OCS Study

BOEM 2024-053

US Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico Regional Office

Biological Sciences Unit

New Orleans, LA

Vessel Strike Risk to Rice's Whale in 

the Gulf of Mexico: Review of Previous 

Methodologies, Information Gaps, and 

Recommendations for Future Efforts to 

Predict Strike Risks
OCS Study

BOEM 2024-053

US Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico Regional Office

Biological Sciences Unit

New Orleans, LA

Vessel Strike Risk to Rice's Whale in 

the Gulf of Mexico: Review of Previous 

Methodologies, Information Gaps, and 

Recommendations for Future Efforts to 

Predict Strike Risks

July 2024

Authors:

Tara S. Stevens

Mark Fonseca

Mary Jo Barkaszi

Prepared under Purchase Order Number 140M0124P0008

by

CSA Ocean Sciences Inc.

8502 SW Kansas Avenue

Stuart, FL 34997
iii

DISCLAIMER

Study concept, oversight, and funding were provided by the U.S. Department of the Interior, Bureau of 

Ocean Energy Management, Environmental Studies Program, Washington, DC under Purchase Order 

Number 140M0124P0008 with CSA Ocean Sciences Inc. (Stuart, Florida). 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

Download a PDF file of this report at https://espis.boem.gov/Final Reports/BOEM_2024-053.pdf. 

To search for other Environmental Studies Program ongoing and completed studies, visit

https://www.boem.gov/environment/environmental-studies/environmental-studies-information/. 

CITATION

Stevens TS, Fonseca M, Barkaszi MJ. 2024. Vessel strike risk to Rice’s whale in the Gulf of Mexico: 

review of previous methodologies, information gaps, and recommendations for future efforts to 

predict strike risks. New Orleans (LA): U.S. Department of the Interior, Bureau of Ocean Energy 

Management. 61 p. Obligation No.: 140M0124P0008. Report No.: OCS Study BOEM 2024-053. 

ABOUT THE COVER

A Rice’s whale in the Gulf of Mexico from an aerial survey in April 2024. Photo credit: NOAA 

Fisheries/Paul Nagelkirk (Permit #21938). 
iv

Contents

List of Figures .............................................................................................................................................v

List of Tables ...............................................................................................................................................v

List of Abbreviations and Acronyms .......................................................................................................vi

1 Introduction ...............................................................................................................................7

2 Methods......................................................................................................................................9

2.1 Data Sources ...............................................................................................................................9

2.2 Literature Review and Synthesis ...............................................................................................10

3 Results .....................................................................................................................................11

4 Vessel Strike Risk Factors .....................................................................................................11

4.1 Distribution, Occurrence, and Habitat Selection........................................................................12

4.2 Dive and Surface Behaviors ......................................................................................................16

4.3 Vessel Activity and Documented Vessel Strikes .......................................................................17

4.3.1 Vessel Strike Mitigation...........................................................................................................20

5 Existing Vessel Strike Risk Analyses ...................................................................................22

5.1 The Biological Opinion on the Federally Regulated Oil and Gas Program Activities in the 

Gulf of Mexico (NOAA NMFS 2020): ........................................................................................22

5.1.1 Assumptions and Limitations ..................................................................................................23

5.2 Spatial Analysis of Ship-strike Risk for Rice's Whales in the Gulf of Mexico (Unpublished 

Report) (Best 2023)...................................................................................................................23

5.2.1 Assumptions and Limitations ..................................................................................................24

5.3 Risk Assessment to Model Encounter Rates between Large Whales and Vessel Traffic 

from Offshore Wind Energy on the Atlantic OCS (Barkaszi et al. 2021) ..................................25

5.3.1 Assumptions and Limitations ..................................................................................................25

5.4 Modeling Whale Deaths from Vessel Strikes to Reduce the Risk of Fatality to 

Endangered Whales (Rockwood et al. 2021) ...........................................................................26

5.4.1 Assumptions and Limitations ..................................................................................................26

6 Recommendations..................................................................................................................27

6.1 Improving Rice’s Whale Density and Distribution Data .............................................................27

6.2 Improvements in Understanding of Animal Behavior ................................................................28

6.3 Other Considerations.................................................................................................................29

7 Conclusions.............................................................................................................................31

8 References...............................................................................................................................32

Appendix A: Literature Review and Synthesis Matrix...........................................................................39
v

List of Figures

Figure 1. National Marine Fisheries Service (NMFS) proposed Rice’s whale critical habitat.......................8

Figure 2. Rice’s whale core distribution area (orange). ..............................................................................13

Figure 3. Habitat-based predicted Rice’s whale density (January). ...........................................................15

Figure 4. Automatic Identification System (AIS) vessel transit count data for 2022 for all vessel types. ...18

Figure 5. Automatic Identification System (AIS) vessel trackline data for different vessel categories in 

2022. ......................................................................................................................................19

List of Tables

Table A-1. Classification matrix and summary table for primary and secondary data sources..................39
vi

List of Abbreviations and Acronyms

Short Form Long Form

AIS Automatic Identification System

BIA biologically important area

BOEM Bureau of Ocean Energy Management

BSEE Bureau of Safety and Environmental Enforcement

CV coefficient of variation

DOI Department of the Interior

eDNA environmental DNA

EEZ Exclusive Economic Zone

EIS environmental impact statements

ESA Endangered Species Act

GAM generalized additive modeling

GIS Geographic Information System

GOM Gulf of Mexico

GUI geographic user interface

MARU marine autonomous recording units

MMPA Marine Mammal Protection Act

MSP marine spatial planning

NEPA National Environmental Policy Act

NMFS National Marine Fisheries Service

NOAA National Oceanographic and Atmospheric Administration

OCS Outer Continental Shelf

OCSLA OCS Lands Act

OE Office of Environment

PAM passive acoustic monitoring

PMRF Pacific Missile Range Facility

PSO protected species observer

RPA Reasonable and Prudent Alternative

SAR Stock Assessment Reports

SDM spatial density modeling

SEFSC Southeast Fisheries Science Center

SME subject matter experts

SSHA sea surface height anomalies

SST sea surface temperature

TDR time depth recorders

VSR vessel speed reductions
7

1 Introduction

The Bureau of Ocean Energy Management (BOEM) is dedicated to overseeing the development of energy 

and mineral resources on the U.S. Outer Continental Shelf (OCS) in a way that is both environmentally 

and economically responsible. To achieve this, BOEM funds rigorous scientific research to guide policy 

decisions related to OCS energy and mineral development, the National Environmental Policy Act 

(NEPA), and analyses of potential environmental impacts from BOEM-approved activities, such as oil 

and gas extraction, renewable energy projects, carbon sequestration, and mineral resources in Federal 

OCS waters. The Office of Environment (OE) evaluates the environmental impacts of BOEM-regulated 

activities and works to avoid or mitigate these impacts in line with the OCS Lands Act (OCSLA), the 

Endangered Species Act (ESA), and other environmental laws.

On April 15, 2019, National Oceanographic and Atmospheric Administration (NOAA) Fisheries

(National Marine Fisheries Service [NMFS]) issued a final rule listing the Gulf of Mexico (Gulf) Bryde’s 

whale (Balaenoptera edeni) as an endangered subspecies under the ESA. Genetic analysis of the 

population revealed extremely low diversity and an evolutionary distinction from other Bryde’s whale

populations (Rosel and Wilcox 2014). Subsequently, the Gulf Bryde’s whale population was reclassified 

as a separate species, the Rice’s whale (B. ricei) (NOAA NMFS 2021; Rosel et al. 2021). Hereafter, 

reference to the Gulf Bryde’s whale will use the reclassified species name (i.e., Rice’s whale).

Sightings from vessel and aerial surveys indicate that Rice’s whales are primarily located in the 

northeastern Gulf, particularly in De Soto Canyon at depths of approximately 100 to 400 meters (328 to 

1,312 feet), which is considered their core distribution area. There have been several confirmed visual 

sightings in the western Gulf during NOAA visual line-transect surveys in recent years, including one 

individual during summer 2017 (Garrison et al. 2020) and two individuals in April 2024 (NOAA 2024). 

Acoustic monitoring efforts have also detected assumed Rice’s whale calls in the northwestern and 

western GOM, though there is insufficient data to determine any seasonal patterns of use or migration 

between areas (Soldevilla et al. 2024).

Collisions between whales and vessels can cause serious injury or death to the whales and damage the 

vessels. Most reported vessel collisions with marine mammals involve large whales, though smaller 

species are also affected (Van Waerebeek et al. 2007). Research by Laist et al. (2001) shows that most 

severe and fatal injuries to whales occur from collisions with large ships (>80 meters [262 feet]) traveling 

at higher speeds, with 89 percent of ship-strike records involving vessels moving at over 14 knots. 

Rice’s whales may spend up to 88 percent of their time at night–and 70 percent of their time overall–

within 15 meters (39 feet) of the ocean surface (Soldevilla et al. 2017). This makes them particularly 

vulnerable to collisions with large ships. NMFS has documented two instances of Rice’s whales showing 

evidence of vessel strikes: a dead lactating adult female found in Tampa Bay, Florida in 2009 with 

injuries indicative of blunt trauma resulting from a large object (Hayes et al. 2023), and a free-swimming 

whale in the northeastern Gulf in 2019 with a severely deformed spine, also consistent with a vessel strike

(Hayes et al. 2023; Rosel et al. 2021). Neither incident was linked to oil and gas related activities.

NMFS is currently proposing to designate critical habitat for the Rice’s whale (NOAA NMFS 2023).

Proposed critical habitat includes all waters within the 100- to 400-meter (328- to 1,312-foot) isobaths, 

extending from the U.S. Exclusive Economic Zone (EEZ) boundary off Texas to the boundary between 

the South Atlantic Fishery Management Council and the Gulf of Mexico Fishery Management Council 

(GMFMC) off Florida (88 Federal Register [FR] 47453; NOAA NMFS 2023) (Figure 1). NMFS 

anticipates publishing a final rule for Rice’s whale critical habitat in late summer of 2024.
8

To fulfill requirements under the ESA, relative to the Rice’s whale, BOEM must assess vessel strike risk 

posed by vessel traffic specifically related to BOEM-authorized activities. As an initial step, BOEM 

requires a systematic evaluation of previously used methodologies for predicting Rice’s whale strike risk 

in the northern Gulf, which serves as the objective of this BOEM-funded study.

Figure 1. National Marine Fisheries Service (NMFS) proposed Rice’s whale critical habitat.

Source: 88 FR 47453
9 

2 Methods

A review of data sources that address the potential for vessel strike to Rice’s whales in the Gulf was 

undertaken. This included evaluation of peer-reviewed literature, published reports, and data sets that 

describe aspects of Rice’s whale distribution, behavior, and ecology; factors that contribute to or elevate 

their risk for vessel strike were preferentially selected. Since relatively little published literature exist for 

the Rice’s whale, evaluation of other taxonomically similar species (Omura’s whale [B. omuri]) and 

Bryde’s whale subspecies (i.e., Bryde’s whale [B. e. brydei]; Eden’s whale [B. e. edeni]) was also 

conducted, as applicable. In addition, a review of quantitative and qualitative studies and reports or 

predictive tools that evaluate vessel strike risk to Rice's whales and other similar species as deemed 

potentially applicable was conducted. Specifically, the literature review focused on addressing the 

following questions:

• What are the previous efforts (both quantitative and qualitative) that have evaluated vessel strike 

risk to Rice's whales or similar large baleen whale species?

• What was the approach used (e.g., calculations) in each effort?

• What are the strengths and weaknesses of each approach?

• What were the baseline data (e.g., field studies, density models) used in each effort and what are 

the strengths and weaknesses of the data?

• What were the assumptions made in each effort and how did they affect the outcome?

• What were the mitigation measures (e.g., vessel speed restrictions) that were evaluated as part of 

each assessment, the basis for those measures, and how they affected the outcome?

2.1 Data Sources

The identification of relevant source material was based on a search of numerous bibliographic and 

library sources using keywords relevant to the objective of this study. An extensive search for all relevant 

scientific and technical information was conducted using five major sources, described below:

• Proquest Dialog (Dialog); 

• OCLC WorldCat (WorlCat); 

• Internet search engines to locate relevant websites such as conference proceedings and archives 

(e.g., Google, Google scholar); 

• Digital Repositories, including industry-related sites and web-wide open term searches; and

• Relevant government agency websites.

Proquest Dialog is a unique aggregation of the world's leading bibliographic and full text sources and 

offers the largest collection of authoritative content that can be searched at one time. OCLC WorldCat is a 

non-profit, member-driven library community from which relevant books, proceedings, technical reports, 

and gray literature are located. WorldCat is a cooperative database of more than 450 million bibliographic 

records contributed to by more than 30,000 member libraries in 123 countries, making it the world’s 

largest, most complete, and most consulted library union catalog of electronic, print, and digital resources. 

Items found in WorldCat may be purchased or borrowed via the OCLC Interlibrary Loan System.

Internet search engines were also used to locate relevant websites and the digital document repositories, 

which serve as excellent sources of gray literature and conference papers, including web-wide key word 

searches and maintained sites. Government agency websites were also searched for documents relevant to 

NEPA, Marine Mammal Protection Act (MMPA), and ESA contexts. 
10

2.2 Literature Review and Synthesis

A comprehensive literature review followed the systematic identification of potential sources discussed in 

Section 2.1. Title and abstract reviews were conducted as an efficient and effective first step to identify 

potentially relevant documents. Following the abstract review, if the publication was deemed relevant, a 

review of methods, results, and findings was conducted for each publication. All selected literature was

imported from publisher-provided *.ris files or manually entered into bibliographic management software

(i.e., EndNote).

This review and synthesis step recognizes the primary objective of the project––to compare and contrast 

the findings from various analyses regarding potential risk of vessel strike to Rice’s whales. A secondary 

objective of the data review effort was to review, summarize, and synthesize relevant data sources to be 

used by BOEM subject matter experts (SMEs) in future impact assessment tasks (e.g., NEPA documents, 

Biological Opinions, Incidental Take Statements). These objectives underscore a need to: 1) support 

future impact analyses; 2) identify viable and effective mitigation measures; and 3) identify key data gaps 

and provide a basis for future research needs to fill those gaps.

Data sources were evaluated on the basis of three perspectives: 1) scientific merit; 2) applicability and 

relevance; and 3) relative value in a NEPA context (e.g., suitability for use in future environmental impact 

statements [EISs]):

• Scientific merit metric: Broad characterization categories include High and Acceptable. This 

metric provides further insight into the relative scientific merit of each data source relative to this 

study’s objectives. The intent behind this metric is to qualitatively establish the relative value of 

each data source for BOEM’s purposes.

• Rice’s whale vessel strike risk: Categorization levels include Directly Relevant, Moderately

Relevant, and Peripheral Interest relative to this study’s objectives. This metric effectively 

separates Rice’s whale-specific studies from others, creating a de facto listing of primary data 

sources (Directly Relevant), secondary data sources (Moderately Relevant), and tertiary data 

sources (Peripheral Interest).

• ESA/NEPA context: More detailed assessment of data sources using topic categorization as 

likely applicable NEPA-related impact analyses. This metric provides an assessment of each data 

source and its utility in a NEPA-type analysis primarily based on subject/topic review.

All relevant data sources were classified using the metrics noted above in a presence-absence context

(i.e., yes or no). A Microsoft Excel matrix was developed to manage this classification process, as well as 

to organize the subject/topic review, summaries, and evaluation for each relevant data source. The 

classification matrix for primary and secondary data sources is presented in Appendix A (Table A-1).

During this literature review and synthesis, limitations, and data gaps were identified. These were the 

basis for recommendations for BOEM to perform a rigorous, statistically meaningful vessel strike risk 

assessment methodology that could be realistically implemented and peer-reviewed for validating a future 

risk assessment.
11

3 Results

The initial bibliographic literature search yielded 247 potentially relevant publications and reports, 

inclusive of peer-reviewed papers, published and unpublished reports, and dissertations. An initial title 

review eliminated 142 papers that were duplicative or deemed not relevant. The remaining 

105 documents underwent a systematic review of the abstract, methods, results, and findings. Through 

this process, an additional 7 reports were excluded from analysis as they lacked applicability to this study, 

resulting in a total of 98 documents considered for inclusion in this study.

Out of 98 documents considered, 78 were peer-reviewed studies, 13 were government reports, one was an 

unpublished report, and six were other document and data types, including one dissertation. Further 

screening of the 98 documents designated 35 as primary source documents and 10 as secondary source 

documents. The primary and secondary papers were scrutinized for analytical quality and validity of 

conclusions with focus on statistical analyses, if conducted. An additional 53 documents were considered 

tertiary source documents, which provided useful species or other data and were reviewed primarily for 

conclusions. No obvious issues regarding experimental design or application of statistical methods 

(e.g., sample independence, absent transformations) were detected in any study documents assessed.

4 Vessel Strike Risk Factors

Three fundamental components are essential to understanding and assessing vessel strike risk to any 

marine mammal population:

1. Distribution, occurrence, and habitat selection of the population

2. Dive and surface behavior of individuals

3. Vessel characteristics, activity, and mitigation measures

Relatively few publications directly assess Rice’s whale vessel strike risk. However, using what is known 

about the behavior and ecology of the species, and supplementing with behavioral data on the broader 

Bryde’s whale complex, can help to inform risk analyses and NEPA assessments.

Vessel speed and vessel size are of note when assessing strike risk. The speed at which vessels travel 

affects their ability to detect and react to whales in their path. For example, faster vessels have shorter 

reaction times and may pose a higher risk of collision. The size of a vessel also influences its 

maneuverability: larger vessels are unable to change course or slow down to avoid a collision as quickly 

as smaller, more maneuverable vessels. Also, larger vessels have deeper drafts, which increase the threedimensional strike zone ahead of a moving vessel. Vessel size is typically reported in length. However, 

that length is associated with classes of vessel that are also larger in mass (gross tonnage), which 

subsequently influence the vessels maneuverability, including its ability to change course or stop. Wang 

et al. (2007) demonstrated that the lethality of a strike was even more correlated to the vessel’s speed than 

size for vessels over 500 tons. When these factors are combined, strikes involving large vessels (i.e., over 

80 meters [262 feet]) traveling at speeds of 10 knots or faster are more likely to result in severe injuries

and mortality (Laist et al. 2001; Jensen and Silber 2004; Vanderlaan and Taggart 2007; Conn and Silber 

2013). Mitigation measures designed to reduce strike risk typically manage vessel speeds, though other 

methods are also frequently employed (Section 4.3.1).
12

4.1 Distribution, Occurrence, and Habitat Selection

Visual vessel-based and aerial line-transect surveys serve as the baseline for documenting Rice’s whale 

distribution and occurrence patterns in the Gulf. These data indicate Rice’s whale occur in a very small 

area of the Gulf in the northeast portion, particularly around De Soto Canyon and in water depths of 

100 to 400 meters (328 to 1,312 feet) (Mullin and Hoggard 2000; Mullin and Fulling 2004; Maze-Foley 

and Mullin 2006; Mullin 2007; Garrison et al. 2020, 2023; Rappucci et al. 2023). The visual data is 

corroborated by several acoustic studies also documenting their occurrence in the northeast GOM 

(Rice et al. 2014; Sirovic et al. 2014; Soldevilla et al. 2022a)

Water depth, surface chlorophyll concentration, bottom temperature, and bottom salinity are physical

descriptors that broadly define Rice’s whale's habitat that are subsequently influenced by oceanographic 

features like upwelling and circulation patterns to produce key habitat features necessary for biological 

functions (Garrison et al. 2024). The species’ known core distribution area (hereafter referred to as the 

“core area”), as of June 2019, is located in the northeastern Gulf shelf break, particularly around De Soto 

Canyon and in water depths of 100 to 400 meters (328 to 1,312 feet) (Garrison and Rosel 2019)

(Figure 2). This area encompasses the biologically important area (BIA) previously identified for 

Rice’s whales (LaBrecque et al. 2015). The Rice’s whale core area features seasonal advection of low 

salinity, high productivity surface waters, and persistent upwelling driven by winds and Loop Current 

intrusions. The confluence of these features leads to a mixing area with intermediate chlorophyll-a

concentrations, temperatures, and high salinity bottom water, which is where Rice’s whales are most 

commonly observed (i.e., in the mixing area) (Farmer et al. 2022). Though other regions in the Gulf have 

similar features, nearly all Rice’s whale sightings have occurred within the core area.
13

Figure 2. Rice’s whale core distribution area (orange).

Source: Rosel and Garrison (2019)

Rice’s whale presence outside the core area in the northeastern Gulf is considered low, which may be a 

product of its low population size in the Gulf or may indicate limited use of non-core habitats. Indeed, the 

population size of Rice’s whale is very low, with abundance estimated at 51 individuals (coefficient of 

variation [CV] 0.53; confidence interval [CI] 20, 130), based on line-transect surveys conducted during 

2017 and 2018 (Garrison et al. 2020; Hayes et al. 2023). Though their historical presence was likely much 

broader in the Gulf (Reeves et al. 2011), recent acoustic studies (Rice et al. 2014; Soldevilla et al. 2022b; 

Soldevilla et al. 2024) and two visual surveys (Garrison et al. 2020; Rappucci et al. 2023; NOAA 2024)

detected the presence of Rice’s whales outside of the core area, most notably in the northwestern Gulf.

Soldevilla et al. (2022b) collected long-term acoustic data from five locations across the northern Gulf 

shelf break from July 2016 to August 2017 and identified a unique long-moan variant call attributed to 

Rice’s whales outside of the core area. The authors acknowledge uncertainties about the origin of the 

unique call variant as compared to those made by Soldevilla et al. (2022a), which obtained acousticdirected visual confirmation of calls attributable to Rice’s whales in the core area. Confirmed 

identification for calls recorded by Soldevilla et al. (2022b) has not been made but, rather, are presumed 

to originate from Rice’s whales because of their call characteristics and that they are the only resident 

baleen whale in the Gulf. Vocalizations recorded in the western Gulf were relatively sparse in comparison 

to the number of calls recorded in the northeast Gulf (Soldevilla et al. 2022b). For example, fewer calls 

were detected on fewer days in the western Gulf, potentially indicating fewer whales or more sparse 

occurrences of individuals in the northwestern Gulf. In addition, long-term acoustic monitoring in the 

western Gulf detected Rice’s whales regularly and throughout the year in both U.S. and Mexican 
14

EEZ waters, present on up to 33 percent of recorded days (Soldevilla et al. 2024). While it is unknown if 

Rice’s whales occur in the north-central Gulf (e.g., off Louisiana) or travel between the northwestern and 

northeastern areas, the data indicate a persistent presence of Rice's whales in the northwestern and 

western Gulf, which coincides with high levels of anthropogenic activities (Soldevilla et al. 2022b; 2024).

It is important to note that there is a lack of occurrence and genetic data from the southern Gulf and the 

wider Caribbean region. This knowledge gap may be significant, as illustrated by the Omura’s whale: 

initially thought to have a limited distribution in the eastern Indian Ocean, focused data collection over 

the last 15 years revealed that Omura’s whale actually inhabits a much broader area of the Indian Ocean 

and tropical Atlantic (Cherchio et al. 2019). Though isolated populations of Bryde’s whales are well 

documented globally, a lack of data in the broader Gulf and Caribbean limits the ability to fully assess 

Rice’s whale baseline status and risk in the Gulf.

Distribution and occurrence of Bryde’s whales is likely strongly linked to oceanographic variables for 

some populations, with seasonal peaks in occurrence likely related to high prey abundances and 

concentrations (Penry et al. 2011; Salvadeo et al. 2011; Watanabe et al. 2012; Sasaki et al. 2013; Lodi 

et al. 2015; Tardin et al. 2017; Maciel et al. 2018; Purdon et al. 2020). Strong upwelling and high 

productivity as governed by seasonal and inter-annual climate variability patterns in the eastern North 

Pacific, in particular, likely drive prey availability and, in turn, Bryde’s whale occurrence patterns

(Kerosky et al. 2012). In the Gulf, the Rice’s whale core area features seasonal advection of low salinity, 

high productivity surface waters, and persistent upwelling driven by winds and Loop Current intrusions. 

The confluence of these features leads to a mixing area with intermediate chlorophyll-a concentrations, 

temperatures, and high salinity bottom water, which is where Rice's whales are most commonly observed 

(Farmer et al. 2022).

Habitat-based spatial density modeling (SDM) used large vessel survey data from the Gulf from 2003 

through 2019 taking into account the effect of oceanographic variables (in particular, depth, surface 

chlorophyll, bottom temperature, bottom salinity, and geostrophic velocity) to predict the abundance and 

distribution of the species (Garrison et al. 2023). As shown in Figure 3, suitable habitat exists for the 

Rice’s whale well beyond the core area, and potentially exists Gulf-wide along the 100- to 400-meter

(328- to 1,312-foot) isobath. Of note, the densities depicted in Figure 3 are very low, even in the core 

area, due to the assumed limited population size of the Rice’s whale. Areas that share similar bathymetric 

and current characteristics to the core area, particularly near the Campeche Bank (Southern Gulf off the 

Yucatan Peninsula), may also represent suitable Rice’s whale habitat. However, projections of the SDM

beyond the northern Gulf should be treated cautiously, as it assumes consistent species-habitat

relationships. Importantly, this data does not represent recorded animal densities; instead, the SDM 

represents abundance prediction maps generated based on environmental variables understood to likely 

influence habitat selection and suitability. Garrison et al. (2024) found that water depth, surface 

chlorophyll concentration, bottom temperature, and bottom salinity are crucial factors defining the 

whale's habitat, influenced by oceanographic features like upwelling and circulation patterns.
15

Figure 3. Habitat-based predicted Rice’s whale density (January).

Source: Rappucci et al. (2023); Garrison et al. (2023)
16

The proposed designated critical habitat for Rice’s whale includes all Gulf U.S. EEZ waters within the 

100-meter to 400-meter (328- to 1,312-foot) isobaths, spanning from Texas to Florida (Figure 1) and is 

based on occurrence and habitat suitability data (NMFS 2023). Three key habitat attributes that are 

necessary to support Rice’s whale are identified in the proposed rule (NOAA NMFS 2023), which are:

• Sufficient density, quality, abundance, and accessibility of small demersal and vertically 

migrating prey species;

• Marine water with elevated productivity, bottom temperatures of 10 to 19°C, and levels of 

pollutants that do not preclude or inhibit any demographic function; and

• Sufficiently quiet conditions for normal use and occupancy, including intraspecific 

communication, navigation, and detection of prey, predators, and other threats.

These key attributes are applicable to broad regions of the Gulf, including areas where limited or no

Rice’s whale detections (visual or acoustic) have been made. Similar to the discussion above regarding 

the habitat-based SDM, caution must be applied when interpreting the spatial expanse of the proposed 

critical habitat versus actual habitat selection by Rice’s whales. However, Farmer et al. (2022) highlight 

the importance of recognizing broader distinct habitat regions for the Rice's whales (i.e., the core area and 

the extended area) as a conservative measure when assessing risk until more is known about the species' 

distribution and density.

4.2 Dive and Surface Behaviors

Availability for a vessel strike is directly related to the surface presence of the animal and the proportion 

of its time that it spends in the upper water column within the draft of vessels. Tagging data indicate that 

Bryde’s whale subspecies spend up to 91 percent of their time within 12 meters (39 feet) of the ocean's 

surface (Constantine et al. 2015). This is a general understanding that Bryde’s whales spend a large 

portion of their time at the surface. Several studies indicated a higher proportion of deeper dives during 

the daytime, with more resting at the surface and shallower dives overnight (Alves et al. 2010; 

Constantine et al. 2015; Izadi et al. 2022). These generalized behaviors make Bryde’s whales particularly 

vulnerable to being hit by ships, especially at night and by large vessels with limited maneuverability that 

do not see them in time to take evasive action. However, many studied Bryde’s whale populations occur 

in shallow coastal waters (Cerchio et al. 2019), which may not actually be representative of 

Rice’s whales, thus necessitating the need for dedicated dive data analyses for Rice’s whales.

A few studies exist that examine diving behavior of Rice’s whales in the Gulf. Soldevilla et al. (2017) 

tagged a single individual (female; age class not specified) in the core area to track vertical positioning 

with time spent at depth (duration: 2.7 days), plus longer-term tracking with location-only information

(duration: 33 days). The tagged individual exhibited a diel dive pattern, with deep (>70 meters [230 feet], 

maximum: 271 meters [889 feet]) daytime dives and shallow (<30 meters [98 feet]) nighttime dives

(Soldevilla et al. 2017). The authors suggest the animal was likely foraging at or near the bottom, based 

on lunge patterns and supported by additional tagging data from Kok et al. (2023). During the daytime, 

the individual spent 47 percent of its time in the top 15 meters [49 feet] of the water column. During 

nighttime, the animal remained close to the surface, made shallow dives with occasional deeper dives

(maximum: 150 meters [492 feet]); only 3.25 percent of deep dives occurred at night. The whale spent 

88 percent of its time in the top 15 meters (49 feet) of the water column at night. Overall, the animal spent 

70 percent of its total time within 15 meters (49 feet) of the surface, which overlaps with the draft of 

deep-draft vessels that transit the region. These data represent highly consistent diel dive behavior with 

presumed foraging at/near the bottom mainly during the daytime and extended periods spent at the 

surface during the nighttime. (Kok et al. 2023; Soldevilla et al. 2017). Though these studies collectively 

tracked a very small number of individuals, representing severe limitations from which to infer dive and 

surface behaviors of the population, Soldevilla et al. (2017) indicate that it is reasonable to assume that 
17

this behavior is representative of the GOM Rice’s whale population as other tagged baleen whales also 

display similar stereotypical diel dive behaviors linked to foraging and prey distribution.

These dive behaviors are further supported by reported prey selection by Rice’s whales. Based on skin 

and blubber biopsy samples collected in 2010 (n=1) and 2018 to 2019 (n=9), Kiszka et al. (2023) found 

Rice’s whales in the northeastern Gulf preyed primarily on high-energy content species, particularly 

Ariomma bondi (a small schooling fish found in demersal habitats over muddy bottoms at depths of 50 to 

500 meters [164 to 1,640 feet]), while other abundant species appeared less significant in their diet. These 

results suggest Rice’s whales selectively forage on high-energy content prey, even if lower-quality prey 

are more abundant (Kiszka et al. 2023). The authors also postulate that the deep dives exhibited by 

Rice’s whales in the Gulf are energetically expensive and therefore high quality prey would be needed to 

meet their energetic requirements

4.3 Vessel Activity and Documented Vessel Strikes

Vessel traffic in the Gulf is highest in the north-central and western regions; less traffic is evident within 

the core area, though several shipping routes do bisect it, as evidenced by 2022 Automatic Identification 

System (AIS) vessel transit count data (Figure 4). Gulf vessel traffic and port utilization is expected to 

increase into the future. For example, the expected demand for port handling capabilities is expected to 

double or even quadruple by 2050, resulting in either more vessel traffic, larger vessel capacity, or both 

(Hanson and Nicholls 2020). Therefore, it is expected that larger and faster ships will increasingly use the 

Gulf, posing greater risk to Rice’s whales (Rosel et al. 2016). Specific to BOEM-regulated activities, 

BOEM’s oil and gas forecast for 2022 to 2031 (Zeringue et al. 2022) indicates that Gulf oil and gas 

production is expected to experience continued growth and that the transition to renewable sources of 

energy is underway; there is not an expected decrease in oil and gas vessel requirements even with the 

introduction of renewable energy leases, which will introduce a new set of vessel type and transit 

conditions.

As shown in Figure 5, Rice’s whale core distribution area and the proposed critical habitat overlap with 

high volumes of vessel transits by a variety of vessel categories, but most prominently cargo vessels. This 

overlap increases the likelihood of encounters between whales and vessels. Soldevilla et al (2017) 

reported on vessel activity in the BIA from October 2009 to 2010 based on AIS data and found that less 

than one vessel transit was recorded per week in 98.5 percent of the BIA, with the remainder averaging 

one transit per week to two transits per day. Importantly, though, vessel traffic reported by Soldevilla 

et al. (2017) and presented in Figure 4 and Figure 5 is likely underrepresented based on the limitations of

AIS data1

.

1 Recreational vessels, small craft, and military vessels are not required to carry AIS and therefore are not 

represented in the data analyzed by Soldevilla et al. (2017). Additionally, offshore spatial coverage of AIS 

throughout the GOM is limited due to distance from port for port-based AIS receivers, which have a maximum 

receiving rage of approximately 23 miles (37 kilometers) (United States Coast Guard [USCG] n.d.). 
18

Figure 4. Automatic Identification System (AIS) vessel transit count data for 2022 for all vessel types.

Source: BOEM and NOAA 2024a.
19

Figure 5. Automatic Identification System (AIS) vessel trackline data for different vessel 

categories in 2022.

Source: BOEM and NOAA 2024b
20

As noted above, only one Rice’s whale vessel strike mortality is documented within the Gulf: a dead 

lactating adult female found in Tampa Bay, Florida in 2009, likely having been carried into the port 

across the bow of a vessel following a lethal vessel strike (Hayes et al. 2023). A free-swimming Rice’s 

whale in the northeastern Gulf was recorded in 2019 with a severely deformed spine, consistent with a 

vessel strike (Rosel et al. 2021; Hayes et al. 2023). Although only one mortality has been recorded, the 

incidence of Rice’s whale vessel strikes in the Gulf is likely underreported. For example, Williams et al. 

(2011) suggest only about 2 percent (range: 0 to 6.2 percent) of cetacean carcasses are recovered, 

indicating true mortality rates may be much higher than realized.

Globally, populations of the Bryde’s whale complex are also at high risk for vessel strike. Many Bryde’s 

whale populations are non-migratory and occur in coastal areas that overlap with high levels of vessel 

activity (Constantine et al. 2018). Constantine et al. (2015) reports high levels of vessel strike in Hauraki 

Bay, New Zealand, likely driven by their distributional overlap with shipping lanes and their surface/dive 

behavior (i.e., individuals spend 91 percent of their time at night in the top 12 meters [39 feet] of the 

water column). In this study, 85 percent of mortalities with a known cause of death had injuries consistent 

with vessel strike. Soldevilla et al. (2017) report Gulf Bryde’s whales spend 88 percent of their time in the 

top 15 meters [49 feet] of the water column at night, which is when visual detection and aversion would 

be severely limited due to reduced or no visibility. Athayde et al. (2022) report 4 percent (n=3) of adult 

Bryde’s whales identified off the northern coast of São Paulo, Brazil show scars from vessel propellers, 

indicating sub-lethal vessel collisions. Felix and Van Waerebeek (2005) indicate that instances of Bryde’s 

whale complex bow-draping, which is when a struck individual is draped across the bow of the vessel as 

the vessel continues to transit and enter port, is likely lower than that for larger rorquals. This would 

contribute to lower recovery and reporting of vessel strikes. Vessel strikes, though still relatively rare, are 

yet reported for other Bryde’s whale complex populations globally (Van Waerebeek et al. 2007; Van 

Waerebeek and Leaper 2008; Nanayakkara and Herath 2017; Cerchio et al. 2019; Ransome et al. 2021). It 

is likely that underreporting of Bryde's whale complex vessel strikes contributes to underestimates of 

strike risk for the species globally (Laist et al. 2001).

In addition to the implementation of vessel strike mitigation measures (discussed below in Section 4.3.1),

strike risk is also affected by an individual’s response to an approaching vessel, and if any avoidant or 

evasive actions are taken by the individual animal that removes it from the “strike zone” (i.e., the area in 

front of the vessel where a strike would be unavoidable and occur). Aversion behavior may include 

movement away from the vessel or a dive response. However, no evidence suggests that Rice’s 

(or Bryde’s) whales avoid approaching vessels. Dong et al. (2022) observed Bryde’s whales altering their 

dive behavior when approached by whale watching vessels; deep dives (defined as dives greater than 

3 meters [9.8 feet] in a total water column depth of up to 10 meters [33 feet]) decreased by approximately 

17 percent when approached within 300 meters (984 feet). Therefore, it cannot be assumed that 

individuals will take evasive actions to avoid vessel collisions and some reactive behaviors may actually

heighten their strike risk.

4.3.1 Vessel Strike Mitigation

A number of mitigation recommendations have been proposed for some regions with high strike risk. 

Collisions often result in severe injuries, particularly when involving large vessels (i.e., over 80 meters

[262 feet]) traveling at speeds of 10 knots or faster (Laist et al. 2001; Jensen and Silber 2004; Vanderlaan 

and Taggart 2007; Conn and Silber 2013), so measures targeted specifically to these vessel classes and 

activities may have the most success at reducing overall strike risk. For example, vessel speed restriction 

recommendations and observer recommendations are in place for some vessel types (i.e., ferries, cargo 

ships) in the Canary Islands to reduce vessel strikes to Bryde’s whales (Ferreira et al. 2021). In areas 

where busy shipping lanes overlap with high concentrations of large whale species, measures such as

posting visual lookouts, vessel re-routing measures, and establishing areas to be avoided may also

mitigate strike risk for Bryde’s whales and other large whale species (Nanayakkara and Herath 2017; 
21

Flynn and Calambokidis 2019; Redfern et al. 2024). Additionally, public awareness (e.g., voluntary speed 

reduction efforts) and enforcement efforts may increase compliance rates (van der Hoop et al. 2015).

Slowing vessels down can also result in lower underwater noise levels, offering additional benefit to large 

whale species (Findlay et al. 2023). However, exposure to vessel noise increases with transit time.

Ebdon et al. (2020) evaluated the efficacy of mitigation measures specific to Bryde’s whale strike risk in 

the Hauraki Gulf (New Zealand) and found that 10-knot vessel speed restrictions reduced the probability 

of lethal strikes by half. Redfern et al. (2024) analyzed management strategies that aim to reduce vessel 

strike risk to whales along the U.S. East Coast. Using vessel speed, transit distance, and whale density 

and distribution data, the authors found that a 10 knots speed restriction effectively reduces risk and 

benefits multiple large whale species. Modeling exercises also indicate that vessel speed restrictions in 

critical areas (i.e., areas of overlap between high vessel activity and large whale distributions) reduces 

vessel strike risk (Rockwood et al. 2021). However, the actual effectiveness of these measures depends on 

the level of compliance by the shipping industry; low compliance levels with voluntary speed restriction

measures limits the observed effectiveness of these interventions in reducing whale mortality (Rockwood 

et al. 2021).

As part of the Biological Opinion on the Federally Regulated Oil and Gas Program Activities in the Gulf

(NOAA NMFS 2020), BOEM and the Bureau of Safety and Environmental Enforcement (BSEE) 

consulted with NMFS to develop a Reasonable and Prudent Alternative (RPA) with measures designed 

specifically to reduce vessel strike risk to Rice’s whales. These measures are summarized below:

• All vessels of all sizes to observe a 10-knot vessel speed restriction within the “mitigation area”2

during daylight hours year-round (except when necessary for vessel or crew safety).

• No transits permitted in the mitigation area during nighttime (unless necessary for vessel or crew 

safety).

• Visual observers to monitor the vessel strike avoidance zone (500 meters [1,640 feet]).

• All vessels of all sizes to maintain a minimum 500 meters (1,640 feet) separation distance from 

Rice’s whales or unidentified large whale.

• Report transits within the mitigation area.

• All vessels greater than 19.8 meters (65 feet) in length to transmit AIS at all times.

Implementation of the RPA measures, specifically the 10-knot daytime speed restriction and the nighttime 

restriction of all transits in the mitigation area, was determined by NMFS to effectively avoid lethal vessel 

strikes resulting from oil and gas vessel activities in the Gulf (NOAA NMFS 2020).

2 This area is referred to as the “Bryde’s whale area” in NOAA NMFS (2020) and is roughly equitable to the 

Rice’s whale core distribution area (Figure 2). To avoid confusion, this will be referred to as the “mitigation area” 

hereafter.
22

5 Existing Vessel Strike Risk Analyses

The Gulf is a busy maritime region with significant commercial shipping traffic (Section 4.3). The 

constant movement of large vessels through Rice’s whales’ habitat increases the risk of collisions. As of 

August 2024, two analyses directly assess vessel strike risk for Rice’s whales in the Gulf (Best 20233

; 

NOAA NMFS 2020), presented below. An additional BOEM-funded study models vessel strike risk for 

large whales and sea turtles in the Atlantic OCS, but does not include Rice’s (or Bryde’s) whales or the 

Gulf (Barkaszi et al. 2021); its relevance to overall strike risk is discussed below, as well. Finally, 

Rockwood et al. (2021) modeled vessel strike of large whales to evaluate mortality across different 

management scenarios. Similarly, this study does not include Rice’s (or Bryde’s) whales or the Gulf, but 

its relevance to modeling strike risk is presented below. Results of the literature synthesis, including

subject/topic review and summaries for each primary and secondary data source, are provided in 

Appendix A. 

5.1 The Biological Opinion on the Federally Regulated Oil and Gas 

Program Activities in the Gulf of Mexico (NOAA NMFS 2020): 

Section 8.4 of NOAA NMFS (2020) outlines the evaluation of vessel traffic data concerning the potential 

impact on ESA-listed species using BOEM-estimated data (vessel trips based on port calls) and AIS 

(2014 to 2018) data. Use of AIS provides finer resolution regarding vessel routes, distances traveled, and 

speeds, allowing for more realistic estimates of ESA-listed species exposure to vessel traffic. The 

proposed action considered in NOAA NMFS (2020) was estimated to involve a maximum of 

173,002 vessel trips annually, constituting approximately 20 percent (19.77 percent) of the total vessel 

trips in the GOM. 

To estimate the number of vessel strikes to Rice’s and sperm whales that will result from the Oil and Gas 

Program, they combined information on vessel traffic from the aforementioned AIS dataset with data on 

Rice’s and sperm whale distribution and density as described in NOAA NMFS (2020) Section 8.1.2 in 

order to quantify the co-occurrence of whales and vessels, hereafter referred to as vessel strike risk. Focus 

was on vessels traveling >10 knots. It was recognized that there are likely other factors at play that 

determine the probability of an actual vessel strike occurring, such as whale and vessel size, whale diving 

behavior, among others. However, consideration of these factors was seen to not invalidate estimates 

below of the relative risk associated with oil and gas vessel traffic since it was anticipated that these other 

factor(s) would equally affect the probability of vessel strikes from oil and gas vessel traffic compared to 

all vessel traffic.

Oil and gas related vessel traffic analyzed in NOAA NMFS (2020) without the application of mitigation 

measures is likely to result in a total of 23 vessel strikes of Rice’s whales, with 17 of these strikes 

expected to result in serious injury or mortality and six strikes expected to result in minor or no injuries. 

However, NOAA NMFS (2020) further considered that 35 percent of oil and gas traffic occurs within the 

mitigation area, which would be limited to a 10-knot speed restriction and no nighttime transits. If a 10-

knot speed restriction within the mitigation area results in a 90 percent reduction in the number of vessel 

strikes, then the overall reduction of vessel strike risk to Rice’s whales due to oil and gas vessel traffic of 

all speeds would be approximately 31 percent; oil and gas vessel activity over 50 years would therefore 

result in 12 vessel strikes with serious injury or mortality and four with no or minor injuries. Strike risk 

here is equivalent to the default conditions of BOEM’s ship-strike model (Barkaszi et al. 2021; see 

3 Best PB. 2023. Spatial analysis of ship strike risk for Rice's whales in the Gulf of Mexico. Unpublished Report: 

https://ecoquants.com/ricei. 
23

Section 5.3) where an intersection or encounter without any mitigating factors (e.g., vessel draft, time of 

day, aversion [particularly vessel aversion], etc.) represents the highest level of encounter probability. 

Thus, “relative strike risk” is actually “relative encounter probability”4

.

5.1.1 Assumptions and Limitations

A major assumption in NOAA NMFS (2020) was that a reduction in vessel speed to ≤10 knots is likely to 

lead to a 90 percent reduction in the number of all vessel strikes, regardless of severity. Furthermore, in 

using a 10-knot cut off, it was assumed that 100 percent of vessel strikes occurring at speeds of 10 knots 

or greater would likely result in serious injury or mortality, despite known incidents of vessel strikes of 

large whales at speeds greater than 10 knots that did not result in serious injury or mortality (e.g., see 

Figure 2 in Vanderlaan and Taggart [2007]). This analysis also considered stranded animals with 

characteristic injuries to indeed be from strikes that resulted in mortality or serious injury, though this 

might not necessarily be the case. In addition, it was also assumed that effects would be equal among 

different vessel categories, which would only be true if vessel characteristics were equal among both oil 

and gas and non-oil and gas vessels. A major limitation of NOAA NMFS (2020) was that behavioral 

factors that affect strike availability, such as diving and/or surface presence or aversion, were not included

in the analysis.

5.2 Spatial Analysis of Ship-strike Risk for Rice's Whales in the Gulf of 

Mexico (Unpublished Report) (Best 2023)

The unpublished Best (2023) report replicates the ship-strike analysis in NOAA NMFS (2020) using an 

updated whale density model (Litz et al. 20225

) and AIS vessel data from 2015 to 2018, extending the 

analysis Gulf-wide in U.S. EEZ waters. This report uses the updated Rice’s whale SDM from Litz et al. 

(2022), which is based on habitat suitability estimates from Gulf ship-based and aerial surveys conducted 

from 2003 to 2019 and oceanographic variables from 2015 to 2019. The SDMs from Litz et al. (2022) are 

extrapolated to predict high-density areas beyond U.S. waters in the Gulf based primarily on habitat 

suitability (in lieu of sighting events), so these predictions should be interpreted cautiously.

Using these data, Best (2023) suggests a new “whale area” (hereafter called the extended area) in the Gulf 

based on location (25.5o N and higher) and depth (100 to 400 meters [328 to 1,312 feet]). Application of 

10-knot restrictions in all of the Rice’s whale core area and extended area resulted in risk reduction 

regardless of vessel type (oil and gas or all vessels) and speeds (>10 knots and all speeds). For example, 

when considering all vessel types and all vessel speeds, implementing mitigation measures for oil and gas 

traffic from NOAA NMFS (2020) reduced strike risk by 93 percent for the extended area compared to 

only 12 percent in the core area.

Best (2023) replicated NOAA NMFS (2020) methodology (i.e., use of AIS data, grid cell size, risk 

[number of whales multiplied by vessel traffic in distance traveled per grid cell]) but extended the 

geography under consideration and applied updated whale density information. Statistical analyses 

mentioned are previously developed spatial summarization tools. Notably, Best (2023) generalizes to all

oil and gas vessels only by speed, but not vessel type, and inherently assumes accurate whale density data. 

Additionally, strike risk is coarse-grain rank based on vessel and whale abundance and speed but does not 

4 The probability of an encounter is not identical to strike risk, unless the encounter would result in animal exposure 

within the strike zone of the vessel (e.g., draft).

5 The metadata associated with this dataset was recently updated (April 1, 2024). However, this report will refer to 

Litz et al. (2022) as cited in Best (2023) and in accordance with its publication date (July 29, 2022).
24

appear to consider aversion or any mitigation measures in the assessment (other than as related to the 

10-knot vessel speed recommendation).

Use of the Litz et al. (2022) data is a significant extension of the risk map throughout the U.S. EEZ and

notably includes the western Gulf. Risk is shown to be much higher in the areas off Louisiana and east 

Texas than that seen in the original geographic area defined by NOAA NMFS (2020), providing a more 

comprehensive assessment of Gulf-wide Rice’s whale strike risk. Best (2023) used higher resolution 

animal density data (40 km2 vs. previous 100 km2

) so the risk basis is at a 2.5-times greater animal 

resolution than most modeling efforts. This was re-projected to fit 100 km2 square cells to be consistent 

with the Biological Opinion (NOAA NMFS 2020) and done in a manner to preserve to some degree the 

finer grain geometric densities of the animal data. However, the re-projection of the data was scaled to 

provide a total of 51.3 animals to match published abundance estimates, rather than attempt spatial 

extrapolation of whale density, which would require many assumptions. If those estimates underrepresent the population abundance of Rice’s whale, then the strike risk values derived in the paper are an 

underestimate of strike risk. The analytical approach appears robust, but the findings are ultimately driven 

by the spatial organization of the new animal density data provided by Litz et al. (2022) and the decision 

to cap the total number of animals at 51.3. Vessel traffic data were the same date range as in the NOAA

NMFS (2020) Biological Opinion and analyzed similarly, minimizing any influence of the vessel 

component to differences in risk values produced here vs. that of NOAA NMFS (2020).

The risk methodology followed that of NOAA NMFS (2020). However, by capping the number of 

animals at 51.3 and expanding the geographic extent of the critical habitat, this could have an effect of 

substantially diminishing animal density and consequently, strike risk, as seen in Table 3 of Best (2023). 

The strike risk reduction presented is driven by application of the 10-knot slowdown and nighttime 

closure but also to some unknown degree by a potential dilution of the animal density. However, the 

computation of risk reduction arising from the slowdown and closure is not described and therefore the 

accuracy cannot be determined independently.

The efficacy of this result may also turn on the population estimate and agreement as to whether the 

population number needs to be revised and/or revisited because of a larger geographic extent of detection 

or whether the population is accurately represented at 51.3 individuals. An increase in the population size 

will increase the strike risk and Best (2023) would have to be revised accordingly.

5.2.1 Assumptions and Limitations

Since Best (2023) followed the same methodology employed by NOAA NMFS (2020), the main 

assumptions and limitations outlined in Section 5.1.1 for the Biological Opinion are also applicable here. 

In addition, Best (2023) limited the analysis to a population total of 51.3 animals and expanded the 

geographic extent of the Rice’s whales core distribution. In doing this, an artificially low strike risk may 

be presented if population size and habitat selection differ substantially from the key assumptions. In 

addition, if the Rice’s whale is not actually found in the north-central Gulf (e.g., off Louisiana, east 

Texas), the expanded geographic extent is indicating risk in an area where there is effectively none at this 

time.
25

5.3 Risk Assessment to Model Encounter Rates between Large Whales 

and Vessel Traffic from Offshore Wind Energy on the Atlantic OCS 

(Barkaszi et al. 2021)

This study aimed to evaluate the risk of vessel strikes on large whales and sea turtles in Atlantic offshore 

wind energy areas, considering various vessel types. The research comprised four stages: characterizing 

baseline vessel traffic and wind farm operations, developing an analytical framework to calculate 

encounter numbers, creating a quantitative model for strike risk assessment, and building a graphical user 

interface (GUI) for scenario creation and result visualization. The GUI enables users to simulate potential 

encounters between vessels and marine animals, allowing manipulation of vessel activity and animal 

behavioral responses, with capacity for aggregating results from multiple scenarios. The model is limited 

to the Atlantic OCS and does not assess strike risk for the Rice's whale. However, the basis of the model 

uses surface density layers, dive and/or surface behaviors, and animal size and/or swim speed for each 

species to calculate vessel strike risk based on vessel data characteristic of offshore wind energy 

development and project-specific vessel routes, transit numbers, speed, etc. as entered by the user.

The model provides a risk assessment framework to compute the number of potential encounters among 

mammals and turtles with various classes of vessels, at varying speeds along transit routes at given times 

of year. The vessel route is divided into 1-kilometer (0.62-mile) segments, and interactions are modeled 

for each segment. The total encounters along the route are then summed. Parameters (e.g., vessel speed, 

animal density) are assumed constant within each 1-kilometer (0.62-mile) block but can vary across 

different segments based on broader environmental factors. This approach allows for a scalable, 

consistent framework to assess encounter risks while incorporating adjustable aversion factors to better 

simulate real-world conditions.

When used as a planning tool, BOEM’s vessel strike model represents a user-friendly, spatial and 

temporally articulated geographic information system (GIS)-based model with outputs predicting 

potential vessel-animal encounters under a wide range of tunable variables. BOEM is currently expanding 

this model to include all U.S. OCS waters (except Alaska) and to lessen some of the limitations identified 

above. The updated risk assessment tool may be used Gulf-wide for project-specific applications. Strike 

risk is calculated based on the user-defined vessel activity scenario and availability of an animal for 

strike, which includes habitat-based predictive density and dive and/or surface behaviors that will be 

updated for Rice’s whales in the Gulf. BOEM expects to release the updated vessel strike model by 2026.

5.3.1 Assumptions and Limitations

Several assumptions are considered in this vessel strike model. Probabilistic modeling of strike risk is 

conceptualized as being directly proportional to the expected number of animal encounters a given vessel 

will accrue while transiting to its destination. The mathematical equations were derived solely from 

geometric relationships that result from various simplifying assumptions from both the animal’s 

perspective (i.e., generalizing spatial and temporal behaviors for entire species) and the vessels 

(i.e., pre-defined vessel classes only transit along previously identified routes). In addition, the geometric 

model incorporates vessel length to calculate strike risk, such that as length increases, strike risk also 

increases. However, it is not well established in the literature that this proportional relationship is that 

simple, as other factors secondary to vessel length (i.e., maneuverability, turning radius, crash stop 

distance, gross tonnage) also contribute to a vessel’s ability to avoid collision. Finally, aversion 

probability can be input and manipulated by the user, which can lead to inaccurate and non-realistic 

results.
26

5.4 Modeling Whale Deaths from Vessel Strikes to Reduce the Risk of 

Fatality to Endangered Whales (Rockwood et al. 2021)

Rockwood et al. (2021) outline a comprehensive approach to estimate whale mortality due to vessel 

strikes in the Southern California region. The study focused on the area from Point Conception to 

San Diego, California extending 100 kilometers (62 miles) offshore, covering a total area of 39,889 km2. 

This region includes the Santa Barbara and San Pedro Channels, known for high productivity and 

consistent feeding aggregations of large whales. A spatial grid of approximately 1 km2 cells (47,955 in 

total) was created for the study area. Modeled whale density data (Becker et al. 2016, 2017), which used 

environmental predictors and whale sightings from systematic boat-based surveys to develop predictive 

densities, and AIS vessel data from 2012 to 2018 were used, with specific criteria for excluding erroneous 

records (e.g., speeds less than 2 knots or greater than 40 knots).

The core of the methodology of Rockwood et al. (2021) is an encounter probability model, adapted from 

Martin et al. (2016) and previous works by Rockwood et al. (2017, 2020), which calculates vessel-whale 

encounter rates within each grid cell, considering whale and vessel size, velocity, and distance traveled. 

Whale time-at-depth data from multi-sensor tags were used to assess the risk of being in the vertical strike 

zone. Encounter rates were then converted to mortality estimates by incorporating probabilities of 

collision avoidance and mortality given a collision. Several improvements were made to the model to 

enhance accuracy. For example, mortality estimates were calculated independently for each vessel track 

and summed within each grid cell, addressing biases from non-linear relationships between ship 

parameters and strike mortality. The study also explored various vessel speed reduction scenarios, 

including different speed limits and compliance levels, to assess potential changes in whale mortality.

This study uses a detailed and systematic approach to estimate whale mortality from vessel strikes, 

integrating spatial grid analysis, whale density modeling, and an encounter probability framework. The 

results provide valuable insights into the effectiveness of speed management strategies and potential 

improvements for reducing whale fatalities in the region.

5.4.1 Assumptions and Limitations

Rockwood et al. (2021) acknowledge several limitations that impact the accuracy and applicability of 

their findings. The study relies on whale density models which are based on past sightings and 

environmental conditions. These models may not accurately reflect current whale distributions, especially 

considering potential shifts due to changing oceanographic conditions. Additionally, vessel traffic data, 

sourced from the AIS, might have inaccuracies, including errors in recorded speeds and locations. 

Additionally, not all vessels are required to carry AIS transponders, which inherently underestimates the 

volume of vessel traffic within any given region. Rockwood et al (2021) further filtered AIS data to only 

include those greater than 19 meters (62 feet) in length and with a draft of 2 meters (6.6 feet) or greater; 

the authors did not conduct separate analyses of different vessel categories. The encounter probability 

model makes several assumptions, including whale behavior and the probability of whales avoiding 

vessels, which may not hold true in all scenarios; the model's predictions depend heavily on the accuracy 

of whale density data and the assumption that past behavior and distributions are indicative of future 

patterns. The spatial resolution of the grid cells (1 km2) and temporal resolution of the data (2012–2018) 

might not capture finer-scale variations in whale density and vessel traffic, which could lead to 

underestimations or overestimations of risk in certain areas or times. Finally, broader ecological changes, 

such as shifts in prey availability or long-term climatic changes, are not fully accounted for in the models; 

these factors could significantly influence whale distributions and the risk of vessel strikes.
27

6 Recommendations

The review of primary and secondary source documents spanned examination of both Rice’s and 

Bryde’s whale complex distribution, abundance, behavior, feeding, and risk factors. From this, 

recommendations for additional study to inform a statistically meaningful vessel strike risk assessment 

methodology that BOEM could realistically implement and that could be peer-reviewed for validating a 

future assessment were developed. Key emergent information from these papers fell into two categories 

of recommended additional study, (1) improvements in animal density and distribution data and 

(2) improvements in understanding of animal behavior.

One emergent question is whether small sample sizes of Rice’s whale attributes (distribution, behavior, 

acoustic signature, diet) as seen throughout the reviewed studies should be utilized to inform ship-strike 

modeling. Given the very small population size (<100 and perhaps as few as 51), even a sample from one 

animal represents a comparatively large sub-sample of a population. While individual behavior may skew 

the utilization of information (e.g., dive times, surface resting times, etc.) from numerically small 

samples, it is also possible that many aspects of observed behaviors are indeed characteristic of the 

species. Moreover, the urgency of need for actionable data suggests that its use would be appropriate to 

generalize to the species. Further, the behaviors noted for other Bryde’s subspecies generally do not 

contradict what has been found in the limited Rice's whale observations, further suggesting the 

appropriateness of their use.

6.1 Improving Rice’s Whale Density and Distribution Data

Central to both range determination and in particular, ship-strike modeling, is accurate animal density 

data in space and time. Data are often coarse (e.g., animal density averaged at 100 km2 resolution),

inherently mismatching spatial resolution of vessels precisely tracked with AIS data versus that of whales, 

thereby limiting spatial and temporal predictions of strike risk. Therefore, emphasis on refining the spatial 

and seasonal abundance of Rice’s whale may represent one of the most valuable management data sets. In 

particular, understanding Rice’s whale occurrence patterns and behaviors in the northern Gulf and 

identifying if there is any east-west migration between the northwest and western Gulf are critical to 

informing strike risk in these areas.

It is recommended that localized Rice’s whale vocalizations (Soldevilla et al. 2022a, 2022b, 2024) are 

merged with modeling efforts that currently predict Rice’s whale occurrence patterns based on habitat 

suitability and, to a lesser extent, visual detections made from systematic vessel and aerial survey data 

(Litz et al. 2022; Rappucci et al. 2023; Garrison et al. 2023, 2024). This action, which likely would 

require additional long-term passive acoustic monitoring (PAM) buoy deployments (especially in the 

western and central GOM, and potentially deployments outside of U.S. waters), would compile a more 

comprehensive assessment of Rice’s whale distribution and seasonal occurrence patterns in a 

cost--effective way. However, to use acoustic methods, additional resolution of Rice’s whale call 

signatures is prudent (e.g., Soldevilla et al. 2022a) to ensure that acoustic detection is unambiguous.

Short-term studies involving multi-platform approaches (e.g., Izadi et al. 2022) to identify and 

acoustically fingerprint individuals would be appropriate. Being able to defend identification of 

Rice’s whale acoustically will be critical for use of recording devices on a broad geographic extent to 

quantify range extent and frequency of use.

Extracting spatial and temporal detection data (e.g. metadata analysis and recovery) from protected 

species observer (PSO) data (e.g., Barkaszi and Kelly 2024) and oil and gas vessel operator reports would 

be a cost-effective strategy to supplement animal distribution and density data layers. It is strongly 

recommended that a central digital repository of these data be created and maintained so that it may be 

effectively mined for past and ongoing Rice’s whale detections.
28

Anecdotal sightings, if confirmed (i.e., through supporting photo or video documentation), would also 

contribute to our overall understanding of Rice’s whale occurrences. Conducting outreach to boat 

operators and fishermen to provide awareness of Rice’s whales and to request sightings information 

(i.e., through a simplified sightings sheet and/or website) would be a reasonable approach. It is 

acknowledged that reception from local communities around the Gulf to outreach and data requests from 

BOEM may prove challenging. However, partnering with universities or ongoing “citizen science” efforts 

for other marine species (i.e., whale sharks [Rhincodon typus]) may provide a valuable mechanism for 

compiling sighting events.

Assembly of PSO and even anecdotal data would create an up-to-date and authoritative reference base for 

understanding distribution and abundance but also would be useful for detecting spatial and temporal gaps 

in data and to direct additional studies. However, an understanding of the caveats associated with these 

data sources must be cautioned. PSO and vessel operator data are not equivalent to systematic visual 

surveys and only represent presence-absence data. Additionally, detections may be confounded by the use 

of airgun arrays and therefore may not fully represent Rice’s whale distribution patterns. Further, 

anecdotal data can at best represent presence data, but little more than that. Despite these data limitations, 

an expansion of sightings throughout the GOM still represents valuable insight to Rice’s whale 

occurrences. However, one cannot develop a statistical design without a thorough understanding of the 

available data structure, including its resolution and extent in both space and time. Consequently, any 

determination of the appropriate statistical approach (if any) must await data acquisition and structural 

review.

If evident from gap analysis of existing data, a plan of study for utilization of acoustic buoy deployment 

to capture Rice’s whale spatial and seasonal distribution, including possible east–west migrations, would 

be a logical build-out from the extant data. Together with the existing data (and consideration of any 

ongoing studies and surveys), this effort would provide defensible data for designation of critical habitat.

6.2 Improvements in Understanding of Animal Behavior

Several papers review Bryde’s whale diving and feeding behavior (e.g., Kershaw et al. 2013; Sirović et al. 

2014; Dong et al. 2022; Izadi et al. 2022) but few in the GOM and far fewer specifically targeted 

Rice’s whale (e.g., Soldevilla et al. 2017; Kiszka et al. 2023; Kok et al. 2023). The “common” 

understanding is that Rice’s whales may spend a lot of time at the surface, especially at night, making 

them vulnerable to ship strike. However, the data supporting this are quite limited (although even a few 

suction-cup tags on individuals out of a population of 51 individuals would be substantial and worthwhile 

upon which to build generalizations in ship-strike models). Meanwhile, in the face of data paucity, 

extrapolating from Bryde’s whale data is appropriate, though potentially misleading given the different 

habitat characteristics of better-studied populations (see Section 4.2). Therefore, a strong 

recommendation is to improve the specific understanding of Rice’s whale behavior that includes time 

spent feeding, moving, and resting.
29

This field work, requiring tagging and monitoring efforts, would require suitable geographic replication 

and importantly, stratification of sampling by water depths (foraging and diving behavior must be 

strongly water depth dependent). Priority focus should be given to localizing individuals outside the 

Rice’s whale core area (i.e., in the northern and western Gulf) and deploying satellite telemetry tags to 

track long-term movements and multisensory suction-cup tags for higher resolution tracking of dive and 

foraging behaviors, similar to the methodology described by Soldevilla et al. (2017). At minimum, it is 

recommended a target of at least five satellite tags and five multi-sensor tags be deployed (each 

representing approximately 10 percent of the known population). So that our understanding of 

Rice’s whale behavior and habitat selection is not limited to only the northwestern Gulf, it is

recommended that the majority of tags are deployed on individuals outside the core area, with at least one

of each on individuals inside of the core area. Further, tagging individuals (specifically, with the 

multi-sensor suction-cup tags) in different depth strata may reveal different behavior or use patterns not 

currently evident in available data.

Though our recommendation sets a target of sampling 10 percent of the known population, it is 

acknowledged that this may not be achievable is a realistic timeframe, especially when targeting 

individuals outside of the core area. A power analysis would be hard to perform because one must have 

an estimation of variance, which likely does not exist for many variables associated with behavior 

(i.e., tagged and/or tracked limited numbers) and would be extremely challenging to develop. As a result, 

it is recommended that any tagging data be acquired urgently, and suggest that whatever proportion can 

be sampled be taken as representative of the population until proven differently.

Amplification and refinement of previous studies where remote sensing data was used to include 

identification of cues driving aggregation (i.e., food supply) is also needed to build forecasts of spatial 

distribution and density models necessary for accurate ship-strike probabilistic modeling. This effort may 

be combined with those recommended in Section 6.1 to compile and extract observed behavioral records

along with occurrence data.

6.3 Other Considerations

In keeping with BOEM’s attention to realistic implementation, consideration was given to utilization of 

environmental DNA (eDNA). A cursory Google Scholar search shows a relatively recent report(Martinez 

et al. 2024) where biopsy subsamples and eDNA water samples were collected as part of a preliminary 

attempt to utilize this technology to ascertain presence of the species. With establishment of an eDNA 

library for Rice’s whale and understanding of eDNA persistence in nature, this method may represent a 

useful tool for rapid assessment of habitat utilization and buttressing of more expensive derivation of 

distribution information (see review by Suarez-Bregua et al. [2022]). While not a recommended substitute 

for direct observation, tagging, and monitoring, these data, if acceptable across agencies, could provide a 

rapid and comparatively inexpensive means of detecting the regional presence of the species.

Compilation of environmental data to associate with Rice's whale distribution bears consideration.

Environmental data such as chlorophyll-a, sea surface temperature, sea surface height anomalies, and 

depth already exist or can be obtained remotely and synoptically and used as predictors from other studies 

of Bryde's whales to further refine core habitat preferences both spatially and temporally. This would 

reduce costs but would need to be paired with sightings data to build confidence in the forecasted habitat.

Improvements in vessel tracking and vessel data are necessary to better our understanding and refining 

the assessment of vessel strike risk for the Rice’s whale. Specifically, better documentation of vessel 

activity in offshore regions where AIS coverage is more limited is especially necessary to quantify vessel 

strike risk across industries and vessel categories. However, shore-based AIS receivers are limited in their 

coverage for offshore regions and positions may only sporadically be received. For example, port-based 
30

AIS receivers have a maximum receiving rage of approximately 37 kilometers (23 miles) (USCG n.d.). 

While repeater stations may be used to improve and extend coverage (USCG n.d.), AIS coverage in 

offshore regions of the Gulf remains limited. Use of satellite-derived AIS data can serve to fill this data 

gap and improve our understanding of offshore vessel activity in the Gulf (i.e., Metcalfe et al. 2017).

Efforts to ensure compliance with AIS usage, where required, and the accuracy of vessel data input are 

likely ongoing and must be continued. Analysis of improved offshore data must be conducted using a 

multi-year timeframe and rooted in a multivariate analysis of vessel movement patterns (i.e., >10 knots 

and ≤10 knots; daytime and nighttime) relative to Rice’s whale core area and proposed critical habitat for 

different vessel categories and industries. Finally, predictive modeling (i.e., Kaiser 2015) should be 

conducted to forecast vessel activity and offer an outlook perspective for future vessel traffic in the Gulf.

Adaptations to BOEM’s vessel strike model (Section 5.3) that incorporate improved animal density, 

animal behavior, and ship traffic data should be considered a priority for assessing strike risk. Since the 

underlying framework for the mathematical model and risk assessment tool are already built and tested, 

modifying its application to industries beyond offshore wind would be a relatively straightforward and 

cost-effective approach to modeling Rice’s whale strike risk in the Gulf. Further, the model should be 

used to assess the effectiveness of different mitigation strategies, with results that serve as the baseline for 

recommended or required strike avoidance plans.

Finally, it is recommended that BOEM consider incentives for vessel operators to voluntarily reduce 

vessel speeds, to implement technology onboard designed to automatically detect large whales in the 

strike zone so that evasive actions may be taken, and to detect collisions that have occurred. The latter 

would serve to improve our understanding of cryptic mortality and constitute a database of directly 

relevant strike risk in the Gulf. Technology, specifically that which can monitor in low visibility 

conditions (i.e., during nighttime) such as thermal and infrared cameras, has been rapidly developing in 

recent years and offers a cost-effective way to monitor for marine mammals (Verfuss et al. 2018; Baille 

and Zitterbart 2022; Paoletti et al. 2023; Richter et al. 2023). Incentives, whether financial, regulatory, or 

other, can entice companies and vessel operators to implement additional protective measures that 

ultimately serve to mitigate strike risk for Rice’s whales. Voluntary compliance programs have had

limited success elsewhere (Redfern et al. 2019; Rockwood et al. 2021; Morten et al. 2022), so 

consideration of regulatory requirements for large vessels (i.e., over 80 meters [262 feet]) traveling above 

10 knots through Rice’s whale core area (and critical habitat, if implemented) to use alternative 

monitoring technologies at nighttime may be warranted. In addition, future recommendations for any 

vessel speed restrictions should consider vessel class and gross tonnage as these factors may be a better 

indicator of lethality related to vessel strike then length alone. Importantly, these recommendations are 

geared specifically toward improving detection capabilities and reducing vessel strikes resulting in injury 

or mortality for Rice’s whales and are not likely to fully eliminate anthropogenic impacts to the species or 

to the key habitat attributes as identified in the critical habitat proposed rule (Section 4.1). 
31

7 Conclusions

Rice’s whales are especially vulnerable to vessel strike due to their surface behaviors, especially during 

the night when they spend up to 88 percent of their time at the surface, and their habitat selection, which 

overlaps with high levels of vessel traffic in the Gulf. Their very small population size is a confounding 

risk factor such that any serious injuries or mortalities experienced by individuals could lead to 

population-level effects. A comprehensive literature review and synthesis was conducted to better 

understand and assess this risk, which highlighted important data gaps and recommendations for future 

analyses. Most data gaps center around our understanding of the behavior and ecology of the Rice’s

whale. As a result, two categories of recommended additional study were developed, including 

improvements in animal density and distribution data and improvements in understanding the species’ 

behavioral ecology. Additional recommendations were considered that improve our assessment of vessel 

activity within the Gulf and potential mechanisms that could be implemented to provide an overall 

reduction in strike risk in the Gulf.
32

8 References

Alves F, Dinis A, Cascão I, Freitas L. 2010. Bryde’s whale (Balaenoptera brydei) stable associations and 

dive profiles: New insights into foraging behavior. Mar Mammal Sci. 26(1):202-212.

Athayde A, Cardoso J, Francisco A, Siciliano S. 2023. A vessel collision report for Bryde's whales 

(Balaenoptera brydei) off the northern coast of São Paulo, Brazil. Aquat Conserv Mar Freshwater 

Ecosyst. 33(2):226-230.

Baille LM, Zitterbart DP. 2022. Effectiveness of surface-based detection methods for vessel strike 

mitigation of North Atlantic right whales. Endanger Species Res. 49:57-69.

Barkaszi M, Fonseca M, Foster T, Malhotra A, Olsen K (CSA Ocean Sciences Inc., Stuart, FL). 2021. 

Risk assessment to model encounter rates between large whales and vessel traffic from offshore wind 

energy on the Atlantic OCS. Sterling (VA): Department of the Interior, Bureau of Ocean Energy 

Management. 54 p. Obligation No.: 140M0119F0033. OCS Study BOEM 2021-034.

https://espis.boem.gov/final%20reports/BOEM_2021-034.pdf. 

Barkaszi MJ, Kelly CJ. 2024. Analysis of protected species observer data: Strengths, weaknesses, and 

application in the assessment of marine mammal responses to seismic surveys in the northern Gulf of 

Mexico 2002–2015. PLOS ONE. 19(3):e0300658.

Becker EA, Forney KA, Fiedler PC, Barlow J, Chivers SJ, Edwards CA, Moore AM, Redfern JV. 2016. 

Moving towards dynamic ocean management: How well do modeled ocean products predict species 

distributions? Remote Sens. 8(2):149.

Becker EA, Forney KA, Thayre BJ, Debich AJ, Campbell GS, Whitaker K, Douglas AB, Gilles A, 

Hoopes R, Hildebrand JA. 2017. Habitat-based density models for three cetacean species off 

Southern California illustrate pronounced seasonal differences. Front Mar Sci. 4:121.

Bureau of Ocean Energy Management (BOEM) and National Oceanic and Atmospheric Administration 

(NOAA). 2024a. AIS Vessel Transit Counts 2022. [accessed June 14, 2024]. 

https://marinecadastre.gov/data/.

Bureau of Ocean Energy Management (BOEM) and National Oceanic and Atmospheric Administration 

(NOAA). 2024b. AIS Vessel Tracks 2022. [accessed June 14, 2024]. https://marinecadastre.gov/data/.

Cerchio S, Yamada TK, Brownell Jr RL. 2019. Global distribution of Omura’s whales (Balaenoptera

omurai) and assessment of range-wide threats. Front Mar Sci. 6:67.

Conn P, Silber G. 2013. Vessel speed restrictions reduce risk of collision‐related mortality for North 

Atlantic right whales. Ecosphere. 4(4):1-16.

Constantine R, Iwata T, Nieukirk SL, Penry GS. 2018. Future directions in research on Bryde’s whales. 

Front Mar Sci. 5:333.

Constantine R, Johnson M, Riekkola L, Jervis S, Kozmian-Ledward L, Dennis T, Torres LG, de Soto NA. 

2015. Mitigation of vessel-strike mortality of endangered Bryde’s whales in the Hauraki Gulf, New 

Zealand. Biol Conserv. 186:149-157.
33

Dong L, Liu M, Lin W, Li S. 2022. First suction cup tagging on a small and coastal form Bryde’s Whale 

(Balaenoptera edeni edeni) in China to investigate its dive profiles and foraging behaviours. J Mar 

Sci Eng. 10(10):1422.

Ebdon P, Riekkola L, Constantine R. 2020. Testing the efficacy of ship strike mitigation for whales in the 

Hauraki Gulf, New Zealand. Ocean Coast Manage. 184:105034.

Farmer NA, Powell JR, Morris Jr JA, Soldevilla MS, Wickliffe LC, Jossart JA, MacKay JK, Randall AL, 

Bath GE, Ruvelas P. 2022. Modeling protected species distributions and habitats to inform siting and 

management of pioneering ocean industries: A case study for Gulf of Mexico aquaculture. PLOS 

ONE. 17(9):e0267333.

Félix F, van Van Waerebeek K. 2005. Whale mortality from ship strikes in Ecuador and West Africa. Lat 

Am J Aquat Mamm.4(1):55-60.

Ferreira R, Dinis A, Badenas A, Sambolino A, Marrero‐Pérez J, Crespo A, Alves F. 2021. Bryde's whales 

in the North‐East Atlantic: New insights on site fidelity and connectivity between oceanic 

archipelagos. Aquat Conserv Mar Freshwater Ecosyst. 31(10):2938-2950. 

Findlay CR, Rojano-Doñate L, Tougaard J, Johnson MP, Madsen PT. 2023. Small reductions in cargo 

vessel speed substantially reduce noise impacts to marine mammals. Science Advances. 

9(25):eadf2987. 

Flynn KR, Calambokidis J. 2019. Lessons from placing an observer on commercial cargo ships off the 

US West coast: utility as an observation platform and insight into ship strike vulnerability. Front Mar 

Sci. 6:501.

Garrison LP, Ortega-Ortiz J, Rappucci G, Aichinger-Dias L, Mullin K, Litz J (NOAA Southeast Fisheries 

Science Center, Miami, FL). 2023. Gulf of Mexico Marine Assessment Program for Protected 

Species (GOMMAPPS): marine mammals. Volume 2: appendix C: Gulf of Mexico marine mammal 

spatial density models. New Orleans (LA): US Department of the Interior, Bureau of Ocean Energy 

Management. 1264 p. Obligation No.: M17PG00013. Report No.: OCS Study BOEM 2023-042.

https://espis.boem.gov/final%20reports/BOEM_2023-042_v2.pdf. 

Garrison LP, Ortega-Ortiz J, Rappucci G. 2020. Abundance of marine mammals in waters of the US Gulf 

of Mexico during the summers of 2017 and 2018. Southeast Fisheries Science Center, Protected 

Resources and Biodiversity Division. 56 p. Report No.: PRD Contribution: #PRD-2020-07.

Garrison LP, Soldevilla MS, Martinez A, Mullin KD. 2024. A density surface model describing the 

habitat of the Critically Endangered Rice’s whale Balaenoptera ricei in the Gulf of Mexico. Endanger 

Species Res. 54:41-58.

Hanson SE, Nicholls RJ. 2020. Demand for ports to 2050: Climate policy, growing trade and the impacts 

of sea‐level rise. Earth’s Future. 8(8):e2020EF001543.

Hayes SA, Josephson E, Maze-Foley K, Rosel PE, McCordic J, Wallace JE. 2023. U.S. Atlantic and Gulf 

of Mexico marine mammal stock assessment reports 2022. Woods Hole (MA): U.S. Department of 

Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, 

Northeast Fisheries Science Center. June 2023. 262 p.
34

Izadi S, Aguilar de Soto N, Constantine R, Johnson M. 2022. Feeding tactics of resident Bryde's whales 

in New Zealand. Mar Mammal Sci. 38(3):1104-1117.

Jensen AS, Silber GK. 2004. Large whale ship strike database. NOAA technical memorandum NMFSOPR-25. https://repository.library.noaa.gov/view/noaa/23127. 

Kaiser MJ. 2015. Offshore service vessel activity forecast and regulatory modeling in the US Gulf of 

Mexico, 2012–2017. Mar Pol. 57:132-146.

Kerosky SM, Širović A, Roche LK, Baumann-Pickering S, Wiggins SM, Hildebrand JA. 2012. Bryde's 

whale seasonal range expansion and increasing presence in the Southern California Bight from 2000 

to 2010. Deep Sea Res Part I. 65:125-132.

Kershaw F, Leslie MS, Collins T, Mansur RM, Smith BD, Minton G, Baldwin R, LeDuc RG, Anderson 

RC, Brownell Jr RL. 2013. Population differentiation of 2 forms of Bryde’s whales in the Indian and 

Pacific Oceans. J Heredity. 104(6):755-764.

Kiszka JJ, Caputo M, Vollenweider J, Heithaus MR, Aichinger Dias L, Garrison LP. 2023. Critically 

endangered Rice’s whales (Balaenoptera ricei) selectively feed on high-quality prey in the Gulf of 

Mexico. Sci Reps. 13(1):6710.

Kok AC, Hildebrand MJ, MacArdle M, Martinez A, Garrison LP, Soldevilla MS, Hildebrand JA. 2023. 

Kinematics and energetics of foraging behavior in Rice’s whales of the Gulf of Mexico. Sci Reps. 

13(1):8996.

LaBrecque E, Curtice C, Harrison J, Van Parijs SM, Halpin PN. 2015. 3. Biologically important areas for 

cetaceans within U.S. waters-Gulf of Mexico region. Aquat Mamm. 41(1):30.

Laist DW, Knowlton AR, Mead JG, Collet AS, Podesta M. 2001. Collisions between ships and whales. 

Mar Mammal Sci. 17(1):35-75.

Litz J, Dias LA, Rappucci G, Martinez A, Soldevilla M, Garrison L, Mullin K, Barry K, Foster M. 2022. 

Cetacean and sea turtle spatial density model outputs from visual observations using line-transect 

survey methods aboard NOAA vessel and aircraft platforms in the Gulf of Mexico from 2003-06-12 

to 2019-07-31 (NCEI Accession 0256800). Dataset. NOAA National Centers for Environmental 

Information. https://catalog.data.gov/dataset/cetacean-and-sea-turtle-spatial-density-model-outputsfrom-visual-observations-using-line-trans.

Lodi L, Tardin RH, Hetzel B, Maciel IS, Figueiredo LD, Simão SM. 2015. Bryde’s whale 

(Cetartiodactyla: Balaenopteridae) occurrence and movements in coastal areas of southeastern Brazil. 

Zoologia (curitiba). 32:171-175.

Maciel IS, Tardin RH, Simão SM. 2018. Occurrence and habitat use of Bryde’s whales (Balaenoptera 

edeni) in the Cabo Frio region, South-eastern Brazil. J Mar Biol Assoc UK. 98(5):1081-1086.

Martin J, Sabatier Q, Gowan TA, Giraud C, Gurarie E, Calleson CS, Ortega‐Ortiz JG, Deutsch CJ, Rycyk 

A, Koslovsky SM. 2016. A quantitative framework for investigating risk of deadly collisions between 

marine wildlife and boats. Methods Ecol Evol. 7(1):42-50.
35

Martinez A, Abercrombie D, Rappucci G, Garrison LP, Soldevilla M. 2024. Cruise report: NOAA Ship 

Gordon Gunter cruise (GU18-06), November 10–December 4, 2018, Trophic Interactions and Habitat 

Requirements of Rice's Whales. Southeast Fisheries Science Center Reference Document MMTD2024-06.

Maze-Foley K, Mullin K. 2006. Cetaceans of the oceanic northern Gulf of Mexico: Distributions, group 

sizes and interspecific associations. J Cetacean Res Manage. 8(2):203-213.

Metcalfe K, Bréheret N, Chauvet E, Collins T, Curran BK, Parnell RJ, Turner RA, Witt MJ, Godley BJ. 

2018. Using satellite AIS to improve our understanding of shipping and fill gaps in ocean observation 

data to support marine spatial planning. J Appl Ecol. 55(4):1834-1845.

Morten J, Freedman R, Adams JD, Wilson J, Rubinstein A, Hastings S. 2022. Evaluating adherence with 

voluntary slow speed initiatives to protect endangered whales. Front Mar Sci. 9:833206.

Mullin K, Hoggard W. 2000. Visual surveys of cetaceans and sea turtles from aircraft and ships. In: Davis 

R, Evans W, Würsig B, editors (Texas A&M University at Galveston and the National Marine 

Fisheries Service). Cetaceans, sea turtles and seabirds in the Northern Gulf of Mexico: distribution, 

abundance and habitat associations [GulfCet]. New Orleans (LA): Department of the Interior, 

Minerals Management Service; US Geological Survey Biological Resources Division Obligation No.: 

1445-CTO9-96-0004 and 1445-1A09-96-0009. Report No.: USGS/BRD/CR-1999-0006; OCS Study 

MMS 2000-003. p. 346. 

Mullin KD, Fulling GL. 2004. Abundance of cetaceans in the oceanic northern Gulf of Mexico, 1996–

2001. Mar Mammal Sci. 20(4):787-807. https://doi.org/10.1111/j.1748-7692.2004.tb01193.x. 

Mullin KD (Southeast Fisheries Science Center, Pascagoula, MS). 2007. Abundance of cetaceans in the 

oceanic northern Gulf of Mexico from 2003 and 2004 ship surveys. Pascagoula (MS): National 

Marine Fisheries Service, Southeast Fisheries Science Center. 27 p. Southeast Fisheries Science 

Center reference document PRBD 2016-03. 

Nanayakkara RP, Herath H. 2017. Report on 14 large whales that died due to ship strikes off the coast of 

Sri Lanka, 2010–2014. J Mar Sci. 2017:6235398.

National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS). 

2020. Biological Opinion on the Federally Regulated Oil and Gas Program Activities in the Gulf of 

Mexico. OPR-2017-00002. https://doi.org/10.25923/hyeh-mb74v.

National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS). 

2021. Taking and importing marine mammals; taking marine mammals incidental to geophysical 

surveys related to oil and gas activities in the Gulf of Mexico. Federal Register (January 19) 

86(11):4322–5450. 50 CFR 217.

National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS). 

2023. Endangered Species Act Rice’s whale critical habitat report: proposed information basis and 

impact considerations of critical habitat designation. July 2023. 80 p. 

https://www.fisheries.noaa.gov/s3/2023-07/Critical-Habitat-Report-508-Final.pdf. 

National Oceanic and Atmospheric Administration (NOAA). 2024. Rice’s whales spotted in the Western 

Gulf of Mexico. [accessed June 14, 2024]. https://www.fisheries.noaa.gov/feature-story/rices-whalesspotted-western-gulf-mexico. 
36

Paoletti S, Rumes B, Pierantonio N, Panigada S, Jan R, Folegot T, Schilling A, Riviere N, Carrier V, 

Dumoulin A. 2023. SEADETECT: developing an automated detection system to reduce whale-vessel 

collision risk. Res Ideas Outcomes. 9.

Penry GS, Cockcroft VG, Hammond PS. 2011. Seasonal fluctuations in occurrence of inshore Bryde's 

whales in Plettenberg Bay, South Africa, with notes on feeding and multispecies associations. Afr J 

Mar Sci. 33(3):403-414.

Purdon J, Shabangu FW, Yemane D, Pienaar M, Somers MJ, Findlay K. 2020. Species distribution 

modelling of Bryde’s whales, humpback whales, southern right whales, and sperm whales in the 

southern African region to inform their conservation in expanding economies. PeerJ. 8:e9997.

Ransome N, Loneragan NR, Medrano-González L, Félix F, Smith JN. 2021. Vessel strikes of large 

whales in the eastern tropical Pacific: a case study of regional underreporting. Front Mar Sci. 

8:675245.

Rappucci G, Garrison L, Soldevilla M, Ortega-Ortiz J, Reid J, Aichinger-Dias L, Mullin K, Litz J

(Southeast Fisheries Science Center, Miami, FL). 2023. Gulf of Mexico Marine Assessment Program 

for Protected Species (GoMMAPPS): marine mammals, volume 1: report. New Orleans (LA): US 

Department of the Interior, Bureau of Ocean Energy Management. Obligation No.: M17PG00013.

Report No.: OCS Study BOEM 2023-042.

Redfern JV, Hodge BC, Pendleton DE, Knowlton AR, Adams J, Patterson EM, Good CP, Roberts JJ. 

2024. Estimating reductions in the risk of vessels striking whales achieved by management strategies. 

Biol Conserv. 290:110427.

Redfern JV, Moore TJ, Becker EA, Calambokidis J, Hastings SP, Irvine LM, Mate BR, Palacios DM. 

2019. Evaluating stakeholder‐derived strategies to reduce the risk of ships striking whales. Divers 

Distrib. 25(10):1575-1585.

Reeves RR, Lund JN, Smith TD, Josephson EA. 2011. Insights from whaling logbooks on whales, 

dolphins, and whaling in the Gulf of Mexico.Gulf Mex Sci. 29(1):4.

Rice AN, Palmer K, Tielens JT, Muirhead CA, Clark CW. 2014. Potential Bryde's whale (Balaenoptera 

edeni) calls recorded in the northern Gulf of Mexico. J Acoust Soc Am. 135(5):3066-3076.

Richter S, Yurk H, Winterl A, Chmelnitsky E, Serra N, O’Hara PD, Zitterbart D. 2023. Coastal marine 

mammal conservation using thermal imaging-based detection systems. bioRxiv.2023.2008. 

2025.554754.

Rockwood RC, Adams J, Silber G, Jahncke J. 2020. Estimating effectiveness of speed reduction measures 

for decreasing whale-strike mortality in a high-risk region. Endanger Species Res. 43:145-166.

Rockwood RC, Adams JD, Hastings S, Morten J, Jahncke J. 2021. Modeling whale deaths from vessel 

strikes to reduce the risk of fatality to endangered whales. Front Mar Sci. 8:649890.

Rockwood RC, Calambokidis J, Jahncke J. 2017. High mortality of blue, humpback and fin whales from 

modeling of vessel collisions on the US West Coast suggests population impacts and insufficient 

protection. PLOS ONE. 12(8):e0183052.
37

Rosel P, Corkeron P, Engleby L, Epperson D, Mullin K, Soldevilla M, Taylor B. 2016. Status review of 

Bryde's whales, (Balaenoptera edeni) in the Gulf of Mexico under the Endangered Species Act. 

US Department of Commerce. Report No.: NOAA Technical Memorandum NMFS‐SEFSC‐692.

Rosel PE, Garrison LP. 2019. Rice’s whale core distribution map Version: 7 June 2019 (NMFS Southeast 

Fisheries Science Center Reference Document MMTD-2022-01). U.S. Department of Commerce. 

https://www.fisheries.noaa.gov/resource/map/rices-whale-core-distribution-area-map-gis-data. 

Rosel PE, Wilcox LA, Yamada TK, Mullin KD. 2021. A new species of baleen whale (Balaenoptera) 

from the Gulf of Mexico, with a review of its geographic distribution. Mar Mammal Sci. 37(2):577-

610.

Rosel PE, Wilcox LA. 2014. Genetic evidence reveals a unique lineage of Bryde’s whales in the northern 

Gulf of Mexico. Endanger Species Res. 25(1):19-34.

Salvadeo C, Flores-Ramírez S, Gómez-Gallardo A, MacLeod C, Lluch-Belda D, Jaume-Schinkel S, 

Urban J. 2011. Bryde’s whale (Balaenoptera edeni) in the southwestern Gulf of California: 

Relationship with ENSO variability and prey availability. Ciencias Marinas. 37(2):215-225.

Sasaki H, Murase H, Kiwada H, Matsuoka K, Mitani Y, Saitoh Si. 2013. Habitat differentiation between 

sei (Balaenoptera borealis) and Bryde’s whales (B. brydei) in the western North Pacific. Fish 

Oceanogr. 22(6):496-508.

Širović A, Bassett HR, Johnson SC, Wiggins SM, Hildebrand JA. 2014. Bryde’s whale calls recorded in 

the Gulf of Mexico. Mar Mammal Sci. 30(1):399-409.

Soldevilla MS, Debich AJ, Garrison LP, Hildebrand JA, Wiggins SM. 2022b. Rice’s whales in the 

northwestern Gulf of Mexico: Call variation and occurrence beyond the known core habitat. 

Endanger Species Res. 48:155-174.

Soldevilla MS, Debich AJ, Pérez‐Carballo I, Jarriel S, Frasier KE, Garrison LP, Gracia A, Hildebrand JA, 

Rosel PE, Serrano A. 2024. Rice’s whale occurrence in the western Gulf of Mexico from passive 

acoustic recordings. Mar Mammal Sci.1–8.

Soldevilla MS, Hildebrand JA, Frasier KE, Dias LA, Martinez A, Mullin KD, Rosel PE, Garrison LP. 

2017. Spatial distribution and dive behavior of Gulf of Mexico Bryde’s whales: potential risk of 

vessel strikes and fisheries interactions. Endanger Species Res. 32:533-550.

Soldevilla MS, Ternus K, Cook A, Hildebrand JA, Frasier KE, Martinez A, Garrison LP. 2022a. Acoustic 

localization, validation, and characterization of Rice’s whale calls. J Acoust Soc Am. 151(6):4264-

4278.

Suarez-Bregua P, Alvarez-Gonzalez M, Parsons KM, Rotllant J, Pierce GJ, Saavedra C. 2022. 

Environmental DNA (eDNA) for monitoring marine mammals: challenges and opportunities. Front 

Mar Sci. 9:987774.

Tardin RH, Chun Y, Simão SM, Alves MAS. 2017. Modeling habitat use by Bryde’s whale Balaenoptera 

edeni off southeastern Brazil. Mar Ecol Prog Ser. 576:89-103.

United States Coast Guard. n.d. How AIS Works. [accessed July 9, 2024]. 

https://www.navcen.uscg.gov/how-ais-works. 
38

van der Hoop JM, Vanderlaan AS, Cole TV, Henry AG, Hall L, Mase‐Guthrie B, Wimmer T, Moore MJ. 

2015. Vessel strikes to large whales before and after the 2008 ship strike rule. Conserv Lett. 8(1):24-

32.

Van Waerebeek K, Baker AN, Félix F, Gedamke J, Iñiguez M, Sanino GP, Secchi E, Sutaria D, van 

Helden A, Wang Y. 2007. Vessel collisions with small cetaceans worldwide and with large whales in 

the Southern Hemisphere, an initial assessment. Latin Am J Aquatic Mamm. 6(1):43-69.

van Van Waerebeek K, Leaper R. 2008. Second report of the IWC vessel strike data standardisation 

working group. IWC Scientific Committee document SC/60/BC5. 620.

Vanderlaan AS, Taggart CT. 2007. Vessel collisions with whales: the probability of lethal injury based on 

vessel speed. Mar Mammal Sci. 23(1):144-156.

Verfuss UK, Gillespie D, Gordon J, Marques TA, Miller B, Plunkett R, Theriault JA, Tollit DJ, Zitterbart 

DP, Hubert P. 2018. Comparing methods suitable for monitoring marine mammals in low visibility 

conditions during seismic surveys. Map Poll Bull. 126:1-18.

Wang C, Lyons SB, Corbett J, Firestone J, Corbett JJ. 2007. Using ship speed and mass to describe 

potential collision severity with whales: An application of ship traffic, energy and environment model 

(STEEM). In: Compendium of Papers: Transportation Research Board 86th Annual Meeting; 1-18 p.

Watanabe H, Okazaki M, Tamura T, Konishi K, Inagake D, Bando T, Kiwada H, Miyashita T. 2012. 

Habitat and prey selection of common minke, sei, and Bryde’s whales in mesoscale during summer in 

the subarctic and transition regions of the western North Pacific. Fish Sci. 78:557-567.

Williams R, Gero S, Bejder L, Calambokidis J, Kraus SD, Lusseau D, Read AJ, Robbins J. 2011. 

Underestimating the damage: interpreting cetacean carcass recoveries in the context of the Deepwater 

Horizon/BP incident. Conserv Lett. 4(3):228-233.

Zeringue B, Hahn M, Riches T, Maclay D, Wilson M. 2022. US Outer Continental Shelf Gulf of Mexico 

Region oil and gas production forecast: 2022-2031. OCS report BOEM 2022-022. 29 p.
39

Appendix A: Literature Review and Synthesis Matrix

Table A-1. Classification matrix and summary table for primary and secondary data sources. 

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Alves et al. (2010) -- ● -- -- -- -- ● -- ● -- - This study, the first to use time depth recorders (TDRs) on Bryde’s 

whales (Balaenoptera edeni), revealed two distinct dive types and 

highlighted a significant amount of synchronized deep dive 

behavior and social association between two individuals tagged off 

Madeira Island, providing new insights into their foraging patterns 

and social structures. Mean dive duration was 5.0 ± 1.2 minutes, n 

=50; maximum 9.4 minutes. Tag data indicate that feeding 

occurs at depth and mainly at the bottom of a deep dive, with 

deeper dive at night than during the day, likely correlated with 

zooplankton diel migration. From early afternoon through 

early morning, the majority of the tagged individuals' time was 

at the surface, likely engaged in resting and respiration rather 

than traveling or feeding.

Athayde et al. (2023) -- ● ● -- ● -- -- -- -- -- -- Vessel collisions are a significant threat to cetaceans, notably 

larger species like Bryde's whales, which are frequently affected 

globally. Long-term research off the northern coast of 

São Paulo, Brazil, identified 67 Bryde's whales between 2004 

and 2022, with 4 percent (n=3) showing scars (sub-lethal) from 

vessel propellers, indicating vessel collisions, though all 

individuals appeared to have fully recovered. It is likely that 

underreporting of Bryde's whale vessel strikes contributes to 

underestimates of strike risk for the species globally.
40

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Barkaszi et al. (2021)* -- -- ● -- -- -- -- -- -- -- ● This study aimed to evaluate the risk of vessel strikes on large 

whales and sea turtles in Atlantic offshore wind energy areas, 

considering various vessel types. The research comprised four 

stages: characterizing baseline vessel traffic and wind farm 

operations, developing an analytical framework to calculate 

encounter numbers, creating a quantitative model for strike risk 

assessment, and building a geographic user interface (GUI) for 

scenario creation and result visualization. The GUI enables users 

to simulate potential encounters between vessels and marine 

animals, allowing manipulation of vessel activity and animal 

behavioral responses, with capacity for aggregating results from 

multiple scenarios. The model is limited to the Atlantic outer 

continental shelf (OCS) and does not assess strike risk for the 

Rice's whale (B. ricei). However, the basis of the model uses 

surface density layers, dive/surface behaviors, and animal 

size/swim speed for each species to calculate vessel strike risk 

based on vessel data characteristic of offshore wind energy 

development and project-specific vessel routes, transit numbers, 

speed, etc. as entered by the user.

Best (2023)* ● -- ● -- -- -- -- ● -- -- ● Since the release of the Biological Opinion on oil and gas activities 

in the Gulf of Mexico (GOM) in 2020, which used a density surface 

model to describe the distribution of the critically endangered 

Rice’s whale, a new model has become available. This new model 

extends the whale's known habitat from the Eastern GOM to the 

West. This report replicates the ship-strike analysis using the 

updated distribution model and suggests a new whale area that is 

able to reduce ship-strike risk, based on location (25.5o N and 

higher) and depth (100 to 400 meters). Risk reduction was seen 

regardless of vessel type (oil and gas or all vessels) and speeds 

(>10 knots and all speeds), using 2015 to 2018 Automatic 

Identification System (AIS) data. For example, when considering 

all vessel types and all vessel speeds, implementing 

reasonable and prudent measures (RPMs) from the National 

Marine Fisheries Service (NMFS) 2020 Biological Opinion 

reduce strike risk by 93 percent for the extended area 

compared to only 12 percent in the core area.

Constantine et al. (2015)* -- ● ● ● -- -- ● -- -- -- -- Collisions between vessels and wildlife, particularly in areas with 

high vessel traffic like the Hauraki Gulf, New Zealand, pose 

significant threats to whale populations, as evidenced by a high 

mortality rate among Bryde's whales due to vessel strikes. In this 

study, 85 percent of mortalities with a known cause of death 

had injuries consistent with vessel strike. Tag data indicated 

that individuals spent 91 percent of their time at depths within 

the maximum draft of vessels transiting the region. Despite 

challenges in monitoring and mitigating this threat (i.e., re-routing is 

ineffective because whales are widely dispersed; visual monitoring 

at night when whales are more likely at the surface is ineffective; 

passive acoustic monitoring (PAM) is limited to vocalizing 

individuals and subject to masking), collaborative efforts between 

scientists and stakeholders have led to the development of a 

Transit Protocol for Shipping, which includes voluntary speed 

restrictions and monitoring plans to reduce lethal vessel strikes 

and promote conservation in the region. 
41

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Ebdon et al. (2020) -- ● ● ● -- -- -- -- -- -- -- The study investigates the effectiveness of voluntary ship-strike 

mitigation measures introduced in 2013 to protect Bryde's whales 

in New Zealand's Hauraki Gulf. It contrasts the quicker 

implementation of voluntary agreements with the uncertain efficacy 

due to reliance on stakeholder awareness and cooperation. Using 

AIS and whale sighting data from 2014 to 2016, researchers 

mapped whale and shipping routes, finding a significant reduction 

in the risk of lethal ship strikes (from 51 to 26 percent) attributed to 

ships adhering to a voluntary speed limit of 10 knots. Despite low 

reporting rates of whale sightings to Harbor Control and minimal 

ship course diversions based on reports, the study underscores the 

potential of effective voluntary actions in mitigating threats to 

marine wildlife. The efficacy of mitigation measures specific to 

Bryde’s whale strike risk in the Hauraki Gulf (New Zealand) 

were evaluated and show a 10-knot vessel speed restriction 

reduced the probability of lethal strikes by half.

Farmer et al. (2022)* ● -- -- -- ● -- -- ● -- ● -- Marine Spatial Planning (MSP) uses spatial data and models to 

evaluate trade-offs when locating ocean industries, with a focus on 

minimizing conflicts with protected species habitats. In the 

U.S. GOM, a method was developed to identify suitable 

aquaculture areas with minimal impact on endangered species, 

integrating a multi-criteria decision-making framework to optimize 

site selection and mitigate risks to marine wildlife. This paper 

highlights the importance of recognizing broader distinct habitat 

regions for the Rice's whales (i.e., the core area and the extended 

area) as a conservative measure when assessing risk until more is 

known about the species' distribution and density. The Rice’s 

whale core area features seasonal advection of low salinity, 

high productivity surface waters, and persistent upwelling 

driven by winds and Loop Current intrusions. The confluence 

of these features leads to a mixing area with intermediate 

chlorophyll-a (Chl-a) concentrations, temperatures, and high 

salinity bottom water, which is where Rice's whales are most 

commonly observed (i.e., in the mixing zone). While other 

regions in the Gulf have similar bottom temperatures, lower 

surface productivity and higher shipping activity and noise 

may explain the less frequent observations of Rice’s whales in 

those areas.

Ferreira et al. (2021) -- ● ● ● ● -- -- ● -- -- -- The study on Bryde's whales in Macaronesia utilized a 14-year 

photographic database to assess site fidelity and movement 

patterns, identifying 59 individuals in Madeira with both shortterm and long-term site fidelity evident, and some individuals 

moving between Madeira and the Canaries. The findings highlight 

the ecological importance of this region for Bryde's whales, 

emphasizing the need for coordinated conservation efforts among 

countries and the value of open-access photographic data for 

research. Fast ferries in the Canary Islands pose one of the 

biggest ship-strike risk for large whales including Bryde's 

whales. Vessel speed restriction recommendations and 

observer recommendations are in place for some vessel types 

(i.e., ferries, cargo ships) in the Canary Islands to reduce 

vessel strikes.
42

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Garrison et al. (2020)* ● -- -- -- ● -- -- -- -- -- -- The report uses visual line-transect survey data (conducted by the 

NMFS Southeast Fisheries Science Center in the GOM during the 

summers of 2017 and 2018, as part of the GOM Marine 

Assessment Program for Protected Species [GoMMAPPS]) to 

report on abundance, habitats, and spatial distribution of cetaceans 

to support marine mammal stock assessments under the Marine 

Mammal Protection Act (MMPA). The findings contribute to the 

Stock Assessment Reports (SARs) which update the status and 

management of marine mammal stocks in the GOM. Abundance 

from 2017 to 2018 is estimated at 51 individuals (coefficient of 

variation [CV] 0.53; confidence interval [CI] 20, 130; based on 

line-transect surveys conducted during 2017 and 2018 

[Garrison et al. 2020]).

Garrison et al. (2023)* 

and 

Rappucci et al. (2023)* 

● -- -- -- ● -- -- ● -- ● -- From 2003 to 2019, vessel surveys primarily observed Rice's 

whales near the 220-meter isobath off the coast of Florida. 

One confirmed sighting was in the western GOM near Texas. 

Additional sightings from other surveys were included to improve 

detection models. The species distribution model incorporated 

factors such as depth, chl-a concentration, bottom temperature, 

bottom salinity, and geostrophic velocity components. The model 

indicated that Rice's whale density is highest in shallow waters with 

bottom temperatures between 10 and 19°C and intermediate 

Chl-a levels. This habitat mainly occurs in the northeastern GOM, 

with a similar environment along the shelf break in the western 

GOM. Although surveys are limited to the northern GOM, it is 

possible that Rice's whales also inhabit the southern GOM. 

However, projections of the SDM beyond the northern GOM 

should be treated cautiously, as it assumes consistent 

species-habitat relationships. Potential high-density areas 

include regions with similar bathymetric and current characteristics 

to the core area, particularly near the Campeche Bank.

Garrison et al. (2024)* ● -- -- -- -- -- -- ● -- ● -- The study focuses on the newly identified Rice's whale, one of the 

most endangered large whale species, primarily found near the 

continental shelf break in the northeastern GOM. By analyzing 

visual line-transect survey data collected from 2003 to 2019, 

researchers developed spatially explicitly surface density maps to 

understand the whale's habitat preferences. The authors found 

that water depth, surface chlorophyll concentration, bottom 

temperature, and bottom salinity are crucial factors defining 

the whale's habitat, influenced by oceanographic features like 

upwelling and circulation patterns. This research aids in 

designating critical habitat under the Endangered Species Act 

(ESA) and informs MSP to mitigate anthropogenic impacts like 

wind energy and aquaculture development on Rice's whales.
43

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Gonçalves et al. (2016) -- ● -- -- ● -- ● ● -- -- -- This paper presents findings from investigations into Bryde’s

whales along the São Paulo coast, contributing to our 

understanding of their occurrence and behavior, particularly in the 

context of conservation. Sighting surveys conducted from 2003 to 

2005 revealed higher rates of sightings and individuals in coastal 

areas during the summer season, with notable social interactions 

observed at the 1,200-meter isobath; a total of 71 individuals were 

observed, with all sightings between the isobaths of 20 and 3000 

meters. Sighting rates were higher in coastal areas during the 

summer season, suggesting potential feeding in coastal areas 

and breeding offshore. Dive duration was typically shorter 

during foraging dives (maximum = 7 minutes) and longer for 

traveling individuals (maximum = 17 minutes). The behavioral 

patterns revealed by this study could have implications for marine 

protected areas and conservation efforts in Brazil.

Hayes et al. (2023)* ● -- ● -- ● -- -- -- -- -- -- Rice’s whales, closely related to Bryde’s and sei whales, were 

recognized as a distinct species in 2021 and are critically 

endangered with fewer than 100 individuals remaining. They are 

primarily found in the northern GOM near De Soto Canyon, with 

some evidence suggesting occasional sightings along the 

southeastern U.S. Atlantic coast. Historical records indicate a 

broader past range, potentially extending to the southern GOM. 

Recent abundance estimates suggest a population size of around 

51 individuals, showing no significant trend over recent survey 

years. The species faces threats from anthropogenic activities 

such as oil spills, fishing gear entanglements, and vessel strikes, 

and their small population size and limited genetic diversity make 

them highly vulnerable to environmental and demographic 

stochasticity. Abundance from 2017 to 2018 is estimated at 51 

individuals (CV: 0.53; CI: 20, 130, based on line-transect 

surveys conducted during 2017 and 2018 [see Garrison et al. 

2020]).

Helble et al. (2024) -- ● -- -- ● ● ● -- -- -- -- This study examines the acoustic and swimming behaviors of 

Bryde’s whales in the central North Pacific, leveraging data from 

acoustically derived tracks spanning 2011 to 2022 at the U.S. 

Pacific Missile Range Facility in Kaua‘i, Hawai‘i. Findings reveal 

distinct kinematic states and seasonal patterns in movement and 

calling behavior, providing valuable insights into the behavior of 

this understudied whale species. Bryde's whales were tracked 

with faster swim speeds than other mysticetes (1.7 m/s), and 

appeared to have more directional travel during daytime than 

at night, and more between May and August then the 

remainder of the year.

Henry et al. (2022) ● -- ● -- -- -- -- -- -- -- -- Opportunistic reports from 2015 to 2019 were used to calculate 

human-caused serious injury and mortality rates for baleen whale 

stocks along the GOM, U.S. East Coast, and Atlantic Canadian 

provinces. The average annual rates detected were 7.65 for North 

Atlantic right whales, 16.25 for Gulf of Maine humpback whales, 

1.85 for western North Atlantic fin whales, 10.35 for Canadian East 

Coast minke whales, 0.6 for Nova Scotian sei whales, 0 for 

western North Atlantic blue whales, and 0 for northern GOM 

Bryde’s whales, with actual levels potentially being much higher 

due to unreported cases. The Bryde’s whale human-caused 

mortality/serious injury (M/SI) value remains 0 for the 2015 to 

2019 time period; there were no reported injuries and 

1 mortality from natural causes for this time period.
44

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Izadi et al. (2022) -- ● -- -- -- -- ● -- ● -- -- Baleen whales, despite being the largest predators, primarily 

consume plankton and small fish. Unlike many other baleen 

whales that migrate to polar regions for abundant zooplankton 

prey, Bryde's whales in warm-temperate waters off New Zealand 

adapt to variable prey availability, targeting both fish and 

zooplankton. Through multi-sensor tags and visual observations, it 

was found that Bryde's whales use specialized feeding tactics 

depending on the prey type: they use head-slaps to aggregate 

zooplankton and perform side-lunges, or swim in circles and 

perform multiple lunges when targeting plankton patches. Their 

diet plasticity and dynamic feeding behaviors are believed 

crucial for adapting to changes in prey availability, which is 

characteristic of warm-temperate waters that have limited prey 

resources and are less productive. As cited in Izadi et al. 

(2022), Izadi (2018) (doctoral dissertation) documented tagged 

individuals feeding only during the day and resting most of 

the night, which may save energy resources; daytime foraging 

may be sufficient for this species, which does not undergo 

seasonal migrations like most other large whale species.

Kiszka et al. (2023)* ● -- -- -- -- -- -- -- ● -- -- Understanding prey selection drivers in marine predators is 

essential for investigating ecosystem structure and function. For 

the critically endangered Rice’s whale in the GOM, research using 

Bayesian stable isotope mixing models indicated a preference for

high-energy schooling fish, particularly Ariomma bondi (a small 

schooling fish found in demersal habitats over muddy bottoms at 

depths of 50 to 500 meters), suggesting that energy content, rather 

than prey abundance, primarily drives prey selection. Prey 

availability (by use of trawl data) and prey selection (by use of 

stable carbon and nitrogen isotope mixing models) were assessed 

for Rice's whales in the core area. Ten skin and blubber biopsy 

samples were collected in 2010 (n=1) and 2018 to 2019. The data 

indicate that Rice’s whales in the northeastern GOM primarily 

prey on high-energy content species, particularly A. bondi, 

while other abundant species like M. weitzmani are less 

significant in their diet. These results suggest selective prey 

selection driven mainly by prey energy content as opposed to 

abundance. The authors also note that deep dives are 

energetically expensive and therefore requires high quality prey to 

meet their energetic requirements.

Kok et al. (2023)* ● -- ● -- -- -- ● -- ● -- -- Rorqual foraging behavior is highly variable and critical to Rice’s 

whale fitness, influenced by species, prey type, and foraging 

conditions. The endangered Rice’s whales, with a population of 

fewer than 100 individuals, were studied using suction-cup tags on 

two individuals to reveal their predominantly bottom-focused lungefeeding behavior. The whales exhibited unique patterns, including 

circling before lunging and a lower lunge rate compared to other 

lunge-feeding baleen whales, potentially reflecting their preference 

for fish or different foraging conditions. Lunge-feeding behaviors 

were recorded most commonly near the sea bottom during 6 

to 10 minute foraging dives. Extended periods of time were 

spent at the surface during the night.

LaBrecque et al. (2015) ● -- -- -- -- -- -- ● -- -- -- This review identifies and supports the delineation of 12 

biologically important areas (BIAs) in U.S. waters of the GOM. For 

the Bryde's whale, observations in the GOM are mainly limited 

to the 100 to 300-meter isobaths in the eastern Gulf, which 

denotes the year-round BIA for the species.
45

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Laist et al. (2001)* -- ● ● ● -- -- -- -- -- -- -- Ship strikes with great whales have been a concern since the late 

1800s, becoming more frequent from the 1950s to the 1970s due 

to increased ship numbers and speeds. Among the 11 species 

affected, fin whales are struck most often, followed by right whales, 

humpback whales, sperm whales, and gray whales. Collisions 

often result in severe injuries, particularly when involving ships 

over 80 meters long traveling at speeds of 14 knots or faster, 

posing a significant threat to vulnerable whale populations like the 

northern right whales in the western North Atlantic. Records of 

collisions with Bryde's whales are rare, although strikes may 

be underrepresented for small mysticetes due to their size.

Implementing measures to reduce vessel speeds below 14 knots in 

areas prone to ship strikes could help mitigate this issue.

Litz et al. (2022)* ● -- -- -- ● -- -- -- -- -- -- The National Oceanic and Atmospheric Administration (NOAA) 

Southeast Fisheries Science Center (SEFSC) created spatial 

density models (SDMs) for cetaceans and sea turtles in the GOM, 

using data from ship-based and aerial surveys conducted from 

2003 to 2019. These models, based on generalized additive 

modeling (GAM), analyze the relationship between species 

abundance and environmental variables (oceanographic conditions 

from 2015 to 2019). The models were extrapolated to predict highdensity areas beyond U.S. waters in the GOM, though these 

predictions should be interpreted cautiously. The dataset includes 

19 shapefiles for the SDMs of each species or species group. 

Rice's whale dataset is used and referenced in Best (2023).

Maze-Foley and Mullin 

(2006)* 

● -- -- -- ● -- -- -- -- -- -- The paper provides a comprehensive overview of cetacean 

distribution and behavior in the oceanic northern GOM. It highlights 

the impacts of the MMPA and concerns over oil and gas industry 

activities on cetacean research in the region. The study conducted 

nine ship surveys over 11 years, covering 45,462 kilometers in the 

continental shelf-edge and oceanic waters (>100 meters depth), 

identifying 18 cetacean species from 1,868 sightings. Key findings 

include detailed descriptions of species distribution, group sizes, 

and associations with sea surface temperature, water depth, birds, 

and fish. Bryde's whales were the only balaenopterid whale sighted 

during these surveys (n=15) and were likely not sei whales 

according to the authors (based on morphological features); all 

balaenopterid/Bryde's whale sightings occurred within the 

core area and in a very narrow depth range (199 to 302 

meters) and in small group sizes (<3).

Mullin (2007)* ● -- -- -- ● -- -- -- -- -- -- In the GOM, ship-based surveys conducted during 2003 and 2004 

in oceanic waters over 200 meters deep revealed a diverse 

cetacean community with at least 17 species sighted across 

10,933 kilometers of survey effort. The most commonly sighted 

species included pantropical spotted dolphins, sperm whales, and 

bottlenose dolphins, with some species showing regional 

distributions. Abundance estimates varied, with notable differences 

from previous surveys, particularly for pantropical spotted dolphins 

and sperm whales, reflecting improved counting methodologies.

All Bryde's whale detections occurred in the core area.
46

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Mullin and Fulling (2004)* ● -- -- -- ● -- -- -- -- -- -- Ship-based, line-transect surveys conducted in oceanic waters 

(>200 meters deep) of the northern GOM within U.S. waters from 

1996 to 1997 and 1999 to 2001 provided data on the abundance of 

cetaceans. The surveys covered an area of 380,432 km2 and 

yielded minimum abundance estimates based on 12,162 

kilometers of effort and 512 sightings of at least 19 species. 

Pantropical spotted dolphins, sperm whales, and dwarf/pygmy 

sperm whales were among the most commonly sighted species, 

with variations in abundance and distribution observed among 

different species in the region. All Bryde's whale sightings 

(n=40; mean group size=2) occurred in the Northeast Slope 

region of the GOM.

Mullin and Hoggard 

(2000)* 

● -- -- -- ● -- -- -- -- -- -- The GulfCet I study provided seasonal abundance and distribution 

data for 19 cetacean species in continental shelf and slope waters 

of the north-central and northwestern GOM. Species such as 

pantropical spotted dolphins, bottlenose dolphins, striped dolphins, 

and others were widely distributed across different water depth 

ranges. The GulfCet II program extended these surveys to the 

northeastern GOM, focusing on seasonal patterns and comparing 

abundance estimates with previous studies to establish baselines 

for monitoring trends over time. Bryde's whales were detected 

three times during vessel-based surveys and two times during 

aerial visual surveys; all Bryde's whale detections occurred in 

the core area near the 100-meter isobath.

NOAA NMFS (2020)* ● -- ● ● -- -- -- -- -- -- ● Section 8.4: This section outlines the evaluation of vessel traffic 

data concerning the potential impact on ESA-listed species, 

considering data provided by BOEM and supplemented by 

AIS data for a more detailed representation. While Bureau of 

Ocean Energy Management (BOEM) data measures vessel trips 

based on port calls, AIS data provides finer resolution regarding 

vessel routes, distances traveled, and speeds, allowing for more 

realistic estimates of ESA-listed species exposure to vessel traffic. 

AIS data reveals that Oil and Gas Program vessel traffic comprises 

approximately 43 percent of total traffic in the GOM (measures by 

distance traveled), differing significantly from the 9.23 percent 

estimated by BOEM. The proposed action is estimated to involve a 

maximum of 173,002 vessel trips annually, constituting 

approximately 20 percent (19.77 percent) of the total vessel trips in 

the GOM. Section 8.4.2.1: BOEM-proposed vessel strike 

mitigation is likely to result in a total of 23 vessel strikes of Bryde’s 

whales, with 17 of these strikes expected to result in serious injury 

or mortality and six strikes expected to result in minor or no 

injuries. Assumptions: a reduction in vessel speed to ten knots and 

below is likely to lead to a 90 percent reduction in the number of all 

vessel strikes, regardless of severity, and 35 percent of oil and gas 

traffic occurs within the mitigation area, which would be limited to a 

10-knot speed restriction. If a 10-knot speed restriction results in a 

90 percent reduction in the number of vessel strikes, then the 

overall reduction of vessel strike risk to Bryde’s whales due to oil 

and gas vessel traffic of all speeds is approximately 31 percent. 

The proposed speed restriction RPAs for oil and gas vessel 

activity over 50 years would result in 12 vessel strikes with 

serious injury or mortality and the remaining four with no or 

minor injuries.
47

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

NOAA NMFS (2021)* ● -- ● -- -- -- -- -- -- -- -- The Gulf of Mexico Oil and Gas Program Incidental Take 

Statement (as amended April 26, 2021) indicated the following 

incidental take considering the extent of the RPA in NOAA NMFS 

(2020):

• No take of GOM Bryde’s whales is expected from pile 

driving activities.

• Take is anticipated for GOM Bryde’s whales from seismic 

survey sound associated with geophysical survey activities.

• Take is anticipated for GOM Bryde’s resulting from 

vessel strike. Since NMFS states "Feasible monitoring 

techniques for directly detecting the full extent of take 

(either lethal or nonlethal) of these species resulting 

from vessel strike do not exist", vessel activity level is 

used as a proxy for determining anticipated vessel 

strike take numbers (see Section 8.4 of the NOAA 

NMFS 2020).

•

NOAA NMFS (2023)* ● -- ● -- ● -- ● ● ● -- -- The document provides detailed analysis and information 

necessary for the designation of critical habitat for Rice's whale 

under the ESA. It covers essential biological and physical features, 

management considerations, economic impacts, and potential 

implications for activities in the GOM, along with compliance with 

regulatory requirements under the ESA and the Regulatory 

Flexibility Act. A comprehensive literature review is conducted 

for: species description and life history; vocalizations and 

sound; distribution, movement, and habitat use; diet and 

foraging; and abundance.

Redfern et al. (2024)* -- -- ● ● -- -- -- -- -- -- ● The study focuses on evaluating strategies to reduce the risk of 

vessels striking North Atlantic right, humpback, fin, and sei whales 

on the US East Coast. Evaluating strategies to reduce vessel 

strikes on whales requires balancing species protection with 

economic impacts. Previous simplistic methods lack important 

elements of vessel strike risk, while complex methods often rely on 

unestimated parameters. The study introduces a simple metric, 

Total PLETHd, which incorporates vessel speed, transit distance, 

and whale distribution to assess risk reductions. Findings indicate 

that a 10 knot speed restriction effectively reduces risk and 

broad area speed restrictions are almost as effective as those 

applied across all East Coast waters, benefiting multiple 

whale species. The areas primarily defined to protect right 

whales also provide protection for humpback, fin, and sei 

whales. The study highlights the importance of considering both 

the whale and vessel metrics used in risk estimation.

Rice et al. (2014)* ● -- -- -- ● ● -- -- -- -- -- The study deployed marine autonomous recording units (MARUs) 

in the northeastern GOM from 2010 to 2012 (core area) to study 

Bryde’s whales' acoustic ecology post-Deepwater Horizon oil spill, 

revealing a poorly documented acoustic repertoire for this 

sub-population. The recordings captured numerous 

stereotyped, low-frequency signals, categorized by spectral 

and temporal properties, suggesting Bryde’s whales as the 

likely source. These findings potentially unveil previously 

undocumented calls, which could enhance future PAM efforts to 

assess the population dynamics and status of Bryde’s whales in 

this region.
48

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Rockwood et al. (2021)* -- -- ● ● -- -- -- -- -- -- ● Vessel strikes are a major cause of large whale mortality globally. 

The Santa Barbara Channel, California, has one of the highest 

predicted whale mortality rates from vessel strikes in U.S. Pacific 

waters. Since 2007, the NOAA has requested voluntary vessel 

speed reductions (VSRs) for ships over 300 gross tonnage (GT) in 

this area to mitigate whale strikes. A study using whale density 

data and AIS vessel data estimated whale mortality under different 

management scenarios. For 2012 to 2018, it was estimated that 

during summer/fall (June to November), an average of 8.9 blue 

whales, 4.6 humpback whales, and 9.7 fin whales were killed by 

ship strikes each year, which is 13 to 26 percent higher than 

previously estimated. Winter/spring (January to April) humpback 

whale mortality was assessed for the first time, estimating an 

average of 5.7 deaths per year. Poor compliance with the VSR 

resulted in minimal or no reduction in strike mortalities. The study 

found that if 95 percent compliance with VSRs were achieved in 

the shipping lanes, whale deaths could be reduced by 22 to 26 

percent. Extending VSRs to additional areas with similar 

compliance could reduce strike mortalities by 30 percent. Overall, 

with 95 percent compliance in all areas, vessel strike mortalities 

could decrease by 21 to 29 percent relative to the entire study 

area. To effectively reduce whale mortalities from vessel strikes in 

this region, the study recommends expanding VSR areas and 

increasing voluntary compliance, or considering mandatory 

speed limits if high cooperation levels are not otherwise 

achievable.

Rosel and Garrison 

(2019)* 

● -- -- -- ● -- -- ● -- ● -- Map of Rice’s Whale Core Distribution Area (June 2019) in the 

northeastern GOM.

Rosel and Wilcox (2014)* ● -- -- -- -- -- -- -- -- -- -- Bryde’s whales are the sole year-round resident baleen whale 

species in the northern GOM, with a severely restricted range and 

an estimated population abundance of 33 individuals. Genetic 

analysis using mitochondrial DNA and nuclear genes revealed 

extremely low diversity, with only two mitochondrial 

haplotypes and limited microsatellite variation. Phylogenetic 

reconstruction indicated that GOM Bryde’s whales are 

evolutionarily distinct from other members of the Bryde’s whale 

complex, suggesting a unique evolutionary trajectory deserving of 

separate taxonomic recognition. The findings highlight 

conservation concerns due to their small population size and low 

genetic diversity.
49

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Rosel et al. (2016)* ● -- ● -- ● -- ● -- -- -- -- The NMFS received a petition from the Natural Resources Defense 

Council on September 18, 2014, to list the GOM population of 

Bryde's whales as an endangered distinct population segment 

under the ESA, which prompted a comprehensive review. The 

petition was based on substantial scientific information indicating 

that an ESA listing may be warranted, given the whales' restricted 

distribution, low genetic diversity, and susceptibility to numerous 

threats including habitat modification, oil spills, vessel collisions, 

and anthropogenic sound. Despite no specific listing 

recommendation from the Status Review Team, it was 

unanimously agreed that the GOM Bryde's whales are at high risk 

of extinction due to their small population size and vulnerability to 

various anthropogenic impacts, highlighting the urgent need for 

conservation and management measures. Vessel traffic in the 

GOM is highest in the north-central and western regions; 

comparatively less traffic (based on AIS tracks) is evident 

within the core area, though several shipping lanes do bisect 

it. Only one Bryde's whale vessel strike is documented within 

the GOM, though collisions are likely underreported. 

Constantine et al. (2015) reports high levels of vessel strike in 

Hauraki Bay, New Zealand, likely driven by their distributional 

overlap with shipping lanes and their surface/dive behavior 

(i.e., individuals spend 91 percent of their time at night in the 

top 14 meters of the water column). Soldevilla et al. (2017) 

report GOM Bryde's whales spend 88 percent of their time at 

night in the top 15 meters of the water column. It is expected 

that larger and faster ships will increasingly utilize the GOM, 

posing greater risk to Bryde's whales.

Rosel et al. (2021)* ● ● -- -- -- -- -- -- -- -- -- Bryde's-like whales constitute a complex of medium-sized baleen 

whales found in tropical waters across all major ocean basins. 

Currently recognized as a single species, Balaenoptera edeni, with 

two subspecies, recent genetic data identified a new, genetically 

distinct lineage of Bryde's-like whale primarily in the northern GOM. 

A comprehensive morphological examination of a complete skull 

from these whales revealed diagnostic features distinguishing them 

from other medium-sized baleen whale taxa. Genetic sampling of 

36 individuals from the GOM lineage supports the existence of 

an undescribed species of Balaenoptera in this region.

Sasaki et al. (2013) -- ● -- -- ● -- -- ● -- ● -- This study investigated habitat differentiation between sei and 

Bryde’s whales in the western North Pacific from May to August 

2004 and 2005, focusing on oceanographic features like sea 

surface temperature (SST), sea surface Chl-a, sea surface height 

anomalies (SSHAs), and depth. It found that the two whale 

species utilized habitats with distinct SST, Chl-a, and 

SSHA ranges, and identified the 0.25 mg/m3 Chl-a contour as a 

significant indicator separating their habitats. Generalized 

linear models revealed that SST influenced habitat use patterns 

and varied seasonally, with the boundary between their potential 

habitats shifting northward.

Sirović et al. (2014)* ● -- -- -- ● ● -- -- -- -- -- Bryde’s whales in the GOM are thought to be a distinct population 

with a restricted range around DeSoto Canyon. This study 

describes the first documented recordings of Bryde’s whale 

calls in the GOM, identifying distinctive pulse pair calls and 

suggesting their possible residency and small population size 

in the region.
50

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Soldevilla et al. (2017)* ● -- ● ● -- -- ● ● ● -- -- Bryde’s whales are the sole resident baleen whale species in the 

GOM, where their population is extremely rare and has a restricted 

distribution, representing a unique evolutionary lineage. This study 

assessed the overlap of GOM Bryde’s whales with commercial 

shipping and fisheries, highlighting potential threats such as vessel 

strikes and entanglement in fishing gear, especially given their diel 

diving behavior and frequent near-surface presence at night. 

Management strategies to mitigate these threats are crucial for the 

recovery of this vulnerable population. Using SEFSC survey data 

(1992 to 2015; 99,652 kilometers shipboard trackline survey), 50 

sightings of 112 Bryde's whale detections were made. Sightings: 

All sightings were in the 100 to 400-meter isobath; 92.9 percent of 

the detections were inside the BIA, with the remainder just outside. 

Sat tag, location: One satellite tag was deployed and remained on 

the individual for 33 days, coving a total track length of 780 

kilometer. The majority of time was spent in the De Soto Canyon 

region (28 days), with one excursion southeast of the canyon and 

back (5 days). Suction-cup tag: one individual was tagged, with tag 

lasting 2.7 days on the animal. The individual exhibited a diel 

dive pattern as follows: deep (>70 meters) daytime dives; 

shallow (<30 meters) nighttime dives. 123 deep dives were 

recorded (only 3.25 percent occurred at night). Daytime dives: 

Deepest dive was 271 meters; animal was likely foraging at or near 

the bottom based on lunge patterns. The tagged animal spent 

47 percent of its time in the top 15 meters of the water column 

during the daytime. Night dives: During nighttime, the animal 

remained close to the surface, made shallow dives with 

occasional deeper dive (deepest three: 125 to 150 meters); 

whale spent 88 percent of its time in the top 15 meters of the 

water column. Vessel activity in the BIA: from October 2009 to 

2010, less than one vessel transit was recorded per week in 98.5 

percent of the BIA, with the remainder averaging one per week to 

two per day, though vessel data is likely underrepresented based 

not he limitations of AIS. Summary: the tag data represent highly 

consistent diel dive behavior with presumed foraging at/near the 

bottom over the 2.7 days the tag was attached. The authors 

indicate that it is reasonable to infer that this behavior is 

representative of the GOM Bryde’s whale population as other 

tagged baleen whales also display similar stereotypical diel dive 

behaviors linked to foraging and prey distribution. Strike Risk: 

Overall, the animal spent 70 percent of its total time within 15 

meters of the surface, which overlaps with the draft of deep-draft 

vessels that transit the region. While only one GOM Bryde's whale 

strike has been recorded, the incidence of ship strike is likely 

underreported (i.e., Williams et al. 2011 suggest 98 to 99.6 percent 

of ship strikes are unrecovered). Mitigation recommendations: 

vessel speed reduction rules and re-routing measures have been 

successful at reducing risk for North Atlantic right whales 

(NARWs); identifying appropriate habitat should be a priority and 

then these measures could be implemented in the GOM.
51

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

Soldevilla et al. (2022a)* ● ● ● The study focuses on the acoustic validation and characterization 

of the recently named Rice’s whale, one of the most endangered 

whales in the world, to improve understanding of its spatiotemporal 

occurrence patterns crucial for conservation efforts. Using 

sonobuoys and passive acoustic tagging (all within the core 

area), the researchers identified and attributed several call 

types, including long-moan calls, downsweep sequences, and 

tonal sequences, to Rice’s whales, while ruling out 

anthropogenic sources. This validation and characterization of 

the call repertoire provide foundational knowledge necessary for 

employing PAM to aid in the conservation of these critically 

endangered whales.

Soldevilla et al. (2022b)* ● -- -- -- ● ● -- -- -- -- -- The Rice’s whale, an endangered species with fewer than 100 

individuals remaining, is the only year-round resident baleen whale 

in the GOM, primarily found near De Soto Canyon. Recent studies 

using long-term passive acoustic recordings reveal the presence of 

6 new variants of Rice’s whale long-moan calls in the northwestern 

GOM, indicating a broader distribution than previously known, 

which has implications for conservation efforts and habitat 

designation. Rice's whale vocalizations were recorded outside 

the core area, but detections were sparce compared to the 

northeastern GOM: fewer calls were detected on fewer days in 

the western GOM, potentially indicating fewer whales or more 

sparse occurrences of individuals in the northwestern GOM. 

However, the data does indicate a persistent presence of 

Rice's whales in the northwest GOM, which coincides with 

high levels of anthropogenic activities. It is unknown if Rice's 

whales occur in the north-central GOM or travel between the 

northwestern and northeastern areas.

Soldevilla et al. (2024)* ● -- -- -- ● ● -- -- -- -- -- Rice's whales, one of the world's most endangered marine 

mammals, are primarily found in the GOM with an estimated 

population of 51 individuals concentrated along the northeastern 

shelf break. Long-term PAM has revealed their presence in waters 

off Texas and Mexico, highlighting the need for comprehensive 

transboundary conservation efforts to mitigate threats from 

industrial activities and climate change. Rice's whale calls were 

detected regularly and throughout the year, present on 25 to 

33 percent of days (depending on the receiver), indicating the 

species' regular occurrence in the northwestern GOM. Calls 

were also detected sporadically in the western GOM in 

Mexican waters (15 percent of days). These occurrences int he 

western GOM overlap with high levels of anthropogenic 

activities.
52

Citation Rice's 

Whale 

(incl. 

GOM 

Bryde's 

Whale)

Bryde’s, 

Omura's, or 

Eden’s 

Whales 

Vessel 

Strike / 

Strike 

Risk 

Mitigation 

Review 

Presence / 

Occurrence 

Data 

Acoustic 

Data 

Dive / 

Surface 

Behavior 

Habitat 

Selection / 

Preference / 

Suitability

Foraging 

Behavior / 

Prey 

Selection 

Models 

Habitat 

Use / 

Suitability

Models 

Vessel 

Strike 

Risk 

Summary

van der Hoop et al. (2013)* ● -- ● -- -- -- -- -- -- -- -- This study evaluated the effectiveness of regulatory actions by the 

U.S. and Canadian governments aimed at reducing whale mortality 

caused by human activities, such as vessel strikes and 

entanglement in fishing gear, from 1970 to 2009. The results 

indicated that human-caused mortalities, primarily due to 

entanglements and vessel strikes, exceeded sustainable levels for 

some whale species, and regulatory measures have not 

significantly reduced these lethal effects on a population-wide 

basis. At least 1,762 confirmed mortalities comprising 8 large 

whale species were recorded from 1970 to 2009. Species were 

identified in 85 percent of cases and cause of death identified in 

42.6 percent of mortalities. Of those, 66.9 percent were resultant 

from human activities. 22.8 percent of mortalities resulted from 

vessel strikes; one Bryde's whale mortality is attributed to vessel 

strike. The leading cause of mortalities for fin and right whales 

were vessel strikes. Bryde's whales died of non-human causes 

more frequently than expected, and sei whales died of vessel 

strike more than expected. Recorded human-caused mortality for 

Bryde's whales (and minke, fin, and sperm whales) was lower than 

their Potential Biological Removal (PBR) level. Williams et al. 

(2011) estimated between 0 to 6.2 percent of mortalities in the 

GOM go undetected. Additionally, coastal species are more 

likely to be recorded and documented than pelagic-dwelling 

species.

Van Waerebeek et al. 

(2007)* 

-- ● ● -- -- -- -- -- -- -- -- Collisions between vessels and large whales in the Northern 

Hemisphere are well documented, but less attention has been 

given to similar incidents in the Southern Hemisphere and to small 

cetaceans globally. A compiled experimental database of 256 

incidents (119 large whales, 137 small cetaceans) was created for 

rapid assessment, revealing confirmed collisions for 25 species 

and probable collisions for 10 others. Species like southern right, 

humpback, and Bryde's whales are heavily impacted, with 

significant mortality observed in southern right whale populations 

off South Africa and eastern South America. A total of 13 vessel 

collisions with Bryde's whales (Australia, New Zealand, and 

Ecuador) were assessed, with 5 confirmed and 8 unconfirmed. 

Collisions with Bryde’s whales, once considered rare, have 

been increasingly documented, suggesting underreporting in 

(sub)tropical regions. Recent cases include specimens found 

dead with propeller-inflicted traumas in Brazil and 

New Zealand, indicating a higher frequency of collisions with 

vessels than previously believed.

Vanderlaan and Taggart 

(2007)* 

-- -- ● ● -- -- -- -- -- -- ● This study analyzes historical records of vessel strikes on large 

whales to determine the relationship between vessel speed and 

the likelihood of causing lethal injuries. The analysis reveals that 

the probability of a lethal injury to whales increases 

significantly with vessel speed, particularly between 8.6 and 

15 knots, where the probability rises from 0.21 to 0.79. The 

probability of lethal injury drops below 50 percent at speeds under 

11.8 knots and approaches near certainty at speeds above 

15 knots. The study suggests that implementing vessel speed 

limits could significantly reduce the lethality of vessel strikes on 

endangered whale species, especially the North Atlantic right 

whale. The findings provide a critical basis for policy decisions 

regarding maritime traffic regulations aimed at protecting large 

whale populations.
53

• = applicable; -- = not applicable; * = primary literature; AIS = Automatic Identification System; BIA = biologically important area; BOEM = Bureau of Ocean Energy Management; Chl-a = chlorophyll-a; CV = coefficient of variation; ESA = Endangered Species Act; 

GAM = generalized additive modeling; GOM = Gulf of Mexico; GoMMAPPS = Gulf of Mexico Marine Assessment Program for Protected Species; GUI = graphic user interface; GT = gross tonnage; MARU = marine autonomous recording unit; M/SI = mortality/serious 

injury; MMPA = Marine Mammal Protection Act; MSP = marine spatial planning; NARW = North Atlantic right whale; NMFS = National Marine Fisheries Service; NOAA = National Oceanic and Atmospheric Administration; OCS = outer continental shelf; PBR = Potential 

Biological Removal; PAM = passive acoustic monitoring; RPM = reasonable and prudent measure; SAR = stock assessment report; SDM = spatial density models; SEFSC = Southeast Fisheries Science Center; SSHA = sea surface height anomaly; SST = sea surface 

temperature; TDR = time depth recorded; VSR = vessel speed reduction.
54

A.1 References

Alves F, Dinis A, Cascão I, Freitas L. 2010. Bryde's whale (Balaenoptera brydei) stable associations and 

dive profiles: New insights into foraging behavior. Mar Mammal Sci. 26(1):202-212.

Athayde A, Cardoso J, Francisco A, Siciliano S. 2023. A vessel collision report for Bryde's whales 

(Balaenoptera brydei) off the northern coast of São Paulo, Brazil. Aquat Conserv Mar Freshwater 

Ecosyst. 33(2):226-230.

Barkaszi M, Fonseca M, Foster T, Malhotra A, Olsen K (CSA Ocean Sciences Inc, Stuart, FL). 2021. 

Risk assessment to model encounter rates between large whales and vessel traffic from offshore wind 

energy on the Atlantic OCS. Sterling (VA): Department of the Interior, Bureau of Ocean Energy 

Management. 54 p. Obligation No.: 140M0119F0033 . OCS Study BOEM 2021-034.

https://espis.boem.gov/final%20reports/BOEM_2021-034.pdf

Constantine R, Johnson M, Riekkola L, Jervis S, Kozmian-Ledward L, Dennis T, Torres LG, de Soto NA. 

2015. Mitigation of vessel-strike mortality of endangered Bryde’s whales in the Hauraki Gulf, New 

Zealand. Biol Conserv. 186:149-157.

Ebdon P, Riekkola L, Constantine R. 2020. Testing the efficacy of ship strike mitigation for whales in the 

Hauraki Gulf, New Zealand. Ocean Coast Manage. 184:105034.

Farmer NA, Powell JR, Morris Jr JA, Soldevilla MS, Wickliffe LC, Jossart JA, MacKay JK, Randall AL, 

Bath GE, Ruvelas P. 2022. Modeling protected species distributions and habitats to inform siting and 

management of pioneering ocean industries: A case study for Gulf of Mexico aquaculture. PLOS 

ONE. 17(9):e0267333.

Ferreira R, Dinis A, Badenas A, Sambolino A, Marrero‐Pérez J, Crespo A, Alves F. 2021. Bryde's whales 

in the North‐East Atlantic: New insights on site fidelity and connectivity between oceanic 

archipelagos. Aquat Conserv Mar Freshwater Ecosyst. 31(10):2938-2950. 

Garrison LP, Ortega-Ortiz J, Rappucci G, Aichinger-Dias L, Mullin K, Litz J (NOAA Southeast Fisheries 

Science Center, Miami, FL). 2023. Gulf of Mexico Marine Assessment Program for Protected 

Species (GOMMAPPS): marine mammals. Volume 2: appendix C: Gulf of Mexico marine mammal 

spatial density models. New Orleans (LA): US Department of the Interior, Bureau of Ocean Energy 

Management. 1264 p. Obligation No.: M17PG00013. Report No.: OCS Study BOEM 2023-042.

https://espis.boem.gov/final%20reports/BOEM_2023-042_v2.pdf 

Garrison LP, Ortega-Ortiz J, Rappucci G. 2020. Abundance of marine mammals in waters of the US Gulf 

of Mexico during the summers of 2017 and 2018. Southeast Fisheries Science Center, Protected 

Resources and Biodiversity Division. 56 p. Report No.: PRD Contribution: #PRD-2020-07.

Garrison LP, Soldevilla MS, Martinez A, Mullin KD. 2024. A density surface model describing the 

habitat of the Critically Endangered Rice’s whale Balaenoptera ricei in the Gulf of Mexico. Endanger 

Species Res. 54:41-58.

Gonçalves LR, Augustowski M, Andriolo A. 2016. Occurrence, distribution and behaviour of Bryde's 

whales (Cetacea: Mysticeti) off south-east Brazil. J Mar Biol Assoc UK. 96(4):943-954.
55

Hayes SA, Josephson E, Maze-Foley K, Rosel PE, McCordic J, Wallace JE. 2023. U.S. Atlantic and Gulf 

of Mexico marine mammal stock assessment reports 2022. Woods Hole (MA): U.S. Department of 

Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, 

Northeast Fisheries Science Center. June 2023. 262 p.

Helble TA, Alongi GC, Guazzo RA, Allhusen DR, Martin CR, Martin SW, Durbach IN, Henderson EE. 

2024. Swimming and acoustic calling behavior attributed to Bryde’s whales in the central North 

Pacific. Front Mar Sci. 11:1305505.

Henry A, Garron M, Morin D, Smith A, Reid A, Ledwell W, Cole TV. 2022. Serious injury and mortality 

determinations for baleen whale stocks along the Gulf of Mexico, United States East Coast, and 

Atlantic Canadian Provinces, 2015-2019.

Izadi S, Aguilar de Soto N, Constantine R, Johnson M. 2022. Feeding tactics of resident Bryde's whales 

in New Zealand. Mar Mammal Sci. 38(3):1104-1117.

Kiszka JJ, Caputo M, Vollenweider J, Heithaus MR, Aichinger Dias L, Garrison LP. 2023. Critically 

endangered Rice’s whales (Balaenoptera ricei) selectively feed on high-quality prey in the Gulf of 

Mexico. Sci Rep. 13(1):6710.

Kok AC, Hildebrand MJ, MacArdle M, Martinez A, Garrison LP, Soldevilla MS, Hildebrand JA. 2023. 

Kinematics and energetics of foraging behavior in Rice’s whales of the Gulf of Mexico. Sci Rep. 

13(1):8996.

LaBrecque E, Curtice C, Harrison J, Van Parijs SM, Halpin PN. 2015. 3. Biologically important areas for 

cetaceans within U.S. waters-Gulf of Mexico region. Aquat Mamm. 41(1):30.

Laist DW, Knowlton AR, Mead JG, Collet AS, Podesta M. 2001. Collisions between ships and whales. 

Mar Mammal Sci. 17(1):35-75.

Litz J, Dias LA, Rappucci G, Martinez A, Soldevilla M, Garrison L, Mullin K, Barry K, Foster M. 2022. 

Cetacean and sea turtle spatial density model outputs from visual observations using line-transect 

survey methods aboard NOAA vessel and aircraft platforms in the Gulf of Mexico from 2003-06-12 

to 2019-07-31 (NCEI Accession 0256800). Dataset. NOAA National Centers for Environmental 

Information. https://catalog.data.gov/dataset/cetacean-and-sea-turtle-spatial-density-model-outputsfrom-visual-observations-using-line-trans.

Maze-Foley K, Mullin K. 2006. Cetaceans of the oceanic northern Gulf of Mexico: Distributions, group 

sizes and interspecific associations. J Cetacean Res Manage. 8(2):203-213.

Mullin K, Hoggard W. 2000. Visual surveys of cetaceans and sea turtles from aircraft and ships. In: Davis 

R, Evans W, Würsig B, editors (Texas A&M University at Galveston and the National Marine 

Fisheries Service). Cetaceans, sea turtles and seabirds in the Northern Gulf of Mexico: distribution, 

abundance and habitat associations [GulfCet]. New Orleans (LA): Department of the Interior, 

Minerals Management Service; US Geological Survey Biological Resources Division Obligation No.: 

1445-CTO9-96-0004 and 1445-1A09-96-0009. Report No.: USGS/BRD/CR-1999-0006; OCS Study 

MMS 2000-003. p. 346. 

Mullin KD, Fulling GL. 2004. Abundance of cetaceans in the oceanic northern Gulf of Mexico, 1996–

2001. Mar Mammal Sci. 20(4):787-807. https://doi.org/10.1111/j.1748-7692.2004.tb01193.x. 
56

Mullin KD (Southeast Fisheries Science Center, Pascagoula, MS). 2007. Abundance of cetaceans in the 

oceanic northern Gulf of Mexico from 2003 and 2004 ship surveys. Pascagoula (MS): National 

Marine Fisheries Service, Southeast Fisheries Science Center. 27 p. Southeast Fisheries Science 

Center reference document PRBD 2016-03. 

National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS). 

2020. Biological Opinion on the Federally Regulated Oil and Gas Program Activities in the Gulf of 

Mexico. OPR-2017-00002. https://doi.org/10.25923/hyeh-mb74v.

National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS). 

2021. Taking and importing marine mammals; taking marine mammals incidental to geophysical 

surveys related to oil and gas activities in the Gulf of Mexico. Fed Reg. (January 19) 86(11):4322–

5450. 50 CFR 217.

National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS). 

2023. Endangered Species Act Rice’s whale critical habitat report: proposed information basis and 

impact considerations of critical habitat designation. July 2023. 80 p. 

https://www.fisheries.noaa.gov/s3/2023-07/Critical-Habitat-Report-508-Final.pdf. 

Redfern JV, Hodge BC, Pendleton DE, Knowlton AR, Adams J, Patterson EM, Good CP, Roberts JJ. 

2024. Estimating reductions in the risk of vessels striking whales achieved by management strategies. 

Biol Conserv. 290:110427. 

Rice AN, Palmer K, Tielens JT, Muirhead CA, Clark CW. 2014. Potential Bryde's whale (Balaenoptera 

edeni) calls recorded in the northern Gulf of Mexico. J Acoust Soc Am. 135(5):3066-3076.

Rockwood RC, Adams JD, Hastings S, Morten J, Jahncke J. 2021. Modeling whale deaths from vessel 

strikes to reduce the risk of fatality to endangered whales. Front Mar Sci. 8:649890.

Rosel P, Corkeron P, Engleby L, Epperson D, Mullin K, Soldevilla M, Taylor B. 2016. Status review of 

Bryde's whales, (Balaenoptera edeni) in the Gulf of Mexico under the Endangered Species Act. 

US Department of Commerce. Report No.: NOAA Technical Memorandum NMFS‐SEFSC‐692.

Rosel PE, Garrison LP. 2019. Rice’s whale core distribution map Version: 7 June 2019 (NMFS Southeast 

Fisheries Science Center Reference Document MMTD-2022-01). U.S. Department of Commerce. 

https://www.fisheries.noaa.gov/resource/map/rices-whale-core-distribution-area-map-gis-data. 

Rosel PE, Wilcox LA, Yamada TK, Mullin KD. 2021. A new species of baleen whale (Balaenoptera) 

from the Gulf of Mexico, with a review of its geographic distribution. Mar Mammal Sci. 37(2):577-

610.

Rosel PE, Wilcox LA. 2014. Genetic evidence reveals a unique lineage of Bryde’s whales in the northern 

Gulf of Mexico. Endanger Species Res. 25(1):19-34.

Sasaki H, Murase H, Kiwada H, Matsuoka K, Mitani Y, Saitoh Si. 2013. Habitat differentiation between 

sei (Balaenoptera borealis) and Bryde’s whales (B. brydei) in the western North Pacific. Fish 

Oceanogr. 22(6):496-508.

Širović A, Bassett HR, Johnson SC, Wiggins SM, Hildebrand JA. 2014. Bryde's whale calls recorded in 

the Gulf of Mexico. Mar Mammal Sci. 30(1):399-409.
57

Soldevilla MS, Debich AJ, Garrison LP, Hildebrand JA, Wiggins SM. 2022b. Rice’s whales in the 

northwestern Gulf of Mexico: Call variation and occurrence beyond the known core habitat. 

Endanger Species Res. 48:155-174.

Soldevilla MS, Debich AJ, Pérez‐Carballo I, Jarriel S, Frasier KE, Garrison LP, Gracia A, Hildebrand JA, 

Rosel PE, Serrano A. 2024. Rice's whale occurrence in the western Gulf of Mexico from passive 

acoustic recordings. Mar Mammal Sci.1–8.

Soldevilla MS, Hildebrand JA, Frasier KE, Dias LA, Martinez A, Mullin KD, Rosel PE, Garrison LP. 

2017. Spatial distribution and dive behavior of Gulf of Mexico Bryde’s whales: potential risk of 

vessel strikes and fisheries interactions. Endanger Species Res. 32:533-550.

Soldevilla MS, Ternus K, Cook A, Hildebrand JA, Frasier KE, Martinez A, Garrison LP. 2022a. Acoustic 

localization, validation, and characterization of Rice's whale calls. J Acoust Soc Am. 151(6):4264-

4278.

Suarez-Bregua P, Alvarez-Gonzalez M, Parsons KM, Rotllant J, Pierce GJ, Saavedra C. 2022. 

Environmental DNA (eDNA) for monitoring marine mammals: challenges and opportunities. Front 

Mar Sci. 9:987774.

van der Hoop JM, Vanderlaan AS, Cole TV, Henry AG, Hall L, Mase‐Guthrie B, Wimmer T, Moore MJ. 

2015. Vessel strikes to large whales before and after the 2008 ship strike rule. Conserv Lett. 8(1):24-

32.

Van Waerebeek K, Baker AN, Félix F, Gedamke J, Iñiguez M, Sanino GP, Secchi E, Sutaria D, van 

Helden A, Wang Y. 2007. Vessel collisions with small cetaceans worldwide and with large whales in 

the Southern Hemisphere, an initial assessment. Latin Am J Aquatic Mamm. 6(1):43-69.

Vanderlaan AS, Taggart CT. 2007. Vessel collisions with whales: the probability of lethal injury based on 

vessel speed. Mar Mammal Sci. 23(1):144-156.
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