June 2025

Welcome to interactive presentation, created with Publuu. Enjoy the reading!

Oceanography | Vol. 38, No. 2

46

centered at the seven ADEON bottom lander sites during a

three-year period. Volume backscatter data were gridded both

horizontally (100 m) and vertically (5 m; Figure 4c) to produce

variogram range estimates, the distance over which data are spa­

tially autocorrelated, providing a proxy for scatterer patch size

and representative distance (Legendre and Fortin, 1989). Patch

horizontal lengths were consistently 2–4 km among the seven

ADEON locations (Blair et al., 2021).

A second study compared the spatial and temporal autocor­

relations of vessel survey and stationary backscatter data using

two approaches. First, virtual backscatter transects were cre­

ated by advecting stationary echosounder data using measured

current velocities from the vessel-mounted acoustic Doppler

current profiler during the FSASs at each site. This was done

during the same night an FSAS occurred, so spatial autocorrela­

tion could be estimated for both data types. Next, the tempo­

ral autocorrelation of the two-week-long time series of hourly

backscatter (Figure 4d,e) centered in time on each applicable

FSAS date for three sites (VAC, HAT, and JAX) was converted

into a distance estimate to compare with the FSAS variogram

ranges (Figure 4e). This methodology allowed for longer time

periods (up to two weeks instead of 12 hours) to be analyzed

and for associated autocorrelation patterns to be detected. The

resulting autocorrelation distances from the stationary systems

(0.8–3.4 km) were similar to those (1.3–3.8 km) from vertically

integrated FSAS data from the same three sites (Blair, 2023).

The spatial characteristics of epi- and mesopelagic scattering

layers are rarely measured in the horizontal dimension, yet they

are imperative information for the design and implementation

of monitoring and management programs for pelagic ecosys­

tems (Horne and Jacques, 2018). These findings demonstrate the

importance of considering scale when designing active acoustics

monitoring networks and sampling protocols. Comparing scales

of space and time in the dynamic ocean is a nontrivial task, and

it remains unknown whether the characteristics measured along

the US eastern continental shelf are representative of shelf-slope

environments in other regions.

Acoustic Propagation Modeling

Soundscape modeling is among a number of considerations used

for policy decisions related to ocean sound. It is important to

know the performance accuracy of soundscape models (Heaney

et al., 2024), and measurements from the ADEON project are

extremely valuable for this purpose. For the acoustic modeling

component of ADEON, a wind and shipping soundscape model

was developed for the Atlantic OCS. This permitted evaluation of

the spatial and temporal distributions of the soundscape beyond

the data collected at the lander locations. Acoustic propagation in

the ocean is sensitive to temperature and salinity fields, bathyme­

try, seafloor sediment type, and sea surface roughness (a function

of wind speed) (Jensen et al., 2011). The soundscape modeling

approach consisted of three steps: (1) identify the distributions of

sources contributing to sound in the region and collect the rele­

vant environmental information, (2) compute the acoustic prop­

agation loss from all sources to all receiver positions, and (3) sum

the contributions and compute the SPL.

The regional SPL was computed for the years 2018, 2019,

and 2020 for decidecade bands centered at 20, 50, 100, 200, and

400 Hz. A single snapshot and a monthly average of the SPL

for 50 Hz at the seafloor is shown in Figure 5 panels a and b,

respectively. The temporal observation window was three hours

for 2018 and 2020 and 10 minutes for 2019. The 2019 model

was generated first, and the 10-minute temporal observation

window proved computationally expensive with an extensive

storage requirement; thus, the observation windows for 2018

and 2020 were expanded to three hours. This massive model­

ing product dataset is served to the public on the ADEON web­

site (https://ADEON.unh.edu) as explained in the visualiza­

tion section below. One observation of this modeling study was

that the SPL on the seafloor was often 3 dB higher than that at

10 m depth, due to the downward refraction of shipping sound

(Heaney et al., 2024).

The wind and shipping sound levels for each of the lander

positions were computed with a higher resolution time obser­

vation window of five minutes. Sediment uncertainty, oceano­

graphic variability, and shipping source depth and level uncer­

tainties were incorporated using a Monte Carlo framework. The

sediment uncertainty drives the modeled SPL, permitting an

estimate of the local sediment characteristics when compared

with the observed data. Figure 5c shows the modeled 125 Hz

decidecade band SPL (5th, 50th, and 95th percentiles) along

with the measurements for the WIL site for the first week of

January 2019. The percentiles relate to the weekly mean SPL dis­

tribution across the sediment types. The data match the 5th per­

centile model across the ensemble with only a few passing ships

above the wind noise floor. The comparison of the SPLs using

the best sediment value for BLE (sediment grain size parame­

ter, phi = 5.68) is shown in Figure 5d. The short time duration

peaks are nearby passing surface ships, and the slowly varying

low SPL regions are wind levels. The differences between the

two sites can be attributed to the number of passing ships and

the sediment (WIL having more ships and sediment with higher

acoustic impedance, and BLE having both fewer ships and lower

impedance sediment).

Ecosystem Modeling

The ADEON ecological modeling component focused first on

describing the temporal abundance patterns of marine mam­

mals across the entire study region (Figure 6a). This informa­

tion was then used to quantify the variability in marine mammal

distribution via call density as it related to changing oceano­

graphic conditions. Both the diversity in calling marine mam­

mals as well as the species-specific detection rates were analyzed

concurrent with the lander and remotely sensed oceanographic

Made with Publuu - flipbook maker