Statistical analyses
To investigate the effect of ecological distance on insect biodiversityper se , we calculated generalized linear models of the formbiodiversity ~ distance *
host-tree-genera + RDA1 and a negative binomial distribution. To
account for effects of local, environmental conditions we calculated a
constrained redundancy analysis (RDA) conditioned on the tree genera for
each Heteroptera, Coleoptera and total insects respectively. We used the
Hellinger transformed insect diversity matrix as dependent variable and
diameter at breast height, total tree height and the amount of congenera
within a ten-meter radius as independent variables. This RDA represents
the explained variance of the insect diversity matrix with respect to
the tree individual fitness (in form of growth parameters) and the
proximity of congeners, which both might influence the diversity and
abundance of insects. We use the first axis of the RDA (RDA1) to account
for these local environmental effect in the analysis. We removed all
individuals with the respective biodiversity measure of zero prior to
calculations of the generalized model. For the models with Shannon index
we used a gaussian distribution and for models of number of species and
number of individuals we used a negative binomial distribution. The
chosen distributions reflect common distributions in biodiversity
analysis for the respective parameters. We report marginal r² values,
Pearsons r² for models with gaussian distribution and trigamma
calculated r² for models with negative binomial distribution following
Nakagawa et al. (2017). To compare if the distance effects are stronger
for phytophageous then non-phytophageous insects, we followed a similar
procedure as described above, though including insect genera as
covariate within the generalized linear model: biodiversity~ distance * host-tree-genera *
insect-genera + RDA1 . Model assumptions were assessed visually
(QQplots) and tested for overdispersion using package DHARMa(Hartig 2022). All analysis and visualization were done using R and the
packages tidyverse (Wickham 2023), MASS (Venables &
Ripley 2002), DHARMa (Hartig 2022), effects (Fox et
al. 2022), lme4 (Bates et al. 2023), MuMIn (Barton
2020).