Data from: Temporal scale-dependence of plant-pollinator networks

The study of mutualistic interaction networks has led to valuable insights
into ecological and evolutionary processes. However, our understanding of
network structure may depend upon the temporal scale at which we sample
and analyze network data. To date, we lack a comprehensive assessment of
the temporal scale-dependence of network structure across a wide range of
temporal scales and geographic locations. If network structure is
temporally scale-dependent, networks constructed over different temporal
scales may provide very different perspectives on the structure and
composition of species interactions. Furthermore, it remains unclear how
various factors—including species richness, species turnover, link
rewiring, and sampling effort—act in concert to shape network structure
across different temporal scales. To address these issues, we used a large
database of temporally-resolved plant-pollinator networks to investigate
how temporal aggregation from the scale of one day to multiple years
influences network structure. In addition, we used structural equation
modeling to explore the direct and indirect effects of temporal scale,
species richness, species turnover, link rewiring, and sampling effort on
network structural properties. We find that plant-pollinator network
structure is strongly temporally-scale dependent. This general pattern
arises because the temporal scale determines the degree to which temporal
dynamics (i.e. phenological turnover of species and links) are included in
the network, in addition to how much sampling effort is put into
constructing the network. Ultimately, the temporal scale-dependence of our
plant-pollinator networks appears to be mostly driven by species richness,
which increases with sampling effort, and species turnover, which
increases with temporal extent. In other words, after accounting for
variation in species richness, network structure is increasingly shaped by
its underlying temporal dynamics. Our results suggest that considering
multiple temporal scales may be necessary to fully appreciate the causes
and consequences of interaction network structure.