Tuesday, August 5, 2008

PS 33-168: Eigenvector analysis of connectivity in food webs

Jonathan L. Bowers1, Albert J. Meier1, and Stuart R. Borrett2. (1) Western Kentucky University, (2) Univeristy of North Carolina Wilmington

Background/Question/Methods

Food webs and matrices are important tools assisting in the understanding of feeding relationships and ecology.  One way of presenting the direct relationships between predators and prey is with an adjacency matrix, a binary matrix which utilizes with direct links shown as one’s and no direct link between nodes as zero’s.  Species alterations were performed on a variety of published food webs ranging from pine forests in the United States to tussock grasslands in New Zealand.  This produced a set of food webs varying in number of distinguishable taxa present, functional diversity; and from various climates and habitats.  By diversifying habitats and taxa, results are then not specific to a given system.  Predators and prey were chosen from observed food webs by using those which best fit linkage density for the given system and altered into universal predators and universal prey.  Identification of standardized eigenvectors reporting the spatial distribution of energy throughflow potential were obtained through the use of R programming language.  In addition, further efforts have created a function capable of modifying matrices and extracting their eigenvectors to report this throughflow potential. 

Results/Conclusions

By artificially creating a universal predator species as well as a universally consumed prey species the number of indirect paths of length n as well as indirect relationships throughout the systems increased.  When a universal prey species was created, however, a greater number of nodes experienced indirect links and a greater number of total paths were observed. Creation of a universal predator also increased paths but this effect was more localized to top predators than was observed with the creation of a universal prey item. The role and ecological interpretation of eigen analysis in the broader Network Environ Analysis may contribute more to the role of system structure and connectivity to the stability of ecosystems.