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Marine Geospatial Ecology Tools

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Presentation on theme: "Marine Geospatial Ecology Tools"— Presentation transcript:

1 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:

2 MGET is an ArcGIS toolbox
Over 250 Tools It can also be invoked from most programming languages

3 MGET is used worldwide ~2300 installs since August 2009
81 countries (map is missing 25)

4 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

5 Let’s see some examples from each toolset…
Tour of the tools Let’s see some examples from each toolset…

6 Convert data

7 MGET supports a growing list of products and algorithms
Let’s look at some examples…

8 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

9 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:

10 Leatherback Track Video (click link above while viewing slide show)

11 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.

12 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

13 Cayula & Cornillon algorithm
~120 km Daytime SST 03-Jan-2005 28.0 °C 25.8 °C Mexico Front Step 1: Histogram analysis ArcGIS model Bimodal Optimal break 27.0 °C Frequency Temperature Example output Step 2: Spatial cohesion test Mexico Strong cohesion  front present Weak cohesion  no front

14 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: /rspb

15 Miller’s composite front maps
FF UF CSF % Areas of Additional Pelagic Ecological Importance (AAPEI) Summer frequent front map Miller P, et al. (in review) Frequent locations of ocean fronts as an indicator of pelagic diversity: application to marine protected areas and renewables

16 Detecting mesoscale eddies
This tool detects eddies in SSH images collected by NASA/CNES radar altimeters

17 Gulf stream eddies Image from

18 Okubo-Weiss eddy detection
SSH anomaly Example output Aviso DT-MSLA 27-Jan Red: Anticyclonic Blue: Cyclonic Negative W at eddy core

19 Eddy Detection Video (click link above while viewing slide show)

20 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.

21 Longline catch per unit effort (1993-2005)

22 Effects of Other Parameters on CPUE
Results Species CPUE in eddy habitats Effects of Other Parameters on CPUE 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)

23 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:

24 Querying OBIS Query OBIS’s ~30 million records
Filter by taxon, bounding box, dates, etc. Download results as GIS point features

25 Map species biodiversity

26 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

27 Yellowfin and swordfish have different seasons

28 Sparse data for bluefin  noisy periodogram
Possible lunar and seasonal patterns Noise due to sparse data – ignore! Bigeye CPUE highest in full moon Annual harmonics at 121 and 91 days: short season

29 How does this work? How do we identify cycles in complicated-looking data? CPUE

30 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) 3 component signals Original signal

31 Larval density rasters
Model larval connectivity Habitat patches Ocean currents data Larval density rasters Tool downloads data for the region and dates you specify Edge list feature class representing dispersal network

32 Larval Dispersal Video 1 Larval Dispersal Video 2 (click links above while viewing slide show)

33 Invoke R from ArcGIS

34 Model species habitat Point observations of species
Probability of occurrence predicted from environmental covariates Predictive model Gridded environmental data Binary classification Chlorophyll SST Bathymetry

35 Application: rockfish habitat models
Young 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: 247–261.

36 Bathy-derived predictor variables

37 Results: yellowtail rockfish

38 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:

39 Thanks for coming! (or Google “MGET”)
Download MGET: (or Google “MGET”) me: 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:

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