Data from: A new tool to improve the estimates of interaction rewiring considering the whole community composition

Understanding temporal dynamics in ecological networks is crucial to
predicting their capability to cope with global changes. Despite this,
proper quantification of network dynamics still remains a challenge.
Temporal dynamics are typically studied using data of interaction networks
over time, through the evaluation of interaction turnover and its two
components: changes related to species turnover (species gains and losses)
or rewiring (switching partners among the set of species shared over
time). However, with this approach based exclusively on network data,
dynamics are computed similarly for species that are truly missing from
the community at a given temporal period, and for species occurring in the
community but that do not interact with any other. This might lead to an
underestimation of the real extent of rewiring, while overestimating the
species turnover component of interaction turnover. We used data on 20
plant-pollinator communities to calculate interaction turnover components
accounting also for the species that occurred in the communities at
different temporal periods but did not appear in some of the temporal
interaction networks (non-interacting species), and then compared these
estimates with conventional ones. Besides, we used empirical data and
simulations to evaluate the extent to which dynamics estimates were
affected by sampling effort when including and excluding non-interacting
species. As expected, disregarding the non-interacting species that occur
in the communities at different temporal periods leads to the
underestimation of rewiring and the overestimation of species turnover as
components of interaction turnover. Effect size was moderate when
independent pollinator data were included, and large when including plants
or both trophic levels. Simulations indicated that, in general,
considering the non-interacting species reduced biases at the time of
identifying changes due to the different interaction turnover components.
Accounting for non-interacting species was particularly important to
reduce bias when sampling effort was low and when dynamics were calculated
seasonally. Despite sampling effort effects, phenology was the main
determinant of species’ rewiring frequencies. Our approach contributes to
reducing biases and improving the estimates of interaction flexibility in
networks, which are necessary to comprehend the response of communities in
the face of global change.