Statistical analyses
Data analysis was done in R (version 3.6.1). We checked distributional
assumptions using the fitdistrplus package (Delignette-Muller & Dutang
2015) and inspected model residuals for constant variance.
We used generalized linear mixed-effects models (package glmmTMB, Brooks
et al., 2017) with tweedie errors and management nested in blocks as
random effects to analyze data on flower visitor diversity and total
arthropod diversity, which is expressed as Shannon’s entropy (Jost 2007)
and its numbers equivalent (exponential of Shannon’s diversity) in the
whole manuscript. This index was calculated using the package vegan
(Oksanen et al. 2019). As fixed effects we used 1) crop
diversity*management and 2) crop mixture*management in separate models.
We generated plant-flower visitor networks using the bipartite package
(Dormann et al. 2009) for all crop mixtures and management
intensities separately (N=4). For calculations of network metrics, we
pooled our data per block, crop mixture and management intensity, and
then analyzed effects of crop mixture and management on network metrics
using the same model structure as above with generalized poisson and
compois errors. We used the number of interactions, number of flower
visitor species, and Shannon’s diversity of interactions as metrics to
describe the networks, as more complex indices need a minimal network
size to function reliably (Dormann et al. 2009), which was not
the case in this study.
The number of flower visits and the total number of arthropod
individuals were analyzed as described above with generalized poisson
errors. Crop biomass was analyzed using tweedie errors. Means were
compared using Type Ⅱ Wald chisquare tests from the car library (Fox &
Weisberg 2019).