2.3.1 Network structure
We used foraging frequency to construct the interaction network, and
divided the whole year into four seasons, spring (March, April, and
May), summer (June, July, and August), autumn (September, October, and
November), and winter (December, January, and February), to analyze the
differences in plant-frugivore network characteristics between different
seasons. We characterized the structure of weighted interaction network
using the following nine statistics through the function
“network-level” in the “bipartite” package (R Core Team, 2022): (1)
number of bird species (b); (2) number of plant species (p); (3) network
size (b×p); (4) number of links (n); (5) connectance (C), the proportion
of links that are realized among the pool of all possible links (n/b×p)
(Cruz et al., 2013); (6) nestedness (nestedness), which quantifies the
degree to which species with few interactions are connected to highly
connected species and has been proposed to be associated with network
stability (Ramos-Robles et al., 2016); (7) specialization
(H2´ ),
which quantifies the overall specialization within a network, that is,
whether species in a network tend to separate or share their interaction
partners (Blüthgen et al., 2006); (8) interaction diversity
(H2 ), a Shannon index based measure of diversity
estimated from interaction frequencies, which reflect whether the links
are strong (high interaction frequencies) or weak (low interaction
frequencies) (Zhang et al., 2022); (9) interaction evenness
(E2 ), which depicts heterogeneity in the
distribution of interactions across species in the network, with high
values indicating more even distribution (Sakai et al., 2016).
We used the function “null model” to randomize plant-frugivore
interactions, and compared the differences of structure between the
observed network and null model (1000 iterations). Randomizations can
determine which nodes (species) interact with one another and how strong
the interactions are under a simple null hypothesis and determine
whether interaction frequencies between consumers and resources are a
consequence of the relative abundances of the potential resources
(Vaughan et al., 2018). The null model can reshuffle interactions while
maintaining the observed matrix dimensions and connectance to reduce the
influence of sampling effects on the network interpretation (Pigot et
al., 2016).