The temporal dimension in individual-based plant pollination networks
Metadatos
Mostrar el registro completo del ítemEditorial
Wiley
Materia
Pollinator turnover Phenology Erysimum mediohispanicum
Fecha
2015Referencia bibliográfica
Oikos 125: 468–479, 2016 doi: 10.1111/oik.02661
Patrocinador
Spanish Ministerio de Economia y Competitividad (CGL2009-07015, CGL2012–34736, CGL2013- 47558-P), including EU-FEDER funds, and Plan Andaluz de Investigación (P11-RNM-7676); Fellowship (BES-2010-030067) from MINECOResumen
The pollination success of animal-pollinated plants depends on the temporal coupling
between flowering schedules and pollinator availability. Within a population, individual
plants exhibiting disparate flowering schedules will be exposed to different pollinators when
the latter exhibit temporal turnover. The temporal overlap between individual plants and
pollinators will result in a turnover of interactions, which can be analyzed through a network
approach. We have explored the temporal dynamics of individual-based plant networks
resulting from pairwise similarities in pollinator composition. During two flowering seasons,
we surveyed the phenology and pollinator fauna of the individual plants from a population of
Erysimum mediohispanicum (Brassicaceae). We analyzed the topology of these networks by
means of their modularity, clustering, and core-periphery structure. These metrics are related
to network functional properties such as cohesion, transitivity and centralization respectively.
Afterwards, we analyzed the influence of each pollinator functional group on network
topology. We found that network topology varied widely over time as a consequence of the
differences in plant phenology and the idiosyncratic and contextual effect of pollinators.
When integrating all temporary networks, the network became cohesive (non modular),
transitive (locally clusterized), and centralized (core-periphery topology). These topologies
could entail important consequences for plant reproduction. Our results highlight the
importance of considering the entire flowering season and the necessity of making
comprehensive temporal sampling when trying to build reliable interaction networks.





