 10.3     4.82     14.4
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE  df t.ratio p.value
##  FF - FS    0.4643 1.57 113   0.296  0.9909
##  FF - SF    1.8690 1.61 112   1.161  0.6523
##  FF - SS    1.7940 1.57 113   1.140  0.6655
##  FS - SF    1.4048 1.54 112   0.910  0.7996
##  FS - SS    1.3297 1.48 112   0.896  0.8070
##  SF - SS   -0.0751 1.55 112  -0.049  1.0000
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

Regression to Peak - for females mated twice

Does the rate to the peak fecundity differ between treatments?

#to peak outliers - #4,12,101
to_peak_outliers <- two.topeak %>%
  group_by(indv) %>%
  mutate(rate_of_lay=(max(egg_count)-min(egg_count))/(max(day)-min(day))) %>%
  filter(rate_of_lay!="NaN") %>%
  filter(row_number(rate_of_lay) == 1)%>%
  ungroup() %>%
  filter(rate_of_lay<1)

to_peak_nooutliers <- two.topeak %>%
  filter(!indv %in% c(4,12,101))

Graph

Model

## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## mate_order       3635    1212     3 216.20  1.2217    0.3027    
## day             75417   75417     1 516.87 76.0403 < 2.2e-16 ***
## mate_order:day  21393    7131     3 518.10  7.1898 9.786e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## $emtrends
##  mate_order day.trend    SE  df lower.CL upper.CL
##  FF             0.808 0.316 521    0.187     1.43
##  FS             2.974 0.585 511    1.824     4.12
##  SF             2.064 0.505 524    1.072     3.06
##  SS             3.787 0.727 512    2.359     5.21
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE  df t.ratio p.value
##  FF - FS    -2.166 0.665 514  -3.258  0.0066
##  FF - SF    -1.256 0.596 523  -2.109  0.1516
##  FF - SS    -2.979 0.793 514  -3.759  0.0011
##  FS - SF     0.910 0.773 517   1.177  0.6416
##  FS - SS    -0.813 0.933 512  -0.871  0.8197
##  SF - SS    -1.723 0.885 516  -1.947  0.2100
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

Model - no outliers

## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## mate_order       4790    1597     3 219.46  1.5241 0.2091309    
## day             77089   77089     1 492.71 73.5893 < 2.2e-16 ***
## mate_order:day  18279    6093     3 493.92  5.8162 0.0006537 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## $emtrends
##  mate_order day.trend    SE  df lower.CL upper.CL
##  FF             0.968 0.354 499    0.271     1.66
##  FS             2.915 0.599 485    1.738     4.09
##  SF             2.026 0.517 498    1.011     3.04
##  SS             4.012 0.770 489    2.499     5.52
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE  df t.ratio p.value
##  FF - FS    -1.947 0.696 489  -2.797  0.0274
##  FF - SF    -1.058 0.627 498  -1.689  0.3305
##  FF - SS    -3.044 0.848 491  -3.592  0.0020
##  FS - SF     0.889 0.791 491   1.123  0.6752
##  FS - SS    -1.097 0.975 487  -1.125  0.6743
##  SF - SS    -1.986 0.927 492  -2.142  0.1414
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

Regression from Peak - for females mated twice

Does egg rate to death differ between treatments?

#to peak outliers removed from peak - #4,12,101

from_peak_outliers <- two.frompeak %>%
  group_by(indv) %>%
  mutate(rate_of_lay=(min(egg_count)-max(egg_count))/(max(day)-min(day))) %>%
  filter(rate_of_lay!="NaN") %>%
  filter(row_number(rate_of_lay) == 1)%>%
  ungroup()


from_peak_nooutliers <- two.frompeak %>%
  filter(!indv %in% c(4,12,101))

Graph

Model

## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
## day            174073  174073     1 447.68 183.5810 < 2.2e-16 ***
## mate_order       6858    2286     3 246.42   2.4109   0.06748 .  
## day:mate_order  25126    8375     3 402.17   8.8330 1.105e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## $emtrends
##  mate_order day.trend    SE  df lower.CL upper.CL
##  FF             -1.04 0.158 186    -1.35   -0.731
##  FS             -2.47 0.366 394    -3.20   -1.754
##  SF             -2.19 0.316 640    -2.81   -1.573
##  SS             -2.23 0.298 628    -2.82   -1.647
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE  df t.ratio p.value
##  FF - FS    1.4322 0.399 349   3.590  0.0021
##  FF - SF    1.1510 0.353 516   3.261  0.0065
##  FF - SS    1.1893 0.337 494   3.530  0.0026
##  FS - SF   -0.2811 0.484 494  -0.581  0.9377
##  FS - SS   -0.2428 0.472 482  -0.514  0.9557
##  SF - SS    0.0383 0.434 634   0.088  0.9998
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

Model - noo outliers

## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
## day            173333  173333     1 435.25 181.2764 < 2.2e-16 ***
## mate_order       6963    2321     3 240.30   2.4273   0.06612 .  
## day:mate_order  25525    8508     3 389.50   8.8983 1.024e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## $emtrends
##  mate_order day.trend    SE  df lower.CL upper.CL
##  FF             -1.03 0.158 178    -1.34   -0.715
##  FS             -2.47 0.367 386    -3.19   -1.746
##  SF             -2.19 0.317 628    -2.81   -1.564
##  SS             -2.23 0.299 616    -2.81   -1.641
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE  df t.ratio p.value
##  FF - FS    1.4397 0.400 340   3.602  0.0020
##  FF - SF    1.1580 0.354 502   3.271  0.0063
##  FF - SS    1.1995 0.338 480   3.548  0.0024
##  FS - SF   -0.2817 0.485 484  -0.581  0.9377
##  FS - SS   -0.2401 0.473 472  -0.508  0.9573
##  SF - SS    0.0416 0.435 623   0.096  0.9997
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

Total Fecundity and Longevity

General Data Description

trial = experimental trial (1-8)
mate_order = male morph mating order in either of 4 treatments: “FF”, “FS”, “SS”, “SF”
day = the day of egg laying starting at the mating day relates to female longevity
egg_count = the number of eggs the female laid
times_mated = how many times a female mated either 1 or 2 times
replicate = replicate vial within a treatment (1-5)
indv = unique identification number for female
total = total fecundity per female
day = longevity of the female

Model

Does total fecundity differ between treatments groups where females mated twice?

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: total ~ mate_order + (1 | trial)
##    Data: double.mated
## 
## REML criterion at convergence: 1595.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8037 -0.8392 -0.1028  0.7295  2.1807 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  trial    (Intercept)  3501     59.17  
##  Residual             26154    161.72  
## Number of obs: 125, groups:  trial, 8
## 
## Fixed effects:
##              Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)    283.10      36.25  24.28   7.810 4.45e-08 ***
## mate_orderFS   -47.25      41.32 114.73  -1.143    0.255    
## mate_orderSF   -19.59      42.33 114.47  -0.463    0.644    
## mate_orderSS   -60.85      40.79 114.91  -1.492    0.139    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mt_rFS mt_rSF
## mate_ordrFS -0.584              
## mate_ordrSF -0.571  0.505       
## mate_ordrSS -0.592  0.528  0.515

## $emmeans
##  mate_order emmean   SE   df lower.CL upper.CL
##  FF            283 36.3 26.3      209      358
##  FS            236 35.7 24.3      162      310
##  SF            264 36.9 27.4      188      339
##  SS            222 35.1 22.8      150      295
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE  df t.ratio p.value
##  FF - FS      47.3 41.4 115   1.142  0.6646
##  FF - SF      19.6 42.4 115   0.462  0.9671
##  FF - SS      60.8 40.9 115   1.489  0.4476
##  FS - SF     -27.7 41.7 115  -0.664  0.9104
##  FS - SS      13.6 39.9 114   0.341  0.9863
##  SF - SS      41.3 41.0 115   1.006  0.7460
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

Model - no outliers

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: total ~ mate_order + (1 | trial)
##    Data: double.mated.noout
## 
## REML criterion at convergence: 1554.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7484 -0.8031 -0.1381  0.7314  2.2482 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  trial    (Intercept)  2200     46.91  
##  Residual             26208    161.89  
## Number of obs: 122, groups:  trial, 8
## 
## Fixed effects:
##              Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)    299.49      34.87  31.63   8.588 8.99e-10 ***
## mate_orderFS   -61.11      42.06 111.41  -1.453   0.1491    
## mate_orderSF   -34.31      43.05 111.13  -0.797   0.4272    
## mate_orderSS   -69.75      41.87 112.32  -1.666   0.0985 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mt_rFS mt_rSF
## mate_ordrFS -0.640              
## mate_ordrSF -0.625  0.522       
## mate_ordrSS -0.642  0.540  0.526

## $emmeans
##  mate_order emmean   SE   df lower.CL upper.CL
##  FF            299 34.9 36.3      229      370
##  FS            238 33.4 30.2      170      307
##  SF            265 34.6 34.5      195      335
##  SS            230 33.3 28.8      162      298
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE  df t.ratio p.value
##  FF - FS     61.11 42.1 113   1.450  0.4712
##  FF - SF     34.31 43.1 112   0.796  0.8563
##  FF - SS     69.75 42.0 113   1.661  0.3493
##  FS - SF    -26.80 41.7 112  -0.642  0.9180
##  FS - SS      8.64 40.3 112   0.215  0.9965
##  SF - SS     35.44 41.4 112   0.856  0.8275
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

Survival

Does survival differ between treatment groups?

Survival - No outliers

Model

## Cox mixed-effects model fit by maximum likelihood
##   Data: longevity.fert
##   events, n = 145, 145
##   Iterations= 2 16 
##                     NULL Integrated    Fitted
## Log-likelihood -580.0343  -569.9017 -569.8478
## 
##                   Chisq   df         p  AIC    BIC
## Integrated loglik 20.27 8.00 0.0093785 4.27 -19.55
##  Penalized loglik 20.37 7.05 0.0049846 6.27 -14.72
## 
## Model:  Surv(day) ~ mate_order * egg_count + (1 | indv) 
## Fixed coefficients
##                                coef exp(coef)   se(coef)     z     p
## mate_orderFS            0.386942692 1.4724721 0.26738043  1.45 0.150
## mate_orderSF            0.545855553 1.7260845 0.26727810  2.04 0.041
## mate_orderSS            0.342274684 1.4081470 0.26163686  1.31 0.190
## egg_count               0.023533489 1.0238126 0.01408043  1.67 0.095
## mate_orderFS:egg_count -0.012326383 0.9877493 0.01512498 -0.81 0.420
## mate_orderSF:egg_count -0.030741473 0.9697262 0.01604497 -1.92 0.055
## mate_orderSS:egg_count  0.006051695 1.0060700 0.01551183  0.39 0.700
## 
## Random effects
##  Group Variable  Std Dev     Variance   
##  indv  Intercept 0.019981891 0.000399276

## $emtrends
##  mate_order egg_count.trend      SE  df asymp.LCL asymp.UCL
##  FF                 0.02353 0.01408 Inf -0.004064   0.05113
##  FS                 0.01121 0.00578 Inf -0.000112   0.02253
##  SF                -0.00721 0.00764 Inf -0.022190   0.00777
##  SS                 0.02959 0.00692 Inf  0.016018   0.04315
## 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate      SE  df z.ratio p.value
##  FF - FS   0.01233 0.01512 Inf   0.815  0.8474
##  FF - SF   0.03074 0.01604 Inf   1.916  0.2212
##  FF - SS  -0.00605 0.01551 Inf  -0.390  0.9798
##  FS - SF   0.01842 0.00962 Inf   1.913  0.2223
##  FS - SS  -0.01838 0.00876 Inf  -2.099  0.1534
##  SF - SS  -0.03679 0.01038 Inf  -3.545  0.0022
## 
## P value adjustment: tukey method for comparing a family of 4 estimates

##                      chisq df     p
## mate_order            0.75  3 0.861
## egg_count             3.43  1 0.064
## mate_order:egg_count  5.40  3 0.145
## GLOBAL                6.94  7 0.435

Model - no outliers

## Cox mixed-effects model fit by maximum likelihood
##   Data: longevity.fert
##   events, n = 145, 145
##   Iterations= 2 16 
##                     NULL Integrated    Fitted
## Log-likelihood -580.0343  -569.9017 -569.8478
## 
##                   Chisq   df         p  AIC    BIC
## Integrated loglik 20.27 8.00 0.0093785 4.27 -19.55
##  Penalized loglik 20.37 7.05 0.0049846 6.27 -14.72
## 
## Model:  Surv(day) ~ mate_order * egg_count + (1 | indv) 
## Fixed coefficients
##                                coef exp(coef)   se(coef)     z     p
## mate_orderFS            0.386942692 1.4724721 0.26738043  1.45 0.150
## mate_orderSF            0.545855553 1.7260845 0.26727810  2.04 0.041
## mate_orderSS            0.342274684 1.4081470 0.26163686  1.31 0.190
## egg_count               0.023533489 1.0238126 0.01408043  1.67 0.095
## mate_orderFS:egg_count -0.012326383 0.9877493 0.01512498 -0.81 0.420
## mate_orderSF:egg_count -0.030741473 0.9697262 0.01604497 -1.92 0.055
## mate_orderSS:egg_count  0.006051695 1.0060700 0.01551183  0.39 0.700
## 
## Random effects
##  Group Variable  Std Dev     Variance   
##  indv  Intercept 0.019981891 0.000399276

## $emtrends
##  mate_order egg_count.trend      SE  df asymp.LCL asymp.UCL
##  FF                 0.02353 0.01408 Inf -0.004064   0.05113
##  FS                 0.01121 0.00578 Inf -0.000112   0.02253
##  SF                -0.00721 0.00764 Inf -0.022190   0.00777
##  SS                 0.02959 0.00692 Inf  0.016018   0.04315
## 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate      SE  df z.ratio p.value
##  FF - FS   0.01233 0.01512 Inf   0.815  0.8474
##  FF - SF   0.03074 0.01604 Inf   1.916  0.2212
##  FF - SS  -0.00605 0.01551 Inf  -0.390  0.9798
##  FS - SF   0.01842 0.00962 Inf   1.913  0.2223
##  FS - SS  -0.01838 0.00876 Inf  -2.099  0.1534
##  SF - SS  -0.03679 0.01038 Inf  -3.545  0.0022
## 
## P value adjustment: tukey method for comparing a family of 4 estimates

##                      chisq df     p
## mate_order            0.75  3 0.861
## egg_count             3.43  1 0.064
## mate_order:egg_count  5.40  3 0.145
## GLOBAL                6.94  7 0.435

Mating Behaviour

General Data Description

trial = experimental trial (1-8)
mate.order = First mating with first morph, second mating with second morph
male.morph = male morph as fighter (F) or scrambler (S) in either of 4 treatments: “FF”, “FS”, “SS”, “SF”
time = time to initiate mating (in seconds)
replicate = replicate vial within a treatment (1-8)
mate.type = the type of mating pair either the same (FF/SS) or different (SF/FS)
male = male morph presented to female: “Fighter” or “Scrambler”

Mating Rejection

Graph

Model

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: n ~ morph * as.factor(mate.order) + (1 | trial)
##    Data: rej.count
## 
## REML criterion at convergence: 33
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -0.9720 -0.8585 -0.4294  1.0452  1.5011 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev.
##  trial    (Intercept) 0.0009791 0.03129 
##  Residual             0.2646200 0.51441 
## Number of obs: 22, groups:  trial, 8
## 
## Fixed effects:
##                                        Estimate Std. Error        df t value
## (Intercept)                            1.500399   0.365076 12.147494   4.110
## morphScrambler                        -0.001628   0.516295 12.148026  -0.003
## as.factor(mate.order)2                -0.056724   0.403026 17.156366  -0.141
## morphScrambler:as.factor(mate.order)2 -0.220322   0.570282 15.454700  -0.386
##                                       Pr(>|t|)   
## (Intercept)                            0.00141 **
## morphScrambler                         0.99754   
## as.factor(mate.order)2                 0.88971   
## morphScrambler:as.factor(mate.order)2  0.70451   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mrphSc a.(.)2
## mrphScrmblr -0.707              
## as.fctr(.)2 -0.904  0.640       
## mrphS:.(.)2  0.640 -0.905 -0.707

## Type III Analysis of Variance Table with Satterthwaite's method
##                               Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## morph                       0.040619 0.040619     1 12.977  0.1535 0.7016
## as.factor(mate.order)       0.090738 0.090738     1 17.053  0.3429 0.5658
## morph:as.factor(mate.order) 0.039497 0.039497     1 15.455  0.1493 0.7045

Do females reject fighter males more often than scrambler males?

Mating Latency

Graph

Model

Does mating latency differ between treatment groups?

Between Treatment Groups

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(time) ~ morph * as.factor(mate.order) + (1 | female)
##    Data: all.late
## 
## REML criterion at convergence: 609.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9308 -0.7153  0.0734  0.8069  2.2503 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  female   (Intercept) 0.01239  0.1113  
##  Residual             0.39836  0.6312  
## Number of obs: 308, groups:  female, 154
## 
## Fixed effects:
##                                       Estimate Std. Error       df t value
## (Intercept)                             3.4162     0.0735 303.9999  46.479
## morphScrambler                         -0.1021     0.1032 303.7216  -0.989
## as.factor(mate.order)2                  0.1325     0.1032 241.7954   1.285
## morphScrambler:as.factor(mate.order)2   0.1516     0.1460 303.4374   1.039
##                                       Pr(>|t|)    
## (Intercept)                             <2e-16 ***
## morphScrambler                           0.323    
## as.factor(mate.order)2                   0.200    
## morphScrambler:as.factor(mate.order)2    0.300    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mrphSc a.(.)2
## mrphScrmblr -0.712              
## as.fctr(.)2 -0.702  0.507       
## mrphS:.(.)2  0.503 -0.707 -0.717

## Type III Analysis of Variance Table with Satterthwaite's method
##                             Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## morph                       0.0518  0.0518     1 303.91  0.1299 0.718747   
## as.factor(mate.order)       3.3414  3.3414     1 152.37  8.3881 0.004333 **
## morph:as.factor(mate.order) 0.4297  0.4297     1 303.44  1.0787 0.299810   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## $emmeans
##  morph     mate.order emmean     SE  df lower.CL upper.CL
##  Fighter            1   3.42 0.0737 304     3.27     3.56
##  Scrambler          1   3.31 0.0728 304     3.17     3.46
##  Fighter            2   3.55 0.0737 304     3.40     3.69
##  Scrambler          2   3.60 0.0728 304     3.45     3.74
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                  estimate    SE  df t.ratio p.value
##  Fighter 1 - Scrambler 1     0.1021 0.104 304   0.983  0.7594
##  Fighter 1 - Fighter 2      -0.1325 0.104 241  -1.280  0.5764
##  Fighter 1 - Scrambler 2    -0.1820 0.103 243  -1.770  0.2905
##  Scrambler 1 - Fighter 2    -0.2347 0.103 243  -2.282  0.1052
##  Scrambler 1 - Scrambler 2  -0.2841 0.102 239  -2.782  0.0296
##  Fighter 2 - Scrambler 2    -0.0495 0.104 304  -0.476  0.9643
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 4 estimates

Does day of last lay differ between treatments?

lay_time_double <- read.csv("C:/Users/z5218946/OneDrive - UNSW/Research/Mites Fecundity/fecundity_time_lay.csv")

str(lay_time_double)

## 'data.frame':    1209 obs. of  7 variables:
##  $ ï..trial   : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ mate_order : chr  "FF" "FF" "FF" "FF" ...
##  $ day        : int  2 3 5 7 9 12 14 17 19 21 ...
##  $ egg_count  : int  0 0 0 0 0 0 0 0 0 24 ...
##  $ times_mated: int  2 2 2 2 2 2 2 2 2 2 ...
##  $ replicate  : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ indv       : int  1 1 1 1 1 1 1 1 1 1 ...

colnames(lay_time_double)[1]="trial"
lay_time_double$egg_count <- as.numeric(lay_time_double$egg_count)
lay_time_double$trial <- as.numeric(lay_time_double$trial)
lay_time_double$day <- as.numeric(lay_time_double$day)
lay_time_double$rep <- as.factor(lay_time_double$rep)
lay_time_double$indv <- as.numeric(lay_time_double$indv)

day_of_last_lay_double <- lay_time_double %>%
  group_by(indv) %>%
  top_n(1, day)

ggplot(day_of_last_lay_double, aes(mate_order,day)) + geom_boxplot()

mod_lay_double <- lmer(day~mate_order + egg_count + (1|trial), day_of_last_lay_double)
summary(mod_lay_double)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: day ~ mate_order + egg_count + (1 | trial)
##    Data: day_of_last_lay_double
## 
## REML criterion at convergence: 1074.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8415 -0.6445 -0.1645  0.4420  4.3314 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  trial    (Intercept)  22.03    4.694  
##  Residual             101.75   10.087  
## Number of obs: 144, groups:  trial, 8
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   24.64750    2.47370  19.43973   9.964 4.46e-09 ***
## mate_orderFS  -1.81489    2.40699 132.08498  -0.754 0.452186    
## mate_orderSF  -2.68077    2.42000 132.50913  -1.108 0.269974    
## mate_orderSS  -4.10060    2.36397 132.51190  -1.735 0.085132 .  
## egg_count     -0.10467    0.02816 138.19126  -3.717 0.000292 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) mt_rFS mt_rSF mt_rSS
## mate_ordrFS -0.457                     
## mate_ordrSF -0.451  0.497              
## mate_ordrSS -0.506  0.497  0.496       
## egg_count   -0.291 -0.065 -0.084  0.069

hist(resid(mod_lay_double))

emmeans(mod_lay_double, pairwise~mate_order)

## $emmeans
##  mate_order emmean   SE   df lower.CL upper.CL
##  FF           21.7 2.37 17.5     16.7     26.7
##  FS           19.9 2.39 18.1     14.8     24.9
##  SF           19.0 2.40 18.3     14.0     24.0
##  SS           17.6 2.35 17.0     12.6     22.5
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE  df t.ratio p.value
##  FF - FS     1.815 2.41 132   0.754  0.8750
##  FF - SF     2.681 2.42 133   1.107  0.6861
##  FF - SS     4.101 2.37 133   1.733  0.3109
##  FS - SF     0.866 2.42 132   0.358  0.9843
##  FS - SS     2.286 2.40 133   0.954  0.7758
##  SF - SS     1.420 2.40 133   0.591  0.9348
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates

Mites in Conflict

Anastasia Shavrova

2022-02-25

Working Title

Authors

Description

Hypotheses

Fecundity Over Time

General Data Description

Graph

Average between all individuals

Model

First Fecund

Does egg laying on the first day differ between treatment groups?

Model

Model - no outliers

Peak Fecundity

Regression to Peak - for females mated twice

Does the rate to the peak fecundity differ between treatments?

Graph

Model

Model - no outliers

Regression from Peak - for females mated twice

Does egg rate to death differ between treatments?

Graph

Model

Model - noo outliers

Total Fecundity and Longevity

General Data Description

Model

Does total fecundity differ between treatments groups where females mated twice?

Model - no outliers

Survival

Does survival differ between treatment groups?

Survival - No outliers

Model

Model - no outliers

Mating Behaviour

General Data Description

Mating Rejection

Graph

Model

Do females reject fighter males more often than scrambler males?

Mating Latency

Graph

Model

Does mating latency differ between treatment groups?

Between Treatment Groups

Does day of last lay differ between treatments?