STATISTICAL ANALYSIS OF LIFE HISTORY TRAIT DATA
We argue that alternatives to popular analytical approaches in life
history theory should be considered more often [47], and judiciously
applied. Tractable generalisations of life history variation frequently
require analyses to collapse multivariate trait datasets and associated
covariates into a smaller set of latent variables. Common statistical
means of reducing dimensionality have both strengths and drawbacks (Box
3). The most popular approach in this context is the Principal
Components Analysis (PCA). Although PCA is a well-established
exploratory tool, it should not be used naïvely [48].
First, researchers should be aware of their own interpretations and
biases when interpreting dimension reduction analyses. Prior to
analysis, careful consideration should be given to the aim of the study:
for example, PCA informs on correlative rather than causative
relationships between variables. Interpreting results of multivariate
analyses can be challenging, as composite axes are combinations of
underlying traits. Second, careful consideration should be given to how
the chosen data may affect outcomes. Indeed, sets of life history traits
that are derived algebraically from vital rate schedules may be
correlated due to mathematical relationships as much as natural
covariation [49]. If a trait set includes measures that are a
function of multiple vital rates (e.g ., generation time), or that
are measured in different units (e.g ., energy for metabolic rate
and time for mortality), structuring axes from PCAs (principal
components) may be challenging to determine. Even if the multivariate
trait set is carefully chosen, it is likely to be difficult to draw fair
comparisons between studies that use different trait sets and therefore
have component axes with different orientations. Third, the statistical
shortcomings of analyses must be carefully considered. In particular, a
weakness of PCA is that, in its simplest form, it does not consider
measurement errors (but see [50]). Fourth, PCAs do not account for
non-linearities among life history traits [45]. Finally, and perhaps
most importantly, inquiry should be hypothesis-driven: PCA is designed
to inform on how variation in multivariate data is partitioned, rather
than to test associations between specific traits (Box 3).