BOX 3. STATISTICAL METHODS FOR COMPARATIVE ANALYSES OF LIFE
HISTORIES
A set of measurable, correlated traits can be described using a smaller
number of emergent, orthogonal variables representing dominant axes of
life history variation, such as the fast-slow continuum. Principal
component analysis (PCA) is one common statistical method to achieve
this goal [3–5,7,9,26,27,29,30,33,34,74,75]. Although some -largely
arbitrary- guidance exists regarding how many dimensions explain
meaningful amounts of variation [48], it is less clear how to
compare dominant axes across independent analyses with data sets
comprising different traits, taxa, and sampling methodologies.
It is also possible to explore relationships between two different
multivariate data sets (e.g ., climatic and life history data).
Canonical Correlation Analysis (CCA) yields two sets of emergent
uncorrelated variables by calculating axes with highest correlation
between variable sets. This approach recognises that dominant axes of
variation (e.g. , fast-slow) may not yield strong relationships
with other ecological or evolutionary processes.
Unlike both PCA and CCA, Factor Analysis (FA) [47,76] treats
measured variables as functions of latent variables, with associated
measurement and/or residual error. As FA does not require latent
variables to be uncorrelated, the approach offers solutions to some
problems of PCA and CCA [47]. We suggest that FA is better suited to
the testing of hypothetical, rather than data-driven, axes of variation.
Dimension-reduction analyses fit axes through data, rather than measure
the multidimensional boundaries of life history variation. Cluster
Analyses (CA) may help understand, after standardising for the
species-specific ranking on the fast-slow continuum (by usinge.g. generation time), which and why certain life history
strategies do not exist, as much as the clustered patterning of those
that do. Hierarchical CA has previously been used to identify
substructure in life history variation [77] and could prove a useful
tool to apply much more widely in comparative life history theory
[78–82].
Depending on the researcher’s perspective, a given environmental,
phylogenetic, or morphological variable may drive life history
variation, or be a nuisance covariate to deal with statistically
[83]. Comparative analyses must account for the non-independence
from shared evolutionary history [9,47]. Phylogenetic methods exist
for PCA [83], but not yet for FA or CA, forcing researchers to use
phylogenetically independent contrasts. Correlates of life history
traits, particularly organism size, cause further challenges: including
these correlates in analyses risks deriving life history axes defined by
non-life-history traits. Moreover, using residuals in multivariate
analyses drawn from regressions of life history against body size can
introduce statistical biases [84].