Marine Sanctuaries Conservation Series ONMS-10-03

U.S. Department of Commerce

National Oceanic and Atmospheric Administration

National Ocean Service

Office of Ocean and Coastal Resource Management

Office of National Marine Sanctuaries July 2010

Variation in Planning-Unit Size and 

Patterns of Fish Diversity: Implications 

for Design of Marine Protected Areas

 
About the Marine Sanctuaries Conservation Series

The National Oceanic and Atmospheric Administration’s National Ocean Service (NOS) 

administers the Office of National Marine Sanctuaries (ONMS). Its mission is to identify, 

designate, protect and manage the ecological, recreational, research, educational, 

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accomplish the diverse needs of NOAA’s resource protection mandate.
Variation in Planning Unit-Size and Patterns of Fish Diversity: 

Implications for Design of Marine Protected Areas

Chiu-Yen Kuo and Peter J. Auster

Department of Marine Sciences and National Undersea Research Center

University of Connecticut, Groton

and

Jason Parent

Department of Natural Resources and the Environment

University of Connecticut, Storrs

Silver Spring, Maryland

July 2010

U.S. Department of Commerce

Gary Locke, Secretary

National Ocean and Atmospheric Administration

Jane Lubchenco, Ph.D.

Under Secretary of Commerce for Oceans and Atmosphere

National Ocean Service

David M. Kennedy, Acting Assistant Administrator

Office of National Marine Sanctuaries

Daniel J. Basta, Director
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 web site at http://sanctuaries.noaa.gov. 

Cover

Juvenile Acadian redfish (Sebastes fasciatus) at a boulder reef in Stellwagen Bank 

National Marine Sanctuary. Acadian redfish have relatively narrow habitat requirements. 

Spatial planning to conserve diversity must consider nesting multiple habitats within 

planning units in order to capture the greatest diversity in the smallest area. Image from 

University of Connecticut’s ROV Kraken 2 courtesy of Peter Auster. 

Suggested Citation

Kuo, C-Y., P.J. Auster, J. Parent. 2010. Variation in planning-unit size and patterns of 

fish diversity: implications for design of marine protected areas. Marine Sanctuaries 

Conservation Series ONMS-10-03. U.S. Department of Commerce, National Oceanic and 

Atmospheric Administration, Office of National Marine Sanctuaries, Silver Spring, MD. 

52 pp.

Contact

Peter J. Auster, Department of Marine Sciences and Northeast Underwater Research 

Technology & Education Center, University of Connecticut at Avery Point, 1080 

Shennecossett Rd., Groton, CT 06340 USA. Email: peter.auster@uconn.edu
i

Abstract

Marine protected areas (MPAs) have commonly been used to conserve or protect 

communities and habitats sensitive to disturbance, provide refuge for juveniles and 

spawning adults of exploited species, and serve as a hedge against management 

miscalculations or abnormal conditions. Species-richness hotspots are often used as an 

important focus for identifying conservation targets. We investigated how variation in 

planning-unit size (i.e. 10x10 km, 20x20 km, 40x40 km, and 80x80 km) affected spatial 

patterns of fish species richness and identification of diversity hotspots in the Gulf of 

Maine - Georges Bank region of the northwest Atlantic. Data from region-wide seasonal 

bottom trawl surveys from 1975-2004 were used to calculate total and mean richness 

estimates at each spatial scale. We found that planning-unit size and spatial variation in 

sampling effort had a profound influence on emergent spatial patterns of diversity. Spatial 

patterns of sample effort were uneven and contributed to variation in patterns based on 

total richness, especially at the smallest planning-unit size. A bootstrap approach was 

subsequently used to standardize effort and to estimate mean richness in each grid cell. 

Hotspots, defined as those planning units representing the top 10% of species richness,

shifted from coastal areas to offshore sites with steep topography near Georges Bank at 

coarser planning-unit sizes, for both total richness and standardized effort approaches. 

Hotspots with similar species composition, based on cluster analysis, had discontinuous 

distributions at 10-km and 20-km scales. Regressions of analysis of similarity (ANOSIM) 

R values versus distance between hotspot pairs, at each planning-unit size, did not indicate 

any strong linear relationships. Furthermore, ANOSIM procedures at each planning-unit 

size showed that the 10-km scale had the highest species dissimilarity (based on Global R 

values) among individual hotspots. These results may be attributed to the patchy 

distribution of multiple species based on variation in habitat affinities and fewer habitat 

types occurring in small planning-units. It is difficult to conclude that a large MPA is 

better than several small MPAs; however, we suggest that increasing planning-unit size 

can reduce the effect of sample size on the selection of hotspots, increase confidence in the 

results of such analyses, and increase probability of encompassing representative species 

at regional scales. 

Key Words

Spatial planning, GIS, biodiversity, hotspot, species richness, Gulf of Maine, Georges 

Bank, MPA
ii

Table of Contents

Topic Page

Abstract................................................................................................................................ i

Key Words........................................................................................................................... i

Table of Contents................................................................................................................ ii

List of Figures and Tables..................................................................................................iii

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

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

Results................................................................................................................................. 6

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

Acknowledgements........................................................................................................... 27

Literature Cited ................................................................................................................. 27

Appendix 1 – List of scientific and common names of fishes captured ........................... 34

Appendix 2 – Results of the curve estimation procedure on the relationship between 

sample size and species richness at 10-km scale. ........................................ 39

Appendix 3 – Results of the curve estimation procedure on the relationship between 

sample size and species richness at 20-km scale. ........................................ 40

Appendix 4 – Results of the curve estimation procedure on the relationship between 

sample size and species richness at 40-km scale. ........................................ 41

Appendix 5 – Results of the curve estimation procedure on the relationship between 

sample size and species richness at 80-km scale. ........................................ 42

Appendix 6 – SIMPER results for comparison of hotspot group 2 versus hotspot 

group 1 at 10-km spatial scale.. ................................................................... 43

Appendix 7 – SIMPER results for comparison of hotspot group 2 versus hotspot 

group 3 at 10-km spatial scale.. ................................................................... 44

Appendix 8 – SIMPER results for comparison of hotspot group 3 versus hotspot 

group 1 at the 10-km spatial scale................................................................ 45

Appendix 9 – SIMPER results for comparison of hotspot group 2 versus hotspot 

group 1 at the 20 km spatial scale................................................................ 46

Appendix 10 – SIMPER results for comparison of hotspot group 2 versus hotspot 

group 3 at the 20 km spatial scale................................................................ 47

Appendix 11 – SIMPER results for comparison of hotspot group 3 versus hotspot 

group 1 at 20 km spatial scale...................................................................... 48

Appendix 12 – SIMPER results for comparison of hotspot group 1 versus hotspot 

group 2 at the 40 km spatial scale................................................................ 49
iii

List of Figures 

Figure Number and Title Page

Figure 1 – The Gulf of Maine and Georges Bank region in the northwest Atlantic........... 3

Figure 2 – Location of trawl survey sample stations from 1975 to 2004. .......................... 5

Figure 3 – Spatial patterns of species richness at the 10-km scale: (A) total species 

richness (B) mean species richness based on the effort-standardization 

procedure........................................................................................................... 7

Figure 4 – Spatial patterns of species richness at the 20-km scale: (A) total species 

richness (B) mean species richness based on the effort-standardization 

procedure........................................................................................................... 7

Figure 5 – Spatial patterns of species richness at the 40-km scale: (A) total species 

richness (B) mean species richness based on the effort-standardization 

procedure. ......................................................................................................... 8

Figure 6 – Spatial patterns of species richness at the 80-km scale: (A) total species 

richness (B) mean species richness based on the effort-standardization 

procedure........................................................................................................... 8

Figure 7 – Relationship between sample size and species richness at multiple spatial 

scales............................................................................................................... 10

Figure 8A – Cluster analysis of species similarity among hotspots at the 10-km scale.. . 16

Figure 8B – Distribution of the three cluster groups at the 10-km scale......................17

Figure 9A – Cluster analysis of species similarity among hotspots at the 20-km scale.. . 18

Figure 9B – Distribution of the three cluster groups at the 20-km scale......................19

Figure 10A – Cluster analysis of species similarity among hotspots at the 40-km scale. 20

Figure 10B – Distribution of the two cluster groups at the 40-km scale......................21 

Figure 11 – The relationship of dissimilarity (R value) and distance between hotspots 

at (A) 10-km scale (B) 20-km scale, and (C) 40-km scale. ............................ 22
iv

List of Tables

Table Number and Title Page

Table 1 – ANCOVA of planning-unit size, sample size in each cell (logSS), and their 

interaction on the estimation of species richness in each cell......................... 11

Table 2 – Results of pair-wise comparisons based on Wilcox modification of the 

Johnson-Neyman procedure............................................................................ 11

Table 3 – Summary of changes in the location of hotspots based on the observation 

of total species richness and the effort-standardization procedure. ................ 11

Table 4 – SIMPER results for comparison of hotspot group 2 versus hotspot group 

1 at 10-km spatial scale................................................................................... 12

Table 5 – SIMPER results for comparison of hotspot group 2 versus hotspot group 

3 at 10-km spatial scale................................................................................... 12

Table 6 – SIMPER results for comparison of hotspot group 3 versus hotspot group 

1 at the 10-km spatial scale............................................................................. 13

Table 7 – SIMPER results for comparison of hotspot group 2 versus hotspot group 

1 at the 20 km spatial scale. ............................................................................ 13

Table 8 – SIMPER results for comparison of hotspot group 2 versus hotspot group 

3 at the 20 km spatial scale.. ........................................................................... 14

Table 9 – SIMPER results for comparison of hotspot group 3 versus hotspot group 

1 at 20 km spatial scale.. ................................................................................. 14

Table 10 – SIMPER results for comparison of hotspot group 1 versus hotspot group 

2 at the 40 km spatial scale. ............................................................................ 15

Table 11 – Regression results for R value versus distance between each hotspot pair. ... 15
1

Introduction

Marine protected areas (MPAs) are considered important management tools for 

conserving biological diversity in the sea in general and fishes in particular (Auster & 

Shackell 2000; Mora et al. 2006). Both empirical and theoretical modeling studies 

suggest that if properly designed, MPAs can aid in the conservation of species, 

populations, communities and habitats as well as the important biological and physical 

interactions that affect patterns of diversity and vice versa (Gell & Roberts 2003; Halpern 

& Warner 2002; Lindholm et al. 2001; Murawski et al. 2005). The potential benefits of 

protecting ecologically meaningful areas for marine conservation include: increasing the 

abundance, size, and fecundity of exploited species; spillover of propagules, juveniles 

and adults to serve as source populations to unprotected as well as other protected areas; 

maintaining the complexity and quality of habitats for sustaining populations and 

communities; conserving local examples of biological diversity; maintenance and 

restoration of community processes; and increasing resiliency from management 

miscalculations or abnormal conditions (Allison et al. 1998; Auster & Shackell 2000; 

Friedlander 2001; Roberts et al. 2003). 

Much of what we understand about the performance of MPAs comes from studies of

coral reef and kelp forest communities as well as other nearshore spatially complex 

habitats (Halpern 2003; Halpern & Warner 2002; 2003). In general, such studies 

demonstrate that protection increases populations of exploited species, habitat 

complexity, and biological diversity. While spatial management techniques have been 

used as tools across wide areas of continental shelf for marine conservation (e.g., 

spawning closures for key species), the outcomes of such management approaches are 

not always clear-cut. Quantitative studies measuring the performance of MPAs indicate 

there are benefits for some but not all species and habitats (Collie et al. 2000; Lindholm 

et al. 2004; Link 2005; Murawski et al. 2005). For example, epifaunal communities on 

gravel substratum on Georges Bank demonstrate a positive response to closure attributed 

to the elimination of direct physical disturbance by fishing gear. In contrast, shallow 

sand communities in the same area (i.e. less than 80 m depth) have shown little 

demonstrable effect likely due to continued disturbance by tidal and storm currents and a 

resident fauna with life history patterns adapted to such disturbance regimes. MPA sites 

have been delineated using a variety of biological criteria (Auster & Shackell 2000; 

Botsford et al. 2003) including: (1) location of nursery areas and spawning sites for 

exploited species, (2) areas consistently utilized by highly migratory species including 

marine mammals, (3) areas containing habitats and communities important for key taxa 

or sensitive to disturbance such as seagrass meadows and coral reefs, and (4) areas of 

high biological diversity. 

Conserving hotspots has become a primary goal for conservation as such sites include the 

greatest diversity, however measured, in the smallest area allowing other uses outside 

protected sites. However, diversity hotspots are defined in multiple ways including areas 

of high species richness, diversity (based on various indices of richness and evenness as 

well as higher level taxonomic relationships), and numbers of endemic or endangered 
2

species (Myers et al. 2000; Reid 1998). In any case species are not evenly distributed 

across space, so identifying hotspots requires data on species distribution. Many studies, 

primarily from terrestrial ecosystems, revealed that patterns of species diversity are 

strongly spatial scale dependent (Bohning-Gaese 1997; Connor & McCoy 1979; Lennon 

et al. 2001; Palmer & White 1994; Rahbek 2005; William 1986; Willis & Whittaker 

2002; Zagmajster et al. 2008). This scale-dependent phenomenon is associated with one 

of the oldest paradigms in ecology, the species-area relationship (SAR), which

demonstrates that species richness increases at a rate related to increases in the spatial 

scale of observation (Arrhenius 1921). For example, Lennon et al. (2001) demonstrated 

that diversity hotspots of birds in the United Kingdom move northwards when planningunit size increased. This suggests that the choice of spatial scale for planning may be a 

critical factor for identifying the distribution of hotspots and meeting conservation 

objectives.

If scale-dependent changes indeed result in shifts in the actual location of hotspots, then 

identifying how and why such changes occur, and how conservation planning efforts 

might address such patterns, can aid the process of MPA site selection. Few studies have 

focused on the relationship between the spatial scale of observation and patterns of 

diversity in marine ecosystems. Herein we evaluated the effects of planning-unit size on 

emergent patterns of species-richness hotspots across the Gulf of Maine-Georges Bank 

Large Marine Ecosystem. We used data on the distribution of fishes as information for 

this taxon is the most geographically and temporally comprehensive due to their 

economic value and requirements for active and ongoing management. We also 

investigated both how spatial scale affects geographic patterns of species richness as well 

as the effect of sample size per planning unit on such patterns. An effort-standardization

approach based on a bootstrap procedure was used to address variation in sample effort 

across the geographic region. In order to understand the equivalence of hotspots in terms 

of conservation value, we also investigated patterns of species similarity among hotspots 

across the gradient of planning-unit size.

Methods

The Gulf of Maine and Georges Bank (herein after the Northeast Large Marine 

Ecosystem or NELME) form a distinctive sub-region of the North American continental 

shelf (Sherman et al. 1996). Data on the distribution and abundance of fishes were 

obtained from shelf-wide research trawl surveys conducted by the National Marine 

Fisheries Services (NMFS) Northeast Fisheries Sciences Center from 1975 to 2004 (see 

Auster et al. 2006 for details of this data set). These multi-species surveys were designed 

to monitor trends in abundance and distribution of the demersal fish species inhabiting 

the region. Although these broad-scale trawl surveys cover continental shelf waters from 

Cape Hatteras to Nova Scotia, we focused this analysis on the NELME (Figure 1). Each 

station was sampled using a No. 36 Yankee (or similar) bottom trawl deployed for 30 

minutes and towed at a speed of 6.5 km h-1. Tows for each survey were conducted at 

350-400 sampling stations using a stratified-random sampling design (NEFC 1988). 

Twenty-six strata were delineated based on temperature and depth (Azarovitz 1981; 

Grosslein 1979). Sample sites within strata were chosen at random for each survey based
3

Figure 1. The Gulf of Maine and Georges Bank (NELME) region in the northwest Atlantic. 

on one tow per 680 km2 but with a minimum of five samples per stratum. Samples were 

collected in depths of 27 m to 350 m; however, greater depths were occasionally sampled 

in the deep canyons along the continental margin. Individuals in the catch were sorted to 

species, counted, weighed (to the nearest 0.1 kg), and measured (to the nearest cm). 

While sampling of northwest Atlantic fishes has routinely been done in all four seasons, 
4

the major focus has been in spring (March-May) and fall (September-November). Catch 

per unit effort (CPUE) was standardized for all tows. We aggregated data for both the 

spring and fall survey periods and across all years in order to produce a general 

perspective on patterns of species diversity in the NELME. A total of 7554 tows and 157 

species was observed in trawl samples and included in the data set (Appendix 1 lists both 

scientific and common names). 

In order to assess the role of planning-unit size on patterns of species richness, we 

partitioned sample data across seasons and years into grid cells at multiple spatial scales 

(i.e. 10x10 km, 20x20 km, 40x40 km, and 80x80km). The Create Vector Grid tool in the 

Hawths Tools extension in ArcGIS 9.1 (ESRI, Redlands, CA, USA) was used to produce 

uniform grids at each scale with trawl survey data linked to the geographic coordinates of 

each tow start location. Grid cells were nested, such that the smallest grid cell (i.e.

planning-unit) was 25% of the size of the next largest unit, and so on. For example, a 20-

km unit contained four 10-km units, thus survey data from four adjacent smaller cells 

were combined to estimate the species richness in the larger cell. For the NELME study 

area, there were 1353 planning-units at the 10- km scale, 366 planning-units at the 20-km

scale, 106 planning-units at the 40-km scale, and 32 planning-units at the 80-km scale. 

Total species richness was then calculated for each planning- unit at each scale and 

plotted on a map. Patterns of spatial variation in species richness were identified by 

parsing the data into quintiles and plotting the results. Diversity hotspots were then 

identified as the planning-units in the top 10% of richness values, based on an a priori

choice of threshold value (sensu Roberts et al. 2002), and serve as the primary focus of 

subsequent analyses. (Here the top quintile of richness values placed hotspots in a larger 

spatial context of high diversity sites and provides an example for use of alternative high 

diversity sites in the public process of negotiating locations of MPAs.) 

The literature amply demonstrates the effects of sample size on estimates of species 

richness. In this study the random selection of sample sites within survey strata, as well 

as the practical limits on where trawl sampling can be conducted due to complex 

topography, resulted in an uneven distribution of samples across the region and between 

grid cells at all planning-unit sizes (Figure 2). This variable pattern of effort will be 

problematic for delineating diversity hotspots based on inclusion of planning-units as 

false positives and exclusion of units as false negatives. Further, the effects of variable 

effort will vary across the gradient of grid cell size since larger grid cells have larger 

sample sizes. To understand the effect of variation in sampling effort on the estimation 

of species richness we calculated sample size in each grid cell at each planning-unit size. 

The relationship between sample size and species richness within each planning-unit size 

was then calculated by using the curve estimation procedure in SPSS (SPSS Inc., 

Chicago, IL, USA) to determine the most appropriate regression model to fit the data. 

Further, in order to understand if the relationships between sample size per grid cell and 

species richness were significantly different at fine planning-unit sizes, models were 

compared using analysis of covariance (ANCOVA; Quinn & Keough 2003) to determine 

if there were significant differences among the slopes of the regressions. The null 

hypotheses that regression slopes are homogeneous, i.e. β10km=β20km=β40km=β80km, was 

tested by examining whether the interaction between grid size (categorical predictor) and
5

Figure 2. Location of trawl survey sample stations from 1975 to 2004.

sample size (continuous predictor) equals zero. When the results of the ANCOVA reject 

the null hypothesis, the Wilcox modification of the Johnson-Neyman procedure (Quinn & 

Keough 2003) was used to compare models in pair-wise groups to determine which 

regression models contributed to the difference. 

To reduce the effect of spatial variation in this effort, we re-sampled the data using a 

bootstrap sub-sampling approach based on matching sample density across grid cells, at 

each planning-unit size. The appropriate sample size for each grid cell was determined 

by dividing the total area of each grid by 50 km2 in order to accommodate grids along the 
6

coastal margin that contain less area then complete grid cells offshore. Data were then 

randomly re-sampled without replacement based on standard sample sizes. We chose 

sampling without replacement to maximize the probability of including all species, 

especially numerically rare species, from the samples in each cell. Grid cells were 

eliminated if the required sample size for re-sampling was larger than the actual sample 

size (180 grid cells for the 10-km scale, 54 for the 20-km scale, 22 for the 40-km scale, 

nine cells for the 80-km scale were eliminated). The effort-standardization procedure was 

repeated 1000 times in each grid cell and at each planning-unit size. Species richness in 

each cell was estimated by averaging the species richness calculated from each resample 

procedure. Species-richness maps were produced for each spatial scale and richness 

hotspots were identified as above.

Different hotspots may support the same number of species but the composition of 

species among hotspots may not be the same. To understand if species compositions 

were similar or different among the hotspots, we calculated Bray-Curtis similarity indices 

for all pair-wise comparisons of species composition (species and abundance based on 

numbers) in each planning unit from the bootstrap data set. We also visualized the 

similarities in species composition using hierarchical clustering and used the results to 

assign planning units to cluster groups for an Analysis of Similarities (ANOSIM; Clarke 

& Warwick 2001). The null hypothesis for ANOSIM is there are no differences in 

species composition between different hotspots. Alternatively, if two or more sample 

sites are different in species composition, then the dissimilarities between the groups 

should be greater than those within groups. The resultant global R value of this test is a 

measure of variation between samples compared to variation within samples with high R 

values indicating greater dissimilarity (Clarke 1993). Similarity percentage routines 

(SIMPER) were used to identify species that contributed most to the average dissimilarity 

between units. To understand how geographic distance between individual hotspots (i.e., 

individual planning-units) plays a role in similarity of species composition, we conducted 

an ANOSIM based on all pair-wise comparisons at each spatial scale. R values for each 

pairwise comparison were plotted against the geographic distance between planning 

units. If, in general, proximate planning-units were more similar then distal sites, then 

there should be a positive relationship between R and distance (i.e., less dissimilarity at 

short versus long distances). Abundance data were log(y+1) transformed and 

standardized between samples.

Results

Our results demonstrated that the locations of the species-richness hotspots varied with 

planning-unit size, both using observed patterns of richness and the effort-standardization 

method. Based on observed species richness, grid cells identified as hotspots were located 

primarily along the northern edge of Georges Bank and the coastal region of the western 

Gulf of Maine at fine scales (10 km and 20 km; Figures 3A and 4A). At larger planningunit sizes, hotspots shifted from coastal areas to offshore sites with steep topography near 

Georges Bank (Figure 5A and 6A). There were only two hotspots identified at the largest 

planning-unit size and they were located along the northern edge of Georges Bank and

the deep-water continental margin area along southern Georges Bank (Figure 6A). 
7

Figure 3. Spatial patterns of species richness at the 10-km scale: (A) total species richness (B) mean 

species richness based on the effort-standardization procedure. All planning-units are classified by 

quantile rank. The highlighted grid cells (blue) are those in the top 10 % of diversity values. 

Planning-units that did not have enough samples for the sub-sample procedure are blank.

Figure 4. Spatial patterns of species richness at the 20-km scale: (A) total species richness (B) mean 

species richness based on the effort-standardization procedure. See Figure 3 for explanation of map 

details. 
8

Figure 5. Spatial patterns of species richness at the 40-km scale: (A) total species richness (B) mean 

species richness based on the effort-standardization procedure. See Figure 3 for explanation of map 

details. 

Figure 6. Spatial patterns of species richness at the 80-km scale: (A) total species richness (B) mean 

species richness based on the effort-standardization procedure. See Figure 3 for explanation of map 

details. 
9

Not surprisingly, estimates of species richness in grid cells increased with sample size per 

cell (Figure 7). From the curve estimation procedure, power functions best described the 

relationship between richness and sample size between grid cells based on the highest rsquare value and significance values (all significant at p<0.05, see detailed results in 

Appendices 2-5). Data were log10 transformed to perform further analysis. The results of 

ANCOVA indicated that grid size, sample size, and their interaction all had significant 

effects on the estimation of total species richness (Table 1). The significance value of the 

interaction between grid scale and sample size suggested that the regression slopes were 

significantly different. Comparison of the slopes of the power functions indicated that 

sample size has more of an effect on species richness when the sample size was small. 

The results of the Wilcox pair-wise comparisons indicated that the slope at the 10-km 

scale was significantly higher than that at all other scales (Table 2). A visual inspection of 

the resultant maps from the effort-standardization procedure show significant differences 

in the location of hotspots when compared to the results based on total species richness 

per cell (Figures 3-6). Approximately half of all hotspot locations shift based on the 

richness estimation method used for each cell regardless of scale (Table 3). From a 

geographic perspective at the 10-km scale, the hotspots along the northern edge of 

Georges Bank delineated using all observations disappear and new hotspots appear in the 

central basin of the Gulf of Maine and at more locations west of Nova Scotia based on 

the effort-standardization approach (Figure 3B). When the planning-unit size was 

increased to the 20-km scale, there was a pattern in reallocating hotspots from the north 

and south edges of Georges Bank to the coastal area (Figure 4B). At the 40-km scale, 

hotspots were found on northern and southern Georges Bank, and at two coastal sites 

along southern coastal Gulf of Maine based on observed richness. The southern Georges 

Bank sites disappeared and three additional hotspots were identified along the coastal 

region based on the effort-standardization procedure (Figure 5B). Species-richness 

hotspots were identified on the northwest and southwest edges of Georges Bank based on 

observed richness but shifted to the southern edge at the 80-km scale (Figure 6B). 

Overall, there was a shift in the location of hotspots from coastal areas to areas with steep 

topography as planning-unit size increased.

The number of hotspots decreased with increased planning-unit size but the total number 

of species appearing in the hotspots increased. 117 hotspots were selected by the subsample method at the 10-km scale, and 50 % of the fish species (79 out of 157 species) 

were included in the hotspots. 31 hotspots were selected at the 20-km scale, and 50 % of 

the fish species (79 out of 157 species) were included in the hotspots. Eight hotspots were 

selected at the 40-km scale, and 55 % of the fish species (86 out of 157 species) were 

included in the hotspots. Only two hotspots were selected at the 80-km scale, and 63 % of 

the fish species (99 out of 157 species) were included in the hotspots. 

We assigned hotspots to either two or three groups based on the results of the cluster 

analyses, except for the 80-km scale that had only two hotspot planning units (Figures 

8A-10A). ANOSIM Global R values represented the overall degree of similarity among

cluster groups with a value of 1 indicating a maximum dissimilarity between samples. 
10

Figure 7. Relationship between sample size and species richness at multiple spatial scales. The best 

fits for the relationships between sample size and species richness at each spatial scale were: (A) 

Y=11.6X0.354 (R2

 = 0.927, n=1262 , P<0.01) at 10-km scale (B) Y=13.55X0.291 (R2=0.9174, n=347, 

P<0.01) at 20-km scale (C) Y=15.25X0.252 (R2

=0.8324, n=97, P<0.01)at 40-km scale (D) Y=16.72X0.234 

(R2

=0.8572, n=30, P<0.01) at 80-km scale. The results indicate that sample size in each grid cells has 

a positive effect on the patterns of species richness. 

Comparisons of species composition among the cluster groups resulted in Global R 

values of 0.644 at the 10-km scale (P<0.001), 0.692 at 20 the km-scale (P<0.001), and 

0.677 at the 40 km-scale (P=0.029). All comparisons suggested that species 

compositions were significantly different between any two of the hotspot groupings 

(based on a critical value of p=0.05). Figures 8B-10B illustrates the spatial distributions 

of the hotspot groups at each spatial scale. Hotspots with similar species composition, 

based on cluster analysis, had discontinuous distributions at 10-km and 20-km scales 

while the hotspots were separated along a north and south axis at 40-km scale. The results 

of the SIMPER analyses demonstrated that species that made the greatest contribution to 

the dissimilarity between hotspot groups for the 10-km, 20-km and 40-km scales varied 

in composition and percent contribution to dissimilarity (Tables 4-10). The contribution 

of individual species to the level of dissimilarity was relatively small (i.e. less than 5.5% 

maximum contribution). 
11

Table 1. ANCOVA of planning-unit size, sample size in each cell (logSS), and their interaction on the 

estimation of species richness in each cell. In order to perform ANCOVA, logarithmic transformed 

valuables were used for species richness and sample size. 

Source

Type III 

Sum of 

Squares Df

Mean 

Square F Sig.

Corrected Model 43.550(a) 7 6.221 872.116 <0.001

Intercept 21.626 1 21.626 3031.506 <0.001

Planning-unit Size .215 3 .072 10.064 <0.001

Logs 6.217 1 6.217 871.545 <0.001

Planning-unit * logSS .630 3 .210 29.428 <0.001

Error 12.327 1728 .007

Total 3321.909 1736

Corrected Total 55.877 1735

Table 2. Results of pair-wise comparisons based on Wilcox modification of the Johnson-Neyman 

procedure.

Pairwise comparisons Significant range of covariate Significance level

10 km VS. 20 km logSS < 1.02 P<0.05

10 km VS. 40 km logSS <1.46 P<0.05

10 km VS. 80 km logSS <1.81 P<0.05

20 km VS. 40 km -999.00 NS

20 km VS. 80 km -999.00 NS

40 km VS. 80 km -999.00 NS

Table 3. Summary of changes in the location of hotspots based on the observation of total species 

richness and the effort-standardization procedure. Total number of hotspots = number from all 

observations/number from effort-standardization procedure (hotspots were of equal number in both 

approaches); Number of stable hotspots = number of hotspots that did not shift locations between 

procedures; Percent constancy in location = number of stable hotspots/total hotspots.

Planning-Unit 

Size

Total 

hotspots

Number of stable 

hotspots 

Percent constancy in 

location

10 km 117 55 0.47

20 km 31 13 0.41

40 km 8 4 0.5

80 km 2 1 0.5
12

Table 4. SIMPER results for comparison of hotspot group 2 versus hotspot group 1 at 10-km spatial 

scale. 27 species contributed to 80 % of dissimilarity (see Appendix 6 more details). The top 10

species are listed here.

Species

Av.Abund 

Group 2

Av.Abund 

Group 1 Av.Diss Diss/SD Contrib% Cum.%

Helicolenus dactylopterus 0.06 1.02 1.74 3.29 5.42 5.42

Argentina silus 0.05 0.57 1.73 3.28 5.4 10.82

Myoxocephalus 

octodecemspinosus 10.96 0 1.72 3.3 5.35 16.18

Pseudopleuronectes americanus 2.56 0 1.45 1.84 4.51 20.69

Limanda ferruginea 3.48 0 1.17 1.34 3.64 24.32

Artediellus sp. 0.24 0.37 1.08 1.15 3.37 27.69

Brosme brosme 0.24 0.59 1.08 1.15 3.36 31.05

Enchelyopus cimbrius 1.69 0.11 1.07 1.15 3.32 34.37

Alosa sapidissima 0.3 0 1.02 1.1 3.18 37.56

Alosa aestivalis 2.11 0.07 1 1.06 3.11 40.67

Table 5. SIMPER results for comparison of hotspot group 2 versus hotspot group 3 at 10-km spatial 

scale. Thirty-six species contributed to 80 % dissimilarity (see Appendix 7 for details). The top 10 

species are listed here.

Species

AV.Abund 

Group2

Av.Abund 

Group3 Av.Diss Diss/SD Contrib% Cum.%

Enchelyopus cimbrius 1.69 0 1.25 1.62 3.89 3.89

Malacoraja senta 0.82 0.07 1.12 1.28 3.49 7.38

Leucoraja erinacea 0.96 6.66 1.02 1.13 3.15 10.53

Leucoraja ocellata 0.55 7.52 1.01 1.13 3.15 13.68

Alosa aestivalis 2.11 0.25 0.98 1.13 3.05 16.73

Alosa sapidissima 0.3 0.04 0.94 1.08 2.92 19.65

Triglops murrayi 0.62 2.29 0.93 1.05 2.9 22.55

Scomber scombrus 1.69 2.07 0.91 1.01 2.82 25.37

Hippoglossus hippoglossus 0.12 0.27 0.89 1.01 2.78 28.15

Ammodytes dubius 3.01 1.69 0.89 1.01 2.76 30.91
13

Table 6. SIMPER results for comparison of hotspot group 3 versus hotspot group 1 at the 10-km 

spatial scale. Twenty-nine species contributed to 80 % dissimilarity (see Appendix 8 for details). The 

highest top 10 species are listed here.

Species

Av.Abund 

Group3

Av.Abund 

Group1 Av.Diss Diss/SD Contrib% Cum.%

Pseudopleuronectes americanus 23.76 0 1.94 8.55 4.99 4.99

Myoxocephalus 

octodecemspinosus 35.65 0 1.94 8.55 4.99 9.97

Helicolenus dactylopterus 0.04 1.02 1.82 3.56 4.69 14.66

Limanda ferruginea 8.47 0 1.81 3.58 4.66 19.32

Leucoraja erinacea 6.66 0 1.6 2.06 4.11 23.43

Brosme brosme 0.06 0.59 1.6 2.06 4.1 27.54

Aspidophoroides monopterygius 0.91 0 1.58 2.07 4.07 31.61

Leucoraja ocellata 7.52 0 1.57 2.07 4.03 35.64

Argentina silus 0.15 0.57 1.49 1.73 3.83 39.47

Triglops murrayi 2.29 0 1.14 1.16 2.92 42.39

Table 7. SIMPER results for comparison of hotspot group 2 versus hotspot group 1 at the 20 km 

spatial scale. Twenty-nine species contributed to 80 % dissimilarity (see Appendix 9 for details). The 

highest top 10 species are listed here.

Species

Av.Abund 

Group2

Av.Abund 

Group1 Av.Diss Diss/SD Contrib% Cum.%

Lepophidium profundorum 0 0.31 1.16 21.69 5.13 5.13

Lumpenus lumpretaeformis 0 0.62 1.16 21.69 5.13 10.25

Lumpenus maculatus 0.11 3.07 0.93 1.94 4.11 14.37

Maurolicus weitzmani 0.12 0 0.81 1.49 3.58 17.95

Dipturus laevis 0.1 0 0.69 1.19 3.04 20.98

Tautogolabrus adspersus 0.14 0.8 0.59 0.97 2.62 23.6

Prionotus carolinus 0.26 0 0.58 0.97 2.57 26.18

Myoxocephalus aenaeus 0.13 0.03 0.58 0.97 2.56 28.74

Artediellus sp. 0.07 0.02 0.58 0.97 2.56 31.3

Pomatomus saltatrix 0.01 0.03 0.58 0.97 2.56 33.87
14

Table 8. SIMPER results for comparison of hotspot group 2 versus hotspot group 3 at the 20 km 

spatial scale. Thirty-five species contributed to 80 % dissimilarity (see Appendix 10 for details). The 

highest top 10 species are listed here.

Species

Av.Abund 

Group2

Av.Abund 

Group3 Av.Diss Diss/SD Contrib% Cum.%

Cryptacanthodes maculatus 0 0.15 1 2.07 4.19 4.19

Lumpenus lumpretaeformis 0 0.38 0.97 1.91 4.09 8.28

Myoxocephalus aenaeus 0.13 0 0.72 1.19 3.03 11.31

Dipturus laevis 0.1 0.01 0.69 1.14 2.91 14.21

Lumpenus maculatus 0.11 1.77 0.66 1.09 2.79 17.01

Cyclopterus lumpus 0.04 0.08 0.65 1.07 2.75 19.75

Ammodytes dubius 33.97 1.74 0.65 1.04 2.73 22.48

Triglops murrayi 1.95 0.12 0.64 1.04 2.71 25.2

Brosme brosme 0.11 0.11 0.62 0.99 2.59 27.79

Prionotus carolinus 0.26 0.02 0.61 0.99 2.58 30.37

Table 9. SIMPER results for comparison of hotspot group 3 versus hotspot group 1 at 20 km spatial 

scale. Twenty-nine species contributed to 80 % dissimilarity (see Appendix 11 for details). The 

highest top 10 species are listed here.

Av.Abund Av.Abund

Species Group 3 Group 1 Av.Diss Diss/SD Contrib% Cum.%

Lepophidium profundorum 0.01 0.31 1.05 2.85 5.16 5.16

Cyclopterus lumpus 0.08 0 0.79 1.45 3.85 9.01

Tautogolabrus adspersus 0.07 0.8 0.68 1.15 3.35 12.36

Maurolicus weitzmani 0.16 0 0.67 1.15 3.3 15.66

Ammodytes dubius 1.74 987.61 0.63 1.04 3.09 18.75

Triglops murrayi 0.12 0.06 0.63 1.04 3.06 21.81

Melanostigma atlanticum 0.06 0.01 0.59 0.98 2.87 24.69

Pomatomus saltatrix 0.01 0.03 0.59 0.98 2.87 27.56

Stenotomus chrysops 0.03 0.01 0.59 0.98 2.87 30.43

Ulvaria subbifurcata 0.6 0.41 0.59 0.98 2.87 33.31
15

Table 10. SIMPER results for comparison of hotspot group 1 versus hotspot group 2 at the 40 km 

spatial scale. Thirty-five species contributed to 80 % dissimilarity (see Appendix 12 for details). The 

highest top 10 species are listed here.

Species

Av.Abund 

Group1

Av.Abund 

Group2 Av.Diss Diss/SD Contrib% Cum.%

Lumpenus maculatus 0 3.1 0.88 16.05 4.33 4.33

Helicolenus dactylopterus 0.24 0.01 0.68 1.68 3.35 7.68

Syngnathus fuscus 0.02 0 0.67 1.67 3.28 10.96

Stenotomus chrysops 0.01 0.02 0.66 1.66 3.24 14.2

Petromyzon marinus 0.02 0 0.66 1.66 3.21 17.41

Scomberesox saurus 0.05 0 0.56 1.25 2.75 20.16

Urophycis regia 0 0.1 0.55 1.24 2.7 22.86

Dipturus laevis 0.06 0.01 0.55 1.24 2.69 25.55

Merluccius albidus 0 0.03 0.55 1.24 2.68 28.22

Maurolicus weitzmani 0.05 0.13 0.46 0.97 2.23 30.45

Table 11. Regression results for R value versus distance between each hotspot pair.

Planning-Unit Size N R Slope P value

10 km 6785 0.114 0.001 <0.001

20 km 464 0.059 0.001 <0.001

40 km 27 0.002 8E-005 0.836
16

Figure 8A. Cluster analysis of species similarity among hotspots at the 10-km scale. Planning-units 

are clustered into three groups. Each number represents a grid cell identified as a hotspot (n=117).
17

Figure 8B. Distribution of the three cluster groups at the 10-km scale.
18

Figure 9A. Cluster analysis of species similarity among hotspots at the 20-km scale. Planning-units 

are clustered into three groups. Each number represents a grid cell identified as a hotspot (n=31).
19

Figure 9B. Distribution of the three cluster groups at the 20-km scale. 
20

Figure 10A. Cluster analysis of species similarity among hotspots at the 40- km scale. Planning-units 

are clustered into three groups. Each number represents a grid cell identified as a hotspot (n=8).
21

Figure 10B. Distribution of the two cluster groups at the 40-km scale. 
22

Figure 11. The relationship of dissimilarity (R value) and distance between hotspots at (A) 10-km 

scale (B) 20-km scale, and (C) 40-km scale. 
23

Global R values from the ANOSIM conducted for all comparisons among hotspots were 

0.349 (P<0.001) at the 10-km scale, 0.224 (P<0.001) at the 20-km scale, 0.183 at the 40-

km (P<0.001) scale, and 0.021 (P=0.084) at the 80-km scale. The results demonstrated 

that the overall species similarity among hotspots increased when the spatial scale 

increased. If distance did significantly influence the degree of dissimilarity, we would 

expect a positive relationship between ANOSIM R value and distance. Regressions of 

ANOSIM R value versus distance between each hotspot pairs, at each planning-unit size, 

did not demonstrate any strong linear relationship (Figure 11). While regressions for the 

10 km and 20 km scales were significant (p < 0.05), correlation coefficients were very 

low and slopes were close to zero. The regression at the 40 km scale was not significant 

(Table 11). 

Discussion

Here we have demonstrated that planning-unit size and spatial variation in sampling 

effort can substantially influence emergent spatial patterns of diversity. Not surprisingly 

these results illustrated that total species richness was positively correlated with sample 

size. However, the smaller the sample size the greater the effect on the estimation of 

species richness tended to be as the regressions of sample size versus richness were 

steeper at small sample sizes in all cases. This pattern likely reflects the effect of small 

planning-units containing fewer samples with lower diversity than larger units resulting 

in an increased rate of species accumulation as sample size increased. Note that the slope 

value of the power curves decreased from 0.36 to 0.23 as planning-unit size increased 

from 10 km2 to 80 km2 and the Wilcox pair-wise comparison of regressions indicated that 

the slope value of the regression model at the 10-km scale was significantly higher than 

at larger planning-unit sizes. These results illustrated that the effect of sample size at 

small planning-unit sizes was significantly higher. With small planning-unit sizes, we 

suggest that small sample sizes are problematic for assessing spatial variation of species 

richness. Furthermore, about 50 % of hotspots changed regardless of planning-unit size 

when we compared the results from the estimation of total species richness and the effortstandardization approach. The number of unstable planning units was higher at finer 

scales (e.g., 55 planning units at the 10-km scale) and such numbers may have 

problematic consequences in an MPA designation process. 

Results demonstrate that use of the procedure to standardize effort represented in each 

planning unit addresses obvious problems inherent in use of the widely variable number 

of samples collected across the spatial extent of interest. Approaches for marine 

conservation planning vary, and how the data are used as a foundation for decisionmaking are sometimes ignored or at least not explicitly acknowledged. For example, the 

distribution of diversity is often used but variation in sampling effort and effects on 

diversity estimation and geographic pattern are not addressed (Andrew 2002). Hence, it is 

possible that we have identified hotspots in the past due in part to variation in sampling 

effort and missed some number of other sites that may have better met conservation goals 

or provided additional alternatives for consideration. 
24

In this study, we present an initial investigation on the subject of the spatial patterns of 

species diversity in relation to planning-unit size in the Gulf of Maine. We used empirical 

data to reduce the effect of variation in sample size instead of other nonparametric 

techniques to predict species richness in each grid cell based on widely variable sample 

sizes. A robust and accurate estimator should be insensitive to sample size (Chazdon et 

al. 1998), however, multiple studies have demonstrated that sample size has a crucial 

effect on the nonparametric estimation on species richness (Andrew et al. 2003; Brose et 

al. 2003; Chazdon et al. 1998). Moreover, it is difficult to estimate the total number of 

species because there is always a high chance of excluding a large number of rare species 

(Bunge & Fitzpatrick 1993), especially if they have a clumped distribution.

Standardization approaches are needed to address variation of sample size within 

planning units in order to compare datasets (Gotelli & Colwell 2001). We re-sampled the 

empirical data by programming to overcome the issues of uneven sample size, thus each 

grid cell was comparable to all others. We randomly re-sampled tows 1000 times from 

the dataset in each of the grids and calculated average number of species represented by 

the re-sampled tows. Our procedure was similar to sample-based rarefaction (Gotelli & 

Colwell 2001), except that we re-sampled data from an empirical dataset and used mean 

values to represent the results. This procedure was designed to select hotspots, at least 

based on relative differences in species richness among planning units, rather than on 

estimation of predicted species richness. However, while the effort standardization 

approach can overcome the issues of large sample size resulting in overexpression of 

species richness, grid cells with small sample sizes still remain an issue as the procedure 

repetitively re-samples the same tows. 

The locations of species-richness hotspots are used for many aspects of MPA design. The 

goal of many MPA designations is to conserve as many species as possible (Higgs & 

Usher 1980; McNeill & Fairweather 1993; Palmer et al. 1997; Roberts et al. 2003; Rowe 

and Sedberry 2006; Semmens et al. 2010). From an ecosystem management perspective, 

one of the most critical benefits of conserving species diversity is the redundancy that 

species provide within functional guilds and thus increase the resilience of ecosystems 

(Palmer et al. 1997; Sandin et al. 2008, Auster & Link 2009). In this study, patterns of 

species richness were clearly dependent on the spatial scale of planning-units and the 

location of hotspots changed across multiple spatial scales. The shifts in hotspot patterns

may be due to the variation in the patchiness of habitats and attendant variation in the 

habitat affinities of the fish fauna (e.g., Auster 2002; Auster et al. 2001, 2006; 

Rosenzweig 1995). Increasing planning-unit sizes likely encompass more habitats and 

hence greater diversity of the fish fauna as diverse habitat-specific faunas merge 

(Rosenzweig 1995; Zagmajster et al. 2008). In particular, species-richness hotspots 

along the coast proportionally decreased and shifted to the complex topography along the 

edges of Georges Bank when planning-unit size increased. The habitats near the edge of 

Georges Bank are complex with high variation of bathymetry, sediment type, 

temperature, and circulation systems (Butman & Beardsley 1987). 

Choices for threshold values to define hotspots can be rather arbitrary. Here we chose 

10% based on use in a previous study but others have used different threshold values for 
25

hotspots (e.g., top quintile from each of six diversity indices in Auster et al 2006, top 5% 

in Lennon et al. 2001, top 15% in Zagmajster et al. 2008). We also mapped richness 

based on the distribution of quintiles and found similar trends in shifts in distribution 

based on planning unit size. From an applied perspective such maps are useful for 

assessing alternative protected area scenarios in terms of trade-offs of planning units in 

the top 10% of richness with other high diversity sites in the top quintile. The 

designation of MPAs, at least in the United States, is governed by a regulatory process 

that involves assessment of alternative strategies and a nested social process of 

negotiation. While hotspots may serve as set of ideal sites, alternative sites may 

ultimately be used due to overarching conflicts with various stakeholder interests. 

Another issue related to MPA design is to decide how large sites should be. Some 

researchers suggest a number of small reserves protect more species than a single large 

reserve (Higgs & Usher 1980; McNeill & Fairweather 1993). In this study, the two 

hotspots identified at the 80-km scale included more species than the 117 hotspots 

identified at the 10-km scale. However, at the 10-km scale, the highest global R value and 

a higher proportion of pair-wise R values reached the value of one, indicating that species 

compositions were more dissimilar at finer planning-unit sizes. As discussed above, 

species distributions are demonstrably correlated with habitat features at these spatial 

scales (Auster et al. 1998; Langton and Watling, 1990) and small planning units 

generally contain fewer habitat types. If we take the total area of hotspots and species 

dissimilarity into account, it is possible that several small MPAs would protect more 

species than a single large MPA as there is a greater level of similarity at larger planningunit sizes. Interestingly, distance was not a significant factor that affected species 

similarity between hotspots as there were no strong relationships based on distance 

between hotspots and species similarity at any planning-unit size. 

We also found that there were more negative ANOSIM R values at the 10-km scale. R is 

a measure of variation between samples within grid cells compared to variation between 

grid cells (Clarke 1993). Theoretically, negative values are unlikely because they may 

represent greater dissimilarity among replicates within grid cells than between grid cells 

(Chapman & Underwood 1999). However, negative ANOSIM R values happened 

commonly in other studies and the phenomenon has been attributed either to inadequate 

sampling of species within delineated areas (e.g., habitats) or inappropriate grouping of 

samples for analysis (Chapman & Underwood 1999). The higher proportion of negative 

ANOSIM R values in this study was more common at fine spatial scales and may be 

indicative such differences among planning units. For example, when sample size within 

planning units was small, less temporal variability was represented, thus the variation 

among samples from different years may be higher than the variation among planning 

units. 

Many species that contributed most to dissimilarity between two groups of hotspots were 

relatively common at the scale of the NELME but may be linked to particular habitats or 

have restricted distributions at the scale of planning units. Hence small planning-unit size 

increases differences in species compositions and differences between groups. For 

example, cusk (Brosme brosme) had high contribution to the dissimilarity between 
26

hotspot groups at the 10-km and 20-km scale but not at the 40-km scale. Cusk occur 

primarily in boulder, ledge and gravel habitats (Auster & Lindholm 2005; Collette & 

Klein-MacPhee 2002) that are primarily limited to the upper slopes and peaks of offshore 

banks (except Georges Bank), coastal Maine, and the heads of submarine canyons. 

Hence, the contribution of cusk to the dissimilarity of hotspots will depend on inclusion 

of such habitats within planning units, with larger planning units having an increased 

probability of encompassing such features. 

In this study we focused on patterns of species richness based on combining samples 

across the entire time series in the data set. Issues of sub-population structure, variation 

in use of multiple habitats, and temporal variation of community composition remain. 

However, sample size within individual planning units was minimal even with the 

aggregated data and temporal subsets of data would have been inadequate to support the 

analysis in most of the grid cells at fine planning-unit sizes. Although it is possible that 

several smaller MPAs can protect more species than a large MPA, we suggest that there 

would be more biased information from fine grid cells because of the effect of sample 

size and patchy distribution of fishes if we want to use empirical data for hotspot 

analysis. In other words, selecting hotspots from coarser spatial scale is less influenced 

by sample size and patchiness. Some studies indicate that larger protected areas with 

diverse habitats may maintain more stable conditions (Auster 2002; Hilborn et al. 2003). 

From a study in the Gulf of Maine region, species diversity declined at Stellwagen Bank 

National Marine Sanctuary (at a smaller scale) over time but has remained stable at the 

scale of the Gulf of Maine region overall (Auster 2002).

There is a great need for identifying proxies for predicting the distribution of biological 

diversity to aid in conservation planning (Ward et al. 1999), as surveys across broad 

taxonomic groups are time-consuming and costly. The distribution and abundance of 

fishes are well known in many areas with active fisheries and understanding their role as 

a proxy (i.e. based on single taxa, community structure, measures of diversity) would be 

useful in this regard (Auster et al. 2001; Cook & Auster 2006). However, there are few 

empirical tests to demonstrate such relationships. There is a degree of spatial 

concordance of geographic boundaries based of Georges Bank fish communities with 

benthic communities in the same region (Auster & Shackell 2000; Overholtz & Tyler 

1985; Theroux & Grosslein 1987) but further study is needed to demonstrate the 

robustness of such correlations. 

This study provides a foundation for marine conservation planning based on 

identification of diversity hotspots using geographically comprehensive data on demersal 

fishes, a type of data set common to regions and nations with developed commercial 

fisheries. The spatial extent of the region of interest is a critical decision to be made prior 

to any analyses. Here we addressed the region of the NELME. However, smaller or 

larger regions based on biogeographic patterns, oceanographic regimes, and habitat or 

landscapes of interests can be used to define the region of study (Cook & Auster 2007). 

Here we have demonstrated that larger planning-unit size reduces problems related to 

estimation of diversity and identification of hotspots. Given this result, identifying 

hotspots over larger regions may be useful as a first step and then subsequently 
27

investigating patterns of diversity within such planning units may lead to more 

strategically targeted MPA designs. 

Acknowledgements

The authors gratefully acknowledge the support provided by the Taiwanese Ministry of 

Education (to CYK), the Census of Marine Life Gulf of Maine Program (to PA), and 

NOAA’s National Undersea Research Program. Dr. Randal Clark of NOAA’s National 

Centers for Coastal Ocean Science provided the data set used in this analysis. Drs. Eric

Schultz and Daniel Civco provided many useful comments over the course of this project 

that greatly improved the final product. Drs. Ming-Hui Chen and Xia Wan provided

important statistical advice. The authors also thanks three anonymous reviewers for 

comments that greatly improved the manuscript. The opinions expressed herein are those 

of the authors and do not necessarily reflect the opinions of the funding organizations or 

their sub-agencies.

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34

Appendix 1. List of scientific and common names of fishes captured in spring and fall 

NMFS trawl samples within the NELME, 1975-2005.

Scientific name Common name

Alosa aestivalis blueback herring

Alosa mediocris hickory shad

Alosa pseudoharengus Alewife

Alosa sapidissima American shad

Aluterus schoepfi orange filefish

Amblyraja radiata thorny skate

Ammodytes americanus American sand lance

Ammodytes dubius northern sand lance

Anarhichas lupus Atlantic wolfish

Anchoa hepsetus striped anchovy

Anchoa mitchilli bay anchovy

Anguilla rostrata american eel

Antennarius radiosus singlespot frogfish

Antigonia capros deepbody boarfish

Antimora rostrata blue hake

Apeltes quadracus fourspine stickleback

Archosargus probatocephalus sheepshead 

Arctozenus rissoi white barracudina

Argentina silus Atlantic argentine

Argentina striata striated argentine

Argyropelecus aculeatus silver hatchetfish

Ariomma bondi silver rag

Artediellus sp hookear sculpin

Aspidophoroides monopterygius alligatorfish 

Balistes capriscus gray triggerfish

Brevoortia tyrannus Atlantic menhaden

Brosme brosme cusk 

Caranx crysos blue runner

Centropristis ocyurus bank sea bass

Centropristis striata black sea bass

Ceratoscopelus maderensis horned lanternfish

Chauliodus sloani viperfish 

Chaunax stigmaeus redeye gaper

Chlorophthalmus agassizi shortnose greeneye

Citharichthys arctifrons gulf stream flounder

Clupea harengus Atlantic herring

Coelorhynchus carminatus long-nosed grenadier
35

Scientific name Common name

Conger oceanicus conger eel

Cookeolus japonicus bulleye 

Cryptacanthodes maculatus wrymouth 

Cubiceps pauciradiatus bigeye cigarfish

Cyclopterus lumpus lumpfish 

Dactylopterus volitans flying gurnard

Decapterus macarellus mackerel scad

Decapterus punctatus round scad

Dipturus laevis barndoor skate

Enchelyopus cimbrius fourbeard rockling

Engraulis eurystole silver anchovy

Epigonus pandionis bigeye 

Etropus microstomus smallmouth flounder

Etrumeus teres round herring

Eumicrotremus spinosus Atlantic spiny lumpsucker

Fistularia petimba red cornetfish

Fistularia tabacaria bluespotted cornetfish

Foetorepus agassizi spotfin dragonet

Gadus morhua Atlantic cod

Gasterosteus aculeatus threespine stickleback

Glyptocephalus cynoglossus witch flounder

Helicolenus dactylopterus blackbelly rosefish

Hemitripterus americanus sea raven

Hippoglossoides platessoides american plaice

Hippoglossus hippoglossus Atlantic halibut

Hyperoglyphe perciformis barrelfish 

Hyporhamphus unifasciatus silverstripe halfbeak

Laemonema barbatulum shortbeard codling

Lepophidium profundorum fawn cusk-eel

Leucoraja erinacea little skate

Leucoraja garmani rosette skate

Leucoraja ocellata winter skate

Limanda ferruginea yellowtail flounder

Liparis atlanticus Atlantic seasnail

Liparis inquilinus inquiline snailfish

Lophius americanus goosefish 

Lopholatilus chamaeleonticeps blue tilefish

Lumpenus lumpretaeformis snakeblenny 

Lumpenus maculatus daubed shanny
36

Scientific name Common name

Lycenchelys verrilli wolf eelpout

Macrorhamphosus scolopax longspine snipefish

Macrourus berglax roughhead grenadier

Macrozoarces americanus ocean pout

Malacocephalus occidentalis western softhead grenadier

Malacoraja senta smooth skate

Mallotus villosus capelin 

Maurolicus weitzmani weitzmans pearlsides

Melanogrammus aeglefinus haddock 

Melanostigma atlanticum Atlantic soft pout

Menidia menidia Atlantic silverside

Merluccius albidus offshore hake

Merluccius bilinearis silver hake

Monacanthus hispidus planehead filefish

Monolene sessilicauda deepwater flounder

Morone saxatilis striped bass

Mustelus canis smooth dogfish

Myctophum humboldti humboldts lanternfish

Myoxocephalus aenaeus grubby 

Myoxocephalus octodecemspinosus longhorn sculpin

Myoxocephalus scorpius shorthorn sculpin

Myxine glutinosa Atlantic hagfish

Naucrates ductor pilotfish 

Nemichthys scolopaceus slender snipe eel

Nezumia bairdi marlin-spike 

Ogcocephalus corniger longnose batfish

Ogcocephalus nasutus shortnose batfish

Ophichthus cruentifer margined snake eel

Ophidion selenops mooneye cusk-eel

Opsanus tau oyster toadfish

Orthopristis chrysoptera pigfish 

Osmerus mordax rainbow smelt

Paralepis coregonoides sharpchin barracudina

Paralichthys dentatus summer flounder

Paralichthys oblongus fourspot flounder

Parasudis truculenta longnose greeneye

Peprilus triacanthus butterfish 

Peristedion miniatum armored searobin

Petromyzon marinus sea lamprey
37

Scientific name Common name

Pholis gunnellus rock gunnel

Pollachius virens pollock 

Polyipnus clarus slope hatchetfish

Polymetme thaeocoryla lightfish 

Pomatomus saltatrix bluefish 

Priacanthus arenatus bigeye 

Prionotus carolinus northern searobin

Pristigenys alta short bigeye

Pseudopleuronectes americanus winter flounder

Reinhardtius hippoglossoides greenland halibut

Rhomboplites aurorubens vermilion snapper

Sarda sarda Atlantic bonito

Scomber japonicus chub mackerel

Scomber scombrus Atlantic mackerel

Scomberesox saurus Atlantic saury

Scophthalmus aquosus windowpane 

Scyliorhinus retifer chain dogfish

Sebastes fasciatus acadian redfish

Selar crumenophthalmus bigeye scad

Selene setapinnis Atlantic moonfish

Selene vomer lookdown 

Seriola zonata banded rudderfish

Simenchelys parasiticus snubnose eel

Sphoeroides maculatus northern puffer

Squalus acanthias spiny dogfish

Stenotomus chrysops scup 

Stomias boa scaly dragonfish

Symphurus civitatus offshore tonguefish

Symphurus diomedianus spottedfin tonguefish

Symphurus plagiusa blackcheek tonguefish

Syngnathus fuscus northern pipefish

Tautoga onitis tautog 

Tautogolabrus adspersus cunner 

Torpedo nobiliana Atlantic torpedo

Trachurus lathami rough scad

Trichiurus lepturus Atlantic cutlassfish

Triglops murrayi moustache sculpin

Ulvaria subbifurcata radiated shanny

Urophycis chesteri longfin hake
38

Scientific name Common name

Urophycis chuss red hake

Urophycis regia spotted hake

Urophycis tenuis white hake

Zenopsis conchifera buckler dory
39

Appendix 2. Results of the curve estimation procedure on the relationship between 

sample size and species richness at 10-km scale. 

Equation 

Model Summary Parameter Estimates 

R Square F df1 df2 Sig. Constant b1 

Linear .830 107.541 1 22 .000 16.744 .872 

Logarithmic .934 311.951 1 22 .000 8.682 8.378 

Power .963 567.325 1 22 .000 11.608 .364 

Growth .739 62.146 1 22 .000 2.837 .035 

Exponential .739 62.146 1 22 .000 17.059 .035 

Logistic .739 62.146 1 22 .000 .059 .965 
40

Appendix 3. Results of the curve estimation procedure on the relationship between 

sample size and species richness at 20-km scale.

Equation 

Model Summary Parameter Estimates 

R Square F df1 df2 Sig. Constant b1 

Linear .830 107.541 1 22 .000 16.744 .872 

Logarithmic .934 311.951 1 22 .000 8.682 8.378 

Power .963 567.325 1 22 .000 11.608 .364 

Growth .739 62.146 1 22 .000 2.837 .035 

Exponential .739 62.146 1 22 .000 17.059 .035 

Logistic .739 62.146 1 22 .000 .059 .965 
41

Appendix 4. Results of the curve estimation procedure on the relationship between 

sample size and species richness at 40-km scale.

Equation 

Model Summary Parameter Estimates

R Square F df1 df2 Sig. Constant b1 

Linear .720 164.778 1 64 .000 28.742 .194 

Logarithmic .757 199.013 1 64 .000 7.372 8.986 

Power .832 317.815 1 64 .000 15.250 .252 

Growth .654 121.070 1 64 .000 3.363 .005 

Exponential .654 121.070 1 64 .000 28.882 .005 

Logistic .654 121.070 1 64 .000 .035 .995 
42

Appendix 5. Results of the curve estimation procedure on the relationship between 

sample size and species richness at 80-km scale.

Equation 

Model Summary Parameter Estimates 

R Square F df1 df2 Sig. Constant b1 

Linear .675 53.882 1 26 .000 37.535 .080 

Logarithmic .764 84.372 1 26 .000 5.080 10.295 

Power .857 156.084 1 26 .000 16.718 .234 

Growth .616 41.783 1 26 .000 3.599 .002 

Exponential .616 41.783 1 26 .000 36.579 .002 

Logistic .616 41.783 1 26 .000 .027 .998 
43

Appendix 6. SIMPER results for comparison of hotspot group 2 versus hotspot group 

1 at 10-km spatial scale. Twenty-seven species contributed to 80 % of dissimilarity.

Species

Av.Abund 

Group 2

Av.Abund 

Group 1 Av.Diss Diss/SD Contrib% Cum.%

Helicolenus dactylopterus 0.06 1.02 1.74 3.29 5.42 5.42

Argentina silus 0.05 0.57 1.73 3.28 5.4 10.82

Myoxocephalus 

octodecemspinosus 10.96 0 1.72 3.3 5.35 16.18

Pseudopleuronectes americanus 2.56 0 1.45 1.84 4.51 20.69

Limanda ferruginea 3.48 0 1.17 1.34 3.64 24.32

Artediellus sp 0.24 0.37 1.08 1.15 3.37 27.69

Brosme brosme 0.24 0.59 1.08 1.15 3.36 31.05

Enchelyopus cimbrius 1.69 0.11 1.07 1.15 3.32 34.37

Alosa sapidissima 0.3 0 1.02 1.1 3.18 37.56

Alosa aestivalis 2.11 0.07 1 1.06 3.11 40.67

Aspidophoroides monopterygius 0.5 0 0.97 1.06 3.03 43.69

Scomber scombrus 1.69 0.18 0.97 1.02 3.01 46.71

Myxine glutinosa 0.22 0.2 0.92 0.98 2.87 49.58

Peprilus triacanthus 0.98 0.44 0.9 0.94 2.8 52.38

Lumpenus maculatus 1.92 0.53 0.88 0.95 2.74 55.12

Anarhichas lupus 0.5 0.18 0.86 0.89 2.68 57.8

Paralichthys oblongus 0.46 0 0.82 0.9 2.55 60.35

Hippoglossus hippoglossus 0.12 0.07 0.78 0.86 2.43 62.78

Malacoraja senta 0.82 0.67 0.77 0.8 2.41 65.19

Leucoraja erinacea 0.96 0 0.71 0.79 2.2 67.39

Hemitripterus americanus 2.13 0.51 0.7 0.73 2.19 69.59

Leucoraja ocellata 0.55 0 0.7 0.79 2.17 71.76

Cyclopterus lumpus 0.1 0 0.67 0.74 2.1 73.86

Lycenchelys verrilli 0.02 0.07 0.63 0.75 1.98 75.83

Triglops murrayi 0.62 0 0.59 0.7 1.84 77.67

Argentina striata 0 0.2 0.58 0.71 1.8 79.47

Lumpenus lumpretaeformis 0.46 0 0.57 0.68 1.77 81.24
44

Appendix 7. SIMPER results for comparison of hotspot group 2 versus hotspot group 

3 at 10-km spatial scale. Thirty-six species contributed to 80 % dissimilarity. 

Species

AV.Abund 

Group2

Av.Abund 

Group3 Av.Diss Diss/SD Contrib% Cum.%

Enchelyopus cimbrius 1.69 0 1.25 1.62 3.89 3.89

Malacoraja senta 0.82 0.07 1.12 1.28 3.49 7.38

Leucoraja erinacea 0.96 6.66 1.02 1.13 3.15 10.53

Leucoraja ocellata 0.55 7.52 1.01 1.13 3.15 13.68

Alosa aestivalis 2.11 0.25 0.98 1.13 3.05 16.73

Alosa sapidissima 0.3 0.04 0.94 1.08 2.92 19.65

Triglops murrayi 0.62 2.29 0.93 1.05 2.9 22.55

Scomber scombrus 1.69 2.07 0.91 1.01 2.82 25.37

Hippoglossus hippoglossus 0.12 0.27 0.89 1.01 2.78 28.15

Ammodytes dubius 3.01 1.69 0.89 1.01 2.76 30.91

Aspidophoroides monopterygius 0.5 0.91 0.85 0.94 2.65 33.56

Myxine glutinosa 0.22 0.03 0.85 0.98 2.65 36.21

Paralichthys oblongus 0.46 0.74 0.85 0.97 2.65 38.86

Sebastes fasciatus 33.73 0.75 0.84 0.94 2.6 41.47

Peprilus triacanthus 0.98 21.63 0.83 0.92 2.58 44.04

Scophthalmus aquosus 0.19 3.72 0.81 0.92 2.51 46.56

Brosme brosme 0.24 0.06 0.79 0.89 2.46 49.01

Lumpenus maculatus 1.92 0 0.75 0.88 2.33 51.34

Cyclopterus lumpus 0.1 0.05 0.74 0.85 2.31 53.65

Tautogolabrus adspersus 0.15 0.67 0.73 0.84 2.25 55.91

Anarhichas lupus 0.5 0.77 0.69 0.78 2.15 58.06

Limanda ferruginea 3.48 8.47 0.68 0.76 2.12 60.18

Glyptocephalus cynoglossus 6.32 0.72 0.68 0.76 2.12 62.3

Artediellus sp 0.24 0.08 0.67 0.77 2.07 64.37

Ulvaria subbifurcata 1.52 0.02 0.54 0.68 1.69 66.06

Lumpenus lumpretaeformis 0.46 0 0.53 0.68 1.66 67.71

Cryptacanthodes maculatus 0.11 0 0.5 0.65 1.54 69.25

Maurolicus weitzmani 0.11 0.15 0.48 0.62 1.5 70.75

Argentina silus 0.05 0.15 0.48 0.61 1.48 72.23

Prionotus carolinus 0.02 1.44 0.44 0.59 1.37 73.59

Myoxocephalus aenaeus 0.03 0.06 0.43 0.58 1.34 74.94

Pseudopleuronectes americanus 2.56 23.76 0.4 0.52 1.23 76.17

Dipturus laevis 0.02 0.06 0.39 0.54 1.22 77.39

Lophius americanus 0.97 0.35 0.39 0.5 1.2 78.59

Alosa pseudoharengus 9.67 2.47 0.37 0.49 1.16 79.75

Amblyraja radiata 2.37 2.07 0.36 0.48 1.11 80.86
45

Appendix 8. SIMPER results for comparison of hotspot group 3 versus hotspot group 

1 at the 10-km spatial scale. Twenty-nine species contributed to 80 % dissimilarity. 

Species

Av.Abund 

Group3

Av.Abund 

Group1 Av.Diss Diss/SD Contrib% Cum.%

Pseudopleuronectes americanus 23.76 0 1.94 8.55 4.99 4.99

Myoxocephalus 

octodecemspinosus 35.65 0 1.94 8.55 4.99 9.97

Helicolenus dactylopterus 0.04 1.02 1.82 3.56 4.69 14.66

Limanda ferruginea 8.47 0 1.81 3.58 4.66 19.32

Leucoraja erinacea 6.66 0 1.6 2.06 4.11 23.43

Brosme brosme 0.06 0.59 1.6 2.06 4.1 27.54

Aspidophoroides monopterygius 0.91 0 1.58 2.07 4.07 31.61

Leucoraja ocellata 7.52 0 1.57 2.07 4.03 35.64

Argentina silus 0.15 0.57 1.49 1.73 3.83 39.47

Triglops murrayi 2.29 0 1.14 1.16 2.92 42.39

Artediellus sp 0.08 0.37 1.13 1.16 2.91 45.3

Malacoraja senta 0.07 0.67 1.09 1.13 2.8 48.11

Ammodytes dubius 1.69 0 1 1.03 2.58 50.69

Hippoglossus hippoglossus 0.27 0.07 0.99 1 2.54 53.22

Sebastes fasciatus 0.75 1.23 0.92 0.92 2.36 55.59

Scomber scombrus 2.07 0.18 0.9 0.89 2.3 57.89

Scophthalmus aquosus 3.72 0 0.8 0.82 2.05 59.94

Peprilus triacanthus 21.63 0.44 0.78 0.82 2 61.94

Anarhichas lupus 0.77 0.18 0.77 0.76 1.98 63.92

Paralichthys oblongus 0.74 0 0.76 0.82 1.94 65.86

Glyptocephalus cynoglossus 0.72 3.3 0.73 0.73 1.89 67.75

Alosa aestivalis 0.25 0.07 0.71 0.79 1.83 69.58

Hemitripterus americanus 6.14 0.51 0.7 0.7 1.81 71.39

Tautogolabrus adspersus 0.67 0 0.67 0.72 1.73 73.11

Myxine glutinosa 0.03 0.2 0.67 0.76 1.72 74.83

Enchelyopus cimbrius 0 0.11 0.65 0.7 1.66 76.49

Lumpenus maculatus 0 0.53 0.59 0.7 1.52 78.01

Lycenchelys verrilli 0 0.07 0.59 0.7 1.52 79.53

Argentina striata 0 0.2 0.59 0.7 1.52 81.04
46

Appendix 9. SIMPER results for comparison of hotspot group 2 versus hotspot group 

1 at the 20 km spatial scale. Twenty-nine species contributed to 80 % dissimilarity. 

Species

Av.Abund 

Group2

Av.Abund 

Group1 Av.Diss Diss/SD Contrib% Cum.%

Lepophidium profundorum 0 0.31 1.16 21.69 5.13 5.13

Lumpenus lumpretaeformis 0 0.62 1.16 21.69 5.13 10.25

Lumpenus maculatus 0.11 3.07 0.93 1.94 4.11 14.37

Maurolicus weitzmani 0.12 0 0.81 1.49 3.58 17.95

Dipturus laevis 0.1 0 0.69 1.19 3.04 20.98

Tautogolabrus adspersus 0.14 0.8 0.59 0.97 2.62 23.6

Prionotus carolinus 0.26 0 0.58 0.97 2.57 26.18

Myoxocephalus aenaeus 0.13 0.03 0.58 0.97 2.56 28.74

Artediellus Sp 0.07 0.02 0.58 0.97 2.56 31.3

Pomatomus saltatrix 0.01 0.03 0.58 0.97 2.56 33.87

Stenotomus chrysops 0.08 0.01 0.58 0.97 2.56 36.43

Urophycis regia 0.01 0.01 0.58 0.97 2.56 38.99

Lycenchelys verrilli 0.01 0.07 0.58 0.97 2.56 41.56

Malacoraja senta 0.71 0.28 0.58 0.97 2.56 44.12

Melanostigma atlanticum 0 0.01 0.58 0.97 2.56 46.68

Morone saxatilis 0.16 0.01 0.58 0.97 2.56 49.25

Cryptacanthodes maculatus 0 0.11 0.58 0.97 2.56 51.81

Gasterosteus aculeatus 0.02 0.01 0.58 0.97 2.56 54.38

Ammodytes americanus 0 0.29 0.58 0.97 2.56 56.94

Ariomma bondi 0 0.01 0.58 0.97 2.56 59.5

Brosme brosme 0.11 0.02 0.58 0.97 2.56 62.07

Ulvaria subbifurcata 0.03 0.41 0.58 0.97 2.56 64.63

Centropristis striata 0.02 0.01 0.58 0.97 2.56 67.19

Helicolenus dactylopterus 0.05 0 0.57 0.97 2.54 69.73

Enchelyopus cimbrius 0.3 2.76 0.48 0.8 2.12 71.85

Cyclopterus lumpus 0.04 0 0.47 0.79 2.07 73.93

Scomberesox saurus 0.05 0 0.46 0.8 2.04 75.97

Citharichthys arctifrons 0.02 0 0.46 0.8 2.04 78.01

Hippoglossus hippoglossus 0.03 0 0.46 0.79 2.02 80.03
47

Appendix 10. SIMPER results for comparison of hotspot group 2 versus hotspot group 

3 at the 20 km spatial scale. Thirty-five species contributed to 80 % dissimilarity. 

Species

Av.Abund 

Group2

Av.Abund 

Group3 Av.Diss Diss/Sd Contrib% Cum.%

Cryptacanthodes maculatus 0 0.15 1 2.07 4.19 4.19

Lumpenus lumpretaeformis 0 0.38 0.97 1.91 4.09 8.28

Myoxocephalus aenaeus 0.13 0 0.72 1.19 3.03 11.31

Dipturus laevis 0.1 0.01 0.69 1.14 2.91 14.21

Lumpenus maculatus 0.11 1.77 0.66 1.09 2.79 17.01

Cyclopterus lumpus 0.04 0.08 0.65 1.07 2.75 19.75

Ammodytes dubius 33.97 1.74 0.65 1.04 2.73 22.48

Triglops murrayi 1.95 0.12 0.64 1.04 2.71 25.2

Brosme brosme 0.11 0.11 0.62 0.99 2.59 27.79

Prionotus carolinus 0.26 0.02 0.61 0.99 2.58 30.37

Tautogolabrus adspersus 0.14 0.07 0.61 0.99 2.57 32.94

Helicolenus dactylopterus 0.05 0.02 0.61 0.99 2.56 35.5

Artediellus Sp 0.07 0.22 0.6 0.97 2.52 38.01

Ulvaria subbifurcata 0.03 0.6 0.59 0.97 2.5 40.51

Hippoglossus hippoglossus 0.03 0.09 0.59 0.96 2.49 43.01

Maurolicus weitzmani 0.12 0.16 0.58 0.93 2.42 45.43

Scomberesox saurus 0.05 0.02 0.55 0.9 2.33 47.76

Scophthalmus aquosus 1.03 0.27 0.55 0.89 2.32 50.08

Citharichthys arctifrons 0.02 0.01 0.54 0.88 2.26 52.35

Enchelyopus cimbrius 0.3 1.65 0.52 0.83 2.19 54.54

Urophycis chesteri 0.04 0 0.48 0.81 2 56.54

Stenotomus chrysops 0.08 0.03 0.47 0.79 2 58.54

Melanostigma atlanticum 0 0.06 0.44 0.76 1.87 60.41

Centropristis striata 0.02 0.01 0.44 0.75 1.87 62.28

Lycenchelys verrilli 0.01 0.06 0.43 0.74 1.82 64.1

Gasterosteus aculeatus 0.02 0.02 0.43 0.73 1.79 65.89

Syngnathus fuscus 0.03 0.01 0.42 0.72 1.79 67.67

Alosa aestivalis 1.48 1.98 0.42 0.72 1.78 69.45

Myxine glutinosa 0.21 0.2 0.41 0.69 1.71 71.16

Paralichthys oblongus 1.44 0.45 0.4 0.67 1.67 72.83

Alosa sapidissima 0.12 0.44 0.39 0.68 1.66 74.49

Aspidophoroides 

monopterygius

 

0.55 0.34 0.39 0.67 1.66 76.15

Petromyzon marinus 0.02 0 0.39 0.68 1.64 77.8

Leucoraja erinacea 4.19 1.12 0.39 0.67 1.63 79.42

Liparis atlanticus 0.02 0 0.36 0.65 1.53 80.96
48

Appendix 11. SIMPER results for comparison of hotspot group 3 versus hotspot group 

1 at 20 km spatial scale. Twenty-nine species contributed to 80 % dissimilarity. 

Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum.%

Group 3 Group 1

Lepophidium profundorum 0.01 0.31 1.05 2.85 5.16 5.16

Cyclopterus lumpus 0.08 0 0.79 1.45 3.85 9.01

Tautogolabrus adspersus 0.07 0.8 0.68 1.15 3.35 12.36

Maurolicus weitzmani 0.16 0 0.67 1.15 3.3 15.66

Ammodytes dubius 1.74 987.61 0.63 1.04 3.09 18.75

Triglops murrayi 0.12 0.06 0.63 1.04 3.06 21.81

Melanostigma atlanticum 0.06 0.01 0.59 0.98 2.87 24.69

Pomatomus saltatrix 0.01 0.03 0.59 0.98 2.87 27.56

Stenotomus chrysops 0.03 0.01 0.59 0.98 2.87 30.43

Ulvaria subbifurcata 0.6 0.41 0.59 0.98 2.87 33.31

Urophycis regia 0.04 0.01 0.59 0.98 2.87 36.18

Malacoraja senta 0.41 0.28 0.59 0.98 2.87 39.05

Morone saxatilis 0 0.01 0.59 0.98 2.87 41.93

Myoxocephalus aenaeus 0 0.03 0.59 0.98 2.87 44.8

Ammodytes americanus 0 0.29 0.59 0.98 2.87 47.68

Ariomma bondi 0 0.01 0.59 0.98 2.87 50.55

Artediellus Sp 0.22 0.02 0.59 0.98 2.87 53.42

Centropristis striata 0.01 0.01 0.59 0.98 2.87 56.3

Lycenchelys verrilli 0.06 0.07 0.59 0.98 2.87 59.17

Cryptacanthodes maculatus 0.15 0.11 0.59 0.98 2.87 62.04

Gasterosteus aculeatus 0.02 0.01 0.59 0.98 2.87 64.92

Brosme brosme 0.11 0.02 0.59 0.98 2.87 67.79

Lumpenus maculatus 1.77 3.07 0.51 0.84 2.48 70.27

Hippoglossus hippoglossus 0.09 0 0.5 0.84 2.43 72.7

Scophthalmus aquosus 0.27 0.47 0.44 0.75 2.15 74.85

Paralichthys oblongus 0.45 0.83 0.38 0.67 1.86 76.71

Aspidophoroides 

monopterygius 0.34 1.18 0.32 0.59 1.57 78.28

Leucoraja erinacea 1.12 1.33 0.31 0.59 1.53 79.8

Scomberesox saurus 0.02 0 0.31 0.59 1.5 81.3
49

Appendix 12. SIMPER results for comparison of hotspot group 1 versus hotspot group 

2 at the 40 km spatial scale. Thirty-five species contributed to 80 % dissimilarity. 

Species

Av.Abund 

Group1

Av.Abund 

Group2 Av.Diss Diss/SD Contrib% Cum.%

Lumpenus maculatus 0 3.1 0.88 16.05 4.33 4.33

Helicolenus dactylopterus 0.24 0.01 0.68 1.68 3.35 7.68

Syngnathus fuscus 0.02 0 0.67 1.67 3.28 10.96

Stenotomus chrysops 0.01 0.02 0.66 1.66 3.24 14.2

Petromyzon marinus 0.02 0 0.66 1.66 3.21 17.41

Scomberesox saurus 0.05 0 0.56 1.25 2.75 20.16

Urophycis regia 0 0.1 0.55 1.24 2.7 22.86

Dipturus laevis 0.06 0.01 0.55 1.24 2.69 25.55

Merluccius albidus 0 0.03 0.55 1.24 2.68 28.22

Maurolicus weitzmani 0.05 0.13 0.46 0.97 2.23 30.45

Prionotus carolinus 0.17 0.01 0.45 0.97 2.22 32.67

Pomatomus saltatrix 0.02 0.01 0.45 0.96 2.21 34.89

Hippoglossus hippoglossus 0.02 0.03 0.45 0.97 2.21 37.09

Argentina silus 0.9 0.01 0.45 0.97 2.21 39.3

Myoxocephalus aenaeus 0.12 0.02 0.45 0.97 2.19 41.49

Ulvaria subbifurcata 0.04 1.97 0.45 0.96 2.19 43.68

Gasterosteus aculeatus 0.02 0.03 0.45 0.96 2.19 45.87

Citharichthys arctifrons 0.02 0 0.44 0.97 2.18 48.04

Paralichthys dentatus 0.01 0.01 0.44 0.96 2.17 50.22

Torpedo nobiliana 0 0.01 0.44 0.96 2.17 52.39

Melanostigma atlanticum 0.04 0.09 0.44 0.97 2.16 54.55

Pholis gunnellus 0.01 0.01 0.44 0.97 2.16 56.71

Urophycis chesteri 0.31 0.02 0.44 0.97 2.16 58.87

Nemichthys scolopaceus 0 0 0.44 0.96 2.14 61.01

Liparis atlanticus 0.03 0 0.44 0.96 2.14 63.15

Lumpenus lumpretaeformis 0 0.67 0.44 0.96 2.14 65.29

Lepophidium profundorum 0 0.09 0.43 0.96 2.11 67.4

Brevoortia tyrannus 0 0.01 0.43 0.96 2.1 69.5

Mallotus villosus 0 0.04 0.42 0.96 2.08 71.58

Lycenchelys verrilli 0.01 0.02 0.34 0.75 1.65 73.23

Myoxocephalus scorpius 0 0.01 0.33 0.75 1.62 74.84

Centropristis striata 0.04 0.02 0.33 0.75 1.62 76.46

Morone saxatilis 0.14 0 0.32 0.75 1.58 78.03

Ariomma bondi 0 0 0.32 0.75 1.58 79.61

Ammodytes americanus 0.39 0.04 0.32 0.75 1.58 81.19
50

 

NMSP CONSERVATION SERIES PUBLICATIONS

To date, the following reports have been published in the Marine Sanctuaries Conservation Series. All 

publications are available on the Office of National Marine Sanctuaries website 

(http://www.sanctuaries.noaa.gov/).

Examples of Ecosystem-Based Management in National Marine Sanctuaries: Moving from Theory to 

Practice (ONMS-10-02)

The Application Of Observing System Data In California Current Ecosystem Assessments (ONMS-10-01)

Reconciling Ecosystem-Based Management and Focal Resource Conservation in the Papahānaumokuākea 

Marine National Monument (ONMS-09-04)

Preliminary Comparison of Natural Versus Model-predicted Recovery of Vessel-generated Seagrass 

Injuries in Florida Keys National Marine Sanctuary (ONMS-09-03) 

A Comparison of Seafloor Habitats and Associated Benthic Fauna in Areas Open and Closed to Bottom 

Trawling Along the Central California Continental Shelf (ONMS-09-02) 

Chemical Contaminants, Pathogen Exposure and General Health Status of Live and Beach-Cast 

Washington Sea Otters (Enhydra lutris kenyoni) (ONMS-09-01) 

Caribbean Connectivity: Implications for Marine Protected Area Management (ONMS-08-07) 

Knowledge, Attitudes and Perceptions of Management Strategies and Regulations of FKNMS by 

Commercial Fishers, Dive Operators, and Environmental Group Members: A Baseline Characterization and 

10-year Comparison (ONMS-08-06) 

First Biennial Ocean Climate Summit: Finding Solutions for San Francisco Bay Area's Coast and Ocean 

(ONMS-08-05) 

A Scientific Forum on the Gulf of Mexico: The Islands in the Stream Concept (NMSP-08-04) 

M/V ELPIS Coral Reef Restoration Monitoring Report Monitoring Events 2004-2007 Florida Keys 

National Marine Sanctuary Monroe County, Florida (NMSP-08-03)

CONNECTIVITY Science, People and Policy in the Florida Keys National Marine Sanctuary (NMSP-08-

02) 

M/V ALEC OWEN MAITLAND Coral Reef Restoration Monitoring Report Monitoring Events 2004-2007 

Florida Keys National Marine Sanctuary Monroe County, Florida (NMSP-08-01) 

Automated, objective texture segmentation of multibeam echosounder data - Seafloor survey and substrate 

maps from James Island to Ozette Lake, Washington Outer Coast. (NMSP-07-05) 

Observations of Deep Coral and Sponge Assemblages in Olympic Coast National Marine Sanctuary, 

Washington (NMSP-07-04) 

A Bioregional Classification of the Continental Shelf of Northeastern North America for Conservation 

Analysis and Planning Based on Representation (NMSP-07-03) 
51

M/V WELLWOOD Coral Reef Restoration Monitoring Report Monitoring Events 2004-2006 Florida Keys 

National Marine Sanctuary Monroe County, Florida (NMSP-07-02) 

Survey report of NOAA Ship McArthur II cruises AR-04-04, AR-05-05 and AR-06-03: Habitat 

classification of side scan sonar imagery in support of deep-sea coral/sponge explorations at the Olympic 

Coast National Marine Sanctuary (NMSP-07-01) 

2002 - 03 Florida Keys National Marine Sanctuary Science Report: An Ecosystem Report Card After Five 

Years of Marine Zoning (NMSP-06-12) 

Habitat Mapping Effort at the Olympic Coast National Marine Sanctuary - Current Status and Future Needs 

(NMSP-06-11)

M/V CONNECTED Coral Reef Restoration Monitoring Report Monitoring Events 2004-2005 Florida Keys 

National Marine Sanctuary Monroe County, Florida (NMSP-06-010) 

M/V JACQUELYN L Coral Reef Restoration Monitoring Report Monitoring Events 2004-2005 Florida 

Keys National Marine Sanctuary Monroe County, Florida (NMSP-06-09) 

M/V WAVE WALKER Coral Reef Restoration Baseline Monitoring Report - 2004 Florida Keys National 

Marine Sanctuary Monroe County, Florida (NMSP-06-08) 

Olympic Coast National Marine Sanctuary Habitat Mapping: Survey report and classification of side scan 

sonar data from surveys HMPR-114-2004-02 and HMPR-116-2005-01 (NMSP-06-07) 

A Pilot Study of Hogfish (Lachnolaimus maximus Walbaum 1792) Movement in the Conch Reef Research 

Only Area (Northern Florida Keys) (NMSP-06-06) 

Comments on Hydrographic and Topographic LIDAR Acquisition and Merging with Multibeam Sounding 

Data Acquired in the Olympic Coast National Marine Sanctuary (ONMS-06-05) 

Conservation Science in NOAA's National Marine Sanctuaries: Description and Recent Accomplishments 

(ONMS-06-04) 

Normalization and characterization of multibeam backscatter: Koitlah Point to Point of the Arches, 

Olympic Coast National Marine Sanctuary - Survey HMPR-115-2004-03 (ONMS-06-03) 

Developing Alternatives for Optimal Representation of Seafloor Habitats and Associated Communities in 

Stellwagen Bank National Marine Sanctuary (ONMS-06-02) 

Benthic Habitat Mapping in the Olympic Coast National Marine Sanctuary (ONMS-06-01) 

Channel Islands Deep Water Monitoring Plan Development Workshop Report (ONMS-05-05) 

Movement of yellowtail snapper (Ocyurus chrysurus Block 1790) and black grouper (Mycteroperca bonaci

Poey 1860) in the northern Florida Keys National Marine Sanctuary as determined by acoustic telemetry 

(MSD-05-4) 

The Impacts of Coastal Protection Structures in California's Monterey Bay National Marine Sanctuary 

(MSD-05-3) 

An annotated bibliography of diet studies of fish of the southeast United States and Gray's Reef National 

Marine Sanctuary (MSD-05-2) 

Noise Levels and Sources in the Stellwagen Bank National Marine Sanctuary and the St. Lawrence River 

Estuary (MSD-05-1) 
52

Biogeographic Analysis of the Tortugas Ecological Reserve (MSD-04-1) 

A Review of the Ecological Effectiveness of Subtidal Marine Reserves in Central California (MSD-04-2, 

MSD-04-3) 

Pre-Construction Coral Survey of the M/V Wellwood Grounding Site (MSD-03-1) 

Olympic Coast National Marine Sanctuary: Proceedings of the 1998 Research Workshop, Seattle, 

Washington (MSD-01-04) 

Workshop on Marine Mammal Research & Monitoring in the National Marine Sanctuaries (MSD-01-03) 

A Review of Marine Zones in the Monterey Bay National Marine Sanctuary (MSD-01-2)

Distribution and Sighting Frequency of Reef Fishes in the Florida Keys National Marine Sanctuary (MSD01-1) 

Flower Garden Banks National Marine Sanctuary: A Rapid Assessment of Coral, Fish, and Algae Using the 

AGRRA Protocol (MSD-00-3) 

The Economic Contribution of Whalewatching to Regional Economies: Perspectives From Two National 

Marine Sanctuaries (MSD-00-2) 

Olympic Coast National Marine Sanctuary Area to be Avoided Education and Monitoring Program (MSD00-1) 

Multi-species and Multi-interest Management: an Ecosystem Approach to Market Squid (Loligo 

opalescens) Harvest in California (MSD-99-1)