| Grant number: | 14/20572-1 |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| Start date: | January 14, 2015 |
| End date: | January 13, 2016 |
| Field of knowledge: | Biological Sciences - Ecology - Theoretical Ecology |
| Principal Investigator: | Paulo Roberto Guimarães Junior |
| Grantee: | Marília Palumbo Gaiarsa |
| Supervisor: | Jason M. Tylianakis |
| Host Institution: | Instituto de Biociências (IB). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
| Institution abroad: | University of Canterbury (UC), New Zealand |
| Associated to the scholarship: | 13/13319-5 - Ecological and evolutionary cascades in mutualistic networks, BP.DR |
Abstract Given the current Biodiversity crises and the critical importance of many species for ecosystems functioning and provisioning of services on which we depend, it is important to predict which species are most likely to be affected by perturbations, like species extinction and changes in species abundance. Species in ecological communities are linked through ecological interactions, and perturbations in one species can cascade throughout the entire system. This work will explore how the combined effects of network topology and biological attributes influence cascading effects in mutualistic networks. More specifically, we will investigate (1) how species differ in their likelihood of being affected by a cascading effect and (2) how network topology and patterns of interactions of individual species affect the amount of time needed for a perturbation to cascade throughout the network. We expect that a perturbation will rapidly spread in networks in which there are species that interact with several species and that strongly rely upon these interactions to survive. Also, we hypothesize that species that interact with a great number of other species will both be affected by, and propagate, the cascading effects more rapidly than species that interact with a small number of other species. We will also explore how the dynamics of cascading effects change after the inclusion of biological attributes known to affect species interactions, like body size and species abundances for example. To explore this question we will combine analysis of network structure with information theory and natural history data. (AU) | |
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