1. Marine Geospatial Ecology Tools
Jason Roberts, Ben Best, Dan Dunn, Eric Treml and Pat Halpin
Duke Marine Geospatial Ecology Lab
The development of
MGET was funded by:

2. MGET is an ArcGIS toolbox
Over 250 Tools
It can also be invoked from most programming languages

3. MGET is used worldwide ~2300 installs since August 2009
81 countries (map is missing 25)

4. More MGET facts Free, open-source software Requires Windows and ArcGIS
These requirements are slowly disappearing
Easy installation (“just click Next, Next, Next”)
Written in Python, R, MATLAB, and C/C++
Uses free MATLAB Component Runtime

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

6. Convert data

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

8. Easily acquire oceanographic data in GIS-compatible formats
MGET provides customized tools for each data product that it supports
The tool shown here is a simple one: it downloads ocean color data in a GIS-compatible format
This may seem trivial but GIS users regularly cite data import as 80% of the work of any project

9. 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:

10. Leatherback Track Video (click link above while viewing slide show)

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

12. 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

13. Cayula & Cornillon algorithm
~120 km
Daytime SST 03-Jan-2005
28.0 °C
25.8 °C
Mexico
Front
Step 1: Histogram analysis
ArcGIS model
Bimodal
Optimal break 27.0 °C
Frequency
Temperature
Example output
Step 2: Spatial cohesion test
Mexico
Strong cohesion  front present
Weak cohesion  no front

14. Application: albatross habitat suitability
SST Front Activity Index
Žydelis, R, RL Lewison, SA Shaffer, JE Moore, AM Boustany, JJ Roberts, M Sims, DC Dunn, BD Best, Y Tremblay, MA Kappes, PN Halpin, DP Costa, and LB Crowder (2011) Dynamic habitat models: Using telemetry data to project fisheries bycatch. Proceedings of the Royal Society B. doi: /rspb

15. Miller’s composite front mapsFF UF
CSF %
Areas of Additional Pelagic Ecological Importance (AAPEI)
Summer frequent front map
Miller P, et al. (in review) Frequent locations of ocean fronts as an indicator of pelagic diversity: application to marine protected areas and renewables

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

17. Gulf stream eddies
Image from

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

19. Eddy Detection Video (click link above while viewing slide show)

20. 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.

21. Longline catch per unit effort (1993-2005)

22. Effects of Other Parameters on CPUE
Results
Species
CPUE in eddy habitats
Effects of Other Parameters on CPUE
SST
Bait Depth
Lightsticks
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
+ = positively correlated with CPUE
̶ = negatively correlated with CPUE
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)

23. 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:

24. Querying OBIS Query OBIS’s ~30 million records
Filter by taxon, bounding box, dates, etc.
Download results as GIS point features

25. Map species biodiversity

26. Temporal periodicity analysis for swordfish
Top histogram shows how CPUE varies over time
Periodogram shows periods of cycles detected in the data
First find large spikes, then look up period on x axis
Important periods:
365 days: annual cycle
29.5 days: lunar cycle
1 day: diurnal cycle
Radial histograms shows CPUE by day of year and lunar phase
365 days 
annual cycle

27. Yellowfin and swordfish have different seasons

28. Sparse data for bluefin  noisy periodogram
Possible lunar and seasonal patterns
Noise due to sparse data – ignore!
Bigeye CPUE highest in full moon
Annual harmonics at 121 and 91 days: short season

29. How does this work?
How do we identify cycles in complicated-looking data?
CPUE

30. We use methods such as the Discrete Fourier Transform (DFT) to decompose the original signal into a series of sine waves that, when added together, reproduce it.
The MGET tool uses the Lomb-Scargle method, developed by astronomers to find cycles in phenomena that are only observed infrequently (e.g. rotating stars)
3 component signals
Original signal

31. Larval density rasters
Model larval connectivity
Habitat patches
Ocean currents data
Larval density rasters
Tool downloads data for the region and dates you specify
Edge list feature class representing dispersal network

32. Larval Dispersal Video 1 Larval Dispersal Video 2 (click links above while viewing slide show)

33. Invoke R from ArcGIS

34. Model species habitat Point observations of species
Probability of occurrence predicted from environmental covariates
Predictive model
Gridded environmental data
Binary classification
Chlorophyll
SST
Bathymetry

35. 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.

36. Bathy-derived predictor variables

37. Results: yellowtail rockfish

38. 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:

39. Thanks for coming! http://mgel.env.duke.edu/mget (or Google “MGET”)
Download MGET:
(or Google “MGET”)
me:
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: