1. Marine Geospatial Ecology Tools (MGET) November 2013 Overview Jason Roberts, Ben Best, Daniel Dunn, Eric Treml, and Pat Halpin Duke Marine Geospatial Ecology Lab The development of MGET was funded by:

2. Duke Marine Geospatial Ecology Lab Lab Director: Dr. Patrick N. Halpin Duke Main Campus Durham, North Carolina Staff and Students: Dr. Andre Boustany Ben Donnelly Daniel Dunn Ei Fujioka Hunter Jones Jesse Cleary Liza Hoos Shay Viehman Washington, D.C. Dr. Ari Friedlaender Connie Kot Corrie Curtice Daniel Dunn Erin LaBrecque Jerry Moxley Duke Marine Lab Beaufort, North Carolina Jason Roberts

3. What we do Marine Ecology Ecoinformatics Geospatial Analysis Linking biological, satellite and ocean observing data Developing innovative data analysis and visualization tools Field data collection, analysis and modeling Applied marine science for sustainable use & conservation

4. Ecological research New analytic methods “From data… Analysis and decision support tools …to decisions”

5. MGET is an ArcGIS toolbox It can also be invoked from many programming languages Over 280 Tools

6. Many users access MGET from the ModelBuilder capability of ArcGIS

7. Installation is easy! Free, open-source software Requires Windows + ArcGIS + a free Python lib For full functionality, you need other free software Open-source GIS may be supported in the future Easy installer (“just click Next, Next, Next”) Download and instructions are here: http://mgel.env.duke.edu/mget/download

8. 99 countries ~3800 installs since August 2009 The user community as of Nov. 2013

9. Tour of the tools Let’s see some examples from each toolset…

10. The first tools we developed: generic HDF/netCDF converters

11. These evolved to tools specific to popular products

12. MGET supports a growing list of products and algorithms Let’s look at some more examples…

13. Sample 3D and 4D products Chai, F, RC Dugdale, TH Peng, FP Wilkerson, and RT Barber (2002). One-dimensional ecosystem model of the equatorial Pacific upwelling system. Part I: model development and silicon and nitrogen cycle. Deep Sea Research Part II: Topical Studies in Oceanography 49: 2713-2745.

14. Application: analyzing the movements of leatherback sea turtles tracked by satellite telemetry Click here while viewing the slide show to see an animation of one of the tracklineshere (requires player for.wmv files)

15. Sampling in 4 dimensions: lat, lon, depth, time Black bars are the depths most frequented by turtle on that day. Research question: are turtles choosing locations and depths based on mesozooplankton density (ROMS-CoSiNE zz2 variable)?

16. Leatherback movement modeling Schick RS, Roberts JJ, Eckert SA, Halpin PN, Bailey H, Chai F, Shi L, Clark JS (accepted) Pelagic movements of Pacific Leatherback Turtles (Dermochelys coriacea) reveal the complex role of prey and ocean currents. Movement Ecology. Schick et al (2008) Bayesian animal movement model

17. Detecting SST fronts MGET provides tools that detect oceanographic features in remote sensing images These are some of the most popular tools in MGET Terra Aqua

18. Cayula & Cornillon algorithm ~120 km Daytime SST 03-Jan-2005 28.0 °C 25.8 °C Mexic o Front 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

19. Application: Modeling density of critically endangered right whales Roberts, Best, Halpin, et al. (in prep)

20. Detecting mesoscale eddies This tool detects eddies in SSH images collected by NASA/CNES radar altimeters

21. Gulf stream eddies Image from http://www.oc.nps.edu/

22. Okubo-Weiss eddy detection Aviso DT-MSLA 27-Jan-1993 Red: Anticyclonic Blue: Cyclonic Negative W at eddy core SSH anomaly Example output

23. Application: fisheries ecology Are tuna and swordfish catches in the northwest Atlantic correlated with eddies? Eddies Hsu A, Boustany AM, Roberts JJ, Halpin PN (in review) The effects of mesoscale eddies on tuna and swordfish catch in the U.S. northwest Atlantic longline fishery. Fish. Oceanogr.

24. Results Species CPUE in eddy habitats Effects of Other Parameters on CPUE SSTBait DepthLightsticks Bluefin A > N > C ̶̶̶ Yellowfin C > N+ ̶̶ Bigeye C > A > N ̶̶̶ Swordfish N > C > A+++ A = In anticyclonic eddies C = In cyclonic eddies N = Not in eddies For tunas, CPUE is higher inside eddies than outside eddies (p < 0.05) For swordfish, CPUE is lower inside eddies than outside eddies (p < 0.05) + = positively correlated with CPUE ̶ = negatively correlated with CPUE

25. Chelton’s eddy database MGET also includes tools that provide easy access to data products published by other NASA grantees By improving access to these products from GIS, we hope to increase use by ecologists Chelton, DB, MG Schlax, and RM Samelson (2011). Global observations of nonlinear mesoscale eddies. Progress in Oceanography 91: 167-216.

26. Spatiotemporal analysis of fisheries

27. Swordfish exhibits annual periodicity Bigeye CPUE highest in full moon

28. Daniel Dunn, et al. (2013) Empirical move-on rules to inform fishing strategies: a New England case study. Fish and Fisheries. To avoid damage by slime eels, move on by 3 km for 5 days

29. Model larval connectivity Habitat patches Ocean currents data Tool downloads data for the region and dates you specify Larval density rasters Edge list feature class representing dispersal network Methods described in Treml et al. (2008, 2012)

30. Application: modeling dispersal of coral larvae in the Caribbean to assist in systematic conservation planning Click here while viewing the slide show to see a simulation of the dispersal of coral larvaehere (requires player for.mp4 files) Animation by George Raber

31. Schill S, Raber G, Roberts JJ, Treml EA, Brenner J (in prep) Designing for Resilience: A regional coral marine connectivity model for the Caribbean Basin and Gulf of Mexico based on NOAA’s Real-Time Ocean Forecast System (RTOFS).

32. Invoke R from ArcGIS

33. Chlorophyll SST Bathymetry Point observations of species Gridded environmental data Predictive model Probability of occurrence predicted from environmental covariates Binary classification Predictive species distribution modeling

34. Application: rockfish habitat models Young MA, Iampietro PJ, Kvitek RG, Garza CD (2010) Multivariate bathymetry- derived generalized linear model accurately predicts rockfish distribution on Cordell Bank, California, USA. Marine Ecology Progress Series 415: 247– 261.

35. Bathymetry-derived predictor variables Young et al. (2010)

36. Results: yellowtail rockfish Young et al. (2010)

37. MGET is not just useful for marine species. How about a terrestrial example involving homo sapiens sapiens habitat? Using Predictive Modeling Methods as a Way of Examining Past Settlement Patterns: An Example From Southern Poland Anna Luczak, University of Wroclaw, Institute of Archaeology, Wroclaw, Poland

38. Results: Predicted Neolithic Sites Anna Luczak

39. Acknowledgements A special thanks to the many developers of the open source software that MGET is built upon, including: Guido van Rossum and his many collaborators; Mark Hammond; Travis Oliphant and his collaborators; Walter Moreira and Gregory Warnes; Peter Hollemans; David Ullman, Jean-Francois Cayula, and Peter Cornillon; Stephanie Henson; Tobias Sing, Oliver Sander, Niko Beerenwinkel, and Thomas Lengauer; Frank Warmerdam and his collaborators, Howard Butler; Timothy H. Keitt, Roger Bivand, Edzer Pebesma, and Barry Rowlingson; Gerald Evenden; Jeff Whitaker; Roberto De Almeida and his collaborators; Joe Gregorio; David Goodger and his collaborators; Daniel Veillard and his collaborators; Stefan Behnel, Martijn Faassen, and their collaborators; Paul McGuire and his collaborators; Phillip Eby, Bob Ippolito, and their collaborators; Jean-loup Gailly and Mark Adler; the developers of netCDF; the developers of HDF Thanks to our funders:

40. Thank you! Download MGET: http://mgel.env.duke.edu/mget (or Google “MGET”) Email me: jason.roberts@duke.edu If you use MGET, please cite our paper: Roberts, JJ, Best BD, Dunn DC, Treml EA, Halpin PN (2010) Marine Geospatial Ecology Tools: An integrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C++. Environmental Modelling & Software 25: 1197-1207.