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Bridging Species Niche Modeling and Multispecies Ecological Modeling and Analysis

Jeffery Cavner, J.H. Beach, Aimee Stewart, CJ Grady jcavner@ku.edu, beach@ku.edu ,astewart@ku.edu, cjgrady@ku.edu Biodiversity Institute University of Kansas. Bridging Species Niche Modeling and Multispecies Ecological Modeling and Analysis.

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Bridging Species Niche Modeling and Multispecies Ecological Modeling and Analysis

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  1. Jeffery Cavner, J.H. Beach, Aimee Stewart, CJ Grady jcavner@ku.edu, beach@ku.edu ,astewart@ku.edu, cjgrady@ku.edu Biodiversity Institute University of Kansas Bridging Species Niche Modeling and Multispecies Ecological Modeling and Analysis

  2. Species DiversityLmRAD (Lifemapper Range and Diversity) • Biodiversity - describe, visualize and analyze different aspects of the numbers and abundances of taxa in time and space. • Patterns of species richness - constituent species ranges sizes and spatial locations of those ranges. • Patterns related to species associations, co-occurrence, and species interactions requires testing against randomized distributions. • Species richness and species range can be summarized and linked by one basic analytical tool, the presence/absence matrix (PAM).

  3. Lifemapper as an overarching architecture • LmRAD is built on top of the existing Lifemapper architecture • Lifemapper is an archival and species distribution modeling platform consisting of a computational pipeline, specimen data archive, predicted species distribution model archive • Distribution models are built on-demand using openModeller. • Inputs: climate scenario data and aggregated specimen occurrences from GBIF and user provided occurrence points.

  4. The Presence Absence Matrix (PAM) Data Matrix Grid

  5. Most existing indices of biodiversity are simple combinations of : • Vectors: species richness sizes of distributions “dispersion fields” “diversity fields” • Whitaker’s beta diversity • The dimensions of the PAM

  6. Constraints • Construction of PAMs can be an extremely time consuming data management task • Current methods for working with these matrices can be computationally slow

  7. Toovercomecomputational restraints we use a Python implementation of the Web Processing Service standard on a compute cluster, exposing spatial and statistical algorithms. Allows a variety of species inputs Extendable clients including Quantum GIS (QGIS) and VisTrails that share a common client library Approach

  8. Clients

  9. Randomizing the PAM • To test the null hypothesis • By producing the same richness and range patterns while ignoring realistic species combinations • Two Types of Randomization: Swap and Dye Dispersion • Swap : keeps species richness and range size totals intact.

  10. Additional Randomization methods Dye Dispersion • Geometric constraints model • Assumes range continuity • Reassembles ranges • Keeps range size intact

  11. QGIS is used as a WPS client

  12. Using QGIS and WPS to construct a grid

  13. The asynchronous nature of WPS combined with a computational pipeline and compute cluster allow a user to intersect hundreds of species layers at a time with the data grid to populate the PAM.

  14. Terrestrial Mammals Proportional Species Richness High Yellow Moderate Red Low Blue Per-site Range Size

  15. Statistical services provide diversity indices and plots using WPS

  16. By-species range-diversity plot

  17. The plug-ins use a simple MVC pattern with QT threads for asynchronous WPS requests and a client library for the communication layer

  18. Conclusion

  19. Jeffery Cavner, J.H. Beach, Aimee Stewart, CJ Grady jcavner@ku.edu, beach@ku.edu, astewart@ku.edu, cjgrady@ku.edu Biodiversity Institute University of Kansas

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