Methods
Survey data. We used information on well-surveyed avian
communities from published studies and our own field work (Sol et al.
2020b, c). The term community is used to describe assemblages within a
region that could be unambiguously assigned to one of five land-use
classes: native vegetation, rural, little urbanized (e.g. urban parks),
moderately urbanized (suburbs) and highly urbanized (city centers). Our
criteria to accept data were that, at each study region, bird species
were exhaustively surveyed and that at least one of the communities was
invaded by one or more non-indigenous bird species. The resulting
database comprised information on 1367 species —63 non-indigenous in
one or more communities— in 277 communities from 50 study regions. In
47 of these study regions, surveys were conducted both in urbanized
areas and in the surrounding non-urbanized areas, allowing us to assess
the effect of human-related disturbances on invasiveness. The entire
dataset is available in Sol et al. (2020a).
Introduced species information. We extracted information on bird
species introduced and established in regions outside their native range
from the Global Avian Invasion Atlas GAVIA (Dyer et al. 2017),
the most complete dataset of historical introductions currently
available. The dataset covers > 27,000 introductions of 971
bird species and also provides information of the year of introduction
for established exotics.
Invasion success. We used two classic measures of invasion
success: 1) the presence/absence of the invader in communities from a
same region, 2) the abundance of exotic species relative to native
species within the invaded communities. The Gavia dataset (see
above) confirmed that the exotic species analyzed were introduced
sufficiently time ago (range from 1853 to 1975) to make their abundances
little influenced by time lags or introduction effort. The density of
occurrences of an animal in a given place may however be influenced by
their size, either directly (e.g. by increasing the demands of food) or
indirectly (by correlating with the fast-slow continuum of life history
variation). We evaluated the importance of body size by including it as
a co-variate in the models (see below).
Phylogenetic information. We extracted from the BirdTree database
(http://www.birdtree.org) (Jetz et al. 2012), two samples
of 5,000 phylogenies that included all native and exotics species
included in this study. Each sample was based two alternative backbone
topologies: Hackett et al. (2008) and Ericson et al. (2006).
Phylogenetic distances were estimated based on two summary trees
calculated from both samples. These were computed as the maximum clade
credibility tree using the program TreeAnnotator (included in the
package BEAST v1.8.0) (Drummond et al. 2012). Because results
from both phylogenies were highly coincident, we only present the
results of analyses based on Ericson et al. (2006).
Functional traits information. We described the foraging niche of
species based on three types of functional traits extracted from Pigot
et al. (Pigot et al. 2020): 1) eight morphological traits (all
log-transformed), 2) seven diet categories, and 3) 30 categories of
foraging behavior collected from the literature, as described in Sol et
al. (Sol et al. 2020b). Diet and foraging behavior were described
as fuzzy variables, ranking each category from 0 to 10 as a function of
the degree of use (Pigot et al. 2020). In addition, we extracted
information on relative brain size, resource niche breadth and brood
value from Sol et al. (Sol et al. 2014a, 2020b) and Sayol et al.
(Sayol et al. 2016). To estimate niche breadth, we used Rao’s
quadratic entropy (De Cáceres et al. 2011), based on the
functional distances matrix (see below) derived from niche information.
Phylogenetic and functional distance metrics. We used two
complementary metrics of phylogenetic distance, the average and nearest
phylogenetic distance between each exotic species and all the native
species of the community (Tucker et al. 2017). For each invader
from a given site, we calculated the mean (MPD) and nearest phylogenetic
distance (NPD) between invaders and all native species of each community
regardless of whether the exotic species was present or not (see
justification below). Summary trees were pruned down to the species
present in the communities and phylogenetic distances among species were
calculated by means of the function “cophenetic” in the R package
“Picante” (Kembel et al. 2010). To consider the fact that most
native species in the community were rare, we used weighted versions of
MPD and NPD in which the distance of the invader to each native species
was multiplied by 1 – Abr , whereAbr is the relative abundance of the invader in
the community. This increases the distance of rarer species relative to
more common species. Functional distances were estimated the same way,
but applying Gower’s distance (Gower 1971) to morphological, diet and
foraging behavior traits (the last two coded as fuzzy variables).
Spatial scale and biases. We used our community dataset to assess
how the nearest phylogenetic distance between exotic and native species
varied with spatial scale (i.e. community, region, country and
continent). We used simulations to explore whether spatial patterns were
affected by the increase in species with scale or by the fact that
exotic and native species came from different regions. The former was
investigated by randomly sampling without replacement pairs of species
in different numbers (from 1 to 125) from the entire avian phylogeny
(n=9993). We compared all species as well as the subset that were
introduced in regions outside their native range and the subset of them
that have become successfully established in the new region. The
analysis of geographic effects was conducted comparing species within
and across biogeographic realms that have been either donors
(Palearctic) or receptors of invaders (Nearctic and Australian).