OCS Study

BOEM 2020-020

US Department of the Interior

Bureau of Ocean Energy Management

Office of Renewable Energy Programs

Occurrence of Commercially Important 

and Endangered Fishes in Delaware

Wind Energy Areas Using Acoustic 

Telemetry
OCS Study

BOEM 2020-020

US Department of the Interior

Bureau of Ocean Energy Management

Office of Renewable Energy Programs

Occurrence of Commercially Important 

and Endangered Fishes in Delaware 

Wind Energy Areas Using Acoustic 

Telemetry

January 2020

Authors:

Danielle Haulsee, Post-Doctoral Researcher

College of Earth, Ocean, and the Environment

University of Delaware, Lewes, DE 19958

Matthew J. Oliver, Professor

College of Earth, Ocean, and the Environment

University of Delaware, Lewes, DE 19958

Dewayne Fox, Professor

College of Agriculture and Related Sciences 

Delaware State University, Dover, DE 19901

Prepared under Cooperative Agreement M16AC00009
DISCLAIMER

Study collaboration and funding were provided by the US Department of the Interior, Bureau of Ocean 

Energy Management (BOEM), Environmental Studies Program, Washington, DC, under Agreement 

Number M16AC00009. This report has been technically reviewed by BOEM, and it has been approved 

for publication. The views and conclusions contained in this document 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.

REPORT AVAILABILITY

To download a PDF file of this report, go to the US Department of the Interior, Bureau of Ocean Energy 

Management Data and Information Systems webpage (http://www.boem.gov/Environmental-StudiesEnvData/), click on the link for the Environmental Studies Program Information System (ESPIS), and 

search on 2020-020. The report is also available at the National Technical Reports Library at 

https://ntrl.ntis.gov/NTRL/.

CITATION

Haulsee DE, Fox DA, Oliver MJ. 2020. Occurrence of Commercially Important and Endangered Fishes in 

Delaware Wind Energy Areas Using Acoustic Telemetry. Lewes (DE): US Department of the 

Interior, Bureau of Ocean Energy Management. OCS Study BOEM 2020-020. 80 p.

ABOUT THE COVER

Captain Wes Townsend redeploys an acoustic receiver mooring in the Delaware Wind Energy Area array 

in the early morning.

ACKNOWLEDGMENTS

We would like to extend our sincere gratitude and thanks to Brian Hooker (BOEM) for his role 

in administering project funding. Members of the Atlantic Cooperative Telemetry (ACT) 

Network graciously provided species identification of fish detected during this project. In 

particular, we thank Dr. Dave Secor and Ellie Rothermal for their contributions to the project. 

We thank Dr. Vince Guida, MARACOOS, and Rutgers University for the environmental data 

used in our analyses. We appreciate the invaluable field assistance provided by Captain Kevin

Wark for expertise in the safe capture and firsthand knowledge of Atlantic sturgeon in the mid‐

Atlantic region. In addition, we are grateful for the assistance of Captain Wes Townsend with 

designing, deploying and maintaining the acoustic telemetry receivers.
4

Contents

List of Figures................................................................................................................................................6

List of Tables.................................................................................................................................................8

Executive Summary ......................................................................................................................................9

Introduction .........................................................................................................................................11

1.1 Project objectives.......................................................................................................................12

Methods - Delaware Wind Energy Area Study Design.......................................................................13

2.1 Study Region - Physical Setting ................................................................................................13

2.2 Acoustic Receiver Array ............................................................................................................15

Deployment ...........................................................................................................................15

Maintenance and Recovery...................................................................................................16

Acoustic Telemetry Data Distribution ....................................................................................17

Methods - Atlantic Sturgeon and Winter Skate Tagging.....................................................................19

3.1 Fish Sampling ............................................................................................................................19

3.2 Atlantic Sturgeon Transmitter Implantation ...............................................................................22

3.3 Winter Skate Transmitter Implantation ......................................................................................23

Methods - Seasonal Occurrence in DE WEA .....................................................................................24

4.1 Temporal Occurrence and Spatial Distribution..........................................................................25

4.2 Residency and Rate of Movement ............................................................................................25

4.3 Environmentally Driven Predictive Model..................................................................................26

Results - Recovery Efficiency of Standard vs. Acoustic Release Receiver........................................29

5.1 Interaction with Acoustic Seafloor Survey Vessel .....................................................................29

Species Occurrence and Distribution..................................................................................................32

6.1 Atlantic Sturgeon .......................................................................................................................32

6.2 Winter Skate ..............................................................................................................................34

6.3 All Other Species .......................................................................................................................36

Atlantic Sturgeon Residency and Rate of Movement .........................................................................40

7.1 Residency ..................................................................................................................................40

7.2 Non-Residency ..........................................................................................................................41
5

Predictive Habitat Models ...................................................................................................................43

8.1 Association with Bottom Type....................................................................................................43

8.2 GAM Selection and Performance ..............................................................................................44

8.3 Seasonal Predicted Distribution in DE WEA .............................................................................48

Discussion...........................................................................................................................................50

9.1 Observed Species Occurrence..................................................................................................50

9.2 Atlantic Sturgeon Residency and Movement.............................................................................51

9.3 Areas of Intensive Use...............................................................................................................51

Conclusions and Recommendations .........................................................................................52

References.................................................................................................................................52

Appendix A: Wentworth grain size chart.....................................................................................................57

Appendix B: All Species Observed Occurrences by Month........................................................................58
6

List of Figures

Figure 1. Map of study region. ....................................................................................................................13

Figure 2. Map of bathymetry and sediment type in DE WEA. ....................................................................14

Figure 3. Acoustic receiver mooring designs..............................................................................................15

Figure 4. Photo demonstrating the proximity to acoustic release receiver station. ....................................17

Figure 5. Fish sampling aboard F/V Dana Christine...................................................................................19

Figure 6. Collecting metadata for adult Atlantic sturgeon and Winter skate in live well. ............................20

Figure 7. Atlantic sturgeon with field datasheet before release..................................................................21

Figure 8. Length frequency for Atlantic sturgeon captured between 2 April 2017 and 7 May 2017...........22

Figure 9. Example of adult female gonadal tissue and interrupted suture closure of incision....................23

Figure 10. Length frequency for winter skate captured between 2 April 2017 and 7 May 2017. ...............24

Figure 11. Winter skate acoustic transmitter surgical implantation.............................................................25

Figure 12. Climatology of SST and modeled bottom temperature for July 3-11 in the DE WEA. ..............27

Figure 13. Map of DE WEA VEMCO acoustic receiver array compared to vessel traffic...........................30

Figure 14. Accumulation of acoustic detections over time compared to peaks in ambient noise level in DE 

WEA. ...........................................................................................................................................................31

Figure 15. Individual Atlantic sturgeon occurrences by month and region of the DE WEA........................32

Figure 16. Spatial distribution of detections of Atlantic sturgeon on receivers of the DE WEA broken down 

by month......................................................................................................................................................33

Figure 17. Individual winter skate occurrences by month and region of the DE WEA. ..............................34

Figure 18. Spatial distribution of detections of winter skate on receivers of the DE WEA by month..........35

Figure 19. Number of tagged individuals detected .....................................................................................36

Figure 20. Occurrences of all species detected in the DE WEA throughout time. .....................................37

Figure 21. Individual occurrences by month and region of the DE WEA....................................................38

Figure 22. Spatial distribution of detections of all species on receivers of the DE WEA broken down by 

month. .........................................................................................................................................................39

Figure 23. Duration of residency events for Atlantic sturgeon in the DE WEA over time...........................40

Figure 24. Calculated transit rates for Atlantic sturgeon in Mid-Atlantic WEAs..........................................41
7

Figure 25. Time series of calculated transit rates for Atlantic sturgeon in Mid-Atlantic WEAs. ..................42

Figure 26. Calculated transit rates for Atlantic sturgeon in Mid-Atlantic WEAs..........................................42

Figure 27. Associations between Atlantic sturgeon and Winter skates and sediment types observed in DE 

WEA. ...........................................................................................................................................................43

Figure 28. Variable importance of predictors in best performing GAM model for predicting Atlantic 

sturgeon occurrence in DE WEA. ...............................................................................................................46

Figure 29. Predictor variable response functions for generalized additive models predicting the binomial 

response of Atlantic sturgeon in the DE WEA. ...........................................................................................47

Figure 30. Predicted Atlantic sturgeon occurrence for best GAM model including bottom type 

characteristics. ............................................................................................................................................49
8

List of Tables

Table 1. Cumulative number of individuals per species detected on DE BOEM acoustic array between 

deployment 27 Feb 2017 and recovery 23 Feb 2019.................................................................................18

Table 2. Summary of VEMCO acoustic receivers lost during study. ..........................................................29

Table 3. Generalized additive model performance statistics for predicting Atlantic sturgeon occurrence in 

the DE WEA including bottom type characteristics.....................................................................................45
9

Executive Summary

The Mid-Atlantic Bight has been identified as an area with great offshore wind potential, creating interest 

in the development of offshore wind energy projects in Wind Energy Lease Areas (WEAs). The Delaware 

(DE) WEA starts approximately 11 km off the coast, but little information about the distribution and 

occurrence of marine species in this area is known. Between 2017-2019, an extensive acoustic telemetry 

project monitored the timing and location of Atlantic sturgeon, winter skate, and other acoustically

telemetered species in the DE WEA (Figure 1). This study was conducted to better understand the 

potential of encountering these species during future development in the DE WEA, as well as address 

potential impacts to commercial fisheries. The primary objectives of this study were: (1) Acoustically tag 

additional Atlantic sturgeon (n=50) and winter skate (n=50) in the DE coastal ocean (2) Deploy an 

acoustic receiver array for two years in the DE WEA (3) Determine seasonal usage of Atlantic sturgeon, 

winter skate and other acoustically telemetered species as provided by the ACT Network in the DE WEA 

(4) Develop environmentally driven models of Atlantic sturgeon and winter skate in the DE WEA (5) 

Determine transit times of acoustically telemetered species in the DE WEA and nearby coastal ocean (6) 

Deliver acoustic detection data to acoustic telemetry networks (ACT, MATOS, OTN) (7) Compare the 

recovery efficiency of standard and acoustic release receivers. Our key findings are highlighted below: 

(1) Acoustic detections from all receivers in the array documented the presence of 26 different 

marine fish and mammals, which creates a baseline to inform future monitoring efforts.

(2) Atlantic sturgeon occurred in the DE WEA nearly year-round, however distribution shifted from 

the shallow northwestern corner in the spring-summer, to the deeper offshore waters in the fall 

and winter. Atlantic sturgeon detections were highest in November and December, with far fewer

Atlantic sturgeon detections during the summer, especially August. These observations expand 

upon previous studies for Atlantic sturgeon which characterized their occurrence as much more 

seasonal than documented in our study. Patterns in the timing and distribution of Atlantic 

sturgeon were consistent between years.

(3) Using detections from collaborators monitoring the Maryland (MD) WEA and our detections in 

the DE WEA, we estimated the transit rates for Atlantic sturgeon migrating north in the spring, 

and south in the fall. Transit rates were consistent with those reported by other studies in the MidAtlantic. In addition, we quantified residency behavior in the DE WEA, which was highest in 

November and December. This provides further evidence that these months are likely when there 

is the greatest risk of encountering Atlantic sturgeon in the DE WEA. 

(4) A generalized additive model (GAM) was created to predict the occurrence and distribution of 

Atlantic sturgeon using remotely sensed ocean surface parameters (temperature, color), modeled 

ocean bottom temperature, and seafloor characteristics (bathymetry, sediment grain size, and 

bottom temperature). This model expands upon similar models produced for Atlantic sturgeon in 

the area but captures the distribution of Atlantic sturgeon further offshore than previously 

observed. Model outputs reflect the seasonality of the distribution and occurrence of Atlantic 

sturgeon in the DE WEA. We also show selection for coarse sand by Atlantic sturgeon for the 

first time in this region.

(5) Few tagged winter skate were detected in the DE WEA, indicating that there is a low risk of 

impacting the commercial stock of winter skate due to offshore wind development in this WEA. 

The few winter skate that briefly entered the DE WEA did so in the spring and fall. Based on the 

acoustic detections of all tagged species detected, it appears the spring and fall are important 
10

seasons in the study region, with high numbers of species occurring in the DE WEA. This is 

presumably related to the seasonal migrations of these species.

(6) We observed a large variety of elasmobranch species occurring in the DE WEA, mostly during 

the summer months. In addition, we detected two marine mammals (seals), which entered the DE 

WEA in the spring. Further research is needed to better understand the occurrence and 

distribution of these species in this region, and to establish baselines for their abundance predevelopment.

(7) While not a focal species, we noted a high number of detections of striped bass; a species which 

supports a large commercial and recreational fishery. Striped bass occurrence was highly 

seasonal, with fish occurring in high numbers in November through January slightly more inshore 

and in March through May slightly more offshore. 

(8) Our study demonstrated the utility of acoustic-release transceivers, which minimized gear loss 

and enabled more reliable data collection of acoustic detections for tagged marine animals in this 

region. In addition, acoustic-release receivers allowed us to quantify excess ambient noise in the 

study region due to seafloor mapping efforts being conducted in anticipation for offshore wind 

energy development in the DE WEA. 
11

Introduction

Technological advances and recent changes in national energy policy objectives have increased the 

interest in offshore wind energy. The mid-Atlantic continental shelf, which includes Delaware, has been 

identified by the National Renewable Energy Laboratory as the US coastal region with the greatest 

offshore wind resource potential in waters less than 60 m deep and therefore is very well suited for nearterm offshore wind energy technology (Musial and Ram 2010). This region also supports many 

commercial and recreational fisheries that contribute significantly to the coastal economy. A challenge 

with managing a multi-use coastal ocean is developing a spatial management plan that accounts for the 

dynamic nature of marine species distributions and human activities (Foley et al. 2010). At present, there 

is limited site-specific information on the seasonal patterns of distribution and habitat use of economically 

important species (e.g. striped bass, spiny dogfish, summer flounder, and winter skate) as well as species 

protected under the Endangered Species Act (e.g. Atlantic sturgeon), in and around the proposed offshore 

wind energy areas in DE. This information is necessary for BOEM to minimize interactions with these 

species for compliance with the National Environmental Policy, Endangered Species, and MagnusonStevens Fishery Conservation and Management Acts.

The Mid-Atlantic Bight coastal ocean is shallow, and the warm summers and cold winters in the 

Northeast US cause large seasonal changes in sea temperature that drive large scale migrations of 

commercially important and protected species. While this shallow region may appear featureless, ancient 

river channels, sand waves and heterogeneous substrate distributions may also influence the occurrence 

and distribution of marine species, especially those that are benthically associated. In this study, we 

focused on the federally protected Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) and the 

commercially important winter skate (Leucoraja ocellata), through the use of a passive acoustic telemetry 

array deployed around the perimeter of the Delaware Wind Energy Lease Area (DE WEA). Both of these 

species are associated with the benthos and are thought to seasonally occur in the study region. Although 

our primary focus was these two species, we also leveraged an additional 48 species of acoustically 

telemetered marine fishes, sea turtles and mammals totaling 4,509 active tags currently registered in the 

Atlantic Cooperative Telemetry (ACT) Network database. 

Sturgeons (family Acipenseridae) are highly subject to overexploitation and extinction leading to their 

recognition as the most imperiled group of organisms on the IUCN Red List (IUCN 2010). Atlantic 

sturgeon experienced severe declines due to habitat destruction and overfishing beginning in the late 19th 

century. Lack of recovery coupled with concerns over continued loss/degradation of habitat, ship strikes, 

and bycatch in commercial fisheries resulted in NOAA-NMFS listing five Distinct Population Segments 

(DPS) under provisions of the Endangered Species Act in 2012 ( United States Office of the Federal 

Registry. 2012). The mid-Atlantic, which includes the Delaware and Hudson Rivers, historically 

supported the largest populations of Atlantic sturgeon (Secor and Waldman 1999). While there is a 

growing body of information on the riverine habitat requirements for this species, information on their 

marine habitats is severely lacking (Dunton et al. 2010; Erickson et al. 2011; Oliver et al. 2013). Studies 

in the Mid-Atlantic Bight have shown evidence that Atlantic sturgeon seasonally migrate along the East 

Coast of the U.S. and through the Mid-Atlantic Bight, but these studies have been limited to the nearshore 

environment (< 16 km; Erickson et al. 2011; Breece et al. 2018). This lack of information on marine 

habitat use is of particular concern given the fact that Atlantic sturgeon spend the vast majority (>90%) of 

their adult lives in coastal and offshore waters and are completely dependent on this region for food 

resources. Atlantic sturgeon are a large (max weight > 400kg) and highly mobile species, as a result they 
12

are not commonly encountered in traditional gillnet or trawl surveys due to issues surrounding gear 

selectivity (Dunton et al. 2010). Telemetry is therefore a much more effective technique for understanding 

the seasonal presence, habitat use and movement pathways of sturgeon.

Similar to sturgeons, skates (family Rajidae) are highly vulnerable to overexploitation due to a 

combination of life history traits (Swain et al. 2005). Once widely considered a “trash fish” the winter 

skate, one of the largest and most common skates in mid-Atlantic and New England waters, now supports 

large scale gillnet and trawl fisheries throughout much of its range (Frisk et al. 2008). At present there is 

growing concern over the long-term sustainability of these fisheries resulting in calls for improved data to 

manage this species (Kelly and Hanson 2013). Winter skates are commonly found on sandy to gravelly 

bottom in waters shallower than 111m (McEachran and Musick 1975) and are known to occur in the DE 

WEA, although information required to develop environmentally driven occupancy models are not 

available at this time (Packer et al. 2003). Beyond their commercial value, Winter skate play a key role in 

structuring benthic communities through their foraging (Link 2007). Based on conventional markrecapture and trawl surveys, winter skate are believed to make both inshore-offshore and north-south 

migrations based on relative age/sex and time of year (Packer et al. 2003) although we are not aware of 

any telemetry studies providing finer scale information. 

At the conclusion of this two-year survey, we have a baseline dataset of the occurrence and distribution of 

our focus species, as well as over 20 additional telemetered species that traveled through the acoustic 

array. These results will help establish pre-construction standards of marine animal behavior, occurrence 

and distribution in the DE WEA.

1.1 Project objectives

The main objective of this study was to provide information on the seasonal patterns of occurrence and 

habitat use as well as explore the underlying causal mechanisms and environmental correlates for Atlantic 

sturgeon and winter skate habitat selection in the DE WEA. Our approach provides a robust estimate of 

Atlantic sturgeon and winter skate distribution and habitat use in the DE WEA through the use of acoustic 

telemetry arrays. Following the BOEM guidelines for providing information on fisheries for renewable 

energy development, the specific objectives of this study were to:

1. Acoustically tag additional Atlantic sturgeon and winter skate in the DE coastal ocean.

2. Deploy an acoustic receiver array for two years in the DE WEA.

3. Determine seasonal usage of Atlantic sturgeon, winter skate and other acoustically telemetered 

species as provided by the ACT Network in the DE WEA.

4. Develop environmentally driven models of Atlantic sturgeon and winter skate in the DE WEA.

5. Determine transit times of acoustically telemetered species in the DE WEA and nearby coastal 

ocean.

6. Deliver acoustic detection data to acoustic telemetry networks (ACT, MATOS, OTN)

7. Compare the recovery efficiency of standard and acoustic release receivers.
13

Methods - Delaware Wind Energy Area Study Design

2.1 Study Region - Physical Setting

The DE WEA is located approximately 11-23 miles east of Rehoboth Beach, DE (southeast of the mouth 

of the Delaware Bay), and covers 96,430 acres (Figure 1; Guida et al. 2017). This lease area is located 

between two shipping channels that lead to the entrance of one of the largest shipping ports in the United 

States (Figure 1; Mangone 1988). Water depth in the region ranges from 10 to 34 m, with an average of 

approximately 25 m (Figure 2A; Guida et al. 2017). Previous surveys of the bottom topography in the 

lease area have documented the presence of several N-S and NE-SW oriented “mega-ripples”, with the 

largest sand ripples in the NW corner (Figure 2; Guida et al. 2017). The bottom substrate consists largely 

of sediment between -2 to 3 Φ (very fine gravel to fine sand) on the Wentworth scale, with a distinct NESW oriented sand channel between areas of coarser sand and gravel (Figure 2B).

Figure 1. Map of study region. 

BOEM Outer Continental Shelf (OCS) wind energy areas off of Delaware and Maryland, and the Delaware Bay 

shipping lanes. Black points represent locations of VEMCO acoustic receiver stations deployed between 27 Feb 

2017 and 23 Feb 2019.
14

A)

B)

Figure 2. Map of bathymetry and sediment type in DE WEA.

Panel A) Bathymetry data presented is from the NOAA National Centers for Environmental Information, US

Coastal Relief Model. Panel B) Sediment grain size (Φ) shown is the interpolated predicted average sediment 

type (Wentworth Classification) from DE WEA physical samples (adapted from Guida et al. 2017).
15

2.2 Acoustic Receiver Array

A total of 25 passive acoustic receivers were anchored in gate formations (straight lines) around the 

perimeter of the DE WEA (Figure 1). Having the receivers arranged around the perimeter of the WEA 

allowed us to minimize distance between receivers and maximize the probability that telemetered fish 

would be detected entering and exiting the study area. In addition, having receivers placed at the top and 

bottom of the WEA would allow us to calculate transit rates as telemetered fish migrated through the 

array. The original array design consisted of 21 traditional VEMCO VR2W (VEMCO Ltd. Halifax, Nova 

Scotia), and 4 VEMCO VR2AR acoustic release receivers, allowing us to compare the loss rates of the 

two different receiver mooring designs in the study region. 

Deployment

The design of the acoustic receiver moorings (traditional and acoustic release), was a collaboration 

between the scientific investigators of the project and commercial pot fisherman, who together have over 

70 years of commercial fishing and mooring experience along the Delaware coastal ocean. The VR2W 

VR2W Mooring Design

VR2AR Mooring Design

Figure 3. Acoustic receiver mooring designs.

Traditional VR2W acoustic receiver mooring design with redundant surface expression for recovery, and 

redundant anchoring systems to hold receivers on station. Acoustic release mooring design with nonrecoverable 

concrete anchors and reusable crush resistant trawl ball floats (also see inset picture).
16

receiver moorings consisted of two surface floats (attached with weak-links), marking the locations of a 

Danforth anchor and an 80 lb concrete block connected by a ground line (Figure 3). An acoustic receiver 

was held upright in the water column, by a crush resistant trawl ball float, approximately 4 m off the 

seafloor and connected to the ground line. A second concrete block was also attached to the ground line

for stability (Figure 3). The second concrete block was added after heavy coastal storms and strong tidal 

flows caused the receiver moorings to become entangled and move off station. The acoustic release 

receivers do not require surface expression for recovery and were moored on the bottom by two concrete 

blocks, with 3-4 crush resistant trawl ball floats holding the receivers upright approximately 4 m off the 

seafloor (Figure 3). Acoustic receivers were deployed 27 February 2017 and spaced at approximately 3 

km intervals along the northern, western and eastern edges (Figure 1). Acoustic detection range is 

estimated to be approximately 1 km, so while there was no overlap in detection ranges between receivers, 

this distance maximized receiver coverage given the number of receivers available for this study. 

Maintenance and Recovery

The acoustic receiver array was maintained on an approximately bi-monthly basis depending on weather 

and vessel availability. All maintenance occurred from a commercial pot fishing vessel. The majority of 

the maintenance trips were conducted on a single day, although occasionally two separate trips were 

needed to maintain all 25 receivers. During each maintenance trip, traditional VR2W receiver moorings 

were recovered and biofouling was removed from lines, floats and receivers. Occasionally, strong tidal 

flows or coastal storms caused the mooring to become tangled, often holding one surface float under 

water until the mooring was untangled. Often, one of the surface floats was missing, likely cut off by 

vessels passing through, and required replacement. After each mooring was recovered, the acoustic 

receiver was downloaded, and the mooring was redeployed. If a mooring was unable to be located by the 

surface expression, echosounder or grapple, or the receiver was missing from the mooring, it was replaced

with a spare, or converted to an acoustic release station if receiver loss at that station was persistent. 

Acoustic release receivers were not downloaded on every maintenance trip to reduce the number of 

sacrificial anchoring mechanisms used. In addition, the absence of any surface expression meant that 

there were fewer interactions between vessels and the acoustic release moorings. If the VR2AR was not 

acoustically released from its mooring for download, the status (battery, depth, tilt angle, memory) was 

checked using the VEMCO VR-100 and transponding hydrophone. Occasionally moorings were shifted 

off station 500 m when it was determined the presence of fishing activity (sea bass pot fisherman in the 

NE corner), or shipping activity (tanker ships anchoring in the S corner) were likely to, or already had, 

interfered with our moorings (Figure 4). 

The acoustic receiver array was recovered during multiple trips between 30 March 2018 and 1 June 2018. 

Acoustic detection data for analysis was constrained between 27 February 2017 and 23 February 2019 to 

cover 2 full calendar years with near complete coverage by the array.
17

Acoustic Telemetry Data Distribution

The Atlantic Cooperative Telemetry (ACT) Network was critical to the success of our project. While we 

deployed 100 additional acoustic transmitters associated with Atlantic sturgeon and winter skate as part of 

this project (Section 3), approximately 55% of the individual acoustic transmitters in marine animals 

detected in this study were associated with species other than our focal species. During our two-year 

study, receivers in the DE WEA array recorded 124,710 detections, from 899 transmitters, belonging to

49 different researchers or research collectives. Of the 899 transmitters, we were able to identify the 

species associated with 877 of the transmitters (Table 1). Detections were distributed via email to the 

researcher associated with the transmitter code in the ACT Network database on a bi-monthly to quarterly 

basis. Upon completion of the study, detections of transmitters that were unable to be identified through 

the ACT network were sent to VEMCO who alerted the researcher of our project. This process allowed us 

to identify 132 of the 899 total transmitters detected, belonging to 13 of the 49 researchers. 

Figure 4. Photo demonstrating the proximity to acoustic release receiver station. 

Trawl ball floats attached to acoustic release receiver are circled in red.
18

Table 1. Cumulative number of individuals per species detected on DE BOEM acoustic array 

between deployment 27 Feb 2017 and recovery 23 Feb 2019.
19

Methods - Atlantic Sturgeon and Winter Skate Tagging

To supplement the existing populations of acoustically telemetered Atlantic sturgeon and winter skate, we 

deployed 100 additional VEMCO acoustic transmitters (50 each species). This ensured that there was an 

adequate number of active transmitters in our focal species that were captured in close proximity to the 

DE WEA and reduced our reliance on transmitters deployed by other research groups.

3.1 Fish Sampling

Sampling for adult and large sub-adult Atlantic sturgeon and adult winter skate was conducted in the 

spring of 2017, during their coastal migration along the Delaware coastline. The timing and location

(approximately 20 km to the SW of the DE WEA based on knowledge of high densities of these animals 

Figure 5. Fish sampling aboard F/V Dana Christine.

Captain Kevin Wark, Michael Lohr and Dr. Danielle Haulsee (L-R) watch as gill net is reeled onto drum. Wooden 

live well (bottom left) contains an adult Atlantic sturgeon under anesthetic.
20

from prior research and fishing experience) of sampling was adapted to minimize potential interactions 

with marine mammals as well as to intercept the coastal movements of Atlantic sturgeon and Winter 

skate. Sampling for adult sturgeon and large sub-adult Atlantic sturgeon, as well as adult Winter skate,

was conducted simultaneously using anchor gill nets. Nets were built and fished in compliance with both 

the Harbor Porpoise Take Reduction Requirement as well as the Atlantic Large Whale Take Reduction 

Program. Nets were fished in accordance with developed protocols (Damon-Randall et al. 2010) and 

sampling was conducted in compliance with the NOAA- NMFS permit to take protected species for 

scientific purposes #16507.

Between 2 April 2017 and 7 May 2017, we sampled on 16 days, and deployed 70 gill net sets. Each set 

consisted of up to twenty monofilament anchor gillnet panels (91.0m x 6.1m) strung together and fished 

as one long net. Nets had mesh sizes ranging from 30.5 to 33.0 cm stretch, with twine sizes ranging from 

0.92-0.98mm, and a hanging ratio of 0.5. During suitable conditions, up to two nets were fished at the 

same time and were tended every 2-4 hours to reduce bycatch and minimize stress and/or potential 

mortality of Atlantic sturgeon and Winter skate. Gillnets were compliant with large whale take reduction 

measures and outfitted with acoustic pingers (Fumunda, 10 kHz, 132 dB) at the beginning of the net and 

at the junction of each gillnet panel to help deter marine mammal interactions. The GPS coordinates for 

the both ends of the gillnet were recorded as the set location, as well as the soak time and local 

environmental conditions (temperature, salinity, dissolved oxygen, depth). In total, 68 Atlantic sturgeon

and 199 winter skate were collected during the sampling efforts.

Figure 6. Collecting metadata for adult Atlantic sturgeon and Winter skate in live well.

Left Photo: Dr. Danielle Haulsee measuring the fork length and total length of an anesthetized Atlantic sturgeon 

in preparation for transmitter implantation. Right Photo: Adult Winter skate in live well where it will be the disc 

width was measured.
21

Upon capture, all Atlantic sturgeon were placed in a live well with water pumped directly from the 

sampling site, maintaining ambient temperature, salinity and dissolved oxygen levels (Figure 6, left 

photo). Total length (TL), fork length (FL) (cm) (Figure 6) and weight (kg) were measured and recorded

(Figure 7). Using an AVID® Power Tracker 8, each Atlantic sturgeon was scanned for the presence of an 

existing PIT tag (passive integrated transponder). If no tag was found, a PIT tag was inserted on the left 

side of the fish at the base of the dorsal fin (Fuller et al. 2008). Using a PIT tag is common practice 

among Atlantic sturgeon researchers along the East Coast and allows researchers to track fish 

economically even if they are not tagged with an acoustic transmitter. A 1-1.5 cm2 genetic sample was

collected from the pectoral fin and placed in 95% ethanol for future genetic analysis. 

Similarly, captured winter skate that were appropriate for acoustic transmitter implantation (healthy, and

appropriate sex to maintain 50:50 sex ratio) were immediately transferred to a live well with fresh 

seawater pumped from the sampling site. Disc width (cm, measured at the widest part of the skate) and 

sex were recorded for each winter skate.

Figure 7. Atlantic sturgeon with field datasheet before release.

All captured Atlantic sturgeon were photographed with a field datasheet documenting their length (FL and TL), 

weight, sex (if determined), transmitter information, samples taken, and set captured information. In addition, any 

injuries or unusual morphologies were photographed and documented.
22

3.2 Atlantic Sturgeon Transmitter Implantation

As our focus was on adult Atlantic sturgeon (> 1.30 m FL, (Van Eenennaam et al. 1996)), we restricted 

transmitter implantation to individuals > 1.30 m FL (n=3 Atlantic sturgeon < 1.30 FL). For the 68

Atlantic sturgeon captured, the mean weight was 46.2 kg (range 5-112 kg), and mean fork length was 

165.3 cm (range 88-235 cm, Figure 8). We implanted acoustic transmitters (VEMCO V-16, 6-H, 69.0 

kHz, 120s duty cycle with an estimated battery life of 10 years), in 50 Atlantic sturgeon (20 male, 18 

female and 12 unknown sex) using implantation protocols developed for Gulf sturgeon (A. o. desotoi) 

(Fox et al. 2000) as modified by (Damon-Randall et al. 2010). Anesthetic (tricaine methane sulfonate MS222®) was added to the live well at a dosage of 50-125 mg/l (Harms and Bakal 1994). When the Atlantic 

sturgeon could not maintain equilibrium and exhibited signs of slowed respiration, it was placed in a 

sling, ventral side up, while its head and mouth remained submerged. An incision was made off the 

centerline of the abdomen; a gonadal tissue sample was taken and placed in a vial containing 10% 

Figure 8. Length frequency for Atlantic sturgeon captured between 2 April 2017 and 7 May 

2017.

Bars are colored by the approximated sex of the fish, as determined from gonadal tissue samples taken during 

the tagging procedure. “Unknown” indicates that the sex was either not able to be determined from gonadal 

tissue, or the fish was not tagged and no gonadal tissue was sampled. 
23

buffered formalin for sexual identification and reproductive stage and the transmitter was inserted into the 

coelomic cavity (Figure 9). Transmitters were coated with Dow Corning Silastic®, a biologically inert 

silicone elastomer to minimize rejection rates (Boyd Kynard, USGS, Conte Anadromous Fish Laboratory, 

personal communication). Two to three interrupted sutures were used to close the incision using sterilized 

suture material (PDSII, size 1, Figure 9). Oxytetracycline was administered as an antibiotic at a dose of 

1.0 ml per 20 kg body weight near the base of the dorsal fin. Atlantic sturgeon were allowed to recover in 

a second live well with fresh seawater and were then released near the location of capture when they 

returned to an active state.

3.3 Winter Skate Transmitter Implantation

The transmitter implantation process for winter skates was similar to that for Atlantic sturgeon. Nearly all 

winter skate collected during sampling were adults (Figure 10). Winter skates were also generally in 

excellent condition after capture, and we were able to select 25 adult females, and 25 adult males for 

transmitter implantation. Average disc width for tagged winter skates was 50.6 cm (range 41-60 cm, 

Figure 10). Winter skates were implanted with a slightly smaller acoustic transmitters (VEMCO V-16, 4-

H, 69.0 kHz), to lessen the burden on the winter skate, however these tags were programmed with the 

same 120 s duty cycle with an estimated battery life of 6.7 years. Like those for Atlantic sturgeon, these 

transmitters were coated with silicone elastomer to minimize transmitter rejection. In preparation for the 

implantation surgery, winter skate were placed ventral side up in a shallow water bath, which seemed to 

Figure 9. Example of adult female gonadal tissue and interrupted suture closure of incision.

Left photo: Black immature oocytes fill the abdominal cavity of an adult female Atlantic sturgeon. Right photo: An 

anesthetized adult Atlantic sturgeon, implanted with an acoustic receiver, and the interrupted suture incision 

closure. 
24

induce tonic immobility and caused the skate to become calm enough that anesthetic was not necessary. A 

small incision off the midline was made into the coelomic cavity, using forceps or tweezers to pull the 

dermis and peritoneum away from internal organs to prevent injury to internal organs from the scalpel 

blade (Figure 11). The sterilized transmitter was inserted into the coelomic cavity (Figure 11) and the 

incision was closed with one or two interrupted sutures using sterilized absorbable suture material (PDSII, 

size 1). At the completion of tagging, the winter skate was allowed to recover prior to being released near 

the location of capture.

Methods - Seasonal Occurrence in DE WEA

Detections from the DE WEA acoustic receiver array were filtered to remove single detections (likely 

false detections). The data was also carefully reviewed visually to identify and remove spurious 

detections that were outliers based on the timing and location of detection for that species. To reduce 

autocorrelation in the dataset due to individual fish being detected on the same receiver multiple times in 

a row, detections were reduced to detections per individual per receiver per day (detection days, Breece et 

al. 2018). Analyses and data management were primarily done in R (R Core Team 2017).

Figure 10. Length frequency for winter skate captured between 2 April 2017 and 7 May 2017.

Bars are colored by the sex of the skate, as determined by presence or absence of claspers. 
25

4.1 Temporal Occurrence and Spatial Distribution

To identify potential areas of conflict- and times of the year when conflict with human uses of the DE 

WEA are more likely to occur, we summarized the temporal occurrence and spatial distribution of our 

focal species, as well as the other species detected by the receivers in the DE WEA. The temporal 

occurrence of all species detected on receivers in the DE WEA array was mapped, with station locations 

scaled to the number of cumulative detection days for that species over the two-year study. In addition, 

the presence and absence of all species detected throughout time was displayed on an abacus plot to show 

the seasonal occurrence of the identified species detected in the DE WEA array. 

For our focal species, we also visualized the number of unique individuals recorded by any receiver in the 

DE WEA, broken down by month, as well as unique individuals on the “Northern Line”, “Eastern Line” 

and “Western Line”. Preliminary data exploration indicated that there were shifts in the regions of 

intensive use by Atlantic sturgeon and winter skate throughout the year, and in this way, we simplified the 

25 arrays into three regions. This allows us to see the times of the year when there is the highest number 

of individual fish residing in or passing through the study area. Because the detection range of the 

receivers in the array did not overlap, there were gaps in the array meaning it was possible for fish to 

enter and exit the array without being detected. Having gaps in the array likely results in the 

underestimation of fish occurrence in the array, and therefore results presented are conservative estimates 

of occurrence and distribution. 

4.2 Residency and Rate of Movement

While it is important to quantify the occurrence of our focal species in the array, and the seasonality of 

their occurrence, it is also important to understand their behavior while they occur in this region. 

Estimating the residency and rate of movement of these species will allow managers to have a better 

Figure 11. Winter skate acoustic transmitter surgical implantation. 

Left Photo: Tweezers were used to pull the dermal tissue of the Winter skate away from the delicate internal organs 

within. Right Photo: A VEMOCO V16-4H is inserted into the body cavity of a Winter skate. 
26

understanding of the impacts that the construction and presence of man-made structures like offshore 

wind turbines might have on these species. Animals with high residency may be more likely to be 

negatively affected than species quickly passing through.

Due to the shape of the array, we did not observe many Atlantic sturgeon making clear migratory 

movements through the DE WEA. Therefore, to aid in the calculation of residency and rates of movement 

in the Mid-Atlantic Offshore region, supplementary detections of Atlantic sturgeon tagged by our group 

were obtained from collaborators maintaining acoustic receivers deployed in the MD WEA (D. Secor and 

E. Rothermal). To understand the changes in behavior of Atlantic sturgeon detected in the DE WEA, we 

calculated periods of residency and movement using the R package Vtrack (Campbell et al. 2012). The 

Vtrack software package is specifically designed to qualify the behavior of animals detected using 

acoustic telemetry, and has been proven useful to classify the behavior of acoustically telemetered 

Atlantic sturgeon in the Mid-Atlantic ocean (Breece, Fox, and Oliver 2018; Ingram et al. 2019). For our 

analysis, we defined residence events for each individual sturgeon when they were detected at one 

receiver station for at least 12 hours (Breece, Fox, and Oliver 2018). A residence event was ended when 

12 hours elapsed with no additional detections of the individual or if the individual was detected by 

another receiver. Movement events occurred in between times of residency and were filtered to be those 

that occurred in less than seven days to eliminate spurious detections between distant receiver stations 

across seasons. This is adequate time for an Atlantic sturgeon to transit the approximately 54 km from the 

southernmost extent of the MD WEA to the northernmost extent of the DE WEA based on published 

mean swimming speeds of 0.31 (SD= 0.20) m/s (54 km / 7 days = 0.089 m/s, (Ingram et al. 2019). A 

distance matrix was calculated for all receivers that detected an Atlantic sturgeon assuming a detection 

radius of 600 m (Kilfoil et al. 2017), from which we derived the rate of movement (ROM) during 

movement events between stations. 

Detections of winter skate were too sparse (n = 609 detections) to include in this analysis. Although

tagged winter skate were detected as near to shore as 3 miles and as far offshore as 25 miles within the 

MD WEA, the DE WEA and the NY WEA during this study, they were not detected in large numbers. 

While some of our tagged winter skate were detected in the MD WEA, Vtrack was unable to extract 

movement or residency behaviors and statistics due to limitations of the dataset, therefore we are unable 

to determine potential transit rates for this species through our study region at this time. 

4.3 Environmentally Driven Predictive Model

Defining species distribution models for Atlantic sturgeon is challenging because of their complex 

interactions between marine and riverine ecosystems and physical forcings. While the location of 

sediment and benthic structure is relatively stable over ecological time, ocean variables that affect 

Atlantic sturgeon such as temperature, salinity, and turbidity are forced over shorter time scales. These 

factors may also influence behavior (residency vs. movement) and population parameters (e.g. growth and 

fecundity) as adult and large juvenile Atlantic sturgeon are thought to forage extensively in the marine 

and estuarine environment (Stein et al. 2004). Understanding environmental parameters (static and 

dynamic) that are associated with Atlantic sturgeon occurrence on daily and seasonal time scales, gives 

managers a tool to predict when and where Atlantic sturgeon will occur, when more direct observations 

are not feasible.
27

To determine the associations between Atlantic sturgeon and environmental parameters, potential 

predictor variables were extracted for each day and station location during the two-year study. Static 

variables included water depth (Figure 2A; NOAA National Centers for Environmental Information, US 

Coastal Relief Model), modeled bottom type (sediment type, Wentworth Classification, Figure 2B, Guida 

et al. 2017), and terrain metrics (slope, aspect, rugosity, Guida et al. 2017). Dynamic variables included 

one day composites of remotely sensed sea surface variables from the NASA MODIS-Aqua satellite 

platform. These variables included sea surface temperature (SST, Figure 12A), and 12 ocean color 

absorption channels (available at http://tds.maracoos.org/thredds/MODIS.html and 

http://basin.ceoe.udel.edu/thredds/catalog.html). Ocean color has been shown to be useful for predicting 

Atlantic sturgeon (Breece et al. 2018) and Sand Tiger shark (Haulsee et al. 2018) occurrence in the MidAtlantic Bight, and is likely a proxy for ocean productivity, nutrient fields, river plumes or other water 

masses that drive oceanic species occurrence. In addition, because Atlantic sturgeon are a benthic 

associated fish, we also extracted modeled daily bottom temperatures for each receiver station from the 

Experimental System for Predicting Shelf and Slope Optics ROMS model (ESPreSSO, available between 

23 Feb 2017 to 31 Oct 2017, http://www.myroms.org/espresso/) and DOPPIO ROMS model (Figure 12B; 

available between 1 Nov 2017 to 27 Feb 2019, 

http://tds.marine.rutgers.edu/thredds/catalog/roms/doppio/2017_da/avg/catalog.html?dataset=roms/doppi

o/2017_da/avg/Averages_Best). These environmental conditions were then matched to Atlantic sturgeon 

presence (detection days per individual) and absences. Absence records were created by extracting 

receivers where individual Atlantic sturgeon were not detected between the time of first detection and last 

detection of each individual on the receiver array, and accounting for when receivers were deployed 

(Breece et al. 2018; Haulsee et al. 2018). Presence and absence records for all identified Atlantic sturgeon

Figure 12. Climatology of SST and modeled bottom temperature for July 3-11 in the DE WEA.

Panel A) Sea surface temperature from the MODIS-Aqua satellite platform. Panel B) Modeled bottom temperature 

from the DOPPIO ROMS model (Rutgers University).

A) B)
28

detected in the DE WEA were used for this analysis, including those individuals tagged by other research 

groups. This increased our sample size of Atlantic sturgeon from 123 to 360 and greatly improved the 

strength of our modeling analysis. 

The goal of predictive species distribution modeling is to determine the fewest predictor variables that 

contain the most information about the response variable, in this case the binomial presence and absence 

of Atlantic sturgeon. Therefore, variable reduction techniques were used to eliminate predictor variables

that were highly correlated (|r| > 0.7) and contained the highest information values (R package 

InformationValue, (Prabhakaran 2016). 

Generalized additive models (Hastie and Tibshirani 1990) were used to quantify the non-linear 

relationships between the binomial presence/absence of Atlantic sturgeon and environmental predictor 

variables using the mgcv package in R (Wood 2011). Thin plate shrinkage smoothers (ts) were used to fit 

the complex relationships between predictors and the response and shrink non-significant variables out of 

the model, with tensor product (te) smoothers used to test interactions (Wood 2003). Models were 

systematically tested in a forward stepwise selection protocol, and model performance was evaluated by 

the reduction of Akaike’s Information Criterion (AIC) and increasing deviance explained (Wood 2006). 

The least complex model, with the lowest AIC was chosen as the “best” model. Models were cross 

validated by systematically removing 20% of the training data, building the model on the remaining 80% 

of the data and comparing those predictions to the reserved 20%. The cross-validation process calculated 

model performance statistics, explained deviance (r2

), sensitivity (presences correctly predicted), 

specificity (absences correctly predicted), optimal threshold (predicted prevalence where sensitivity 

equals specificity), percent correctly classified (PCC), and the area under the receiver operating curve 

(AUC), using the ‘Presence Absence’ package in R (Freeman and Moisen 2008).

Model predictions were visualized using environmental variable layers interpolated to the same resolution 

as the lowest resolution input variables, the remotely sensed environmental variables (4 km2 resolution). 

For dynamic environmental variables (SST, ocean color, and bottom temperature), monthly climatologies 

were used as inputs to create probability of occurrence maps. 

Detections of winter skate were too sparse to create a dynamic habitat model for this species in this 

region, but we were able to use simple habitat preference associations to characterize preliminary 

information about their occurrence and distribution as mentioned below. 

During the complex GAM model building process, it became apparent that there were strong associations 

between Atlantic sturgeon and sediment type. The Wentworth sediment type associated with the presence 

and absence of both species at receiver stations was converted into a categorical variable using the 

established ranges of φ (Appendix A.1.1., Hobson 1979). A simple habitat selection analysis for Atlantic 

sturgeon and winter skate was performed to test for selection of certain sediment types. A chi-squared

goodness of fit test was used to determine if the proportion of sediment types associated with detections 

of Atlantic sturgeon and winter skate (“used”) was significantly different from the proportion of sediment 

types available.
29

Results - Recovery Efficiency of Standard vs. Acoustic Release 

Receiver

Throughout the two-year study, six acoustic receivers were lost despite our efforts to prevent gear loss 

(Table 2). We attempted to prevent gear loss in two ways: 1) We consulted with a professional pot 

fisherman, Captain Wes Townsend, who has decades of experience fishing seabass and lobster pots in this 

region. His livelihood depends on the reliability of this mooring designs, and he made every effort to 

assist us in designing and setting our gear to with the local conditions in mind, to prevent equipment loss. 

2) Regular maintenance, as weather permitted, allowed us to untangle gear after storms, replace missing 

surface floats, and replace anchors and blocks as needed to ensure we would be able to locate gear upon 

successive maintenance trips. Despite these preventative measures, we lost six VR2Ws during the study 

(Table 2). We did not lose any acoustic release receivers, likely because they did not have any surface 

expression floats and lines in the water (Table 2).

For five of the traditional VR2W receivers, the entire mooring was not able to be located visually (unable 

to locate surface floats or see mooring underwater using the vessels echosounder), or with a grapple. For 

one of the receivers, the mooring was recovered, but the receiver was sheared off of the mooring (zip-tie 

attachments were broken). One station (BOEM_E_15) was lost twice. This station is located in the lower 

corner of the array, where large tankers and shipping vessels often anchor or transit through the study area 

before entering the nearby shipping lanes (Figure 13). After the second loss at the same station, and our 

assumption that shipping traffic was likely responsible for these losses, we converted the station to a 

bottom anchored VR2AR acoustic release receiver station, which prevented future station losses. 

These losses not only represent monetary losses equivalent to the cost of the receivers and mooring gear 

but also in the loss of the valuable data that was archived on the equipment (Table 2).

Table 2. Summary of VEMCO acoustic receivers lost during study.

Losses were likely due to interactions with fishing activities, shipping activities or weather. Stations highlighted in 

italics were lost after the cut-off date of our study (23 Feb 2019), so losses in data did not impact data analyses. In 

addition, it is possible these stations may still be recoverable. 

5.1 Interaction with Acoustic Seafloor Survey Vessel

We were concerned that the acoustic survey to classify bottom type, performed by contractors hired by 

Deepwater Wind/Orsted (http://dwwind.com/press/deepwater-wind-accelerates-skipjack-wind-farmmulti-million-dollar-ocean-floor-survey/), would interfere with the ability of the receivers to detect 

acoustic tags. The VEMCO VR2AR receivers have the ability of measuring ambient noise at 69 kHz to 

measure how much noise interference (from sea state, turbidity, rainfall, air bubbles, boat traffic, depth 

sounders, construction, biological (i.e. snapping shrimp), etc.) may be interfering with the ability of the 
30

receivers to detect acoustic transmitters in fish. Comparing the pattern of the cumulative number of 

detections over the study, and the peak noise levels measured by the acoustic release receivers in the 

array, there does not appear to be any obvious gap in transmitter detections that correlates with increasing 

ambient noise (Figure 14A). Highest average noise was recorded between mid-September and earlyOctober at stations BOEM_W_21, BOEM_W_19, BOEM_W_17, BOEM_W_18, and BOEM_E_14 

(Figure 14B). These receivers are concentrated in the lower corner of the DE WEA, where the proposed 

wind turbines for the Skipjack Wind Farm will be located (https://orsted.com/en/Our-business/Offshorewind/Our-offshore-wind-farms). While we believe our receivers did record increases in ambient noise 

levels during the survey, it is unclear whether the ability of the receivers to detect tagged animals was 

affected, or if there were any changes in animal behavior as a result. 

Figure 13. Map of DE WEA VEMCO acoustic receiver array compared to vessel traffic. 

Red points indicate receiver stations where a standard VR2W receiver with surface expression was lost between 

Feb 2017 and Mar 2019. Example concentration of vessel traffic (2011) indicated by colors, with cool colors 

indicating low concentration and warm colors indicating high concentration 

(https://coast.noaa.gov/arcgis/rest/services/MarineCadastre/2011VesselDensity/MapServer). Inset shows heavy 

anchorage used by large shipping vessels waiting offshore before using shipping lane to enter DE Bay. Note the 

circular patterns in concentration due to ships swinging on their anchors as the tide changes.
31

Figure 14. Accumulation of acoustic detections over time compared to peaks in ambient noise 

level in DE WEA. 

Panel A) Cumulative acoustic detections from all species and all stations in the DE WEA VEMCO receiver array 

between deployment 27 Feb 2017 and recovery 23 Feb 2019. Panel B) Recorded average noise level (at 69 kHz) 

from VR2AR receivers between deployment 27 Feb 2017 and recovery 23 Feb 2019. Black vertical lines on both 

graphs represent peak noise levels, 26 Sept 2018. Black dots in receiver array map inset represent stations where 

peak average noise levels were recorded.

A)

B)
32

Species Occurrence and Distribution

With two years of data, we were able to see trends in the seasonal pattern of spatial distribution of all 

species in the array. The array was frequented by multiple different species, including our focal species, 

which varied in the region of the array where they occurred, and in how much time they spent in the area.

6.1 Atlantic Sturgeon

Between 23 Feb 2017 and 27 Feb 2019, receivers in the DE WEA recorded 43,620 detections of 360 

individual Atlantic sturgeon. These Atlantic sturgeon were originally tagged by 11 different primary 

researchers and included 32 (out of 50) individuals tagged for this study. 

Figure 15. Individual Atlantic sturgeon occurrences by month and region of the DE WEA. 

Grey bars show the number of unique individual Atlantic sturgeon detected in the DE WEA observed over 

the two-year study. Colored lines represent the region (corner) of the array where these individual fish 

were detected.
33

Atlantic sturgeon were detected during all months of the year in the DE WEA; however their occurrence 

is lowest in August, and highest in November and December (Figure 15). More Atlantic sturgeon occur in 

the northern corners (northeast and northwest) throughout the year, however the number of unique 

Atlantic sturgeon recorded increases in all three corners in November and December (Figure 15). A 

smaller peak in the number of individual Atlantic sturgeon recorded occurs between April and May, 

where Atlantic sturgeon occur in the northwest corner (Figure 15). These patterns of occurrence are 

Figure 16. Spatial distribution of detections of Atlantic sturgeon on receivers of the DE WEA 

broken down by month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all Atlantic 

sturgeon recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
34

shown in more detail in Figure 16. Specific receiver stations near the center of the gate of receivers that 

runs from the northwest to southern corner accumulated many more detection days than other receivers 

consistently throughout the year (Figure 16). 

6.2 Winter Skate

Winter skate occurrence was low throughout the two-year study. In total, receivers in the DE WEA 

recorded 609 detections of 13 winter skate. These fish were all tagged as a part of this study. Similar to 

Atlantic sturgeon, peaks in winter skate occurrence occurred between March and May, and again in 

November and December, when considering the diversity of individual winter skate detected in the DE 

WEA (Figure 17). These individuals occur most frequently near receivers in the northwest corner during 

both peaks, with the southern corner again detecting the fewest number of individuals (Figure 17). These 

patterns are also apparent when observing the accumulation of detection days for Winter skate on 

individual receivers in the DE WEA, with most detections occurring on only a few receivers in the 

northwest corner (Figure 18). 

Figure 17. Individual winter skate occurrences by month and region of the DE WEA. 

Grey bars show the number of unique individual winter skate detected in the DE WEA observed over the 

two-year study. Colored lines represent the region (corner) of the array where these individual fish were 

detected.
35

Figure 18. Spatial distribution of detections of winter skate on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all winter

skate recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
36

6.3 All Other Species

In addition to Atlantic sturgeon and winter skate, the receivers deployed in the DE WEA detected 

acoustic transmitters associated with 28 additional species (Figure 19). While we detected a large number 

(n=360) unique Atlantic sturgeon, one of our focal species, we also detected a very large number of 

striped bass (n=320, Figure 19). Over the two-year study, receivers logged 124,710 detections, which 

were often seasonal in nature (Figure 20). As discussed above, Atlantic sturgeon occurred almost all year 

round, however species such as striped bass and multiple species of sharks (white shark, dusky shark, 

blacktip shark) occurred on seasonal cycles (Figure 20, Appendix B). Striped bass occurred most 

frequently in the winter and spring, likely during times of migration along the East Coast (Figure 20). In 

contrast, elasmobranchs occurred most frequently in the summer months (Figure 20). 

The highest numbers of unique individual animals carrying acoustic transmitters were detected in 

November and December (Figure 21). Between 300-400 unique animals occurred in the DE WEA in 

November and December throughout the study period (Figure 21). During these months, more individuals 

occur in the northern corners than the southern corner (Figure 21). However, during a secondary peak in 

number of animals observed in April, more individuals were recorded in the southern corner. 

Figure 19. Number of tagged individuals detected 

Colored points represent the presence of at least one tagged individual for 30 species detected in the DE WEA. 
37

Figure 20. Occurrences of all species detected in the DE WEA throughout time. 

Colored points represent the presence of at least one tagged individual for 30 species detected in the DE WEA. 
38

Figure 21. Individual occurrences by month and region of the DE WEA. 

Grey bars show the number of unique individuals detected in the DE WEA observed over the two-year study. 

Colored lines represent which region (corner) of the array these individual fish were detected occurring.
39

Plotting detection days on receiver stations by month for all species emphasizes the heavy use of all of the

acoustic receivers in the DE WEA during January, November and December (Figure 22). In fact, for the 

majority of the year, detections of at least one species were recorded on all receiver stations, with the 

exception of February and August during which the fewest number of individuals were detected for the 

fewest numbers of detection days (Figures 20-22). The summarized occurrences for all species detected 

are shown in Appendix B.

Figure 22. Spatial distribution of detections of all species on receivers of the DE WEA broken 

down by month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all species 

recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National Centers for 

Environmental Information, US Coastal Relief Model.
40

Atlantic Sturgeon Residency and Rate of Movement

Due to the high number of individual Atlantic sturgeon detected in the DE WEA, we were able to use 

established analytical techniques to describe residency and non-residency behaviors exhibited by Atlantic 

sturgeon. Residency events are seasonal, and generally short in duration.

7.1 Residency

Using the software package VTrack, residency events were identified between 12.01 hrs and 29 days 

(Figure 23). Residency events were centered around July and November-December during both years of 

the study (Figure 22). The longest residency events occurred in December 2017 and December 2019 (15 

days and 29 days respectively (Figure 23). The longest residency event (29 days) was for Atlantic 

sturgeon with transmitter code A69-9001-17245. This fish was tagged by Carter Watterson from the U.S. 

Navy in the Chesapeake Bay in September 2017. In December of 2018, this fish was detected by station 

BOEM_N_7 for multiple weeks before being detected by 5 other stations in the array and eventually 

leaving (Figure 23 inset). This fish was then detected again in July of 2019 (Figure 23 inset), further 

confirming that this residency event was not the result of a mortality, or an expelled transmitter from the 

fish.

Figure 23. Duration of residency events for Atlantic sturgeon in the DE WEA over time. 

The duration of residency events calculated by Vtrack for Atlantic sturgeon detected in the DE WEA. Inset shows 

the supplementary detection at station information for Atlantic sturgeon A69-9001-17425, which exhibited the 

longest residency event (29 days) in December 2018.
41

7.2 Non-Residency

Periods between residency events were classified as non-residency events in VTrack. During these times 

we assume Atlantic sturgeon are transiting between sites and we used these movements to calculate 

transit rates within the DE WEA (movements between receivers in the array), and between the DE WEA 

and the MD WEA (for Atlantic sturgeon tagged only by Dr. Dewayne Fox). The density distribution of 

transit rates are similar for Atlantic sturgeon movements within the DE WEA and between the DE WEA 

and MD WEA, however there is a slight shift in the distribution to faster transit rates for fish moving 

between the DE WEA and MD WEA compared to fish only transiting within the DE WEA (Figure 24). 

Calculated transit rates were between 0.003 and 1.480 m/s, with an average transit rate of 0.284 m/s 

(Figure 24). There are slight seasonal patterns where transit events occurred, with movements within the 

MD WEA only occurring more often in November-December, and transit events within the DE WEA 

occurring in July and January (Figure 25). When fish moved between the DE WEA and MD WEA, we 

were able to calculate an approximate direction of movement (Figure 26). Most transits of this type were 

for Atlantic sturgeon swimming south from the DE WEA to the MD WEA during December, although 

there are northerly movements during this period as well (Figure 26). The few observations of transits 

between the two WEAs that occurred in the late spring were fish swimming north, however this behavior

is less commonly observed (Figure 26). The distribution of transit speeds does not appear to change 

depending on the direction of travel (Figure 26), however, it is possible we did not detect the majority of 

the population when they were transiting north in the spring based on model observations produced by 

Breece et al (2018), which observed Atlantic sturgeon migrating closer to shore in the spring compared to 

the fall.

Figure 24. Calculated transit rates for Atlantic sturgeon in Mid-Atlantic WEAs.

Transit rates for Atlantic sturgeon moving within the DE WEA and between the DE WEA and the MD 

WEA calculated for the non-residency event output from VTrack. 
42

Figure 25. Time series of calculated transit rates for Atlantic sturgeon in Mid-Atlantic WEAs.

Transit rates for Atlantic sturgeon moving within the DE WEA and between the DE WEA and the MD WEA 

calculated for the non-residency event output from VTrack over time. 

Figure 26. Calculated transit rates for Atlantic sturgeon in Mid-Atlantic WEAs.

Transit rates for Atlantic sturgeon moving within the DE WEA and between the DE WEA and the MD WEA 

calculated for the non-residency event output from VTrack.
43

Predictive Habitat Models

Predictive habitat models were created for our focal species using two different methods. A simple 

bottom type habitat selection analysis tested for associations for certain bottom types by Atlantic sturgeon 

and winter skate. The high number of detections of Atlantic sturgeon also allowed us to use generalized 

additive models to model the complex associations between sturgeon and environmental predictors. 

8.1 Association with Bottom Type

Using the Wentworth grain size scale (Appendix A.1.1.) to classify the modeled sediment type output into 

categories, presences and absences of Atlantic sturgeon and winter skate were matched to three sediment 

types; very coarse sand, coarse sand and medium sand (in decreasing grain size order). Comparing 

sediment types at each receiver station used, versus the sediment types at each receiver station that were 

available, we were able to see that Atlantic sturgeon and winter skate are selecting for different sediment 

types (Figure 27). The proportion of sediment types used by Atlantic sturgeon, compared to what was 

available, was significantly different (Figure 24A, X2 = 115.82, df = 2, p-value < 0.0001). This 

relationship was driven by a preference for coarse sand, and the slight avoidance of very coarse sand and 

strong avoidance of medium sand (Figure 25A). In contrast, Winter skate were observed using sediment 

Figure 27. Associations between Atlantic sturgeon and Winter skates and sediment types 

observed in DE WEA. 

Proportion of sediment types used vs. available for A) Atlantic sturgeon and B) Winter skate. Sediment types were 

those observed nearest to the acoustic receiver station, and were categorized from the modeled Wentworth 

sediment grain size output provided by (Guida et al. 2017).

A) B)
44

types differently than Atlantic sturgeon (Figure 24B, X2 = 5.1597, df = 2, p-value = 0.075), preferring

coarse sand and avoiding coarse sand and medium sand. At this time, we are unable to determine the 

biological significance of these sediment type preferences for each species, but it is likely that these 

sediment types may be related to food availability (both species are benthic feeders), or related to 

preferences for local benthic and oceanographic features such as the occurrence of strong tidal currents 

moving through a channel. Further research is needed to understand if these sediment types are 

biologically important, or spuriously correlated.

8.2 GAM Selection and Performance

Generalized additive models were tested for Atlantic sturgeon detected in the DE WEA only. Models 

were built in a forward-selection method and evaluated using cross-validation and reduction in AIC 

techniques. Based on the pairwise correlation between predictor variables and information values, we 

determined that bottom temperature, depth, Wentworth sediment type, sea surface temperature (SST), 

absorption at 443 nm (a443; sr-1

) and 555 nm (a555; sr-1), and bottom slope were likely predictive of 

Atlantic sturgeon presence and absence. Ocean color absorption at 443 nm (blue) and 555 nm (green) are 

related to variables used to estimate chlorophyll concentrations in the surface ocean. The relationship 

between these variables and Atlantic sturgeon is likely correlated to the preference for different water 

types (coastal, productive, terrigenous in origin etc.) that has been documented in the literature (Breece et 

al. 2018). Day of year is also highly related to Atlantic sturgeon occurrence but is highly correlated with 

bottom temperature. We therefore used bottom temperature as a variable that is more ecologically 

interpretable because it is thought that Atlantic sturgeon spend most of their time near the seafloor 

(Erickson et al. 2011). Models were built using combinations of these predictors, as well as interactions 

between pairs of predictors. For all models tested, a555 was not a significant contributor to the model, and 

the effective degrees of freedom were smoothed to zero, effectively removing that variable from each 

model, therefore this predictor is not shown in the model results. The best performing model included the 

additive effects of bottom temperature, Wentworth sediment type, bottom slope, a443 and the interaction 

of depth and SST (Table 3). Variable importance This model was the least complex model tested that had 

the lowest AIC and highest explained deviance (Table 3). Cross-validation analysis revealed that this 

model captures about 1% of the deviance of Atlantic sturgeon occurrence, correctly classifying 79% of 

sturgeon presences (sensitivity), and 75% of absences (specificity, Table 3). The AUC was high (0.84), 

indicating that the model predicts sturgeon occurrence much better than random (AUC=0.5, Table 3). 

In terms of contribution to the model, water temperature (bottom and surface) were most important, 

followed by bottom characteristics (depth, sediment grain size, bottom slope), and a443 (Figure 28). 

Partial variable effect plots show the complex relationships between the occurrence of Atlantic sturgeon

and environmental predictors (Figure 29). Ocean bottom temperature between ~10-25 °C are predictive of 

Atlantic sturgeon occurrence, with bottom temperatures less than 10 °C associated with Atlantic sturgeon 

absence (Figure 29). In the previous section we showed a significant relationship between sturgeon and 

coarse sand, and an avoidance of very coarse sand and medium sand, however in the effects plot of the 

continuous variable Wentworth sediment grain size, there we see evidence for avoidance of large grain 

sizes (small Φ, Appendix A1.1.), and preference for smaller grain sizes (higher Φ, Appendix A1.1., 

Figure 29). We also see preference for stations that were characterized by higher bottom slope, likely 

related to the strong preference for receivers near the large sand ripple and trough in the northwest corner 

of the array (Figure 2A, 29) The partial effect of a443 is relatively weak, contributing to slight avoidance 
45

at higher a443 (green) absorption values (Figure 29). This may be related to the seasonality of Atlantic 

sturgeon, which occur less frequently during the summer months, when “green” water, or highly 

productive surface water would be present in this region. Finally, there is a complex relationship between 

Atlantic sturgeon, SST and bottom depth, with preference for cooler water at all depths, and avoidance of 

deep water when the SST is warm (Figure 29). 

Table 3. Generalized additive model performance statistics for predicting Atlantic sturgeon

occurrence in the DE WEA including bottom type characteristics.

Environmental predictor variables are abbreviated for brevity. Bottemp = bottom temperature, wentworth = 

modeled Wentworth sediment type, a443 = absorption at 443 nm, sst = sea surface temperature. AIC = 

Akaike’s information criterion, Exp Dev = explained deviance, PCC = percent correctly classified, Sens = 

sensitivity, Spec = specificity, AUC = area under the curve. 
46

Figure 28. Variable importance of predictors in best performing GAM model for predicting 

Atlantic sturgeon occurrence in DE WEA. 

SST = sea surface temperature, Slope = bottom topography slope, a443= absoption at 443 nm.
47

Figure 29. Predictor variable response functions for generalized additive models predicting the 

binomial response of Atlantic sturgeon in the DE WEA.

Black lines: smoothed curve of partial additive variable effect on probability of presence; dashed lines: 95% 

confidence interval. Partial variable responses > 0 are predictive of sturgeon occurrences, while partial variable 

responses < 0 are predictive of absences. Short vertical lines (rug) on the x-axis of single variable plots represent 

the distribution of variable observations upon which the model’s response was built. Wentworth = modeled 

Wentworth sediment grain size (higher Φ = smaller grain size), a443= absoption at 443 nm (green), SST = sea 

surface temperature, Slope = bottom topography slope.
48

8.3 Seasonal Predicted Distribution in DE WEA

Using monthly climatologies of environmental predictor variables, we created prediction maps to assess 

the spatial output of the best performing GAM model for predicting Atlantic sturgeon occurrence in the 

DE WEA. Model performance statistics are comparable to published models for Atlantic sturgeon in the 

general Mid-Atlantic region (Breece et al. 2018; Ingram et al. 2019), differing slightly in the partial 

effects plots and model design. Predicted occurrence patterns for Atlantic sturgeon seem to match up well 

in both space and time with observed Atlantic sturgeon occurrences (Figures 16, 30). Predicted presence 

of Atlantic sturgeon is low January through April, with low occurrence predicted to occur in the northeast 

corner of the DE WEA, as well as in the seafloor channel in the northwest corner of the WEA (Figure 30). 

Beginning in May, as the Atlantic sturgeon migrate into the area in higher abundance, the model predicts 

heavy use of the northern portion of the WEA, in particular the shallowest areas in the northwestern 

corner by Atlantic sturgeon (Figure 30). This pattern is predicted to persist through October, when 

Atlantic sturgeon distribution expands across the entire WEA for November through December (Figure 

30). These predicted patterns based on environmental parameters correlate well with patterns discussed in 

earlier sections and give us confidence that this model is useful to understand the distribution of Atlantic 

sturgeon throughout the entire array, even though our receivers were only able to monitor the perimeter. 
49

Figure 30. Predicted Atlantic sturgeon occurrence for best GAM model including bottom type 

characteristics.

Climatologies of environmental predictor variables and the best performing GAM model were used to create 

predicted occurrence maps for Atlantic sturgeon in the DE WEA. Cool colors represent areas of low probability of 

occurrence, and warm colors are areas of high probability of occurrence.
50

Discussion

The objectives of this study were to document the occurrence and behavior of telemetered species that 

occurred in the potential offshore energy lease site off coastal Delaware. This information is necessary for 

BOEM to meet its obligations under the National Environmental Policy Act, the Endangered Species Act, 

and the Magnuson-Stevens Fishery Conservation and Management Act. Baseline acoustic telemetry data 

on these species has documented their seasonal presence/absence, habitat use, and migratory behavior 

which will inform environmental impact assessments for offshore renewable energy. The species

observed, and especially one of our focal species (Atlantic sturgeon), could be negatively impacted by 

offshore wind energy development (Breece et al. 2018) through noise disturbance and displacement from 

feeding grounds, masking of their communication calls, and disruption of their migration pathways. In 

addition, the DE WEA is situated between shipping lanes near the mouth of DE bay and includes

important fishing grounds (Bureau Of Ocean Energy Management 2012). Therefore, this multi-use area is 

important for both the marine animals that occur within the study region and for humans as well. 

9.1 Observed Species Occurrence

This project documented use of the DE WEA by 26 different marine species that were carrying VEMCO 

acoustic transmitters. These species included recreationally and commercially important fish species 

(Atlantic cod, black sea bass, cobia, smooth dogfish, spiny dogfish, striped bass, winter skates, etc.), apex 

predators (blacktip sharks, bull sharks, sandbar sharks, thresher sharks, tiger sharks, white sharks, etc.), 

marine mammals (gray seal, harbor seal), and endangered species or species of concern (Atlantic bluefin 

tuna, Atlantic sturgeon, dusky sharks, sand tiger sharks). This study confirms the presence of and 

therefore the likelihood of impacting many of the species highlighted by BOEM in early site 

characterization documents (Bureau Of Ocean Energy Management 2012, Section 4.1.2.7.1.1). Atlantic 

sturgeon and striped bass were the most commonly occurring species and are therefore most likely to be 

encountered. However, while Atlantic sturgeon occurrence was recorded year-round, striped bass 

presence was highly seasonal.

Patterns in occurrence were consistent across the two years of our study, with repeated seasonal patterns 

in distribution. Use of the DE WEA by marine fish and mammal species occurred year-round, although 

heaviest use occurred in the spring (Mar-May), and fall (Nov-Dec), driven by the high numbers of 

individual Atlantic sturgeon and striped bass migrating through the study area. High occurrences of 

Atlantic sturgeon during the late fall/early winter supports previous studies, which hypothesized that 

Atlantic sturgeon use coastal waters further offshore, including the DE WEA, during their southward fall 

migration (Figure 16, 26, Breece et al. 2018). Conversely, we observed very few northward migration 

events, as these events are likely occurring much closer to shore (Figure 16, 26, Breece et al. 2018).

Winter skate were observed in low abundance, but also occurred in the spring and late fall/early winter

indicating that their presence in the study region may have been related to their migratory behaviors, and 

not long-term residency in the WEA. It is possible that their occurrence was low because the site is near 

the southern extent of their geographic range, or that this species prefers waters further inshore as their 

occurrence in the DE WEA was concentrated in the shallowest region (Michael G. Frisk and Miller 

2009).
51

The timing of the occurrence of these species is likely related to water temperature preferences. Breece et 

al. 2018 found that SST and day of year largely explained the timing of Atlantic sturgeon presence in the 

nearshore coastal ocean. Similarly, our models included SST, further supporting that this is an important 

environmental variable for this species. Our models did not include day of year, but instead used modeled 

bottom temperature data which was highly correlated to day of year. While the ocean temperature likely 

defines the timing of Atlantic sturgeon and winter skate occurrence in the DE WEA, it does not vary on a 

small enough scale to explain the fine scale preferences for areas of intensive use that were observed 

during our study. 

9.2 Atlantic Sturgeon Residency and Movement

Understanding the residency and movement behavior of Atlantic sturgeon in the study region allows 

managers to understand the likelihood of impacting this species. Residency events were observed for 

Atlantic sturgeon in the DE WEA most commonly in either late fall/early winter, when Atlantic sturgeon

are preparing for migration, or are slowly migrating south for the winter (Figure 23, Breece et al. 2018), 

or during July, when the sturgeon may be seeking areas with cooler bottom temperatures offshore of 

Delaware Bay (Figure 12, Breece et al. 2018: Figure 3). The duration of residency events were 

comparable to previously documented residency events for Atlantic sturgeon, with most residency events 

lasting a few days, and the occasional event lasting multiple weeks (Breece et al. 2018; Ingram et al. 

2019). 

Transit rates within the DE WEA were comparable to those observed in the NY WEA and MD WEA 

(Ingram et al. 2019, E. Rothermal, pers comm). In addition, transit rates occurred most commonly in late 

fall/early winter, or in July (Figure 25). Because we observed both residency and non-residency events 

during these two times of the year (across both years of the study), we believe that Atlantic sturgeon

occurrence and activity is highest in the DE WEA during July and Nov-Dec, and these are the months 

when there is the highest likelihood of encountering Atlantic sturgeon during construction or development 

of offshore wind energy projects. 

9.3 Areas of Intensive Use

Species occurred throughout the entire perimeter of the DE WEA, however, we did note patterns in the 

areas of intensive use by key species. Our focal species, Atlantic sturgeon and winter skate, were 

commonly detected in both the northwestern and northeastern corners of the array, as well as along near 

the large sand ripple and seafloor channel along the western edge of the WEA. These areas may be

frequented by these species due to the unique bottom characteristics (bathymetric features and sediment 

grain size) in this portion of the DE WEA. Bathymetry data reveals the presence of a channel that runs 

from the middle of the western edge of the array to the northeastern corner of the array. The northwestern 

corner is also the shallowest area of the DE WEA. However, it is unclear why these features may be 

attractive to Atlantic sturgeon. It is possible that these features are attractive because of small scale 

oceanographic features such as water flow and water mixing that may be enhanced by shallowing of the 

ocean floor, or Atlantic sturgeon may simply be attracted to the sediment grain size that is correlated with 

these features. 
52

Previous surveys of the DE WEA have documented blue mussel reefs in the northwestern corner of the 

WEA, which may be an attractive prey item, or habitat for other types of prey (amphipods, worms, 

crustaceans, etc.) for both Atlantic sturgeon and winter skate (Johnson et al. 1997; Garrison and Link 

2000; Guida et al. 2017). In addition, bottom surveys documented high occurrences of other benthic 

infauna including crustaceans and polychaetes, which occur throughout the DE WEA, but are also likely 

sought after prey items for our focal species (Johnson et al. 1997; Garrison and Link 2000; Guida et al. 

2017). This region of the array is also dominated by crests and troughs of large mega-ripples, which may 

provide an attractive structure to many species, including our focal species (Guida et al. 2017). These 

sand ripples are also indicative of high rates of water flow, which we experienced first-hand when 

maintaining moorings in the middle of the western edge of the array and in the northeastern corner. High 

rates of water flow increase mixing and may overturn new feeding grounds as sand ripples are moved 

during tidal fluxes or strong storm events (Wright 1993; Glenn et al. 2008). Results from our habitat 

selection analyses and predictive species occurrence models indicate that bottom type and topography are 

related to the occurrence of Atlantic sturgeon in this region, and therefore likely explains the importance 

of these regions of intensive use by Atlantic sturgeon, and likely other species of interest as well. 

Conclusions and Recommendations

Based on our initial two year study, we believe that it is likely that Atlantic sturgeon may be impacted by 

the offshore wind energy development projects in this region, however it is still unclear as to the level of 

impact the construction, maintenance and presence of the wind turbines might have on this species. 

Impacts on both Atlantic sturgeon and winter skate would be reduced if construction of wind turbines 

avoided the northern portion of the array. In addition, concentrating construction efforts to the summer 

months (June-September), would also reduce interactions with these species and most other species as 

this was the time of year when acoustic detections were fewest. Late fall and early winter (NovemberDecember) appears to be a very active time in the DE WEA, with multiple species migrating through the 

region. 

To better understand the effects of the construction and presence of offshore wind turbines on species in 

the Mid-Atlantic, we suggest a continuation of this study to record the occurrence of telemetered species 

during the construction and operational phase. This would inform many of the other offshore wind turbine 

development projects in the Mid-Atlantic region, and benefit from having an established baseline dataset 

of species occurrence and distribution as provided by this project. 

References

Breece, M.W., D.A. Fox, D.E. Haulsee, I.I. Wirgin, M. J. Oliver. 2018a. Satellite Driven Distribution 

Models of Endangered Atlantic Sturgeon Occurrence in the Mid-Atlantic Bight. ICES Journal of 

Marine Science 75 (2): 562–71. https://doi.org/10.1093/icesjms/fsx187.

Breece, M.W., D.A. Fox, M.J. Oliver. 2018b. Environmental Drivers of Adult Atlantic Sturgeon 

Movement and Residency in the Delaware Bay. Marine and Coastal Fisheries 10 (2): 269–80. 

https://doi.org/10.1002/mcf2.10025.
53

Bureau Of Ocean Energy Management, OOREP. 2012. Commercial Wind Lease Issuance and Site 

Characterization Activities on the Atlantic Outer Continental Shelf Offshore New Jersey, 

Delaware, Maryland, and Virginia - Draft Environmental Assessment. U S Department of the 

Interior, Bureau of Ocean Energy Management Office of Renewable Energy Programs.

Campbell, H.A., M.E. Watts, R.G. Dwyer, and C.E. Franklin. 2012. V-Track: Software for Analysing and 

Visualising Animal Movement from Acoustic Telemetry Detections. Marine and Freshwater 

Research 63: 815–20.

Damon-Randall, K, R. Bohl, S. Bolden, D.A Fox, C. Hager, B. Hickson, E. Hilton, et al. 2010. Atlantic 

Sturgeon Research Techniques. NOAA Technical Memorandum NMFS-NE-215.

Dunton, K.J., A. Jordaan, K.A. McKown, D.O. Conover, M.G. Frisk. 2010. Abundance and Distribution 

of Atlantic Sturgeon (Acipenser Oxyrinchus) within the Northwest Atlantic Ocean, Determined 

from Five Fishery-Independent Surveys. Fishery Bulletin 108: 450.

Erickson, D.L., A. Kahnle, M.J. Millard, E.A. Mora, M.Bryja, A. Higgs, J. Mohler, et al. 2011. Use of 

Pop-up Satellite Archival Tags to Identify Oceanic-Migratory Patterns for Adult Atlantic 

Sturgeon, Acipenser Oxyrinchus Oxyrinchus Mitchell, 1815. Journal of Applied Ichthyology 27 

(2): 356–65. https://doi.org/10.1111/j.1439-0426.2011.01690.x.

Foley, M.M., B.S. Halpern, F. Micheli, M.H. Armsby, M.R. Caldwell, C.M. Crain, E. Prahler, et al. 2010. 

Guiding Ecological Principles for Marine Spatial Planning. Marine Policy 34 (5): 955–66. 

https://doi.org/10.1016/j.marpol.2010.02.001.

Fox, D.A., J.E. Hightower, F.M. Parauka. 2000. Gulf Sturgeon Spawning Migration and Habitat in the 

Choctawhatchee River System, Alabama–Florida. Transactions of the American Fisheries Society 

129 (3): 811–26. https://doi.org/10.1577/1548-8659(2000)129<0811:GSSMAH>2.3.CO;2.

Freeman, E.A., and G. Moisen. 2008. PresenceAbsence: An R Package for Presence Absence Analysis. 

Journal of Statistical Software 23 (1): 1–31. https://doi.org/10.18637/jss.v023.i11.

Frisk, M.G., T.J. Miller, S.J. D. Martell, K. Sosebee. 2008. New Hypothesis Helps Explain Elasmobranch 

‘Outburst’ on Georges Bank in the 1980s. Ecological Applications: A Publication of the 

Ecological Society of America 18 (1): 234–45.

Frisk, M.G., and T.J. Miller. 2009. Maturation of Little Skate and Winter Skate in the Western Atlantic 

from Cape Hatteras to Georges Bank. Marine and Coastal Fisheries 1 (1): 1–11. 

https://doi.org/10.1577/C08-014.1.

Fuller, A.S., J.P. Henne, J. Seals, V.A. Mudrak. 2008. Performance of Commercially Available Passive 

Integrated Transponder (PIT) Tag Systems Used for Fish Identification and Interjurisdictional 

Fisheries Management. North American Journal of Fisheries Management 28 (2): 386–93. 

https://doi.org/10.1577/M06-019.1.

Garrison, L.P., and J.S. Link. 2000. Dietary Guild Structure of the Fish Community in the Northeast 

United States Continental Shelf Ecosystem. Marine Ecology Progress Series 202: 231–40. 

https://doi.org/10.3354/meps202231.
54

Glenn, S., C. Jones, M. Twardowski, L. Bowers, J. Kerfoot, J. Kohut, D. Webb, O. Schofield. 2008. 

Glider Observations of Sediment Resuspension in a Middle Atlantic Bight Fall Transition Storm. 

Limnology and Oceanography 53 (5part2): 2180–96. 

https://doi.org/10.4319/lo.2008.53.5_part_2.2180.

Guida, V., A. Drohan, H. Welch, J. McHenry, D. Johnson, V. Kentner, J. Brink, D. Timmons, and E. 

Estela-Gomez. 2017. Habitat Mapping and Assessment of Northeast Wind Energy Areas. OCS 

Study BOEM 2017-088. Sterling, VA: US Department of the Interior, Bureau of Ocean Energy 

Management.

Harms, C.A, and R.S. Bakal. 1994. Techniques in Fish Anesthesia. In American Association of Zoo 

Veterinarians Annual Proceedings, 202–210.

Hastie, Trevor, and Robert Tibshirani. 1990. Generalized Additive Models. Chapman & Hall, New York.

Haulsee, D.E., M.W. Breece, L.M. Brown, B.M. Wetherbee, D.A. Fox, and M.J. Oliver. 2018. Spatial 

Ecology of Carcharias Taurus in the Northwestern Mid-Atlantic Coastal Ocean. Marine Ecology 

Progress Series 597 (June): 191–206. https://doi.org/10.3354/meps12592.

Hobson, R.D. 1979. Definition and Use of the Phi Grade Scale. CETA 79-7. Fort Belvoir, Va.: U.S. 

Army, Corps of Engineers, Coastal Engineering Research Center. 

https://apps.dtic.mil/dtic/tr/fulltext/u2/a080983.pdf.

Ingram, E.C., R.M. Cerrato, K.J. Dunton, M.G. Frisk. 2019. Endangered Atlantic Sturgeon in the New 

York Wind Energy Area: Implications of Future Development in an Offshore Wind Energy Site. 

Scientific Reports 9 (1): 12432. https://doi.org/10.1038/s41598-019-48818-6.

IUCN. 2010. Sturgeon More Critically Endangered than Any Other Group of Species. International 

Union for Conservation of Nature. https://www.iucn.org/content/sturgeon-more-criticallyendangered-any-other-group-species.

Johnson, J.H., D.S. Dropkin, B.E. Warkentine, J.W. Rachlin, W.D. Andrews. 1997. Food Habits of 

Atlantic Sturgeon off the Central New Jersey Coast. Transactions of the American Fisheries 

Society 126 (1): 166–70. https://doi.org/10.1577/1548-

8659(1997)126<0166:FHOASO>2.3.CO;2.

Kelly, J.T., and J.M. Hanson. 2013. Maturity, Size at Age and Predator–Prey Relationships of Winter 

Skate Leucoraja Ocellata in the Southern Gulf of St Lawrence: Potentially an Undescribed 

Endemic Facing Extirpation. Journal of Fish Biology 82 (3): 959–78. 

https://doi.org/10.1111/jfb.12030.

Kilfoil, J.P., B.M. Wetherbee, J.K. Carlson, D.A. Fox. 2017. Targeted Catch-and-Release of Prohibited 

Sharks: Sand Tigers in Coastal Delaware Waters. Fisheries 42 (5): 281–87. 

https://doi.org/10.1080/03632415.2017.1306974.

Link, J.S. 2007. Underappreciated Species in Ecology:“Ugly Fish" in the Northwest Atlantic Ocean. 

Ecological Applications 17: 2037–2060.
55

Mangone, G.J. 1988. Ports and Terminals in the Delaware River Basin. In Ecology and Restoration of the 

Delaware River Basin, edited by S.K. Majumdar and E.W. Miller, 273–85. Philadelphia: 

Pennsylvania Academy of Science.

McEachran, J.D., and J.A. Musick. 1975. Distribution and Relative Abundance Of 7 Species of Skates 

(Pisces-Rajidae) Which Occur Between Nova-Scotia and Cape-Hatteras. Fishery Bulletin 73: 

110–136.

Musial, W., and B. Ram. 2010. Large-Scale Offshore Wind Power in the United States: Assessment of 

Opportunities and Barriers. National Renewable Energy Laboratory.

Oliver, M.J., M.W. Breece, D.A. Fox, D.E. Haulsee, J.T. Kohut, J. Manderson, T. Savoy. 2013. Shrinking 

the Haystack: Using an AUV in an Integrated Ocean Observatory to Map Atlantic Sturgeon in the 

Coastal Ocean. Fisheries 38 (5): 210–16. https://doi.org/10.1080/03632415.2013.782861.

Packer, D.B., C.A. Zetlin, J.J. Vitaliano. 2003. Essential Fish Habitat Source Document. Winter Skate, 

Leucoraja Ocellata, Life History and Habitat Characteristics. NMFS-NE-179. NOAA Technical 

Memorandum.

Prabhakaran, S. 2016. InformationValue: Performance Analysis and Companion Functions for Binary 

Classification Models (version R package version 1.2.3). https: //CRAN.Rproject. 

org/package=InformationValue.

Secor, D.H., and J.R. Waldman. 1999. Historical Abundance of Delaware Bay Atlantic Sturgeon and 

Potential Rate of Recovery. In Life in the Slow Lane: Ecology and Conservation of Long-Lived 

Marine Animals., edited by J.A. Musick, 23:203–17. Washington, DC.

Stein, Andrew B., Kevin D. Friedland, Michael Sutherland. 2004. Atlantic Sturgeon Marine Distribution 

and Habitat Use along the Northeastern Coast of the United States. Transactions of the American 

Fisheries Society 133 (3): 527–37. https://doi.org/10.1577/T02-151.1.

Swain, D.P., T. Hurlbut, H.P. Benoît. 2005. Changes in the Abundance, Size and Habitat Associations of 

Skates in the Southern Gulf of St. Lawrence, 1971-2002. J. Northw. Atl. Fish. Sci. 36 (June): 19–

30.

United States Office of the Federal Registry. 2012. Endangered and threatened wildlife and plants; 

threatened and endangered status for distinct population segments of Atlantic sturgeon in the

northeast region. United States Office of the Federal Registry, 77: 5880–5912.

Van Eenennaam, J.P., S.I. Doroshov, G.P. Moberg, J.G. Watson, D.S. Moore, J.Linares. 1996. 

Reproductive Conditions of the Atlantic Sturgeon (Acipenser Oxyrinchus) in the Hudson River. 

Estuaries 19 (4): 769. https://doi.org/10.2307/1352296.

Wood, S.N. 2006. Generalized Additive Models: An Introduction with R. Boca Raton, FL: Chapman and 

Hall/CRC. https://doi.org/10.1201/9781315370279.

Wood, S.N. 2003. Thin Plate Regression Splines. Journal of the Royal Statistical Society Series B 65 (1): 

95–114.
56

Wood, S.N. 2011. Mixed GAM Computation Vehicle with Automatic Smoothness. (version R package 

version 1.8-27).

Wright, L. D. 1993. Micromorphodynamics of the Inner Continental Shelf: A Middle Atlantic Bight Case 

Study. Journal of Coastal Research, 93–124.
57

Appendix A: Wentworth grain size chart

Figure A1.1. Wentworth grain size chart.

Chart used as guide when discussing Wentworth sediment grain size product used to classify habitat selectivity 

for Atlantic sturgeon and winter skate in the DE WEA. From: United States Geological Survey Open-File Report 

2006-1195, “Surficial sediment character of the Louisiana offshore continental shelf region: A GIS Compilation” by 

Jeffress Williams, Matthew A. Arsenault, Brain J. Buczkowski, Jane A. Reid, James G. Flocks, Mark A. Kulp,

Shea Penland, and Chris J. Jenkins.
58

Appendix B: All Species Observed Occurrences by Month

Figure B1.1. Spatial distribution of detections of American shad on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all 

American shad recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA 

National Centers for Environmental Information, US Coastal Relief Model.
59

Figure B1.2. Spatial distribution of detections of Atlantic angel shark on receivers of the DE 

WEA by month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all Atlantic 

angle sharks recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
60

Figure B1.3. Spatial distribution of detections of Atlantic bluefin tuna on receivers of the DE 

WEA by month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all Atlantic 

bluefin tuna recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
61

Figure B1.4. Spatial distribution of detections of Atlantic cod on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all Atlantic 

cod recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National Centers 

for Environmental Information, US Coastal Relief Model.
62

Figure B1.5. Spatial distribution of detections of black sea bass on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all black 

sea bass recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
63

Figure B1.6. Spatial distribution of detections of blacktip shark on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all blacktip 

sharks recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
64

Figure B1.7. Spatial distribution of detections of bull shark on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all bull 

sharks recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
65

Figure B1.8. Spatial distribution of detections of cobia on receivers of the DE WEA by month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all cobia

recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National Centers for 

Environmental Information, US Coastal Relief Model.
66

Figure B1.9. Spatial distribution of detections of cownose ray on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all 

cownose ray recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
67

Figure B1.10. Spatial distribution of detections of dusky shark on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all dusky 

sharks recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
68

Figure B1.11. Spatial distribution of detections of gray seal on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all gray 

seals recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
69

Figure B1.12. Spatial distribution of detections of harbor seal on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all harbor 

seals recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
70

Figure B1.13. Spatial distribution of detections of sand tiger shark on receivers of the DE WEA 

by month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all sand 

tiger sharks recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
71

Figure B1.14. Spatial distribution of detections of sandbar shark on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all 

sandbar sharks recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA 

National Centers for Environmental Information, US Coastal Relief Model.
72

Figure B1.15. Spatial distribution of detections of smooth dogfish on receivers of the DE WEA 

by month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all smooth 

dogfish recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
73

Figure B1.16. Spatial distribution of detections of southern stingray on receivers of the DE 

WEA by month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all 

southern stingray recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA 

National Centers for Environmental Information, US Coastal Relief Model.
74

Figure B1.17. Spatial distribution of detections of spinner shark on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all spinner 

sharks recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
75

Figure B1.18. Spatial distribution of detections of spiny dogfish on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all spiny 

dogfish recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
76

Figure B1.19. Spatial distribution of detections of striped bass on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all striped 

bass recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National Centers 

for Environmental Information, US Coastal Relief Model.
77

Figure B1.20. Spatial distribution of detections of summer flounder on receivers of the DE WEA 

by month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all 

summer flounder recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA 

National Centers for Environmental Information, US Coastal Relief Model.
78

Figure B1.21. Spatial distribution of detections of thresher shark on receivers of the DE WEA 

by month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all 

thresher sharks recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA 

National Centers for Environmental Information, US Coastal Relief Model.
79

Figure B1.22. Spatial distribution of detections of tiger shark on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all tiger 

sharks recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
80

Figure B1.23. Spatial distribution of detections of white shark on receivers of the DE WEA by 

month.

Black circles represent receiver locations and are sized by the cumulative number of detection days for all white 

sharks recorded between 23 Feb 2017 and 27 Feb 2019. Underlying bathymetric map from NOAA National 

Centers for Environmental Information, US Coastal Relief Model.
81

Department of the Interior (DOI)

The Department of the Interior protects and manages the Nation's natural 

resources and cultural heritage; provides scientific and other information about 

those resources; and honors the Nation’s trust responsibilities or special 

commitments to American Indians, Alaska Natives, and affiliated island 

communities.

Bureau of Ocean Energy Management (BOEM)

The mission of the Bureau of Ocean Energy Management is to manage 

development of U.S. Outer Continental Shelf energy and mineral resources in 

an environmentally and economically responsible way.

BOEM Environmental Studies Program

The mission of the Environmental Studies Program is to provide the 

information needed to predict, assess, and manage impacts from offshore 

energy and marine mineral exploration, development, and production activities 

on human, marine, and coastal environments. The proposal, selection, research, 

review, collaboration, production, and dissemination of each of BOEM’s 

Environmental Studies follows the DOI Code of Scientific and Scholarly 

Conduct, in support of a culture of scientific and professional integrity, as set 

out in the DOI Departmental Manual (305 DM 3).