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 with changed 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 analysis Courtesy 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