Modified from hybridInflo Biomass.Rmd (April 2017)

Guides

Purpose

Identify reproductive barriers between two sympatric moth-pollinated plant species, Schiedea kaalae and S. hookeri by fitting a generalized linear mixed model (GLMM).

In the experimental design, the following crosstypes were made:

  • within species, between population (may show outbreeding depression or heterosis)
  • within species, within populations (may show inbreeding depression)
  • hybrids between species (indicates species barrier from pollination to seed production)

In this analysis the response variable is the biomass of the offspring produced by each cross. Other barriers (hybrid survival, flowering) could be analyzed in a similar framework, with appropriate changes to the underlying distribution.

Fixed effects:

  • crosstype - hybrids, within population, between populations
  • species - species of the maternal plant that produced the Inflo Biomass

Potential random effects:

  • mompop - maternal plant population
  • mompid - maternal plant, specified by its population and ID
  • dadpop - paternal plant population

Data Import

ib <- read.table("inflobiomass.csv", header=T, sep="\t", 
                 colClasses=c(collect.date="Date", weigh.date="Date"))
crosses <- read.table("hybrids.csv", header=T, sep="\t", colClasses=c(mompop="factor", dadpop="factor"))

crosscol <- c("green","blue","orange","red")

#treat populations as factors
ib$mompop <- crosses$mompop[match(ib$crossid, crosses$crossid)]
ib$momid <- crosses$momid[match(ib$crossid, crosses$crossid)]
ib$species <- crosses$momsp[match(ib$crossid, crosses$crossid)]
ib$dadpop <- crosses$dadpop[match(ib$crossid, crosses$crossid)]
ib$dadid <- crosses$dadid[match(ib$crossid, crosses$crossid)]
ib$dadsp <- crosses$dadsp[match(ib$crossid, crosses$crossid)]
ib$crosstype <- crosses$crosstype[match(ib$crossid, crosses$crossid)]
ib$cross <- crosses$cross[match(ib$crossid, crosses$crossid)]

#rename crosstype codes
ib$crosstype <- factor(ib$crosstype, levels=c("between", "within", "hybrid"))
#made "between" the first reference level to facilitate comparison between outcrossing populations and hybridizing species 

ib$mompop <- sapply(ib$mompop, mapvalues, from = c("794","866","899","879","892","904","3587"), to = c("WK","WK","WK","879WKG","892WKG","904WPG","3587WP"))
ib$dadpop <- sapply(ib$dadpop, mapvalues, from = c("794","866","899","879","892","904","3587"), to = c("WK","WK","WK","879WKG","892WKG","904WPG","3587WP"))

#define interactions
ib <- within(ib, sxc <- interaction(species,crosstype))
ib <- within(ib, sxcxm <- interaction(species,crosstype,mompop,momid))
ib <- within(ib, mompid <- as.factor(paste(mompop,momid,sep=".")))
ib <- within(ib, dadpid <- as.factor(paste(dadpop,dadid,sep=".")))
ib <- within(ib, smompop <- as.factor(paste(species,mompop,sep="")))

ib$collect.date <- round(difftime(ib$collect.date, "2016-03-01"))
#check final structure
str(ib)
   'data.frame':    1528 obs. of  28 variables:
    $ crossid     : int  1 1 1 1 1 1 1 1 1 1 ...
    $ plantid     : Factor w/ 25 levels "1","10","11",..: 1 15 20 23 24 24 2 2 3 4 ...
    $ cross       : Factor w/ 4 levels "HH","HK","KH",..: 1 1 1 1 1 1 1 1 1 1 ...
    $ block1      : int  12 12 11 12 12 12 12 12 12 12 ...
    $ blockAB     : Factor w/ 3 levels "","A","B": 1 1 1 1 1 1 1 1 1 1 ...
    $ collect.date:Class 'difftime'  atomic [1:1528] 350 353 347 354 325 325 344 344 355 332 ...
     .. ..- attr(*, "units")= chr "days"
    $ regression  : Factor w/ 5 levels "","backup R",..: 4 4 4 4 3 5 3 5 4 3 ...
    $ flrs        : int  NA NA NA NA NA 41 NA 34 NA NA ...
    $ inflo.e     : int  1 4 10 7 31 1 3 1 6 10 ...
    $ inflo       : int  1 4 10 7 32 NA 4 NA 6 11 ...
    $ weigh.date  : Date, format: "17-07-14" "17-05-18" ...
    $ mass        : num  0.053 0.075 1.113 0.44 2.14 ...
    $ initials    : Factor w/ 4 levels "AK","AK ","SS",..: 3 3 3 3 3 3 3 3 3 3 ...
    $ check       : Factor w/ 22 levels "","AK 8/15/17",..: 2 2 2 2 2 2 2 2 2 2 ...
    $ comments    : Factor w/ 66 levels "","11 open fls",..: 1 1 1 1 1 1 21 1 1 1 ...
    $ linenum     : num  1 2 3 4 5 6 7 8 9 10 ...
    $ mompop      : Factor w/ 5 levels "3587WP","WK",..: 3 3 3 3 3 3 3 3 3 3 ...
    $ momid       : Factor w/ 17 levels "1","10","10-1",..: 7 7 7 7 7 7 7 7 7 7 ...
    $ species     : Factor w/ 2 levels "hook","kaal": 1 1 1 1 1 1 1 1 1 1 ...
    $ dadpop      : Factor w/ 5 levels "3587WP","WK",..: 3 3 3 3 3 3 3 3 3 3 ...
    $ dadid       : Factor w/ 23 levels "1","10","10-1",..: 19 19 19 19 19 19 19 19 19 19 ...
    $ dadsp       : Factor w/ 2 levels "hook","kaal": 1 1 1 1 1 1 1 1 1 1 ...
    $ crosstype   : Factor w/ 3 levels "between","within",..: 2 2 2 2 2 2 2 2 2 2 ...
    $ sxc         : Factor w/ 6 levels "hook.between",..: 3 3 3 3 3 3 3 3 3 3 ...
    $ sxcxm       : Factor w/ 510 levels "hook.between.3587WP.1",..: 195 195 195 195 195 195 195 195 195 195 ...
    $ mompid      : Factor w/ 21 levels "3587WP.10","3587WP.14",..: 8 8 8 8 8 8 8 8 8 8 ...
    $ dadpid      : Factor w/ 23 levels "3587WP.10","3587WP.14",..: 9 9 9 9 9 9 9 9 9 9 ...
    $ smompop     : Factor w/ 5 levels "hook879WKG","hookWK",..: 1 1 1 1 1 1 1 1 1 1 ...

Data Inspection

Regression

library(ggplot2)
qplot(log10(mass), log10(flrs), col=cross, data=ib, weight=mass) + geom_smooth(method="lm", se=T) + scale_color_manual(values=crosscol) + ggtitle("cross")

qplot(log10(mass), log10(flrs), col=cross, group=paste(mompop,dadpop), data=ib) +  geom_smooth(method="lm", se=F) + scale_color_manual(values=crosscol) + ggtitle("mompop*dadpop")

#qplot(log10(mass), log10(flrs), col=cross, data=ib, weight=mass) + geom_quantile(quantiles = 0.9) + scale_color_manual(values=crosscol)

#plot(log10(flrs)~log10(mass), data=ib, type="n")
#text(log10(ib$mass), log10(ib$flrs), 1:length(ib$plantid), col=crosscol[ib$cross])

#library(quantreg,log10(flrs)~log10(mass)*cross, data=ib[-exclude,], weights=mass)
#qr <- rq(log10(flrs)~log10(mass)*cross, data=ib[-exclude,], tau = 0.9)
#summary(qr)

mf <- lm(log10(flrs)~log10(mass)*cross, data=ib, weights=mass)
#plot(mf)
mass.flrs <- summary(mf)
mass.flrs
   
   Call:
   lm(formula = log10(flrs) ~ log10(mass) * cross, data = ib, weights = mass)
   
   Weighted Residuals:
        Min       1Q   Median       3Q      Max 
   -0.32549 -0.04318  0.00825  0.05360  0.32532 
   
   Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
   (Intercept)          2.54429    0.07685  33.108  < 2e-16 ***
   log10(mass)          0.91156    0.08689  10.491  < 2e-16 ***
   crossHK              0.22495    0.07812   2.879 0.004158 ** 
   crossKH              0.25375    0.08232   3.082 0.002169 ** 
   crossKK             -0.05870    0.07723  -0.760 0.447550    
   log10(mass):crossHK  0.24313    0.09610   2.530 0.011715 *  
   log10(mass):crossKH  0.22703    0.11299   2.009 0.045047 *  
   log10(mass):crossKK  0.30545    0.09000   3.394 0.000744 ***
   ---
   Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
   
   Residual standard error: 0.08916 on 495 degrees of freedom
     (1025 observations deleted due to missingness)
   Multiple R-squared:  0.9156, Adjusted R-squared:  0.9144 
   F-statistic: 766.8 on 7 and 495 DF,  p-value: < 2.2e-16
slopes <- mass.flrs$coefficients[c(2,6,7,8),"Estimate"]
slopes[c(2,3,4)] <- slopes[c(2,3,4)]+slopes[1]
names(slopes) <- levels(ib$cross)
slopes
         HH       HK       KH       KK 
   0.911560 1.154691 1.138592 1.217012
intercepts <- mass.flrs$coefficients[c(1,3,4,5),"Estimate"]
intercepts[c(2,3,4)] <- intercepts[c(2,3,4)]+intercepts[1]
names(intercepts) <- levels(ib$cross)
intercepts
         HH       HK       KH       KK 
   2.544293 2.769240 2.798040 2.485588
#with(ib, plot(mass,flrs, col=crosscol[cross], xlim=c(0,0.5), ylim=c(0,240)))
#for(i in 1:4) { curve(10^intercepts[i]*x^slopes[i], from=0, to=6, col=crosscol[i], add=T) }
#with(ib, plot(log10(mass),log10(flrs), col=crosscol[cross]))
#for(i in 1:4) { curve(intercepts[i]+x*slopes[i], from=-2, to=1, col=crosscol[i], add=T) }
#Add together envelopes for regression and the rest
ibold <- ib
ib <- aggregate(mass ~ crossid+plantid+cross+mompop+momid+species+dadpop+dadid+dadsp+crosstype+sxc+sxcxm+mompid+dadpid+smompop, ib, sum, na.action=na.pass)
ib$collect.date <- aggregate(collect.date ~ crossid+plantid+cross+mompop+momid+species+dadpop+dadid+dadsp+crosstype+sxc+sxcxm+mompid+dadpid+smompop, ibold, max, na.action=na.pass)$collect.date

pred <- predict(mf, ib, se.fit=T)
ib$flrs <- 10^pred$fit
ib$seflrs <- pred$se.fit

Replication

The sample sizes are unbalanced at all levels, including maternal population:

reptab <- with(ib, table(smompop,crosstype))
mosaic(reptab, pop=F)
labeling_cells(text = reptab, margin = 0)(reptab)

Replication is low for some within-population crosses. The replication is even lower for each maternal plant, so we need to be wary of estimates when subsetting at this level:

with(ib, kable(table(mompid,crosstype)))
between within hybrid
3587WP.10 0 1 0
3587WP.14 15 0 6
3587WP.15 4 0 0
3587WP.7 19 14 8
3587WP.A 5 0 0
3587WP.C 12 1 0
879WKG.10-1 48 1 35
879WKG.2-2 31 35 89
879WKG.G-2 15 5 26
879WKG.H-2 0 0 7
879WKG.N-5 8 6 22
892WKG.1 23 0 6
892WKG.3 1 0 3
892WKG.4 1 0 1
892WKG.5 8 5 6
904WPG.2 13 9 7
904WPG.3 16 27 3
904WPG.5 77 32 17
WK.2 137 18 91
WK.2E- 1 0 0 19
WK.4 55 6 25

Overall data distribution

To identify the best-fitting distribution, we make quantile-quantile plots of the raw data against various distributions. The more points within the confidence interval envelopes, the better the fit. Later, we present quantile-quantile plots of the model residuals to assess model fit.

#QQ plots against various distributions
set.seed(1)
par(mfrow=c(1,3))
normal <- fitdistr(log(ib$flrs+1), "normal")
qqp(log(ib$flrs+1), "norm", main="Normal")
lognormal <- fitdistr(ib$flrs+1, "lognormal")
qqp(ib$flrs+1, "lnorm", main="Log Normal")
#pois <- fitdistr(ib$flrs+1, "Poisson")
#qqp(ib$flrs, "pois", pois$estimate, main="Poisson")
#nbinom <- fitdistr(ib$flrs+1, "Negative Binomial") 
#qqp(ib$flrs+1, "nbinom", size = nbinom$estimate[[1]], mu=nbinom$estimate[[2]], main="Negative Binomial")
gamma <- fitdistr(ib$flrs+1, "gamma")
qqp(ib$flrs+1, "gamma", shape = gamma$estimate[[1]], rate = gamma$estimate[[2]], main="Gamma")

Distributions by fixed factors

ggplot(ib, aes(x = flrs, fill=species)) +
  geom_histogram(data=subset(ib,species == "hook"), aes(y=-..density..),binwidth=500)+
  geom_histogram(data=subset(ib,species == "kaal"), aes(y= ..density..),binwidth=500)+
  coord_flip() + facet_grid(~crosstype) + labs(y="Counts", x="Inflo Bioflrs")

Distributions by random factors

ggplot(aes(y=flrs, x=mompid, color=crosstype), data=ib) + geom_count(alpha=0.8) + coord_flip() + labs(x="Maternal plant", y="flrs")

Homogeneity of variances across subsets

Our mixed model uses one parameter to capture random effect variance, which is assumed to be homogeneous. Plotting on a log scale should uncouple variances from means to assess this visually. Subsets are species * crosstype * maternal plant.

Subset variances are not homogeneous:

ggplot(aes(y=log(flrs+1), x=sxcxm, color=crosstype), data=ib) + geom_boxplot() + coord_flip() + labs(y="ln(Inflo Bioflrs + 1)",x="Subsets")

Subset mean-variance relationship

Various distributions make different assumptions about the mean-variance (µ-Var) ratio.

grpVars <- with(ib, tapply(flrs, list(sxcxm), var))
grpMeans <- with(ib, tapply(flrs, list(sxcxm), mean))
grpCounts <- with(ib, tapply(flrs, list(sxcxm), length))
#set weight=grpCounts to weight loess by sample sizes
ggplot(na.omit(data.frame(grpMeans,grpVars,grpCounts)),
       aes(x=grpMeans,y=grpVars, weight=1))+geom_point(aes(size=grpCounts))+
  guides(colour=guide_legend(title="Fit"),size=guide_legend(title="Sample size")) + labs(x="Subset Mean", y="Subset Variance") + labs(subtitle="Subset: species*crosstype*mompid")

Fixed effects

Effects and interactions in these plots are simply given by the mean, which may be unduly influenced by high values.

intplot <- ggplot(ib,aes(x=crosstype,y=flrs))+
  geom_count(aes(size = ..prop.., group=sxc),alpha=0.5)+
  stat_summary(aes(x=as.numeric(crosstype)),fun.y=mean,geom="line")+ facet_grid(~species)
intplot + aes(group=species, color=species)

Random effects

Maternal population

intplot + aes(group=mompop, color=mompop)

Maternal plant

intplot + aes(group=mompid, color=mompop)

Paternal population

intplot + aes(group=dadpop, color=dadpop)

Run models on subsets

Run many generalized linear models on subsets of the data defined by crosstype | mompid to see if effects estimates are consistent within maternal plants.

Most maternal plant subsets agree, but some are problematic outliers. These plants can be picked out visually from the random effects interaction plot above, the estimated parameters of each subset model, and the QQ plot of the estimated parameters:

 #had to get rid of species or mompid since mompid is nested inside species. dadpop also works
glm.lis <- lmList(log(flrs)~crosstype|mompid,data=ib, family="gaussian")
plot.lmList(glm.lis,scale=list(x=list(relation="free")))
   Loading required package: reshape
   
   Attaching package: 'reshape'
   The following objects are masked from 'package:plyr':
   
       rename, round_any
   The following object is masked from 'package:Matrix':
   
       expand
   Using grp as id variables

qqmath.lmList(glm.lis)#
   Using  as id variables

Models

We constructed the following models with the package glmmADMB. They all have the same fixed effects, species x crosstype, and response variable, log10(flrs)

  • X = standard GL(M)M
Distribution, Random Effects: None Maternal plant Maternal population
normal (norm) X X X 
#Normal (Gaussian) distribution, identity link
ib <- ib[!is.na(ib$collect.date),]
# weights=1/ib$seflrs
sc.norm.l          <- lm(log10(flrs)~species*crosstype+collect.date, data=ib)
sc.mix.mompid.l    <- lmer(log10(flrs)~species*crosstype+collect.date + (1|mompid), data=ib)
sc.nd.mix.momdadpid.l <- lmer(log10(flrs)~species*crosstype + (1|mompid)+ (1|dadpid), data=ib)
sc.mix.mompop.l    <- lmer(log10(flrs)~species*crosstype+collect.date + (1|mompop), data=ib)
sc.mix.momdadpid.l <- lmer(log10(flrs)~species*crosstype+collect.date + (1|mompid) + (1|dadpid), data=ib)
sc.mix.momdadpop.l <- lmer(log10(flrs)~species*crosstype+collect.date + (1|mompop) + (1|dadpop), data=ib)

Model comparison

AIC

We will use the Aikake Information Criterion to pick the model the best fits the data, penalized by the number of parameters. Differences of 2 units are significant.

sc.names <- c("sc.norm.l","sc.mix.mompid.l","sc.mix.mompop.l","sc.mix.momdadpid.l","sc.mix.momdadpop.l")
sc.list <- sapply(sc.names, get, USE.NAMES=T)
sc.AIC <- ICtab(sc.list,mnames=sc.names,type="AIC", base=T, delta=F) # for AICc, nobs=nobs(sc.list[[1]])
class(sc.AIC)<-"data.frame"
all.names <- c(sc.names)
all.list <- sapply(all.names, get, USE.NAMES=T)
all.AIC <- dfun(rbind(sc.AIC))
all.AIC <- all.AIC[order(all.AIC$dAIC),]
kable(all.AIC, format.arg=list(digits=3))
dAIC df
sc.norm.l 0.0 8
sc.mix.mompid.l 34.9 9
sc.mix.momdadpid.l 35.4 10
sc.mix.momdadpop.l 38.0 10
sc.mix.mompop.l 40.0 9

The best-fiting model is a linear model with the following components:

    • response: log(flrs)
    • fixed effects: species, crosstype, species x crosstype

Overdispersion

Looking at the normal, fixed effects model, we see that the residuals are not normal:

shapiro.test(sc.norm.l$residuals)#raw residuals!
   
    Shapiro-Wilk normality test
   
   data:  sc.norm.l$residuals
   W = 0.93352, p-value < 2.2e-16

Coefficients

The coefficients estimated for each model agree qualitatively.

sc.log.names <- sc.names
sc.log <- sapply(sc.log.names, get, USE.NAMES=T)

coefplot2(sc.log, legend.x="topright",legend=T,legend.args=list(cex=0.8, xpd=T, inset=c(-0.1,0)), col.pts=sample(gg_color_hue(length(sc.log.names))), spacing=0.05, lwd.2=2, lwd.1=4, intercept=F)

Inference

We chose the model with nearly the best (lowest) AIC, to carry out inference tests and parameter estimation.

Description

mod <- sc.mix.momdadpid.l
print(mod)
   Linear mixed model fit by REML ['lmerMod']
   Formula: 
   log10(flrs) ~ species * crosstype + collect.date + (1 | mompid) +  
       (1 | dadpid)
      Data: ib
   REML criterion at convergence: 1547.87
   Random effects:
    Groups   Name        Std.Dev.
    dadpid   (Intercept) 0.04882 
    mompid   (Intercept) 0.08912 
    Residual             0.51282 
   Number of obs: 997, groups:  dadpid, 23; mompid, 21
   Fixed Effects:
                   (Intercept)                  specieskaal  
                      3.223588                     0.572270  
               crosstypewithin              crosstypehybrid  
                     -0.044547                     0.659749  
                  collect.date  specieskaal:crosstypewithin  
                     -0.002634                    -0.016366  
   specieskaal:crosstypehybrid  
                     -0.721010

Test significance of random effects

Using a likelihood ratio test, with a null hypothesis of zero variance, the random effect (maternal plant) is not significant:

anova(sc.norm.l, sc.mix.momdadpid.l) #double this p-value. or simulate null by permuting data.

Test significance of interaction

By dropping it from the model and performing a likelihood-ratio test, we see that the species x crosstype interaction is significant for the count model but not the binary model:

sxc.chisq <- drop1(mod, test="Chisq") #load from file
dfun(sxc.chisq)
   Single term deletions
   
   Model:
   log10(flrs) ~ species * crosstype + collect.date + (1 | mompid) + 
       (1 | dadpid)
                     Df   dAIC    LRT   Pr(Chi)    
   <none>                0.000                     
   collect.date       1 13.517 15.517 8.175e-05 ***
   species:crosstype  2 22.493 26.493 1.767e-06 ***
   ---
   Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Model summary

The model estimated the following parameters, with individual parameter significance determined by the Wald z-test, and fixed effect significance determined by analysis of deviance Wald test.

summary(mod)
   Linear mixed model fit by REML ['lmerMod']
   Formula: 
   log10(flrs) ~ species * crosstype + collect.date + (1 | mompid) +  
       (1 | dadpid)
      Data: ib
   
   REML criterion at convergence: 1547.9
   
   Scaled residuals: 
       Min      1Q  Median      3Q     Max 
   -4.5275 -0.5074  0.1988  0.6919  1.8619 
   
   Random effects:
    Groups   Name        Variance Std.Dev.
    dadpid   (Intercept) 0.002384 0.04882 
    mompid   (Intercept) 0.007942 0.08912 
    Residual             0.262984 0.51282 
   Number of obs: 997, groups:  dadpid, 23; mompid, 21
   
   Fixed effects:
                                 Estimate Std. Error t value
   (Intercept)                  3.2235879  0.2231008  14.449
   specieskaal                  0.5722702  0.0743359   7.698
   crosstypewithin             -0.0445469  0.0712783  -0.625
   crosstypehybrid              0.6597495  0.0514799  12.816
   collect.date                -0.0026343  0.0006669  -3.950
   specieskaal:crosstypewithin -0.0163657  0.1012888  -0.162
   specieskaal:crosstypehybrid -0.7210103  0.1039637  -6.935
   
   Correlation of Fixed Effects:
                   (Intr) spcskl crsstypw crsstyph cllct. spcskl:crsstypw
   specieskaal     -0.078                                                
   crsstypwthn     -0.070  0.215                                         
   crsstyphybr     -0.013  0.473  0.322                                  
   collect.dat     -0.972 -0.091 -0.002   -0.122                         
   spcskl:crsstypw  0.036 -0.342 -0.707   -0.239    0.016                
   spcskl:crsstyph  0.019 -0.498 -0.155   -0.597    0.062  0.253
Anova(mod, type=3)
   Analysis of Deviance Table (Type III Wald chisquare tests)
   
   Response: log10(flrs)
                       Chisq Df Pr(>Chisq)    
   (Intercept)       208.774  1  < 2.2e-16 ***
   species            59.266  1  1.377e-14 ***
   crosstype         189.367  2  < 2.2e-16 ***
   collect.date       15.604  1  7.810e-05 ***
   species:crosstype  50.798  2  9.319e-12 ***
   ---
   Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Predicted random effects

These are box and QQ (to check normality) plots of the estimated random effect of each maternal plant.

predre <- setNames(data.frame(RE=ranef(mod)$mompid,SD=ranef(mod, sd=T)$`1`),c("RE","SD"))
ggplot(predre, aes(x = rownames(predre),y=RE)) +
  geom_point(size = 2) + coord_flip()+
  geom_errorbar(aes(ymin = RE-SD, ymax = RE+SD)) + labs(x="Maternal plants", y="Predicted random effects")

#Count
reStack <- ldply(ranef(mod))
print( qqmath( ~`(Intercept)`|.id, data=reStack, scales=list(relation="free"),
                 prepanel = prepanel.qqmathline,
                 panel = function(x, ...) {
                   panel.qqmathline(x, ...)
                   panel.qqmath(x, ...)
                 },
                 layout=c(1,1)))

Least square means

The least square means procedure can generate predictor estimates of each type, and give their significance groupings with a post-hoc Tukey test. S. hookeri-produced hybrids produce less Inflo Bioflrs than either crosses between or within S. hookeri populations. The other differences are not significant, but remember that the fixed effect of hybrid (vs. between) was significant (model summary).

#Count
rg <- ref.grid(sc.nd.mix.momdadpid.l)
   Loading required namespace: lmerTest
#summary(rg)
sxc.lsm <- lsmeans(rg, ~ crosstype*species)
plot(sxc.lsm)

options(digits=4)
cld.mod <- cld(sxc.lsm, Letters=letters) #tukey letterings
cld.mod$response <- 10 ^ cld.mod$lsmean
cld.mod$uSE <- 10 ^ (cld.mod$lsmean+cld.mod$SE)
cld.mod$lSE <- 10 ^ (cld.mod$lsmean-cld.mod$SE)
cld.mod[rev(order(cld.mod$species, cld.mod$crosstype)),]
    crosstype species lsmean      SE    df lower.CL upper.CL .group response
    hybrid    kaal     2.853 0.07984 97.15    2.695    3.012   b       713.5
    within    kaal     2.859 0.07098 45.81    2.716    3.002   b       722.8
    between   kaal     2.913 0.05175 24.92    2.806    3.019   b       818.0
    hybrid    hook     3.003 0.04781 12.47    2.900    3.107   b      1007.8
    within    hook     2.323 0.07459 57.88    2.174    2.473  a        210.6
    between   hook     2.369 0.05217 13.50    2.257    2.481  a        234.0
       uSE   lSE
     857.5 593.6
     851.1 613.8
     921.5 726.1
    1125.0 902.7
     250.1 177.4
     263.8 207.5
   
   Degrees-of-freedom method: satterthwaite 
   Results are given on the log10 (not the response) scale. 
   Confidence level used: 0.95 
   P value adjustment: tukey method for comparing a family of 6 estimates 
   significance level used: alpha = 0.05
H.wb <-  with(cld.mod[cld.mod$species=="hook",], response[crosstype=="within"]/response[crosstype=="between"] - 1)
K.wb <- with(cld.mod[cld.mod$species=="kaal",], response[crosstype=="within"]/response[crosstype=="between"] - 1)
K.H <- with(cld.mod[cld.mod$crosstype=="between",], response[species=="kaal"]/response[species=="hook"] - 1)
maxsp <- ifelse(K.H>0, "kaal","hook")
minsp <- ifelse(K.H<0, "kaal","hook")
maxresp <- with(cld.mod, response[species==maxsp & crosstype=="between"])
minresp <- with(cld.mod, response[species==minsp & crosstype=="between"])
HK.resp <-  with(cld.mod, response[species=="hook" & crosstype=="hybrid"])
KH.resp <-  with(cld.mod, response[species=="kaal" & crosstype=="hybrid"])
HK.int <-   with(cld.mod, ifelse(HK.resp > minresp & HK.resp < maxresp, (HK.resp-minresp)/(maxresp-minresp), 
                                 ifelse(HK.resp < minresp, HK.resp/minresp-1, HK.resp/maxresp-1)))
KH.int <-   with(cld.mod, ifelse(KH.resp > minresp & KH.resp < maxresp, (KH.resp-minresp)/(maxresp-minresp), 
                                 ifelse(KH.resp < minresp, KH.resp/minresp-1, KH.resp/maxresp-1)))

intermed <- (minresp + maxresp) / 2
with(ib, wilcox.test(ib[species=="kaal" & crosstype=="hybrid","flrs"], mu=intermed))
   
    Wilcoxon signed rank test with continuity correction
   
   data:  ib[species == "kaal" & crosstype == "hybrid", "flrs"]
   V = 1300, p-value = 6e-05
   alternative hypothesis: true location is not equal to 526
with(ib, wilcox.test(ib[species=="hook" & crosstype=="hybrid","flrs"], mu=intermed))
   
    Wilcoxon signed rank test with continuity correction
   
   data:  ib[species == "hook" & crosstype == "hybrid", "flrs"]
   V = 42000, p-value <2e-16
   alternative hypothesis: true location is not equal to 526
round(c(H.wb,K.wb,K.H,HK.int,KH.int),2)
   [1] -0.10 -0.12  2.50  0.23  0.82
ggplot(as.data.frame(cld.mod), aes(y=response, x=relevel(crosstype, "within"), fill=species)) +
  geom_col(position=position_dodge2()) +
  geom_linerange(aes(ymin=lSE, ymax=uSE), position=position_dodge(0.9)) +
  labs(x="", y="Flowers",fill="Maternal species") +
  scale_fill_manual(labels = c("S. hookeri  ", "S. kaalae  "), values=brewer.pal(name="Set1", n=3)[c(3,2)]) +
  scale_x_discrete(labels = c("Intrapopulation", "Interpopulation", "Hybrid")) +
  geom_text(aes(label=.group), position=position_dodge(0.9), hjust=0, vjust=-1) +
  scale_y_continuous(expand = expand_scale(add=c(0,0)), breaks = scales::pretty_breaks(n = 5)) +
  theme_classic() + theme(legend.text=element_text(face="italic", size=rel(1)), legend.position="bottom", axis.text = element_text(colour="black", size=rel(1)), text=element_text(size=14), axis.ticks.x = element_blank()) + geom_segment(aes(x=2.5, y=intermed, xend=3.5, yend=intermed))