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Associations between SurficialSediments and Groundfish Distributionsin the Gulf of Maine–Georges BankRegionElizabeth T. Methratta a & Jason S. Link aa National Marine Fisheries Service, Northeast Fisheries ScienceCenter, Food Web Dynamics Program, 166 Water Street, WoodsHole, Massachusetts, 02543, USAPublished online: 09 Jan 2011.

To cite this article: Elizabeth T. Methratta & Jason S. Link (2006) Associations between SurficialSediments and Groundfish Distributions in the Gulf of Maine–Georges Bank Region, North AmericanJournal of Fisheries Management, 26:2, 473-489, DOI: 10.1577/M05-041.1

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Associations between Surficial Sediments and GroundfishDistributions in the Gulf of Maine–Georges Bank Region

ELIZABETH T. METHRATTA*1AND JASON S. LINK

National Marine Fisheries Service, Northeast Fisheries Science Center, Food Web Dynamics Program,166 Water Street, Woods Hole, Massachusetts 02543, USA

Abstract.—The delineation of essential fish habitat is an important element of contemporary fisheries

management. Although local-scale species–habitat relationships have been established for some managed

species, we lack an understanding of these associations at the synoptic spatial scales on which fish populations

and their associated fisheries operate. Interest in habitat delineations has been elevated further by an increased

awareness of ongoing habitat degradation caused by mobile fishing gears and by the advancement of spatial

management tools. Here we examine the associations between surficial sediment grain size and groundfish

distributions in the Gulf of Maine–Georges Bank region. The mean abundances for 58 demersal fish species

were determined for a spatial cell grid (185.2 km2 per cell), which was subsequently joined to a spatially

referenced sediment database in a geographical information systems environment. Multivariate statistical

methods were then used to examine how fish distribution and abundance varied with substrate grain size. Of

the 58 species examined, 12 were consistently associated with particular substrate types. Atlantic cod Gadusmorhua, longhorn sculpin Myoxocephalus octodecemspinosus, sea raven Hemitripterus americanus, and

winter flounder Pseudopleuronectes americanus were consistently abundant in large-grained substrate types,

whereas white hake Urophycis tenuis, goosefish Lophius americanus, red hake U. chuss, silver hake

Merluccius bilinearis, witch flounder Glyptocephalus cynoglossus, and American plaice Hippoglossoidesplatessoides were consistently abundant in fine-grained substrates. Little skate Leucoraja erinacea was most

abundant in sediments of intermediate grain size. Although broadly distributed, spring dogfish Squalusacanthias consistently distinguished assemblages in fine-grained sediments. Given that we were able to detect

even weak associations and that these relationships were consistent with local-scale studies, we recommend

using these relationships to further refine essential fish habitat and that they be given more weight in this and

similar temperate ecosystems.

For species such as demersal fish that live in close

association with the seafloor, benthic sediments may

influence distribution, abundance, and production in

multiple ways. Spawning grounds are often described

in terms of the substrate type present (Collette and

Klein-MacPhee 2002), and substrate grain size is

associated with differential predation and survival rates

for several flatfish and gadid species (Gibson and Robb

1992; Gotceitas and Brown 1993; Tupper and Boutilier

1995; Lindholm et al. 1999). Variation in prey fields

and diet composition that are associated with sediment

type may also have implications for fish trophody-

namics (e.g., McConnaughey and Smith 2000). De-

finitive species–substrate associations have been

established with relatively localized spatial-scale meas-

urements (e.g., Auster et al. 2003a, 2003b). Establish-

ing these relationships at the broader spatial scales on

which marine fish populations and their associated

fisheries operate have been limited by the paucity of

synoptic information on the character and distribution

of benthic substrates (Scott 1982; Mahon and Smith

1989; Auster et al. 1997b; McConnaughey and Smith

2000). Depth and temperature are known to delineate

groundfish distributions at broad scales (Overholtz and

Tyler 1985; Murawski and Finn 1988; Gabriel 1992;

Perry and Smith 1994), but how relationships with

benthic substrates influence patterns at comparable

scales are not well understood.

Delineating essential fish habitat (EFH) is an

important element of fisheries management (Kaiser et

al. 1999; Fluharty 2000; Worm et al. 2003). In the

USA, habitat provisions were incorporated into federal

legislation in 1996 when the Sustainable Fisheries Act

mandated the identification and description of EFH for

federally managed species (Fluharty 2000). Much

research has since sought to refine habitat requirements

for individual species and their life history stages (e.g.,

Collie et al. 2000; Kaiser and de Groot 2000; Phelan et

al. 2001; Manderson et al. 2002). In large marine

systems, most EFH and habitat issues tend to focus on

substrate type (e.g., Langton et al. 1996). Spatial

management tools, such as marine protected areas, area

closures, and related approaches, are enhanced by

a refined understanding of how fish associate with

factors describing EFH (Schmitten 1999; Fluharty

2000; Shipley 2004). The vulnerability of seafloor

* Corresponding author: methratt@sas.upenn.edu1 Present address: Department of Biology, University of

Pennsylvania, Philadelphia, Pennsylvania 19104, USA.

Received March 18, 2005; accepted October 4, 2005Published online May 30, 2006

473

North American Journal of Fisheries Management 26:473–489, 2006� Copyright by the American Fisheries Society 2006DOI: 10.1577/M05-041.1

[Article]

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habitats to degradation has further elevated the

importance of delineating EFH (Jennings and Kaiser

1998; Norse and Watling 1999; Kaiser et al. 2003).

Integrating EFH definitions with spatial management

approaches simultaneously prevents the removal of

some amount of fish biomass (even if only seasonally)

and protects essential habitat from the damaging effects

of mobile bottom-tending gears (Auster and Shackell

2000). Spatial management approaches have had

notably positive effects in tropical reef ecosystems,

where our knowledge of the benthic landscape and how

species relate to that landscape is more detailed (e.g.,

Garcıa-Charton and Perez-Ruzafa 1999; Roberts et al.

2001; Friedlander et al. 2003; Halpern 2003). Positive

effects in the tropics are further incentive to refine

temperate EFH designations (Auster et al. 1997b;

Steneck et al. 1997; Auster and Shackell 2000).

Our objective was to examine groundfish–substrate

relationships in a broad-scale, multispecies context for

the Gulf of Maine–Georges Bank region. Consistently

high abundance in a particular substrate would suggest

a possible functional relationship with that substrate or

with some factor(s) that covaries with substrate type

(e.g., food resources; McConnaughey and Smith 2000).

The Gulf of Maine–Georges Bank region supports

economically important fisheries for multiple species

(Sissenwine et al. 1984; Bax 1991). An enhanced

understanding of how groundfish relate to their habitat

may improve our ability to manage these natural

marine resources.

Methods

Biotic variables.—Biomass data (kg/tow) for multi-

ple finfish species were obtained from the Northeast

Fisheries Science Center (NEFSC) bottom trawl survey

database. The surveys collect data from 350 to 400

sampling stations from Nova Scotia to Cape Hatteras

using a stratified random sampling design (NEFSC

1988). Within each stratum, 2.00 latitude 3 2.5 0

longitude rectangular sampling units are randomly

selected and each station is sampled with a No. 36

Yankee (or comparable) bottom trawl deployed for 30

min at a tow speed of 6.5 km/h. Several parameters,

including the taxonomic identification and biomass of

each species, are recorded for each tow. A more

detailed description of the bottom trawl sampling

design and methodology is published elsewhere

(Azarovitz 1981; NEFSC 1988). We combined fall

and spring survey data from the time block of 1998–

2002 and included species for which total mean

weight/tow was more than 2 kg during this time

period. Using this cutoff value eliminated outlier

species determined from a species biomass frequency

histogram. A grid composed of 699 100 spatial cells

(185.2 km2 per cell) was overlaid on the study area and

TABLE 1.—Names of the 58 species from the Gulf of

Maine–Georges Bank region included in the analysis of

species–substrate relationships, 1998–2002. Principal species

contributed 90% of the average dissimilarity (d) between

substrate types in pairwise comparisons. Species marked with

asterisks consistently distinguished substrates from each other

in pairwise comparisons (i.e., di/ SD(d

i) � 1.3).

Species

Principal species*Spiny dogfish Squalus acanthias*Little skate Leucoraja erinacea*Silver hake Merluccius bilinearis*Atlantic cod Gadus morhua*White hake Urophycis tenuis*Red hake Urophycis chuss*American plaice Hippoglossoides platessoides*Winter flounder Pseudopleuronectes americanus*Witch flounder Glyptocephalus cynoglossus*Longhorn sculpin Myoxocephalus octodecemspinosus*Sea raven Hemitripterus americanus*Goosefish Lophius americanusSmooth skate Malacoraja sentaWinter skate Leucoraja ocellataThorny skate Amblyraja radiataHaddock Melanogrammus aeglefinusPollock Pollachius virensSpotted hake Urophycis regiaSummer flounder Paralichthys dentatusFourspot flounder Paralichthys oblongusYellowtail flounder Limanda ferrugineaWindowpane Scophthalmus aquosusAcadian redfish Sebastes fasciatusOcean pout Zoarces americanus

Other speciesAtlantic hagfish Myxine glutinosaSmooth dogfish Mustelus canisAtlantic torpedo Torpedo nobilianaBarndoor skate Dipturus laevisClearnose skate Raja eglanteriaRosette skate Leucoraja garmaniBay anchovy Anchoa mitchilliStriped anchovy Anchoa hepsetusAtlantic argentine Argentina silusConger eel Conger oceanicusOffshore hake Merluccius albidusLongfin hake Urophycis chesteriFourbeard rockling Enchelyopus cimbriusCusk Brosme brosmeLongnose grenadier Coelorhynchus carminatusGreenland halibut Reinhardtius hippoglossoidesAtlantic halibut Hippoglossus hippoglossusAtlantic croaker Micropogonias undulatusStriped bass Morone saxatilisBlack sea bass Centropristis striataScup Stenotomus chrysopsWeakfish Cynoscion regalisSpot Leiostomus xanthurusTilefish Lopholatilus chamaeleonticepsBlackbelly rosefish Helicolenus dactylopterusLumpfish Cyclopterus lumpusNorthern searobin Prionotus carolinusStriped searobin Prionotus evolansArmored searobin Peristedion miniatumCunner Tautogolabrus adspersusNorthern sand lance Ammodytes dubiusWrymouth Cryptacanthodes maculatusAtlantic wolffish Anarhichas lupusFawn cusk-eel Lepophidium profundorum

474 METHRATTA AND LINK

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the sampling stations (i.e. tows) occurring in each

spatial cell were assigned to that cell. Data for 58

individual species (Table 1) were summed across

stations within a spatial cell. The mean biomass/tow for

each species within a spatial cell was determined by

dividing that sum by the number of stations in the

spatial cell. There were 3.68 6 0.19 (average, 6 95%

CI) bottom trawl sampling stations sampled per spatial

cell during the 1998–2002 time period (Figure 1).

Substrate grain size.—Sediment data were compiled

from 40 separate reports (Poppe et al. 2003). The vast

majority of samples (;95%) were collected with grab

sampling devices and the remainder used dredges and

cores (Poppe et al. 2003). Samples were collected

within 0–5 cm of the sediment–water interface. Data

were collected between the years 1936 and 2002; 96%

of the data were collected since 1960 and 86% were

collected since 1980 (Figure 2A). Because the

sediment data were from a compilation data set

reported by several authors, database elements varied

between studies. We chose to use particle diameter or

mean phi (particle size, mm ¼ 2(–phi)) because this

information was reported by most of the individual

studies. If the sediment lithology was reported without

a particle diameter or phi value, then the average grain

size for that lithology was used. There were 43,661

sediment samples in the data set used. There were

63.09 6 16.86 (average 6 95% CI) sediment samples

per spatial cell and most cells had between 1 and 10

samples (Figure 2B). The spatial distribution of

sediment samples indicates that, in general, shallower

areas were more densely sampled (Figure 3A). The

gridded biomass data were spatially joined to the

surficial sediment data set in a geographical informa-

tion systems environment. The mean grain size was

calculated for each spatial cell during the spatial join

procedure and the spatial cells were then assigned to

one of six grain size (substrate) categories based

generally on the Wentworth scale (Wentworth 1922):

clay (�0.0040 mm), silt (0.0040–0.062 mm), fine sand

(0.062–0.25 mm), coarse sand (0.25–2.0 mm), fine

rock (2.0–8.0 mm), or coarse rock (.8.0 mm; Figure

3B).

We assume that sediment grain size did not change

FIGURE 1.—The number of bottom trawl sampling stations per 100 (185.2-km2) spatial cell during the 1998–2002 time period

in the Gulf of Maine–Georges Bank region (minimum ¼ 1, maximum¼ 22).

SEDIMENTS AND GROUNDFISH DISTRIBUTIONS 475

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significantly during the sediment sampling period. This

is a reasonable assumption given that sediment

accretion rates in this region are on the order of 25–

130 cm every 1,000 years (Bothner et al. 1981) and

given that most of the sediment data were collected

since 1980 (Figure 2A). This assumption is made with

the caveat that other factors, such as severe weather and

mobile bottom fishing gears, probably affected bottom

types during this time period but have not been

quantified for the entire spatial extent of the study. We

also assume that the mean grain size calculated

throughout the spatial range of a 100 spatial cell was

an accurate descriptor of grain size for the whole

spatial cell. This assumption is probably more reason-

able in areas of lower hydrodynamic energy (Ross

1970), but was necessary based on the sediment

information available. We examined other metrics for

sediment grain size per spatial cell (e.g., variance in

grain size, maximum grain size, median grain size) but

did not obtain notably different patterns than when

using mean grain size.

Statistical analysis.—A Bray–Curtis similarity ma-

trix was constructed to determine the similarity

coefficients (S) between all possible pairwise combi-

nations of spatial cells. Biomass was fourth-root

transformed before analyses to downweight the

contribution of the most common species and to allow

intermediately abundant and relatively less common

species as well as common species to contribute to the

analysis (Clarke and Warwick 2001). The similarity

matrix was used in a nonparametric multidimensional

scaling procedure (NMDS) to examine relationships in

assemblage structure between spatial cells. This is an

iterative ordination procedure that continues to refine

interpoint distances on the ordination plot until

interpoint distances most closely reflect the similarity

(or dissimilarity [d ¼ S–1]) relationships within the

matrix. In the resulting ordination plots, the points that

are closer to each other represent spatial cells that have

assemblages that are more similar in species biomass

composition, whereas those further apart are more

dissimilar. The stress value associated with the

ordination plot describes how closely the ordination

reflects the relationships between samples in the

similarity matrix.

One-way analyses of similarity (ANOSIM) and

planned pairwise contrasts of the six substrate types

were used to test the null hypothesis of no difference in

species composition between substrate types with

Primer 5 software (Clarke and Gorley 2001; Clarke

and Warwick 2001). The similarity percentages routine

(SIMPER) was then used to determine the percentage

contribution of each species to the average between-

group dissimilarity (d) in species composition. The

average contribution (di) from the ith species to the

overall dissimilarity between two substrates was

determined for the species that contributed 90% of

the average dissimilarities between groups. Species for

which the average dissimilarity contribution (di) was

relatively high and the associated standard deviation

(SD[di]) relatively low in between-group comparisons

were considered to distinguish those two substrates

from each other consistently (i.e., di

/SD[di] � 1.3).

These species not only contribute to the dissimilarity

between two substrates, but do so consistently when

comparing samples within substrates (Clarke and

Warwick 2001).

Bonferroni corrections for multiple comparisons

were applied when examining the significance of all

ANOSIM tests. For ANOSIM, we report all significant

FIGURE 2.—Frequency of Gulf of Maine–Georges Bank

substrate data by (A) the year in which the data were collected

(expressed as the percentage of the total number of data

points) and (B) the sample sizes per 100 spatial cell. In (B), the

values on the x-axis represent the maximum number of

samples in each bin.

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results, but caution that when large sample sizes are

used with ANOSIM it is possible for the test statistic

(R) to be small but still reported as significant by the

software package (Clarke and Warwick 2001). The

concept of power is difficult to assess in nonparametric

tests such as ANOSIM, which make no distributional

assumptions (Clarke and Warwick 2001). However,

because statistical power is expected to increase with

sample size (Clarke and Warwick 2001), we expect

that this test is sufficient to detect existing patterns in

the data (n¼ 699 spatial cells). The same methods that

are described above were also carried out with species

abundance (numbers of individuals) instead of biomass

(kg/tow), and because the results of those tests yielded

similar results, they are not described here.

Results

The most common substrate type in this system was

coarse sand; there were 278 100 spatial cells assigned to

this category based on mean substrate grain size

(Figure 3B). One hundred fifty spatial cells were

assigned to the fine sand category and 108 spatial cells

were assigned to the fine rock category. The remaining

three substrates were less common: 84 spatial cells

were assigned to the silt category, 64 were assigned to

the coarse rock category, and 15 were assigned to the

clay category.

The NMDS ordination plot had a stress of 0.16,

suggesting that it is a potentially useful two-dimen-

sional representation of the relationships in the

dissimilarity matrix (Figure 4; Clarke and Warwick

2001). The interpoint distances within substrate types

are large for most substrates. This indicates a large

amount of variance within individual substrate types,

a result not unexpected. Interpoint distances between

substrate types on the ordination were greatest between

the three largest and three smallest grain sizes. The

ANOSIM global R-statistic was low but significant (R¼ 0.138; P , 0.01). The results of the ANOSIM

showed significant differences in assemblage structure

between substrates, particularly between the three

largest and three smallest grain sizes (Table 2).

However, the significance of these R-values should

be interpreted with caution because of their relatively

small magnitude and because of the large sample size

of the analysis (Clarke and Warwick 2001).

The similarity percentages routine (SIMPER) iden-

tified the species that contributed the most to the

dissimilarities between substrate types. Average dis-

similarity between substrate types (d) varied between

44.6% and 67.2% (Table 2). The 24 principal species

in Table 1 were those that contributed 90% of the

overall dissimilarity between substrate types in all

pairwise comparisons. Four general patterns were

identified. First, the biomass of Atlantic cod, haddock,

winter flounder, longhorn sculpin, sea raven, and

winter skate increased with increasing substrate grain

size (Figure 5). Second, the biomass of goosefish,

white hake, red hake, silver hake, witch flounder,

American plaice, and thorny skate increased with

decreasing grain size (Figure 6). Third, the biomass of

little skate, ocean pout, summer flounder, fourspot

flounder, yellowtail flounder, windowpane, and spotted

hake was highest in habitats of intermediate grain size

(Figure 7). Fourth, smooth skate, spiny dogfish,

Acadian redfish, and pollock were broadly distributed

across most substrate types (Figure 8).

Of these 24 species, the SIMPER routine identified

12 species that had di

/SD(di) values of 1.3 or greater

(i.e., they consistently contributed to dissimilarity

between particular substrate types). These species

included Atlantic cod, longhorn sculpin, sea raven,

and winter flounder, which were most abundant in

large-grained substrate types in northern coastal New

England and portions of Georges Bank (Figure 9). In

particular, longhorn sculpin and winter flounder

consistently distinguished coarse rock from all other

substrate types (Table 3). Similarly, relatively higher

abundances of Atlantic cod consistently distinguished

fine rock from all finer-grained substrate types (Table

3). Goosefish, white hake, red hake, silver hake, witch

flounder, and American plaice were more consistently

abundant in finer-grained substrates (Table 3). Goose-

fish was most prevalent in southern New England,

whereas white hake, red hake, American plaice, and

witch flounder were most abundant in the Gulf of

Maine (Figure 10). Silver hake had a wide spatial

distribution (Figure 10d), but was consistently more

abundant in clay than in coarse sand substrates (Table

3). Spiny dogfish was also broadly distributed across

sediment types (Figure 11A), but was consistently

more abundant in coarse sand habitats than in clay

(Table 3). In intermediate-sized substrates, little skate

was consistently abundant, particularly in southern

New England (Figure 11B; Table 3).

DiscussionSpecies Distributions and Surficial Sediments

Defining fish habitats in large marine ecosystems

remains a unique challenge. In small freshwater (e.g.,

ponds and streams) and tropical reef systems, where

fish have relatively small home ranges and high site

fidelity, delineating habitat is relatively straightforward

(e.g., Friedlander and Parrish 1998; Holtgren and Auer

2004; Lewin et al. 2004). In large marine ecosystems,

local-scale relationships have been identified for some

species (e.g., Lough et al. 1989; Phelan et al. 2001;

Auster et al. 2003a, 2003b). However, at the more

SEDIMENTS AND GROUNDFISH DISTRIBUTIONS 477

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synoptic scales at which fish populations and their

associated fisheries operate, definitive population-level

habitat associations are masked by changing levels of

habitat fidelity through ontogeny (e.g., Swain 1993;

Steves et al. 1999; Sullivan et al. 2000), associations

with local-scale topographic features on the seafloor

(e.g., Auster et al. 1991), and the high mobility and low

site fidelity of fish in these systems (Metcalfe et al.

2002). Thus, even detecting habitat associations at

broad, appropriately resolved scales are not trivial for

these species.

The species–substrate relationships that we did

observe occurred in the smallest and largest substrate

types and were related to the biology of individual

species. Species consistently associated with larger

substrate grain sizes included Atlantic cod, longhorn

sculpin, sea raven, and winter flounder. For Atlantic

cod, rock and cobble substrates in nursery areas

provide spatial refuges for juveniles and adults that

reduce predation rates and increase survivorship

(Lough et al. 1989; Gotceitas and Brown 1993; Tupper

and Boutilier 1995; Lindholm et al. 1999). Shallow

rocky areas also provide ambush sites and spawning

grounds for species such as sea raven and spawning

grounds for longhorn sculpin (Scott and Scott 1988;

Auster et al. 1991; Collette and Klein-MacPhee 2002).

Similarly, winter flounder spawn in inshore waters

where coarse sediments are common (Pereira et al.

1999) and larger individuals may prefer coarser-

grained sediments (Phelan et al. 2001).

Those species that were consistently abundant in the

smaller substrate grain sizes included goosefish, red

hake, silver hake, white hake, American plaice, and

witch flounder. Goosefish and flatfish, such as

American plaice and witch flounder, use sandy sedi-

ments to bury themselves while ambushing prey and

hiding from predators (Gibson and Robb 1992; Steimle

et al. 1999b). Red hake and silver hake also commonly

occur on soft sediments where they are often found

associated with sand waves or resting in depressions in

the sediment (Edwards and Emery 1968; Auster et al.

1991, 2003a; Steimle et al. 1999a).

FIGURE 3.—The spatial distribution of substrate data for the Gulf of Maine–Georges Bank region by (a) sample size (minimum

¼ 1, maximum¼ 2,570) and (b) substrate type at the spatial resolution of 100 spatial cells.

478 METHRATTA AND LINK

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Little skate was the only species that was consis-

tently abundant in intermediate-sized sediments. This is

consistent with previous reports of little skate associ-

ations with sandy bottoms, biogenic depressions, and

flat sand that have been reported at fine scales

(McEachran and Musick 1975; Auster et al. 1995).

Spiny dogfish was particularly abundant in coarse sand

habitats but was also common in most substrate types.

The ovoviviparous lifestyle of spiny dogfish, coupled

with its high mobility and pelagic diet, reduces

associations of this species with the benthos (McMillan

and Morse 1999).

Fish assemblages or communities in large, temperate

marine ecosystems are usually defined by similarity in

spatial distributions and the temporal consistency of

spatial overlap (e.g., Gabriel 1992; Garrison 2000).

These patterns are generally thought to be maintained

by depth gradients and hydrographic circulation

regimes (Gabriel 1992). Some research suggests that

substrate type may also influence community-level

patterns through mediation of trophic relationships

(Scott 1982; Auster et al. 1997a; McConnaughey and

Smith 2000). In the Gulf of Alaska, for example,

substrate type appears to mediate food habits for

flatfish (McConnaughey and Smith 2000). These

benthic predators feed on benthic invertebrates whose

spatial distribution is correlated with surficial sedi-

ments (Grebmeier et al. 1989). Similarly, in the Middle

Atlantic Bight region of the northeastern U.S. Conti-

nental Shelf, local-scale research suggests that the

demonstrated relationship between juvenile silver hake

and sand–silt bottoms is related to their association

with tube-dwelling amphipods Amphipoda spp., which

may provide a food source (bristled longbeak Diche-

lopandalus leptoceras and sand shrimp Crangon

septemspinosa; Auster et al. 1997a). However, the

degree to which substrate-mediated food habits may

affect bottom-type associations at broad scales in the

Gulf of Maine–Georges Bank region is not well known

and requires further study.

Broad-scale studies such as presented here comple-

ment and expand upon those conducted at a more

limited spatial extent. For most species, the patterns

described here are consistent with those described

FIGURE 3.—Continued.

SEDIMENTS AND GROUNDFISH DISTRIBUTIONS 479

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FIGURE 4.—Nonmetric multidimensional scaling (NMDS) plot illustrating the dissimilarity in fish assemblages among (A�F)substrate types in the Gulf of Maine–Georges Bank region. The NMDS plot was separated into six identically scaled plots based

on substrate type so that symbols could be readily identified and interpoint distances could be compared between substrate types.

Stress¼ 0.16.

TABLE 2.—Test statistics (R) from the pairwise analysis of similarity (ANOSIM) between-group comparisons. The average

between-group dissimilarity (d) given by the similarity percentages (SIMPER) routine is shown in parentheses. The overall effect

of substrate was significant (R ¼ 0.138; P , 0.01). Bonferroni-corrected significance levels are as follows: P , 0.05*, P ,

0.01**; a nonsignificant result is indicated by ‘‘ns.’’

Substrate Coarse rock Fine rock Coarse sand Fine sand Silt Clay

Coarse rock ns ns 0.217** 0.340** 0.145*(60.4%) (62.1%) (67.2%) (63.9%) (64.9%)

Fine rock ns 0.224** 0.277** 0.225**(60.5%) (66.4%) (63.9%) (64.8%)

Coarse sand 0.143** 0.185** 0.276**(63.4%) (63.2%) (67.0%)

Fine sand ns ns(57.2%) (59.2%)

Silt ns(44.6%)

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locally (e.g., Auster et al. 1995; Tupper and Boutilier

1995, 1997). However, some species included in this

analysis, such as Atlantic wolffish, fourbeard rockling,

cunner, and tilefish, did not have the substrate

associations suggested by previous smaller-scale stud-

ies (Able et al. 1993; Auster et al. 1998, 2001). This

departure from smaller-scale work may be related to an

enhanced ability to detect habitat heterogeneity and

microhabitat features at local scales (e.g., Able et al.

1993; Auster et al. 1995). Additionally, some species

known to have regular substrate relationships, such as

haddock, did not show consistent sediment associations

here. However, previous studies have specifically

focused on juveniles (e.g., Lough et al. 1989; Brickman

2003), which tend to have greater habitat fidelity owing

to the survival advantage gained in particular habitat

types (Tupper and Boutilier 1995, 1997; Steves and

Cowen 2000; Sullivan et al. 2000). Additionally, other

factors such as bathymetry, thermal variation, seasonal

variation, and prey field may influence distribution

patterns and could obscure definitive substrate relation-

ships (Overholtz and Tyler 1985; Murawski and Finn

1988; Perry and Smith 1994; McConnaughey and

Smith 2000). Although local-scale studies contribute to

our mechanistic understanding of fish–habitat associ-

ations, more work is needed at the broad spatial scales

on which fish populations occur and are managed.

Implications for Fisheries Management

Delineating EFH is an important issue for fisheries

in the USA and around the world (Kaiser et al. 1999;

Fluharty 2000; Worm et al. 2003), particularly as

fisheries managers continue to explore spatial manage-

ment options (Shipley 2004). Marine reserves, area

closures, and related approaches have had notable

success in tropical reef ecosystems (e.g., Roberts et al.

2001). In temperate systems, similar approaches have

been most beneficial for shallow-water sedentary

groups, such as flatfish and skates, but have been less

so for more migratory species (Murawski et al. 2000).

Despite these mixed results from temperate systems,

area closures still prevent the removal of some amount

of biomass, serve as mortality refugia (even if only

seasonally), and may provide potential source popula-

tions, even for species that are not tightly linked to the

benthos (Murawski et al. 2000, 2004). Thus, from

a precautionary perspective, spatial management ap-

proaches are important tools for fisheries management

(Fogarty 1999; Shipley 2004).

Habitat degradation caused by mobile fishing gears

is a consideration for both fish populations and their

habitats (e.g., Jennings and Kaiser 1998). Damage and

removal of structure-providing epifauna, smoothing of

sedimentary bedforms, and the removal of benthic

macrofauna that actively produce structures have been

well documented (e.g., Kaiser et al. 1999; Norse and

TABLE 3.—Species that consistently distinguished assemblages in particular substrate types in pairwise between-substrate-type

comparisons (i.e., di/SD [d

i] � 1.3). Upward-pointing arrows indicate that species biomass is higher in the row substrate type

than in the column substrate type (e.g., longhorn sculpin biomass is higher in coarse rock than in fine rock substrates).

Downward-pointing arrows indicate that species biomass is lower in the row substrate type than in the column substrate type.

Substrate Coarse rock Fine rock Coarse sand Fine sand Silt Clay

Coarse Longhorn sculpin" Longhorn sculpin" Longhorn sculpin" Longhorn sculpin" Longhorn sculpin"rock Winter flounder" Winter flounder" Winter flounder" Winter flounder" Winter flounder"

Little skate# Sea raven" Sea raven"Goosefish# Goosefish#Red hake# Red hake#American plaice# White hake#Witch flounder# Witch flounder#

Fine Atlantic cod" Atlantic cod" Atlantic cod" Atlantic cod"rock Little skate# Longhorn sculpin" Longhorn sculpin"

Goosefish# Goosefish#Witch flounder# Witch flounder#

White hake#American plaice#

Coarse Little skate" Little skate" Little skate"sand Red hake# Red hake# Red hake#

White hake# White hake#Witch flounder# Witch flounder#

American plaice#Silver hake#Spiny dogfish"Goosefish#

Fine White hake#sand American plaice#silt

SEDIMENTS AND GROUNDFISH DISTRIBUTIONS 481

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Watling 1999; Collie et al. 2000; Kaiser and de Groot

2000). Species that associate with more complex

substrates may be especially vulnerable to habitat

degradation because of the long recovery time expected

for more complex habitats after disturbance (Collie et

al. 1997; Dernie et al. 2003). Integrating EFH

designations into spatial management approaches for

temperate demersal fish may mitigate such disturban-

ces to physical habitats (Auster and Shackell 2000).

The paucity of comprehensive information on the

character and distribution of benthic habitats has been

an impediment to establishing broad-scale species–

habitat relationships. Although the sediment data set

used here is one of the most comprehensive for any

large system, this information still must be broadly

interpolated to explore macroecological patterns. In

addition to sediment grain size, little is known in

a synoptic sense about other sea bottom attributes, such

as biogenic structures, waveforms, and burrows that

may influence distributional patterns (Auster et al.

1995, 2003a, 2003b; Kaiser et al. 1999). Further

refinements of the factors delineating EFH, increased

knowledge of habitat distributions, and an integration

of this information across spatial scales will advance

our ability to manage living marine resources.

Fish–habitat relationships remain coarsely resolved

at the spatial scale of fisheries operations and

management. Yet, the delineation of EFH is mandated

by federal legislation, the use of spatial management

tools is increasing, and the degradation of benthic

habitats is ongoing. Given these considerations and

given that we were able to detect even weak

FIGURE 5.—Mean biomass across spatial cells in each substrate type for (A�F) the six species generally associated with large-

grained sediments in the Gulf of Maine–Georges Bank region. Values represent meansþ 95% confidence intervals.

482 METHRATTA AND LINK

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associations, we recommend integrating this informa-

tion with other known habitat factors (e.g., depth,

temperature, etc.) to further refine EFH and that it be

given more weight in this and similar temperate

ecosystems.

Acknowledgments

We acknowledge the efforts of all those at the

NEFSC who have contributed to the planning and

execution of the bottom trawl surveys. We thank the

staff of the NEFSC for their dedicated work in auditing

and maintaining the database for the bottom trawl

surveys. We also thank Bill Overholtz, John Man-

derson, Lance Garrison, and three anonymous re-

viewers for their helpful comments on earlier versions

of this manuscript. This work was supported, in part,

FIGURE 6.—Mean biomass across spatial cells in each substrate type for (A�G) the seven species generally associated with

fine-grained sediments in the Gulf of Maine–Georges Bank region. Values represent means þ 95% confidence intervals.

SEDIMENTS AND GROUNDFISH DISTRIBUTIONS 483

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by a postdoctoral research associateship awarded by

the National Research Council (USA) to E. Methratta.

Reference to trade names does not imply endorsement

by the U.S. Government.

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