Measuring multivariate trait ‘syndromes’ to explain ecological
outcomes
Organisms’ responses to their environment are governed by complex suites
of correlated traits that confer important about performance under
specific sets of environmental filters (Figure 1). Strong correlation
among trait types that can recur across unrelated species—trait
‘syndromes’ or ‘typologies’—underpin trait-based community assembly
theory for plants (Grime 1988) and behavioural syndromes in animals (Sih
et al. 2004). Yet single trait-type studies make up roughly half of the
research we reviewed (387 papers) compared with multi-trait (i.e. 3 or
more traits) studies (176 papers, 21%), and we estimate that the true
ratio of single vs. multi-trait studies to be more significantly skewed
towards single-trait studies. Our review is likely conservative in
assessing their prevalence because an unknown number of studies have
likely been excluded from this review because they do not self-identify
as trait-based investigations of ecological processes, and rather simply
identify the particular trait investigated in relation to species
distributions or abundances. In considering single traits at a time, as
a function of a species’ or ecological community’s response to gradients
of change, we risk overlooking the combined effect that a range of
traits may have in explaining those responses to change (e.g.
multivariate traits for restoration design to resist invasion; Funk et
al. 2008, and plant trait typologies along ecological gradients; Diaz et
al. 2004). While there has been a trend towards identifying a subset of
traits that are strong indicators for ecosystem processes (e.g. Hausner
et al. 2003), increasingly sophisticated statistical tools and computing
power exist to deal with the greater model complexity that comes with
multi-trait based analyses of ecological relationships. For example,
multi-matrix modeling solutions enable the simultaneous assessment of
relationships between species abundances and/or distribution data,
environmental gradients of change and the role that traits play in
mediating changes in biodiversity and assemblages in the face of
environmental change (Dray et al. 2014, Wang et al. 2012, Brown et al.
2014). Comparatively simple techniques available in standard
multivariate statistical tool kits and packages include parametric and
non-parametric techniques for variable reduction and synthesis of trait
combinations. Traditionally, techniques such as principal component
analysis, non-metric dimensional scaling and clustering are most
commonly used for identifying trait groupings and trait typologies,
while systematically reducing variables included in models, and these
remain powerful techniques fit for that purpose (Legendre & Rogers
1972, Legendre & Legendre 1998).