Data analysis
Community structure
We calculated species richness and Shannon diversity index in the forest
sites. A rarefaction-extrapolation technique was used to standardise
species richness based on a constant number of individuals using iNEXT
package in R. We tested the significance of the differences in Shannon
diversity index among the forest sites using permutation tests in PAST
statistical package version 2.17c (Hammer et al., 2001).
Community abundance of lianas was compared among the forest sites by
running nested ANOVA, where sampling area was nested within forest site.
We employed aov function in the stats package in R to perform the nested
ANOVA. Using the equation of Harper et al. (2005, 2015), we calculated
magnitude of edge influence (MEI) on abundance for individual liana
species with abundance ≥ 10 stems. The equation is given as:\(MEI=\frac{e-i}{e+i}\), where e = species abundance in edge site,
and i = species abundance in non-edge site, which was obtained by
finding the average of the values of interior and deep-interior sites.
The values of MEI ranges from −1 (negative edge influence) to +1
(positive edge influence). MEI value of zero indicates no edge
influence. The strength of MEI was determined as follows (Ofosu-Bamfo et
al., 2019): 0 (no edge influence), ≤0.19 (very weak), 0.20–0.39 (weak),
0.40–0.59 (moderate), 0.60–0.79 (strong), 0.80–1.0 (very strong).
Network structure of liana-tree interactions
Liana-tree network structure was quantified using the following network
metrics: nestedness, modularity, degree of specialization (H2’, d’),
connectance, module connectivity and interactions (c and z values),
species co-occurrence. We used quantitative liana-tree species matrices
except in the species co-occurrence test where binary matrices were
employed. Each of matrices was made up of liana species assigned to rows
and tree species assigned to columns. We also represented the various
networks in graphs using plotweb function in the bipartite package in R.
Nestedness
Nestedness occurs when the more specialist species interact only with
subsets of the species interacting with the more generalist species
(Bascompte et al., 2003; Ponisio et al., 2019). This means that
generalists interact with one another, and specialists tend to interact
with generalists, but specialist-specialist interactions are often
absent (Bascompte et al., 2003). We calculated weighted nestedness
metric, WNODF with the networklevel function in bipartite package in R
(Dormann et al., 2020), in accordance with the nestedness equation of
Almeida-Neto and Ulrich (2010). The WNODF metric ranges from 0 (fully
non-nested) to 100 (fully nested). There are two forms of non-nested
pattern described in literature: (1) when nestedness value is consistent
with the null model expectation, and (2) when nestedness value is
significantly less than that of the null model. The aforementioned
patterns of nestedness refer to two different community assembly (random
and non-random assembly, respectively) and therefore must be
distinguished. We therefore used anti-nestedness to refer to the
situation where observed nestedness values were significantly lower than
those expected by chance, while we referred to networks that presented
observed nestedness values which were consistent with null model
expectation as not nested.
Degree of specialisation
The degree of specialisation was determined for the various networks and
the individual species in the networks as follows:
Using the H2’ index, we quantified network specialisation of the various
forest sites. The index measures the extent to which observed
interactions deviate from the interactions that would be expected given
the marginal totals of the interactions per species (Blüthgen et al.,
2006). Generally, higher values of the H2’ index indicate that the
species in the network are more selective, resulting in higher
specialisation of the network. The index ranges from 0 (no
specialisation) to 1 (complete specialisation). The H2’ index was run
with H2fun function in the bipartite package.
The degree of species specialisation was determined by calculating d’
index, using dfun function in the bipartite package. This index is
defined as the deviation from a conformity expected by the overall
utilisation of potential partners (Blüthgen et al., 2007).
Network connectance
Weighted connectance was calculated to express network connnectance in
the study. It is defined as the linkage density divided by number of
species in the network (Dormann et al., 2020; van Altena et al., 2016).
The values of weighted connectance range from 0 (no interaction) to 1
(perfectly connected). Weighted connectance was run with the
networklevel function in the bipartite package.
Modularity
We measured modularity index (Q) with the DIRTLPAwb+ algorithm using
computeModules function within the bipartite package (Beckett, 2016).
Modularity measures the tendency of a network to form modules of
interacting species, which interact more with one another than with
species of other modules (Carstensen et al., 2016; Dormann et al.,
2020). The Q index ranges from 0 for networks with clustering not
different from random to 1 for networks with perfect modules. The Q
index calculation followed the equations in Newman (2006).
Test of statistical significance of the metrics
The above mentioned network metrics were tested for their statistical
significance by generating 1,000 null models and comparing them with the
observed metric values using the Patefield algorithm (Patefield, 1981)
in the bipartite package.
Module connectivity and interactions
The topological roles of liana and tree species with respect to network
modularity was assessed based on the number of links of the species. We
achieved this by calculating the weighted standardised among-module
connectivity (c) and within-module interactions (z), using species
strength of interaction (Watts et al. 2016). To obtain the corresponding
appropriate c and z thresholds for the species, we generated 100 null
models of the original networks using DIRTLPAwb+ algorithm, and 95 %
quantiles as threshold c- and z-values. Based on the c and z values
generated, the species were grouped into four categories of topological
roles (Olesen et al., 2007) indicated below:
- Peripherals: species with lower c- and z-values compared to the
threshold values.
- Network hubs: species with higher c- and z-values compared to the
threshold values.
- Connectors: made up of species with higher c-values and lower z-values
compared to the threshold values.
- Module hubs: made up of species with higher z-values and lower
c-values compared to the threshold values.
Species co-occurrence
Liana species co-occurrence patterns were determined with the
cooc_null_model function from EcoSimR package (Gotelli et al., 2015).
We used the C-score metric, which is the average number of checkerboards
for two species (Stone & Roberts, 1990), to measure species
co-occurrence. The metric was calculated according to the equation
described by Almeida-Neto & Ulrich (2011). To assess the patterns of
co-occurrence, 10,000 null models were generated by the quasiswap
algorithm and compared with the observed c-score values. The c-score
measures the tendency of species to not co-occur (Stone & Roberts,
1990). Thus, the greater the c-score in relation to the null model, the
greater the tendency of the species to not co-occur (i.e., segregation),
and the smaller the c-score value in relation to the null model, the
higher the tendency of species to co-occur (i.e., aggregation).
RESULTS