Among-individual behavioural variation
Below are tutorials associated with “Avoiding the misuse of BLUP in behavioural ecology“ (Houslay & Wilson, 2017), published in the ISBE Behavioral Ecology journal. These tutorials are aimed at researchers interested in multivariate methods for modelling among-individual variation in labile traits (we focus on personality, behavioural syndromes, and individual variation in behavioural plasticity).
We have provided two versions of each tutorial – one using maximum likelihood via the R interface for VSNi commercial software ASReml, and the other using Bayesian methods via Jarrod Hadfield’s MCMCglmm R package.
The data sets for use with these tutorials are available on figshare:
Multivariate modelling for individual variation
Here we demonstrate the use of multivariate models for directly testing among-individual correlations between ‘personality’ traits (measured repeatedly at the individual level), in addition to testing for associations between these traits and a single measure of fitness (measured once at the individual level).
Data: figshare link
Multivariate modelling for individual plasticity variation
Here we show how to test for individual-by-environment interactions (also known as individual variation in slopes, or individual variation in plasticity) using random regression. We also show how to add a further response variable to this random regression, and test for an association between this variable and individual variation in intercepts/slopes. We then discuss problems with the interpretation of random regression models, and why a ‘character state’ approach might be beneficial. We show how to model individual variation in plasticity with character state models, and add the single fitness measure to this model to demonstrate this might be a more intuitive approach.
Data: figshare link
Multivariate behavioural (co)variation
In this tutorial, we show how to isolate the among-individual (co)variance matrix I by applying a multivariate mixed model to a set of traits. We then examine eigen vectors of I to determine what the major axis of among-individual variation looks like. This is more relevant to personality / behavioural syndrome studies than the oft-used technique of applying univariate mixed models to principal components of multivariate data (since these PCs contain both among- and within-individual trait variation).
ASReml-R: coming soon…
MCMCglmm: coming soon…
Data: coming soon…
More tutorials coming soon…