Migration Within and Out of Africa: Identifying Knowledge Gaps by Datamining the Global Register of Migratory Species

Abstract

Knowledge gaps for birds migrating within Africa, or visiting the continent either for breeding or wintering, have been identified systematically by datamining the Global Register of Migratory Species (GROMS). The Register contains a migratory vertebrate species reference list, GIS maps and a literature database with full-text passages on migration. Using the GROMS literature and accessory tables for data mining, knowledge gaps with respect to migration routes and seasonal timing were diagnosed for more than 150 bird species. This analysis was based on a search for text strings such as “migration unknown”, “poorly known”, “surprisingly little known”, etc. The species list of lesser-known migrants generated by data-mining has been published on the GROMS website (www.groms.de), and provides a starting point for future research. The underlying complex query uses Standard Query Language (SQL), requiring a fully documented relational database. Such documentation is often lacking for current biodiversity information systems, which limits their use for complex data mining.