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).