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Marine Geospatial Ecology Tools (MGET)November 2013 OverviewJason Roberts, Ben Best, Daniel Dunn, Eric Treml, and Pat HalpinDuke Marine Geospatial Ecology LabThe development of MGET was funded by:

Duke Marine Geospatial Ecology Lab

Lab Director:Dr. Patrick N. HalpinDuke Main CampusDurham, North CarolinaStaff and Students:Dr. Andre BoustanyBen DonnellyDaniel DunnEi FujiokaHunter JonesJesse ClearyLiza HoosShay ViehmanWashington, D.C.Dr. Ari FriedlaenderConnie KotCorrie CurticeDaniel DunnErin LaBrecqueJerry MoxleyDuke Marine LabBeaufort, North CarolinaJason Roberts

What we do

Marine EcologyEcoinformaticsGeospatialAnalysisLinking biological, satellite and ocean observing data Developing innovative data analysis and visualization toolsField data collection, analysis and modelingApplied marine science forsustainable use & conservation

Ecological research

New analytic methodsFrom data

Analysis and decision support toolsto decisions

MGET is an ArcGIS toolboxIt can also be invoked from many programming languagesOver 280 Tools

Many users access MGET from the ModelBuilder capability of ArcGISInstallation is easy!Free, open-source softwareRequires Windows + ArcGIS + a free Python libFor full functionality, you need other free softwareOpen-source GIS may be supported in the futureEasy installer (just click Next, Next, Next)Download and instructions are here:http://mgel.env.duke.edu/mget/download

99 countries~3800 installs since August 2009The user community as of Nov. 2013Tour of the toolsLets 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 growinglist of products and algorithms

Lets look at some more examples

Sample 3D and 4D productsChai, 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 telemetryClick 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, timeBlack bars are the depths most frequented by turtle on that day.Research question: are turtles choosing locations and depths basedon mesozooplankton density (ROMS-CoSiNE zz2 variable)?Leatherback movement modelingSchick 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

Detecting SST fronts

MGET provides tools that detect oceanographic features in remote sensing imagesThese are some of the most popular tools in MGET

TerraAqua

Cayula & Cornillon algorithm~120 km

Daytime SST 03-Jan-2005

28.0 C25.8 CMexicoFront

FrequencyTemperatureOptimal break 27.0 C

Strong cohesion front presentStep 1: Histogram analysisStep 2: Spatial cohesion testWeak cohesion no frontBimodal

Example outputMexico

ArcGIS model

Application: Modeling density of critically endangered right whales

Roberts, Best, Halpin, et al. (in prep)

Detecting mesoscale eddiesThis 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

Aviso DT-MSLA 27-Jan-1993 Red: Anticyclonic Blue: Cyclonic

Negative W at eddy core

SSH anomalyExample outputApplication: fisheries ecologyAre 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.ResultsSpeciesCPUE in eddy habitatsEffects of Other Parameters on CPUESSTBait DepthLightsticksBluefinA > N > CYellowfinC > N+BigeyeC > A > NSwordfishN > C > A+++A = In anticyclonic eddiesC = In cyclonic eddiesN = Not in eddiesFor 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 CPUECheltons eddy databaseMGET also includes tools that provide easy access to data products published by other NASA granteesBy 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 analysisof fisheries

Swordfish exhibitsannual periodicity

Bigeye CPUE highest in full moon

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 dataTool downloads data for the region and dates you specifyLarval density rasters

Edge list feature class representing dispersal network

Methods described in Treml et al. (2008, 2012)Application: modeling dispersal of coral larvae in the Caribbean to assist in systematic conservation planningClick 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 NOAAs Real-Time Ocean Forecast System (RTOFS).Invoke R from ArcGIS

ChlorophyllSSTBathymetryPoint observations of speciesGridded environmental dataPredictive modelProbability of occurrence predicted from environmental covariatesBinary classification

Predictive species distribution modeling

Application: rockfish habitat modelsYoung 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: 247261.

Bathymetry-derived predictor variablesYoung 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 PolandAnna Luczak, University of Wroclaw, Institute of Archaeology, Wroclaw, Poland

Results:Predicted NeolithicSitesAnna LuczakAcknowledgementsA 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 HDFThanks to our funders:

Thank you!Download MGET:http://mgel.env.duke.edu/mget (or Google MGET)Email me:jason.roberts@duke.eduIf 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.