Abstract
Mutualistic networks are highly dynamic, characterized by high temporal turnover of species and interactions. Yet, we have a limited understanding of how the internal structure of these networks and the roles species play in them vary through time. We used 6years of observation data and a novel statistical method (dynamic stochastic block models) to assess how network structure and species' structural position within the network change throughout subseasons of the flowering season and across years in a quantitative plant-pollinator network from a dryland ecosystem in Argentina. Our analyses revealed a core-periphery structure persistent through subseasons and years. Yet, species structural position as core or peripheral was highly dynamic: virtually all species that were at the core in some subseasons were also peripheral in other subseasons, while many other species always remained peripheral. Our results illuminate our understanding of the dynamics of mutualistic networks and have important implications for ecosystem management and conservation.
Highlights
Studies of plant–animal mutualisms have historically focused on the interactions between one or a few plant species and their animal mutualists (Boucher, James, & Keeler, 1982; Faegri & van der Pjil, 1979)
As discussed above, plant–animal mutualistic networks exhibit widespread asymmetric specialization, harbouring many specialized species that interact with a core of highly generalized species, we hypothesized that a core–periphery structure would be pervasive over time; we expected seasonally resolved networks in our study system to be characterized by a persistent core–periphery structure
By applying the dynSBM algorithm, we found that the Villavicencio plant–pollinator network is organized as a core–periphery structure (Figure 2)
Summary
Studies of plant–animal mutualisms have historically focused on the interactions between one or a few plant species and their animal mutualists (Boucher, James, & Keeler, 1982; Faegri & van der Pjil, 1979). As discussed above, plant–animal mutualistic networks exhibit widespread asymmetric specialization, harbouring many specialized species that interact with a core of highly generalized species, we hypothesized that a core–periphery structure would be pervasive over time; we expected seasonally resolved networks in our study system to be characterized by a persistent core–periphery structure. Given that previous studies had found interactions at the network core to be more persistent over time (Chacoff et al, 2018; Fang & Huang, 2016), we hypothesized that the subseasonal networks would be characterized by the consistent presence of certain species at the network core which would form the ‘persistent backbone’ of the network This analysis allowed us to provide a comprehensive picture of the temporal dynamics of the internal structure of this mutualistic network. Because jointly analysing a series of networks is methodologically challenging (Miele, Matias, Robin, & Dray, 2019), we rely on a recent statistical framework dedicated to this kind of data (dynamic stochastic block models, hereafter dynSBM; Matias & Miele, 2017) to quantify the temporal switching of the structural position of plants and pollinators
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