Marine Sanctuaries Conservation Series ONMS-06-02 

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 February 2006 

 

Developing Alternatives for 

Optimal Representation of Seafloor Habitats and 

Associated Communities in 

Stellwagen Bank National Marine Sanctuary
About the Marine Sanctuaries Conservation Series 

The National Oceanic and Atmospheric Administration’s Office of National Marine Sanctuaries (ONMS) 

administers the National Marine Sanctuary Program. Its mission is to identify, designate, protect and 

manage the ecological, recreational, research, educational, historical, and aesthetic resources and qualities of

nationally significant coastal and marine areas. The existing marine sanctuaries differ widely in their natural 

and historical resources and include nearshore and open ocean areas ranging in size from less than one to over 

5,000 square miles. Protected habitats include rocky coasts, kelp forests, coral reefs, sea grass beds, estuarine 

habitats, hard and soft bottom habitats, segments of whale migration routes, and shipwrecks. 

Because of considerable differences in settings, resources, and threats, each marine sanctuary 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 National Marine Sanctuary Program. 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. 
Developing Alternatives for Optimal Representation of 

Seafloor Habitats and Associated Communities 

in Stellwagen Bank National Marine Sanctuary 

Rosamonde R. Cook1 and Peter J. Auster2

1

U.S. National Park Service, 47050 Generals Highway, Three Rivers, CA, 93271. Email: 

Rose_Cook@nps.gov 

2

National Undersea Research Center and Department of Marine Sciences, University of 

Connecticut at Avery Point, 1080 Shennecossett Road, Groton, CT, 06340. Email: 

Auster@UConn.edu 

Silver Spring, Maryland

February 2006 

U.S. Department of Commerce 

Carlos M. Gutierrez, Secretary 

National Oceanic and Atmospheric Administration 

VADM Conrad C. Lautenbacher, Jr. (USN-ret.) 

Under Secretary of Commerce for Oceans and Atmosphere

National Ocean Service 

John H. Dunnigan, 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 National Marine Sanctuaries 

Program web site at www.sanctuaries.nos.noaa.gov. Hard copies may be available from 

the following address:

National Oceanic and Atmospheric Administration

 Office of National Marine Sanctuaries

 SSMC4, N/ORM62 

 1305 East-West Highway 

Silver Spring, MD 20910 

COVER 

Cover photos: Stellwagen Bank National Marine Sanctuary encompasses a wide 

diversity of seafloor habitats including mud filled basins (top right), sand and gravel

ridges (bottom right), and boulder reefs (left). These habitats support a wide diversity of 

species representative of the Gulf of Maine region. (Images by P.J. Auster and P. 

Donaldson, National Undersea Research Center, University of Connecticut). 

SUGGESTED CITATION

Cook, R. and P. Auster. 2006. Developing Alternatives for Optimal Representation of 

Seafloor Habitats and Associated Communities in Stellwagen Bank National Marine 

Sanctuary. Marine Sanctuaries Conservation Series MSD-05-06. U.S. Department of 

Commerce, National Oceanic and Atmospheric Administration, Office of National 

Marine Sanctuaries, Silver Spring, MD. 24 pp. 

CONTACT

Peter J. Auster, Ph.D. 

Science Director, National Undersea Research Center and 

Associate Research Professor, Department of Marine Sciences 

University of Connecticut at Avery Point 

1080 Shennecossett Rd. 

Groton, CT 06340-6048 

860-405-9121 

peter.auster@uconn.edu
ABSTRACT 

The implementation of various types of marine protected areas is one of several 

management tools available for conserving representative examples of the biological 

diversity within marine ecosystems in general and National Marine Sanctuaries in 

particular. However, deciding where and how many sites to establish within a given area 

is frequently hampered by incomplete knowledge of the distribution of organisms and an 

understanding of the potential tradeoffs that would allow planners to address frequently 

competing interests in an objective manner. Fortunately, this is beginning to change. 

Recent studies on the continental shelf of the northeastern United States suggest that 

substrate and water mass characteristics are highly correlated with the composition of 

benthic communities and may therefore, serve as proxies for the distribution of biological 

biodiversity. A detailed geo-referenced interpretative map of major sediment types 

within Stellwagen Bank National Marine Sanctuary (SBNMS) has recently been 

developed, and computer-aided decision support tools have reached new levels of 

sophistication. We demonstrate the use of simulated annealing, a type of mathematical 

optimization, to identify suites of potential conservation sites within SBNMS that equally 

represent 1) all major sediment types and 2) derived habitat types based on both sediment 

and depth in the smallest amount of space. The Sanctuary was divided into 3610 0.5 

min2

 sampling units. Simulations incorporated constraints on the physical dispersion of 

sampling units to varying degrees such that solutions included between one and four site 

clusters. Target representation goals were set at 5, 10, 15, 20, and 25 percent of each 

sediment type, and 10 and 20 percent of each habitat type. Simulations consisted of 100 

runs, from which we identified the best solution (i.e., smallest total area) and four nearoptimal alternates. We also plotted total instances in which each sampling unit occurred 

in solution sets of the 100 runs as a means of gauging the variety of spatial configurations 

available under each scenario. Results suggested that the total combined area needed to 

represent each of the sediment types in equal proportions was equal to the percent 

representation level sought. Slightly larger areas were required to represent all habitat 

types at the same representation levels. Total boundary length increased in direct 

proportion to the number of sites at all levels of representation for simulations involving 

sediment and habitat classes, but increased more rapidly with number of sites at higher 

representation levels. There were a large number of alternate spatial configurations at all 

representation levels, although generally fewer among one and two versus three- and 

four-site solutions. These differences were less pronounced among simulations targeting 

habitat representation, suggesting that a similar degree of flexibility is inherent in the 

spatial arrangement of potential protected area systems containing one versus several 

sites for similar levels of habitat representation. We attribute these results to the 

distribution of sediment and depth zones within the Sanctuary, and to the fact that even 

levels of representation were sought in each scenario. 

KEY WORDS 

Marine protected areas, sanctuaries, habitat representation, benthic communities, 

sediment, fauna, fishing, fisheries, conservation, optimization, MARXAN 

i 
TABLE OF CONTENTS 

Topic Page

Abstract and Key Words......................................................................................... i 

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

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

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

Methods................................................................................................................... 3 

Results .................................................................................................................... 8

Discussion............................................................................................................... 17 

Acknowledgements................................................................................................. 20 

Literature Cited ....................................................................................................... 21 

ii 
LIST OF FIGURES AND TABLES

Figure/Table Number and Title Page

Figure 1 – Map of Stellwagen Bank National Marine Sanctuary........................... 1 

Figure 2 – Marine benthic sediment layer in SBNMS............................................ 3 

Figure 3 – Habitat classification map for SBNMS ................................................. 4 

Figure 4 – Best results for each representation level and number of sites for 

sediment types....................................................................................... 9 

Figure 5 – Best results for each representation level and number of sites for 

habitat classes........................................................................................ 10 

Figure 6 – Boundary length as a function of the number of sites for representation 

of sediment types.................................................................................. 11 

Figure 7 – Boundary length as a function of the number of sites for representation 

of habitat classes................................................................................... 12 

Figure 8 – Slope of regression equations versus number of sites for relationships 

shown in Figures 6 and 7 ...................................................................... 12 

Figure 9 – Summed solutions for four simulations illustrating the relationship between 

number of sites and variation in spatial configuration among alternate 

solutions........ ...................................................................................... 15 

--- 

Table 1 – Total areal extent of sediment types and habitat classes calculated from 

the interpretive maps............................................................................. 4 

Table 2 – BLM, run number, total area, and BL for the best and alternate solutions 

for sediment classes .............................................................................. 13 

Table 3 – BLM, run number, total area, and BL for the best and alternate solutions 

for habitat types..................................................................................... 14 

iii 

1 

INTRODUCTION 

Stellwagen Bank

National Marine Sanctuary

The sustainable exploitation of marine fishes and the maintenance of healthy ecosystems 

can best be achieved in the context of ecosystem management, including measures for the 

protection of habitats critical for survival, growth, and reproduction of marine organisms 

(Sherman et al. 1993, Jennings and Kaiser 1998, Peterson et al. 2000). A major form of 

habitat disturbance to seafloor communities on the continental shelf of New England is 

the use of bottom-tending mobile gears such as otter trawls and scallop dredges, used to 

exploit commercially valuable fish and invertebrate populations (Auster et al. 1996, 

Auster and Langton 1999). Use of these gears, particularly in sensitive habitats, can

cause significant and long lasting impacts to the structure of the habitat itself and to the 

diversity, abundance, and composition of the biological communities living there (Collie 

et al. 1997). Further, the removal of high trophic level predators, such as Atlantic cod 

(Gadus morhua), can cause trophic cascades that alter the composition and abundance of 

prey communities associated with the seafloor (Jackson et al. 2001). Hence, both direct 

and indirect forms of human disturbance can impact biological diversity at both small and 

large spatial scales. 

Figure 1. Location of Stellwagen Bank National Marine Sanctuary in the Gulf of Maine. 

Remote sensing image courtesy of Paskevich, V. 2003. SEAGRD.TIF: Image 

representation of the NGDC Coastal Relief Model bathymetry for the project 

area. U.S. Geological Survey, Coastal and Marine Geology Program. Woods 

Hole Field Center, Woods Hole, Massachusetts. 
Stellwagen Bank National Marine Sanctuary (SBNMS), located 25 miles east of Boston 

in the Gulf of Maine (Figure 1), is the only National Marine Sanctuary within the 

Acadian biogeographic province of the northeastern United States. While mineral 

extraction is prohibited under law, current sanctuary regulations do not provide protection 

for marine communities from most types of fishing, including the use of fishing gears 

that alter seafloor habitats. 

Support has been gaining in recent years among scientists, resource managers, and the 

public in the United States and other parts of the world for establishing areas in the sea 

that would receive a level of protection similar to that provided by U.S. National Parks on 

land, in which all forms of commercial resource extraction is prohibited and 

anthropogenic disturbance is highly controlled (e.g., Earle et al. 2004). A common theme 

among calls for greater protection of marine habitats is the identification of areas that 

would, if protected, preserve a representative portion of a region’s biological diversity 

(e.g., Thackway 1996, National Research Council 2001). 

Conservation of marine and terrestrial ecosystems poses different types of challenges 

(Carr et al. 2003). Protection of terrestrial ecosystems is often constrained by acquisition 

costs or availability of land, whereas marine conservation, especially in coastal areas, 

usually requires changes in management regulations that affect various user groups. 

Large-scale distributions of marine species and communities are also generally lesser 

known than are those of terrestrial ecosystems. However, recent studies on the 

continental shelf of the northeastern United States, including portions of SBNMS, 

suggest that substrate and water mass characteristics are highly correlated with the 

composition of benthic communities (e.g., Auster et al. 2001, Skinder 2002) and may 

therefore, serve as proxies for the distribution of biological biodiversity where detailed 

information on the distributions and abundances of species is lacking. Water mass 

characteristics have been well defined within the Gulf of Maine and modern tools such as 

multibeam sonar are improving our ability to map the distribution of seafloor habitats. A 

detailed geo-referenced interpretative map of major sediment types within SBNMS has 

recently been developed to aid resource management within the Sanctuary. 

Any efforts to increase habitat protection in parts of the SBNMS will likely meet with the 

greatest success if they attempt to balance the interests of resource users with those of 

conservation. A good starting point in any such discussion might be to identify the 

minimum amount of habitat required to preserve representative examples of the 

Sanctuary’s indigenous biological diversity. The purpose of this paper is to demonstrate 

a means by which this could be done in an objective and transparent manner. 

Methods involve the use of a type of mathematical optimization known as simulated 

annealing, a name that derives from the physical process of heating and slowly cooling a 

substance to obtain a strong crystalline structure (Kirkpatrick et al. 1983). Simulated 

annealing is one of several classes of algorithm that have been developed, tested, and 

applied to real world conservation planning for more than 20 years (see good reviews by 

Nichols and Margules 1993, Humphries et al. 1996). Most studies have focused on 

terrestrial applications although similar models have increasingly been used in marine 

2 
ecoregional planning efforts as well (e.g., Beck and Odaya 2001, Airame et al. 2003, 

Cook and Auster 2005). Simulated annealing has a variety of advantages over other 

methods (Pressey et al. 1993), particularly in its ability often to find multiple solutions, or 

alternate spatial configurations, to meet specific conservation objectives (Possingham et 

al. 2000). Here we demonstrate the use of simulated annealing as a decision-support tool 

for identifying potential sets of alternative habitat conservation areas within SBNMS 

using the map of sediment types described above and a derived set of data for habitats 

classified by sediment type and depth. Habitat conservation targets were chosen only for 

demonstration purposes. These and the results presented in this report are not intended as 

prescriptions for management. 

METHODS 

The data 

Boundaries for SBNMS were digitized from coordinates published in the Federal 

Register and are stored at the Massachusetts Office of Coastal Zone Management. The 

marine sediment layer was created by Technology Planning and Management 

Corporation (TPMC) of Scituate, Massachusetts using multi-beam echo sounder data 

collected by the United States Geological Survey (Valentine et al. 2001). These data 

have an accuracy of 10 m2

 and were used to construct a composite seabed backscatter 

(reflectivity) image of the entire Sanctuary. The strength of the reflectance (gray scale) 

values is generally associated with the composition of seafloor substrates, with harder 

substrates producing stronger signals. A digital two-dimensional map of the major 

sediment types (Figure 2) was generated by defining thresholds for values associated with 

hard and soft sediments as follows: 1 - 75 for mud, 76 – 165 for sand, and 166 – 255 for 

gravel. 

3 

Figure 2. Interpretive marine benthic sediment layer based on

reflectance values from multi-beam echo sounder data 

in Stellwagen Bank National Marine Sanctuary. 

Sediment Classes

Gravel

Sand

Mud

N

W E

S

0 30 Kilometers
We created a map of benthic habitat classes (Figure 3) by reclassifying the sediment layer 

to include three depth zones (0-60m, 61-150m, and >150m) based on information on the 

distributions of water masses and fish species assemblages in the Gulf of Maine (Brown 

et al. 1996, Sherman et al. 1996, Auster 2002). 

Overall, gravel represented the most abundant sediment class, with sand and mud present 

in lesser but approximately equal amounts (Table 1). Most of the Sanctuary 

(approximately 63%) occurs at depths between 60 and 150m. Gravel and mud were 

present in approximately equal proportions in this zone, with lesser amounts of sand. 

Alternatively, sand and gravel were present in roughly equal proportion at depths from 0 

to 60m, with lesser amounts of mud. Mud was the most abundant sediment at depths 

>150m, occupying approximately 77% of substrate area at this depth. 

0 30 Kilometers

N

W E

S

Habitat Classes

Gravel <= 60 m

Gravel >60 m

Gravel >150 m

 Mud <= 60 m

Mud >60 m

Mud >150 m

Sand <= 60 m

Sand >60 m

Sand >150 m

Figure 3. Habitat classification map for Stellwagen 

Bank National Marine Sanctuary with classes 

defined by combinations of sediment type 

(Figure 2) and depth zone. 

Table 1. Total areal extent of sediment types and habitat classes calculated from the 

interpretive maps

Depth Range (m) Gravel (km2

) Mud (km2) Sand (km2) Totals (km

2

)

All depths 866.1 650.4 674.2 2190.7

Surface to 60 332.2 118.5 330.6 781.3

Between 60 and 150 533.2 516.2 339.5 1388.9

Greater than 150 0.7 15.8 4.2 20.6

4 
We sampled the sediment types and habitat classes on the maps by overlaying a grid of 

0.5 min squares (described in more detail below) to create a set of sampling units which 

provided the raw data for the algorithm. 

The algorithm 

We used MARXAN, Version 1.8 software (Ball and Possingham 2001) to perform

simulated annealing, a Monte Carlo procedure used to minimize multivariate functions 

associated with a physical or biological system. MARXAN’s objective function 

calculates the total cost associated with a set of sampling units as: 

Total Cost = ∑ SU costs + BLM ∑ boundary lengths + ∑ CFPF * penalty + Threshold Penalty su’s su’s cf’s

where SU refers to sampling units, CFPF to “Conservation Feature Penalty Factor”, and 

BLM to “Boundary Length Modifier”. The CFPF weights the cost associated with failure 

to meet the representation target of each conservation feature specified, these being 

sediment or habitat types in the present analysis. The threshold penalty represents a cost 

for exceeding some maximum desired size of solutions. 

The BLM is a weighting used to control the spatial aggregation of sampling units, where 

the boundary length is equal to the sum of the lengths of the sides of sampling units that 

do not adjoin another unit. If equal to zero, the algorithm performs without spatial 

constraint. BLM values greater than zero increase the cost of the boundary length and 

encourage solutions that aggregate sampling units into fewer, compact clusters with 

shared boundaries. For example, the boundary length of four unconnected squares is 16 

(based on all sides of each square), while that of four squares sharing boundaries is 8-10, 

depending on configuration. The BLM can be adjusted through an iterative process to 

obtain a desired number of clustered sites. 

Simulations are initialized with a set of sampling units drawn at random from the larger 

data set. Sampling units are then added and removed at random from the set in a long

series of iterations, with the total cost of each new set compared to the previous. Changes 

that reduce the total cost are always retained. Those that do not are accepted with some 

probability that diminishes over the course of the iterations. The rate of diminishing 

probability is set by an “annealing schedule” which includes the number of iterations and 

the number of cooling steps (times the probability of acceptance is reduced). By 

occasionally accepting changes that increase or do not change the total cost during earlier 

iterations, the algorithm is able to explore all possible sets, avoiding local optima in 

search of the global optimum, which will eventually be found given sufficient iterations. 

In the present analysis, we wished to identify those parts of the Sanctuary that represent 

the full diversity of sediment and habitat types in specific quantity (our target levels of 

representation) in the smallest total area. We set the cost of individual sampling units as 

equal to their size so that their summed cost was a measure of total included area. We

identified the smallest value of the BLM that resulted in solutions that met our target 

levels of representation in one to four cohesive groups of sampling units (or sites) 

following the methods described below. We set the threshold penalty to zero and the 

5 
CFPF high enough that our targets were always met. In general, larger areas are needed 

to meet specific representation targets where solutions are constrained to a smaller 

number of sites. In other words, the fewer sites allowed in solutions, the larger the total 

included area. Thus, in our analysis, optimization represented a tradeoff between total 

area and number of sites. 

Sampling units 

The use of reflectance as a proxy for sediment type is spatial scale-dependent. While 

there was little correlation between phi values of sediment grab samples and reflectance 

(P. Valentine personal communication), video observations showed general sediment 

classification and reflectance values to be highly correlated (e.g., Auster et al. 2001). 

Based on the latter analysis, we considered it reasonable to assume sampling units of 0.25 

min squares or larger would represent sediment types reasonably well. 

Therefore, we created three square sampling grids, of 0.25, 0.5, and 1.0 minute latitude 

and longitude in the geographic coordinate system, and compared these for the effect that 

sampling scale might have on the outcome of optimization. The grids were clipped to the 

Sanctuary boundaries such that most sampling units on the boundary consisted of partial 

squares. The size of whole sampling units varied depending on their position along the 

north-south and east-west axes being larger at the northern end of the Sanctuary than the 

southern and in the west than the east, but differences were minor. For example, the 

greatest difference in size between any two whole 0.5 min squares was 0.006 km2

. 

We quantified the amount of each sediment and habitat class per sampling unit by 

intersecting the data and grid layers, quantifying the areas of the newly created polygons, 

and summing by class. The sediment and habitat layers were essentially projected 

against a flat surface to calculate areal coverage. Thus, our estimates of areal coverage 

do not account for topography and will be underestimated in regions where depth varies. 

We chose a flat projection because the alternative would have favored the selection of

sites with high relief. Such solutions could tend to exclude species with preferences for 

habitats in low relief environments. 

We performed simulations on each data set with target levels of representation equal to 

10% and 20% of the three main sediment types. Results obtained with the 0.25 min grid 

consisted of either many small site clusters (at least nine to ten) or the entire study area. 

This occurred because the boundaries of planning units on the outer edge of the study 

area did not count toward the summed cost of the boundary length, and because the 

combined cost of the sampling units and the boundary length (the total cost score without 

penalties) rapidly became larger than the cost of all sampling units under even small 

values of the BLM. 

These problems never arose in tests on the 0.5 min and 1.0 min grids. We compared total 

included area in one, two, and four-site solutions at 10% and 20% target representation

levels and chose for comparison the top ten solutions as described in the next section. 

There was no difference (P > 0.05) between mean total area among any set of solutions at 

the 10% representation level, or for single-site solutions at 20% representation. Multiple6 
site solutions obtained with 0.5 min2

 sampling units did however, cover significantly less 

total area than did those of 1.0 min2

 sampling units (2 sites: F = 13.86, P = 0.002; 4 sites: 

F = 10.23; P = 0.0056). We therefore chose to use the 0.5 min sampling unit grid, with a 

total of 3610 sampling units (mean 0.78 km2

), for all subsequent analyses. 

Controlling spatial configuration 

Boundary lengths were obtained from Arc attribute tables (containing “lpoly” and 

“rpoly” attributes) following conversion from the shape files containing the sampling unit 

grids. The boundaries on the outer edge of the Sanctuary were defined as irremovable 

(Ball and Possingham 2001, page 26). Amounts of each sediment, or habitat, type were 

transformed to the square root of their original area so that they would scale with the 

boundary lengths. Thus, total cost, included in the output for each run of the algorithm, is 

an index of total area. The transformation was necessary in order to keep the values of 

the BLMs required to obtain a desired number of sites (clustered sampling units) within a 

reasonable range and had no effect on the outcome. 

We performed a large number of experimental simulations to determine the effect that the 

BLM had on the size of solutions (total included area), their boundary lengths, and 

number of sampling units. Each trial consisted of 100 runs, as did each of those used to 

produce the results presented in this report. Each run was performed with 10,000,000 

iterations and 50,000 cooling steps. Larger BLM values were associated with a larger 

range in the number of sampling units among solutions and larger maximum areas, but 

smaller maximum boundary lengths. Although the boundary length decreased with 

increasing values of the BLM, the minimum number of sampling units and total included 

area varied little. The mean number of clusters also varied with the BLM. It was 

therefore possible to find solutions in which the number of clusters differed but total 

included area was essentially fixed. That is, for similar total area, solutions could be 

found that consisted of one or more site clusters. Larger boundary lengths were generally 

associated with more clusters. 

Identifying minimum sets 

Identifying a true minimum set of sampling units to meet any representation goal is a 

difficult mathematical problem. Simulated annealing can find the true minimum given an 

adequate annealing schedule. For most real-world conservation applications, it is often of 

little practical value to know the true minimum, as near-minimum sets often come very 

close and other decision factors will ultimately determine the exact size and location of

conservation areas (Kirkpatrick et al. 1983). Furthermore, it is often the case that a 

greater number of near-minimum versus true minimum sets exists, and this can be an

advantage in cases where planners wish to examine a range of alternatives. We present 

all of the results provided with this report as near-minimum sets. However, the procedure 

outlined below identified sets that we believe to approximate the true minima. 

We defined the “best” solution, out of 100 runs, for any set of inputs to be the one having 

the fewest sampling units and the smallest boundary length. We used counts of sampling 

units as a proxy for total area because MARXAN output provides counts of sampling 

units but not summed area of units and because of the negligible differences in size of 

7 
whole sampling units. In addition to the best solution, we also identified four “alternates” 

that were closest to the best in terms of size and boundary length. To identify the 

smallest number of sampling units needed to represent sediment, or habitat, types at each 

of the five target levels (5, 10, 15, 20, and 25% of their distribution in the Sanctuary), we 

ran simulations with BLM values equal to zero. In each case, the set of 100 runs 

produced solutions with the same number of sampling units. These constituted true 

minimum sets because the sampling units chosen included only one sediment type each. 

Site clusters, on the other hand, will include sampling units that span more than one 

sediment type where the size of clusters exceeds the scale of homogeneity in the substrate 

map. To approximate true minima in subsequent runs where the BLM was greater than 

zero, we identified those solutions that contained an equal or near-equal number of

sampling units to those obtained with BLM values equal to zero. 

It was usually necessary to test a range of BLM values to obtain solutions with one to 

four site clusters. We started with BLM values equal to one and increased these in 

regular increments until single-site solutions had a highly rectangular shape (i.e., a 

minimization of the boundary length). The number of sites identified from each of 100 

runs at BLM values identified in this way varied from one and two in some cases to more 

than two in others. To obtain solutions with more sites, we reduced the BLM in small 

increments until this was achieved. We were therefore able to identify multiple-site 

solutions with the smallest boundary lengths (i.e., those with highly rectangular shapes). 

We sorted results of all runs by their boundary lengths. Boundary lengths were highly 

correlated with the number of site clusters, so it was a simple matter to identify the 

smallest boundary length that corresponded with the desired number of sites. We chose 

as best and alternates, solutions with the smallest boundary lengths and number of 

sampling units closest to the minimum identified from runs with BLM = 0. 

For simulations with habitat classes, we followed a slightly different procedure because 

total included area and the number of site clusters were not as tightly associated as they 

had been for the three sediment classes. In these cases, we ordered the results by 

boundary length and chose as best and alternative solutions, those that had the fewest 

sampling units for a given number of site clusters. 

RESULTS 

Here we present results for simulations run with target representation levels of 5, 10, 15, 

20, and 25% of each sediment type and 10 and 20% of each habitat class. These were 

adequate to demonstrate the general patterns that emerged with the application of

simulated annealing to our two data sets. 

Representation targets were met in all simulations. Multiple solutions existed in every

case, with sampling units aggregated into one or more site clusters. Of these, we plotted 

the best solutions obtained for each representation level and number of sites for sediment 

types (Figure 4) and habitat classes (Figure 5). 

8 
Figure 4.1. Best results, as defined in this report, 

for 5% representation of each

sediment type, distributed among A:

1 site cluster, B: 2 site clusters, C: 3

site clusters, and D: 4 site clusters. 

A) B)

C) D)

A) B)

C) D)

Figure 4.2. Best results, as defined in this report, 

for 10% representation of each 

sediment type, distributed among A: 1 

site cluster, B: 2 site clusters, C: 3 site 

clusters, and D: 4 site clusters. 

A) B)

C) D)

Figure 4.3. Best results, as defined in this report, 

for 15% representation of each 

sediment type, distributed among A: 1 

site cluster, B: 2 site clusters, C: 3 site 

clusters, and D: 4 site clusters. 

A) B)

C) D)

Figure 4.4. Best results, as defined in this report, 

for 20% representation of each 

sediment type, distributed among A: 1 

site cluster, B: 2 site clusters, C: 3 site 

clusters, and D: 4 site clusters. 

9 
10 

A) B)

C) D)

Figure 5.1. Best results, as defined in this report, for 10% 

representation of each habitat type, distributed

among A: 1 site cluster, B: 2 site clusters, C: 3

site clusters, and D: 4 site clusters. 

A) B)

C) D)

Figure 4.5. Best results, as defined in this report, 

for 25% representation of each 

sediment type, distributed among A: 1 

site cluster, B: 2 site clusters, C: 3 site 

clusters, and D: 4 site clusters. 

Figure 5.2. Best results, as defined in this report, for 20% 

representation of each habitat type, distributed

among A: 1 site cluster, B: 2 site clusters, C: 3 

site clusters, and D: 4 site clusters. 

A) B)

C) D)
Mean percent area of the entire Sanctuary in solutions from each set of 100 runs on the 

sediment data was equal to the percent representation of each sediment type, with a mean 

standard deviation from all runs of 0.011 (range: 0.003-0.048). Thus, for example, with 

minor deviation, the outcome of runs for which 10% of each sediment type was 

represented covered 10% of the area of the Sanctuary. This relationship did not vary with 

the number of site clusters. Results were similar where the nine habitat classes were 

represented except that total included area was slightly greater on average (but less than 

1% of the area of the Sanctuary) than representation levels for solutions with one or two 

sites. Mean standard deviation from these runs was 0.112 with a range of 0.004-.378, 

somewhat higher than those obtained with the sediment data. 

Boundary length increased in direct proportion to number of sites at all levels of 

representation for simulations with the sediment (Figure 6) and habitat data (Figure 7). 

However, boundary lengths increased more rapidly with number of sites at higher levels 

of representation (Figure 8). 

0

25

50

75

100

125

150

175

200

225

0 1 2 345

Number of Sites

Boundary Lengt

h (

k

m) Figure 6. Boundary length as a function of the number of sites for best and alternate solutions of

5% ( ), 10% ( ), 15% ( ), 20% ( ), and 25% ( ) representation of sediment 

types. 

11 
0

20

40

60

80

100

120

140

160

180

0 1 2 3 4 5

Number of Sites

B

oundary Le

n

gth (

k

m) Figure 7. Boundary length as a function of the number of sites for best and alternate solutions of

10% ( ) and 20% ( ) representation of habitat classes.

 

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30

Level of Representation

R

egr

e

s

sio

n Slope

Figure 8. Slope of regression equations versus number of sites for the relationships shown in

Figure 6 ( ) and Figure 7 ( ). 

12 
Differences in total area between the best and alternate solutions for sediment types were 

no more than 1 km2

, although differences in boundary length were as much as 15 km or 

8% longer (Table 2). By contrast, differences in total area between the best and alternate 

solutions for habitat classes were as much as 21 km2

, but still only about 1% of the total 

area of the Sanctuary. Differences in boundary length were at most 12 km (Table 3). 

Table 2.Boundary Length Modifier (BLM), run number, total area (in km2), and boundary length (BL) (in

km) of the best and four alternate solutions as defined in this report for 5, 10, 15, 20, and 25% 

representation of three sediment classes under four spatial configurations. 

13 
Table 3.Boundary Length Modifier (BLM), run number, total area, and boundary 

length (BL) of the best and four alternate solutions as defined in this report for 

10 and 20% representation of nine habitat types under four spatial

configurations. 

14 
It is evident that the locations of sites selected by the algorithm varied among solutions, 

depending on representation level, number of clusters, and area included. However, the 

probability of selection varied among individual sampling units and cluster sites, and 

therefore some sampling units and sites were represented more frequently in solutions 

that others. We can represent frequency of inclusion by plotting summed solutions in 

which the number of times each sampling unit is included in a solution is summed over 

all runs and displayed as a graded color scheme. MARXAN will output summed 

solutions if specified. 

We plotted summed solutions from four simulations to illustrate the range of variation in 

spatial configuration of alternate solutions and how this varies with the target number of

sites (Figure 9). 

Figure 9.1. The summed solution for 100 runs of a 

MARXAN simulation. Target 

representation levels were 10% of each 

sediment type. The BLM was 5. Times 

selected is the number of times that a 

sampling unit was included in a solution 

set. 

Figure 9.2. The summed solution for 100 runs of a

MARXAN simulation. Target 

representation levels were 10% of each 

sediment type. The BLM was 0.5. 

Times selected is the number of times 

that a sampling unit was included in a 

solution set. 

15 
Figure 9.3. The summed solution for 100 runs of a 

MARXAN simulation. Target 

representation levels were 10% of each 

habitat class. The BLM was 10. Times 

selected is the number of times that a 

sampling unit was included in a solution 

set. 

Figure 9.4. The summed solution for 100 runs of a 

MARXAN simulation. Target 

representation levels were 10% of each 

sediment type. The BLM was 1. Times 

selected is the number of times that a 

sampling unit was included in a solution 

set. 

Simulations producing mostly one and two-site solutions exhibited less variation in the 

location of sites selected across runs and fewer sampling units with a moderate to high

probability of being selected. Differences were most pronounced among simulations 

where sediment rather than habitat type representation was the goal. The outcome of 

simulations on the sediment data further exhibited a tighter clustering of moderate to high 

probability sampling units in the center region of the Sanctuary (Figure 9.1) compared to 

simulations producing three- and four-site solutions (Figure 9.2). Differences were less 

pronounced among simulations targeting representation of the nine habitat types (Figure 

9.3 and Figure 9.4). 

16 
DISCUSSION 

Results of this analysis suggest a great deal of flexibility in the selection of protected 

areas for equal representation of benthic habitats in SBNMS. Not only do targeted levels 

of representation of sediment types and habitat classes, expressed in terms of percent 

cover, correlate strongly with percent of the study area needed to represent them in a 

minimal amount of space, but this area remains fairly constant under a variety of spatial 

design scenarios. These patterns are consistent over a range of representation levels as 

well. 

These patterns were not anticipated and differ from results reported in a similar analysis 

but using abundance distribution data of demersal fishes from research trawl surveys 

across the eastern continental shelf of the United States (Cook and Auster 2005). The 

most likely explanation for the present results is that each of the three sediment types 

occurred in roughly equal proportions within the Sanctuary and there was a good mix of 

all three in most areas. Thus, solutions were very efficient in that none of the targeted 

amounts of sediment or habitat types were exceeded in order to adequately represent all 

of them under the spatial constraints imposed (one to four sites). There was likely less

spatial variation among simulations targeting habitat versus sediment type representation 

because deep-water habitats (>150m) are much more localized within the Sanctuary than 

are the others. Nevertheless, the additional cost of representing the nine habitat classes 

versus three sediment types in terms of total included area was quite small (less than 1%

more of Sanctuary area).

Although the total habitat area included in solutions was not affected by aggregation, 

boundary length increased in direct proportion to the number of sites, given a constant 

representation of sediment and habitat types. Thus, all things being equal, total boundary 

length and boundary to area ratio would be smaller for conservation strategies that 

emphasized a single protected area versus those that allocated the same amount of habitat 

area among two or more sites. A single large protected area might be favored over 

several smaller ones for a number of reasons (see Fogarty 1999 and Dayton et al. 2000 

for good discussions). Among these, smaller boundary lengths can reduce social and 

economic impacts of habitat protection as well as the difficulty and expense of 

enforcement. Smaller boundary-to-area ratios also tend to reduce movement rates of 

mobile organisms from inside to out (Polacheck 1990, Lindholm et al. 2001), thus larger 

areas may offer more protection to their inhabitants, particularly if exploitation occurs 

right on the boundaries. On the other hand, multiple sites increase both redundancy and 

the likelihood of including greater biodiversity. Since boundary length increases faster 

with number of sites at higher levels of representation, a preferred design strategy might 

differ depending on how much habitat were to be protected.

Each of the 100 runs of a simulation represents an optimal or near-optimal solution to a 

set of representation targets given the constraints defined by the optimization function. 

Near-optimal solutions varied little from the best (optimal) solutions in terms of total area 

and boundary length. Therefore, each of the 100 runs of a simulation could be viewed as 

a good solution, where ‘good’ is defined in terms of a conservative use of space in 

17 
representing sediment type diversity. The summed solutions provide an indication of 

how much flexibility exists in the number and location of these solutions. 

In SBNMS, there are fewer good one- and two-site solutions than three- and four-site 

solutions to the equal representation of sediment types because of the distribution of 

sediments. Sediment type heterogeneity is highest toward the center of the Sanctuary. 

Therefore, simulations constrained by the BLM to produce one- and two- site solutions 

will be concentrated in this area because this is where equal representation can be 

achieved most efficiently (note however, that if representation goals had not been equal, 

these results might have been quite different). Since the three sediment types are broadly 

distributed within the Sanctuary, efficient representation can be achieved through a large 

number of different spatial configurations. Thus, greater flexibility in spatial 

configuration is achieved with a greater number of sites. 

However, the choice of sites based on sediment type alone would ignore the relationships 

that many organisms have with habitats defined by a greater number of physical and 

biological factors. Depth is an important, although not necessarily the only other, 

determinate of distribution for many marine organisms. Results of the our analysis 

indicate that considerably less variability exists in the number of potential sites, where 

representation must include each of the nine habitat classes defined by our three sediment 

types and depth zones. These are concentrated in the central portion of the northern half 

of the Sanctuary because this is the only area where three habitat types occur at depths 

exceed 150 meters. Even when the BLM allows for more than 2 sites, most solutions will 

include one or more in this region, although there will likely exist greater flexibility in the 

location of the other sites. 

The underlying assumption in this study is that patterns in the distribution of species and 

communities are highly correlated with sediment type and water temperature and these 

factors can serve as proxies for representative communities of organisms. Such patterns 

have been demonstrated for both fishes (Langton et al. 1995, Brown et al. 1996, Auster et 

al. 1998, 2001, 2002) and benthic invertebrates (Kostylov et al. 2001, Skinder 2002) in 

the Gulf of Maine and are common patterns for a wide range of benthic and demersal 

taxa in other parts of the world ocean (e.g., MaCall and Tevesz 1982, Etter and Grassle 

1992, Morrisey et al. 1992, Zajac et al. 2000). For example, sea pens (Pennaltula 

acculeata) are common structure-forming fauna in deep mud habitats (mud sediment type 

and Maine deep water) of SBNMS (Auster unpublished data). Use of only sediment type 

as a habitat attribute reduces the probability of inclusion of this species in any 

management scheme focused on representation. Use of sediment type and temperature as 

a proxy for habitat in conservation planning, in the absence of robust data on the 

distribution and abundance of fauna at the spatial scales of individual protected areas, 

allows managers to develop conservation alternatives in a precautionary manner (sensu

Auster et al. 1997) to meet goals for the conservation and sustainable use of biological 

diversity within National Marine Sanctuaries. 

The purpose of this study was to demonstrate the utility of simulated annealing as a 

decision support tool for resource management planning in SBNMS and to examine some

18 
of the general patterns discovered in an application that varied representation of marine 

habitats within the range often advocated for regional inclusion in marine protected areas 

(National Research Council 2001). There are several ways in which the present analysis 

could be expanded or improved. 

First, we made the simplifying assumption that the “cost” of sampling units was directly 

proportional to their size. In so doing, we were able to directly compare tradeoffs in total 

area and spatial structure of solutions equivalent in habitat representation. In future 

analyses, the cost function could be expanded to include additional social or economic 

terms, such as those related to commercial and recreational fishing, with appropriate 

values assigned to individual sampling units. Weighting factors could be applied to these 

terms in order to evaluate the effects on solutions and the trade-offs associated with one 

or more of these factors. 

Secondly, boulder pile and rocky reef habitats were not included in our sediment data. 

These relatively localized habitats cannot be distinguished easily from other gravel 

substrates using multi-beam data. However, it is possible to interpret pattern in gray 

scale to differentiate boulder and reef habitats based on small-scale patterns in variation 

of reflectivity. A map product has been developed based on visual interpretation of the 

multibeam record (P. Valentine unpublished data) but was not completed in time for use 

in the present study. Auster et al. (1998) showed that the composition and abundance of 

fishes differed between gravel and boulder reef-dominated habitats, so subsequent 

analyses should differentiate between these habitats to provide a more complete range of

sediment types, and by extension, of biological diversity. 

Finally, use of habitat or species-specific research results, as well as traditional ecological 

knowledge (e.g., from the fishing community), could be used to prioritize habitats in this 

type of analysis. For example, known habitat patches (planning units) that are important 

for key species (e.g., locations of nursery or spawning habitat) can be identified a priori 

to be included in any solution. Such planning units can be selected for species of 

ecological or economic importance as well as those that are sensitive to disturbance, rare 

or are of particular interest to managers or the public. 

There are several ways to develop alternatives for spatial management regimes in marine 

protected areas. These include methods where stakeholders develop a priori maps with 

defined management units and defend their assumptions or priorities represented by each. 

Final solutions require iterative renditions based on changing sets of assumptions and 

priorities. Simulated annealing using MARXAN or related programs provides managers 

and stakeholders with a tool that can incorporate a priori ecological, economic, and other 

societal priorities in a manner that precedes the display of solutions. Such an approach 

can garner results that are acceptable to diverse user groups as the variety of optimal 

solutions represents the amalgam of stakeholder requirements. 

19 
ACKNOWLEDGEMENTS 

We thank Craig MacDonald and Benjamin Cowie-Haskell for permission to use the 

sediment data layer, Hugh Possingham and Ian Ball for permission to use MARXAN, 

Technology Planning and Management Corporation for technical assistance, Kevin Joy 

for generating the boundary length data, Rikki Grober-Dunsmore and an anonymous 

referee for helpful suggestions, and Kathy Dalton for editing the manuscript. RC was 

supported by a grant from Environmental Defense. PA was supported by NOAA’s 

Undersea Research Program, Stellwagen Bank National Marine Sanctuary and the 

Census of Marine Life Gulf of Maine Program. The views expressed herein are those of 

the authors and do not necessarily reflect the views of the funding agencies. 

20 
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24 
ONMS 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/). 

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) 

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 (MSD01-03) 

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 (MSD-01-1) 

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 (MSD-00-1) 

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

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

25