Core-periphery dynamics in a plant-pollinator network

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 6 years 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.

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 6 years 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.