Pollinator and plant traits
For pollinators, we collected several functional trait data (Table 1).
Activity length and seasonality (i.e., abundance and richness at
different seasons) data were collected for all pollinator groups. For
body size, we used the inter-tegular distance (ITD) for bees and the
wingspan for Lepidoptera. Hymenoptera pollinators were further
categorized based on (1) their nesting behaviour: above ground (tree,
wood, stem, above ground cavity) or below ground (within existing
tunnels or excavators), (2) sociality: social, solitary
or
parasitic and (3) diet specialization;
polylectic
or oligolectic depending on if they feed on various or a particular
plant taxon (Michener 2007). Due to data limitations, functional traits
were collected mainly for Hymenoptera and Lepidoptera (for details, see
Table 1).
We categorized each flower as non-radial (e.g., Lotus spp.) or
radial (e.g., Asteraceae) to test if flower morphology mediates the
effects of urbanisation on plant reproductive success.
Effect size calculation andhierarchical
meta-analysis
We used Hedges’ d , weighted by sample size, as our effect size.r -type and statistic values -type data were transformed
into
Cohen’sd and then into Hedges’ d using standard mathematical
formulas (Koricheva et al. 2013; Borenstein et al. 2021;
Table S1). Hedges’ d was calculated using the R statistical
software (R Core Team 2021). In all cases, a negative value of Hedges’d reflects negative effects of urbanisation on pollinator
communities. Effect sizes were considered statistically significant if
their 95% bias-corrected bootstrap confidence intervals (CI) did not
overlap with zero (Koricheva et al. 2013; Borenstein et
al. 2021).
Some publications provided more than one effect size, which may result
in pseudoreplication, so we carried out a hierarchical meta-analysis
that allows nesting effects within papers/studies (Tuck et al.2014). We included a publication-level random effect as a nesting factor
to incorporate this non-independency. We first performed a random
effects meta-analysis to calculate the overall mean effect size of
urbanisation on pollinator abundance and richness, flower abundance and
richness, functional traits, and pollination separately. Second, we
incorporated moderators, including the climatic region of the study, the
origin of the species, the taxonomic group of pollinators, and the
symmetry of the flowers. To assess the levels of the heterogeneity of
effect sizes, we calculated the P-value of the Qtstatistics. When they were statistically significant (P <
0.05), the influence of the moderators on the effects of urbanisation
was examined using Qm.
To test whether a change in floral diversity
(Hedges’d for flower abundance and richness) could predict a change in
pollinator diversity (Hedges’ d for pollinator abundance and
richness), we fitted maximum likelihood meta-regression models
(Filazzolaet al. 2020). The adjustment of Knapp and Hartung was then used
to account for uncertainty in the variance between studies, with overall
model significance against an F-distribution (Knapp & Hartung 2003).
All analyses were conducted in R statistical software using themetafor package
(Viechtbauer
2010; R Core Team
2021).