2.3 | Analyses
To understand the evolution of behavioral niche in the Mygalomorphae and
identify cases of niche convergence, we conducted ancestral state
reconstruction (ASR) on our two behavioral characters. We compared the
results of two methods: we conducted a maximum-likelihood (ML) approach
(Pagel, 1999) on the genus-level phylogram and chronogram using thecorHMM R package (Beaulieu et al., 2021), and the Maximum
Parsimony (MP) approach (Swofford & Maddison, 1987) on the supertree
using Mesquite v3.51 (Maddison, 2008). For the ML reconstructions, we
compared AICc scores across both alternate branch length sets (i.e., the
chronogram and phylogram, see Wilson et al., 2022b) and across alternate
state-transition models, and chose the branch-length set and model that
minimized AICc (Appendix C).
Next, to visualize how mygalomorph somatic morphology relates to the
behavioral niches that they inhabit, we conducted non-metric
multi-dimensional scaling (NMDS) using the complete 55-character
morphological dataset, revealing the position in two dimensional
‘morpho-space’ of all genera included in the study and the
behavioral/ecological optima present in the infraorder. This analysis
involved first calculating the Gower similarity coefficient (Gower,
1971) between all pairs of taxa based on the morphological characters,
using the Claddis R-package (Lloyd, 2016) before using the
resultant pairwise-similarity matrix to conduct the NMDS analysis, using
the R-package vegan (Oksanen et al., 2013).
Finally, to identify the specific morphological features associated with
different behavioral niches, and thereby better understand their
function, we conducted a series of phylogenetic tests for correlated
evolution between morphological features and behavior (Table 1). A
morphological feature was tested for correlation with behavior if: (i)
an association between the feature and behavior has been proposed
previously in the literature; (ii) the function of the feature is known
and is tied with a particular behavior; or (iii) a strong association
between a feature and behavior was perceived while scoring characters
for this study. We tested all selected morphological features for
correlation with five key behaviors, all of which have evolved multiple
times in mygalomorphs: (a) construction of a web (sheet, funnel, or
curtain) at the entrance to the retreat; (b) opportunistic retreat
construction (as opposed to construction of a burrow or nest); (c)
construction of a burrow; (d) structural modification of the retreat
entrance (with a purse, collar, turret, or trapdoor); and (e)
construction of a hinged trapdoor at the retreat entrance.
We tested hypotheses in two steps. Firstly, we used the pairwise
comparisons method (Maddison, 2000; Read & Nee, 1995) to test
correlation between each morphological feature and all five behaviors.
This method was applied as a stringent first pass because it is
relatively robust to the ‘pseudoreplication problem’ that causes many
other phylogenetic correlation tests to identify significant correlation
in questionable scenarios (see Maddison & FitzJohn, 2015). Because this
method does not consider branch lengths, it was conducted using the
supertree to benefit from the additional taxa. The analysis was
performed twice for each character, the first time using only pairs that
contrasted in both characters (i.e., morphology and behavior), and the
second time using pairs that varied in at least one of the two
characters (i.e., morphology and/or behavior) (Maddison, 2000; Read &
Nee, 1995). For each approach we identified 1000 alternative pairing
schemes, and from these we took the highest possible P -value as
our significance threshold, thereby reducing the chance of type-1 error.
After using this first step to identify significant cases of
correlation, we then analysed these cases using Maximum Likelihood
methods (sensu Pagel, 1994). For each case, we generated
likelihood values using four different structured-Markov models: a model
of independence (i.e., no correlation), and of morphological dependence
on behavior, behavioral dependence on morphology, and
morphological/behavioral interdependence (i.e., three alternate models
of correlated evolution). We then estimated the delta-AICc for these
four models to assess their relative strength. This allowed us not only
to compare the aforementioned models of independence and dependence for
each particular case (the best model will have a delta-AICc of 0), but
also provided a way to compare hypotheses of correlation between a
particular morphological feature and alternate behaviors, with the
expectation that the strongest hypothesis will return the highest
delta-AICc value for the independent model (indicating the relative
weakness of this model compared to the strongest model of correlation
for that feature/behavioral combination).