Publication details.

Paper

Year:2017
Author(s):A. Traveset, C. Tur, V. Eguíluz
Title:Plant survival and keystone pollinator species in stochastic coextinction models: Role of intrinsic dependence on animal-pollination
Journal:Scientific Reports
ISSN:2045-2322
JCR Impact Factor:4.122
Volume:7
Issue No.:1
Pages:1-10
D.O.I.:10.1038/s41598-017-07037-7
Web:https://dx.doi.org/10.1038/s41598-017-07037-7
Abstract:© 2017 The Author(s). Coextinction models are useful to understand community robustness to species loss and resilience to disturbances. We simulated pollinator extinctions in pollination networks by using a hybrid model that combined a recently developed stochastic coextinction model (SCM) for plant extinctions and a topological model (TCM) for animal extinctions. Our model accounted for variation in interaction strengths and included empirical estimates of plant dependence on pollinators to set seeds. The stochastic nature of such model allowed us determining plant survival to single (and multiple) extinction events, and identifying which pollinators (keystone species) were more likely to trigger secondary extinctions. Consistently across three different pollinator removal sequences, plant robustness was lower than in a pure TCM, and plant survival was more determined by dependence on the mutualism than by interaction strength. As expected, highly connected and dependent plants were the most sensitive to pollinator loss and collapsed faster in extinction cascades. We predict that the relationship between dependence and plant connectivity is crucial to determine network robustness to interaction loss. Finally, we showed that honeybees and several beetles were keystone species in our communities. This information is of great value to foresee consequences of pollinator losses facing current global change and to identify target species for effective conservation.

Related staff

  • Anna Traveset Vilagines
  • Related departments

  • Oceanography and Global Change
  • Related research groups

  • Global Change Research