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Assessing the Vulnerability of Marine Benthos to Fishing
Gear Impacts
Jonathan H. Grabowskia, Michelle Bachmanb, Chad Demarestc, Steve Eayrsd, Bradley P.
Harrise, Vincent Malkoskif, David Packerg & David Stevensonh
a Marine Science Center, Northeastern University, Nahant, Massachusetts, USA
b New England Fishery Management Council, Newburyport, Massachusetts, USA
c NOAA/NMFS Northeast Fisheries Science Center, Woods Hole, Massachusetts, USA
d Gulf of Maine Research Institute, Portland, Maine, USA
e Department of Environmental Science, Alaska Pacific University, Anchorage, Alaska, USA
f Massachusetts Division of Marine Fisheries, New Bedford, Massachusetts, USA
g NOAA National Marine Fisheries Service, Highlands, New Jersey, USA
h NOAA Northeast Regional Office, Gloucester, Massachusetts, USA
Published online: 25 Apr 2014.
To cite this article: Jonathan H. Grabowski, Michelle Bachman, Chad Demarest, Steve Eayrs, Bradley P. Harris, Vincent
Malkoski, David Packer & David Stevenson (2014) Assessing the Vulnerability of Marine Benthos to Fishing Gear Impacts,
Reviews in Fisheries Science & Aquaculture, 22:2, 142-155
To link to this article: http://dx.doi.org/10.1080/10641262.2013.846292
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Reviews in Fisheries Science & Aquaculture, 22(2):142–155, 2014
Copyright C
Taylor and Francis Group, LLC
ISSN: 2330-8249 print / 2330-8257 online
DOI: 10.1080/10641262.2013.846292
Assessing the Vulnerability of Marine
Benthos to Fishing Gear Impacts
JONATHAN H. GRABOWSKI,1MICHELLE BACHMAN,2CHAD DEMAREST,3
STEVE EAYRS,4BRADLEY P. HARRIS,5VINCENT MALKOSKI,6
DAVID PACKER,7and DAVID STEVENSON8
1Marine Science Center, Northeastern University, Nahant, Massachusetts, USA
2New England Fishery Management Council, Newburyport, Massachusetts, USA
3NOAA/NMFS Northeast Fisheries Science Center, Woods Hole, Massachusetts, USA
4Gulf of Maine Research Institute, Portland, Maine, USA
5Department of Environmental Science, Alaska Pacific University, Anchorage, Alaska, USA
6Massachusetts Division of Marine Fisheries, New Bedford, Massachusetts, USA
7NOAA National Marine Fisheries Service, Highlands, New Jersey, USA
8NOAA Northeast Regional Office, Gloucester, Massachusetts, USA
The Magnuson-Stevens Fishery Conservation and Management Act (MSA) requires US fishery management plans to minimize,
to the extent practicable, the adverse effects of fishing on essential fish habitats (EFHs). To meet this requirement, fishery
managers would ideally be able to quantify such effects and visualize their distributions across space and time. Here,
we develop a framework to quantify and assess benthic impacts of the six most common bottom-tending gears (>99% of
bottom-tending fishing effort) in New England: otter trawls, scallop dredges, hydraulic clam dredges, gillnets, longlines,
and traps. We first conducted a comprehensive review of the habitat impacts literature relevant to Northeast USA fishing
gears and seabed types. We then used this information to develop a framework for generating and organizing quantitative
susceptibility (based on percent loss of structural habitat from a single interaction with the gear) and recovery (i.e., the time
required for recovery of lost structure) parameters for each biological (e.g., sponges, ascidians, mollusks) and geological
(e.g., mud burrows, sand ripples, cobble, and boulder piles) feature common to the following five substrates: mud, sand,
granule–pebble, cobble, and boulder in low- and high-energy environments.
In general, we found that both susceptibility and recovery scores were highest for hydraulic dredges, slightly lower for otter
trawls and scallop dredges, and much lower for fixed gears (i.e., gillnets, longlines, and traps). For bottom trawls and scallop
dredges, geological features in mud, sand, and cobble-dominated substrates were more susceptible to gear impacts than
features found in granule–pebble and boulder substrates. Meanwhile, biological features were largely equally susceptible to
impacts across the five substrate types. Average susceptibility scores for both biological and geological substrate features
were not affected by energy level. Average recovery times for geological features affected by bottom trawls and dredges were
much longer in low-energy granule–pebble, and low- and high-energy cobble and boulder than in mud and sand substrates.
Meanwhile, there was no difference among substrates or energy levels for biological feature recovery times. These results
collectively suggest that cobble and boulder substrates are the most vulnerable to impacts from mobile bottom-tending gear.
Recovery from the relatively minor impacts caused by fixed gear required slightly longer in the three coarser substrate types
than in mud and sand. Our findings highlight the importance of considering the resilience of specific components of habitat
such as emergent epifauna or geological formations that serve as EFH by providing shelter and a source of food for fish.
When coupled with the distribution of geological substrates and energy environments that exist in a particular region, our
framework offers fisheries resource managers a tool to assess gear-specific spatial impacts on benthic substrates and identify
benthic habitat vulnerability hotspots.
Keywords fishing gear impacts, fixed gear, mobile gear, recovery, susceptibility, vulnerability assessment
Address correspondence to Jonathan H. Grabowski, Marine Science Center,
Northeastern University, 430 Nahant Road, Nahant, MA 01908, USA. E-mail:
J.grabowski@neu.edu
INTRODUCTION
Marine and estuarine benthic habitats have long been rec-
ognized as essential for critical life-history stages of demersal
142
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FISHING GEAR IMPACTS TO BENTHIC HABITAT 143
fish and shellfish. Essential fish habitat (EFH) is defined by
the Magnuson-Stevens Fishery Conservation and Management
Act (MSA) as “those waters and substrate necessary to fish for
spawning, breeding, feeding, or growth to maturity.” Several
fish species recruit to and exhibit higher survival on specific
habitats such as seagrass beds, bivalve reefs, cobble and boul-
der bottom, kelp beds, and soft sediment habitats; meanwhile,
many adult species utilize benthic habitat as foraging and repro-
ductive grounds (Thayer et al., 1978; Stoner, 1983; Summerson
et al., 1984; Coen et al., 1999; Peterson et al., 2003). When
the amount or quality of habitat limits fish recruitment, growth,
and/or reproductive success, the population-level productivity
of the fish species is affected (Dayton et al., 1995; Auster et al.,
1996; Fogarty and Murawski, 1998). These critical relationships
between essential habitat elements and fish population produc-
tion underlie the MSA requirement for all fishery management
plans in the U.S. to minimize, to the extent practicable, the
adverse effects of fishing on EFHs.
An overarching impact of fishing on benthic habitats is the
loss of habitat complexity by altering or removing geological
and biogenic structures. In particular, both static and mobile
fishing gear can result in damage to and the loss of emergent
epiflora including seagrasses, and macroalgae (Peterson et al.,
1983; Fonseca et al., 1984; Peterson et al., 1987; Guillen et al.,
1994) as well as epifauna such as ascidians, hydroids, bivalves,
and polychaetes (Van Dolah et al., 1987; Thrush et al., 1995;
Auster et al., 1996; Collie et al., 1997, 2000, 2005; Engel and
Kvitek, 1998; Freese et al., 1999; Kaiser et al., 1999; Smith
et al., 2000; Kenchington et al., 2006). In addition to these
biogenic structures, fishing gears can impact physical struc-
tures such as cobble and boulder piles, pebble–gravel pavement,
sand ripples, and burrows, mats, and depressions in soft bottom
habitat (Bridger, 1972; Auster et al., 1996; Currie and Parry,
1996; Watling and Norse, 1998). Finally, fishing can disturb
biogeochemical processes associated with benthic habitats by
increasing surficial sediment loss from burial and resuspension,
increasing bottom-water turbidity,and modifying sediment oxy-
gen levels (Mayer et al., 1991; Watling et al., 2001).
Habitat associations for economically valuable species have
been established for almost all estuarine and marine habitats,
so that virtually all of these habitats serve as EFH for some
species. Yet, determining which habitats limit the productivity
of economically valuable fisheries species is extremely difficult,
particularly for highly mobile nearshore and offshore species.
Thus, determining which physical and biological components
of habitat are most vulnerable to fishing impacts also provides
insight into how best to protect EFH. Specifically, habitat com-
ponents that are highly susceptible to gear impacts and recover
more slowly should be of particular concern (Hiddink et al.,
2006a, b). For instance, emergent, slow growing species such
as many sponges and anemones (e.g., Cerianthus sp.) and other
habitat forming invertebrates are highly vulnerable to fishing
impacts from gears that crush, sever, or bury them. A quan-
titative framework assessing gear-specific impacts on biologi-
cal and geological features associated with particular substrates
and natural disturbance regimes would facilitate ongoing and
future marine spatial management initiatives. Furthermore, spa-
tial management efforts aimed at protecting EFH should assess
which habitats are most vulnerable and identify those that most
enhance the productivity of fisheries species.
Generally, there are three tools available to fishery managers
for minimizing the adverse effects of fishing on fish habitats:
area closures, gear modifications, and effort reductions (NRC,
2002). Protected areas and closures have been used globally over
the past couple of decades and can be effective for reducing im-
pacts on the seabed, protecting marine biodiversity, reducing
harvest pressure on fishery species within the closure as well as
potentially having positive spill-over effects on adjacent open
areas (Lester et al., 2009; White et al., 2011). However, both
seasonal and year-round fishing area closures result in effort
displacement if they are not accompanied by commensurate
catch or effort controls (Rijnsdorp et al., 2001; Dinmore et al.,
2003). Furthermore, if fishers displaced by the closures have
to fish longer to land the same amount of fish as before, then
their economic rents are dissipated (Holland, 2004), and the
spatial extent and severity of their gear’s impact could poten-
tially be larger. Hiddink et al. (2006a, b) examined the effects of
area closure and effort control tools on the biomass, production,
and species richness of benthic communities in the North Sea,
and concluded that the efficacy of closures for fish habitat de-
pended on the level of local target species productivity and the
rates of habitat recovery. Fishing gear conservation engineer-
ing can minimize benthic impacts by reducing gear–benthos
contact time and contact type through improved catching effi-
ciency and design modifications (e.g., off-bottom trawl doors,
ground-gear lifting elements; He and Winger, 2010). Addition-
ally, reduced fishing effort will result in lower levels of fishing
effects on benthic habitats, but these measures may also have
severe negative socioeconomic impacts on the fishery. The opti-
mal suite of management measures for minimizing the impacts
of fishing on EFH will depend on local factors and may include
a combination of these three general tools. Thus, to evaluate
ongoing and future EFH protection measures, a quantitative
framework is needed to assess the tradeoffs associated with an
array of area closure, gear modification, and effort reduction
options.
Here, we develop a framework to assess the vulnerability
of benthic habitats to fishing gear impacts for six bottom-
tending fishing gears used in New England to harvest state
and federally managed species: trawls, scallop dredges, gill-
nets, longlines, traps, and hydraulic clam dredges. First, we
characterized the dominant biological and geological features
(e.g., emergent bryozoans, anemones, rock piles, sand ripples)
associated with each of the following geological substrate types:
mud, sand, granule–pebble, cobble, and boulder. We then con-
ducted a literature review of studies that evaluated the suscep-
tibility and recovery potential of each feature common to a
particular substrate and subject to disturbance by each gear
type in each substrate type and in low and high natural en-
ergy environments. We then used this information to develop
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144 J. H. GRABOWSKI ET AL.
a method for scoring the susceptibility and recovery potential
for each feature–gear–substrate–energy combination. Applying
this method, we determined which biological and geological
features, and hence which substrates (in either low or high en-
ergy), are most vulnerable to each gear type. Furthermore, we
examined how impacts from each gear type compare within
and among the five substrate types. This vulnerability assess-
ment is currently being used to redesign the existing closure
area network in the U.S. portions of the Northwest Atlantic
to protect the habitats that are particularly vulnerable to gear
impacts.
METHODS
Defining Habitat
EFH includes both the living and non-living aquatic features
used by individual life-history stages of managed species. How-
ever, impacts to fish habitat conceptualized in this collective
sense are difficult to summarize quantitatively and represent
spatially. Therefore, a vulnerability assessment was conducted
to evaluate more concretely the interaction between fishing ac-
tivity and fish habitat for federally managed fisheries in the U.S.
portions of the Northwest Atlantic, and consequently provide
data needed to quantify the impacts of mobile and fixed fishing
gears on different types of benthic habitat. Marine substrates
were defined using the Wentworth scale into the following five
categories: mud, sand, pebble–granule, cobble, and boulder (Ta-
ble 1 and Figure 1). These categories were derived from review-
ing available substrate data for the region, and thus provide the
most meaningful classification scheme for use in regional spatial
management efforts.
Two physical energy levels were defined based on benthic
boundary shear stress and depth. Sand sediments dominate the
Northwest Atlantic continental shelf (Poppe et al., 2005; Harris
and Stokesbury, 2010) so the benthic boundary shear stress
sufficient to initiate motion in coarse sand (0.194 N·m−2)was
used to define high and low physical energy levels. Where shear
stress data were not available, a depth of 60 m was used based
on evidence of physical sediment movement (sand ripples and
waves) resulting from semi-diurnal solar and lunar tides and
winter storm events occurring primarily on seafloor bottom at
depths <60 m (Butman, 1987a, b; Harris et al., 20012). Thus,
Tab l e 1 Substrate classes by particle size range (based on Wentworth, 1922)
Substrate Particle size range Corresponding wentworth class
Mud <0.0039–0.0625 mm Clay (<0.0039 mm) and silt
(0.0039–0.0625 mm)
Sand 0.0625–2 mm Sand (0.0625–2 mm)
Granule–pebble 2–64 mm Granule (2–4 mm) and pebble
(4–64 mm)
Cobble 64–256 mm Cobble (64–256 mm)
Boulder >256 mm Boulder (>256 mm)
Figure 1 Images of each of the five substrates taken on Georges Bank. Images
were collected by the University of Massachusetts Dartmouth, School for Marine
Science and Technology Scallop Video Survey (K. Stokesbury, unpubl. image
library).
the five substrate and two physical energy classes resulted in 10
basic physical habitat types.
For this assessment, structures underlying the waters and
associated biological communities are specified as individual
features that occur in areas identified as having particular sedi-
ment compositions. While recognizing the influence that water
column properties such as temperature, salinity, and flow have
on an area’s suitability as fish habitat, this review addresses
physical changes to seafloor substrates and biological commu-
nities exclusively because it is assumed that fishing gear does not
alter the water itself in any substantive way. Individual features
were identified based on their known or assumed importance to
managed species, and were differentiated to the extent required
to capture broad differences in their susceptibility to and recov-
ery from fishing disturbance. Substrate features were defined
as the living and non-living seabed structures used by managed
species or their prey for shelter, and were classified as either
geological (non-living), or biological (living). Furthermore, bi-
ological features were comprised of functionally similar groups
of species that were comparable in susceptibility to and recovery
from potential gear impacts. The various geological (Table 2)
and biological (Table 3) features were then assigned to each
seafloor substrate–energy class where the particular feature may
be found in the NE region.
Literature Review
A Microsoft Access database was developed to organize the
review and to identify in detail the gear types and habitat features
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FISHING GEAR IMPACTS TO BENTHIC HABITAT 145
Tab l e 2 Geological habitat features and their inferred distribution by substrate and energy
Mud Sand Granule–pebble Cobble Boulder
Feature (h) (l) (h) (l) (h) (l) (h) (l) (h) (l)
Sediments X X X X
Biogenic burrows X X X X
Biogenic depressions X X X X
Bedforms X
Gravel, scattered XXXXXX
Gravel pavement X X
Gravel piles XXXX
Shell deposits X X X X
Features present in each substrate type are denoted with an X. Energy regime categories: h-high and l-low.
evaluated by each study. In addition to identifying gear types
and features, the database included fields to code for basic infor-
mation about study location and related research; study design,
relevance, and appropriateness to the vulnerability assessment;
depth and energy environment; whether recovery of features
is addressed; and substrate types found in the study area. Most
studies were reviewed and coded by a single expert initially, and
then checked by one or more additional experts at a later time.
The most recent studies that were evaluated in this assessment
were published in 2009.
Many types of fishing gears are used throughout the North-
west Atlantic. However, here only seabed impacts from bottom-
tending gears that account for significant landings, revenue,
and/or days at sea were evaluated. Key fishing gears were iden-
tified out of 45 gear types associated with landings of federal or
state-managed species as reported in National Marine Fisheries
Service Vessel Trip Reports (VTR) from 1996 to 2008. Eight
gear types individually accounted for roughly 1% or greater of
landings, revenues and/or days absent: ocean quahog/surf clam
dredge, sea scallop dredge, sinking gillnet, bottom longline, bot-
tom otter trawl (combining fish, scallop, and shrimp), mid-water
otter trawl, lobster pot, and purse seine. Of these, mid-water otter
trawls and purse seines were not evaluated because these gear
types are thought to have little to no bottom contact.
Susceptibility and Recovery Matrices
Fishery managers in the US are required to minimize the
adverse impacts of fishing on EFH with an “adverse impact”
defined as any impact that “reduces the quantity or quality
of EFH.” Adverse impacts do not need to be minimized un-
less they are “more than minimal and not temporary” (Na-
tional Marine Fisheries Service, Magnuson-Stevens Act Pro-
visions, 67 FR 2343). Therefore, this vulnerability assessment
organizes quantitative estimates of both the magnitude of the
impacts that result from the physical interaction of fish habi-
tats and fishing gears (susceptibility), and the duration of re-
covery following those interactions (recovery). With respect to
each feature–gear–substrate–energy combination, “vulnerabil-
ity” represents the extent to which the effects of fishing gear on
a feature are adverse. In particular, vulnerability is defined as the
combination of how susceptible the feature is to a gear effect
and how quickly it can recover following the fishing impact.
Specifically, susceptibility (S) was defined as the percent
Tab l e 3 Biological habitat features and their inferred distribution by substrate and energy
Mud Sand Granule–pebble Cobble Boulder
Feature (h) (l) (h) (l) (h) (h) (l) (h) (l) (h)
Amphipods X X X X
Anemones, actinarians X X X X X X
Anemones, cerianthids X X X X X X
Ascidians X X X X X X X X
Brachiopods X X X X X X
Bryozoans X X X X X X
Corals, sea pens X X
Hydroids XXXXX XXXXX
Macroalgae X X X
Mollusks,mussels XXXXXXXXXX
Mollusks, scallops X X X X X X
Polychaete, F. implexa X X X X X X
Polychaetes, other X X X X X X
Sponges X X X X X X X X
Features present in each substrate type are denoted with an X. Energy regime categories: h-high and l-low.
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146 J. H. GRABOWSKI ET AL.
reduction in the functional value of a particular habitat fea-
ture as a result of contact with fishing gear during a hypothetical
single pass fishing event (one tow or haul) that were reduced
in functional value. Here, functional value refers to the relative
usefulness of a given habitat feature in its intact form to a fish
species requiring shelter. However, because functional value is
difficult to assess directly, and will vary for each species and life
stage using the feature, percent feature removal or damage was
used as a proxy for general reduction in functional value. Mean-
while, recovery (R) was defined as the time in years required for
the functional value of that feature to be restored. Both suscepti-
bility and recovery values were grouped into an ordinal scale (0
to 3; see Table 4) to facilitate comparison among the magnitude
of susceptibility and recovery values, since susceptibility and
recovery are closely related. Susceptibility scores ranged from
0 (0–10% impacted) to 3 (>50% impacted), and recovery scores
ranged from 0 (<1 year) to 3 (5–10 years).
Susceptibility and recovery were scored for each feature–
gear–substrate–energy combination based on information found
in the scientific literature, to the extent possible, combined with
expert judgment where research results were lacking or incon-
sistent. In some cases, studies from gears with similar physical
contact characteristics or species with similar structural vul-
nerabilities were used. For example, otter trawl studies were
used to inform some of the scallop dredge scores when direct
information on scallop impacts to a particular feature was lack-
ing. In other cases, susceptibility scores derived from published
studies of the effects of a given gear type on a habitat fea-
ture in one substrate type were applied to other substrates that
lacked information. For benthic organisms, characteristics such
as fragility and size were assessed when assigning susceptibil-
ity scores. In addition, for each feature–gear–substrate–energy
combination, studies that were deemed more relevant based on
attributes of the study design, location, and metrics quantified
were weighted more heavily when determining susceptibility
and recovery scores. Recovery times for biological feature types
(e.g., all sponges) were evaluated based on information or esti-
mates of the lifespan and/or growth rates for the more common
species in the feature type. All feature–substrate–gear–energy
combinations were evaluated with the exception of hydraulic
dredges, which were scored for sand and granule–pebble sub-
strates only since this gear cannot be fished in other substrates.
Susceptibility and recovery scoring was reviewed at five New
England Fishery Management Council Habitat Plan Develop-
Tab l e 4 Categorical scores given to geological and biological features based
on their susceptibility to and time needed to recover from gear impacts
Susceptibility Recovery
Scores (%) (Years)
0 0–10% <1
1>10–25% 1–2
2 25–50% 2–5
3>50% >5
ment Team (PDT) meetings between January and August 2009.
These group discussions ensured that each team member had
the same understanding of what was meant by susceptibility
and recovery, and understood the assumptions underlying the
assessment and scoring methods. During this period, matrices
were evaluated in three iterations. After two initial rounds of
scoring, the PDT divided into small groups of 3–4 members
each to evaluate each gear type in detail. Individual members
submitted completed matrices to the group, including justifi-
cation for each score, and the small groups developed con-
sensus scores for each feature. Once consensus was reached
for each gear type, the matrices were considered by the en-
tire team and scores were compared across substrates, energy
regimes, and gear types to achieve internal consistency among
team members in their approaches (see Table 5 for an example
matrix).
The following vulnerability assessment assumptions and
rules evolved from the above series of meetings. First, sus-
ceptibility was evaluated for the entire swath of seabed affected
by the gear during one fishing event (e.g., tow, set) based on the
nominal area swept of the gear. In most cases, a feature is small
(<1 m) in comparison with the path of the gear (tens of meters).
In the case of larger features, (e.g., sand waves), or gears with
narrower footprints (e.g., fixed gears), impacts to the portion of
the feature in the path of the gear were evaluated.
Second, because there was no information to suggest oth-
erwise, susceptibility was generally assumed to be similar for
both high- and low-energy areas and therefore a single score was
given for both. Meanwhile, recovery was assumed to vary such
that separate high- and low-energy scores could be assigned as
appropriate. For some geological features, variation in recov-
ery times in low- and high-energy environments depended on
how quickly habitat structure (e.g., sand waves or ripples) was
expected to recover given the strength of tidal bottom currents
or storm waves in each energy regime. Furthermore, recovery
times varied for some biological features in high- and low-
energy regimes if the more common members of the feature
class had notably different life histories.
Third, four trawl gear subtypes (generic/fish, shrimp, squid,
raised footrope) are commonly used in New England, but vulner-
ability matrices for each type were not completed for each sep-
arately because research on shrimp, squid, and raised footrope
trawl impacts is limited. Furthermore, differences in impacts
between these gears can be accounted for when the vulnera-
bility assessment is applied to fishing effort by varying gear
dimensions and the estimated degree of contact with the seabed.
Fourth, the intention of the susceptibility scoring was to con-
sider loss or damage of features in the path of the gear for the
portion of the gear that was actually in contact with the seabed.
For example, 100% of the trawl doors likely contact the bottom,
whereas a lower percentage of the ground cables likely contact
the bottom. Thus, efforts to apply the vulnerability assessment to
model gear impacts of specific regions of the Northwest Atlantic
or elsewhere should account for the degree to which different
gear components contact the bottom.
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FISHING GEAR IMPACTS TO BENTHIC HABITAT 147
Tab l e 5 Example of the matrices that were derived to determine the susceptibility and recovery scores to each type of gear for each biological and geological
feature within a particular substrate. This matrix is for otter trawl impacts in granule–pebble substrate
High energy Low energy
Feature name and class Gear impacts (#)aSR(#)
aSR
Gravel pavement (G) Burial, mixing, homogenization 0 1 0 N/A N/A N/A
Gravel, scattered (G) Burial, mixing 1 1 0 4 1 2
Shell deposits (G) Burying, crushing, displacing 2 1 1 2 1 2
Anemones, actinarian (B) Breaking, crushing, dislodging, displacing 8 2 2 3 2 2
Anemones, cerianthid (B) Breaking, crushing, dislodging, displacing 5 2 2 0 2 2
Ascidians (B) Breaking, crushing, dislodging, displacing 4 2 1 1 2 1
Brachiopods (B) Breaking, crushing, dislodging, displacing 1 2 2 1 2 2
Bryozoans (B) Breaking, crushing, dislodging, displacing 10 1 1 1 1 1
Hydroids (B) Breaking, crushing, dislodging, displacing 10 1 1 2 1 1
Macroalgae (B) Breaking, dislodging 0 1 1 N/A N/A N/A
Mollusks, mussels (B) Breaking, crushing, dislodging, displacing 7 2 3 1 2 3
Mollusks, scallops (B) Breaking, crushing 7 1 2 1 1 2
Polychaetes, F. i m p l e x a
(B)
Breaking, crushing, dislodging, displacing 6 2 2 1 2 2
Polychaetes, other (B) Crushing, dislodging 6 2 1 1 2 1
Sponges (B) Breaking, dislodging, displacing 11 2 2 1 2 2
Feature type categories: G-geological and B-biological.
aFor each energy regime, (#) refers to the amount of studies that provided results on the impacts of trawling on the particular feature type.
Fifth, all gear components (e.g., trawl door, ground cable,
sweep) were considered together when evaluating susceptibility
because previous studies do not disaggregate gear effects by
component. However, analysts considered the relative contribu-
tion of each gear component to area swept when evaluating the
matrices.
Sixth, the matrix scores are hypothesized for a single pass,
with no baseline state of the seabed or features assumed. Gener-
ally, areas within Northwest Atlantic as well as study sites in the
fishing impacts literature have been subjected to repeated fish-
ing disturbance for many years. The single pass approach made
the results of some studies more difficult to apply to the scor-
ing of susceptibility and recovery. While there are a number of
studies among the 97 evaluated that examine habitat impacts at
this level, many do not. It can be argued that such experimental
impact studies are simply not practicable at “relevant” temporal
and spatial scales (Tillin et al., 2006; Hinz et al., 2009), but com-
parative studies also have drawbacks. For instance, comparative
studies can be somewhat difficult to evaluate and extrapolate
insights from because the scale of fishing disturbance may vary
widely between studies and is often vaguely quantified as high
or low (Hinz et al., 2009). More generally, a challenge inher-
ent to evaluating the result of the fishing impacts literature is
the lack of true control sites and the confounding of natural
variation that predisposes an area to fishing in comparison with
a nearby “control” area with the actual effects of trawling on
seabed features (Tillin et al., 2006; Hinz et al., 2009).
Seventh, recovery rates of features assumed the absence of
additional fishing pressure. That is, each additional fishing event
results in “new” impacts rather than impacting already disturbed
structures. Finally, in the absence of any data to the contrary,
the different features were assumed to be evenly distributed
within each substrate–energy combination. We also assumed
that the susceptibility of each feature within a substrate–energy
combination did not differ as a function of its size or density.
Thus, we assumed that the one pass of the gear would have
the same relative effect on the functional value of the feature
regardless of its dimensions or abundance.
RESULTS
Summary of Literature Review
In total, 97 studies of the impacts of fishing gear on habitat
features were identified and used in this vulnerability assess-
ment. While these studies collectively span much of the world,
only studies with information relevant to Northwest Atlantic
fishing gears and substrate features were included. About half
of the 97 studies used experimental approaches to assess gear
impacts on benthic habitats; however, only 25 of these were be-
fore/after impact studies directly applicable to the assessment of
the susceptibility of habitat features to the effects of single tows
or sets. The remaining studies used comparative approaches
(e.g., evaluations of habitat conditions in areas open and closed
to fishing, or where fishing intensity was heavy versus light) to
assess gear impacts. While these studies provided useful infor-
mation, they were deemed less informative than experimental
studies when assigning susceptibility and recovery scores.
Over 70 of the gear-impact studies focused largely on the
effects of demersal trawling on biological and geological sub-
strate features. Most of these studies considered “generic” otter
trawls, making it difficult to discern the effects of modified ot-
ter trawls (e.g., raised footrope or squid trawls) on substrate
features. In addition, very few studies provided enough de-
tails regarding specific trawl design, configuration, and fishing
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148 J. H. GRABOWSKI ET AL.
practices, which would have been required to assign S and R
scores for individual trawl types. Studies of the remaining gear
types were far more limited: scallop dredges (11 studies), hy-
draulic dredges (17), and fixed gear (5).
Susceptibility
Feature susceptibilities varied by gear type (Table 6, Fig-
ures 2–6). Geological and biological features were generally
most susceptible to impacts from hydraulic dredges (average
scores for all features in a particular substrate and energy en-
vironment ranged from 2.5 to 2.8 out of 3); intermediate for
otter trawls and scallop dredges (S scores ranged from 1.0 to
2.0); and lowest for fixed (traps, longlines, and gillnets) gear (S
scores ranged from 0 to 1.0), with very little variation among
them.
Only three studies were identified that directly evaluated ef-
fects of the New Bedford style scallop dredge, all of which only
addressed impacts on geological features. We assumed that im-
pacts from scallop gear are most similar to those from trawling,
and assigned identical scores for the two gears across all fea-
tures, substrates, and energies with the exception of the bivalve
mollusk/scallop feature itself, which was estimated to have a
slightly higher susceptibility to scallop dredges. This assump-
tion seemed reasonable since the disturbance caused by both
gears is similar; aside from the trawl doors, both gears cause a
scraping and smoothing of bottom features and a re-suspension
of fine sediments, and these effects are primarily limited to the
sediment surface.
For all five gears that are used in every substrate type, im-
pacts on biological features generally did not differ much among
substrates, although there was a slight trend of higher average
susceptibility scores in coarser substrates. These differences in
average scores are due to differences in the susceptibility of the
suite of features inferred to areas dominated by each substrate
(Tables 3 and 6). Average susceptibility scores for hydraulic
dredges were slightly higher in sand than in granule–pebble
substrates. For trawls and scallop dredges, the average S scores
were notably higher for geological features than for biological
features in mud and slightly higher in sand and cobble,
whereas biological features were, on average, more susceptible
in granule–pebble and boulder habitats. Biological features in
coarser sediments were equally susceptible to impacts, whereas
mud and sand habitats were slightly less susceptible on aver-
age. For trawls, scallop dredges, and fixed gears, geological
features in mud, sand, and cobble habitats were generally more
susceptible than in granule–pebble and boulder habitats.
Most average susceptibility scores did not vary by more
than 0.1 in low- and high-energy habitats for any particular
substrate–gear combination (Table 6). The exceptions (δS=0.2
or 0.3) were all for geological features which were higher for
scallop dredges in low than in high energy cobble, higher for hy-
draulic dredges in high- than in low-energy granule–pebble, and
higher for gillnets and longlines in low- than in high-energy cob-
ble and for traps in low- than in high-energy sand. This similarity
in average susceptibility scores is almost certainly due to insuf-
ficient information on the extent to which natural disturbance
(e.g., strong vs. weak bottom currents and susceptibility to im-
pacts from storm events) modifies the susceptibility of benthic
habitat features to fishing gear and not due to a true difference
in susceptibility in high- versus low-energy environments.
Recovery
Similar to susceptibility scores, mean recovery (R) scores
were generally higher (slower recovery) for mobile than for
fixed gears (Table 7, Figures 2–6). Mean Rscores for biological
features were highest for hydraulic dredges (1.8–2.2), slightly
lower for scallop dredges and trawls (1.5–1.7), and lowest for
fixed gears (0.6–1.2). For scallop dredge and trawl gear, there
was little difference among substrates or energy levels, with
the high energy-mud combination having the lowest Rscore
(1.5), and the low-energy granule–pebble combination having
the highest Rscore (2.2). Biological Rscores for fixed gear
were lower for sand and mud substrates (0.6–0.9) than for the
three hard substrates (1.1–1.2). For fixed gears, average recovery
times for biological features were slightly longer in high- versus
low-energy mud and sand habitats.
Tab l e 6 Average susceptibility scores for all biological and geological substrate features for each gear type
Mud Sand Granule–pebble Cobble Boulder
Feature type (h) (l) (h) (l) (h) (l) (h) (l) (h) (l)
Trawl G 2.02.01.81.81.01.01.72.01.01.0
B1.31.41.51.61.71.71.61.71.71.8
Scallop dredge G 2.02.01.81.81.01.01.72.01.01.0
B1.31.41.61.71.81.81.71.81.71.8
Hydraulic dredge G 2.82.82.72.5
B2.62.72.82.8
Longline, gillnet G 0.30.30.40.50.00.00.30.50.00.0
B0.80.80.60.70.80.80.80.80.90.9
Trap G 1.01.00.60.80.00.00.30.50.00.0
B0.80.80.60.70.90.90.90.91.01.0
Energy regime categories: h-high and l-low. Feature type categories: G-geological and B-biological.
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FISHING GEAR IMPACTS TO BENTHIC HABITAT 149
Figure 2 Mean susceptibility (% damaged) and recovery (time in years) of biological and geological features from otter trawl gear impacts; hatched verticals
error bars are ±1SE.
Figure 3 Mean susceptibility (% damaged) and recovery (time in years) of biological and geological features from scallop dredge gear impacts; hatched verticals
error bars are ±1SE.
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150 J. H. GRABOWSKI ET AL.
Figure 4 Mean susceptibility (% damaged) and recovery (time in years) of biological and geological features from hydraulic dredge gear impacts; hatched
verticals error bars are ±1SE.
Figure 5 Mean susceptibility (% damaged) and recovery (time in years) of biological and geological features from longline and gillnet gear impacts; hatched
verticals error bars are ±1SE.
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FISHING GEAR IMPACTS TO BENTHIC HABITAT 151
Figure 6 Mean susceptibility (% damaged) and recovery (time in years) of biological and geological features from trap gear impacts; hatched verticals error bars
are ±1SE.
Mean recovery times for geological features varied among
substrates far more than they did for biological features. Once
again, Rscores for geological features were highest for hydraulic
dredge gear (0.6–2.0), intermediate for trawl and scallop dredge
gear (0–2.0), and lowest for the three fixed gears (0–1.5). Across
all gears and both energy levels, the geological features associ-
ated with mud substrate were deemed to fully recover within 1
year (R=0). Geological Rscores for sand substrate were also
0 for fixed gears, but slightly higher (0.2–0.5) for trawls and
scallop dredges and substantially higher (0.6–1.5) for hydraulic
dredges. Similarly, Rscores for granule-pebble were 0 for all
fixed gears, higher for trawls and scallop dredges (0.3–2.0),
and highest for hydraulic dredges (1.3–2.0). Mean Rscores for
geological features for cobble and boulder substrates were mod-
erate to high for all gears evaluated (1.0–1.5). For each gear type,
recovery values were consistently higher on geological compo-
nents of habitat in coarse grained substrates than in sand and
mud substrates, reflecting the increased contribution of features
with recovery times of 2–5 and 5–10 years.
In general, geological features recover more slowly from
the effects of fishing in low-energy environments than in
high-energy environments. The largest differential in average
Tab l e 7 Average recovery scores for all biological and geological substrate features for each gear type
Mud Sand Granule–pebble Cobble Boulder
Feature type (h) (l) (h) (l) (h) (l) (h) (l) (h) (l)
Trawl G 0.00.00.20.50.32.01.01.51.51.5
B1.51.61.61.71.71.71.61.71.61.7
Scallop dredge G 0.00.00.20.50.32.01.01.51.51.5
B1.51.61.61.71.71.71.61.71.61.7
Hydraulic dredge G 0.61.51.32.0
B1.81.81.82.2
Longline, gillnet G 0.00.00.00.00.00.01.01.51.51.5
B0.80.60.90.81.21.21.11.11.21.2
Trap G 0.00.00.00.00.00.01.01.51.51.5
B0.80.60.90.81.21.21.11.11.21.2
Energy regime categories: h-high and l-low. Feature type categories: G-geological and B-biological.
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152 J. H. GRABOWSKI ET AL.
recovery times (δR=1.7) was between high- and low-energy
levels for bottom trawls and scallop dredges in granule–pebble
habitats, where recovery times for low-energy levels were much
longer (Figure 2). Average Rscores were also slightly higher
(δR=0.3–0.5) in low-energy sand and cobble habitats exposed
to trawls and scallop dredges, in low-energy granule–pebble
habitats exposed to clam dredging, and in low-energy cobble
habitats where fixed gears are used (Table 7).
DISCUSSION
The Magnuson Stevens Fisheries Act mandates that marine
habitats be protected to the extent practicable from adverse
effects caused by fishing. However, a major impediment to
achieving this mandate is the lack of a quantitative framework
categorizing the habitat-specific impacts from mobile and fixed
gear. We conducted a comprehensive evaluation of fishing gear
impacts on benthic habitats to inform managers in the northeast-
ern US on which ones are the most susceptible to fishing, which
gears cause the greatest impacts, and what are the expected re-
covery times for habitat features that are affected. In general,
this evaluation revealed that substrates were most vulnerable
to impacts from hydraulic dredging, slightly less vulnerable to
those from trawling and scallop dredging, and least vulnerable
to fixed gears. Furthermore, hard substrates (cobble, boulder,
and to a lesser degree, granule-pebble) were typically more vul-
nerable to fishing gear impacts than soft-sediment substrates
(sand, mud).
For the three mobile bottom-tending gears (trawls, scallop
dredges, and hydraulic dredges), biological features associated
with each substrate were, on average, roughly equivalent in
vulnerability to gear impacts. Meanwhile, geological features
were far more vulnerable in hard substrates even though soft-
sediment features can be highly disturbed (i.e., >25%) because
recovery of geological features typically occurs much faster in
soft than in hard substrates. Thus, the slow recovery (except
for high-energy granule–pebble) rates and intermediate to high
susceptibility of geological features in hard substrates explain
why they are the most vulnerable substrates to impacts from
mobile gears.
Several previous studies have focused primarily on mobile
gear impacts to biological features because they provide bio-
genic structure that acts as refuge and foraging grounds for
fishes, and these features are highly susceptible to mobile fish-
ing gears such as dredges and otter trawls (Collie et al., 1997;
Kaiser et al., 1998; Turner et al., 1999; Thrush et al., 2001; Diaz
et al., 2003; Stevenson et al., 2004; Hinz et al., 2009; Mangano
et al., 2013). In our assessment, we determined that different
taxonomic groups of structure-forming epifaunal organisms are
differentially vulnerable to fishing gear impacts depending on
their size, fragility, growth rates, and longevity. For instance,
sponges growing in granule–pebble substrates are more vulner-
able to trawls than ascidians or bryozoans, which either have
faster recovery times than sponges or are smaller and more
flexible and, therefore, more likely to pass under the footrope
of a trawl with minimal damage. However, when the Sand R
scores are averaged across all biological (or geological) features
in a substrate, the differences between substrates (or gears or
energy levels) are often reduced.
Our assessment revealed that geological features such as cob-
ble and boulder piles (many of glacial origin) are perhaps even
more important habitats to protect since they recover far more
slowly (if ever) than do geological features in soft sediments
such as burrows, depressions, and dead shell deposits. Simi-
larly, some benthic species, such as horse mussels (Modiolus
modiolus) and many sponge species have very long lifespans
and grow very slowly, and consequently are extremely vulnera-
ble to fishing activities that sever, crush, or bury them because
they require such a long time to recover. We chose not to in-
clude deep water coral species in our review because NOAA
and the NEFMC are addressing corals separately in attempting
to protect areas known to support coral aggregations such the
canyons on the south edge of Georges Bank by excluding all
fishing (New England Fishery Management Council, Omnibus
EFH Amendment 2; http://www.nefmc.org/habitat/index.html).
Furthermore, these species are relatively rare in the shal-
lower continental shelf habitats that were the focus of the
assessment, and are primarily found on the continental slope
and submarine canyon environments. One type of coral, sea
pens, was included, because they are more widespread in
their distribution, particularly in mud habitats in the Gulf of
Maine.
For fixed gears, geological features once again explained
much of the variation in vulnerability among substrates because
geological features recover far more quickly in soft-sediment
and granule–pebble substrates than in cobble and boulder sub-
strates. Slightly higher susceptibility and recovery rates for bi-
ological features in hard versus soft substrates reinforced this
trend. However, these findings are hampered by the paucity of
existing studies on fixed gear effects on fish habitat (but see
Kaiser et al., 2000; Eno et al., 2001). Thus, future efforts to
quantify and compare the impacts of mobile and fixed gear
would benefit from a greater understanding of how these gear
types affect geological and biological seabed structures. Further-
more, efforts to apply our vulnerability assessment framework
to assess potential and previous fixed gear impacts would ben-
efit from a greater understanding of the effective footprint of
each fixed gear. The degree to which these gears drift and are
dragged on the bottom during normal fishing operations has yet
to be quantified.
Less than one-third of the studies reviewed examined re-
covery times of biological and/or geological features following
impact. Furthermore, many of these only considered immediate
recovery (<5 years), which is likely due to the constraints of
grants rarely stretching longer than 3–5 years. An exception to
this trend is provided by the studies that focused on recovery
times in areas that had been closed for a decade or longer, when
available. The use of a maximum recovery duration of ten years
may not capture the occasional slow growing benthic species
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FISHING GEAR IMPACTS TO BENTHIC HABITAT 153
that requires decades to recover functional value. When apply-
ing the vulnerability assessment to actual or simulated fishing
effort data, we suggest that sensitivity analyses be conducted
to determine the degree to which selection of highly vulnerable
hotspots is influenced by the duration of the maximum recovery
rate.
Two potential limitations of the vulnerability assessment de-
serve consideration. First, in cases where there was not clear
support for a difference in scores, there was a tendency to
assign the same scores between features, thus smoothing the
estimates of impacts across habitat types. This tendency also
occurred when comparing the same feature between gear types
and/or energies. For example, there was no difference for most
Rand Sscores between traps and the other two fixed gears.
Furthermore, while it is plausible that longlines and gillnets im-
pact benthic habitat similarly given how these gears contact the
bottom, the relative lack of studies on fixed gears explains in
part why the resulting vulnerability assessments were identical.
The few studies that were available for fixed gear types support
the inference that impacts from fixed gears are generally lower
than those for mobile gears; however, substrate-specific find-
ings for fixed gear need to be tested further with experimental
manipulations of fishing effort. And second, experts tended to
avoid categorizing features as 0 (little to no impact/recovery
within a year) or as a 3 (greater than 50% impact/recovery
time greater than five years) unless there was ample evidence in
support of these rankings. By ranking features with no sup-
port from the literature toward moderate impact levels, we
likely reduced the spread between the most and least impactful
gears.
A major assumption of this vulnerability assessment that
deserves to be tested further is whether the susceptibility and
recovery scores should be held constant regardless of the current
condition of the substrate. In other words, the Sand Rscores are
measures of the effects of a single gear encounter with the bot-
tom, with the impacts from one encounter independent from the
next. This implies that the functional relationship between habi-
tat vulnerability (i.e., percent features lost and recovery rates)
and the amount of gear use (e.g., number of tows or sets) in an
area is constant, so that there is no difference in the magnitude
of impact from each additional unit of gear use in a particu-
lar location. Thus, although the framework that we developed
can be used to examine cumulative effects of fishing by adding
multiple fishing events at one location over time, the recovery
of substrate features assumes that recovery from an individual
event is independent of subsequent fishing events. We used this
assumption in the absence of studies that conclusively establish
a relationship between the amount of gear use and functional
loss in a given area for any of the gears examined in our review
(but see Jennings and Kaiser, 1998; Hiddink et al., 2006a,b;
Queir´
os et al., 2006). If this relationship is nonlinear, then ef-
forts to examine cumulative impacts will likely be biased. It is
plausible, and has been hypothesized, that the first pass is rel-
atively more impactful than subsequent passes in a particular
location. We would expect this to be particularly true in habitats
with relatively little to no natural disturbance and climax com-
munities consisting of species with long lifespans.
It is likely that our approach would underestimate recov-
ery rates at high levels of cumulative impacts. Areas that are
heavily disturbed are predicted to have lower levels of diver-
sity (Sousa, 1979, 1984). Furthermore, substrates may not re-
cover if disturbances from gear impacts are large enough to
cause a shift from one stable state to another (Lewontin, 1969)
and the alternative stable state does not provide the same habi-
tat function for fish. For instance, areas that are more heavily
disturbed typically are more susceptible to species invasions
that can drive large shifts in community composition and
ecosystem functioning. In addition, our vulnerability assess-
ment does not account for variability in the resilience, or the
ability to withstand state shift (Holling, 1973), of features or
communities.
The effects of cumulative gear impacts on processes such
as benthic species recovery rates and population resilience are
likely influenced by larger, landscape-scale processes. The indi-
vidual patch size and spatial mosaic of substrates could impact
recovery rates, as could the extent of fishing in neighboring
substrate patches. Hypothetically, recovery rates of features im-
pacted by gear would likely be quicker if a fishing impact oc-
curs in a larger patch and is relatively isolated from other fish-
ing impacts. Conversely, patches that are relatively small and
heavily disturbed might have more limited recovery potential
if it hinges on replenishment from local recruitment. Further-
more, as the spatial extent and frequency of impacts increase,
the result is a highly fragmented landscape with reduced re-
silience, connectivity, and ecosystem functioning (Micheli and
Peterson, 1999; Thrush et al., 2008; Grabowski et al., 2012;
Thrush et al., 2013). These potential limitations of our vul-
nerability assessment framework should be carefully consid-
ered when applying it to actual and simulated fishing efforts to
explore gear impacts in particular fishing regions such as the
Northwest Atlantic.
Here, we develop a quantitative framework to assess the vul-
nerability of benthic habitats to mobile and fixed fishing gears
common to New England. By parsing the effects of each gear
type on geological and biological substrate features associated
with each substrate, we have provided a framework that can be
used to compare relative impacts among gear types and across
substrates and energy levels. Moreover, when combined with
spatially explicit information on benthic habitat distributions
from a particular region, this framework can be used to quantify
which areas are most vulnerable to fishing with different types
of bottom-tending gear to guide spatial management efforts.
Protecting habitat from fishing impacts requires displacing fish-
ers with area closures, modifying gear, or fishing less. All of
these are potentially costly to the industry and can result in net
increases in overall adverse impacts if effort redistribution is not
accounted for. Therefore, our quantitative framework should en-
hance resource managers’ ability to objectively evaluate, select,
and protect habitat vulnerability hotspots while also minimizing
the economic impact on the fishery.
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154 J. H. GRABOWSKI ET AL.
ACKNOWLEDGMENTS
David Preble, the chair of the NEFMC’s Habitat Committee,
provided valuable feedback throughout conducting the litera-
ture review and developing the susceptibility and recovery ma-
trices. The NEFMC’s Science and Statistical Committee (SSC)
reviewed early versions of this assessment—Dr. Jacob Kritzer
spearheaded this SSC review. The NEFMC provided travel sup-
port for Habitat PDT members to participate in the meetings
that occurred throughout the process.
FUNDING
Support was provided by NSF’s Coupled Natural and
Human System Program (NSF OCE-0709527) and Bio-
logical Oceanography Program (NSF OCE-1203859) for
Grabowski’s time. Support was provided by NOAA for Harris’
time (NOAA/NMFS NA08NMF4720554, NA09NMF4720256,
NA10NMF4720288, NA09NMF4540129).
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