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Marine GeospatialEcology ToolsJason Roberts, Ben Best, Dan Dunn, Eric Treml and Pat HalpinDuke Marine Geospatial Ecology Lab

The development of MGET was funded by:

MGET is an ArcGIS toolboxIt can also be invoked from most programming languagesOver 250 ToolsMGET is used worldwide

81 countries (map is missing 25)~2300 installs since August 2009More MGET factsFree, open-source softwareRequires Windows and ArcGISThese requirements are slowly disappearingEasy installation (just click Next, Next, Next)Written in Python, R, MATLAB, and C/C++Uses free MATLAB Component RuntimeTour of the toolsLets see some examples from each toolset

Convert data

MGET supports a growinglist of products and algorithms

Lets look at some examples

Easily acquire oceanographicdata in GIS-compatible formatsMGET provides customized tools for each data product that it supportsThe tool shown here is a simple one: it downloads ocean color data in a GIS-compatible formatThis may seem trivial but GIS users regularly cite data import as 80% of the work of any project

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.

Leatherback Track Video(click link above while viewing slide show)Leatherback movement modelingSchick, 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 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: albatross habitat suitabilityydelis, 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 IndexMillers composite front maps

FFUFCSF

%

Miller P, et al. (in review) Frequent locations of ocean fronts as an indicator of pelagic diversity: application to marine protected areas and renewablesAreas of Additional Pelagic Ecological Importance (AAPEI)Summer frequent front map

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 outputEddy Detection Video(click link above while viewing slide show)Application: 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.Longline catch per unit effort (1993-2005)

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.

Querying OBIS

Query OBISs ~30 million recordsFilter by taxon, bounding box, dates, etc.Download results as GIS point features

Map species biodiversity

Temporal periodicity analysis for swordfishTop histogram shows how CPUE varies over timePeriodogram shows periods of cycles detected in the dataFirst find large spikes, then look up period on x axisImportant periods:365 days: annual cycle29.5 days: lunar cycle1 day: diurnal cycleRadial histograms shows CPUE by day of year and lunar phase365 days annual cycle

Yellowfin and swordfish have different seasons

Bigeye CPUE highest in full moonSparse data for bluefin noisy periodogramPossible lunar and seasonal patternsAnnual harmonics at 121 and 91 days: short seasonNoise due to sparse data ignore!How does this work?How do we identify cycles in complicated-looking data?

CPUEWe 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 signal3 component signals

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

Larval Dispersal Video 1Larval Dispersal Video 2(click links above while viewing slide show)Invoke R from ArcGIS

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

Model species habitat

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.

Bathy-derived predictor variablesResults: yellowtail rockfish

AcknowledgementsA 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:

Thanks for coming!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.