Googling Food Webs: Can an Eigenvector Measure Species’ Importance for Coextinctions?
Predicting the consequences of species’ extinction is a crucial problem in ecology. Species are not isolated, but connected to each others in tangled networks of relationships known as food webs. In this work we want to determine which species are critical as they support many other species. The fact that species are not independent, however, makes the problem difficult to solve. Moreover, the number of possible “importance’” rankings for species is too high to allow a solution by enumeration. Here we take a “reverse engineering” approach: we study how we can make biodiversity collapse in the most efficient way in order to investigate which species cause the most damage if removed. We show that adapting the algorithm Google uses for ranking web pages always solves this seemingly intractable problem, finding the most efficient route to collapse. The algorithm works in this sense better than all the others previously proposed and lays the foundation for a complete analysis of extinction risk in ecosystems.
Stefano Allesina and Mercedes Pascual
PLoS Computational Biology Vol 5, Issue 9, e1000494
The authors have developed an algorithm based on the one Google uses to rank web-pages to order species in a network in terms of their importance for coextinctions. Their algorithm outperformed other measures of robustness to species loss. When examining 12 published food webs their results suggest that the position of a species in the food web is an important determinate of impact on extinction cascades.
Compartments in a marine food web associated with phylogeny, body mass, and habitat structure
Ecology Letters (2009) 12(8), 779-788
Enrico L. Rezende, Eva M. Albert, Miguel A. Fortuna, Jordi Bascompte
doi: 10.1111/j.1461-0248.2009.01327.x
Rezende et al. examined network structure in a marine food web (containing 3313 interactions between 249 species/trophic groups!) and unequivocally showed the presence of compartments (or subunits) in this network. These are link-dense regions of the network where species interact more closely with other species within the module than between modules. Modules may may be important for the propagation of disturbance impacts throughout a network. More importantly Rezende et al. identified some potential mechanisms behind this interesting network structure (body size, phylogeny and spatial structure). Shark species played an important role in this network.