Evaluating the structure of commensalistic epiphyte–phorophyte networks. A comparative perspective of biotic interactions

Dataset

To compare epiphyte–phorophyte networks with
mutualistic networks we compiled a dataset of 122 interaction networks (See Table S1). We restricted data to tropical
and subtropical areas to minimize potential biases in topological metrics due
to heterogeneous environmental conditions. Twelve published datasets that
measured quantitatively interactions between vascular epiphyte and host species
in tropical and subtropical areas were included in the study. Epiphyte species
vary across studies, but they are distributed mainly among the families
Orchidaceae and Bromeliaceae (Table S1). The dataset also included 86 pollination,
13 seed dispersal and 11 ant-mymercophyte (plant-ant) networks from published
studies considering only studies carried out in tropical and subtropical areas,
available in Web of life (http://www.web-of-life.es) and additional works
(Table S1). The main characteristics of the analysed networks are described in
the electronic supplementary material, Table S1; detailed description of datasets
and field sampling procedures can be found in the attached references.

Data
analysis

For each of the 122 networks, data
were completely reanalysed to compute the following network metrics: i) nestedness, ii) complementary specialisation and iii) modularity. Nestedness quantifies the degree to which species
with few interactions are connected to highly connected species. Nestedness was evaluated with the NODF index.
As NODF metric is dependent on network size and sampling intensity. To characterize, network specialization at the community level, we
used the index of complementary specialisation H’2, which quantifies the degree of niche divergence of elements
within an entire bipartite network, that is, whether species in a network tend
to partition or share their interaction partners. Modularity was estimated using the QuaBiMo algorithm (Q), which is based on a hierarchical random graph approach, adapted
for quantitative bipartite networks. As the
algorithm is a stochastic process, results may vary among computations. For
each network, we therefore ran the QuaBiMo algorithm 10 times and
retained the optimal modular configuration, i.e. the iteration with
highest Q value. To characterize
specialization at the species level, we quantified the roles of species within
networks with two species‐level metrics: complementary specialization (d’) and between‐module connector values
(c‐values).