1. for the CoML Modeling and Visualization Workshop Jason Roberts and Ben Best 3-Feb-2009, Long Beach, CA

2. What is MGET? A collection of geoprocessing tools for marine ecology Oceanographic data management and analysis Habitat modeling, connectivity modeling, statistics Highly modular; designed to be used in many scenarios Emphasis on batch processing and interoperability Free, open source software Written in Python, R, MATLAB, and C++ Minimum requirements: Win XP, Python 2.4 ArcGIS 9.1 or later needed for some tools ArcGIS and Windows are only non-free requirements

3. Talk outline Overview of MGET’s software architecture Quick tour of the tools Live demonstration Questions Ask questions when needed Short discussions encouraged Long discussions may need to be deferred

4. MGET’s software architecture MGET “tools” are really just Python functions, e.g.: MGET exposes them to several types of external callers: def MyTool(input1, input2, input3)

5. Integration The Python functions can invoke C++, MATLAB, R, ArcGIS, and COM classes.

6. MGET interface in ArcGIS The MGET toolbox appears in the ArcToolbox window

7. MGET interface in ArcGIS Drill into the toolbox to find the tools Double-click tools to execute directly, or drag to geoprocessing models to create a workflow

8. Quick tour of the tools

9. Analyzing coral reef connectivity Coral reef ID and % cover maps Ocean currents data Tool downloads data for the region and dates you specify Larval density time series rasters Edge list feature class representing dispersal network Original research by Eric A. Treml

10. Converting data

11. Batch processing Copy one raster at a time

12. Batch processing Copy rasters that you list in a table

13. Batch processing Copy rasters from a directory tree

14. Tools for specific products Downloads sea surface height data from http://opendap.aviso.oceanobs.com/thredds

15. Identifying SST fronts ~120 km AVHRR Daytime SST 03-Jan-2005 28.0 °C 25.8 °C Mexico Front Cayula and Cornillion (1992) edge detection algorithm Frequency Temperature Optimal break 27.0 °C Strong cohesion  front present Step 1: Histogram analysis Step 2: Spatial cohesion test Weak cohesion  no front Bimodal Example output Mexico ArcGIS model

16. Identifying geostrophic eddies Aviso DT-MSLA 27-Jan-1993 Red: Anticyclonic Blue: Cyclonic Negative W at eddy core SSH anomaly Available in MGET 0.8 Example output

17. Sampling raster data Sampling is the procedure of overlaying points over a map and storing the map’s value as an attribute of each point. Chlorophyll-a Density Chl attribute of the points filled with values from the map MGET has sampling tools for various scenarios

18. Modeling habitat (demo) Chlorophyll SST Bathymetry Presence/absence observations Sampled environmental data Multivariate statistical model Probability of occurrence predicted from environmental covariates Binary classification

19. Live demonstration

20. Acknowledgements Thanks to OBIS SEAMAP and its data providers for sharing the data used here. Thanks to our funders: http://seamap.env.duke.edu

21. For more information Download MGET: http://code.env.duke.edu/projects/mget Contact us: jason.roberts@duke.edu, bbest@duke.edu Learn more about habitat modeling: Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135, 147–186. Thanks for attending!