Project Summary
Our initiative will contribute to raising quantitative proficiency at the undergraduate level through (1) integrating mathematical approaches and statistics into the freshman biology laboratory course, (2) developing a stand-alone statistics course in the context of biological problems, and (3) enabling faculty to integrate quantitative approaches and statistics into their courses through tune-up workshops and walk-in clinics.

The need for this initiative comes from the tremendous developments in biology, in particular at the molecular level where massive amounts of data are now available to generate questions we could only dream about 20 years ago. This richness of data has fundamentally changed the way we create new knowledge: data acquisition, data analysis, modeling, and experimentation are increasingly integrated, and computational tools are now routinely used. We will witness a similar revolution in the environmental sciences, where the development of new technologies (such as wireless sensors) will allow us to monitor environmental variables continually across spatial and temporal scales. This too will generate massive amounts of data, and the data will often be quite complex and diverse, ranging from numerical data to images, which will require novel ways of analysis. Biology education, especially at the undergraduate level, is lagging far behind in training life scientists in the quantitative skills needed to take advantage of these new opportunities across all levels of biological organization.

Quantitative training of biology undergraduates is currently almost exclusively restricted to a one-year calculus course at the freshman level. To gain competency in using and developing quantitative tools for biology, quantitative training must be integrated at all levels of the education of a biology major. The laboratory component of a freshman biology course provides a great opportunity to integrate development of hypotheses, data collection, data analysis, and, if appropriate, model development. The stand-alone course in statistics for biology majors will allow students to experience statistics in the context of biological data and will go beyond traditional statistics.

While the first two goals will primarily benefit undergraduate majors in biology, the third goal focuses on faculty development to enable them to effectively integrate quantitative aspects into current biology courses. Quantitative training must be incorporated across the curriculum; isolated courses will not provide sufficient training to equip students with the skills they will need. The proposed quantitative training of current faculty is thus a logical step to broaden the base of biology faculty with quantitative skills. By helping current faculty integrate quantitative approaches into their courses, we will more quickly reach the goal of teaching quantitative skills to biology students.

The proposed activities provide career development opportunities for undergraduate and graduate students and postdoctoral researchers through training in teaching and curriculum development. The proposed courses will be disseminated through textbooks and individual learning modules. Learning modules that serve as tune-ups for current faculty will be made available on the Web and will be designed for self-study. Transferability to other institutions will be emphasized throughout.

Research Summary
As a mathematician, I work at the interface of ecology and evolution. Our research addresses effects of spatial structure on community dynamics, in particular, the effect of competition on the spatial structure of competitors and the effect of pathogens or mutualists on the spatial distribution of their hosts. In addition, we are conducting research in population genetics where we have developed statistical tools based on genealogical analysis of gene samples.

One of the questions we have pursued is why so many species coexist in ecological communities. Mathematical models have been used to investigate mechanisms that contribute to coexistence. Many of these models do not take into account the local interactions of organisms that are simply due to spatial proximity. The rigorous analysis of mathematical models that incorporate space explicitly has been a particularly vexing problem because of the mathematical difficulties stemming from spatial correlations that are a hallmark of spatially explicit models in which species interact locally. Our work has demonstrated, for instance, that local interactions can impede coexistence, countering a commonly held belief that space could be a mechanism that would facilitate coexistence since it could be viewed as an additional niche.

Current work is shedding light on the evolution of specialization in spatially explicit host-symbiont systems. This work has confirmed that specialization is facilitated in coarse-grained habitats and has given new insights into the effects of feedback between hosts and their symbionts that can alter the spatial structure and thus significantly influence the evolutionary trajectory of symbionts.

Last updated September 2006