Combining critical transition indicator to compare the stable structure of epiphyte–host networks

Abstract

Networks have been widely used to describe a range of biotic interactions in ecosystems; topological indicators of ecological networks provide systematic way to characterize patterns of different interacting systems. However, it is hampered by sampling bias and detecting the true structures to compare the stability of ecological communities. In this context, we aimed to employ innovative approach of identifying critical transition indicator to compare stable network structure by simulating dynamics of network assembly. Four epiphyte–host networks were compiled, including two groups (epiphytic bryophyte and epiphytic vascular plant) of tropical rainforest (TRF) and subtropical forest (STF). The dynamics of three structure indictors, i.e. connectance, nestedness and modularity were simulated to explore what changes occurred as sampling effort increases. The critical transition indicator was detected to compare the structure of four networks at a stable state. Meanwhile, the variation dynamics of host size and epiphyte richness were fitted to analyze the underlying processes shaping the stable network structure. Using a dynamic simulation, the network structures showed the critical transition to stable state when sampling size approximated 30 hosts for epiphytic bryophytes and 100 hosts for epiphytic vascular plants. After critical transition, connectance and nestedness were higher in the STF than in the TRF. In contrast, modularity was higher in the TRF than in the STF. The coefficient of variations (CV) of host size and epiphyte richness tended to be stable at the same critical transition point; the distribution of host size and epiphyte richness could be the fundamental factors for the assembly of stable epiphyte–host network. Our study emphasized the importance of considering sampling effort while evaluating the network structure. Combining critical transition indicator with structure indicator dynamics could be a powerful method to compare the stable structure of different ecological networks.