New model identifies critical species in food webs and social networks
6 November 2013
A common scientific question to identify critical species in different ecosystems has been looked at by an international research team who have developed mathematical tools that can estimate which species are most influential in a food web.
Image by Catherine Trigg
The researchers from the University of Bristol, the Max Planck Institute for Physics of Complex Systems and the US Geological Survey have taken a new modeling approach to the question. The team, using the new mathematical tools, found that long-lived, generalist top predators — such as otters— play the most influential roles within a food web. The findings are published today [Wednesday 6 November] in Proceedings of the Royal Society B.
Helge Aufderheide of the Max Planck Institute and University of Bristol, who led the research, said: “The interactions in an ecosystem are so complex that one can often only guess about the roles that each species plays. Therefore, knowing how to find the key players makes all the difference for understanding where to focus studies.”
Long-lived, generalist top predators can highly influence ecosystems because they feed on different types of prey that occupy different parts of the food web. For example, otters feed on a wide variety of aquatic prey and can influence multiple species throughout the course of their relatively long lifespan. Removing otters from the ecosystem would cause long-term disruptions to all those species, a theory that the new models can now confirm for other species and ecosystems.
The interactions in an ecosystem are so complex that one can often only guess about the roles that each species plays.
Kevin Lafferty, an author of the paper from USGS, said: “As a biologist who studies food webs, I'm hopeful that we can use this approach to help focus our field work.”
The new approach has non-ecological applications as well. Even though the research team applied the computational tools on food webs, their approach also can be applied to other types of complex systems —from electricity grids to online social networks — to identify influential components.
Paper: Predicting community responses in the face of imperfect knowledge and network complexity, Helge Aufderheide (1,2), Lars Rudolf (2), Thilo Gross(2), and Kevin D. Lafferty (3), Proceedings of the Royal Society B, 6 November 2013.
1. Max-Planck-Institute for the physics of complex systems, Dresden, Germany
2. University of Bristol, Department of Engineering Mathematics, Merchant Venturers School of Engineering, Bristol, UK
3. US Geological Survey, Western Ecological Research Center, Marine Science Institute, University of California, Santa Barbara