Collaborative research: ABI Development: Making Advanced Statistical Tools Accessible for Quantitative Research Synthesis and Discovery in Ecology and Evolutionary Biology

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An award is made to build an easy-to-use program for carrying out and teaching meta-analysis in in the fields of ecology and evolution (E&E), by adapting and developing an existing medical meta-analysis program for the unique needs of the scientific community in these fields. Meta-analysis is the quantitative synthesis of independent studies. It has become an important approach for summarizing research results and generalizing from their findings in E&E, as it has in medicine and the social sciences. There is a compelling need for new statistical software for meta-analysis in E&E employing current, cutting edge methodology. Making these statistical tools widely available is highly likely to propel new scientific advances and insights, as did the first generation of meta-analysis software in E&E. This new software, called OpenMeta for Ecology and Evolution (OpenMEE), will be open-source and cross-platform program, will facilitate implementation of statistical methods appropriate for meta-analysis of ecological and evolutionary data, and will include a user interface specifically designed for the unique challenges and conventions of research synthesis in these fields. Then intuitive graphical user interface (GUI) will seamlessly integrate with analytic routines written in the popular open-source statistical programming language R. Novice users will thus be able to take advantage of such routines to carry out sophisticated meta-analyses without needing to know advanced programming. Furthermore, OpenMEE is designed so that those users who are competent programmers can incorporate new statistical developments in a straightforward manner, allowing OpenMEE to be developed and updated indefinitely by the user community. OpenMEE will be introduced with cutting edge statistical developments for meta-analysis in E&E, including complex meta-regression models and phylogenetic analyses. Putting much more powerful tools for quantitative research synthesis into the hands of scientists in E&E will have dramatic effects on research progress in these fields.The scientists working on this project have a solid record of successfully mentoring high school students, undergraduates, graduate students, and postdoctoral fellows, including those belonging to underrepresented groups, and will continue to work actively to recruit and train people at all of these levels. OpenMEE will be disseminated in workshops and short courses, as well as electronically. In recruiting participants for these courses, the researchers will work to identify and attract graduate students and postdoctoral researchers from underrepresented groups as well as others; they will be able to structure the fees for the courses so as to be able to offer travel fellowships for students lacking support to attend the courses. Finally, underrepresented minority students and others will be recruited for research experience through the NSF‐funded Center for Science and Mathematics Education at Stony Brook University, and by recruiting from the rich resource of the high underrepresented student population at University of South Florida. More information about this project may be found at: http://www.cebm.brown.edu/OpenMEE.