  Understanding the architectural subtleties of ecological networks, believed
to confer them enhanced stability and robustness, is a subject of outmost
relevance. Mutualistic interactions have been profusely studied and their
corresponding bipartite networks, such as plant-pollinator networks, have been
reported to exhibit a characteristic "nested" structure. Assessing the
importance of any given species in mutualistic networks is a key task when
evaluating extinction risks and possible cascade effects. Inspired in a
recently introduced algorithm --similar in spirit to Google's PageRank but with
a built-in non-linearity-- here we propose a method which --by exploiting their
nested architecture-- allows us to derive a sound ranking of species importance
in mutualistic networks. This method clearly outperforms other existing ranking
schemes and can become very useful for ecosystem management and biodiversity
preservation, where decisions on what aspects of ecosystems to explicitly
protect need to be made.
