READ ME.

Attached data files (.csv) contain the raw for both experiment 1 and 2 as listed in the main manuscript.  Experiment 1 varies insect release patterns (everywhere, whack a mole, Control) while Experiment 2 is a factorial design with two release patterns (whack a mole, everywhere) crossed with two  selection pressure treatments (constant and heterogeneous)
Note that there are three subpopulations (labeled sub) in each replicate population (rep gives unique number for each population that can be used as a random factor in mixed models).

***Important.  Note that bioassays were carried out at the level of the replicate population- each  of  the three bioassay rows of data repeats the total values for each population.  Number of survivors in toxin diet in cages (toxin_pupae) and total pupae (tot-pupae) were recorded at the sub-population level and allows for analysis with another layer of nesting.  
We found that binomial error structures did not produce robust mixed effect models for complex models- all analyses of proportions were repeated with used the more conservative arc-sine sqrt transformation.  I do not expect anyone will gain any extra satisfaction from trying to optimize the binomial models, but be my guest.
I have not provided analysis code as between myself and my post there was poor adherence to labeling conventions across experiments and this would create confusion and discord.  However, population level analysis summed population sizes across sub-populations within reps and used mixed effects models with a relatively simple random effects structure such as 
for experiment 1:
as_ass2<-lmer(asin_ass_mortality ~generation+trt+(generation|rep),data=metatotal)
for experiment 2:
het_as_ass1<-lmer(asin_assay_mortalty~selection+generation*trt+ generation|meta),data=heterototal)

When checking analyses with data at the sub population level we used the R library nlme and models such as :
mix3<-lme(total_pupae~generation+gen2*trt+refuge,random=~1|replicate/sub,data=het_sub,method="REML")
mlmix3<-update(mix3,method="ML")
where gen2 is generation squared, and sub refers to subpopulation within replicate.
It should be relatively straightforward to reconstruct our analysis from the raw data and this outline.

BR.  Penryn, Oct 2018