However, longitudinal analysis differs from typical multivariate because there is an underlying continuum (‘time’) and the sequential nature of assessments (height at age 5 comes first than height at age 8) creates patterns of variation.

Longitudinal data can be considered as a special case of multivariate analysis, because the ‘same trait’ is measured each time. For example, tree height at ages 2, 5, 8 and 12 years, or data coming from increment cores. Longitudinal data arise when an individual is repeatedly assessed at different times. Now it is the time to start with problems that breeders face when dealing with multiple assessments of a trait.