Statistical analysis
In order to identify potential relationships between morphological data
(body mass and skull dimensions) on one hand and seed and fruit sizes
and seed dispersal distance data on the other hand we used a ”two-block
partial least squares” (2B-PLS) approach (Rohlf & Corti 2000). This
method makes it possible to quantify the degree of association between
two tables of data, recorded for the same species. It is a descriptive
multivariate analysis robust to multicollinearity between variables and
therefore suitable for the use of morphometric and dietary variables.
These analyses generate axes that explain the covariance between two
data tables. A PLS correlation coefficient (Rpls) and
the covariance percentage for each axis produced are obtained using the
function “pls2b” in R from the Morpho library (Schlager 2013). The
result comes from a set of 1000 permutations. Next, a sampling
distribution of coefficients is obtained by resampling.
The P 95‐value is calculated by comparison of the
observed PLS coefficient to those obtained after resampling. The
significance of each linear combination is assessed by comparing the
singular value (PLS coefficient) to those obtained from permuted blocks.
If the PLS coefficient is higher than those obtained from permutated
blocks, then its associated P 95‐value is
considered as significant. For each significant analysis, a graph and
histograms of the variables are generated by using the Geomorph library
(Adams et al. 2013).
Species share a part of their evolutionary history and therefore cannot
be treated as independent data points. Thus, we also conducted these
analyses (2B-PLS phylogenetic) with the consideration of phylogeny. We
used the ”phylo.integration” function (Adams et al. 2014) in R
from the Geomorph library. This function allowed us to quantify the
degree of covariance of two data tables but under the Brownian evolution
model (Adams et al. 2014). The blocks are phylogenetically
corrected and the PLS coefficient (Rpls) between the two
blocks is evaluated.
As body mass is known to impact morphology, we ran Spearman correlation
tests (non-parametric) and linear regressions of each of the skull
dimensions as a function of body mass and extracted the residuals. These
were then used for 2B-PLS and phylogenetic 2B-PLS analyses to explore
covariation between skull dimensions and fruit and seed size without the
confounding effect of body mass.
We used R statistical environment (R core team (2019), version 3.5.2)
for these analyses. All data were Log10-transformed
before analyses to assure normality and homoscedascity.