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:
Duke Marine Geospatial Ecology Lab
Duke Main Campus
Durham, North Carolina
Washington, D.C.
Jason Roberts
Lab Director:
Dr. Patrick N. Halpin
Staff and Students:
Dr. Andre Boustany
Ben Donnelly
Daniel Dunn
Ei Fujioka
Hunter Jones
Jesse Cleary
Liza Hoos
Shay Viehman
Duke Marine Lab
Beaufort, North Carolina
Dr. Ari Friedlaender
Connie Kot
Corrie Curtice
Daniel Dunn
Erin LaBrecque
Jerry Moxley
What we do
“From data…
New analytic methods
Ecological research
…to decisions”
Analysis and
decision
support tools
MGET is an ArcGIS toolbox
Over 280 Tools
It can also be invoked from many programming languages
Many users access MGET
from the ModelBuilder
capability of ArcGIS
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
The user community as of Nov. 2013
~3800 installs since August 2009
99 countries
Tour of the tools
Let’s see some examples from each toolset…
The first tools we developed:
generic HDF/netCDF converters
These
evolved
to tools
specific to
popular
products
MGET supports a growing
list of products and algorithms
Let’s look at some
more examples…
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.
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 tracklines
(requires player for .wmv files)
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)?
Leatherback movement modeling
Schick et al (2008) Bayesian
animal movement model
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.
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
Cayula & Cornillon algorithm
Daytime SST 03-Jan-2005
Mexic
o
Step 1: Histogram analysis
ArcGIS model
Frequency
Bimodal
Optimal
break
27.0 °C
Temperature
Step 2: Spatial cohesion test
28.0 °C
Front
25.8 °C
~120 km
Strong cohesion Weak cohesion
 front present  no front
Example output
Mexico
Application: Modeling density of
critically endangered right whales
Roberts, Best, Halpin, et al. (in prep)
Detecting mesoscale eddies
 This tool detects eddies in SSH
images collected by NASA/CNES
radar altimeters
Gulf stream eddies
Image from http://www.oc.nps.edu/
SSH anomaly
Okubo-Weiss eddy detection
Example output
Aviso DT-MSLA 27-Jan-1993
Red: Anticyclonic Blue: Cyclonic
Negative W at eddy core
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.
Results
CPUE in eddy Effects of Other Parameters on CPUE
Species
habitats
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)
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.
Spatiotemporal analysis
of fisheries
Bigeye CPUE highest
in full moon
Swordfish exhibits
annual periodicity
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
Model larval connectivity
Habitat patches
Ocean currents data
Tool downloads data for the
region and dates you specify
Edge list feature class representing
dispersal network
Methods described in Treml et al. (2008, 2012)
Larval density rasters
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 larvae
(requires player for .mp4 files)
Animation by George Raber
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).
Invoke R from ArcGIS
Predictive species distribution modeling
Point observations of species
Probability of occurrence predicted
from environmental covariates
Predictive model
Gridded environmental data
Bathymetry
SST
Chlorophyll
Binary classification
Application: rockfish habitat models
Young MA, Iampietro PJ,
Kvitek RG, Garza CD (2010)
Multivariate bathymetryderived generalized linear
model accurately predicts
rockfish distribution on
Cordell Bank, California,
USA. Marine Ecology
Progress Series 415: 247–
261.
Bathymetry-derived predictor variables
Young et al. (2010)
Results: yellowtail rockfish
Young et al. (2010)
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
Results:
Predicted
Neolithic
Sites
Anna Luczak
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:
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.