Slide 1Marine Geospatial
Ecology Tools
Jason Roberts, Ben Best, Dan Dunn, Eric Treml and Pat Halpin
Duke Marine Geospatial Ecology Lab
The development of
It can also be invoked from most programming languages
Over 250 Tools
~2300 installs since August 2009
More MGET facts
Free, open-source software
Easy installation (“just click Next, Next, Next”)
Written in Python, R, MATLAB, and C/C++
Uses free MATLAB Component Runtime
Tour of the tools
Convert data
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, 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.
Schick et al (2008) Bayesian animal movement model
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
Weak cohesion no front
Application: albatross habitat suitability
ydelis, 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
SST Front Activity Index
FF
UF
CSF
%
Miller P, et al. (in review) Frequent locations of ocean fronts as an indicator of pelagic diversity: application to marine protected areas and renewables
Areas of Additional Pelagic Ecological Importance (AAPEI)
Summer frequent front map
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/
Okubo-Weiss eddy detection
Negative W at eddy core
SSH anomaly
Example output
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
Species
SST
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
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
Filter by taxon, bounding box, dates, etc.
Download results as GIS point features
Map species biodiversity
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
Possible lunar and seasonal patterns
Annual harmonics at 121 and 91 days: short season
Noise due to sparse data – ignore!
How does this work?
CPUE
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)
Original signal
Tool downloads data for the region and dates you specify
Larval density rasters
Edge list feature class representing dispersal network
Invoke R from ArcGIS
Binary classification
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:
If you use MGET, please cite our paper: