<html><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">DEPARTMENT OF COMPUTER SCIENCE<div><br class="webkit-block-placeholder"></div><div>UNIVERSITY OF CHICAGO</div><div><br class="webkit-block-placeholder"></div><div>Date: Wednesday, April 16, 2008</div><div>Time: 2:30 p.m.</div><div>Place: Ryerson 251, 1100 E. 58th Street</div><div><br class="webkit-block-placeholder"></div><div>----------------------------------------------------------</div><div><br class="webkit-block-placeholder"></div><div>Speaker:<span class="Apple-tab-span" style="white-space: pre; "> </span>Stefano Allesina</div><div><br class="webkit-block-placeholder"></div><div>From:<span class="Apple-tab-span" style="white-space: pre; "> </span>National Center for Ecological Analysis and Synthesis</div><div><br class="webkit-block-placeholder"></div><div>Web page:<span class="Apple-tab-span" style="white-space: pre; "> </span><span class="Apple-style-span" style="color: rgb(0, 0, 255); "><a href="http://web.mit.edu/jakebeal/www/">www.nceas.ucsb.edu/~allesina</a></span></div><div><br class="webkit-block-placeholder"></div><div>Title: The Spider and The Web: The Structure, Robustness and Dynamics of Ecological Networks</div><div><br class="webkit-block-placeholder"></div><div>Abstract: Karl Popper once said that one can learn more on a spider by looking at its web than on the web by looking at the spider. During the last 30 years, ecologists collected particular types of webs: ecological networks describing communities of interacting species. Despite the efforts, learning about the "spider" behind these networks has been harder then expected. Here I tackle this issue from three different angles, presenting results on the structure, robustness and dynamics of ecological networks.</div><br>A fundamental challenge is that the complex network structures are not readily converted into numbers to perform statistical analyses. A way to approach this problem is through the use of "null" models. The rationale is that if a model is capable to produce "realistic" networks, then it is including in its formulation basic forces that are responsible for the shape of real networks. I show how such models can be evaluated using likelihoods and how this technical advance opens several new lines of research.<br><br>The robustness of ecosystems to species losses is a central question in ecology given the current pace of extinctions and the many species threatened by human impacts, including habitat destruction and climate change. Clearly, network structure is a major determinant of robustness to extinction in food webs. I present tools taken from computer science that can advance our understanding of causes and consequences of extinction.<br><br>Finally, I present results on a connection between the structure of networks and their dynamical properties based on the analysis of network motifs. I show how "local" dynamical properties cascade into "global" attributes such as stability and resilience to changes. This scaling-up of properties has important implications for biological conservation, but also for genetic and metabolic networks.<br><br>In developing my arguments I underline future ecological, mathematical and computational challenges that arise from the study of these issues.<p class="MsoNormal">---------------------------------------------------------</p><p class="MsoNormal">Host:<span class="Apple-tab-span" style="white-space: pre; "> </span>Nina Hinrichs</p></body></html>