Alternative titles:
* Making the most of your data: Advantages of mixed effects/hierarchical models for the analysis of biodiversity monitoring and other complex data 
* ?
GROUP x: Your data probably demand it
Advantage 1: Mixed effects models are typically the correct model for your data
Poster: "Account for “pseudoreplication”"
GROUP 2: Make use of all your hard-earned data
Advantage x: Improve inference for individual species by Leverage information across groups/sites species (on partial pooling etc)
Poster: "Make use of all of your hard-earned data"
-Lloyd case study: model rare species,
-Faaborg case study
-Costa Rica
Avoid data exclusion rules (made figure for this for poster)
Potential problem: Does including rare groups introduce bias
Poster: "Minimal bias due to inclusion rules"
Advantage x: Improve inference across species by using multi-level modeling
-poster: "Directly model group-level variation"
-trait modeling (Costa Rica; Mencia? Faaborg?)
Advantage z: Using all your data can increase power (vs. end point analysis)
Advantage c: Use variance partitioning to better understand your data
-can I characterize time series variation using varcomp? which time series is more variable (Lloyd, Aceitillar, Faaborg)
-which time period is more varible - early 2000s or more recent?
-Crone?
GROUP X: Don't throw the baby out with the bathwater
Advantage x: They preserve the direct biological meaning of the data (vs. remedial measures)
Advantage z: Say Ciao to Bonferonni
Advantage q: Smooth out pesky “outliers”!
GROUP X: Keeping your nose clean
Advantage z: Avoiding the garden of forking paths / data dredging
-simulation of how likely you are to get a significant trend when you study x-species
Tying in to other fields
-deer exclosure studies: studies that model focal species