Environmental variables
To test whether there are differences in niche width and range size
between species with different nest types we used geographical
distribution information from BirdLife (2019). We used a published
dataset (Cally et al. 2021) of 19 bioclimatic variables from
Worldclim (Fick & Hijmans 2017) (details in Cally et al. 2021). These
variables were sampled in 1000 random points across the distribution of
each species, and provide information on temperature and rainfall across
the range. We also extracted information on range size from Cally et al.
2021 (n=3174). For a smaller set of species for which breeding range
information was available (n=3049), we extracted information on the same
19 bioclimatic variables following the protocol in Cally et al. 2021,
but this time we used a more recently developed dataset (CHELSA), which
has an algorithm that predicts precipitation patterns more precisely
than Worldclim (Karger & Zimmermann 2019). For each climatic variable,
and each species, we calculated the standard deviation across the 1000
points sampled, to estimate climatic variability across the species
range (and breeding range). Since species restricted to islands are
limited both in the extent of their range and the niche width, we
performed analyses using both the whole dataset and only continental
species.
To summarise information on variation in temperature and variation in
precipitation across a species range, we performed two principal
component analyses, one for temperature and one for precipitation
variables. We split precipitation from temperature because we expected
that nest types would be more linked to temperature than precipitation
variables, given the proposed thermoregulatory capabilities of domed
nests (Martin et al. 2017). The same was done for the breeding
range, leading to four principal component analyses that summarise
information on how variable temperature and precipitation are both
within total and only breeding ranges. We report the PC loadings and
percentage of variance explained for PCs. We refer to the first
principal component from temperature variables as PCTEMPand to the first principal component based on precipitation variables as
PCPRE. These PCs (four in total) were used each as a
different response variable in statistical analyses, but many of them
were very highly correlated (e.g. r2 = 0.99, supp.
material Figure 1). Given that results were very similar for both
breeding and whole range, we present in the main text the results from
the whole range, because it holds a larger sample.