Lifemapper II: Finding the Good Life

Aimee Stewart James H. Beach, C.J. Grady, David A. Vieglias

Biodiversity Institute, KU

Introduction• Components

– Continuously updated database– Computational pipeline– Research tools

• Goals– Research – Education

Lifemapper Database• Biodiversity Institute MOU with GBIF

– Our copy updated ~ once/month

• New models generated withchanged data

• Multiple algorithms

Pipeline• Initializes

experiments • Dispatches

experiments to 64-node cluster

• Retrieves cluster output

• Catalogs results

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

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

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

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