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Mapping Biodiversity Data: Challenges and Opportunities

Explore the complexities of biodiversity data, including geographical, species-related, and historical aspects. Learn how different classifications play a role in understanding ecosystems and adapt to the evolving nature of biodiversity. Discover the need for digitization and the importance of comprehensive data collection for research and conservation efforts.

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Mapping Biodiversity Data: Challenges and Opportunities

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  1. Workshop on Biodiversity and Ecosystem Informatics • Sponsored by NSF, NASA, USGS • June 22-23, 2001 • http://bdi.cse.ogi.edu

  2. What is Biodiversity Data Like?

  3. Geo-Referenced • Most of it is connected to a place • But can be imprecise: North America

  4. Species-Referenced Most data also connected with species • Genetic: by species & subspecies • Invasions and Extinctions • Ecosystems: number and distribution of species Incomplete, 2 for 1, 1 for 2 Like cartography in the 15th century • Unmapped areas • Disagreement on Names • Agree on names, disagree on place Seems like earth is changing, but mostly it’s our knowledge of it

  5. Other Classifications • Vegetative cover • Plant community • Soil type • All vary by time, place, discipline • But need to access past observations that make use of old schemes

  6. Flux • Looking at change in range, distribution, genetics, populations over time • Not spanned by one data set • When were the various exogenous species of shellfish introduced into each Great Lake?

  7. Historical Information • A least last 250 years of information is important • books, journals • field notebooks • observation files • Sometimes handwritten

  8. How to Digitize a Daisy? • 750 Million natural history specimens in the US • Would like at least to capture labels

  9. Small-Scale Features in Large Regions • Kudzu in western US: Plots of 1/4 and 1/8 acre in 1 million sq. miles • •

  10. Data is Never as Complete as Desired • Some research or management question will always benefit from more data • Over a larger area • Sampling regimen at smaller granularity • Shorter or longer time interval • Census versus sample

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