Ecologists and microbiologists are increasingly concerned about changes in diversity patterns of species and microorganism communities, and how these patterns influence ecosystem functioning and stability. However, scientists may not be aware of statistical and visual analysis techniques in other fields, such as organizational management, that may help improve their own understanding. Reciprocally, understanding concerns and analysis techniques of diversity in ecosystems may widen the perspectives of researchers and human resources (HR) managers who study diversity in human organizations.
What do analyses of species diversity, microbial diversity, and workgroup diversity have in common? Which common analytical tasks and visualization techniques are particularly useful in exploring diversity data?
This interdisciplinary work, led by a group of ecologists, microbiologists, and visualization researchers at Oregon State University (Pham and colleagues), aims to address those questions and presents the first cross-disciplinary synthesis study targeting visual analysis of diversity . Built upon the lessons from designing diversity visualizations in previous work [2, 3, 4, 5], the study abstracts diversity concerns across the analyses of species diversity (ecology), microbial diversity (microbiology), and workgroup diversity (organizational management) in an alignment framework. It also offers an operationalization of these concerns in terms of data behaviors, common analytical tasks (see image below), and data visualization best practice. Feedback from scientists validated and refined the alignment framework across the three examined areas. The results from this study aim to provide scientists, visualization researchers, and designers with common vocabulary and design abstractions for designing, discussing, and evaluating different visual-analysis tools targeting diversity analysis. DCO scientists, especially those who study deep life, may find the study results useful for cross-comparing microbial diversity and macrobiotic species diversity studies in terms of analysis approach, characteristics of diversity data, information needs, and commonly used visualization techniques.
Images (courtesy of Tuan Pham): Top: Diversity and abundance patterns of common moths visualized using multiple histogram representation by EcoDATE tool . The structure of the moth data set is described in . The interactive version of the visualization is available at http://purl.oclc.org/ecodate/commonmoth.
Bottom: Proposed classification of analytical tasks for exploration of diversity data organized at three levels of abstractions: (1) Generic Tasks, (2) Data-centric Queries, and (3) Low-level Analytical Operations