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Project Details

Mapping and Modeling Mid-Atlantic Seabirds to Support Spatial Planning of Offshore Renewable Energy

Project Status: This project began in June 2011 and was completed in June 2014

Seabirds and other birds that spend time at sea are wide-ranging and highly mobile, and mapping their at-sea distributions presents a significant challenge. We are producing high-resolution, predictive maps of seabird occurrence and abundance offshore of the U.S. mid-Atlantic coast. These maps will help guide placement of offshore renewable energy facilities, such as wind farms, to reduce potential impacts on birds, filling an important spatial planning information gap in the region.

Why We Care
In a May 2011 Memorandum of Understanding, NOAA and the Bureau of Ocean Energy Management (BOEM) agreed to collaborate on scientific, environmental, and technical issues related to the development and deployment of environmentally sound and sustainable offshore wind and marine and hydrokinetic renewable energy technologies.

An understanding of marine bird distribution and abundance is important to environmentally responsible development of offshore renewable energy resources such as wind farms. Spatial information on marine birds can help reduce potential conflicts between offshore activities and important bird areas. High quality, high resolution maps of marine bird occurrence and abundance are a key information gap for spatial planning and environmental assessment in the mid-Atlantic region. To fill this data gap, NCCOS is working with BOEM’s Environmental Studies Program and the U.S. Geological Survey’s (USGS) Patuxent Wildlife Research Center (PWRC) to develop seasonal and annual maps of marine bird occurrence and abundance. These maps merge existing at-sea seabird survey data with environmental variables in a predictive modeling framework.

What We Are Doing
We are producing maps and predictive models of bird distributions and areas of persistent aggregations of birds offshore of the U.S. mid-Atlantic region. We are using data from the Compendium of Avian Information database compiled by USGS PWRC and the U.S. Fish and Wildlife Service with BOEM funding. 

We are developing high-resolution (2-km) statistical model predictions of the long-term average spatial distributions of important marine bird species and species groups for the mid-Atlantic region, and producing map products that support regional marine planning efforts and provide information that will assist with siting and environmental assessment for offshore renewable energy facilities. These maps are vetted by subject matter experts, and comparisons of historical and recent survey data are also being used to validate newly developed model predictions. The format of the final map products takes into account management-specific needs, in communication with the Mid-Atlantic Regional Council on the Ocean (MARCO), BOEM, and other relevant management and policy authorities.

Project tasks include:

1. Extract sightings from database by species/group for Mid-Atlantic U.S. Exclusive Economic Zone (EEZ).

  • Identify species and groups of interest.
  • Standardize by effort.
  • Develop dataset and species-specific uncertainty estimates where possible.
  • Develop and refine species specific corrections for detection probability and method-based measurement errors.
  • Combine SPUE (sightings per unit effort) across surveys (standardize for different survey methods).
  • Combine species into functional groups where necessary.

2. Compile high-resolution predictors for Mid-Atlantic U.S. Exclusive Economic Zone (EEZ).

  • Acquire, process, and format and document available predictors for the Mid-Atlantic US EEZ.

3. Adapt modeling framework developed by NCCOS for the New York Bight area (Kinlan et al. 2012) to apply to the Mid-Atlantic EEZ region.

  • Account for multiple datasets with different levels of confidence/measurement error.
  • Account for different spatial, temporal support of different datasets.
  • Perform improvements to model methods to maximize predictive performance across the Mid-Atlantic U.S. EEZ.

4. Produce continuous, high resolution (2-km) predictive maps of presence probability and SPUE for bird species and groups of interest

  • Produce maps of seasonal climatological means and/or quantiles.
  • Integrate seasonal maps to produce annual climatology.
  • Produce uncertainty maps for seasonal and annual climatologies.

5. Produce and deliver peer-reviewed report to BOEM on predictive modeling methods and results for Mid-Atlantic U.S. EEZ region. Report will include maps of predicted relative abundance and sighting probability for all species/groups of interest, both annually and by season. Additional maps of relative uncertainty of predictions and statistics summarizing model predictive skill will also be included.

6. Deliver digital versions of predictive maps and uncertainty in format suitable for serving via Multipurpose Marine Cadastre and other relevant GIS data portals and geospatial data access/decision support tools.

What We Found
We have produced seasonal and annual maps of occurrence probability and relative abundance for 27 species of marine birds occurring in waters offshore of the U.S. mid-Atlantic. Distribution and abundance varies both seasonally and spatially.

Benefits of Our Work
Maps of marine bird occurrence probability and abundance are being used by BOEM, other federal and state agencies, and non-governmental organizations to aid marine spatial planning and offshore energy planning in the mid-Atlantic region.

Next Steps
Through a follow-on project with BOEM, NCCOS will be expanding these predictive models and maps to cover the U.S. Atlantic from Maine to the Straits of Florida.

Related Regions of Study: Atlantic Ocean, Atlantic Seaboard

Primary Contacts: Brian Kinlan, John Christensen

Research Area: Science for Coastal Ecosystem Management (Ecological Forecasts and Tools, Biogeographic Assessment, Marine Spatial Planning)

Related NCCOS Center: CCMA


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* Printed on October 25, 2014 at 2:59 AM from http://coastalscience.noaa.gov/projects/detail?key=202.