Introduction: Generalized linear mixed-effects models (GLMMs) are an extension of linear mixed-effects models (Module M.5) used when there is clustering (i.e., nested data structures) or non-independence (i.e., repeated measurements) among observations and the response variable is not normally distributed. As generalized linear models (Module M. 6), GLMMs consider distributions from an exponential family (e.g., binomial, Poisson) but can also incorporate random effects.

In this module, you will expand your skills on linear models (Modules M.4) to GLMMs while testing associations between Cayo Santiago rhesus macaque male social dispersal and population density.



Upon completion of this module, you will be able to:


References:


Extra training:


Associated literature:


Expand for R Notation and functions index

Notation:


Commands:




Testing associations between social dispersal and population density


Rhesus macaque populations are socially organized into multi-male, multi-female social groups (Fig 1). At Cayo Santiago, these groups formed naturally and most females remain in their natal groups their entire life. However, males migrate out of their natal social group at puberty (social dispersal; Fig 1). This behavior likely evolved to avoid inbreeding. Although natal social group dispersal is expected, individual males show high variability in the timing of migration. One important factor driving annual social dispersal in males could be high population density due to increased competition. As Cayo Santiago is an 15.2 ha island, metrics of population density are accurately estimated.