We developed these seasonal spatially-explicit density distribution maps under the AMAPPS project using animal distribution data collected during shipboard and aerial line
transect surveys during 2010-2017, dive time data derived from tagged animals, and satellite and model-based static and dynamic environmental information
(Palka et al. 2017; Palka et al. in press).
To display the distribution of animal density (animals/km²) of a North Atlantic cetacean species within the spatial cells of the study area:
Select the species of interest from the drop-down menu.
Select the seasonal data available to display from the drop-down menu. Note: Not every season is available for each species.
Select the symbology of the type of data to be displayed. The data available includes mean density (animals/km²), coefficient of variation (CV), lower
confidence interval (Low Density) and upper confidence interval (High Density).
Click on the Submit button.
Use the mouse scroll wheel to zoom-in and out on a specific region.
For more information about the species of interest click on the link "Information About"
To select a specific region, click on the Select Data button. Note: This button will be grayed out until a selection has been submitted.
Press and hold down the left or right mouse button to start the area selection and release the button to finish the selection. A new pop-up window will display a
summary of the area selected with the mean density/or number of animals, confidence interval, and coefficient of variation. In addition the pop-up window will display
the details for each of the cells selected.
There are two different ways to download the species specific data displayed on the maps. The downloaded data are in CSV format and includes the following fields:
Cell area in km²
Species Name (only available when downloading all species data)
Season (only available when downloading all species data)
Mean Abundance (rounded to the nearest integer)
Low Density (defined as the lower confidence interval (2.5%))
High Density (defined as the upper confidence interval (97.5%))
Coefficient of Variation (CV)
To download the values for the entire seasonal species maps available for the AMAPPS study area in CSV format, click the "Download All" button.
After a user-defined region has been selected, the information for each selected cell can be downloaded by clicking on the "Download as CSV" button.
Distribution and abundance of wildlife is largely driven by physical and biological environmental factors, including climate, habitat characteristics, and prey
distribution (Ainley et al. 2005). To account for this, these spatially- and temporally-explicit density maps were based on animal density -
environmental generalized additive statistical models that were fit to visual shipboard and aerial survey line-transect data, associated survey
conditions, animal group characteristics, spatially- and temporally-explicit static and dynamic environmental data, and species-specific availability
bias correction factors. For a more completed description, see Palka et al. (2017)
and Palka et al. (in press).
Visual line-transect data from ships and planes may result in incorrect density estimates when visibility biases are not
accounted for (McLaren 1961). There are two types of visibility bias:
Availability bias (due to animals that were missed because they were submerged and thus not available to be detected)
Perception bias (due to animals that were available to be detected but were missed because of a variety of other reasons, such as distance from
the platform or poor sighting conditions due to sun glare or sea state)
In this analysis, we accounted for both perception and availability bias. We used two-team visual line-transect data to address perception bias, and ancillary dive
time data to address availability bias. We used the following general workflow:
We collected shipboard and aerial survey line transect data that included the location of the sighting relative to the sighting platform, species
identification, group size, group behavior and activities, number of animals in the group, and general sighting conditions
(Beaufort sea state, cloud cover, swell height, etc). For a more complete description, see Palka (2020) and Garrison (2020).
We collected static and dynamic habitat data over the same spatial and temporal scales as the line transect data.
We defined the study area and strata by dividing all data into standardized spatial grid cells (10 x 10 km²) and standardized temporal
time periods (8-days), hereafter referred to as spatial-temporal cells.
We conducted quality control checks, processed, and then collated all input data into a common database.
We estimated species and platform specific ocean surface density estimates accounting for perception bias for each species (or species guild)
within each spatial-temporal cell that had survey effort using
techniques (Thomas et al. 2010). We used 4 platforms that collected data (northeast ship, northeast plane,
southeast ship and southeast plane), where the northeast surveys were conducted by the
Northeast Fisheries Science Center
and the southeast surveys were conducted by the
Southeast Fisheries Science Center.
The Distance analysis techniques that account for perception bias involve the estimation of a detection function, and p(0) - the probability
of detecting a group on the track line – using significant survey related covariates (such as, sighting conditions, group size, animal behavior, etc.).
We estimated a species-platform specific availability bias correction factor using information on the average surface and dive times, group sizes,
and viewing area from the platform.
We estimated the bias-corrected density estimates that account for availability and perception bias for each spatial-temporal cell that had survey
effort by applying the species-platform specific availability bias correction factor to the species-platform ocean surface density estimate that
accounted for perception bias.
We next developed an animal density-habitat
Generalized Additive Model
(GAM model) using data from each spatial-temporal cell that had survey effort where we used several goodness-of-fit tests to choose the best fitting
model of the relationship between the cell’s bias-corrected density and associated static and dynamic environmental variables.
We predicted animal density and associated measures of uncertainty for all spatial-temporal cells using the GAM modeled animal
We calculated seasonal mean density (number of animals per km²) and abundance (number of animals), along with associated measures of uncertainties.
Finally, we displayed results by plotting maps of spatially explicit densities and associated measures of uncertainty. We generated the maps
using the oblique Mercator Projected defined as:
CRS(‘+proj=omerc +lat_0=35 +lonc=-75 +alpha=40 +k=0.9996 +x_0=0 +gamma=40 + y_0=0 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0’).
AMAPPS phase I was during 2010–2014, phase II was during 2015–2019, while phase III is during 2019-2023. For more on the AMAPPS program, visit the
and Palka et al. 2017.
The objectives of the phase I of the AMAPPS program were to investigate both fine and broad scale patterns. Specifically the objectives were:
Collect broad-scale data over multiple years on the seasonal distribution and abundance of marine mammals (cetaceans and pinnipeds), marine turtles,
and seabirds using direct aerial and shipboard surveys of coastal US Atlantic Ocean waters;
Collect similar data at finer scales at several (~3) sites of particular interest to NOAA’s partners using visual and acoustic survey techniques;
Conduct tag telemetry studies within surveyed regions of marine turtles, pinnipeds and seabirds to develop corrections for availability bias in
the abundance survey data and collect additional data on habitat use and life-history, residence time, and frequency of use;
Explore alternative platforms and technologies to improve population assessment studies;
Assess the population size of surveyed species at regional scales;
Develop models and associated tools to translate these survey data into seasonal, spatially-explicit density estimates incorporating habitat characteristics.
Because marine ecosystems are complex and involve dynamic assemblages of many co-existing species, to understand these marine ecosystem
processes and achieve the AMAPPS objectives, research was integrated across taxonomic groups, among trophic levels and used a suite
of data collection and analytical techniques. To enumerate distribution and abundance, the following types of data were collected:
Sightings of cetaceans, seabirds, sea turtles, and seals from shipboard and aerial surveys;
Acoustic detections of vocalizing cetaceans and fish from
ship-towed and bottom-mounted passive acoustic recorders;
Location/depth information derived from various types of tags that were deployed on turtles,
seals and cetaceans.
To place all of these data into an ecosystem context, spatially- and temporally-explicit static and dynamic remotely sensed
data were compiled, in situ data were collected on physical oceanographic characteristics and biological data of plankton,
fish, and other trophic levels, and these ecosystem habitat-type data were compared to the local densities of target species.
This work represents the efforts of many individuals and funders. Users of these map products are encouraged to use the following citation when the data
or maps are included in reports or publications:
Palka D, Aichinger Dias L, Broughton E, Chavez-Rosales S, Cholewiak D, Davis G, DeAngelis A, Garrison L, Haas H, Hatch J, Hyde K, Jech M, Josephson E,
Mueller-Brennan L, Orphanides C, Pegg N, Sasso C, Sigourney D, Soldevilla M, Walsh H. 2021. Atlantic Marine Assessment Program for Protected Species:
FY15 – FY19. Washington DC: US Department of the Interior, Bureau of Ocean Energy Management. OCS Study BOEM 2021-051
Ainley, D.G., L. Spear, C.T. Tynan, J.A. Barth, S.D. Pierce, R. Glenn Ford, T. Cowles. 2005. Physical and biological variables affecting seabird
distributions during the upwelling season of the northern California Current. Deep Sea Research II 52(2):123–143.
Garrison LP. 2020. Abundance of marine mammals in waters of the U.S. east coast during summer 2016. Southeast Fisheries Science Center, Protected
Resources and Biodiversity Division, 75 Virginia Beach Dr., Miami, FL 33140.
PRBD Contribution #PRBD-2020-04; 17 pp.
McLaren IA. 1961. Methods of determining the numbers and availability of ringed seals in the eastern Canadian Arctic. Arctic 14:162-175.
Palka D. 2020. Cetacean abundance in the US Northwestern Atlantic Ocean summer 2016. US Dept Commer, Northeast Fish Sci Cent Ref Doc. 20-05; 60 pp.
Palka D, Aichinger Dias L, Broughton E, Chavez-Rosales S, Cholewiak D, Davis G, DeAngelis A, Garrison L, Haas H, Hatch J, Hyde K, Jech M,
Josephson E, Mueller-Brennan L, Orphanides C, Pegg N, Sasso C, Sigourney D, Soldevilla M, Walsh H. 2021. Atlantic Marine Assessment Program
for Protected Species: FY15 – FY19. Washington DC: US Department of the Interior, Bureau of Ocean Energy Management. OCS Study BOEM 2021-051
Questions concerning the data and/or methodology should be directed to Debra Palka.
We obtained partial funding for this study from 2 Interagency Agreements with the U.S. Department, National Oceanic and Atmospheric Administration,
National Marine Fisheries Service, Northeast Fisheries Science Center: (a) Interagency Agreements M10PG0075, M14G00005, and M19PG00007 with the
US Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies Program, Washington, DC and (b) Interagency Agreements
NEC 11-009, NEC 15-004, and NEC-16-011-01 with the Department of Defense, U.S. Navy.
The views and conclusions contained in this document and website are those of the authors and should not be interpreted as representing the opinions
or policies of the US Government, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. These maps
represent estimates of seasonal average densities and abundances. Thus, it is not expected to exact numbers will be present on any particular day.