1. Ben Best Patrick Halpin Jason Roberts Ei Fujioka Ben Donnely Jesse Cleary

2. Polar Synthesis Macroscope Team Duke University NC 26-28 Oct 2008

3. Partners: Other Visualization Experts (NESCent National Evolutionary Synthesis Center ) Xianhua Liu / David Kidd (NESCent National Evolutionary Synthesis Center ) GeoPhyloBuilder Reference: Kidd, D. M. and M. G. Ritchie (2006). "Phylogeographic information systems; Putting the geography into phylogeography." Journal of Biogeography 33: 1851-1865.

4. Google Earth’s Oceans - Octopuses

5. Antarctica exports oxygen-rich cold bottom water ocean surrounded by continent continent surrounded by ocean contrast

6. Geo/Phylo/Hab Workflow Get Observations extent taxa points phylogeny GeoPhyloBuilder 3Dgeophylo Calculate Species Richness Get Environment Fit Model Predict Model variable environment grid(s) diversity grid model prediction

7. Oceanographic Data Online Pointers: CoMLmaps.org > HowTo > Layers and ResourcesCoMLmaps.org

8. OceanWatch LAS Formats txt netcdf kml OPeNDAP http://las.pfeg.noaa.gov/oceanWatch/oceanwatch_safari.php

9. OPeNDAP Open-source Project for a Network Data Access Protocol OPeNDAP form (.html.dds.das): http://oceanwatch.pfeg.noaa.gov/thredds/dodsC/satellite/MB /chla/8day.html http://oceanwatch.pfeg.noaa.gov/thredds/dodsC/satellite/MB /chla/8day.html MATLAB command: loaddods('http://las.pfeg.noaa.gov/OceanWatch- FDS/LAS/MB/chla8day?MBCHLA[1033:1033][0:0][3218:3379][4558 :4760]') Python: pydap

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

11. 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

12. 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

13. Interoperability MGET “tools” are really just Python functions with input and output parameters: def DoSomething(input1, input2, output1) Python programmers can call MGET functions directly. To facilitate interoperability, MGET exposes these functions as COM Automation objects and ArcGIS tools. MGET COM Automation class DoSomething COM-capable program: C / C++ / C#, Visual Basic, R, MATLAB, Java, etc. ArcGIS geoprocessing tool

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

15. Typical observation data IATTC Olive Ridley Encounters 1990-2005 Fishery catch and bycatch records Surveys Argos satellite tracks Figure courtesy of Scott Eckert

16. Typical workflow Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Create derived oceanographic datasets Sample oceanographic data Explore maps of oceano. and observations MGET includes tools that assist with all of these steps Analyze/model species habitat or behavior

17. Typical workflow Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Create derived oceanographic datasets Sample oceanographic data Explore maps of oceano. and observations Analyze/model species habitat or behavior

18. Species observations Skipping the details of this step to save time Ultimately you must produce a point shapefile or feature class that shows locations where the species was present and where it was absent Species presence field: 1 = present, 0 = absent Date field records date of observation

19. Typical habitat modeling workflow Import species observations into GIS Download oceanographic datasets Prepare oceanographic data for use Create derived oceanographic datasets Sample oceanographic data Explore maps of oceano. and observations Analyze/model species habitat or behavior

20. Options for obtaining data 1. Download files from data providers using FTP Nearly all data products are available with FTP Powerful, free downloaders exist (e.g. SmartFTP) But must often convert files to ArcGIS-compatible formats 2. Download using MGET or other tool (e.g. NOAA EDC) The tool hides details of download, using FTP, OPeNDAP or other protocols, and writes ArcGIS- compatible formats Not many such tools exist 3. Order files on CD-ROM or DVD-ROM Use this if your Internet connection is slow

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

22. Example SSH and currents data with turtle track

23. Typical workflow Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Create derived oceanographic datasets Sample oceanographic data Explore maps of oceano. and observations Analyze/model species habitat or behavior

24. Preparing oceanography for use Most oceanographic datasets are not immediately usable by ArcGIS Common preprocessing steps include: Converting to an ArcGIS-supported format Projecting to a desired projection Clipping to region of interest Performing basic calculations (via map algebra) E.g. converting integers given by the original data provider to floats that represent the real values Building pyramids

25. Converting data

26. Sea surface temperature NOAA NODC 4km AVHRR Pathfinder v5 GOES 10/12 from PO.DAAC NOAA CoastWatch AVHRR Also: MODIS Aqua and Terra, GOES 9

27. Sea surface chlorophyll density SeaWiFS from the NASA GSFC OceanColor Group Also: MODIS Aqua and combined MODIS/SeaWiFS

28. QuikSCAT ocean winds from PO.DAAC Katrina 28-Aug-2005 Also: BYU QuikSCAT Sigma-0 (approximates sea surface rougness)

29. Global bathymetries ETOPO2 GEBCO S2004 Map shows S2004 clipped to eastern Pacific ocean

30. Typical workflow Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Create derived oceanographic datasets Sample oceanographic data Explore maps of oceano. and observations Analyze/model species habitat or behavior

31. 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

32. 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

33. Typical workflow Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Create derived oceanographic datasets Sample oceanographic data Analyze/model species habitat or behavior Explore maps of oceano. and observations

34. 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

35. Typical workflow Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Create derived oceanographic datasets Sample oceanographic data Analyze/model species habitat with statistics Explore maps of oceano. and observations

36. MGET statistics tools Lots of tools, many more planned Built from Ben Best’s ArcRStats / HabMod projects Tools require the R statistics program to be installed on your computer

37. Exploratory analysis Scatterplot Matrix tool Density Distance to nesting beach (m) Density Histogram tool Turtle present Turtle absent

38. Fitting statistical models Term plots ROC plots

39. Predicting habitat maps from the model Predicted species presence Binary habitat (cutoff = 0.025) Bayesian probability that predicted presence ≥ 0.025 Predict GAM tool Input #1: The fitted model Input #2: Cutoff value Input #3: Rasters for predictor variables

40. 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

41. Calculate Species Diversity Available in MGET 0.7 alpha 10

42. More Information Census of Marine Life Map & Vis www.comlmaps.org info@comlmaps.org Marine Geospatial Ecology Tools code.env.duke.edu/projects/mget