NOAA Professional Paper NMFS 17

Potential overlap between cetaceans 

and commercial groundfi sh fl eets that

operate in the California Current 

Large Marine Ecosystem Blake E. Feist

Marlene A. Bellman

Elizabeth A. Becker

Karin A. Forney

Michael J. Ford

Phillip S. Levin

March 2015
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NOAA Professional Paper NMFS 17

Potential overlap between cetaceans 

and commercial groundfi sh fl eets that 

operate in the California Current 

Large Marine Ecosystem

Blake E. Feist

Marlene A. Bellman

Elizabeth A. Becker

Karin A. Forney

Michael J. Ford

Phillip S. Levin

March 2015

U.S. Department of Commerce

Seattle, Washington
Suggested reference

Feist, Blake E., Marlene A. Bellman, Elizabeth A. Becker, Karin A. Forney, Michael 

J. Ford, and Phillip S. Levin. 2015. Potential overlap between cetaceans and commercial groundfi sh fl eets that operate in the California Current Large Marine Ecosystem. NOAA Professional Paper NMFS 17, 27 p. doi:10.7755/PP.17

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CONTENTS

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Literature cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

iii

1

Potential overlap between cetaceans and 

commercial groundfish fleets that operate in 

the California Current Large Marine Ecosystem 

Blake E. Feist (contact author)1

Marlene A. Bellman2

Elizabeth A. Becker3

Karin A. Forney3

Michael J. Ford1

Phillip S. Levin1

Email address for the contact author: blake.feist@noaa.gov 

1 Conservation Biology Division

Northwest Fisheries Science Center

National Marine Fisheries Service, NOAA

2725 Montlake Blvd E.

Seattle, Washington 98112

2 Fishery Resource Analysis and Monitoring Division

Northwest Fisheries Science Center

National Marine Fisheries Service, NOAA

2725 Montlake Blvd E.

Seattle, Washington 98112

3 Protected Resources Division

Southwest Fisheries Science Center

National Marine Fisheries Service, NOAA

110 Shaffer Road

Santa Cruz, California 95060

Abstract—Cetacean populations 

are confronted by many anthropogenic threats, including commercial 

whaling, noise, vessel collisions, gear 

entanglement, exploitative competition, habitat disturbance, and global 

climate change. Evidence indicates 

that commercial fishing activities can 

have both direct (e.g., gear entanglement and bycatch) and indirect (e.g., 

prey reduction and noise) effects on 

cetaceans. However, few studies have 

addressed the potential vulnerability 

of a given cetacean species to an entire fishing fleet that operates over 

a large marine ecosystem. In this 

study, we overlaid spatially explicit 

multiyear predicted mean densities 

of 11 cetacean species and 1 species 

guild within the California Current 

Large Marine Ecosystem with data 

for commercial fishing effort of the 

fixed-gear, at-sea hake mid-water 

trawl, and bottom trawl fleets of 

the west coast groundfish fishery. 

We quantified the exposure of each 

species to each fleet type by multiplying the predicted mean cetacean 

density by the measured fishing fleet 

effort. We found large interspecific 

and interfleet variability in the overlap between cetaceans and fishing 

fleets. Although many of the species 

had relatively low overlap rates, others had substantial exposure to some 

of the fishing fleets, particularly 

those species with more nearshore 

distributions. Direct mortality from 

these fleets has been documented 

to be low, but our results indicate 

that there is opportunity for fisheries interactions with some cetacean 

species, particularly in the fixed-gear 

fleet. Our analyses make up an important first step in generating formal risk assessments for quantification of the impacts of various fishing fleets on populations of cetacean 

species that occur in the California 

Current.

Introduction

Risk is a function of the likelihood 

that a subject will experience adverse 

consequences of exposure to a single 

or many threats (Burgman, 2005; 

Suter, 2007). In the context of endangered species management, risk assessment evaluates the degree to which 

human activities or natural processes 

interfere with the achievement of 

management objectives related to recovery of imperiled species (Levin et 

al., 2009). Therefore, a risk assessment for endangered species fundamentally requires an understanding of 

the distribution and intensity of potentially harmful activities.

Cetaceans confront a number of 

anthropogenic pressures. Commercial 

whaling was once the primary risk to 

the viability of many whale populations, but many populations have rebounded since the International Whaling Commission’s ban on commercial 

whaling began in 1986 (Clapham et 

al., 1999; Reeves et al., 2003). During 

the past few decades, the expansion of 

global fisheries has increased concern 

about the direct and indirect risks to 

cetaceans from fishing activities. Commercial fishing can affect cetaceans directly (e.g., contact with fishing gear) 

(Beverton, 1985; Read et al., 2006) or 

indirectly (e.g., exploitative competition) (Bearzi et al., 1999; DeMaster et 

al., 2001; DeMaster et al., 2006). For 

example, incidental catch of vaquitas 

(Phocoena sinus) in gillnet fisheries 

that operate in the northern Gulf of 

California has resulted in significant 

mortality and currently threatens this 

species with extinction (Gerrodette 

and Rojas-Bracho, 2011). Larger cetacean species occupy broader geographic extents and can encounter 

entanglement risks throughout their 

range. The majority of North Atlantic 
2 Professional Paper NMFS 17 

right whales (Eubalaena glacialis) and humpback whales 

(Megaptera novaeangliae) (Johnson et al., 2005), for 

example, have scarring associated with entanglement 

with fishing gear in a variety of areas along the Eastern Seaboard. Although evidence of commercial fishing 

fleets competing with cetaceans for prey resources exists 

(Trites et al., 1997; Herr et al., 2009; Gomez-Campos et 

al., 2011), population-level consequences of these interactions have proven difficult to demonstrate (Matthiopoulos et al., 2008).

The California Current Large Marine Ecosystem 

(CCLME) (Fig. 1) is actively fished by a variety of commercial fleets, and those fleets that are known or suspected of causing harm to cetaceans are monitored with 

observer programs. Other fleets that are considered less 

of a threat to cetaceans, such as the ones that use midwater and bottom trawl gear (Jannot et al.1), still have 

nearly 100% observer coverage to monitor catches of 

fish species, therefore reducing the likelihood that these 

gear types pose undetected threats to cetaceans. However, even with 100% observer coverage, indirect threats, 

such as exploitative competition, may exist. Certain 

fleets, such as those fleets that use fixed gear, have low 

levels of observer coverage yet employ gear types that 

pose risks to cetaceans (e.g., pots or traps with long lines 

and floats) and often are left unattended during fishing 

activities. Consequently, quantification of the co-occurrence of the fixed-gear fleet and cetacean populations is 

needed to better understand the potential risk imposed 

by these fisheries. 

In some studies, the intensity of commercial fishing 

has been inferred from landings data recorded at ports 

along a given coast. These data then are used to infer 

the spatial pattern of commercial fishing vessels (Kaschner, 2004; Kaschner et al.2; Kaschner and Pauly3). Such 

approaches are not ideal because the specific locations 

of fishing activities are unknown. Studies that use direct 

observations of fishing effort provide a basis for generating reliable estimates of the exposure of cetaceans to 

fishing activities (Carretta et al., 2004; Herr et al., 2009; 

Vanderlaan et al., 2011).

The CCLME is home to several cetacean species that 

are listed as endangered under the U.S. Endangered Spe1 Jannot, J., E. Heery, M. A. Bellman, and J. Majewski. 2011. Estimated bycatch of marine mammals, seabirds, and sea turtles 

in the US west coast commercial groundfish fishery, 2002–2009, 

104 p. [Available from West Coast Groundfish Observer Program, Northwest Fisheries Science Center, National Marine Fisheries Service, 2725 Montlake Blvd. E., Seattle, WA 98112.] 2 Kaschner, K., R. Watson, V. Christensen, A. W. Trites, and D. 

Pauly. 2001. Modeling and mapping trophic overlap between marine mammals and commercial fisheries in the North Atlantic. In 

Fisheries impacts on North Atlantic ecosystems: catch, effort and 

national/regional data sets. (D. Zeller, R. Watson, and D. Pauly, 

eds.), p. 35–45. [Available from Fisheries Centre, Univ. British 

Columbia, 2204 Main Mall, Vancouver, BC, Canada V6T 1Z4.]

3 Kaschner, K., and D. Pauly. 2004. Competition between marine 

mammals and fisheries: food for thought, 28 p. [Available from 

Humane Society, 2100 L St. NW, Washington, DC 20037.]

cies Act (NMFS4). The CCLME also is considered a 

hotspot of extinction risk (Davidson et al., 2012), because of the high diversity of species with life history 

traits that make them vulnerable. Therefore, a rigorous 

determination of the magnitude and frequency of exposure of cetaceans to fisheries is necessary, especially for 

fisheries with low levels of observer coverage. In this paper, we address: 1) the general patterns of overlap of 11 

cetacean species and 1 species guild with 3 of the major 

fishing fleets operating in the CCLME, and 2) interspecific and interfleet differences in the overlap patterns. We 

also present quantification of the potential for overlap 

between each cetacean species and fishing fleet. This spatially explicit assessment provides a measure of the potential risk from 3 specific fishing fleets that operate in 

the CCLME; however, actual bycatch rates will depend 

on the gear types used and cetacean species involved.

Materials and methods

To quantify the co-occurrence of cetacean populations 

and specific fishing activities, we overlaid 2 types of geospatial data layers: modeled cetacean density and observed commercial fishing effort. We compared general 

patterns of effort by 3 commercial fleets by gear type 

(bottom trawl, mid-water trawl for Pacific hake [Merluccius productus], and fixed gear) with general density patterns of 11 cetacean species and 1 species guild that occur in the CCLME (Fig. 1). The CCLME is one of many 

large marine ecosystems throughout the world that are 

adjacent to continents in coastal waters and generally 

exhibit higher primary productivity compared with open 

ocean areas (for more about large marine ecosystems, 

see the website Large Marine Ecosystems of the World, 

http://lme.edc.uri.edu/, accessed December 2014).

Cetacean data

We used estimates of cetacean density from habitat models that were generated by the NOAA Southwest Fisheries Science Center for a study area of approximately 

1,141,800 km2 off the U.S. west coast (Barlow et al., 

2009; Forney et al., 2012). They used data from 5 systematic ship-based cetacean and ecosystem assessment 

surveys conducted during the period from June through 

November in 1991, 1993, 1996, 2001, and 2005 to build 

habitat-based density models for 11 species and 1 species 

guild. Models were built for striped dolphin (Stenella 

coeruleoalba), short-beaked common dolphin (Delphinus delphis), Risso’s dolphin (Grampus griseus), Pacific white-sided dolphin (Lagenorhynchus obliquidens), 

4 NMFS (National Marine Fisheries Service). Endangered and 

threatened marine mammals. Office of Protected Resources, National Marine Fisheries Service, 1315 East-West Highway, Silver 

Spring, MD 20910. [Available from http://www.nmfs.noaa.gov/

pr/species/esa/listed.htm#mammals, accessed 9 April 2013.]
3

Figure 1

Maps of the boundary of the California Current Large Marine Ecosystem, which was the focus for this study of the 

overlap of cetaceans and fishing effort of commercial groundfish fleets, and its general location relative to the west 

coast of North America. The zoomed in map at right depicts the region for which density patterns of 11 species and 1 

guild of cetaceans for the period between 1991 and 2005 were available from models generated by the NOAA Southwest Fisheries Science Center (Barlow et al., 2009; Forney et al., 2012). The yellow line indicates the 1600-m isobath, 

within which most groundfish fishery activity occurs.
4 Professional Paper NMFS 17 

northern right whale dolphin (Lissodelphis borealis), 

Dall’s porpoise (Phocoenoides dalli), sperm whale (Physeter macrocephalus), fin whale (Balaenoptera physalus), 

blue whale (B. musculus), humpback whale (Megaptera

novaeangliae), Baird’s beaked whale (Berardius bairdii), 

and a guild of small beaked whales (including Cuvier’s 

beaked whale [Ziphius cavirostris] and beaked whales of 

the genus Mesoplodon). Four of these species are listed 

as endangered under the U.S. Endangered Species Act 

(Table 1). 

Generalized additive models (GAMs) were used by 

the Southwest Fisheries Science Center to predict cetacean densities from habitat variables that included 

remotely sensed measures of sea-surface temperature 

(SST) and the coefficient of variation of SST (to serve 

as a proxy for frontal regions); in situ measures of seasurface salinity, water depth, mixed-layer depth (the 

depth at which temperature is 0.5°C less than surface 

temperature), and sea-surface concentration of chlorophyll-a; and geographic information system (GIS)-derived values of bathymetric slope and distance to the 

nearest 2000-m isobath (Barlow et al., 2009; Forney et 

al., 2012). Model validation was performed on a novel 

data set (2005), and selected models were then re-fit to 

the complete set of data (1991–2005). Predicted densities for each of the 5 individual years (1991, 1993, 

1996, 2001, and 2005) were smoothed and then averaged to produce a composite grid that represents the 

best estimate of average cetacean density and distribution for the months from June through November. The 

grids were created at a resolution of approximately 25 

km and covered most of the CCLME off the coasts of 

Washington, Oregon, and California. Further details of 

these models can be found in Barlow et al. (2009) and 

Forney et al. (2012). For the analyses in our study, we 

used predicted multiyear mean density (number of animals per square kilometer), which is the predicted density of each species or species guild in any given year 

between 1991 and 2005. 

Commercial fishing effort

Fishing effort was represented on grids of 10 km for the 

bottom trawl fleet and Pacific hake mid-water trawl fleet 

and 20 km for the fixed-gear fleet. We used data that 

were provided by the At-sea Hake Observer Program (ASHOP) and the West Coast Groundfish Observer Program (WCGOP) of the Fishery Resource Analysis and 

Monitoring Division, NOAA Northwest Fisheries Science Center (NWFSC). Results from analyses were adjusted for the differing grid-cell sizes of these data layers, 

which are described later in the “Materials and methods” 

section.

Data of commercial fishing effort are subject to restrictions that preserve confidentiality as required under 

the Magnuson-Stevens Fishery Conservation and Management Reauthorization Act of 2006. Because of these 

restrictions, data cannot be presented to the general 

public unless they represent information from 3 or more 

vessels. To comply with these confidentiality restrictions, 

grid cells in the final mapped overlap indices that contained data from 2 or fewer vessels are not displayed 

herein. However, we ran all of our analyses with the full 

set of grid cells, which included those grid cells representing 2 or fewer vessels.

Fishing effort of the fleet that uses mid-water trawls 

to fish for Pacific hake (hereafter called “hake fleet”) 

Table 1

The 11 species and 1 guild of cetaceans represented in the geospatial data layer of predicted cetacean densities from habitat 

models that were generated by the NOAA Southwest Fisheries Science Center for a study area off the U.S. west coast (Barlow 

et al., 2009; Forney et al., 2012).

Endangered Species

Cetacean Suborder Family Act status

Baird’s beaked whale (Berardius bairdii) Odontoceti (toothed) Ziphiidae (beaked whales) –

Blue whale (Balaenoptera musculus) Mysticeti (baleen) Balaenopteridae Endangered

Fin whale (B. physalus) Mysticeti (baleen) Balaenopteridae Endangered

Short-beaked common dolphin (Delphinus delphis) Odontoceti (toothed) Delphinidae (dolphins) –

Risso’s dolphin (Grampus griseus) Odontoceti (toothed) Delphinidae (dolphins) –

Northern right whale dolphin (Lissodelphis borealis) Odontoceti (toothed) Delphinidae (dolphins) –

Pacific white-sided dolphin (Lagenorhynchus obliquidens) Odontoceti (toothed) Delphinidae (dolphins) –

Humpback whale (Megaptera novaeangliae) Mysticeti (baleen) Balaenopteridae Endangered

Dall’s porpoise (Phocoenoides dalli) Odontoceti (toothed) Phocoenidae (porpoises) –

Sperm whale (Physeter macrocephalus) Odontoceti (toothed) Physeteridae (sperm whales) Endangered

Striped dolphin (Stenella coeruleoalba) Odontoceti (toothed) Delphinidae (dolphins) –

Small beaked whales (Ziphius and Mesoplodon) Odontoceti (toothed) Ziphiidae (beaked whales) –
5

was collected directly by the A-SHOP (NWFSC5). The ASHOP collects information on total catch (fish discarded 

and retained) from all vessels that process Pacific hake at 

sea. All data were collected according to standard protocols and data quality control established by the A-SHOP 

(NWFSC5). 

Fishing effort of the bottom trawl fleet (hereafter 

called “trawl fleet”) (NWFSC6) was derived from fleetwide logbook data submitted by state agencies to the Pacific Fisheries Information Network (PacFIN) regional 

database, maintained by the Pacific States Marine Fisheries Commission (PacFIN Coastwide Trawl Logbook Subsystem, http://pacfin.psmfc.org/pacfin_pub/data.php, accessed January 2011). A common-format logbook is used 

by Washington, Oregon, and California. Trawl logbook 

data are regularly used in analyses of the bottom trawl 

groundfish fishery observed by the WCGOP (Bellman et 

al.7).

For spatial data for both the trawl and hake fleets, a 

trawl towline model (straight line drawn from the start 

to end location of a trawl tow) was used to allocate data 

to grid cells that were 10 × 10 km. We did not use the 

trawl towline transect data directly because those data 

depict the exact locations of vessels and those locations 

are considered confidential. 

5 NWFSC (Northwest Fisheries Science Center). 2011. At-sea Hake 

Observer Program, observer sampling manual, 47 p. [Available from Fishery Resource Analysis and Monitoring Division, 

NWFSC, 2725 Montlake Blvd. E., Seattle, WA 98112.]

6 NWFSC (Northwest Fisheries Science Center). 2010. West coast 

groundfish observer training manual, 665 p. [Available from West 

Coast Groundfish Observer Program, NWFSC, 2725 Montlake 

Blvd. E., Seattle, WA 98112.]

7 Bellman, M. A., A. W. Al-Humaidhi, J. Jannot, and J. Majewski. 2011. Estimated discard and catch of groundfish species in 

the 2010 U.S. west coast fisheries. [Available from West Coast 

Groundfish Observer Program, Northwest Fisheries Science Center, National Marine Fisheries Service, 2725 Montlake Blvd. E., 

Seattle, WA 98112.]

Fixed gear is generally defined as fishing gear that 

is anchored to the bottom of the ocean, as opposed to 

gear that is towed through the water. The fishing effort 

of the fixed-gear fleet (hereafter called “fixed fleet”) was 

collected directly by the WCGOP from the following 

commercial groundfish fixed-gear sectors: limited entry 

sablefish primary (target: sablefish), limited entry nonsablefish endorsed (target: sablefish and groundfish), 

open access fixed gear (target: groundfish), and Oregon 

and California state-permitted nearshore fixed gear (target: nearshore groundfish). We used the following gear 

types recorded by the WCGOP: historic longline, vertical 

hook and line, other hook and line, fish pot, fixed hook 

longline, and snap gear longline. We excluded troll and 

rod-and-reel gear from our analyses because those gear 

types represent a small fraction of the fishing effort and 

no cases of injury or mortality associated with these gear 

types have been documented for cetacean species off the 

U.S. west coast at the time that analyses were conducted 

(NMFS8). Gill nets were not included in our analysis because the groundfish fleets monitored by the WCGOP do 

not use them.

The observed portion of overall fishing effort in each 

sector of the fixed fleet varied by coverage level (Table 

2). Coverage rates were calculated for each sector as the 

observed retained weight of target species divided by the 

sector-wide landed weight of target species for each sector. Because not all fishing operations were observed, neither the maps nor the data can be used to characterize 

the fishery completely. We did not correct our overlap indices to account for this underrepresentation because we 

did not have information about the spatial consistency 

of this deficiency. Both the observed fixed-gear set (start 

location of fishing) and haul (location of gear retrieval) 

8 NMFS. 2011. 2012 List of Fisheries (LOF). NMFS, NOAA, Silver Spring, MD. [Available from: http://www.nmfs.noaa.gov/pr/

interactions/lof/final2012.htm, accessed 17 September 2012.]

Table 2

Fixed-gear fishing effort, as represented in data from the West Coast Groundfish Observer Program, by sector 

observed for the period 2002–2009: the proportion of total observed effort (cumulative number of hours 

during which a gear was deployed), the observed sector coverage rate calculated as the observed retained 

catch weight of target species divided by the fleet-wide landed weight of target species, and the assumed 

proportion of total fleet-wide effort.

Proportion of

Sector Total duration (%) Coverage rate duration represented

Limited-entry sablefish primary 59.38% 26.12% 15.51%

Limited-entry non-tier-endorsed fixed gear 17.00% 7.41% 1.26%

Open access fixed gear 18.63% 3.00% 0.56%

Oregon nearshore fixed gear 3.83% 5.20% 0.20%

California nearshore fixed gear 1.16% 3.43% 0.04%

 Total proportion of duration represented=17.57%
6 Professional Paper NMFS 17 

were assigned to grid cells that were 20 × 20 km. In cases 

where a set and haul line crossed a given 20-km grid 

cell, fishing effort was divided proportionately using the 

length of the line that intersected any given grid cell as 

a weighting factor. Using a weighting factor accurately 

proportioned the duration of the contributions of a given 

set and haul line to multiple grid cells that had been intersected. As was the case with the data for the trawl and 

hake fleets, we did not use transect data for the set and 

haul locations directly because exact locations of vessels 

are confidential.

Fishing effort was expressed as the cumulative number of hours a given fishing fleet (trawl, hake, or fixed) 

had gear deployed in the water. All of the fishing effort 

data were reported as monthly sums for each type of 

fishing gear; therefore, for each year from 2002 through 

2009, we calculated cumulative fishing effort (in hours) 

from June through November, the period that corresponded to the months over which the data were collected each year for building the habitat-based cetacean 

density models. 

For the hake and trawl fleets, the data represent total fishing effort (100%). The National Marine Fisheries Service requires all hake vessels (catcher-processors 

and mother ships) longer than 38 m (125 ft) to carry 

2 observers, and vessels under 38 m carry 1 observer 

(Bellman et al.7). PacFIN fleet-wide logbook data are assumed to represent the entire trawl fleet for our analysis. 

However, all fishing operations may not be recorded in 

logbooks, and logbook submission may not be complete. 

Because observer data did not capture 100% of the fishing effort for the fixed fleet, we calculated the proportion 

(C) of the fleet that was represented by the observer data:

C = ts

T

× ws(obs)

Ws(land)

⎛

⎝

⎜

⎜

⎜

⎜

⎞

⎠

⎟

⎟

⎟

⎟⎟ s=1

5

∑ , (1)

where s corresponds to each of the 5 sectors, t is the 

total time (in hours, 2002–2009) a given sector was 

observed with gear in the water, T is the total time (in 

hours, 2002–2009) during which all 5 of the sectors 

were observed with gear in the water, w is the total retained weight of target fish species caught on vessels with 

observers present (reported by sector, 2002–2009), and 

W is the total landed weight of target fish species by all 

vessels (reported by sector, from 2002–2009).

Cetacean and fishery overlap

We created maps of the index of overlap (for the period 

2002–2009) for each of the 11 cetacean species and the 

1 species guild, and we calculated the population overlap 

for each species with each of the 3 fleets as well as a cumulative overlap index.

We assumed that the various fishing fleets and cetacean species were randomly distributed in any given grid 

cell; therefore, we did not account for cetaceans avoiding 

commercial fishing activities (because of noise, general 

disturbance, or other disruption) or being attracted to 

these fishing activities (depredation by cetaceans in longline and other fisheries). The former would reduce the 

apparent influence of commercial fishing activity, whereas the latter would increase the potential effect.

We generated a simple index of predicted overlap (Rs) 

with this equation:

Rs = t × ρ, (2)

where t is fishing effort (total time, in hours, that a gear 

was in the water), and ρ is the predicted density of cetaceans (number of animals per square kilometer).

Maps We calculated the overlap indices for each of 

the combinations of species and fleet types (12×3=36) 

throughout the study area. Because the grid-cell size 

of the cetacean data (~25 km) was not the same as for 

the fishing effort data (10 or 20 km), we calculated an 

area-weighted mean cumulative fishing effort for each 

year that corresponded to each respective cetacean grid 

cell. Although all of the geospatial data layers were on 

regular grids, they were vector based, allowing us to 

explicitly account for varying sizes of grid cells. First, 

we combined each cetacean grid with the 3 fishing fleet 

grids through the use of the INTERSECT command in 

ArcGIS (vers. 9.3, Esri, Redlands, CA). Then, we used the 

information from this intersection to calculate an areaweighted mean (AWM) fishing effort for each cetacean 

grid cell with the following equation:

tawm = tn 1

n

∑ (an)

⎡

⎣

⎢

⎢

⎤

⎦

⎥

⎥

/A, (3)

where t is the fishing effort in hours for a given portion 

of a given cetacean grid cell, a is the corresponding area 

for that effort, and A is the total area of the corresponding cetacean grid cell. This equation effectively weighted 

fishing effort for fragments of grid cells that fell within 

any given 25-km cetacean grid cell by multiplying the 

area of any given fragment by the corresponding value of 

the fishing effort in that grid cell.

Finally, we multiplied the AWM fishing effort (t) for 

each grid cell by the corresponding cetacean density (ρ) 

to yield the final map of predicted overlap index. We 

used ArcGIS to join the corresponding predicted overlap 

index for each combination of species and gear type to 

the original cetacean density grid to create 36 gridded 

maps, which we used to explore spatiotemporal patterns 

in the overlap of cetaceans and fishing fleets.

Population overlap index Given that species densities can vary by more than 3 orders of magnitude, standardizing mapped overlap indices by total abundance 

is important for evaluation of proportional overlap. To 

compare interspecific and fishery overlap relative to all 

of the modeled individuals in a given species, we used 
7

the following equation to calculate what fraction of the 

modeled population for each cetacean species overlapped 

with areas where commercial fishing occurred within the 

CCLME study area:

Rp = pn(an)

1

n

∑ / Pn(an)

1

n

∑ , (4)

where ρ is the modeled cetacean density for a given grid 

cell that experienced commercial fishing by a given fleet, 

a is the area of the corresponding grid cell, and Ρ is the 

modeled cetacean density for a given grid cell, regardless 

of whether or not that grid cell experienced commercial 

fishing by any of the fleets.

Cumulative overlap index We calculated with the following equation a cumulative overlap index over the 

area that each fleet operated for each combination of cetacean species and fishing fleet for all years from 2002 

through 2009:

Rc = R(an)

1

n

∑ /A, (5)

where R is the predicted overlap index for a given 25-km 

grid cell, an is the area of the corresponding grid cell, and 

A is the total area over which a given fleet operated. This 

approach allowed us to compare patterns of interspecific 

and fishery overlap that were standardized by the total 

area where the fleet operated.

Population-weighted cumulative overlap index We 

calculated a population-weighted cumulative overlap 

index by taking the product of population overlap and 

cumulative overlap indices over the entire study area for 

each combination of cetacean species and fishing fleets 

for all years from 2002 through 2009. This weighting 

adjusted the scores in the cumulative overlap index to 

better reflect the overall influence of each fleet on the 

modeled cetacean populations, as a function of fishing 

effort, cetacean density, and spatial distribution of cetacean populations.

Results

Commercial fishing effort

Overall, spatial and temporal patterns of fishing effort 

varied widely over the study area. The cumulative levels 

of effort during the months from June through November in 2002–2009 for the fixed, hake, and trawl fleets 

were 187,015; 24,132; and 287,886 hours, respectively.

For the fixed fleet, the fishing effort captured by observers varied across sectors (Table 2). In general, observers captured approximately 17.57% of the total fixedgear effort (as a function of the cumulative hours during which a gear was deployed) that occurred over the 

entire study area, a finding based on the proportion of 

effort in 2002–2009 from each observed sector and the 

WCGOP coverage rate of fishery landings by sector for 

all years combined. Therefore, our indices for overlap of 

fixed-gear fishing with the various cetacean species likely 

underestimate the actual magnitude of this fleet’s overlap 

by a factor of 5. However, this underrepresentation did 

not alter the proportion of each population that overlapped with the observed fixed fleet. 

Interannual patterns Cumulative annual effort varied 

over time for each of the fleets (Fig. 2). Observed cumulative effort of the fixed fleet had peaks in 2003 and 

2005, with a downward trend from 2005 to 2009 (Fig. 

2). The hake fleet gradually increased in cumulative effort level until 2008 and decreased in 2009 (Fig. 2). The 

trawl fleet had a drop in cumulative annual fishing effort 

in 2004 but returned to 2002 levels of effort by 2009 

(Fig. 2).

Monthly interannual and intraannual patterns There 

was considerable interannual, intra-annual, and interfishery variability in the cumulative effort, an observation based on the monthly data (Fig. 3). The fixed fleet 

had the greatest interannual and intraannual variability 

in observed effort. This fleet generally had peak efforts 

during the summer months (Fig. 3A). However, there 

was usually a second peak of effort in the fall (Fig. 

3A). Effort was lowest during the months of January, 

Figure 2

Interannual trends in fishing effort, expressed as the 

cumulative number of hours per year during which 

fishing gear was deployed in the water, for the period 

June through November in 2002–2009 for each of the 

3 fleets in the commercial groundfish fishery that operate off the U.S. west coast (the fixed-gear, at-sea hake 

mid-water trawl, and bottom trawl fleets).
8 Professional Paper NMFS 17 

February, November, and December (Fig. 3A). The hake 

fleet had the least interannual but the greatest intraannual variability in effort. The hake fleet does not fish 

from January to April each year, but it clearly had its 

maximum effort in May and June, with a smaller peak 

often occurring in the late fall (Fig. 3B). The trawl fleet 

had higher interannual but moderate intraannual variability in effort. The trawl fleet generally has considerable and consistent effort year round, but it tends to 

taper off towards the end of the year (Fig. 3C). In 2002, 

however, there was a strong peak of effort from October 

through November by this fleet.

Spatial and temporal patterns There was substantial 

inter-fishery variability in the spatial extent of cumulative fishing effort (Fig. 4). For the period 2002–2009, 

various levels of observed effort of the fixed fleet occurred from the U.S.–Mexico border north to the U.S.–

Canada border (Fig. 4). There were concentrations of effort off the California coastal areas of Los Angeles, San 

Diego, Caspar, and Eureka and off the northern half of 

the Oregon coast (Fig. 4). The patchy distribution of the 

observed effort of the fixed fleet is assumed to be representative of overall fishing patterns, but there is a lack 

of logbook or other data sources to corroborate fleetwide spatial patterns of distribution. Fishing efforts of 

the hake fleet occurred over a much smaller region, spanning the coasts of Oregon and Washington (Fig. 4). The 

hake fleet effort was not as patchy compared with the 

observed effort of the fixed fleet, but there were areas 

of increased effort (Fig. 4). Trawl fleet efforts, occurring 

consistently from Point Conception, California, north to 

the U.S.–Canada border, were not quite as widespread as 

the observed efforts of the fixed fleet (Fig. 4). Effort of 

the trawl fleet was not as patchy compared with that of 

the observed effort of the fixed fleet, but there were areas 

where effort was higher (Fig. 4).

Interannual spatial variability was greatest and most 

patchy for the observed fixed fleet (figures are unavailable because of confidentiality restrictions). In some 

years (e.g., 2002), large expanses of hundreds of kilometers or more had no effort whatsoever. The effort of 

the hake fleet also became more patchy when examined 

on an annual basis, but there were few large areas that 

were unexploited in a given year. Of the 3 gear types, the 

trawl fleet had the most consistent effort over space and 

time. However, there was still considerable interannual 

variability among various 10-km grid cells in effort for 

this fleet.

Mapped overlap index: cetaceans and fishing 

efforts 

Generally, there was low overlap spatially between the 

11 cetacean species and 1 species guild and the 3 commercial fishing fleets (Figs. 5–16). Where there was overlap between the various cetacean species and the 3 commercial fishing fleets, there was considerable variation 

in the mapped overlap indices. Highly abundant species 

(short-beaked common dolphin and Dall’s porpoise) and 

species with the greatest densities in shelf and slope waters, such as the Pacific white-sided dolphin, Risso’s dolphin, and northern right whale dolphin, had the greatest predicted overlap with fisheries. Species with more 

offshore distributions, such as the sperm whale, beaked 

whales, and the striped dolphin, had very little overlap 

with these 3 fishing fleets. In the rest of this section, we 

describe species-specific spatial patterns of the predicted 

overlap index, in order of decreasing population-weighted overlap indices.

Overlap of the observed fixed fleet and the Dall’s porpoise was concentrated from Astoria Canyon, Oregon, 

south to around Stonewall Bank, Oregon (Rs>630; Fig. 

5). Maximum overlap of this species with the hake fleet 

was near Cape Flattery, Washington, and in the region 

off Oregon from Astoria Canyon south to around Heceta 

Valley (Rs>40; Fig. 5). The trawl fleet overlapped fairly 

Figure 3

Monthly trends in commercial groundfish fishing effort, expressed as the cumulative number of hours per 

month during which fishing gear was deployed in the 

water, in 2002–2009 off the U.S. west coast for each 

of the 3 fleets: (A) observed fixed gear, (B) hake midwater trawl, and (C) bottom trawl. Error bars represent 

+1 standard error.
9

Figure 4

Patterns of commercial groundfish fishing effort along the U.S. west coast, expressed as the cumulative number 

of hours per grid cell during which fishing gear was deployed in the water, for all months in 2002–2009 for each 

of the following 3 fleets: (A) fixed-gear fleet, on a grid of 20 × 20 km, ~17% observer coverage; (B) hake midwater trawl fleet, on a grid of 10 × 10 km, 100% observer coverage; and (C) bottom trawl fleet, on a grid of 10 

× 10 km, 100% observer coverage.
10 Professional Paper NMFS 17 

Figure 5

Maps of (A) modeled mean density of Dall’s porpoise (Phocoenoides dalli), expressed as the number of individuals per 

square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 1991–2005), and 

of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, and (D) bottom 

trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape Flattery, 2) Astoria 

Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape Mendocino, 8) Vizcaino 

Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. Note that the density scales 

differ in Figures 5–16.
11

Figure 6

Maps of (A) modeled mean density of short-beaked common dolphins (Delphinus delphis), expressed as the number of 

individuals per square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 

1991–2005), and of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, 

and (D) bottom trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape 

Flattery, 2) Astoria Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape 

Mendocino, 8) Vizcaino Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. 

Note that the density scales in Figures 5–16 differ.
12 Professional Paper NMFS 17 

consistently from Cape Flattery all the way south to 

Cape Mendocino, California (Rs>124; Fig. 5).

The short-beaked common dolphin overlapped the 

most with the fixed fleet from south of the Channel Islands, California, down to the U.S.–Mexico border 

(Rs>1076; Fig. 6). Overlap of this species with the hake 

fleet was greatest just southwest of Cape Flattery, Washington, near Astoria Canyon, Oregon, and east of the 

Astoria Sea Channel off Oregon (Rs>17; Fig. 6). Overlap 

indices with the trawl fleet were more evenly distributed 

over the entire fishing area than were the overlap indices for this dolphin with the fixed and hake fleets, with 

maximum overlap occurring just southwest of Cape Flattery, just north of Cape Mendocino, California, and off 

the coast of San Francisco (Rs>83; Fig. 6). Density predictions for this species ranged from very low to zero off 

the coasts of Oregon and Washington (Fig. 6A); therefore, overlap indices for this species with the hake and 

trawl fleets are very low overall.

Overlap of the Pacific white-sided dolphin with the 

observed fixed fleet occurred near Astoria Canyon and 

Stonewall Bank off Oregon and near Trinidad Canyon, 

California (Rs>289; Fig. 7). Overlap with the hake and 

trawl fleets was most pronounced for this species near 

Cape Flattery, Washington (Rs>28 and Rs>128, respectively; Fig. 7).

The overlap of the observed fixed fleet with Risso’s 

dolphin was greatest near Astoria Canyon and Stonewall 

Bank off Oregon, just north of Cape Mendocino, California, and from the Northeast Bank, California, south 

to the U.S.–Mexico border (Rs>129; Fig. 8). Overlap 

with the hake fleet was greatest just southwest of Cape 

Flattery, Washington, and over the stretch from the Astoria Canyon south to Stonewall Bank (Rs>7; Fig. 8). 

Maximal overlap with the trawl fleet occurred over fairly 

large areas near Cape Flattery and in a fairly large area 

near Astoria Canyon (Rs>23; Fig. 8).

Maximum overlap between the northern right whale 

dolphin and the observed fixed fleet occurred near Astoria Canyon, Oregon, and Trinidad Canyon, California (Rs>115; Fig. 9). The hake fleet overlapped the most 

near Cape Flattery, Washington (Rs>9, Fig. 9), and trawl 

fleet efforts overlapped the most near Cape Flattery but 

had evenly distributed overlap all the way south to Cape 

Mendocino, California, and beyond (Rs>33; Fig. 9).

For the observed fixed fleet, peak areas of overlap with the humpback whale (Rs>17) occurred north 

of Cape Mendocino, California, off the central Oregon 

coast, and off Astoria Canyon, Oregon (Fig. 10). For the 

trawl fleet, the highest overlap indices with this species 

occurred along the northern portion of the coast from 

Cape Mendocino to Cape Flattery, Washington, with areas of overlap >3 (Fig. 10). The highest overlap indices 

for the hake fleet occurred near Cape Flattery and were 

<2 (Fig. 10).

The highest degree of spatial overlap for the blue 

whale with the fleets of the west coast groundfish fishery occurred with the observed fixed fleet, with some local overlap index values exceeding 20 near San Diego 

and just north of Cape Mendocino, California (Fig. 11). 

Overlap with the trawl fleet was much lower, with a few 

overlap indices that exceeded ~4 near Cape Mendocino 

and off of San Francisco Bay (Fig. 11). Overlap with the 

hake fleet was very limited, and it was <0.5 in all locations (Fig. 11).

The highest areas of spatial overlap for the fin whale 

with the fleets of the west coast groundfish fishery occurred from Astoria Canyon, Oregon, northward, with 

overlap indices for the observed fixed fleet >20 near Astoria Canyon and indices for the trawl fleet >3 along the 

Washington coast (Fig. 12). The highest overlap index 

with the hake fleet was <2, off the coast of northern 

Washington (Fig. 12).

Maximum overlap of the observed fixed fleet with 

the guild of small beaked whales occurred near Astoria 

Canyon and Stonewall Bank off Oregon, Trinidad Canyon and Vizcaino Knoll in California, and off the coast 

of San Diego (Rs>11; Fig. 13). Overlap indices were the 

highest for the fishing effort of the hake fleet that occurred near Cape Flattery off Washington, Astoria Canyon, and Stonewall Bank (Rs>0.6; Fig. 13). Finally, trawl 

fleet operations overlapped the most near Cape Flattery, 

Astoria Canyon, Stonewall Bank, Oregon’s Heceta Valley, Trinidad Canyon, south of Cape Mendocino off California, and off the coast of San Francisco (Rs>2; Fig. 13). 

As was the case with the short-beaked common dolphin, 

density predictions for small beaked whales ranged from 

very low to zero off the coasts of Oregon and Washington (Fig. 13); therefore, overlap indices for this guild 

with the hake and trawl fleets were very low overall.

The observed fixed fleet overlapped the most (Fig. 14) 

with Baird’s beaked whale (Rs>3.1) near Astoria Canyon 

and Stonewall Bank off Oregon and near Trinidad Canyon, California. Overlap with the hake fleet was considerably lower, with maxima occurring just southwest of 

Cape Flattery, Washington (Rs>0.239; Fig. 14). For the 

trawl fleet, overlap was generally higher in the northern 

two-thirds of the fishing grounds, with maxima occurring just southwest of Cape Flattery and north of Cape 

Mendocino, California (Rs>0.65; Fig. 14).

Overlap indices between the distribution of the sperm 

whale and the groundfish fisheries were generally lower 

compared with the overlap indices for other whales. For 

the observed fixed fleet, the maximum values were <6 

and occurred in only a few places north of Cape Mendocino, California (Fig. 15). Overlap indices for the trawl 

fleet were fairly low and uniform from San Francisco to 

Cape Flattery, Washington, and generally <1 (Fig. 15). 

Overlap indices for the hake fleet were all <0.3 (Fig. 15).

The distribution of the striped dolphin overlapped 

most with the observed fixed fleet near Astoria Canyon 

and Stonewall Bank off Oregon and Trinidad Canyon, 

California, and over a fairly large area running south of 

Cape Mendocino down to just north of Cordell Bank off 
13

Figure 7

Maps of (A) modeled mean density of Pacific white sided dolphins (Lagenorhynchus obliquidens), expressed as the number 

of individuals per square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 

1991–2005), and of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, 

and (D) bottom trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape 

Flattery, 2) Astoria Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape 

Mendocino, 8) Vizcaino Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. Note 

that the density scales in Figures 5–16 differ.
14 Professional Paper NMFS 17 

Figure 8

Maps of (A) modeled mean density of Risso’s dolphins (Grampus griseus), expressed as the number of individuals per 

square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 1991–2005), 

and of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, and (D) bottom trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape Flattery, 2) 

Astoria Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape Mendocino, 

8) Vizcaino Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. Note that the 

density scales in Figures 5–16 differ.
15

Figure 9

Maps of (A) modeled mean density of Northern right whale dolphins (Lissodelphis borealis), expressed as the number of 

individuals per square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 

1991–2005), and of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, 

and (D) bottom trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape 

Flattery, 2) Astoria Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape 

Mendocino, 8) Vizcaino Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. 

Note that the density scales in Figures 5–16 differ.
16 Professional Paper NMFS 17 

Figure 10

Maps of (A) modeled mean density of humpback whales (Megaptera novaeangliae), expressed as the number of individuals per square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 

1991–2005), and of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, 

and (D) bottom trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape 

Flattery, 2) Astoria Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape 

Mendocino, 8) Vizcaino Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. 

Note that the density scales in Figures 5–16 differ.
17

Figure 11

Maps of (A) modeled mean density of blue whales (Balaenoptera musculus), expressed as the number of individuals per 

square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 1991–2005), 

and of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, and (D) bottom trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape Flattery, 2) 

Astoria Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape Mendocino, 

8) Vizcaino Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. Note that the 

density scales in Figures 5–16 differ.
18 Professional Paper NMFS 17 

Figure 12

Maps of (A) modeled mean density of fin whales (Balaenoptera physalus), expressed as the number of individuals per 

square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 1991–2005), 

and of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, and (D) bottom trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape Flattery, 2) 

Astoria Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape Mendocino, 

8) Vizcaino Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. Note that the 

density scales in Figures 5–16 differ.
19

Figure 13

Maps of (A) modeled mean density of small beaked whales (Ziphius and Mesoplodon), expressed as the number of individuals per square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 

1991–2005), and of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, 

and (D) bottom trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape 

Flattery, 2) Astoria Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape 

Mendocino, 8) Vizcaino Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. 

Note that the density scales in Figures 5–16 differ.
20 Professional Paper NMFS 17 

Figure 14

Maps of (A) modeled mean density of Baird’s beaked whales (Berardius bairdii), expressed as the number of individuals per square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 

1991–2005), and of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, 

and (D) bottom trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape 

Flattery, 2) Astoria Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape 

Mendocino, 8) Vizcaino Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. 

Note that the density scales in Figures 5–16 differ.
21

Figure 15

Maps of (A) modeled mean density of sperm whales (Physeter macrocephalus), expressed as the number of individuals per square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 

1991–2005), and of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, 

and (D) bottom trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape 

Flattery, 2) Astoria Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape 

Mendocino, 8) Vizcaino Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. 

Note that the density scales in Figures 5–16 differ.
22 Professional Paper NMFS 17 

Figure 16

Maps of (A) modeled mean density of striped dolphins (Stenella coeruleoalba), expressed as the number of individuals per square kilometer, off the U.S. west coast (on the basis of survey data collected during summer and fall in 

1991–2005), and of overlap indices for this species and the (B) observed fixed-gear fleet, (C) hake mid-water trawl fleet, 

and (D) bottom trawl fleet. Numbers indicate geographic reference locations in the study area, which include 1) Cape 

Flattery, 2) Astoria Canyon, 3) Astoria Sea Channel, 4) Stonewall Bank, 5) Heceta Valley, 6) Trinidad Canyon, 7) Cape 

Mendocino, 8) Vizcaino Knoll, 9) Cordell Bank, 10) Santa Lucia Bank, 11) Channel Islands, and 12) Northeast Bank. 

Note that the density scales in Figures 5-16 differ.
23

the coast of California (Rs>3; Fig. 16). In contrast, overlap with the hake fleet was concentrated over a fairly 

large area from Astoria Canyon, south to the border of 

Oregon and California (Rs>0.06; Fig. 16). Overlap with 

the trawl fleet was also fairly homogeneous and was consistently high from latitude 45° N south to Santa Lucia 

Bank, California (Rs>0.7, Fig. 16). Because density predictions for this species ranged from very low to zero off 

the coasts of Oregon and Washington (Fig. 16A), overlap indices with the hake and trawl fleets were very low, 

overall.

Population overlap index

Variability was considerable in the proportion of each 

modeled cetacean population that overlapped with the 3 

fleet types for the period 2002–2009 (Fig. 17A). Overall, 

the humpback whale, Dall’s porpoise, and Pacific whitesided dolphin had the greatest proportions of their populations that overlapped with each of the 3 fleets. The 

population overlap index generally was highest for the 

observed trawl fleet but not always (e.g., short-beaked 

common and Risso’s dolphin; Fig. 17A).

Cumulative overlap index

The patterns of the cumulative overlap index were different from the population overlap index (Fig. 17B). The 

cumulative overlap index provides a measure of overlap between a population of a given species relative to 

the total fishing effort of each fleet. Overall, the shortbeaked common dolphin, Dall’s porpoise, and Pacific 

white-sided dolphin had the greatest cumulative overlap indices with each of the 3 fleets. The largest cumulative overlap indices occurred in the observed fixed fleet, 

and these indices were about 40 times and 2.5 times the 

cumulative overlap indices of the hake and trawl fleets, 

respectively. The striped dolphin, Baird’s beaked whale, 

and sperm whale had the lowest cumulative overlap index (Fig. 17B). Within each of the 3 fleets, there was considerable variability in the cumulative overlap indices, 

with dolphins and porpoises experiencing the highest cumulative overlap indices, although large whales had the 

lowest cumulative overlap indices (Fig. 17B).

Population-weighted cumulative overlap index

Weighting the population overlap index with the cumulative overlap index accounted for the wide range of total modeled population size, cetacean density, and fishing 

effort. Although the weightings reduced the magnitude 

of interspecific differences of exposure, they did not appreciably alter the overall ranking of overlap (Fig. 17C). 

There were marked differences in the population-weighted cumulative overlap indices of the different cetacean 

species, but the magnitude in the differences changed 

(Fig. 17C). For example, the population-weighted cumulative overlap index for the short-beaked common 

dolphin with the fixed fleet was 115 times that with the 

hake fleet and more than 6 times that with the trawl 

fleet. The population-weighted cumulative overlap index 

for the fixed fleet was 3 orders of magnitude greater than 

the index for the hake fleet and more than 12 times the 

index of the trawl fleet.

Discussion

Overall, it is clear that in the California Current commercial fishing overlaps with the 11 cetacean species 

and 1 species guild included in our analyses. There were 

pronounced inter-fleet and interspecific differences in 

overlap, but the distribution of fishing effort varied over 

time. Our results indicate that some cetaceans have significantly more exposure to fishing gear than do others.

Although most of the cetacean populations in this 

study are increasing (Carretta et al., 2011) and direct 

population impacts that result from bycatch and vessel 

collisions are minimal (Jannot et al.1), indirect impacts, 

such as physical disturbance to benthic habitats and exploitative competition, may nonetheless pose threats to 

the various cetacean populations in the CCLME. Spatially explicit overlap analyses facilitate quantitative 

measures of population-level risk once the magnitude of 

those risks has been quantified and are useful to resource 

managers who need to know when and where potential 

risks to cetaceans occur. The methods we have employed 

could easily be used in other systems where risks from 

commercial fishing are a concern.

Impacts from commercial fisheries on cetaceans can 

be direct (or “operational,” as described by Beverton 

[1985]) or indirect from trophic effects. In our study, we 

focused on direct effects. The greatest source of direct 

impacts is bycatch (Lien, 1994; Read et al., 2006; Reeves 

et al., 2003; Young and Ludicello, 2007), although impacts can also occur through vessel collisions (Panigada 

et al., 2006), stress (Curry, 1999; Fair and Becker, 2000), 

noise (National Research Council, 2003; Nowacek et al., 

2007; Romano et al., 2004), and toxins, such as hydrocarbons and exhaust (Jarman et al., 1996; Marsili et al., 

2001).

Although bycatch is often the single greatest deleterious direct impact of commercial fishing on cetaceans, the 

severity of this impact varies among geographic regions. 

In the California Current, in 2002–2009, observers documented 5 cases of cetacean bycatch across the 3 fleets examined in our study (Jannot et al.1). Each of these cases 

involved a single individual, and each individual was a 

different species of cetacean. Three were species included 

in our analyses: Pacific white-sided dolphin, Risso’s dolphin, and sperm whale. The other 2 bycatch events were 

of a single bottlenose dolphin (Tursiops truncatus) and 

a single harbor porpoise (Phocoena phocoena). The cetacean modeling data we used did not include density 
24 Professional Paper NMFS 17 

Figure 17

For each of 11 cetacean species and 1 species guild, (A) population overlap index, 

(B) cumulative overlap index, and (C) population-weighted overlap index of each 

population within the entire modeled area that overlapped with each of the fishing 

effort of 3 commercial fishing fleets, the fixed-gear, at-sea hake mid-water trawl, 

and bottom trawl fleets, off the U.S. west coast in 2002–2009. Overlap indexes were 

produced for the Dall’s porpoise (Phocoenoides dalli), short-beaked common (SBC) 

dolphin (Delphinus delphis), Pacific white-sided (PWS) dolphin (Lagenorhynchus 

obliquidens), Risso’s dolphin (Grampus griseus), northern right whale (NRW) dolphin (Lissodelphis borealis), humpback whale (Megaptera novaeangliae), blue whale 

(Balaenoptera musculus), fin whale (B. physalus), Baird’s beaked whale (Berardius 

bairdii), sperm whale (Physeter macrocephalus), striped dolphin (Stenella coeruleoalba), and a guild of small beaked (SB) whales. Note that the proportions displayed 

by the bars in panel A could not be summed because there was overlap between the 

different fleet types.
25

predictions for these latter 2 species; therefore, we were 

unable to run spatial overlays accordingly. On the basis 

of the high levels of observer coverage, the 3 fishing fleets 

considered in our analyses do not appear to pose a significant bycatch threat to the cetaceans with which they 

overlap.

Vessel collisions can pose a serious threat to some 

cetaceans, especially larger whales (Laist et al., 2001). 

Overall, fishing vessels account for approximately 3% 

of all cetacean collisions (Jensen and Silber, 2003), with 

vessel speed and size as major factors in vessel collisions; 

vessels traveling in excess of 14 kn or more than 80 m in 

length are most likely to cause serious injury or mortality 

(Jensen and Silber, 2003; Laist et al., 2001). Consequently, because of the relatively small size (average ~19 m in 

length [Jannot et al.1]) and slow speeds (typical fishing 

speeds of 2–5 kn) of fishing vessels in the CCLME, it is 

unlikely that collisions with fishing vessels are a significant threat to cetaceans in this region. Indeed, collisions 

with vessels in the commercial groundfish fishery of the 

U.S. west coast are rarely observed—between 2002 and 

2009 there was only one reported collision, between a 

sperm whale and a non-nearshore fixed-gear vessel (targeting sablefish) (Jannot et al.1). Although reported collisions with large cetaceans are rare, it is important to 

note that most components of the open access fixed fleets 

have very low observer coverage (Table 2), and if collisions occur when observers are not present, they may not 

be reported.

Entanglement with a variety of fishing gear can be 

fatal for many cetaceans, but it may also leave animals 

in a compromised condition where feeding, mating, or 

abilities for predator avoidance are diminished (Moore 

et al., 2007). Pot and gillnet gear account for the vast 

majority of fishing-gear entanglements of North Atlantic 

right and humpback whales in the western North Atlantic, although many other types of gear have been implicated (Johnson et al., 2005). Larger cetacean species are 

less likely to be captured as bycatch but are more likely 

to become entangled in actively fished or derelict gear 

(Laist, 1997; Read et al., 2006; Read, 2008). For example, among U.S. Pacific cetaceans that are monitored, the 

humpback whale has the highest rate of entanglementrelated incidents, predicted at approximately 3.2 animals 

per year (Carretta et al., 2011), that are associated with 

trap and pot fishery gear in the CCLME. This rate is well 

below the predicted potential biological removal rate of 

11.3 animals per year. The potential biological removal 

rate is defined in the Marine Mammal Protection Act as 

“the maximum number of animals, not including natural 

mortalities, that may be removed from a marine mammal 

stock while allowing that stock to reach or maintain its 

optimum sustainable population.”

These entanglement data, however, are opportunistic 

and not based on observer data; therefore, they likely 

underestimate entanglements. Further, reports of dead or 

stranded cetaceans in the western North Atlantic with 

trailing fishing gear or scarring associated with fishing 

gear (Johnson et al., 2005) lend caution to the conclusion that unobserved mortality equates to no mortality. 

Nevertheless, the estimated annual population growth 

rate between 1991 and 2009 for the North Pacific stock 

of the humpback whale was between 7% and 8% (Carretta et al., 2011), a finding that indicates that the impacts of commercial fishing fleets are not large enough to 

prevent recovery of this species. 

During the years for which we analyzed data, fishing 

fleets operated under a bimonthly system for landings 

limit management, in which any given vessel was given 

a landings limit every 2 months. However, in 2011 management of the trawl and hake fleets moved to a catch 

share system. The move to catch shares may have substantial effects on the level of exposure of cetaceans to 

groundfish vessels and gears because there are incentives 

to move from trawls to fixed gear (to reduce bycatch 

associated with trawling [Kaplan et al., 2010]). An increasing concentration of fixed gear in regions of high 

whale density could have unintended negative effects. 

Because bycatch of depleted species has constrained fishing on more abundant species (Hilborn et al., 2004), the 

reduction of bycatch associated with catch shares may 

increase the time and area that is currently fished (Kaplan et al., 2010), potentially having negative effects on 

whales. Finally, under catch shares, fishers can choose 

when to catch their quota, thereby allowing individuals 

to distribute their effort over longer time periods. Again, 

this potential expansion of the temporal window of fishing effort may change the exposure of cetaceans to fisheries activities. Regardless of exactly how fisheries effort 

changes, it is clear that it is a time of flux for the west 

coast groundfish fishery, and these changes have implications for the risks to cetaceans posed by these fleets.

An important caveat of our analysis is that we did not 

consider the impacts of gillnet fleets, because we focused 

solely on groundfish fleets which did not include gillnetters. Gill nets can present a significant risk to cetaceans 

(Read et al., 2006); however, the primary type of gill net 

used along the U.S. west coast that can catch cetaceans 

must now be deployed with pingers that alert cetacean 

species to the presence of the net, reducing mortality associated with gillnet gear (Barlow and Cameron, 2003).

Importantly, exposure to fishing gear alone cannot 

be used as an estimate of the direct risk experienced 

by cetaceans from fisheries (Samhouri and Levin, 2012). 

Rather, this risk is a product of both their exposure to 

gear and the consequence of coming into contact with 

the gear. In this study, for the first time, we quantified 

the relative level of exposure of whales to gear deployed 

by 3 fishing fleets in the CCLME; however, we did not 

quantify the potential consequences of this exposure. 

Nonetheless, these analyses that quantify exposure compose an important first step in characterization of the 

potential risk to cetaceans from commercial fishing fleets 

in the CCLME.
26 Professional Paper NMFS 17 

Given our incomplete understanding of the risks imposed by the groundfish fleets, especially indirect ones, 

running more comprehensive risk assessments for cetaceans in the CCLME becomes problematic. However, 

adopting the methodologies developed by Samhouri and 

Levin (2012) would facilitate a more quantitative and 

comprehensive risk assessment. Their approach calculates relative risk as a function of various “drivers and 

pressures” or stressors, accounts for data quality, and 

can incorporate disparate types of quantitative data. 

Applying this robust approach would be a next step for 

cetacean risk assessments in the CCLME and could incorporate risk from other threats that are not from the 

groundfish fishery. Finally, studies directed at quantifying the risks of threats not considered in our assessment 

would be beneficial to improve our ability to thoroughly 

assess the risks to cetaceans posed by various anthropogenic stressors found in the CCLME.

Acknowledgments

We thank J. Samhouri for significantly improving the 

quality of this manuscript. M. Sheer assisted with database manipulations of the groundfish fishery data. We 

also wish to thank the Fishery Resource Analysis and 

Monitoring Division at the NWFSC for collecting and 

processing the data on commercial fishing effort that 

we used. We dedicate this paper to the memory of West 

Coast Groundfish Observer Program observer Christopher Langel, who perished aboard the Lady Cecelia, 

which was lost at sea on 10 March 2012. 

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