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GIS based tools for marine habitat determination and marine spatial planning Tiffany C. Vance NOAA/NMFS/Alaska Fisheries Science Center C.J. Beegle-Krause,

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Presentation on theme: "GIS based tools for marine habitat determination and marine spatial planning Tiffany C. Vance NOAA/NMFS/Alaska Fisheries Science Center C.J. Beegle-Krause,"— Presentation transcript:

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, David Steube ASA / Applied Science Associates Sharon M. Mesick NOAA National Oceanographic Data Center / Coastal Data Development Center

2 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

3 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

4 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

5 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

6 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

7 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

8 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

9 System Architecture Data SourcesData ServerClients 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

10 ASA-IDV Data Connector Ocean Model Data (ROMS) Curvilinear grid Single file netCDF CF compliant Works ‘out of the box’

11 ESRI Data Connections Physical 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

12 Analysis Capabilities Shape characterization Statistics –Landscape metrics –Fractal dimension –Mean center Path of organism through habitat

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14 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

15 For additional information contact: Tiffany.C.Vance@noaa.gov Guide to the ASA IDV plugin available in the back. Plugin available at www.asascience.com

16 Terrestrial Habitat for Ducklings http://www.ducks.ca/aboutduc/news/archives/2004/040531. html


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