Numerous are the challenges of visualization and analysis of biological taxonomies. First, taxonomies are large and will become larger. It is estimated that only 20 percent of the planet's species have been identified. Secondly, taxonomies are dynamic. Research in the taxonomy field constantly leads to new discoveries and corrections. For example, a taxonomic review can reveal that what was considered for many years as one species, actually corresponds to two or more species. Conversely, two apparently different species may actually be the same.  All these situations generate changes at both topological and nomenclature levels of the taxonomy. Third, taxonomy information is scattered in journals, books, and private databases of taxonomists and organizations around the world; this makes the conciliation of information particularly complex. Experts might also have different opinions on how to classify species. International initiatives have as a main goal the standardization and integration of worldwide taxonomic databases that come from multiple sources of information. On the other hand, a common understanding of taxonomy information is fundamental to document biodiversity, seek conciliation, and support conservation. We are working in the development of an information visualization software tool for the comparison of biological taxonomies. This tool is expected to be useful to biodiversity scientists to support the curation of taxonomic databases. The comparison involves the automatic identification of changes such as splits and merges between the taxonomies. During our research, we identified ten users´ visualization tasks: 1. Identify congruence, 2. Identify corrections (splits, merges, moves and naming corrections), 3. Identify additions, 4. Overview changes (obtain an overview of different types of changes), 5. Summarize (obtain a numerical understanding of change), 6. Find inconsistencies, 7. Filter, 8. Retrieve details, 9. Focus, and 10. Edit. The challenge is to identify and visualize differences and similarities as well as to visualize a number of discovered conditions simultaneously in a limited screen space.  In our talk, we will discuss the characteristics of the tool as well as how these tasks could be accomplished through different information visualization techniques such as edge drawing, coloring, animation, matrix representations, and agglomeration. We expect to obtain feedback on data visualization alternatives to effectively convey information for taxonomic work.
