Abstract

The Northeast Regional Ocean Council and the Mid-Atlantic Regional Council on the Ocean (MARCO) recently collaborated to fund the development of maps of marine life to support ocean planning and management. Researchers at several institutions who work collaboratively as the Marine-life Data and Analysis Team (MDAT) assembled a collection of new maps that represents one of the largest known efforts globally to assemble and disseminate spatial data for multiple species and taxa of marine life. NOAA National Centers for Coastal Ocean Science developed the maps of marine bird abundance and distribution for the entire Atlantic coast. The methods used to produce the bird maps are published in a Bureau of Ocean Energy Management Office of Environmental Studies Program report. The MarineCadastre.gov team has selected a subset of the bird layers to include in the National Viewer.

Avian individual species products

The individual species maps represent the results of predictive modeling applied to data from the ‘Compendium of Avian Occurrence Information for the Continental Shelf waters along the Atlantic Coast of the U.S.’ database developed and maintained by USGS and USFWS. See a table of the surveys from the Compendium that were incorporated into the models. The modeling framework enabled predictions beginning 1-2km offshore and extending to the US EEZ boundary along the entire US Atlantic coast. As a result, model predictions are not available for nearshore (0-2km) areas, embayments, or estuaries, such as Long Island. See a table of the environmental covariates used in the modeling process.

Model predictions
  1. Model predictions within area of 95% survey effort
  2. Model predictions masked because of distance from sightings
  3. Model predictions in area of low survey effort; interpret cautiously
  4. Masked model predictions in area of low survey effort

Avian Relative Abundance probabilty model results are the long-term average relative abundance of individuals per strip transect segment. Source data used to create the models are from January 1978 through April 2014. Model resolution is 2km x 2km grid cells, and models were generated with an original extent of approximately the entire US east coast EEZ. Model results are masked (grayed out) beyond 100 km from a minimum-distance path connecting the raw sighting location data. It is important to recognize that the model predictions do not represent absolute occurrence or abundance, rather they are indices of occurrence or abundance. Avian relative abundance predictive maps may inform users in answering the question “relative to other areas, how many more of species X are there likely to be in this area?” Please refer to the full Technical Report for more details.

Avian Relative Occurrence probability model results are the long-term average relative occurrence probability per strip transect segment. Source data used to create the models are from January 1978 through April 2014. Model resolution is 2km x 2km grid cells, and models were generated with an original extent of approximately the entire US east coast EEZ. Model results are masked (grayed out) beyond 100 km from a minimum-distance path connecting the raw sighting location data. It is important to recognize that the model predictions do not represent absolute occurrence or abundance, rather they are indices of occurrence or abundance. Avian relative occurrence maps may inform users in answering questions like “relative to other areas, how much more likely is it that species X occurs in this area.” Please refer to the full Technical Report for more details.

For seasonal models, seasons are defined as:

  • Winter: December 1 to February 28/29
  • Spring: March 1 to May 31
  • Summer: June 1 to August 31
  • Fall: September 1 to November 30

For species of high conservation concern, occurrence probability maps may be more useful than abundance maps. For example, it may be more useful to know how likely it is that a species of conservation interest will occur in a specific area relative to another area, rather than relative differences in abundance. In cases where the abundance model has high uncertainty, the occurrence model component may still be a useful resource.

Characterizations of model uncertainty

90% Confidence Interval Range - From model fit bootstrap procedure. Measure of uncertainty in model predictions originating from observation error, model structural uncertainty, and variability in the occurrence and abundance of birds at a given location over time that is not captured by the long-term average.

Coefficient of Variation - From model fit bootstrap procedure. The CV is a measure of model uncertainty representing the standard deviation of predictions as a proportion of the mean prediction. The magnitude of the CV is less affected by the mean prediction than is the 90% confidence interval range, so it better reflects relative uncertainty across the study area and between models. Values greater than 1, i.e. where the standard error is greater than the density estimate, indicate substantial uncertainty. When high CVs occur where the density estimate is very low, as is often the case, there is little cause for concern. But when high CVs occur where the density estimate is high, it suggests the model cannot predict density well there.

Citation

Any use of the data should be accompanied by the following citations:

Kinlan, B.P., A.J. Winship, T.P. White, and J. Christensen. 2016. Modeling At-Sea Occurrence and Abundance of Marine Birds to Support Atlantic Marine Renewable Energy Planning: Phase I Report. U.S. Department of the Interior, Bureau of Ocean Energy Management, Office of Renewable Energy Programs, Sterling, VA. OCS Study BOEM 2016-039. xvii+113 p. Available online: https://www.data.boem.gov/PI/PDFImages/ESPIS/5/5512.pdf

Curtice, C., Cleary J., Shumchenia E., Halpin P.N. 2016. Marine-life Data and Analysis Team (MDAT) technical report on the methods and development of marine-life data to support regional ocean planning and management. Prepared on behalf of the Marine-life Data and Analysis Team (MDAT). Accessed at: http://seamap.env.duke.edu/models/MDAT/MDAT-Technical-Report-v1_1.pdf

Acknowledgements

NOAA National Centers for Coastal Ocean Science. This study was funded in part by the U.S. Department of the Interior, Bureau of Ocean Energy Management through Interagency Agreement M13PG00005 with the U.S. Department of Commerce, National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Centers for Coastal Ocean Science (NCCOS). This product represents results of predictive modelling applied to data from the 'Compendium of Avian Occurrence Information for the Continental Shelf waters along the Atlantic Coast of the U.S.' database developed and maintained by USGS and USFWS. For more information, please contact Brian Kinlan (NCCOS Biogeography Branch, brian.kinlan@noaa.gov).