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Seafloor Mapping and Fisheries Management in Massachusetts Kathryn Ford, Ph.D., Fisheries Habitat Project Lead Daniel W. Sampson, GIS/Data Manager.

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Presentation on theme: "Seafloor Mapping and Fisheries Management in Massachusetts Kathryn Ford, Ph.D., Fisheries Habitat Project Lead Daniel W. Sampson, GIS/Data Manager."— Presentation transcript:

1 Seafloor Mapping and Fisheries Management in Massachusetts Kathryn Ford, Ph.D., Fisheries Habitat Project Lead Daniel W. Sampson, GIS/Data Manager

2 Presentation objectives Part 1:Part 1: –Overview of USGS-CZM mapping cooperative –Seabed habitat classification pilot –Next steps…holistic UKSeaMap approach Part 2:Part 2: –How habitat information is used in fisheries management, some examples

3 CZM-USGS Seafloor Mapping Cooperative Established Partnership Identified Goal Produce high-resolution maps and geospatial data of seafloor geology and topography Prioritized Mapping Needs Determined Effective Methods Implemented Mapping Publish Results and Advertise Availability

4 Standard seafloor mapping methods Stolen from USGS

5 Seafloor Mapping Status existing mapping data of Stellwagen Bank and Massachusetts Bay Buzzards Bay Vineyard Sound Completed Areas Nahant to Gloucester Boston Harbor Cape Ann to Salisbury Hull to Duxbury Northern Cape Cod Bay Eastern Cape Cod Current Mapping Areas Buzzards Bay Vineyard Sound

6 Seabed classification pilot Approx. area = 134 km 2 Depth = 4 – 48 m A variety of bottom types ranging from soft mud to bedrock

7 What was our approach? 1. Create potential habitat polygons based on: –Sediment type –Rugosity 2. Classify the polygons according to Greene et al. habitat classification schema –Scale –Physiography –Induration –Geomorphology...a set of distinct seafloor conditions that may be found in the future to qualify as habitat. Goal:...a set of distinct seafloor conditions that may be found in the future to qualify as habitat.

8 Potential habitat polygons 4,828 polys total Average size = km 2 Std. Dev. = 0.38 km 2

9 Assign attributes to polys All per Greene et al.All per Greene et al. Upgridded both the sediment and rugosity grids to 25m 2Upgridded both the sediment and rugosity grids to 25m 2 Combined the data sets with a logical statement in ArcGISCombined the data sets with a logical statement in ArcGIS i Ss(s/m)f_u1A Soft bottom continental shelf zone composed of unconsolidated sand & mud; flat with very low rugosity & slope

10 Next steps Better examine biology-substrate correlationsBetter examine biology-substrate correlations Further examine auto-classification techniquesFurther examine auto-classification techniques Create an overall accuracy assessment or error budget:Create an overall accuracy assessment or error budget: –Thematic accuracy –Spatial accuracy Combine with other datasets (water column) and follow a UK SeaMap/DFO type of habitat mapping exercise.Combine with other datasets (water column) and follow a UK SeaMap/DFO type of habitat mapping exercise.

11 How does habitat data get used in Fisheries Management (FM)? Part 2: Disclaimer A view from below: A geologic seafloor mapper in a state fisheries agency

12 FM in Mass: Marine Fisheries Advisory Commission (MFAC) Division of Marine Fisheries Legislative – Executive – Judicial Branches Dept. of Fish and Game Executive Office of Energy & Env. Affairs Resource Assessment SportfishShellfish Lobster Statistics Fisheries Habitat Environmental review Policy Habitat research Licensing Conservation Engineering Fisheries Dependent Investigations Northern ShrimpPolicy & Management Protected Species DEP, DCR, CZM? etc Division of Fish & Wildlife, Office of Boating Access, etc

13 Mass. Fish Habitat in the Federal System: Habitat Committee Bottom Mapping Fish Habitat Characterization Working Group ACFHP Reef Committee Habitat Committee Habitat PDT Mass. Division of Marine Fisheries Fisheries Habitat Project ASMFCNEFMC These groups make recommendations to the Council/Commission and address specific tasks assigned by the Council/Commission

14 Management Decisions Based on three legs: –Science –Politics –Fisheries Issues Use Conflict Industry Sustainability Non-fishing Impacts

15 How Strong is the Science Leg? The weakest? –Science is not often definitive –The dollar value of resource impacts due to management decisions is hard to quantify (especially for habitat) –The scientists arent as squeaky* as fishermen or politicians As in the squeaky wheel gets the grease (English idiom).

16 Examples of Habitat Science Used in Fisheries Mgt.: Inshore closures to protect vulnerable habitat and life stages: Cod Conservation Zone Year round mobile gear closures, north shore.

17 Examples of Habitat Science Used in FM: Sinking groundline regs for pot fisheries (Humpback whale illustration courtesy of Scott Landry of Center for Coastal Studies & gear illustration by David Gabriel) Cape Cod Bay Critical Habitat Jan 1- May 15 gear restrictions

18 Examples of Habitat Science Used in FM: Shellfisheries –Red Tide –Eelgrass Western Gulf of Maine Modeling, courtesy of USGS g.html ECOM-si, a variant of the Blumberg and Mellor 3D coastal ocean model

19 But FM in Mass. also covers… Non-fishing impacts Habitat restoration Basic research: lobster, eelgrass, horseshoe crabs, shellfish, cod, striped bass, anadromous spp…. These all use maps! Maps used to extent possible? View of Horns Rev wind farm, Denmark; 8.7 miles offshore MW windmills

20 Habitat Research Priorities How do species use different seafloor types –Are all hard bottom areas the same? –Classification/landscape modeling –Basic life history information What are indicator and keystone species? What should we really be paying attention to? Linkages to the water column Multiple maps for multiple questions

21 Obstacle: Communication Fisheries Scientists: Stock assessments– relative abundance – allowable catches [how many fish] Habitat Scientists: Spatial distribution – non-managed species – resolution [where are the fish] Managers: Different timelines, different pressures, not always knowing the right question to ask.

22 Obstacle: Technology Fisheries data in multiple databases Habitat data in multiple databases Software availability Education Information overload/dueling scientists (is Joint Fact Finding a solution?)

23 Thanks to: Tony Wilbur, Bruce Carlisle David Pierce, Vincent Malkoski, Michael Hickey, Jeremy King, Bob Glenn Weve got a long way to go! Ask us about the Mass. Ocean Plan!


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