How to Interpret binomial GLMM results - r

Question

I have a large dataset (24765 obs)
I am trying to look at how does cleaning method effect emergence success(ES).
I have several factors: beach(4 levels), cleaning method(3 levels) -->fixed
I also have a few random variables: Zone (128 levels),Year(18 years) and Index(24765)
This is an ORLE model to account for overdispersion.
My best fit model based on AIC scores is:
mod8a<-glmer(ES.test~beach+method+(1|Year)+(1|index),data=y5,weights=egg.total,family=binomial)
The summary showed:
summary(mod8a)#AIC=216732.9, same affect at every beach
Generalized linear mixed model fit by maximum likelihood (LaplaceApproximation) ['glmerMod']
Family: binomial ( logit )
Formula: ES.test ~ beach + method + (1 | Year) + (1 | index)
Data: y5
Weights: egg.total
AIC BIC logLik deviance df.resid
214834.2 214891.0 -107410.1 214820.2 24758
Scaled residuals:
Min 1Q Median 3Q Max
-1.92900 -0.09344 0.00957 0.14682 1.62327
Random effects:
Groups Name Variance Std.Dev.
index (Intercept) 1.6541 1.286
Year (Intercept) 0.6512 0.807
Number of obs: 24765, groups: index, 24765; Year, 19
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.65518 0.18646 3.514 0.000442 ***
beachHillsboro -0.06770 0.02143 -3.159 0.001583 **
beachHO/HA 0.31927 0.03716 8.591 < 2e-16 ***
methodHTL only 0.18106 0.02526 7.169 7.58e-13 ***
methodno clean 0.05989 0.03170 1.889 0.058853 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) bchHll bHO/HA mtHTLo
beachHllsbr -0.002
beachHO/HA -0.054 0.047
mthdHTLonly -0.107 -0.242 0.355
methodnclen -0.084 -0.060 0.265 0.628
What is my "intercept" (as seen above)? I am missing levels of fixed factors, is that because R could not compute it?
I tested for Overdispersion:
overdisp_fun <- function(mod8a) {
+ ## number of variance parameters in
+ ## an n-by-n variance-covariance matrix
+ vpars <- function(m) {
+ nrow(m)*(nrow(m)+1)/2
+ }
+
+ model8a.df <- sum(sapply(VarCorr(mod8a),vpars))+length(fixef(mod8a))
+ rdf <- nrow(model.frame(mod8a))-model8a.df
+ rp <- residuals(mod8a,type="pearson")
+ Pearson.chisq <- sum(rp^2)
+ prat <- Pearson.chisq/rdf
+ pval <- pchisq(Pearson.chisq, df=rdf, lower.tail=FALSE)
+ c(chisq=Pearson.chisq,ratio=prat,rdf=rdf,p=pval)
+ }
> overdisp_fun(mod8a)
chisq ratio rdf p
2.064765e+03 8.339790e-02 2.475800e+04 1.000000e+00
This shows the plot of mod8a
I would like to know why I am getting such a curve and what it means
Lastly I did a multicomparion analysis using multcomp
ls1<- glht(mod8a, mcp(beach = "Tukey"))$linfct
ls2 <- glht(mod8a, mcp(method= "Tukey"))$linfct
summary(glht(mod8a, linfct = rbind(ls1, ls2)))
Simultaneous Tests for General Linear Hypotheses
Fit: glmer(formula = ES.test ~ beach + method + (1 | Year) + (1 |
index), data = y5, family = binomial, weights = egg.total)
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
Hillsboro - FTL/P == 0 -0.06770 0.02143 -3.159 0.00821 **
HO/HA - FTL/P == 0 0.31927 0.03716 8.591 < 0.001 ***
HO/HA - Hillsboro == 0 0.38696 0.04201 9.211 < 0.001 ***
HTL only - HTL and SB == 0 0.18106 0.02526 7.169 < 0.001 ***
no clean - HTL and SB == 0 0.05989 0.03170 1.889 0.24469
no clean - HTL only == 0 -0.12117 0.02524 -4.800 < 0.001 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)
At this point help with interpreting for analysis would help and be greatly appreciated. (Especially with that sigmoid curve for my residuals)