2.4. Taxonomic, functional and phylogenetic diversity metrics
For abundance estimates of each bird species, we pooled the data using
the maximum abundance recorded between the two survey periods, per
point, per year. We calculated several diversity metrics to characterize
each community and community type, based on taxonomic diversity (TD),
functional diversity (FD) and phylogenetic diversity (PD). As a measure
of TD, we used the species richness observed. To have a comprehensive
understanding of the functionality of bird communities, we computed two
metrics of FD based on the 10 functional traits: functional divergence
(FDiv), and functional evenness (FEve). FEve was used as proxy for the
resilience of communities (Lee & Martin, 2017; Morelli et al., 2020).
We assessed the amount of phylogenetic variation of each community
through one PD metric: Mean Pairwise Distance (MPD). To calculate
phylogenetic diversity, 1,000 phylogenetic trees based on the Hackett
backbone (Hackett et al., 2008) were downloaded from
http://birdtree.org/ (Jetz et al., 2012). Since PD metrics are dependent
on species richness, we calculated standardised effect size (ses) for
the MPD metric (sesMPD). To do so, we compared the observed MPD of a
community with the expected MPD of 999 null communities generated with
null models dependent on the “independent swap” algorithm (Swenson,
2014). This procedure removed the effect of species richness (Swenson,
2014). Both MPD and sesMPD metrics were calculated as a mean value from
the 1,000 values for each community (based on the 1,000 trees; Cosset
and Edwards, 2017). Then, we used a two-tailed Wilcoxon signed rank test
to test the difference between observed and expected MPD (Erdős et al.,
2022). We calculated all the metrics using the packages ‘FD’ (Laliberte
& Legendre, 2010), ‘adiv’ (Pavoine, 2020) and ‘picante’ (Kembel et al.,
2010) in R version 4.1.1 (R Development Core Team, 2021).
2.5. Data analysis
Comparison of diversity metrics between community types
We compared TD, FD and PD
metrics between community types, following the three tests used in
Loiola et al., 2018: 1) comparing non-invaded and invaded communities
(non-invaded vs invaded; Test 1); 2) comparing native
species of non-invaded communities and native species of invaded
communities (non-invaded vs invaded no alien; Test 2); and
3) within invaded plots including and excluding alien species
(invaded vs invaded no alien; Test 3). We performed the
above tests across and within the main three land-uses,
trying to assess the effect of environmental filtering on the specific
species pool within a given land-use (Loiola et al., 2018). To evaluate
whether differences of the various metrics between community types were
significant, we performed univariate analysis of variance (ANOVA)
followed by Tukey’s test.
Effect of landscape configuration and composition on diversity
metrics
To assess the effect of landscape variables on diversity metrics, we
built linear mixed-effect models (GLMM) with normal errors using the
maximum log-likelihood method, considering all landscape composition and
configuration variables as fixed effects. Moreover, to account for the
dependence of observations from the same location in different years, we
used point ID as a random effect. Models were run separately for each
community type. We calculated Akaike’s Information Criterion (AIC) to
rank each candidate model. Then, we carried out model averaging
(Richards, 2008) on all models with < 2 ∆AICc using the R
package MuMIn (Bartoń, 2022). To account for spatial autocorrelation, we
used spline correlograms (Bjørnstad & Falck, 2001) with 1,000 bootstrap
resamples, both on raw data and on model residuals (Santana et al.,
2017). We inspected spline correlogram plots of full model residuals
(Zuur et al., 2009), and assumed absence of spatial autocorrelation when
95% Confidence Intervals included (see Supporting Information 2).