Plant–pollinator metanetworks in fragmented landscapes: a simulation study

Abstract

BackgroundThe topology of the plant–pollinator network can be explained by the species’ abundance and their random interactions. Plant–pollinator networks can be studied in the context of a landscape, because each patch can accommodate a certain local network. Local populations of pollinators in the landscape can be connected through migration and then constitute a metanetwork that is known as a combination of spatial and ecological networks. In this regard, habitat fragmentation can affect the topology of plant–pollinator metanetworks through changes in the species abundance and limiting their interactions. However, it is not clear what pattern (fragmented or aggregated) of the landscape structure can accommodate networks with a higher degree of specialization.Methodswe created simulated landscapes with different forest proportions scenarios (from 5% to 50% of the total landscape) and degrees of fragmentation. Then, for each landscape, we limited the proportion of pollinators to the forest patch. We assumed that plants and pollinators are randomly distributed around the landscape and interact randomly. We used landscape metrics to measure different aspects of landscape structure and bipartite metrics for calculating the degree of specialization in plant–pollinator networks.ResultsThe statistical relationship between bipartite and landscape metrics showed that the relationship between the topology of plant–pollinator networks and the landscape structure is affected by the forest amount in the landscape and the degree of forest fragmentation. We also found that according to the nestedness and H2 (a measure of specialization) metrics, fragmented landscapes contain more general plant–pollinator networks.ConclusionsOur findings suggest that fragmented landscapes, characterized by scattered forest patches, can promote higher levels of interaction between limited pollinators and diverse flowers, leading to more general plant–pollinator networks.