Landscape characteristics and climatic data
We defined the degree of urbanization around focal trees as the
percentage of impervious surface (including roads and buildings) in a
buffer with a radius of 200 m centered on the focal oaks based on oak
coordinates as retrieved from Google Maps by project partners (Meyer et
al., 2020; Parsons and Frank, 2019). We also calculated the percentage
of local canopy cover within a 20 m buffer (excluding open areas and
grasslands). We used this buffer size of local canopy cover because the
local abundance of trees is a strong driver of urban biodiversity
(Herrmann et al., 2012; Long and Frank, 2020; Meyer et al., 2020;
Parsons and Frank, 2019; Stemmelen et al., 2020). To that aim we used
the High Resolution Layers of the CORINE land cover datasets with 10 m
resolution and with reference year 2018 (± 1 year). Tree Cover Density
extracted from the CORINE dataset consists of tree cover density in a
range from 0 to 100%, while the urbanization extracted from the CORINE
dataset consists of artificially sealed areas (imperviousness ranging
from 1 to 100%). We assumed that landscape characteristics did not
change during the survey period (2018-2020).
To control for variability in herbivory that is influenced by local
climatic conditions (Valdés-Correcher et al., 2021), we extracted spring
temperature and precipitation (mean temperature and precipitation in
April-June) data from the WorldClim database (Hijmans et al., 2005) on
the basis of the oak coordinates. Spring temperature and precipitation
correspond to the period when most of the partners collected the leaves
and also the main period of activity of insect herbivores on oak.
Urbanization and local canopy cover were slightly negatively correlated
(Pearson r = -0.38, P < 0.05), and were independent of
latitude (Urbanization: Pearson r = 0.02, P > 0.05;
Local canopy cover: Pearson r = 0.04, P > 0.05) and
climate (Temperature and urbanization: Pearson r = -0.02, P> 0.05; Temperature and local canopy cover: Pearson r =
-0.12, P < 0.05; Precipitation and urbanization:
Pearson r = 0.03, P > 0.05; Precipitation and local
canopy cover: Pearson r = 0.01, P > 0.05). Although
latitude was negatively correlated with temperature (Pearson r = -0.76,P < 0.05) and precipitation (Pearson r = -0.70,P < 0.05) which could have caused collinearity issue, a
previous study found that climatic variables were better predictors of
variation in herbivory and therefore decided to only include climatic
variables in the models (Valdés-Correcher et al., 2021).