Introduction:
Over the last thirty years, there has been a massive increase in the
number of published studies using species distribution models (SDMs)
(reviewed in Lobo et al. 2010 and Melo-Merino et al. 2020). Species
distribution models are used to identify areas of potentially suitable
habitat by linking species occurrences to environmental variables
(Loyola et al., 2012; Silva et al., 2014). These predictions of suitable
habitats have many applications (Elith & Leathwick 2009), including:
the estimation of potential distribution under different climate change
scenarios (Marshall at al. 2018; Lima et al. 2020), the estimation of
suitable areas for a species (Suzuki-Ohno et al. 2017) and assessing the
potential invasiveness of an exotic species (Srivastava et al. 2019).
A key to understanding distribution patterns in insects lies in
understanding their relationships with other organisms, e.g. pollinators
and their floral resources (Willmer 2011). This biotic information is
rarely included directly in distribution models, despite the fact that
biotic interactions can theoretically improve our understanding and
predictions of the distribution of species through different mechanisms
(Wisz et al. 2013). Previous studies showed an improvement in the
statistical performance of spatial models when including parasitic
(Mathieu‐Bégné et al. 2021), facilitative (Heikkinen et al 2007),
resource-consumer (Kissling et al. 2007; Bateman et al. 2012; Roslin et
al. 2017; Atauchi et al. 2018; Herrera et al. 2018), competitive (Leach
et al 2016; Mpakairi et al. 2017) and plant-pollinator interactions
(Araújo & Luoto 2007; Espíndola & Pliscoff 2019; Kass et al. 2020).
However, the improvements made by including biotic interactions depend
on the spatial scale of the biotic variable in the model (Heikkinen et
al 2007), and correlative relationships observed in models do not always
represent biotic interactions (Giannini et al. 2013).
The biotic variable can be included in the SDM as an explanatory
variable (Araújo & Luoto 2007; Kass et al. 2020) and it can also be
introduced as either the raw distribution (Giannini et al. 2013; Leach
et al. 2016) or a modelled distribution of the species that the modelled
species interacts with (Bateman et al 2012; Giannini et al. 2013)
(hereafter “interacting species”). The biotic variable can also be
introduced as an approximation of the intensity of the interaction, e.g.
the genomic background of parasite hosts can help identify populations
with resistance genes (Mathieu‐Bégné et al. 2021), distance towards
sighting of a competitor for competition (Mpakairi et al. 2017) or the
distribution of diet resources for resource-consumer interactions
(Araújo et al. 2014).
Besides the methodological considerations, spatial resolution may
strongly affect the contribution of biotic interactions to modelled
distribution patterns (Pearson and Dawson, 2003; Soberón & Peterson,
2005; Wisz et al. 2013). Heikkinen et al. (2007) showed that the impacts
of facilitation between owls and woodpeckers are more visible at a
resolution of 10 km than 40 km. This is consistent with Pearson and
Dawson (2003), who hypothesized that at broader scales and coarse
resolutions, climate variables are more dominant and biotic interactions
less apparent (Heikkinen et al. 2007). However, an obligate parasite
with a strong interaction with its host may always be more dependent on
its interacting species’ distribution at any resolution. There is
insufficient evidence as to how the explanatory power of biotic factors
changes with spatial resolution, which is crucial for improving SDMs of
species with strong hypothesized biotic interactions.
Whether the interaction is essential for survival depends on factors
that include body size, dietary breadth, the distribution of the
interacting species and the dependency on one another, e.g. whether the
flowers are similarly dependent on the pollinator as the pollinator on
the plant. The bee body size shows a strong relation with the foraging
distance of different bees (Greenleaf et al. 2007; Kendall et al. 2019
and references therein) and smaller bees with a smaller foraging
distance would require their host plant closer to their nest. It has
been hypothesized that the dietary breadth of the species could
influence the importance of the biotic factor in the models (e.g.
specialist vs generalist; Araújo et al. 2014). In the case of bees, it
has been shown that the population trend of specialist bees is
correlated to the plant that they are dependent on for their pollen
(Scheper et al. 2014). We expect that a smaller distribution range of
the interacting species would have a higher contribution to the models,
as it more likely to be the limiting factor of the modelled species.
Specialist bee species have a tendency to decline more than generalist
bee species and their decline is correlated to their host plant
(Biesmeijer et al. 2006) and this leads us to expect that the specialist
species show a higher contribution of the interacting species to their
models.
In this paper, we aim to use a priori knowledge to investigate
the importance of biotic interactions in species distribution models of
bees at different spatial resolutions. Wild bees are a group of
well-studied organisms that include species with a great importance to
ecosystem resilience and that play a key role in pollination services to
wild plants and crops (Kleijn et al. 2015; Senapathi et al. 2015;
Weekers et al. 2022). Bees depend on pollen and nectar provided by
plants and diets range from narrow (oligolectic bees, using few plant
species) to broad (polylectic bees, using many plant species) (Rasmussen
et al. 2020). Other species, up to 30%, are cleptoparasitic, meaning
they are brood parasites which lay eggs in nests of other bee species
(Cardinal et al. 2010). They may have one or multiple host bee species.
The Netherlands is a good case study for the effects of biotic
interactions on the distribution of wild bees, as there are more than
300 species of wild bees (Reemer 2018) and there is extensive data on
plant-pollinator interactions, hosts of cleptoparasitic bees and
occurrence data. By integrating knowledge of plant visitation and
cleptoparasitic interactions, we aim to (1) assess the importance of
biotic factors in explaining distributions of polylectic, oligolectic
and cleptoparasitic bees and (2) assess the relative importance of
different factors, including flower and host specialization, spatial
resolution, taxonomic level, distribution of the interacting species and
bee body size (which relates to foraging range) in explaining the
contribution of the interacting species to the models. By modelling a
large number of bee species and using different input variables and
methods, we identified important factors that are related to the
implementation of the biotic interactions to the models.