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:
  1. Peripherals: species with lower c- and z-values compared to the threshold values.
  2. Network hubs: species with higher c- and z-values compared to the threshold values.
  3. Connectors: made up of species with higher c-values and lower z-values compared to the threshold values.
  4. 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