Figure captions:
Figure 1: Schematic overview of the modelling workflow. The
elements are represented in boxes and consist of models, information
from databases or variables (brown), variables, indices, evaluation
measures or experimental conditions (e.g. no data is used or the
resolution of the used variables). The arrows indicate the information
flow. The evaluation measures are: (i) the corrected Akaike information
criteria (AICc; Burnham & Anderson 2002), (ii) the continuous Boyce
index (Boyce index; Hirzel et al. 2006) and (iii) area under the curve
of the receiver operating characteristics curve (AUC) applied to species
distribution models (Elith et al. 2006).
Figure 2: The effect of the resolution and taxonomic level on
the contribution of the biotic variable to the model, expressed as the
ranking of the biotic variable contribution per species (from high to
low: 1-5; 2A) and the difference in variable contribution between the
species that the modelled species interacts with (interacting species)
added at species and genus taxonomic level per species (2B). The arrows
indicate the direction, where the variable contribution is the highest
for the respective taxonomic level. The resolution is the scale in
longitudinal and latitudinal direction at which the interacting species
is observed. The gray area is the standard deviation.
Figure 3: The differences between models including host plant
or parasitic host interactions and models with only land use, climate
and soil variables. Evaluation measurements include Area Under the
receiver operating characteristic Curve (AUC) value of the evaluation
dataset (figure 3A), Continuous Boyce Index (CBI) of the evaluation
dataset (figure 3B), Aikake Information Criteria for small sample sizes
for both the evaluation and calibration data (AICc; Burnham & Anderson
2002; figure 3C) and AICc of the evaluation data only (figure 3D). Host
plants and hosts of parasites were either included at the species or
genus level. The difference in evaluation metrics for models with and
without biotic factors, or difference from zero, is tested for
significance with a One-Sample Wilcoxon Signed Rank Test
(p<0.05*; p<0.01**, p<0.001***). For the
AICc both the calibration and evaluation dataset were included, because
66 modelled bee species did not have enough evaluation datapoints to
calculate the AICc.
Figure 4: The different boxplots represent the summed
contribution of the five climate variables, the sixteen land use
variables, the eight soil variables and the single biotic variable,
averaged over the modelled species in the functional groups. The biotic
variable is averaged over the species and genus taxonomic level of the
visited plant or host bee. The different letters above the boxplots
indicate significant differences between variable groups within the
functional trait groups (p<0.05).
Figure 5: The comparison of the biotic interaction models to
models with random interactions, described as null models, with plants
(for the oligolectic and polylectic bees) or bees (for the
cleptoparasitic bees). Figure 4A shows the distribution of the
performance of the biotic interaction models, expressed as the rank of
the evaluation AUC among all interaction models divided by the total
number of models. The y-axis represents the total number of modelled
species that fall within the performance threshold on the x-axis. For
example, the performance in evaluation AUC of the known interaction was
compared to the other 306 plant species and ranked based on the
position. If the known interaction was the third best performing model,
the modelled species would have the value of 0.98% (the percentage rank
would be 3/307 * 100 = 0.98%) and fall within 0-2.5% best performing
models. The two lines indicate the threshold of 5% and 25% best
performing models. Figure 4B summarizes the results, comparing the
percentage of modelled species that fall within the 5% best performing
ranks, indicating a significant difference from the null models with p
< 0.05 (5% best performing models), and 25% best performing
ranks. Although the percentage of models that fall within the 5% best
performing models is higher for the oligolectic bees and cleptoparasitic
bees, the polylectic bees show a high percentage of performance within
the 25% best performing models, showing a more general preference of
biotic interactions. The number of random interactions for every null
model are 306 interactions for the flower visiting bees with the
interacting species at species level, 160 interactions for the flower
visiting bees with the interacting species at genus level, 99
interactions for the cleptoparasitic bees with the interacting species
at species level and 100 or 15 interactions for the cleptoparasitic bees
with the interacting species at genus level (see Appendix S3 in
supporting information).
Figure 6: The results of the Generalized Linear Models (GLMs)
show the relation between flower specialization (Shannon-Wiener index of
number of plants genera interacted with) and the contribution of the
biotic variable to the models of the oligolectic and polylectic bees
(Shannon 1948; figure 6A). Figure 6B shows effect of distribution of the
most visited genus on the contribution of the biotic variable to the
model. Figure 6C shows the relation between the distribution of the host
bees and the contribution of the biotic variable to the models of the
cleptoparasitic bees.