NSF Postdoctoral Fellowship in Biology FY 2010

Edelman Andrew J, Albuquerque NM

Investigators

Abstract

This action funds an NSF Postdoctoral Research Fellowship for FY 2010. The fellowship supports a research and training plan entitled "Understanding Cooperation on Dynamic Social Networks through Actor-Driven Models of Network Evolution" for Andrew J. Edelman. The host institution for this research is the University of Wyoming, and the sponsoring scientist is David McDonald. Cooperation is a common feature of biological systems, occurring at many levels of organization (e.g., genes, cells, individuals, and groups) and a wide variety of species. In particular, humans and many animal species exhibit complex cooperation among unrelated individuals. Understanding the evolutionary processes that favor complex cooperation is a fundamental challenge of modern biology and crucial to promoting peaceful societies. Recent theoretical models have been successful in explaining some forms of cooperation through indirect reciprocity, which is based on the principle "I scratch your back and someone else will scratch mine." Indirect reciprocity occurs when individuals that act altruistically are more likely to receive altruistic acts in the future. This research tests whether indirect reciprocity explains the dynamics of social networks in male long-tailed manakins (Chiroxiphia linearis). Males of this species perform cooperative displays that attract females to lek sites for mating, but only the top-ranked alpha male mates. Participation in displays appears to determine male social status and rise to alpha rank. The research employs graph theory, network analysis, and statistical modeling to test indirect reciprocity models on a long-term dataset of manakin social dynamics. Specifically, actor-driven network models developed by sociologists are being implemented to determine whether indirect reciprocity explains cooperation among male manakins. Training will include: 1) social network analysis, 2) statistical modeling of dynamic networks, 3) long-term data analysis, and 4) student mentoring. Broader impacts of this research will include: 1) mentoring students, including those from underrepresented groups, in bioinformatics skills, 2) disseminating research to a broad audience through public-outreach publications and presentations, 3) providing empirical-based feedback regarding the applicability of leading-edge theory to natural systems, 4) novel applications of bioinformatics to animal social networks, and 5) understanding the conditions that can foster cooperation and discourage aggression.