U.S. Department of Commerce

Wilbur Ross, Secretary

National Oceanic andAtmospheric Administration

U.S. Department of Commerce

TIME-SERIES PATTERNS OF SPECIES 

RICHNESS, DIVERSITY, AND COMMUNITY 

COMPOSITION OF FISHES AT 

STELLWAGEN BANK NATIONAL MARINE 

SANCTUARY (1970-2017)

April 2019 | sanctuaries.noaa.gov

NATIONAL MARINE SANCTUARIES CONSERVATION SERIES ONMS-19-04
U.S. Department of Commerce

Wilbur Ross, Secretary

National Oceanic and Atmospheric Administration

Dr. Neil Jacobs, Administrator (Acting)

National Ocean Service

Nicole LeBoeuf, Assistant Administrator (Acting)

Office of National Marine Sanctuaries

John Armor, Director

Report Authors:

Peter J. Auster1,2 & Christian W. Conroy2

1

Mystic Aquarium – Sea Research Foundation, 55 

Coogan Blvd., Mystic, Connecticut 06355, USA

2

Department of Marine Sciences, University of 

Connecticut, 1080 Shennecossett Rd., Groton, 

Connecticut 06340, USA

Suggested Citation:

Auster, P.J. and C.W. Conroy. 2019. Time-series patterns 

and dynamics of species richness, diversity, and community 

composition of fishes at Stellwagen Bank National Marine 

Sanctuary (1970-2017). Marine Sanctuaries Conservation 

Series ONMS-19-04. U.S. Department of Commerce, 

National Oceanic and Atmospheric Administration, Office of 

National Marine Sanctuaries, Silver Spring, MD. 58 pp. 

Cover Photo:

A diversity of fish species are found at Stellwagen Bank 

National Marine Sanctuary. Photos: NOAA
i

About the National Marine Sanctuaries

Conservation Series

The Office of National Marine Sanctuaries, part of the National Oceanic and Atmospheric 

Administration, serves as the trustee for a system of underwater parks encompassing more 

than 620,000 square miles of ocean and Great Lakes waters. The 13 national marine 

sanctuaries and two marine national monuments within the National Marine Sanctuary 

System represent areas of America’s ocean and Great Lakes environment that are of special 

national significance. Within their waters, giant humpback whales breed and calve their 

young, coral colonies flourish, and shipwrecks tell stories of our maritime history. Habitats 

include beautiful coral reefs, lush kelp forests, whale migration corridors, spectacular deepsea canyons, and underwater archaeological sites. These special places also provide homes 

to thousands of unique or endangered species and are important to America’s cultural 

heritage. Sites range in size from less than one square mile to more than 582,000 square 

miles. They serve as natural classrooms and cherished recreational spots, and are home to 

valuable commercial industries.

Because of considerable differences in settings, resources, and threats, each site within the 

National Marine Sanctuary System has a tailored management plan. Conservation, 

education, research, monitoring, and enforcement programs vary accordingly. The 

integration of these programs is fundamental to marine protected area management. The 

Marine Sanctuaries Conservation Series reflects and supports this integration by providing 

a forum for publication and discussion of the complex issues currently facing the sanctuary 

system. Topics of published reports vary substantially and may include descriptions of 

educational programs, discussions on resource management issues, and results of scientific 

research and monitoring projects. The series facilitates integration of natural sciences, 

socioeconomic and cultural sciences, education, and policy development to accomplish the 

diverse needs of NOAA’s resource protection mandate. All publications are available on 

the Office of National Marine Sanctuaries website (http://www.sanctuaries.noaa.gov).
ii

Disclaimer

Report content does not necessarily reflect the views and policies of the Office of National 

Marine Sanctuaries or the National Oceanic and Atmospheric Administration, nor does the 

mention of trade names or commercial products constitute endorsement or recommendation 

for use.

Report Availability

Electronic copies of this report may be downloaded from the Office of National Marine 

Sanctuaries website at http://sanctuaries.noaa.gov. 

Contact

Peter J. Auster

Senior Research Scientist

Mystic Aquarium – Sea Research Foundation

35 Coogan Blvd.

Mystic, Connecticut 06355 USA

and 

Research Professor Emeritus

Department of Marine Sciences

University of Connecticut at Avery Point

Groton, Connecticut 06340

860-405-9118

pauster@mysticaquarium.org and peter.auster@uconn.edu
iii

Table of Contents

Table of Contents...............................................................................................................iii

Abstract.............................................................................................................................. iv

Key Words......................................................................................................................... iv

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

Methods............................................................................................................................... 2

Northeast Fisheries Science Center Bottom Trawl Survey Data .................................... 2

Diversity Metrics ............................................................................................................ 3

Community Composition................................................................................................ 4

Results................................................................................................................................. 5

Discussion........................................................................................................................... 8

Tables................................................................................................................................ 12

Figures............................................................................................................................... 27

Literature Cited ................................................................................................................. 52

Acknowledgements........................................................................................................... 58
iv

Abstract

Here we present analyses conducted in support of the most recent Stellwagen Bank 

National Marine Sanctuary (SBNMS) Condition Report for 2008-2017. Our focus was on 

patterns and trends in species richness, diversity, and community composition of fishes in 

SBNMS and surrounding waters over a nearly 50-year period. These analyses of the 

larger Gulf of Maine, in which SBNMS is nested, are used to compare regional and local 

scale patterns and trends. The NOAA Northeast Fisheries Science Center bottom trawl 

survey data, based on a stratified random sampling design, was used to represent the fish 

fauna of SBNMS (sampling stratum 26) along with 19 additional strata (i.e., 21-40) for 

the larger Gulf of Maine. Our results demonstrate that fish communities in SBNMS have 

changed substantially during the past decade. Following a long period of slowly rising 

species richness through 2006, richness rose rapidly over the last decade. This change

coincided with changes in composition and patterns of numerical dominance for both 

local (i.e., SBNMS) and large-scale (i.e., Gulf of Maine) fish communities as well. Depth 

was the most significant correlate of fish community structure, but the threshold between 

shallow and deep communities has moved from 52.5 to 75.5 m over time. Further, 

composition and distribution of communities were influenced by temperature. For 

example, Acadian redfish were more common when bottom temperature was <5.7°C 

while American plaice, longhorn sculpin, yellowtail and witch flounder, and silver and 

white hake were associated with warmer bottom conditions. Over the past decade, 

shallow communities south of 42°N were characterized by higher abundances of warmtolerant species, like Atlantic mackerel and little skate, while the cold-associated species 

like haddock are much more abundant north of this latitude. Based on related studies, 

these community scale changes are attributed to changes in fisheries management, 

changes in species interactions mediated by changes in species and trophic guild 

abundance, and shifts in the distributions and abundances of fish species due to climate 

change as a direct or indirect driver. While maintaining and enhancing diversity is a 

central mission of the sanctuary, the structural changes to its communities is concerning 

and deserves additional investigation. Identifying the drivers of these changes is 

important and may provide some insight on what policies might mitigate adverse changes 

while not sacrificing the benefits of diversity.

Key Words

Trawl survey, catch per unit effort, multivariate, multidimensional scaling, regression 

tree, condition report
1

Introduction

Stellwagen Bank National Marine Sanctuary (SBNMS) is set at the eastern edge of 

Massachusetts Bay between Cape Ann and Cape Cod in the western Gulf of Maine 

(northwest Atlantic), a sub-region of the Northeast United States Large Marine 

Ecosystem (Sherman et al. 1996, Cook and Auster 2007). SBNMS bounds an area of 

1281 km2 (638 nm2) with diverse seafloor topography including Stellwagen Bank, Tillies

Bank, the southern portion of Jeffreys Ledge, Stellwagen Basin, and numerous smaller 

topographic rises and basins (ONMS 2010). The seafloor habitats and overlying waters 

that produce a complex marine seascape support a diversity of fish species that are 

broadly representative of the Gulf of Maine region (Auster 2002, Auster et al. 1998, 

2006). Designated in 1992 under the National Marine Sanctuaries Act (NMSA 1992, 

2000) as the 12th such sanctuary, SBNMS was established as a means for conservation, 

protection, and enhancement of this productive natural system (ONMS 1993).

National marine sanctuary management plans are reviewed periodically to assess whether 

management policies adequately meet the goals and serve the stated mission of the 

sanctuary. This review process is meant to assess whether management plans and their 

implementation have supported conservation objectives, to identify areas where changes 

to policies could improve conservation, and to identify where additional research is 

needed (Battista et al. 2006). These reviews are both mandated by law (NMSA 2000) and

are a requisite aspect of adaptive management, which is a key component of effective 

conservation in marine protected areas (Edgar et al. 2014, McCook et al. 2010).

The management plan review process relies on evaluating the state and dynamics of 

sanctuary resources in the context of both the past and the wider region. Here we present 

analyses conducted as part of the most recent sanctuary condition report (ONMS in 

press). Our focus was on patterns and trends in species richness, diversity, and 

community composition of fishes in SBNMS and surrounding waters over a nearly 50-

year period. These analyses of the larger Gulf of Maine, in which SBNMS is nested, are 

used to compare regional and local scale patterns and trends. In the decade since the last 

such report was published (see NMSP 2007) there have been substantial changes in and 

around the sanctuary and in the greater region (e.g., patterns of fishing, vessel traffic, and 

associated noise; ONMS in press), and particularly as the rate of ocean warming has 

climbed steeply (Saba et al. 2016). Both natural and anthropogenic drivers influence fish 

population structure and distribution, spatial patterns of fish diversity, and components of 

community structure (e.g., Auster et al. 2006, Auster and Link 2009, Hare et al. 2012). 

The current condition review process facilitated the opportunity to investigate the current 

state of SBNMS’s living resources given these changes.
2

Methods

We used multiple approaches to compare the present and past status of demersal fish 

assemblages within SBNMS and throughout the Gulf of Maine. Since one of the central 

missions of the sanctuary is to “conserve, protect and enhance the biological diversity 

[and] ecological integrity” of SBNMS (ONMS 2010), we attempted to thoroughly 

characterize diversity and community composition. To these ends, we used community 

scale metrics to provide easily interpretable, univariate measures of diversity, followed 

by multivariate analyses of community composition (i.e., the species that were identified 

during sampling and their abundances; species listed in table 1) to investigate observed 

diversity. When combined, these approaches can provide an overall picture of demersal 

fish biodiversity, how diversity has changed through time, what drivers may have 

contributed to these changes, and what individual species may typify observed changes. 

Fishery independent sampling datasets provide an excellent source of abundance data for 

use in characterizing fish diversity at the larger geographic scales in this study (Auster 

2002). Northeast Fisheries Science Center (NEFSC) bottom trawl survey data collected

between 1970 and 2017 were used in these analyses, with each individual tow being 

treated as a community sample (Politis et al. 2014).

Northeast Fisheries Science Center bottom trawl survey data

The NEFSC bottom trawl survey maintains the stratified random sampling design

established in 1963 to sample the U.S. continental shelf from North Carolina through 

Maine. Randomly selected trawl stations within depth- and latitude-based strata are 

sampled during the spring and fall of each year (Grosslein 1969). We used data collected 

in stratum 26 to represent SBNMS and data collected in strata 21-40 for the larger Gulf of 

Maine (figure 1a). Despite the limited number of tows conducted within each stratum 

during any given survey season and year, the geographic scope of the stratum is 

represented over a period of multiple and consecutive years (figure 1b). Similar 

geographic and temporal delineations were used in fish diversity analyses that 

contributed to the 2007 sanctuary condition report (Auster et al. 2006, NMSP 2007).

The bottom trawl survey samples a wide range of demersal and benthic species, including 

both vertebrate and invertebrate fauna. We were interested in vertebrate diversity, 

therefore we limited our analyses to finfish, skates, rays, and sharks. Although survey 

scientists collect a large amount of information during sample processing, including sex 

and stage for some species, we limited our analysis to catch enumerated as number of 

individuals by species. Individual abundance indices were produced for each finfish 

species and tow. However, it is important to note that these indices are not measures of 

raw abundance or density due to variations in catchability by the sampling gear (Politis et 

al. 2014). Abundance indices were log-transformed, ln(x+1), to reduce the influence of 

rare outliers (i.e., large abundances) on results for multivariate procedures 

(McConnaughey and Conquest 1993). Patterns of abundance for individual species, based 
3

on catch-per-unit effort (CPUE; catch-per-tow), are reported as untransformed 

abundances. Additional data recorded for each tow include time, location, depth, 

duration, and temperature. We excluded from our analyses tows that were less than 15 

minutes in duration; short tows sample less bottom area than standard duration tows, 

which would inevitably influence species abundance indices. For all analyses seasonal 

variation was addressed by parsing the data set into spring (February – June) and fall 

(July – December) periods and decadal variation by parsing tows as 1970s (i.e., 1970-

1979), 1980s, 1990s, 2000s, and 2010s. Overall sample size for Stellwagen Bank 

National Marine Sanctuary and adjacent waters (stratum 26) was 488 tows, while region 

wide samples across the Gulf of Maine consisted of 7624 tows. Over the course of the 

time series several vessels and trawl designs were used to collect data. We used raw 

count data from across time as catch by species was positively and significantly 

correlated for most taxa (e.g., Miller et al. 2010).

Diversity metrics

We used five commonly employed metrics (species richness, Shannon, Simpson, Chao 1, 

and Uniques) to characterize diversity in SBNMS (stratum 26) over the 50-year period. 

Species richness (S), and both Shannon (H’ log e) and Simpson (1-lambda’) diversity 

indices were computed for each tow using PRIMER software (v.7.0.13; Clarke and 

Gorely 2015). Species richness is simply a tally of the distinct species (or taxa) identified

within a sample. The Shannon and Simpson indices use the relative proportion of 

individuals belonging to a species within a sample to characterize diversity. Despite their 

similarity, these indices differ in their relative sensitivity to species rarity, as Shannon 

index values increase with species richness regardless of evenness (i.e., relative 

proportion of species within a sample) and Simpson index values increase with increasing 

evenness. We used linear regression models to identify trends in diversity indices through 

time using the R statistical program (v.3.4.2; R Core Team 2017). During this process, 

we used the approach developed by Muggeo (2008) to identify “breakpoints,” locations 

of changes in regression slopes. Breakpoints were confirmed by comparing regression 

coefficients before and after an identified break via tests to detect non-zero changes in 

slope (Davies 1987). We interpreted breakpoints in these models as changes in diversity 

trends and associated 95% confidence intervals of breakpoints as measures of how 

quickly trends changed.

For each decade in the time series, species richness “S (est)” as an estimate of the 

expected number of species in t pooled samples, was calculated based on the Chao 1 

richness estimator with 100 random restarts using Estimate S software (v.9.1.0; Colwell 

2013). The Chao 1 estimator relies on the relative rate of rare species in sampling to 

estimate total diversity asymptotically. The number of unique species “Uniques mean 

(Q1)” as the number of unique species that occur in only one sample over t pooled 

samples was also calculated using Estimate S, along with 95% confidence intervals and

standard deviations for both values. Minitab (v.14; Minitab, Inc. 2000) was used to 

produce boxplots for visualizing annual trends within seasonal samples as well as 

patterns in seasonal abundance, based on CPUE, of select species. The R statistical 
4

program (R Core Team 2017) was used to plot rarefication curves, with associated 

confidence intervals (S and Chao 1) or standard deviation (Q1). Curves were compared 

visually.

The breadth of these approaches belies the difficulty of defining diversity in a meaningful 

way, even in the specific case of demersal fish diversity within a geographically limited 

sanctuary. However, a more complete representation of diversity is possible by 

combining these different indices and estimates, which accounts for the influence of both 

rare and dominant species (Magurran 2004, Morris et al. 2014). By using multiple 

measures to characterize diversity of this time series dataset, we provide a more 

comprehensive accounting of how fish assemblages within SBNMS have changed over 

time relative to those of the region as a whole.

Community composition

We investigated the composition of communities sampled in SBNMS using multivariate 

analyses and data visualization approaches. Multivariate comparisons of species 

composition by season and decade at SBNMS (stratum 26) were calculated using 

PRIMER software (Clarke and Gorely 2015). Non-metric multidimensional scaling 

(nMDS) was used to visualize the similarity in species composition in 2D and 3D plots, 

with calculated stress level indicating the most parsimonious approach for interpreting 

relationships. A Bray-Curtis similarity matrix was calculated to implement the 

multivariate comparisons. Analysis of Similarities (ANOSIM) routines were calculated to 

contrast the significance of differences in community composition over time and 

Similarity-Percentage (SIMPER) routines were used to identify the dominant species 

driving differences.

The influences of potential drivers on observed changes in demersal fish assemblages

both within SBNMS and throughout the larger Gulf of Maine were investigated via a 

multivariate extension of decision tree analysis (multivariate regression trees, MRT; 

De’ath 2002). MRT models are created by recursively splitting the response variables, in 

this case species, into two groups based on the associated values of predictor variables, 

which were geographic location (decimal degrees latitude) and depth (m) at the start of a 

tow, bottom temperature (°C), as well as the date, time, and season when sampling 

occurred. Which predictor variable is the basis of the split and the value at which 

partitioning takes place must maximize the homogeneity of the resulting groups (Breiman 

et al. 1984). The final models are readily interpretable in an ecological context based on 

the limited number of predictor variable thresholds (“partitions” or “splits”) that define 

groups of responses (“terminal nodes” or “leaves”; De’ath and Fabricius 2000, Breiman 

2001). Since temperature, depth, or latitude was missing from some trawl records, we 

employed Breiman et al.’s (1984) majority direction approach for assigning those records 

featuring missing data to nodes, which allowed us to retain all trawl records throughout 

the analysis. Following model construction, we focused on species that drove each 

partition; species were considered “drivers” if they contributed ≥5 percent of the total 

variance explained by the partition. These analyses were conducted using the R statistical 

program (R Core Team 2017).
5

Results

Diversity of demersal fishes within SBNMS has varied substantially year to year (figure 

2). Our direct measures of diversity, based on annual mean values of species richness 

(14.17 ±0.19se), Shannon index (1.58 ±0.02se), and Simpson index (0.68 ±0.01se) all 

fluctuated substantially, as evidenced by their relatively high coefficient of variation 

values of 35.6, 33.1, and 29.1 percent, respectively. Despite this observed variance, 

species richness per tow during both spring and fall periods increased significantly 

throughout the time series (figure 3a). Richness slowly increased over the earlier part of 

the time series (1970 to ca. 2006; β1 = 0.11, F = 150.8, p = 2.2·10-16), but increased 

rapidly over the past decade (β1 = 0.75, F = 53.1, p = 8.7·10-13). However, neither of the 

diversity metrics incorporating relative abundance, Shannon and Simpson indices, 

followed clear trends of increasing or decreasing diversity (figure 3b). Unsurprisingly, 

neither linear models of Shannon (F = 0.40, p = 0.75) or Simpson (F = 0.26, p = 0.85) 

detected clear trends and no breakpoints were identified in the time series. This recent 

rise in species richness was also apparent in decadal rarefaction curves (figure 4); 

noteworthy is that the 95% confidence interval (CI) around the 2010s curve for estimated 

species was outside the range of CIs for earlier decades. Although the Chao 1 richness 

estimate for the current decade was highest, estimates were similar across the decades.

That the number of uniques (Q1) is lowest in the 2010s decade suggests that abundance 

of species that nonetheless are rare may have increased in number and distribution over 

time and appeared in multiple tows.

Analyses of community composition in the SBNMS region (stratum 26) from both spring 

and fall surveys across decades indicate significant shifts over time. Composition during 

the spring survey period was significantly different when comparing the 1980s and 1990s

with the current decade (2010s; figure 5; table 2). SIMPER analysis indicated increased 

abundance of multiple species with haddock, silver hake, and Acadian redfish 

contributing most to observed changes in spring fish communities (tables 3 and 4). For 

fall surveys, the community composition from the 1970s, 1990s, and 2000s were all 

significantly different from the 2010s; figure 6; table 5) with haddock and silver hake

becoming increasingly common (tables 6-8).

The final MRT model of Stellwagen Bank consisted of seven partitions and eight

terminal nodes, explaining 26.7 percent of the variance in species abundances observed in 

trawls conducted within stratum 26 (figure7). The 2010s decade marked a shift in the 

composition of fish communities sampled in the trawl survey, driven by growing 

numbers of several species, most notably haddock, silver hake, and spiny dogfish (figure

8). Prior to 2010, assemblages within SBNMS were largely correlated with depth and 

season. Shallow communities (<52.5 m, figure 9) were characterized by large abundances 

of sand lance, largely absent at depth, and a shift in species starting in September (figure

10), as longhorn sculpin and American plaice decreased and silver and red hake as well 

as yellowtail and winter flounder increased. Deeper areas exhibited (figure 11) increases 
6

in Atlantic herring, spiny dogfish, and silver and red hake during the early summer 

period. In recent years, depth is still an important factor influencing fish communities, but 

season appears less important than temperature and geography. Depth remains the most 

significant correlate of fish community structure, but the apparent threshold between 

shallow and deep has moved to 75.5 m (i.e., from 52.5 m prior to 2010 as above), below 

which spiny dogfish and silver hake are more numerous (figure 12). These deeper 

communities are further influenced by temperature, as Acadian redfish are more common 

when bottom temperature is <5.7° C (figure 13); American plaice, longhorn sculpin, 

yellowtail and witch flounder, and silver and white hake are associated with warmer 

bottom conditions. Over the past decade, shallow communities south of 42° N are 

characterized by higher abundances of warm-tolerant species, like Atlantic mackerel and 

little skate, while the cold-associated species haddock is much more abundant north of 

this latitude (figure 14).

When extended to the entire Gulf of Maine, the final MRT model consisted of five

partitions and six terminal nodes, explaining 19.3% of the variance in species abundance 

and remarkably similar to the model for SBNMS that is smaller in spatial extent (figure

15). As within SBNMS, the year 2010 marked a significant change in the Gulf of Maine, 

as abundances of several species, including silver hake, haddock, and red hake, increased 

in the most recent years (figure 16). Across the region, fish assemblages have been 

consistently correlated with depth (figures 17 and 18). Silver hake, haddock, and Acadian 

redfish remained more abundant in deeper waters, as they had been prior to 2010. 

Although species were also influenced by season and geography prior to 2010 (figures 19 

and 20), variations in species abundances were not correlated with these factors after 

2010 (figure 15).

Temporal dynamics in the abundance of select species illustrates the patterns of decline 

and recovery of ecologically and economically important taxa. Atlantic cod, while at low 

numbers overall, appears to be marginally increasing in abundance during the spring 

period (figure 21, top). Atlantic wolffish and cusk, both Species of Concern under the 

Endangered Species Act (ESA) for the northeast region, remain at low abundance based 

on the trawl survey (figure 21, middle, bottom), especially during the fall, while the 

abundance of longhorn sculpin has increased over the time series (figure 22, top).

Alligatorfish (figure 22, middle), thorny skate (figure 23, top), and barndoor skate (figure 

23, bottom) have experienced a more recent, and moderate, rise in abundance. For 

alligatorfish and barndoor skate, this follows a prolonged period of low abundance.

Atlantic herring generally trended upward over the time series but year to year variation 

in the sanctuary is high (figure 22, bottom).

In addition to these temporal trends in abundance, the vertical distributions of some 

species have changed appreciably. Of the 38 fish species collected within the sanctuary in 

at least 5% of tows over the past two decades, nearly half (45%) have experienced a 

change in mean depth of capture greater than 5 m since 2010 (table 9). Among the 

species that shifted deeper were butterfish, winter flounder, ocean pout, and yellowtail 

flounder; these species also experienced substantial increases in abundance over the same 
7

period. Similar patterns were also observed for those species, except yellowtail founder, 

across the Gulf of Maine.
8

Discussion

Fish communities in Stellwagen Bank National Marine Sanctuary have changed 

substantially during the past decade. Following a long period of fluctuating diversity and 

slowly rising species richness, the number of taxa observed in the sanctuary began to 

increase rapidly. This coincided with changes in composition and patterns of numerical 

dominance at both local (SBNMS) and large spatial scales (Gulf of Maine) . Underlying 

these community scale shifts are changes in fisheries management, variations in species 

interactions mediated by transitions in species and trophic guild abundance, and shifts in 

the distributions and abundances of fish species due to climate change as a direct or 

indirect driver (Auster and Link 2009, NEFSC 2012, Link and Auster 2013, Hare et al. 

2016).

The potential drivers of shifts in species richness become clearer when viewed in the 

context of recent changes in ocean conditions. The early 1980s began a 20-year period of 

steadily rising sea surface temperatures during which waters across the entire northeast 

U.S. continental shelf warmed at a rate three times the global average (Pershing et al. 

2015). Within the sanctuary, Massachusetts state water temperature records document

increases in both surface and bottom water temperature (ONMS in press). Diversity, as 

measured by the number of fish species observed in survey trawls, slowly increased.

Rising variance in local taxa is a characteristic response of communities to shifting 

conditions in cold climates, driven directly by temperature variation or indirectly by 

changing species interactions (Currie 2001, Hawkins et al. 2003). Interestingly, the rise in 

species richness accelerated in the mid-2000s, around the time when ocean warming in 

the Gulf of Maine sped up by nearly an order of magnitude (Mills et al. 2013). 

Several distinct mechanisms might underlie the observed increase in species richness. 

Increasing fish species richness in response to warming in the North Sea was ascribed to 

poleward shifts in the distribution of fish assemblages (Hiddink et al. 2008). Essentially,

diverse fish assemblages shifted north into the region, replacing fish assemblages moving 

north out of the region that were relatively taxa-poor. Shifts in fish distributions have 

been observed in the western North Atlantic but these have not always been in the 

expected northerly direction (Nye et al. 2009). In a terrestrial example of responses to 

increased temperature, butterflies in the United Kingdom exhibited a climate-associated 

rise in species richness largely driven by the arrival of generalist species that were

tolerant of wider temperature ranges and less selective of specific habitat features, rather 

than the wholesale shift of entire communities (Menéndez et al. 2006). Recently, the 

National Marine Fisheries Service conducted a vulnerability assessment of species along 

the northeast U.S. continental shelf, predicting their sensitivity to changing ocean 

conditions (Hare et al. 2016). Most of the fish species we identified in this study as 

driving shifts in community composition through their increased abundance were 

identified by Hare et al. as either likely to benefit as a result of changing ocean climate, 

like butterfish, or less sensitive to changing conditions, such as spiny dogfish, silver and 
9

red hake, American plaice, yellowtail and winter flounder, haddock, and Atlantic herring. 

These assessments were based, in part, on habitat selectivity and temperature tolerance.

Noteworthy is that cold-adapted taxa like cusk are predicted to contract their range in the 

Gulf of Maine due to the interaction of both thermal tolerance limits and restricted habitat 

specific distributions (Hare et al. 2012).

Changes due to warming conditions are taxa specific, complicating any interpretation of 

community-scale diversity measures. These relationships also can be impacted by the 

non-uniformity of climate change, local and regional geography (e.g., habitat and 

landscape variability) and bathymetry, and species biogeography. Species distributions 

follow local changes in temperature, and for many species bottom sea water temperature 

is a larger influence than corresponding sea surface temperature (e.g., silver hake; Nye et 

al. 2011). Although local changes in climate tend to track larger regional and global 

trends, this is not always the case, and the rate of change differs substantially within 

larger regions (Burrows et al. 2011). Climate velocity, the rate and direction of these 

changes, has provided consistent correlations with marine species distributions (Pinsky et 

al. 2013). These responses rely not only on climate, but also geography. Pinsky et al. 

identified fish assemblages within the Gulf of Alaska and Gulf of Mexico as examples of 

fish assemblages reacting to local changes in climate by shifting west or deeper, 

respectively. Likewise, Nye et al. (2009) notes the role of geography within the Gulf of 

Maine, which limits poleward advancement in distribution, and the varied responses of 

species, including deeper “centers of mass” as well as range contractions apparent in 

sampling over time. Regional sea surface temperatures have been rising at a rapid rate

over the past decade, which has driven large shifts in the availability of thermal habitats 

within the Gulf of Maine (Friedland et al. 2013, Mills et al. 2013). As warm surface water 

has expanded deeper, many species within the Gulf of Maine have moved to greater 

depths (Nye et al. 2009). These shifts in vertical distribution are detectable within the 

relatively shallow SBNMS, as the mean depth at capture since 2010 was at least 5 m 

deeper than the previous 10 years for more than 10 percent of fish taxa collected within 

the sanctuary. However, for some species deeper habitats may not contain the attributes 

of habitat necessary for survival and growth. For example, sand lance relies on sandy or 

fine gravel well-oxygenated sediments which comprise most of sediments on the peak of 

Stellwagen Bank, while deeper areas tend to be composed of finer cohesive sediments 

(Meyer et al. 1979, Poppe et al. 2003). The relatively minimal number of sand lance 

collected in the trawl survey since 2010 may reflect, in part, the results of this squeeze 

between warming waters and preferred habitat.

By no means is climate considered the sole driver of change, as other factors have 

inevitably influenced diversity and patterns of community composition. Fishing, 

especially those forms that physically contact the seafloor, can have significant effects on 

habitat and contribute to variation in patterns of habitat use by fishes (Auster 2015, 

Auster and Langton 1999, Auster and Lindholm 2005, Auster et al. 1996, Tamsett et al. 

2010). Repeated fishing effort over time in the same area can lead to reduced species 

richness, but the greatest decreases are found where fishing expands to previously 

unexploited areas (Hiddink et al. 2006). 
10

Atlantic cod, an iconic marine species for the Gulf of Maine-Georges Bank region 

including SBNMS, was once a dominant piscivore in this large marine ecosystem. Now 

the Gulf of Maine cod population is at historical lows for both spawning stock biomass 

and recruitment within the current 2010s decade (to 2016), continuing a decline from the 

low population status in the 2000s decade (NEFSC 2017). The distribution of cod also 

has shifted. While once distributed throughout the Gulf of Maine region, occurrences 

now are primarily in the western Gulf of Maine region, with declining population status 

resulting in spatially limited hyper-aggregations that possibly influences the diversity and 

abundance of co-occurring species through predation and competition (Auster and Link 

2009, Link and Auster 2013, Richardson et al. 2014). Notable is that within SBNMS, 

mean cod abundance per survey tow across decadal time periods has increased during

spring periods from the 1970s-2000s, reflecting the general trend in concentration of cod 

in the western Gulf of Maine, while abundance declined between the 2000s and 2010s 

(this study).

Changes in the abundance of other species can reflect changing mortality rates or shifts in 

spatial distributions with larger consequences for fish communities within the sanctuary.

Alligatorfish, once common in visual surveys of gravel habitats, had since the mid-1990s 

declined in abundance (Auster, unpublished data), suggesting some degree of local 

endangerment in the sanctuary. Rises in catch rates of this small poacher suggest a 

moderate recovery of the local population may be underway. Similarly, thorny skate and 

barndoor skate appear to be recovering within SBNMS (Figure 23), after extremely low 

abundances and consideration for listing under the ESA. Increasing numbers of longhorn 

sculpin, an important ambush predator, could have significant effects on the recruitment 

of other fish, especially within the gravel habitats where this species occurred most 

frequently (Auster et al. 2013). Rising abundances of Atlantic herring, an important prey 

species for a number of medium to large predators, could improve forage conditions for a 

range of other species within the sanctuary. Isolated instances of large catches against the 

background of increasing abundance are indicative of patchy spatial distributions and the 

potential for variation in the functional role of this species as prey. In contrast, while 

abundances of Atlantic wolffish and cusk have been consistently low, supporting their 

designation as Species of Concern under the ESA, the return of high density patches of 

these species to the sanctuary (suggested by similarly rare instances of large catches)

could be viewed as an early indication of recovery. However, persistently low 

abundances of these species during the fall are just as likely to reflect seasonal 

movements to deeper waters in response to warm bottom temperatures in the summer or 

mortality due to greater fishing effort over the mid-months of the year. Both of these 

would be more persistent issues that would greatly impede population recovery. Overall, 

local changes in ocean climate have coincided with substantial shifts in fish community 

dynamics that are still ongoing. It is likely that in another decade, species assemblages 

may look as different from the present as today does from a decade ago.

Despite the substantial bottom trawling effort that has been focused on the sanctuary 

throughout the past half century, species richness of fishes has grown steadily. Exploited 

for centuries, with instances of local depletions dating to the early 19th century (Goode 
11

1887), Gulf of Maine fish assemblages, and marine faunal communities in general, have 

not been in an undisturbed state within the survey’s history (Jackson et al. 2001, Steneck 

et al. 2013). While trends in species richness cannot be ascribed to exploitation generally, 

specific changes in fishing activities have likely had some effect on composition. 

Reductions in fishing effort since the 1990s have likely led to the recent surge in 

abundance of haddock (NEFSC 2017), which was one of the species that drove the 

compositional shifts in fish assemblages around 2010 detected within our analyses. The 

type and direction of species interactions that resulted in this response remains to be 

determined.

While maintaining and enhancing diversity is a central mission of the sanctuary, the 

structural changes to its communities is concerning and deserves additional investigation.

Understanding the effects of local versus regional drivers on species diversity and 

community composition can provide critical insight on what policies might mitigate 

adverse changes while not sacrificing the benefits of diversity (Auster 2002). Welldesigned studies in an adaptive management context can provide such insight while 

involving the stakeholder community in the urgent work to link conservation and 

sustainable use in a world of rapidly changing climate.
12

Tables

Table 1. Fish species collected in all Gulf of Maine strata during the trawl survey from 

1970-2017. Those that also occurred in SBNMS (stratum 26) are denoted by an asterisk. 

common name scientific name stratum 26

slickhead species Alepocephalid spp.

blueback herring Alosa aestivalis *

hickory shad Alosa mediocris

Alewife Alosa pseudoharengus *

American shad Alosa sapidissima *

American sand lance Ammodytes americanus *

northern sand lance Ammodytes dubius *

Atlantic wolfish Anarhichas lupus *

striped anchovy Anchoa hepsetus

bay anchovy Anchoa mitchilli

American eel Anguilla rostrata

deepbody boarfish Antigonia capros

Atlantic argentine Argentina silus *

striated argentine Argentina striata *

silver hatchetfish Argyropelecus aculeatus

silver rag Ariomma bondi *

hookear sculpin uncl Artediellus spp. *

Alligatorfish Aspidophoroides monopterygius *

gray triggerfish Balistes capriscus

Frostfish Benthodesmus simonyi

Atlantic menhaden Brevoortia tyrannus *

Cusk Brosme brosme *

boarfish uncl Caproidae spp.

blue runner Caranx crysos
13

Table 1 (cont’d). Fish species collected in all Gulf of Maine strata during the trawl survey 

from 1970-2017. Those that also occurred in SBNMS (stratum 26) are denoted by an 

asterisk.

common name scientific name stratum 26

black sea bass Centropristis striata *

viperfish Chauliodus sloani

greeneye uncl Chlorophthalmid spp.

shortnose greeneye Chlorophthalmus agassizi

Gulf Stream flounder Citharichthys arctifrons *

Atlantic herring Clupea harengus *

herring uncl Clupeidae spp.

conger eel Conger oceanicus

conger eel uncl Congridae spp.

sculpin uncl Cottidae uncl

wrymouth Cryptacanthodes maculatus *

lumpfish snailfish uncl Cyclopteridae spp.

lumpfish Cyclopterus lumpus *

red dory Cyttopsis roseus

round scad Decapterus punctatus

fourbeard rockling Enchelyopus cimbrius *

silver anchovy Engraulis eurystole

epigonus pandionis Epigonus pandionis

smallmouth flounder Etropus microstomus

round herring Etrumeus teres

Atlantic spiny lumpsucker Eumicrotremus spp.inosus

Atlantic cod Gadus morhua *

threespine stickleback Gasterosteus aculeatus *

snake mackerel uncl Gempylidae spp.

witch flounder Glyptocephalus cynoglossus *

lightfish uncl Gonostomatidae spp.

blackbelly rosefish Helicolenus dactylopterus *

sea raven Hemitripterus americanus *

American plaice Hippoglossoides platessoides *
14

Table 1 (cont’d). Fish species collected in all Gulf of Maine strata during the trawl survey 

from 1970-2017. Those that also occurred in SBNMS (stratum 26) are denoted by an 

asterisk.

common name scientific name stratum 26

Atlantic halibut Hippoglossus hippoglossus *

halfbeak Hyporhamphus unifasciatus

honeycomb cowfish Lactophrys polygonia

smooth puffer Lagocephalus laevigatus

porbeagle Lamna nasus

fawn cusk-eel Lepophidium cervinum *

yellowtail flounder Limanda ferruginea *

seasnail Liparis atlanticus *

striped seasnail Liparis liparis

goosefish Lophius americanus *

snakeblenny Lumpenus lumpretaeformis *

daubed shanny Lumpenus maculatus *

wolf eelpout Lycenchelys verrillii *

grenadier uncl Macrouridae uncl

ocean pout Macrozoarces americanus *

capelin Mallotus villosus

pearlsides Maurolicus muelleri *

pearlsides Maurolicus pennanti

haddock Melanogrammus aeglefinus *

Atlantic soft pout Melanostigma atlanticum *

Atlantic silverside Menidia menidia *

offshore hake Merluccius albidus *

silver hake Merluccius bilinearis *

planehead filefish Monacanthus hispidus

deepwater flounder Monolene sessilicauda

mora uncl Moridae spp.

striped bass Morone saxatilis *

smooth dogfish Mustelus canis

lanternfish uncl Myctophid spp. *
15

Table 1 (cont’d). Fish species collected in all Gulf of Maine strata during the trawl survey 

from 1970-2017. Those that also occurred in SBNMS (stratum 26) are denoted by an 

asterisk.

common name scientific name stratum 26

grubby Myoxocephalus aenaeus *

longhorn sculpin Myoxocephalus octodecemspinosus *

shorthorn sculpin Myoxocephalus scorpius *

Atlantic hagfish Myxine glutinosa *

slender snipe eel Nemichthys scolopaceus

marlin-spike Nezumia bairdi

white barracudina Notolepis rissoi

snake eel uncl Ophichthyidae spp.

cusk-eel uncl Ophidiidae spp.

rainbow smelt Osmerus mordax *

barracudina uncl Paralepidae spp.

paralepis coregonoides Paralepis coregonoides

summer flounder Paralichthys dentatus *

fourspot flounder Paralichthys oblongus *

longnose greeneye Parasudis truculenta *

butterfish Peprilus triacanthus *

sea lamprey Petromyzon marinus *

rock gunnel Pholis gunnellus

longfin hake Phycis chesteri

snake eel Pisoodonophis cruentifer *

righteye flounder uncl Pleuronectidae spp. *

pollock Pollachius virens *

hatchetfish Polyipnus asteroides

polymetme corytheola Polymetme corytheola

beardfish Polymixia lowei

bluefish Pomatomus saltatrix *

bigeye Priacanthus arenatus

northern searobin Prionotus carolinus *

striped searobin Prionotus evolans *
16

Table 1 (cont’d). Fish species collected in all Gulf of Maine strata during the trawl survey 

from 1970-2017. Those that also occurred in SBNMS (stratum 26) are denoted by an 

asterisk.

common name scientific name stratum 26

winter flounder Pseudopleuronectes americanus *

clearnose skate Raja eglanteria

little skate Raja erinacea *

rosette skate Raja garmani

barndoor skate Raja laevis *

winter skate Raja ocellata *

thorny skate Raja radiata *

smooth skate Raja senta *

skate uncl Raja spp.

Greenland halibut Reinhardtius hippoglossoides *

chub mackerel Scomber japonicus

Atlantic mackerel Scomber scombrus *

Atlantic saury Scomberesox saurus *

windowpane Scophthalmus aquosus *

scorpionfish uncl Scorpaenidae spp.

Acadian redfish Sebastes fasciatus *

lookdown Selene vomer

slime eel Simenchelys parasiticus

northern puffer Sphoeroides maculatus

blunthead puffer Sphoeroides pachygaster

northern sennet Sphyraena borealis

barracuda uncl Sphyraenidae spp.

spiny dogfish Squalus acanthias *

scup Stenotomus chrysops *

hatchetfish uncl Sternoptychidae spp.

boa dragonfish Stomias ferox

scaly dragonfish uncl Stomiatidae spp.

tonguefish uncl Symphurus spp.

pipefish seahorse uncl Syngnathidae spp. *
17

Table 1 (cont’d). Fish species collected in all Gulf of Maine strata during the trawl survey 

from 1970-2017. Those that also occurred in SBNMS (stratum 26) are denoted by an 

asterisk.

common name scientific name stratum 26

northern pipefish Syngnathus fuscus *

cunner Tautogolabrus adspersus *

Atlantic torpedo Torpedo nobiliana *

rough scad Trachurus lathami

Atlantic cutlassfish Trichiurus lepturus

moustache sculpin Triglops murrayi *

radiated shanny Ulvaria subbifurcata *

eel uncl unknown eel

hake uncl unknown hate

red hake Urophycis chuss *

Carolina hake Urophycis earlli

spotted hake Urophycis regius *

ling uncl Urophycis spp. *

white hake Urophycis tenuis *

American straptail grenadier Ventrifossa occidentalis

Atlantic moonfish Vomer setapinnis

spotted tinselfish Xenolepidichthys dalgleishi

buckler dory Zenopsis conchifera

eelpout uncl Zoarcidae spp.
18

Table 2. Results of ANOSIM procedure comparing differences in decadal groups for 

spring season tows in SBNMS (Global R = 0.115, significant at 0.1%). An asterisk

denotes significantly high R value (closer to 1 and above background values).

Pairwise Tests R Significance Possible Actual Number ≥

Groups Statistic Level % Permutations Permutations Observed

1970s, 1980s 0.112 0.1 Very large 999 0

1970s, 1990s 0.100 0.1 Very large 999 0

1970s, 2000s 0.093 0.1 Very large 999 0

1970s, 2010s 0.193 0.1 Very large 999 0

1980s, 1990s 0.026 12.2 Very large 999 121

1980s, 2000s 0.081 0.1 Very large 999 0

1980s, 2010s *0.220 0.1 Very large 999 0

1990s, 2000s 0.052 0.5 Very large 999 4

1990s, 2010s *0.239 0.1 Very large 999 0

2000s, 2010s 0.105 0.1 Very large 999 0
19

Table 3. Results of SIMPER procedure identifying principal species driving dissimilarity 

of spring season tows from 1980s and 2010s decades in SBNMS. Species are in 

descending order of percent contribution to decadal differences (average dissimilarity = 

62.21). Analytical results are group average abundance (Avg Abund), average 

dissimilarity (Avg Diss), standard deviation of dissimilarity (SD Diss), percent contribution 

of species x to dissimilarity (Contrib%), and cumulative percent dissimilarity in 

descending order of species contribution (Cum%).

Species Group 1980s Group 2010s Avg Diss SD Diss Contrib% Cum%

Avg Abund Avg Abund

haddock 0.97 3.42 4.44 1.40 7.13 7.13

silver hake 1.19 3.40 3.95 1.38 6.35 13.49

Acadian redfish 0.46 2.71 3.86 1.14 6.21 19.70

yellowtail flounder 1.39 2.92 3.44 1.34 5.53 25.22

Atlantic herring 0.92 2.39 3.29 1.09 5.28 30.51

longhorn sculpin 2.05 3.16 3.21 1.18 5.16 35.66

winter flounder 0.67 2.31 3.09 1.33 4.96 40.62

American plaice 2.88 2.99 3.05 1.38 4.90 45.52

Atlantic cod 1.78 2.63 2.68 1.28 4.31 49.83

ocean pout 1.68 2.18 2.29 1.30 3.68 53.51

spiny dogfish 0.10 1.36 2.17 0.76 3.48 57.00

alewife 0.52 1.47 2.15 0.99 3.45 60.45

red hake 0.52 1.52 2.05 1.10 3.30 63.75

thorny skate 0.96 1.42 1.80 1.29 2.89 66.64

pollock 0.58 0.83 1.62 0.84 2.60 69.23

sea raven 1.00 1.12 1.48 1.26 2.38 71.62
20

Table 4. Results of SIMPER procedure identifying principal species driving dissimilarity 

of spring season tows from 1990s and 2010s decades in SBNMS. Species are in 

descending order of percent contribution to decadal differences (average dissimilarity = 

60.12). Analytical results are group average abundance (Avg Abund), average 

dissimilarity (Avg Diss), standard deviation of dissimilarity (SD Diss), percent contribution 

of species x to dissimilarity (Contrib%), and cumulative percent dissimilarity in 

descending order of species contribution (Cum%).

Species Group 1990s Group 2010s Avg Diss SD Diss Contrib% Cum%

Avg Abund Avg Abund

haddock 0.67 3.42 4.64 1.37 7.73 7.73

silver hake 1.28 3.40 3.99 1.38 6.64 14.36

Acadian redfish 1.38 2.71 3.70 1.21 6.16 20.52

Atlantic herring 1.67 2.39 3.43 1.15 5.70 26.22

yellowtail flounder 1.26 2.92 3.39 1.30 5.63 31.85

longhorn sculpin 2.51 3.16 2.96 1.15 4.92 36.77

American plaice 2.94 2.99 2.86 1.35 4.76 41.53

winter flounder 1.03 2.31 2.84 1.26 4.72 46.25

Atlantic cod 1.93 2.63 2.67 1.15 4.45 50.70

ocean pout 1.64 2.18 2.36 1.28 3.93 54.63

alewife 0.78 1.47 2.15 1.05 3.58 58.21

spiny dogfish 0.26 1.36 2.12 0.78 3.53 61.73

red hake 0.80 1.52 2.04 1.14 3.40 65.13

thorny skate 0.80 1.42 1.80 1.27 2.99 68.12

pollock 0.72 0.83 1.62 0.90 2.69 70.81
21

Table 5. Results of ANOSIM procedure comparing differences in decadal groups for fall 

season tows in SBNMS (Global R = 0.154, significant at 0.1%). An asterisk denotes 

significantly high R value (closer to 1 and above background values).

Pairwise Tests R Significance Possible Actual Number ≥

Groups Statistic Level % Permutations Permutations Observed

1970s, 1980s 0.134 0.1 Very large 999 0

1970s, 1990s 0.133 0.1 Very large 999 0

1970s, 2000s 0.121 0.1 Very large 999 0

1970s, 2010s *0.310 0.1 Very large 999 0

1980s, 1990s 0.090 0.1 Very large 999 0

1980s, 2000s 0.088 0.2 Very large 999 1

1980s, 2010s 0.149 0.1 Very large 999 0

1990s, 2000s 0.140 0.1 Very large 999 0

1990s, 2010s *0.351 0.1 Very large 999 0

2000s, 2010s *0.248 0.1 Very large 999 0
22

Table 6. Results of SIMPER procedure identifying principal species driving dissimilarity 

of fall season tows from 1970s and 2010s decades in SBNMS. Species are in 

descending order of percent contribution to decadal differences (average dissimilarity = 

65.77). Analytical results are group average abundance (Avg Abund), average 

dissimilarity (Avg Diss), standard deviation of dissimilarity (SD Diss), percent contribution 

of species x to dissimilarity (Contrib%), and cumulative percent dissimilarity in 

descending order of species contribution (Cum%).

Species Group 1970s Group 2010s Avg Diss SD Diss Contrib% Cum%

Avg Abund Avg Abund

silver hake 2.44 4.92 3.81 1.49 5.80 5.80

spiny dogfish 1.44 3.86 3.71 1.19 5.64 11.43

haddock 2.15 3.79 3.50 1.51 5.33 16.76

Atlantic herring 0.37 3.07 3.48 1.32 5.29 22.05

butterfish 0.13 2.52 2.94 1.43 4.46 26.51

American plaice 2.64 2.66 2.92 1.35 4.44 30.95

red hake 1.56 3.18 2.91 1.45 4.42 35.38

longhorn sculpin 0.97 2.69 2.75 1.31 4.19 39.57

winter flounder 0.30 2.36 2.75 1.46 4.18 43.75

alewife 0.57 2.45 2.68 1.26 4.08 47.83

yellowtail flounder 0.79 2.18 2.57 1.15 3.90 51.73

Acadian redfish 0.99 2.06 2.49 1.05 3.78 55.51

Atlantic mackerel 0.19 1.67 2.11 0.80 3.20 58.71

Atlantic cod 2.41 1.92 1.94 1.36 2.96 61.66

fourspot flounder 0.15 1.60 1.79 1.46 2.72 64.39

pollock 0.86 1.02 1.75 0.81 2.65 67.04

ocean pout 0.77 1.30 1.51 1.17 2.29 69.33

witch flounder 0.86 0.84 1.47 0.93 2.24 71.57
23

Table 7. Results of SIMPER procedure identifying principal species driving dissimilarity 

of fall season tows from 1990s and 2010s decades in SBNMS. Species are in 

descending order of percent contribution to decadal differences (average dissimilarity = 

58.64). Analytical results are group average abundance (Avg Abund), average 

dissimilarity (Avg Diss), standard deviation of dissimilarity (SD Diss), percent contribution 

of species x to dissimilarity (Contrib%), and cumulative percent dissimilarity in 

descending order of species contribution (Cum%).

Species Group 1990s Group 2010s Avg Diss SD Diss Contrib% Cum%

Avg Abund Avg Abund

haddock 0.90 3.79 3.80 1.48 6.47 6.47

spiny dogfish 1.77 3.86 3.28 1.18 5.60 12.07

silver hake 2.87 4.92 3.09 1.51 5.27 17.34

American plaice 3.66 2.66 2.83 1.37 4.83 22.17

Atlantic herring 3.14 3.07 2.83 1.32 4.83 27.00

butterfish 0.30 2.52 2.54 1.36 4.33 31.33

alewife 0.35 2.45 2.48 1.25 4.23 35.55

Acadian redfish 1.82 2.06 2.39 1.17 4.08 39.64

yellowtail flounder 1.06 2.18 2.35 1.17 4.00 43.64

winter flounder 0.74 2.36 2.34 1.40 3.99 47.63

red hake 2.48 3.18 2.28 1.29 3.89 51.52

longhorn sculpin 2.53 2.69 2.15 1.31 3.67 55.19

Atlantic mackerel 0.26 1.67 1.94 0.82 3.31 58.50

Atlantic cod 2.00 1.92 1.83 1.31 3.12 61.62

pollock 0.99 1.02 1.71 0.84 2.92 64.54

fourspot flounder 0.20 1.60 1.62 1.46 2.76 67.30

thorny skate 0.96 1.37 1.36 1.31 2.31 69.61

ocean pout 1.06 1.30 1.35 1.21 2.30 71.92
24

Table 8. Results of SIMPER procedure identifying principal species driving dissimilarity 

of fall season tows from 2000s and 2010s decades in SBNMS. Species are in 

descending order of percent contribution to decadal differences (average dissimilarity = 

56.99). Analytical results are group average abundance (Avg Abund), average 

dissimilarity (Avg Diss), standard deviation of dissimilarity (SD Diss), percent contribution 

of species x to dissimilarity (Contrib%), and cumulative percent dissimilarity in 

descending order of species contribution (Cum%).

Species Group 2000s Group 2010s Avg Diss SD Diss Contrib% Cum%

Avg Abund AvgAbund

silver hake 2.54 4.92 3.33 1.50 5.85 5.850

haddock 2.29 3.79 3.03 1.52 5.32 11.17

American plaice 2.40 2.66 2.67 1.31 4.68 15.85

spiny dogfish 3.87 3.86 2.65 1.16 4.65 20.50

Atlantic herring 3.05 3.07 2.55 1.34 4.48 24.98

red hake 1.87 3.18 2.51 1.43 4.41 29.40

Acadian redfish 2.06 2.06 2.48 1.21 4.36 33.75

butterfish 0.73 2.52 2.44 1.38 4.28 38.04

alewife 0.86 2.45 2.32 1.28 4.07 42.11

longhorn sculpin 2.17 2.69 2.32 1.35 4.07 46.17

yellowtail flounder 0.97 2.18 2.30 1.16 4.03 50.21

winter flounder 1.25 2.36 2.26 1.40 3.97 54.18

Atlantic cod 2.85 1.92 1.87 1.40 3.27 57.46

Atlantic mackerel 0.22 1.67 1.85 0.81 3.25 60.70

pollock 1.24 1.02 1.82 0.88 3.20 63.90

fourspot flounder 0.28 1.60 1.53 1.46 2.69 66.60

thorny skate 0.50 1.37 1.34 1.27 2.36 68.95

ocean pout 0.48 1.30 1.29 1.12 2.26 71.21
25

Table 9. Change in mean depth of capture comparing the 2000s with 2010s decade for both SBNMS and larger Gulf of Maine 

regions (as defined in text). Negative value in “Δ mean depth” columns indicates deeper mean depth in 2010s than previous 

decade while positive value indicates shallower depth. Arrows (↘ and ↗) are limited to changes in mean depth that exceed SE 

for both 2000 and 2010 depths. Continued on next page.

species tows in Stellwagen Bank NMS (stratum 26) tows throughout Gulf of Maine (strata 21-40)

mean ±SE depth (m) Δ mean depth mean ±SE depth (m) Δ mean depth

2000 2010 2000 to 2010 2000 2010 2000 to 2010

butterfish 60.0 ± 5.99 77.5 ± 3.84 -17.5 ↘ 128.7 ± 3.75 150.1 ± 2.54 -21.4 ↘

winter skate 50.9 ± 6.67 68.2 ± 4.24 -17.3 ↘ 112.3 ± 3.50 119.5 ± 3.43 -7.2 ↘

American shad 76.3 ± 6.57 88.3 ± 4.86 -12.0 ↘ 159.8 ± 3.06 159.7 ± 2.29 0.1

Atlantic hagfish 84.8 ± 6.10 95.2 ± 7.97 -10.4 ↘ 181.7 ± 3.11 181.6 ± 3.20 0.1

Atlantic mackerel 64.1 ± 9.53 72.3 ± 4.07 -8.2 133.7 ± 4.12 152.9 ± 2.61 -19.2 ↘

little skate 49.9 ± 5.10 56.9 ± 3.90 -7.0 ↘ 92.5 ± 2.54 108.6 ± 3.59 -16.1 ↘

cunner 63.3 ± 2.63 69.5 ± 3.33 -6.2 ↘ 68.0 ± 3.16 69.9 ± 2.53 -1.8

winter flounder 65.4 ± 3.00 71.1 ± 2.49 -5.7 ↘ 88.7 ± 1.79 97.8 ± 2.00 -9.2 ↘

ocean pout 73.2 ± 3.14 78.4 ± 2.87 -5.2 ↘ 122.7 ± 2.37 130.2 ± 2.08 -7.5 ↘

hookear sculpin 84.9 ± 3.21 90.0 ± 5.20 -5.1 125.8 ± 6.62 150.4 ± 12.12 -24.6 ↘

yellowtail flounder 68.4 ± 3.43 72.7 ± 2.74 -4.3 ↘ 88.8 ± 1.96 87.4 ± 1.88 1.4

spiny dogfish 73.8 ± 3.49 77.6 ± 2.96 -3.8 ↘ 165.4 ± 1.91 164.4 ± 1.99 1.1

moustache sculpin 68.8 ± 3.78 72.2 ± 3.06 -3.4 109.4 ± 6.72 104.9 ± 6.37 4.5

alligatorfish 83.8 ± 6.17 86.7 ± 2.89 -2.9 120.7 ± 3.72 123.8 ± 2.68 -3.1

haddock 76.4 ± 2.87 79.2 ± 2.64 -2.8 152.2 ± 1.96 158.9 ± 1.87 -6.7 ↘

fourspot flounder 80.5 ± 6.01 83.2 ± 3.25 -2.7 130.2 ± 3.39 127.3 ± 2.86 2.8

red hake 80.7 ± 3.43 83.2 ± 2.96 -2.5 162.0 ± 1.48 164.0 ± 1.62 -1.9 ↘

Atlantic herring 77.0 ± 3.17 78.7 ± 2.92 -1.7 156.3 ± 1.68 160.3 ± 1.68 -4.0 ↘

longhorn sculpin 72.9 ± 2.57 73.6 ± 2.52 -0.7 99.9 ± 1.44 104.0 ± 1.73 -4.1 ↘
26

Table 9 (cont’d). Change in mean depth of capture comparing the 2000s with 2010s decade for both SBNMS and larger Gulf 

of Maine regions (as defined in text). Negative value in “Δ mean depth” columns indicates deeper mean depth in 2010s than 

previous decade while positive value indicates shallower depth. Arrows (↘ and ↗) are limited to changes in mean depth that 

exceed SE for both 2000 and 2010 depths.

species tows in Stellwagen Bank NMS (stratum 26) tows throughout Gulf of Maine (strata 21-40)

mean ±SE depth (m) Δ mean depth mean ±SE depth (m) Δ mean depth

2000 2010 2000 to 2010 2000 2010 2000 to 2010

Atlantic wolffish 80.3 ± 4.05 81.0 ± 3.51 -0.7 115.8 ± 4.20 109.5 ± 4.12 6.4 ↗

sea raven 76.2 ± 3.20 76.8 ± 2.85 -0.6 107.6 ± 1.77 111.4 ± 2.35 -3.8 ↘

thorny skate 82.4 ± 3.39 82.7 ± 2.44 -0.3 160.9 ± 2.35 155.3 ± 2.52 5.5 ↗

alewife 83.5 ± 3.56 83.0 ± 3.20 0.5 155.4 ± 1.87 162.1 ± 1.71 -6.7 ↘

blueback herring 83.4 ± 6.62 82.6 ± 4.57 0.8 148.5 ± 2.73 146.2 ± 2.09 2.3

witch flounder 98.2 ± 4.79 96.1 ± 3.31 2.1 176.0 ± 1.41 174.6 ± 1.54 1.4

Acadian redfish 88.3 ± 2.89 85.3 ± 2.66 3.0 ↗ 172.8 ± 1.50 173.0 ± 1.56 -0.2

silver hake 81.3 ± 3.03 78.3 ± 2.65 3.0 165.5 ± 1.44 161.7 ± 1.66 3.9 ↗

American plaice 82.7 ± 2.79 79.5 ± 2.67 3.2 ↗ 161.2 ± 1.41 161.4 ± 1.67 -0.2

windowpane 70.5 ± 11.58 67.2 ± 5.57 3.3 85.4 ± 2.44 88.6 ± 2.81 -3.2 ↘

Atlantic cod 77.1 ± 2.83 73.3 ± 2.49 3.8 ↗ 132.5 ± 1.89 139.7 ± 2.34 -7.2 ↘

snakeblenny 92.4 ± 5.07 88.5 ± 7.04 3.9 109.8 ± 4.95 100.9 ± 6.44 8.9 ↗

white hake 93.6 ± 5.03 88.0 ± 3.18 5.6 ↗ 181.6 ± 1.57 176.5 ± 1.69 5.1 ↗

fourbeard rockling 100.6 ± 7.50 93.5 ± 4.23 7.1 158.7 ± 2.53 166.7 ± 1.76 -8.0 ↘

pollock 81.6 ± 3.60 72.4 ± 3.51 9.2 ↗ 165.8 ± 2.23 160.0 ± 2.74 5.7 ↗

cusk 99.9 ± 9.23 90.0 ± 6.86 9.9 ↗ 187.4 ± 4.52 184.1 ± 6.97 3.3

goosefish 103.0 ± 5.67 85.9 ± 3.52 17.1 ↗ 173.2 ± 1.68 170.4 ± 1.78 2.8 ↗

smooth skate 112.5 ± 10.92 92.8 ± 3.56 19.7 ↗ 185.3 ± 2.00 180.0 ± 1.79 5.4 ↗

wrymouth 119.7 ± 13.09 97.7 ± 8.86 22.0 ↗ 144.7 ± 5.21 149.1 ± 3.39 -4.4
27

Figures

Figure 1a. Survey strata for National Marine Fisheries Service bottom trawl survey 

(above). SBNMS is contained in stratum 26. The Gulf of Maine region, for the purposes 

of this study, was delineated as strata 21-40.
28

Figure 1b. Bathymetry and distribution of survey tows by decade within the SBNMS 

survey stratum.
29

Figure 2. Box-and-whisker plots to compare distributions by tow for S (top) and 

Shannon (middle) and Simpson diversity (bottom), by year and season (spring left and 

fall right). Note upward trends in S in recent years while no clear trend is observed for 

Shannon and Simpson diversity indices that take both diversity and abundance into 

account. The central line in each box denotes the median value. The top of each box is 

the upper value of the third quartile (75% of data less than or equal to this value) and 

bottom is the upper value of the first quartile (25% of the data are less than or equal to 

this value). The tip of the upper whisker is the highest data value within the limit of Q3 + 

1.5 (Q3 - Q1) while the tip of the lower whisker is the lowest value within the limit of Q1-

1.5 (Q3 - Q1). Stars represent outliers of unusually high or low values outside the limits 

of the whiskers.

Year-Fall

S

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

30

25

20

15

10

5

0

Year-Spring

S

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

25

20

15

10

5

0

Year-Spring

H (loge)

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Year-Fall

H (loge)

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

2.5

2.0

1.5

1.0

0.5

0.0

Year-Spring

1-lambda

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

1.0

0.8

0.6

0.4

0.2

0.0

Year-Fall

1-lambda

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0
30

Figure 3. Segmented regression of species richness per tow over time with both spring 

and fall periods combined (top). Richness exhibited an increase over the earlier part of 

the time series (i.e., 1970 to ca. 2006) but over the past decade there has been a 

marked acceleration. However, neither Shannon (middle) and Simpson indices (bottom) 

exhibited such trends. (See text for details.)
31

Figure 4. Rarefication curves by decade for S (est), Chao 1, and Uniques Mean (top to 

bottom, respectively). Shaded areas are 95% Confidence Interval bands for S(est) and 

mean Chao 1, and 1 Standard Deviation for Uniques Mean.
32

Figure 5. Strata 26 (SBNMS) spring period. NMDS as both 2D and 3D plots. Note stress 

levels above each visualization. Upper right is species accumulation curve for observed 

tows as well as Chao 1 and Michaelis-Menton diversity estimators.
33

Figure 6. Strata 26 (SBNMS) fall period. NMDS as both 2D (tows and bubble centroids) 

and 3D plots. Note stress levels above each visualization. Upper right is species 

accumulation curve for observed tows as well as Chao 1 and Michaelis-Menton diversity 

estimators.
34

Figure 7. Multiple regression tree for the SBNMS (stratum 26) region. Tables at each split indicate the proportion of variance 

explained by each factor in the analysis. Factor with greatest explanatory value (top of table at each split) was used for 

partitioning data.
35

Figure 8. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
36

Figure 9. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
37

Figure 10. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
38

Figure 11. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
39

Figure 12. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
40

Figure 13. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
41

Figure 14. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
42

Figure 15. Results of multivariate regression tree for the Gulf of Maine region. Tables at each split indicate the proportion of 

variance explained by each factor in the analysis. Factor with greatest explanatory value (top of table at each split) was used 

for partitioning data.
43

Figure 16. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
44

Figure 17. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
45

Figure 18. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
46

Figure 19. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
47

Figure 20. Barplot of principal species mean abundance at each side of split referenced above and in MRT. Error bars are 

standard error. Numbers in legend are the proportion of variance explained by each species.
48

Figure 21. Spring (left) and fall (right) patterns of abundance for Atlantic cod, Atlantic 

wolfish, and cusk. Refer to Figure 2 regarding details of box-and-whisker plots.

Year-Spring

Atlantic Cod CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

900

800

700

600

500

400

300

200

100

0

Year-Fall

Atlantic Cod CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

600

500

400

300

200

100

0

Year-Spring

Atlantic Wolffish CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

30

25

20

15

10

5

0

Year-Fall

Atlantic Wolffish CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

30

25

20

15

10

5

0

Year-Fall

Cusk CPUE 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

5

4

3

2

1

0

Year-Spring

Cusk CPUE 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

5

4

3

2

1

0
49

Figure 22. Spring (left) and fall (right) patterns of abundance for longhorn sculpin, 

alligatorfish, and Atlantic herring. Refer to Figure 2 regarding details of box-and-whisker 

plots.

Year-Spring

Longhorn Sculpin CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

1800

1600

1400

1200

1000

800

600

400

200

0

Year-Fall

Longhorn Sculpin CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

600

500

400

300

200

100

0

Year-Spring

Alligatorfish CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

50

40

30

20

10

0

Year-Fall

All gi atorfish CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

120

100

80

60

40

20

0

Year-Spring

Atlantic Herring CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

14000

12000

10000

8000

6000

4000

2000

0

Year-Fall

Atlantic Herring CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

6000

5000

4000

3000

2000

1000

0
50

Figure 23. Spring (left) and fall (right) patterns of abundance for thorny skate and 

barndoor skate. Refer to Figure 2 regarding details of box-and-whisker plots.

Year-Spring

Thorny Skate CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

60

50

40

30

20

10

0

Year-Fall

Thorny Skate CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

40

30

20

10

0

Year-Spring

Barndoor skate CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

0.50

0.25

0.00

-0.25

-0.50

Year-Fall

Barndoor Skate CPUE

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

6

5

4

3

2

1

0
51

Glossary of Acronyms

ANOSIM Analysis of similaries

CI Confidence interval

CPUE Catch-per-unit effort

ESA Endangered Species Act

MRT Multivariate regression tree

NEFSC NOAA Northeast Fisheries Science Center

nMDS Non-metric multidimensional scaling

NMSA National Marine Sanctuaries Act

NOAA National Oceanic and Atmospheric Administration

ONMS NOAA Office of National Marine Sanctuaries

SBNMS Stellwagen Bank National Marine Sanctuary

SIMPER Similarity-percentage
52

Literature Cited

Auster, P.J. 2002. Representation of biological diversity of the Gulf of Maine region at 

Stellwagen Bank National Marine Sanctuary (Northwest Atlantic): patterns of fish 

diversity and assemblage composition. Managing Protected Areas in a Changing World. 

S. Bondrup-Nielson, T. Herman, N.W.P. Munro, G. Nelson and J.H.M. Willison (eds.). 

Science and Management of Protected Areas Association, Wolfville, NS, Canada. 

p.1096-1125.

Auster, P.J. 2015. Can fishery closed areas be considered OECMs (other effective 

conservation measures) for conservation of biological diversity: A case study from the 

Western Gulf of Maine (NW Atlantic). White paper contributed to: International Union 

for Conservation of Nature (IUCN) World Commission on Protected Area’s Task Force 

on OECMs. 6pp. Available at ResearchGate DOI: 10.13140/RG.2.1.5103.0486.

Auster, P.J., R. Clark and R.E.S. Reed. 2006. Chapter 3: Marine fishes. in: An Ecological 

Characterization of the Stellwagen Bank National Marine Sanctuary Region: 

Oceanographic, Biogeographic, and Contaminants Assessment. National Center for 

Coastal Ocean Science, NOAA, Silver Spring, Maryland. NOAA Technical 

Memorandum NOS NCCOS 45: 89-229.

Auster, P.J. and R.W. Langton. 1999. The effects of fishing on fish habitat. American 

Fisheries Society Symposium 22:150-187.

Auster, P.J. and J. Lindholm. 2005. The ecology of fishes on deep boulder reefs in the 

western Gulf of Maine. in: Diving for Science 2005, Proceedings of the American 

Academy of Underwater Sciences. Connecticut Sea Grant, Groton, CT. p.89-107.

Auster, P., J. Lindholm, A. Cramer, M. Nenandovic, C. Prindle and A. Tamsett. 2013. 

The Seafloor Habitat Recovery Monitoring Project (SHRMP) at Stellwagen Bank 

National Marine Sanctuary - Final Report. 31 December 2013. 133pp.

Auster, P.J. and J. Link. 2009. Compensation and recovery of feeding guilds in a 

northwest Atlantic shelf fish community. Marine Ecology Progress Series 382: 163–172.

Auster, P.J., R.J. Malatesta, R.W. Langton, L. Watling, P.C. Valentine, C.L.S. 

Donaldson, E.W. Langton, A.N. Shepard and I.G. Babb. 1996. The impacts of mobile 

fishing gear on seafloor habitats in the Gulf of Maine (northwest Atlantic): implications 

for conservation of fish populations. Reviews in Fisheries Science 4:185-202.

Auster, P.J., C. Michalopoulos, P.C. Valentine and R.J. Malatesta. 1998. Delineating and 

monitoring habitat management units in a temperate deep-water marine protected area. 

In: Science & Management of Protected Areas Association, Wolfville, NS, Canada.

p.169-185.

Azarovitz, T.R. 1981. A brief historical review of the Woods Hole Laboratory trawl 

survey time series. Canadian Special Publication of Fisheries and Aquatic Sciences

58(62): 67pp. 
53

Battista, T., R. Clark and S. Pittman. 2006. An Ecological Characterization of the 

Stellwagen Bank National Marine Sanctuary Region: Oceanographic, Biogeographic, and 

Contaminants Assessment. Prepared by NOAA National Centers for Coastal Ocean 

Science’ Biogeography Team in cooperation with the National Marine Sanctuary 

Program. Silver Spring, MD. NOAA Technical Memorandum NOS NCCOS 45: 356pp.

Breiman, L. 2001. Statistical modeling: The two cultures (with comments and a rejoinder 

by the author). Statistical Science 16(3): 199-231. 

Breiman, L., J. Friedman, C.J. Stone and R.A. Olshen. 1984. Classification and 

regression trees. CRC Press, Boca Raton, FL. 358pp.

Burrows, M.T., D.S. Schoeman, L.B. Buckley, P. Moore, E.S. Poloczanska, K.M. 

Brander, C. Brown, J.F. Bruno, C.M. Duarte, B.S. Halpern and J. Holding. 2011. The 

pace of shifting climate in marine and terrestrial ecosystems. Science 334(6056): 652-

655.

Clarke, K.R. and R.N. Gorley. 2015. PRIMER v7: User Manual/Tutorial. PRIMER-E, 

Plymouth, UK. 296pp.

Colwell, R.K. 2013. EstimateS: statistical estimation of species richness and shared 

species from samples. Version 9. User's guide and application. http://purl. oclc. 

org/estimates.

Cook, R. and P. Auster. 2007. A bioregional classification for the continental shelf of 

northeastern North America for conservation analysis and planning based on 

representation. Marine Sanctuaries Conservation Series NMSP-07-03. 14 pp.

Currie, D.J. 2001. Projected effects of climate change on patterns of vertebrate and tree 

species richness in the conterminous United States. Ecosystems 4(3): 216-225.

Davies, R.B. 1987. Hypothesis testing when a nuisance parameter is present only under 

the alternative. Biometrika 74: 33-43.

De'Ath, G. 2002. Multivariate regression trees: a new technique for modeling species–

environment relationships. Ecology 83(4): 1105-1117.

De'ath, G. and K.E. Fabricius. 2000. Classification and regression trees: a powerful yet 

simple technique for ecological data analysis. Ecology 81(11): 3178-3192.

Edgar, G.J., R.D. Stuart-Smith, T.J. Willis, S. Kininmonth, S.C. Baker, S. Banks, N.S. 

Barrett, M.A. Becerro, A.T. Bernard, J. Berkhout and C.D. Buxton. 2014. Global 

conservation outcomes depend on marine protected areas with five key features. Nature 

506(7487): 216-220.

Friedland, K.D., J. Kane, J.A. Hare, R.G. Lough, P.S. Fratantoni, M.J. Fogarty and J.A. 

Nye. 2013. Thermal habitat constraints on zooplankton species associated with Atlantic 

cod (Gadus morhua) on the US Northeast Continental Shelf. Progress in Oceanography 

116: 1-13.
54

Goode, G.B. 1887. The fisheries and fishery industries of the United States. Section III. 

US Government Printing Office, Washington, DC. 238pp.

Grosslein, M.D. 1969. Groundfish survey methods. Bureau of Commercial Fisheries, 

Biological Laboratory, Woods Hole, MA. 34pp.

Hare. J.A., J.P. Manderson, J.A. Nye, M.A. Alexander, P.J. Auster, D.L. Borggaard, 

A.M. Capotondi, K.B. Damon-Randall, E. Heupel, I. Mateo, L. O’Brien, D.E. 

Richardson, C.A. Stock and S.T. Biegel. 2012. Cusk (Brosme brosme) and climate 

change: assessing the threat to a candidate marine fish species under the US Endangered 

Species Act. ICES Journal of Marine Science 69: 1753-1768.

Hare, J.A., W.E. Morrison, M.W. Nelson, M.M. Stachura, E.J. Teeters, R.B. Griffis, 

M.A. Alexander, J.D. Scott, L. Alade, R.J. Bell and A.S. Chute. 2016. A vulnerability 

assessment of fish and invertebrates to climate change on the Northeast US Continental 

Shelf. PloS One 11(2): e0146756.

Hawkins, S.J., A.J. Southward and M.J. Genner. 2003. Detection of environmental 

change in a marine ecosystem- evidence from the western English Channel. Science of 

Total Environment 310 (1-3): 245-256.

Hiddink, J.G., S. Jennings, M.J. Kaiser, A.M. Queirós, D.E. Duplisea and G.J. Piet. 2006. 

Cumulative impacts of seabed trawl disturbance on benthic biomass, production, and 

species richness in different habitats. Canadian Journal of Fisheries and Aquatic Sciences 

63(4): 721-736.

Hiddink, J.G. and R. Ter Hofstede. 2008. Climate induced increases in species richness 

of marine fishes. Global Change Biology 14(3): 453-460.

Jackson, J.B., M.X. Kirby, W.H. Berger, K.A. Bjorndal, L.W. Botsford, B.J. Bourque, 

R.H. Bradbury, R. Cooke, J. Erlandson, J.A. Estes and T.P. Hughes. 2001. Historical 

overfishing and the recent collapse of coastal ecosystems. Science 293(5530): 629-637.

John, G. H. 1995. Robust Decision Trees: Removing Outliers from Databases. KDD, 95: 

174-179.

Link, J.S. and P.J. Auster. 2013. The challenges of evaluating competition among marine 

fishes: who cares, when does it matter, and what can one do about it? Bulletin of Marine 

Science 89: 213-247.

Magurran, A.E. 2004. Measuring Biological Diversity. Blackwell Science, Oxford, U.K.

260pp.

McConnaughey, R.A. and L.L. Conquest. 1993. Trawl survey estimation using a 

comparative approach based on lognormal theory. Fishery Bulletin 91(1): 107-118.

McCook, L.J., T. Ayling, M. Cappo, J.H. Choat, R.D. Evans, D.M. De Freitas, M. 

Heupel, T.P. Hughes, G.P. Jones, B. Mapstone and H. Marsh. 2010. Adaptive 

management of the Great Barrier Reef: a globally significant demonstration of the 

benefits of networks of marine reserves. Proceedings of the National Academy of 

Sciences 107(43): 18278-18285.
55

Menéndez, R., A.G. Megías, J.K. Hill, B. Braschler, S.G. Willis, Y. Collingham, R. Fox, 

D.B. Roy and C.D. Thomas. 2006. Species richness changes lag behind climate change. 

Proceedings of the Royal Society of London B: Biological Sciences 273(1593): 1465-

1470.

Meyer, T.L., R.A. Cooper and R.W. Langton. 1979. Relative abundance, behavior, and 

food habits of the American sand lance, Ammodytes americanus, from the Gulf of Maine. 

Fishery Bulletin 77(1): 243-253.

Miller, T.J., C. Das, P.J. Politis, A.S. Miller, S.M. Lucey, C.M. Legault, R.W. Brown and 

P.J. Rago. 2010. Estimation of Albatross IV to Henry B. Bigelow calibration factors. 

Northeast Fish Sci Cent Ref Doc. 10-05; 233 p. Available from: National Marine

Fisheries Service, 166 Water Street, Woods Hole, MA 02543-1026.

Mills, K.E., A.J. Pershing, C.J. Brown, Y. Chen, F.-S. Chiang, D.S. Holland, S. Lehuta, 

J.A. Nye, J.C. Sun, A.C. Thomas, and R.A. Wahle. 2013. Fisheries management in a 

changing climate: Lessons from the 2012 ocean heat wave in the Northwest Atlantic. 

Oceanography 26(2): 191-195.

Minitab, Inc. 2000. MINITAB release 14: statistical software for windows. Minitab Inc, 

USA.

Morris, E.K., T. Caruso, F. Buscot, M. Fischer and C. Hancock. 2014. Choosing and 

using diversity indices: insights for ecological applications from the German Biodiversity 

Exploratories. Ecology and Evolution 4: 3514-3524.

Muggeo, V.M.R. 2008. Segmented: an R package to fit regression models with brokenline relationships. R News 8(1): 20-25.

National Marine Sanctuary Program (NMSP). 2007. Gerry E. Studds Stellwagen Bank 

National Marine Sanctuary Condition Report 2007. U.S. Department of Commerce, 

National Oceanic and Atmospheric Administration, National Marine Sanctuary Program, 

Silver Spring, MD. 41pp.

Northeast Fisheries Science Center (NEFSC). 1988. An evaluation of the bottom trawl 

survey program of the Northeast Fisheries Center. NOAA Tech Memo NMFS-NEC-52: 

83pp.

Northeast Fisheries Science Center (NEFSC). 2012. Assessment or Data Updates of 13 

Northeast Groundfish Stocks through 2010. U.S. Department of Commerce, National 

Oceanic and Atmospheric Administration, Northeast Fisheries Science Center, Woods 

Hole, MA. Ref Doc. 12-06: 789pp.

Northeast Fisheries Science Center (NEFSC). 2017. Gulf of Maine Atlantic cod 2017 

Assessment Update Report. Draft Working Paper. U.S. Department of Commerce, 

National Oceanic and Atmospheric Administration , Northeast Fisheries Science Center, 

Woods Hole, MA. 9pp.
56

Nye, J.A., J.S. Link, J.A. Hare and W.J. Overholtz. 2009. Changing spatial distribution of 

fish stocks in relation to climate and population size on the Northeast United States 

continental shelf. Marine Ecology Progress Series 393: 111-129.

Nye, J.A., T.M. Joyce, Y.O. Kwon and J.S. Link. 2011. Silver hake tracks changes in 

Northwest Atlantic circulation. Nature Communications 2: 1-6.

Office of National Marine Sanctuaries (ONMS). 1993. Stellwagen Bank National Marine 

Sanctuary Final Management Plan and Environmental Assessment. U.S. Department of 

Commerce, National Oceanic and Atmospheric Administration, Office of National 

Marine Sanctuaries, Silver Spring, MD.

Office of National Marine Sanctuaries (ONMS). 2010. Stellwagen Bank National Marine 

Sanctuary Final Management Plan and Environmental Assessment. U.S. Department of 

Commerce, National Oceanic and Atmospheric Administration, Office of National 

Marine Sanctuaries, Silver Spring, MD. 419pp.

Office of National Marine Sanctuaries (ONMS). In press. Stellwagen Bank National 

Marine Sanctuary 2007-2017 Condition Report. U.S. Department of Commerce, National 

Oceanic and Atmospheric Administration, Office of National Marine Sanctuaries, Silver 

Spring, MD.

Pershing, A.J., M.A. Alexander, C.M. Hernandez, L.A. Kerr, A. Le Bris, K.E. Mills, J.A. 

Nye, N.R. Record, H.A. Scannell, J.D. Scott and G.D. Sherwood. 2015. Slow adaptation 

in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 

350(6262): 809-812.

Pinsky, M.L., B. Worm, M.J. Fogarty, J.L. Sarmiento and S.A. Levin. 2013. Marine taxa 

track local climate velocities. Science 341(6151): 1239-1242.

Politis, P.J., J.K. Galbraith, P. Kostovick and R.W. Brown. 2014. Northeast Fisheries 

Science Center bottom trawl survey protocols for the NOAA Ship Henry B. Bigelow. 

U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 

Northeast Fisheries Science Center, Woods Hole, MA. NEFSC Reference Document 14-

06: 138pp.

Poppe, L.J., V.F. Paskevich, S.J. Williams, M.E. Hastings, J.T. Kelly, D.F. Belknap, L.G. 

Ward, D.M. FitzGerald and P.F. Larsen. 2003. Surficial Sediment Data from the Gulf of 

Maine, Georges Bank, and Vicinity: A GIS Compilation. U.S. Geological Survey OpenFile Report 03-001, http://pubs.usgs.gov/of/2003/of03-001/index.htm.

R Core Team. 2017. R: A language and environment for statistical computing. R 

Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Richardson, D.E., M.C. Palmer and B.E. Smith. 2014. The influence of forage fish 

abundance on the aggregation of Gulf of Maine Atlantic cod (Gadus morhua) and their 

catchability in the fishery. Canadian Journal of Fisheries and Aquatic Sciences 71: 1349-

1362.
57

Saba, V.S., S.M. Griffies, W.G. Anderson, M. Winton, M.A. Alexander, T.L. Delworth, 

J.A. Hare, M.J. Harrison, A. Rosati, G.A. Vecchi and R. Zhang. 2016. Enhanced 

warming of the Northwest Atlantic Ocean under climate change. Journal of Geophysical 

Research: Oceans 121(1): 118-132.

Sherman, K., N.A. Jaworski and T.G. Smayda, eds. 1996. The Northeast Shelf 

Ecosystem: Assessment, Sustainability, and Management. Blackwell Science, Inc., 

Cambridge, MA. 579pp.

Steneck, R.S., A. Leland, D.C. McNaught and J. Vavrinec. 2013. Ecosystem flips, locks, 

and feedbacks: the lasting effects of fisheries on Maine's kelp forest ecosystem. Bulletin 

of Marine Science 89(1): 31-55.

Tamsett, A., K. Heinonen, P.J. Auster and J. Lindholm. 2010. Dynamics of hard 

substratum communities inside and outside of a fisheries closed area in Stellwagen Bank 

National Marine Sanctuary (Gulf of Maine, NW Atlantic). Marine Sanctuaries 

Conservation Series ONMS-10-05: 53pp.

U.S. National Marine Sanctuaries Act (NMSA). 1992, as amended through 2000. 16 

U.S.C. 1431 et seq.
58

Acknowledgements

We thank the NMFS personnel who have designed, implemented, and conducted the 

bottom trawl surveys upon which these analyses are based. Peter J. Auster was supported 

by a grant from the National Marine Sanctuary Foundation and Stellwagen Bank National 

Marine Sanctuary. The comments of two anonymous reviewers greatly improved the 

presentation of our results. The opinions expressed herein are those of the authors and do 

not necessarily reflect the opinions of funding or supporting agencies and institutions.