The paper titled “Diversity and Complexity of Ecosystems: Exploring Balance and Imbalance in Nature” by Prof. Ilmi Yoon (CS Department) and Neo Martinez (Biology Department) has been chosen as the 2nd place winner in the NetSci Visualizing Network Dynamics Competition at NetSci Conference http://www.nd.edu/~netsci/conference.html.
Description: This work briefly (~4 minutes) introduces the fundamental ecological concept of a food web while illustrating our latest understanding of the structure and dynamics of these complex networks. The structural data visualized include a food web created by the "niche model" (Williams and Martinez 2000). Dynamic time series data are created by a nonlinear, high dimensional, bioenergic model explored in many of my lab's recent publications ( e.g., Williams and Martinez 2004). Visual techniques include 3D Java Open GL program-fed data from highly optimized numerical Java code that efficiently and simultaneously solves up to hundreds of linked ordinary differential equations. The main program used in this visualization employs some of the latest computer science technologies including semantic web technologies for distributed knowledgebases of both empirical and simulated data describing unlimited numbers of species and food webs. The main insights gained from this visualization are an enhanced understanding of food webs as complex dynamic networks along with the fundamental and more rigorous notion of the interdependence of all life including humans.
Scientific Value: The scientific value of this work is both for the introduction of fundamental network concepts in ecology and also as a powerful research tool for computional ecology. We use these visualizations to develop optimized algorithms for arranging network nodes in 3D and 2D so that visual crossing of links is minimized and compelling open source platform is provided for ourselves and others to develop more and improved functionality of the software. Our current research uses the software to simulate the structure and dynamics of thousands of networks that are the base of much of the research being published by our lab. As interest continues to grow in ecological network structure and high dimensional nonlinear dynamics, we will provide our field improved interfaces and knowledge bases so that ecologists without programming skills can effectively conduct sophisticated research in computational ecology.
Educational Value: Beyond the research described above, the educational value of these network visualizations includes providing viewers with a much more compelling and rigorous concept of food-web networks than was available before our visualizations. Simply looking at empirical networks have enabled viewers to accurately guess that the average path length for these networks is about two. However, before 2002 when we published that statistical finding, such short path lengths were an important surprise to researchers. While introductory students gain and retain a clearer concept of food webs, researchers such as ourselves have used these visualizations to detect subtle differences between food-web models and data which directs additional research. For example, they illustrated that the best models including our own underestimate the number of herbivorous species in food webs, something we did not see statistically until compelled so by pronounced discrepancies between network visualization of the models and those of empirical data.