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:
MGET is an ArcGIS toolbox
Over 250 Tools
It can also be invoked from most programming languages
MGET is used worldwide
~2300 installs since August 2009
81 countries (map is missing 25)
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
Tour of the tools
Let’s see some examples from each toolset…
Convert data
MGET supports a growing
list of products and algorithms
Let’s look at some
examples…
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
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.
Leatherback Track Video
(click link above while viewing slide show)
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.
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: 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:10.1098/rspb.2011.0330
Miller’s composite front maps
Areas of
Additional
Pelagic
Ecological
Importance
(AAPEI)
Summer
frequent
front map
FF
CSF
UF
%
Miller P, et al. (in review) Frequent locations of ocean fronts as an indicator of pelagic
diversity: application to marine protected areas and renewables
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
Eddy Detection Video
(click link above while viewing slide show)
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.
Longline catch per unit effort (1993-2005)
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.
Querying OBIS
 Query OBIS’s ~30 million records
 Filter by taxon, bounding box, dates, etc.
 Download results as GIS point features
Map species biodiversity
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
Yellowfin and swordfish have different seasons
Sparse data for bluefin
 noisy periodogram
Possible lunar and seasonal patterns
Bigeye CPUE highest
in full moon
Noise due to
sparse data –
ignore!
Annual harmonics at 121 and 91 days: short season
How does this work?
CPUE
 How do we identify cycles in complicated-looking data?
 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.
3 component
signals
Original signal
 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)
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
Larval density rasters
Larval Dispersal Video 1
Larval Dispersal Video 2
(click links above while viewing slide show)
Invoke R from ArcGIS
Model species habitat
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.
Bathy-derived predictor variables
Results: yellowtail rockfish
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:
Thanks for coming!
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.