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

• 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

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

GDAL - Geospatial Data Abstraction Libraryhttp://www.remotesensing.org/gdal/

GDAL translatorlibrary connected toKepler actors via JNI

GDAL can input ~40different raster formats

Conversion betweendifferent projectionspossible

File format conversionsalso possible

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/

Java Actors for Handling ASC Grid Files

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 = 1Scale Factor = 2

Java Routines for Convex Hull Calculation and Rasterization

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

Start

Rescale and Clip

Merge

Finish

Museum Specimen Data (Digir)

34 ‘hits’ for ‘mephitis’ located in seach

Drag to workflow area to create a datasource

Search for species name (“mephitis”)

Specimen information can be ‘filtered’ using a built-in SQL database

SQL Filter Dialog

Fields included in Digir data

SQL filter specification to returnonly location data

i.e. (longitude, latitude)

Workflow Displaying Filtered Data

Single GARP Calculation of

Presence/Absence

Single-Species Best RulesetWorkflow

Predicted Occurrence LocationsUsing ‘Best’ Rulesets

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