Abstract

Four species of sea turtle can commonly be found on the east coast of the United States, the loggerhead, green, Kemp's ridley, and leatherback turtles. All four species are listed under the United States Endangered Species Act. The hardshell species can be found south of the Outer Banks of North Carolina in colder months, moving north to seasonal foraging habitat in the mid-Atlantic, and the North Atlantic for loggerheads, as waters warm. Leatherbacks can be found throughout the waters of the east coast year-round but are more common in warmer months as individuals move to and from breeding habitat in the Wider Caribbean region. Models elucidating broad-scale patterns of distribution and abundance can provide managers with tools to effectively conserve and manage these species at regional and sub-regional scales.

In 2022, spatial density models estimating long term averages of density, abundance, and distribution for the four species were created. The models were adjusted for availability bias using dive data from the east coast or Gulf of Mexico. Perception bias adjustments were derived from surveys in or near the study area that utilized double observer team protocols. These model products represent the first broad scale in-water estimates of abundance and distribution for these species in the study area since 2007. This work was funded by the U.S. Navy to assist with complying with U.S. laws such as the ESA, which require the Navy to assess the potential impacts to protected marine species resulting from military readiness activities.

East Coast Sea Turtle Density Models

The data are long-term monthly average estimates of density, expressed as the number of individuals per square kilometer within the study area, which ranges from the northern Florida Keys to the Gulf of Maine and out to the United States Exclusive Economic Zone. The study area was gridded into 10x10 kilometer blocks, a resolution that aligned with the sampled covariates. The Sea_Turtle_Density service represents the estimated density (individuals per square kilometer) and the Sea_Turtle_CV service represents the estimated coefficient of variation (CV) for the density estimate, which accounts only for GAM parameter uncertainty. The density estimates represent the monthly mean for each cell, averaged for the period 2003-2019, except for the green turtle model which covers 2010-2019. For most of the study area, density was estimated using a habitat-based density model that correlated local abundance observed on systematic line transect surveys with environmental conditions observed at that same location and time. For unsurveyed areas and times, density was estimated by extrapolation.

The shoreline and other geographic features were delineated from a database provided by the Naval Oceanographic Office (NAVOCEANO).

Web Services

Sea Turtle Density - Long-term monthly average estimates of density.

Sea Turtle CV - Coefficient of Variation. The CV is the ratio of the standard error to the estimated density, and helps inform users about the magnitude of variation in model predictions from one place to another. 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.

Citations

Sparks, Laura M. and Andrew DiMatteo (2023) Sea Turtle Distribution and Abundance on the East Coast of the United States. Technical Report prepared for Naval Undersea Warfare Center Division Newport.

DiMatteo Andrew, Roberts JJ, Jones D, Garrison L, Hart KM, Kenney RD, McLellan WA, Lomac-MacNair K, Palka D, Rickard ME, Roberts K, Zoidis AM, and Sparks L. (2023). Sea turtle density surface models along the United States Atlantic coast. Manuscript in review.

Acknowledgements

The authors would like to thank all the data providers and organizations that contributed line transect survey data, and availability bias and perception bias estimates to this project: Lance Garrison, Heather Haas, Chris Sasso, Robert Kenney, Heather Pettis, Tim Cole, Christin Khan, Meghan Rickard, William McClellan, Ann Pabst, Mitchell Rider, Larisa Avens, Ana Cañadas, Dan Engelhaupt, Amy Engelhaupt, Jessica Aschettino, Mark Cotter, Debi Palka, Josh Hatch, Sue Barco, Sam Chavez, Kristen Hart, Kelsey Roberts, The Southeast and Northeast Fisheries Science Centers, Duke University, The University of Rhode Island, New England Aquarium, The New York Department of Environmental Conservation, The University of North Carolina Wilmington, The University of Miami, HDR Inc, Virginia Aquarium & Marine Science Center, and the United States Geological Survey. Jason Roberts contributed to data processing and provided technical advice. Danielle Jones of Naval Facilities Engineering Command Atlantic assisted with project management and collation of availability bias data. Funding for this project was provided by U.S. Fleet Forces Command and was managed on their behalf by Naval Facilities Engineering Systems Command Atlantic.