Diversification analyses and evolutionary models
We estimated speciation rates across a range of phylogenetic trees using
the
cladogenetic diversification rate shift (ClaDS) Bayesian model (Malietet al. 2019) that infers branch-specific diversification rates.
ClaDS allows for gradual variation in diversification rates and was
shown to perform well in identifying both small and large rate shifts
(Maliet et al. 2019). We used the model that accounts for a
scenario with a constant turnover rate (ClaDS2 model) and the faster
implementation in Julia (v.1.1.0, (Bezanson et al. 2017) that
uses data augmentation (Maliet & Morlon 2020), setting the sampling
probability to 0.63. To test whether nest type is associated with
variation in speciation rates, we used the tip-rate estimates (estimates
for each terminal branch at present) extracted from ClaDS as response
variable in a PGLS model, with nest type and log(range size) as
predictors, since range size has been shown to be associated with
speciation (Cally et al. 2021). The nest type category used for
this model was open vs. domed, given that we are interested in the
actual nest building behaviour, not where the nest is placed. A main
model was run using the MCC tree and we also performed the same analysis
in a sample of 20 phylogenetic trees to account for phylogenetic
uncertainty.
To gain a more mechanistic understanding on the feedback between species
traits and their influence on diversification, we used the Multiple
State Speciation Extinction (MuSSE) model to jointly estimate state
dependent speciation and extinction rates (FitzJohn 2012). Modelling
trait evolution while accounting for the diversification process results
in more appropriate estimates of trait transition rates and ancestral
estimates, while accounting for uncertainty in the species for which we
do not have state information (Maddison et al. 2007; FitzJohn
2012). We modelled the correlated evolution of niche width andnest type data by aggregating them into four different states:
narrow niche and domed nest, narrow niche and open nest, wide niche and
domed nest and wide niche and open nest. We assigned a species as having
a wide or narrow niche based on whether the PCTEMP was
below or above the median value of the whole dataset
(PCTEMP = -0.639). Because we are interested in the
interaction among these states, we disallowed transition rates between
states that effectively would represent simultaneous changes in nest
type and niche width (e.g. going from wide niche and domed nest directly
to narrow niche and open nest is not allowed).
Finally, to allow for unobserved taxonomic variance in the
diversification process, thereby not forcing the model to explain all
the heterogeneity in diversification, we ran HiSSE (Hidden State
Speciation Extinction) with two concealed states (Beaulieu & O’Meara
2016). Both analyses were performed using the MCC tree within a Bayesian
Framework using RevBayes, including ancestral state estimation (details
in supplementary material). To evaluate the effect of phylogenetic
uncertainty, we ran the Maximum Likelihood approach to MuSSE implemented
in the R package ‘castor’ (Louca & Doebeli 2018) across 20 phylogenetic
trees. Because phylogenetic uncertainty did not influence the results,
and results were consistent across methods, we present the HiSSE
analyses in the main text and castor and MuSSE results in the
supplementary material.