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![Page 1: GIS based tools for marine habitat determination and marine spatial planning Tiffany C. Vance NOAA/NMFS/Alaska Fisheries Science Center C.J. Beegle-Krause,](https://reader035.fdocuments.in/reader035/viewer/2022062421/56649ce25503460f949ad80a/html5/thumbnails/1.jpg)
GIS based tools for marine habitat determination and marine spatial
planning Tiffany C. Vance
NOAA/NMFS/Alaska Fisheries Science Center
C.J. Beegle-Krause, David Steube
ASA / Applied Science Associates
Sharon M. MesickNOAA National Oceanographic Data Center / Coastal Data Development
Center
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Why look at habitat?
• Climate studies look at societal impacts – habitat loss/gain/change is analogous for organisms
• Legislative mandate to identify critical habitat• As ecosystem forecasting develops, need for
tools to integrate climate impacts• Element of marine spatial planning –
identifying critical areas and activities that can occur there
• Ecosystem forecasting
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Defining Habitat
• Habitat crucial to survival of organisms
• Habitat can be 2.5 or 3D
• Determining habitat parameters for organisms, e.g. temperature ranges, altitudes or substrate types
• Data gathering vs habitat modeling
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Using GIS to Delineate Marine Habitat
• Seagrass is a typical 2D habitat
• Species interact with the surface
• Bottom type, slope and currents define
‘best’ habitat
• GIS provides many tools to delineate and
model benthic habitats
• Open ocean fish experience a
multidimensional environment
• Species interact with water column
• Optimal pelagic habitat varies by life stage
and is multivariate
• Traditional GIS tools inadequate to
integrate diverse time series data
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• Walleye Pollock produce the largest catch of any single species inhabiting the 200-mile U.S. Exclusive Economic Zone.
• Key forage fish in the ecosystem• One spawning aggregation is in Shelikof Strait.
- Larvae transported down the Strait. - Favorable nursery areas assumed to be inshore- Larval dispersal studied using sampling, drifters and models
Walleye Pollock in Shelikof Strait
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EcoFOCI Forecast Horizon
Years: 1 50
INPUT: Indices ROMS/NPZ IPCC Scenario
OUTPUT : Qualitative Quantitative Quantitative Scenario Prediction Prediction
EXAMPLES : Ecosystems FOCI Recruitment Work in Process Considerations Predictions (Recruitment, Chapter Dominant Stabeno et al., Species (2008) Energy Flow)
EcoFOCI = Ecosystems and Fisheries Oceanography Coordinated Investigations
NPCREP = North Pacific Climate Regimes and Ecosystem Productivity
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HabitatSpace• Pelagic habitat – 3D
• Using in situ data, ocean models and biological data to define habitats
• Interactive not static display
• User can define parameter ranges for organism, iterative
• Statistics to compare habitats
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Software Elements• ArcGIS – extension and standalone tool
• IDV - for analysis and visualization
• netCDF files in a THREDDS server
• EDC - Environmental Data Connector
• ASA COASTMAP
• Statistics toolbox – Python
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System Architecture
Data Sources Data Server Clients
• Ocean Models
- NCOM, ROMS
• Physical data
- temperature, salinity
• Meteorological data
- Wind speed, insolation
• Biological data
-Fish catch abundance
• Larval track
-Modeled using ROMS currents data
Northern Gulf Institute
Ecosystem Data Assembly Center
• Visualization:-Integrate data to define habitats
• ESRI ArcGIS ext or standalone tool
• IDV client
• Statistical Analysis- Hot spot analysis
- Kriging
- Mean center
-User defined, iterative
parameter ranges
-Path of organism through
habitat
• Data Ingest
- ASCII
- NetCDF
- Shapefiles
• Transformation
-From source to standard formats
• Data Service
- THREDDS
- ESRI FGDB
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ASA-IDV Data Connector
• Ocean Model Data (ROMS)• Curvilinear grid• Single file• netCDF CF
compliant • Works ‘out of the
box’
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ESRI Data ConnectionsPhysical
Meteorological
Biological
Particle (Larval) Track
Ancillary (grid)
• Feature data readily ingested
• Point, line & poly
• Raster data readily ingested
• Users specify data rendering with customized menus
• Select and name variables
• Name and save
project files
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Analysis Capabilities• Shape
characterization• Statistics
– Landscape metrics
– Fractal dimension
– Mean center
• Path of organism through habitat
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Conclusions
• Habitat determination is important for marine spatial planning and in determining climate impacts
• GIS can provide tools to describe and model habitats in 3-D
• IDV can be modified to provide visualization and analysis of habitats
• Statistical tools for landscape metrics in 3-D still under development
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For additional information contact:
Guide to the ASA IDV plugin available in the back.
Plugin available at www.asascience.com
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Terrestrial Habitat for Ducklings
http://www.ducks.ca/aboutduc/news/archives/2004/040531.html