Statistical analyses
We analyzed the effect of landscape parameters (arable field cover and
landscape heterogeneity) and different measures of the local flowering
plant community on wild bee and hoverfly species richness and abundance,
i.e. number of caught individuals, of a respective site. Apis
mellifera , the European honey bee, was excluded from all analyses.
Arable field cover, landscape heterogeneity and plant community
attributes were investigated in separate models, but the overall model
structure was the same. We used GLMMs with poisson distribution and a
log-link function for both response variables species richness and
abundance (glmer , R-Package lme4, Bates et al. 2020). As
covariates, we included sampling campaign as categorical fixed-effect
and study site as random-effect to account for the nested design of the
study. About 92% of all hoverfly individuals (n=214) were caught in the
third sampling campaign and 3/4 of the sites within the first two
campaigns did not contain any hoverflies, which is a typical pattern for
agrarian landscapes (Brandt et al. 2017). Due to this highly unequal
distribution across the sampling campaigns, we restricted our analyses
of of local plant attributes on hoverfly species richness and abundance
for the third sampling campaign. For these, we used GLMs with poisson
error for species richness and negative binomial for abundance without
any covariates, as we had no pseudo-replication in this dataset. We
assured that model assumptions (normality and over-/underdispersion of
residuals, heteroscedasticity, spatial autocorrelation of response
variables and model residuals and zero-inflation) were not violated with
R-package DHARMa (Hartig and Lohse 2020).
In order to reveal the effects of arable field cover and landscape
heterogeneity on pollinators (hypothesis 1 and 2), we determined the
scale of effect, i.e. at which spatial scale a landscape parameter has
the largest effect on the response variable (Jackson and Fahrig 2015,
hypothesis 3). For this purpose, we used the multifit -function of
Huais (2018), which compares a series of models that differ solely in
the scale a landscape parameter was quantified (see the specific model
structure above). In our case, we compared models of arable field cover
and landscape heterogeneity that were quantified at radii between 60m
and 3000m. Landscape predictors were standardized (z-scaled) for each
radius in this analysis that parameter estimates are comparable
(Schielzeth 2010). The model with the lowest AIC was considered to be
the best model. Further, we assured that the natural distribution of dry
grasslands in clusters did not affect our results due to possible
pseudoreplication of the landscape parameters (Supplementary
information, Appendix 3). Additionally, we assessed whether the
predictors had a statistically clear effect sensu Dushoff et al.
(2019).
We predicted that functional diversity of flowers and traits associated
with attractiveness have a positive effect on pollinators (hypothesis
4). Functional diversity was estimated with Rao’s quadratic entropy
(FDtrait) of the different traits (see above) and the
‘attractiveness’ with community weighted mean (CWMtrait)
(Fornoff et al. 2017). The frequency that a forb species occurred within
the eight segments of the vegetation survey was used as abundance
measure for the weighting of forb species. In a first step, we checked
for possible correlations between FDtrait,,
CWMtrait and number of flowering forb species
(Supplementary information, Appendix 4). Due to multiple correlations,
we included only parameters that were not strongly related to each other
(|r|<0.6): number of flowering forbs,
FDflowering height,, FDcolor,,
FDnectar access,, FDUV reflectance, and
CWM traits that should attract pollinators, CWMcolor
yellow (percentage of yellow flowering species) and
CWMflowering height in our full model. In order to find
the most likely parameter combination of local plant community
attributes that explain pollinator richness and abundance, we applied an
information theoretic approach and compared all possible submodels
derived from the full model with AICc (Burnham and
Anderson 2002). Since several models performed equally well, we
performed model averaging over the best models (delta
AICc <6, Harrison et al. 2018), in order to
get more reliable parameter estimates (Dormann et al. 2018). Local plant
community attributes were not correlated to the landscape predictors at
any scale. All analyses were carried out in R Version 4.1.0 (R Core Team
2021).