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Lifemapper II: Finding the Good Life
Aimee Stewart James H. Beach, C.J. Grady, David A. Vieglias
Biodiversity Institute, KU
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Introduction• Components
– Continuously updated database– Computational pipeline– Research tools
• Goals– Research – Education
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Lifemapper Database• Biodiversity Institute MOU with GBIF
– Our copy updated ~ once/month
• New models generated withchanged data
• Multiple algorithms
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Pipeline• Initializes
experiments • Dispatches
experiments to 64-node cluster
• Retrieves cluster output
• Catalogs results
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Web services• Standards based (OGC, REST)• Accessible through
– Website– APIs
• Data: Spatial and non-spatial• Services:
– ENM using openModeller (CRIA)– Coming soon!
• Landscape Metrics • Macroecological Analysis
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CI-Team• Foster new research approaches using
CI to mediate barriers
• CI services and tools– Data: IPCC AR4– Services: Landscape metrics &
Dispersal analysisCourtesy of NSF Office of Cyberinfrastructure with UNM, ASU, NAU
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Lifemapper-SAM• Multi-species query • Create and populate ME grids• Statistical analysis• Desktop client based on QGIS
– Visualizing and comparing results– Dynamicaly deconstruct spatial, temporal,
phylogenetic patterns– Submit/catalog multiple experiments
Courtesy of NSF Advances in Biological Informatics with UConn
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Next Steps• Integrate into existing workflow environments• Visualization for grades 7-12
– Link food web models to species niche models– Manipulate inputs and visualize interactions
• Narrative and provenance generation
Courtesy of NSF Discovery Research K-12 Program with Umich & NSF EPSCoR Track II with KSU, OU, OSU