Biodiversity Data Retrieval and Integration Distributed species, data, computation and credit

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Biodiversity Data Retrieval and Integration
Distributed species, data, computation and credit

• James H. Beach
• Biodiversity Research Center
• University of Kansas
• beach_at_ku.edu

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Museums and their Data

• 3 B specimens and data documenting the
  distribution of life on earth
• 2 M species
• 300 years of biological exploration
• Data are held in dynamic, autonomous,
  self-organizing and spatially-distributed
  collections

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Paris Museum Mexican Birds
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British Museum Mexican Birds
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Field Museum Mexican Birds
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KU Museum Mexican Birds
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World Museum Mexican Birds
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The Species Analyst Network

• Direct access to live primary data
• Ownership and control maintained locally
• Z39.50, HTTP, XML data, XML Query

Broadcast query
Data Resources
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Species Analyst HTML Gateway
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Results of Species Analyst Query
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GARP Genetic Algorithm for Rule-set Production

• Developed by David Stockwell, San Diego
  Supercomputer Center
• Takes advantage of multiple algorithms (BIOCLIM,
  logistic regression, etc.)
• Different decision rules may apply to different
  sectors of species distributions
• Uses a genetic algorithm for choosing rules
• Implemented on WWW, and open for public use

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Species Analyst GARP A Powerful Tool

• Integrates distributed biodiversity data
• Provides current information on species ranges
• Models species ecological niches
• Predicts geographic distributions
• Integrates niche models with environmental change
  scenarios, e.g. global climate change and
  biodiversity, invasive species, emerging diseases

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Asian Longhorn Beetle (Anoplophora glabripennis)
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Longhorn Beetle - Modeled Asian Distribution
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Asian Longhorn Beetle Predicted U.S.
Distribution
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A Global Encyclopedia of Life or The World
According to GARP

• Research
• Biogeographic analysis on distributions
• Invasive species predictions
• Monitoring and conservation planning
• Global climate change impacts on Biota
• Outreach, Education and Training
• Backyard biodiversity, spatial data queries, GIS
  functions
• Interactive data entry, observational data
• Data Analysis Services for Museums
• Uniqueness and value of collections holdings
• Data quality issues
• Summary statistics and analyses

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A Global Encyclopedia of Life or The World
According to GARP (2)

• Every documented species with georeferenced
  localities in the Species Analyst Network
• North America, Western Hemisphere, World
• Resolution 1 Km grid NA, 10 Km elsewhere
• 1 M species in collections with data?
• Computational Requirements

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Metacomputing Museum Data

• Global species distributions parallel
  computation
• SETI _at_ Home
• Collaborative computing
• 1 M simultaneous users
• Port GARP to Win32 to run in background or
  foreground

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Lifemapper

• Georeferenced Species Data
• Distributed Query Architecture
• Predictive Modeling
• Distributed Computation
• Spatial Map and Model Archive
• Open Access Web Portal

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Lifemapper Demonstration

• Server
• GARP client

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Lifemapper Future Directions

• Diversify modeling options, add interactivity, 3D
  analysis and visualization
• Add new classes of data layers, remote sensing,
  human impacts element, ecological models
• Add observational species data
• Embed dispersion models, temporal dimension
• Add internet services API, UDDI, SOAP
• Add more value-added services for data providers
• Embed LM data and analysis tools within a
  semantic research and decision support network
• Integrate LM into informal and formal science
  education

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Lifemapper Social Scaling

• Distributed authorship
• Desktop computing
• User preferences
• Value-added collections data analysis
• Acknowledgement and accreditation of
  contributions, ranks and statistics

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Museums as Sensor Networks

• Data are dynamic, servers connections
• Deborah Estrin -- Adaptive self-organization of
  the network, unattended and untethered --
  parallels to curators and collection managers.
• Self-assembling, observational data
• Do not usually have the requirement of real time
• Changes are as important
• Source data (West Nile virus), model outputs
• Frank Vernon mentioned that in many cases it is
  not the data values per se it is the change that
  is of importance
• People as part of the Network
• Doug Goodin people are part of the technological
  system museum are sensors, they are
  observatories, but the latency of bringing the
  data into analysis engines is not measured in
  milliseconds but in field seasons, or decades to
  get formal publication of new scientific
  concepts. Many specimens and data are centuries
  old

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Acknowledgements

• University of Kansas
• Dave Vieglais, Ricardo Pereira, Aimee Stewart,
  Greg Vorontsov, Town Peterson, BRC
• SDSC
• David Stockwell, Environmental Computing
• University of Massachusetts-Boston
• Bob Morris, CS, Rob Stevenson, Biology
• UC Berkeley
• John Wiecorek, Museum of Vertebrate Zoology
• Dan Wertheimer, Space Science Laboratory
• Agriculture Canada
• Derek Munro, ITIS Canada Office
• California Academy of Sciences
• Stan Blum, Informatics