Statistical analysis
To test whether in each case the network metrics deviated from the
expected values and if there were differences between habitats and
between seasons within each habitat, we used the swap algorithm
(Dormann, Fründ, Blüthgen, & Gruber, 2009). The swap algorithm
initially randomizes the network matrix using the Patefield algorithm
(Patefield, 1981), then swaps the interactions while constraining for
connectance. Thus, it produces network matrices with the same
connectance and marginal totals as the original matrix, but produces
networks that are more specialized than other algorithms for
randomization as some swaps are more likely than others and increases
the values of high-value cells (Artzy-Randrup & Stone, 2005; Dormann,
Gruber, & Fründ, 2008). We followed Gotelli & Ulrich (2011) and choose
swap web to randomize the network matrices because it is a more
constrained null model, which are better to avoid type I error. More
constrained null models are more parsimonious and conservative in
testing the hypothesis when the information comes only from the
occurrence matrix (Gotelli & Ulrich, 2012). In order to determine the
sampling completeness of our networks and the proportion of the total
arthropod species richness present in bat diets that have been sampled,
we used the Chao 1 index according to the method proposed by Macgregor,
Evans, & Pocock (2017) for the networks, and individual based
rarefaction curves for the estimation of each bat species diet.
We generated 1,000 random matrices with the same total marginal sum and
connectance as the observed networks, and we used the Monte Carlo
procedure (α= 5%) to check if the observed network metric value was
higher or lower than expected by chance. To assess whether network
structure differed between between seasons within each forest, and also
between forest types across the whole year, for each comparison we
calculated the difference in the observed metric values, and compared
this to a null distribution of 1,000 differences obtained by Monte Carlo
procedure. Due to the high calculation intensity of modularity QuanBiMo,
we generated only 100 random matrices using the swap algorithm to
calculate its significance.
In order to better understand the effects of habitat and seasonality on
the composition and interactions of the network during the ENSO event,
we used the R package betalink (Poisot, Canard, Mouillot et al. 2012)
and calculated the dissimilarity of interaction matrices between
habitats and between seasons within each habitat. The values for network
dissimilarities were calculated based on the dissimilarity in the
species composition of communities in the networks (βS),
based on the differences in the interactions observed between species
common to both networks (βOS), based only on differences
in the interactions between both networks (βWN) and
based on the dissimilarity of the interaction structure that was induced
by the dissimilarity in species composition (βST)
(Poisot et al. 2012). In order to determine the sampling completeness of
our networks and the proportion of the total plant species richness
present in bat diets that have been sampled, we used the Chao 1 index
according to the method proposed by Macgregor et al. (2017) for the
networks, and individual based rarefaction curves for the estimation of
each bat species diet. All statistical analysis and network drawings
were performed using R, version 3.3.2 (R Development Core Team, 2017).