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Using the Species Distribution Workflow

Using the Species Distribution Workflow. Dan Higgins – NCEAS Prepared for: Ecoinformatics Training for Ecologists LTER (Albuquerque) January 8-12, 2007 http://www.kepler-project.org. Goal : Predict Species Locations Based on Known Locations and Climate/Geographic Spatial Data.

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Using the Species Distribution Workflow

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  1. Using the Species Distribution Workflow Dan Higgins – NCEAS Prepared for: Ecoinformatics Training for Ecologists LTER (Albuquerque) January 8-12, 2007 http://www.kepler-project.org

  2. Goal : Predict Species Locations Based on Known Locations and Climate/Geographic Spatial Data • Goal is to correlate spatial data with known locations to predict other locations where a species might exist (Environmental Niche Modeling) • Requires manipulation of geographically referenced point data and GIS raster data from variety of sources

  3. Example Species Data • Mephitis mephitis (skunk) – 814 locations • Zapus trinotatus (Pacific Jumping Mouse ) - 387 locations • Orthogeomys cuniculus (Oaxacan Pocket Gopher) – 2 locations • Pappogeomys gymnurus (Llano Pocket Gopher) – 9 locations Species Examples suggested by Towne Peterson

  4. GDAL - Geospatial Data Abstraction Library http://www.remotesensing.org/gdal/ GDAL translator library connected to Kepler actors via JNI GDAL can input ~40 different raster formats Conversion between different projections possible File format conversions also possible

  5. Java Actors for Handling ASC Grid Files Java-based actors created to read and manipulate ASCII grid files ImageJ image processing package from NIH added as an actor to display and manipulate images ImageJ has macro capabilities & numerous plug-ins ImageJ - http://rsb.info.nih.gov/ij/

  6. Java Routines for Convex Hull Calculation and Rasterization Convex Hull algorithm relatively easy to implement in Java Java routines for the Convex Hull convert the polygon to a shape which can be ‘scaled’ in size Scale Factor = 2 Scale Factor = 1

  7. Java Actors for Rescaling and Merging Java actors can rescale ASCII grid files, changing resolution and extent Both nearest-neighbor and Inverse-Distance-Weighted algorithms implemented Disk-based code allows very large grids to be manipulated (i.e not limited by RAM) Grid rasters can be ‘merged’ with various operations on data in overlapping pixels Rescale and Clip Merge Start Finish

  8. Museum Specimen Data (Digir) Search for species name (“mephitis”) Drag to workflow area to create a datasource Specimen information can be ‘filtered’ using a built-in SQL database 34 ‘hits’ for ‘mephitis’ located in seach

  9. SQL Filter Dialog Fields included in Digir data SQL filter specification to return only location data i.e. (longitude, latitude)

  10. Workflow Displaying Filtered Data

  11. Single GARP Calculation of Presence/Absence

  12. Single-Species Best RulesetWorkflow

  13. Predicted Occurrence LocationsUsing ‘Best’ Rulesets

  14. Acknowledgements • This material is based upon work supported by: • The National Science Foundation under Grant Numbers 9980154, 9904777, 0131178, 9905838, 0129792, and 0225676. • Collaborators: NCEAS (UC Santa Barbara), University of New Mexico (Long Term Ecological Research Network Office), San Diego Supercomputer Center, University of Kansas (Center for Biodiversity Research), University of Vermont, University of North Carolina, Napier University, Arizona State University, UC Davis • The National Center for Ecological Analysis and Synthesis, a Center funded by NSF (Grant Number 0072909), the University of California, and the UC Santa Barbara campus. • The Andrew W. Mellon Foundation. • Kepler contributors: SEEK, Ptolemy II, SDM/SciDAC, GEON

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