NOAA Technical Memorandum NMFS-AFSC-104

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Alaska Fisheries Science Center

September 1999

by

K. R. Smith and R. A. McConnaughey

Surficial Sediments of the

Eastern Bering Sea Continental Shelf:

EBSSED Database Documentation
NOAA Technical Memorandum NMFS

The National Marine Fisheries Service's Alaska Fisheries Science Center

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the Northwest Fisheries Science Center.

This document should be cited as follows:

Smith, K. R., and R. A. McConnaughey. 1999. Surficial sediments of 

the eastern Bering Sea continental shelf: EBSSED database 

documentation. U.S. Dep. Commer., NOAA Tech. Memo. NMFSAFSC-104, 41 p.

Reference in this document to trade names does not imply endorsement by

the National Marine Fisheries Service, NOAA.
September 1999

NOAA Technical Memorandum NMFS-AFSC-104

by

 K. R. Smith and R. A. McConnaughey

Surficial Sediments of the

Eastern Bering Sea Continental Shelf:

EBSSED Database Documentation

Alaska Fisheries Science Center

7600 Sand Point Way N.E., BIN C-15700

Seattle, WA 98115-0070

U.S. DEPARTMENT OF COMMERCE

William M. Daley, Secretary

National Oceanic and Atmospheric Administration

D. James Baker, Under Secretary and Administrator

National Marine Fisheries Service

Penelope D. Dalton, Assistant Administrator for Fisheries
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iii

ABSTRACT

The texture of surficial seafloor sediments, that is, the frequency distribution of grain sizes,

is a fundamental property of the benthic marine environment. It affects such basic physical

characteristics of the seafloor as porosity, permeability, and compaction, which in turn may affect

the distribution of plants and animals. A number of investigations have reported spatial variation in

texture on the eastern Bering Sea (EBS) continental shelf. However, many of these studies were

limited to relatively small portions of the shelf, while others characterized larger areas by spatially

averaging relatively sparse data. The original studies varied somewhat in methods of analyzing

sample texture or in the descriptors used to characterize it. In order to facilitate descriptions of

groundfish habitat over a large portion of the EBS shelf, we have assembled a single

comprehensive database of the point sample data (EBSSED; n = 2,587) from all available sources. 

The database represents sediment variation over the study area with uncompromized (i.e., original)

spatial detail. Textural data in the database are of two main types: 1) standardized statistics

characterizing sample grain size distribution based on laboratory measurements (granulometric

data), including % composition by size grades (e.g., gravel, sand, mud) and size distribution

parameters (e.g., mean size); and 2) sample descriptions from less exacting, more subjective

visual/tactile observations, usually made in the field, establishing size-grade constituents. In

addition the EBSSED database includes two descriptive fields which were each added to

characterize sample grain size distribution by a single, standardized variable based on the original

data. These fields classify samples according to gravel-sand-mud composition using high and low
iv

resolution schemes derived from Folk’s (1954) classic ternary diagram. The high resolution

scheme classifies 903 samples with detailed granulometric data into 15 textural classes, providing

the greater detail regarding textural variation. The low resolution scheme (7 classes) is designed to

allow unambiguous classification of nearly all samples (n = 2457) including those with subjective

visual/tactile descriptions. It represents the maximum number of samples according to a single

common variable and thus provides the most spatially detailed data for the study area, albeit at the

expense of some of the textural detail for samples analyzed in the laboratory.

Overall, the EBSSED database is the most comprehensive and detailed source of

information about surficial sediment textures in the EBS study area. Patterns observed in the data

generally agree with large-scale textural maps and summaries by previous investigators,

particularly a general pattern, with exceptions, of decreasing average grain size with increasing

depth and distance from shore. However, those previous large-scale works spatially smoothed

data for the study area from smaller, more sparsely distributed sets of samples. The EBSSED

database preserves potentially important fine-scale variation.
1

INTRODUCTION

The texture of surficial sediments, defined as the size-frequency distribution of

unconsolidated grains, is an important attribute of the coastal ocean environment. In addition to

purely geological applications, data of this type have great utility in habitat studies seeking to

explain the distribution and abundance of important biological resources. In particular, the

National Marine Fisheries Service now has responsibility for the identification (and protection) of

essential fish habitat, defined broadly to include all life history stages of all managed species

(Magnuson-Stevens Fishery Conservation and Management Act, 16 U.S.C. 1801 et seq., as

reauthorized by the Sustainable Fisheries Act). Although detailed habitat analyses are mandated,

suitable data characterizing spatial variation are generally lacking and collection of new

environmental data or reformatting of historical data is required. Sediment texture is one of many

potential factors influencing the distribution and abundance of groundfish. The relative distribution

of grain sizes affects sediment properties such as porosity, permeability, and resistance to

displacement (Allen 1985, Selley 1988). These properties, in turn, may directly (e.g., self-burial to

reduce exposure to predators) or indirectly (e.g., suitability for essential prey organisms) affect fish

habitat quality, as measured by rates of growth, survival, reproduction, and recruitment.

Surficial Sediments in the Eastern Bering Sea

Sediment Transport and Deposition

The dynamics of sediment deposition on the continental shelf in the eastern Bering Sea

(EBS) have been described by Sharma (1974a), Sharma et al. (1972), Lisitsyn (1966), Knebel et al.
2

(1974), and McManus et al. (1977). These authors conclude that physical characteristics on the

shelf both influence and reflect these processes. The extensive shelf surface constitutes a relatively

shallow and level area of seafloor bounded offshore by an abrupt, steep break-in-grade at 150-170

m depth (Fig. 1). Average water depth over the shelf is 60 m, and there is a relatively uniform

cross-shelf slope (i.e., shelf-width/depth-at-break) averaging < 0.0003. Prevailing sea currents

produce a net flow from the Pacific Ocean and out through the Bering Strait. This is manifested in

the general northerly direction of currents, from which there nevertheless are major deviations,

such as a counter-clockwise gyre in Bristol Bay (Fig. 1). Sediment originates from erosion,

surface runoff, and volcanism on the Alaska mainland, which transport material to the coastal

environment where waves and currents disperse it offshore. The two largest river outflows are

those of the Yukon and Kuskokwim Rivers into the northeast and east-central shelf areas,

respectively, and each contributes considerable amounts of sediment. Shelf surface strata generally

consist of a thin veneer of contemporary sediments, from 1.5 m to over 6 m thick in the

southeastern region. Areas of exposed relict deposits do exist in the north, despite the presence of

considerable recent river sediments.

Observations of Surficial Sediment Texture

A number of geologic, oceanographic, or biological studies have reported textural

characteristics of surficial sediment samples from locations on the EBS shelf (e.g., Table 1). These

studies were performed during various periods over a number of years and varied substantially in

geographic scope and methods, according to the specific objectives of each project and the

available technology for processing multiple samples. Most of the studies focused on selected

regions of the shelf and did not sample the entire area comprehensively (e.g., Cimberg et al. 1986;
3

Armstrong et al. 1987).

In addition, a few characterizations of the entire EBS shelf have been undertaken (Sharma

1974b; Naidu 1988; Lisitsyn 1959, 1966), based largely on combined data from previous smallerscale studies. These large-scale reports describe a pattern in which, with exceptions, grain size

generally decreases with increasing depth and distance from shore. This is particularly the case for

the southeastern Bering Sea shelf (Bristol Bay and westward), where Sharma et al. (1972) report a

classic graded shelf. This condition occurs because settling velocity of particles decreases with

size (Stokes Law), as does the minimum water speed necessary to resuspend or at least tumble

them. Since the kinetic energy of wind-generated waves reaching the bottom decreases with

increasing depth, terrigenous grains entering coastal shallows drift with water movement until they

are deposited according to grain size at the depth where maximum water speed drops below the

minimum required for further transport. This process effectively sorts sediment particles by size,

resulting in the graded pattern. Johnson (1983) observed sufficient irregularities to this pattern in

Bristol Bay to conclude that the dynamic equilibrium of deposition forces necessary for a fully

graded condition to occur there did not exist. She attributed the observed deviations from depthdependent grading to variations in height and intensity of storm waves, and to intermittent scouring

by regional alongshore currents exposing patches of relict deposits. Nevertheless, despite these

fine-scale irregularities Johnson acknowledged a regional trend, offshore of shallow coastal areas,

of decreased grain size with increased distance from shore.

The EBS shelf is characterized by large areas with reasonably homogeneous sediments, due

in part to the relatively broad, shallow shelf and the unusually level slope of the bottom. Overall,

various grades of sand predominate, with significant concentrations of mud occurring at greater-
4

than-average depths. Gravel is rare and generally confined to nearshore areas because of sizelimited transport.

The most comprehensive summaries of surficial textures in the EBS were the maps

produced by Lisitsyn (1959;1966) and Naidu (1988; Fig. 2). These works present classifications

generalized over rather large polygonal areas, thereby revealing large-scale patterns, features, and

characteristics. Because of spatial averaging, however, they do not display all the fine-scale

variation of the original (point) data used to generate the polygons.

OBJECTIVES

To facilitate studies of essential fish habitat, we have assembled all available data on

surficial sediment textures in the EBS shelf study area into a single, comprehensive database. All

of the original (point) sample detail has been retained, Also, we have standardized the occasionally

disparate data by adding two descriptive fields that each apply common criteria to assign samples

to a set of discrete textural classes. These schemes are designed to characterize variation of

possible significance to fish habitat studies. By these classifications, we maximize the number of

available samples represented by a single database variable and maintain an accurate representation

of sampling density and spatial variation, in a format conducive to selective use by individual

investigators. The spatial extent of the database is generally defined by the boundaries of the

standard U.S. National Marine Fisheries Service (NMFS) EBS resource assessment surveys

(Goddard and Walters, 1998), and conforms to the EBS shelf south of a line (~60° 50' N.) that

extends from a point just north of Nunivak Island to the shelf break. On the east and south, the

study area is bounded by the Alaska mainland, and it extends west to the 200 m isobath.
1For a description of EBSSED database fields, see Appendix.

5

THE EBSSED DATABASE: COMPILATION AND CONTENT1

Data Sources

The database characterizes surficial sediments at 2,587 separate locations throughout the

EBS study area (Fig. 3). The original data were collected during the period 1934-97 and reported

by civilian and military institutions customarily involved in marine geological, geophysical, or

biological research (Table 1). In addition to original data, some of these sources contain data

which was first reported in other investigations (e.g., Johnson 1983 contains data from Roberts

1976), and records were screened to prevent inclusion of these duplicates.

In addition to differences in purpose and scope, these studies also differed in the equipment

used to collect samples, as well as in the methods used to describe them. The samples were

collected with variations of the three basic gears commonly used for sediment sampling. Records

indicate samples came from grabs (n = 2,300), corers (n = 217), or dredges (n = 7). In some cases

(n = 63), gear type was not reported. When multiple samples were collected at a single location by

means of different gear types, we include only one sample record in the database. Surface

subsamples from coring devices are generally preferred because of inherently better retention of

fine fractions during retrieval. However, either gravity-cores or grabs are preferred over piston

cores as they are less likely to distort the top strata.

The vertical extent of each sample varied somewhat, according to the penetrating abilities

of the sampling gear used. Because of our interest in surficial properties, we restrict data from

cores to that for the upper stratum of homogeneous sediment, as determined by the original
6

investigator. Core data typically indicated similarity of surface sediment extending down more

than 10 cm and this, along with the performance characteristics of the other gear types (e.g., Word

1977), suggests that our database samples generally represented the sediment to that depth or

greater.

Original Sample Data 

Original data in the database include sample location, water depth, and gear type; as well as

institutional information (e.g., cruise, station, and sample identifiers; vessel; date) that may be

useful when consulting the original reports (Table 1). Textural data result from one of two basic

methods by which original investigators described sediment, with either method characterizing the

complete range of grain size within the sample.

Laboratory Grain Size Analysis

In 1,033 cases, the samples were analyzed in the laboratory and various statistics

characterizing the grain size distribution were computed, including measures of the mean, sorting

coefficient (i.e., standard deviation), skewness, and kurtosis, as well as the percent of sample

weight in each standard size-grade. Grain size is expressed on a logarithmic scale in units of N

(phi, the negative log2 diameter in millimeters). This has advantages for the use of standard

methods in statistical analysis of grain size distributions, since the latter tend toward lognormality

in natural sediments (Folk, 1966). Size frequency (% composition) is by weight, not number of

grains. Although all statistics for a sample help describe texture, two parameters in particular each

encapsule basic characteristics of the size distribution. Mean grain size represents the size at the

distribution center of gravity, while the sorting coefficient is a measure of size variation and thus an

inverse indicator of size sorting (poorly sorted samples contain a relatively high degree of
2Gardner (personal communication; see Table 1) and Johnson (1983) provided multiple measures

(graphical and moment) of each sample parameter. The values entered in the EBSSED database for

samples from these sources are moment measures.

7

variation; Folk 1974). Both parameters have been shown to affect such basic sediment properties

as porosity, permeability and compaction (Allen 1985, Selley 1988). Measures of skewness and

kurtosis index the symmetry and peakedness of the particle size distribution, respectively,

characterizing departure from a normal (i.e., Gaussian) distribution. These last two parameters are

sometimes considered when deposition sources and history are traced. Although possible

significance to fish habitat quality is less apparent, they are nevertheless included. 

The actual measures (e.g., “moment”; “graphical”) used, in the original studies, for the four

basic distribution parameters differed somewhat from the theoretical parameters of standard

statistics (see reviews by Folk 1966, Brenninkmeyer 1982, McCammon 1962). These differences

are due to constraints of time and equipment in calculations regarding very many grains per

sample. Generally, where standard geological methods of calculating the four grain size moment

measures were employed, values are likely to most closely approach the standard statistical

parameters (Inman 1952)2. Alternatively, graphical measures developed for mean N and sorting

coefficient, although varying with the formula used in calculation, generally approach the

respective standard parameters as grain size distributions approach the normal. As such, database

values for mean size (n = 994) are all estimates of the same variable, as are those for the sorting
3

In a few cases (e.g., McMurray et al. 1984, U.S.N. Hydrographic Office 1955), original

investigations used unconventional measures for these two parameters which were not estimates of the

standard parameters mean N and standard N deviation. We recast these values in the appropriate

statistical terms analogous to the standard parameters, for entry in the database. The original measures and

the formulas used for these value changes are noted in the “Param. measures” field.

4

Johnson (1983) included the gravel component as part of the “sand” grade in reporting sample

sand-silt-clay composition. Therefore, for each of the 130 samples from this source the database includes

% coarse sediment (i.e., gravel or sand) but the specific % gravel or % sand are unknown.

8

 coefficient (n = 1,013)3. However, this is not true for skewness and kurtosis values since

graphical measures of those characteristics are not analogs of the standard statistical parameters

but rather are completely different indexes. For example, Inman’s “primary skewness” equals his

graphical mean N estimate minus the median N-value, divided by his graphical standard deviation

("N = (MN - MdN)/FN). This measure is likely to approximate 1/6 the value of the standard

skewness (Inman 1952). Documentation and references detailing parameter calculation are in the

“Param. measures” field in the EBSSED database, or users may wish to consult the original data

sources. Users are advised when employing skewness or kurtosis in an associative study to select

samples with values of the same measure (e.g., all moment measures). The spatial distributions of

sample values of mean N and sorting coefficient in the study area are shown in Figures 4 and 5,

respectively.

Grain size statistics for a sample were commonly (n = 1,033) reported in terms of

composition by major size-grades, usually gravel, sand, and mud (or its subclasses silt and clay),

defined on a standard scale of grain size limits (Wentworth 1922, Krumbein 1934; Table 2).4

Grades range from (the subclass) boulders to clay, and together include all possible particle sizes. 

In actual laboratory analysis, sample material was usually sorted and weighed by incremental grain

size ranges of 1N or even divisions thereof (e.g., 2-2.5N, 2.5-3.0N). Size classes coarser than silt
5

In the one case where data included comprehensive size-class composition but no distribution

parameters (Sharma, 1976; 81 samples), we used the composition data to calculate mean N and sorting

coefficient (moment measures) values for the database. 

9

were usually isolated with a series of U.S. standard sieves (e.g., ASTM standard D 422-63) and, if

silt and clay fractions were of interest, they were calculated from measurements reflecting settling

rates in a column of water (e.g., ASTM standard D 422-63). Alternatively, two studies each

employed a different type of automatic particle-size analyzer to obtain size-class fractions for all or

part of the sand-size range. That used for some of the samples described by Johnson (1983)

employed settling rates to calculate fractions in the -1N to 4N range, while the type used by

GeoSea Consulting Ltd. (1999) utilized laser-light diffraction by suspended sediment to obtain

fractions for all sizes finer than 0.5N. Ultimately size-class weights were combined to determine

percent composition by the major Wentworth grades and were also used to compute the

distribution parameters reported5. Since detailed weight percentages for the individual grain size

ranges were reported for only about 2/3 of the laboratory-analyzed samples, such information is

not included in the database as part of the granulometric descriptions. Readers wishing data of this

sort are referred to the original sources (Table 1).

Field Descriptions of Texture

In the remainder of cases (n = 1,554), the original textural descriptions (“sediment type” in

Appendix) are generally qualitative (e.g., pebbly sand, silty clay), based on more subjective

visual/tactile (field) methods of evaluation. These descriptions utilize the same (root) terms as the

names of size-grades in the Wentworth scale. The exceptions are a few references to “stone” and

“rock”, which are interpreted as particle sizes within Wentworth’s open-ended “gravel” range

(Umbach 1976). Given the subjective nature of field methods, these are less exacting descriptions
10

than the laboratory analyses. However, the data likely have sufficient resolution and variability to

be important in fish habitat studies. Within this group, 33 samples were analyzed in the laboratory

but data were reported using unconventional descriptors. In particular, 17 records (NGDC 1994a,

1994b) provide verbal descriptions of “primary texture” that specify composition by size grades

only within certain broad percent ranges. Another 16 samples (LaFond et al. 1949, Buffington et

al. 1950) report sample composition, in 10% increments only, by each of six grades comprising the

grain-size scale (Emery and Gould 1948). These methods preclude reporting of grain size statistics

in the standard format of this database, hence they are grouped with the other purely descriptive

data.

New Textural Classifications

In addition to the original textural descriptions, the database includes two fields that each

independently classify samples unambiguously into a respective set of common textural types,

based on the occurrence of gravel, sand, and mud specified by the original data. These two

classification schemes differ in the level of detail expressed concerning sample texture, but each

takes advantage of different attributes of the original data to produce new data with likely

importance for habitat studies. The schemes are both based on the standard textural classifications

developed by Folk (1954, 1974) and are defined on respective gravel-sand-mud ternary diagrams

designed to accommodate the information content of the original geological descriptions (Figs. 6a,

6b).

High Resolution Classification

The high resolution scheme (“High res. code” in Appendix) classifies samples with detailed

granulometric data (n = 903) using Folk’s standard ternary diagram with 15 textural types (Fig. 6a;
11

Table 3). Folk’s diagram was selected from the various alternatives (e.g., Trefethen 1950, Shepard

1954) because it most closely reflected variability of sediments found in the study area (Fig. 7). In

particular, the rather narrowly defined classes located near the right side and the base of the

triangle allowed the greatest discrimination of textures in areas of the diagram with high sample

density and relatively minor differences in composition. Conversely, relatively large class-areas

along the left side of the diagram include relatively few samples. This conversion of granulometric

data into a single categorical variable reflecting aspects of both central tendency and dispersion in

the particle size distribution (Plumley and Davis 1956) was done to enable categorical habitat

analysis options. Figure 8 shows the spatial distribution of sample high resolution textural classes

in the study area.

Low Resolution Classification

The low resolution scheme (“Low res. code” in Appendix) was developed so that the maximum

number of samples (n = 2,457) could be represented in a common categorical variable representing

texture, irrespective of the method originally used to describe the sample. This included 903

samples with laboratory grain size analyses, those with field descriptions based on visual/tactile

methods (n = 1521), and the 33 cases with atypical granulometric data insufficient for high

resolution classification but sufficient for this scheme. All were classified according to a simplified

form of Folk’s (1954) standard gravel-sand-mud ternary diagram (Fig. 6b). Some of Folk’s

textural classes were merged, in order to accommodate the less detailed descriptions. The field

descriptions provided qualitative information on the grain-size grades in the sample but lacked the

granulometric data allowing point representation on the ternary graph. These samples were

therefore classified by matching descriptive terms with Folk’s sediment-type names. However, as
12

observations made in the field often lacked details of composition such as grade prevalence,

samples could only be classified using the seven possible combinations of the three major grades

(i.e., gravel, sand, and mud; Table 4). For example, textures described in the field as “sandy

gravel”, “pebbly sand”, and “sand coarse, gravel” were all assigned to the “gravel/sand” class

combining Folk’s “sandy gravel” and “gravelly sand”. Also, it is likely that a size class composing

only a very small fraction of a sample escaped detection with field methods. Therefore, we

eliminated class boundaries differentiating trace (i.e., 0.1-5%) from zero-level (0-0.1%) gravel

compositions, incorporating each original “slightly gravelly” class as part of the appropriate new

non-gravel category on the basis of sand-mud composition. Thus, for example, “slightly gravelly

muddy sand” became part of “sand/mud”. A similar simplifying approach has been used by the

National Geophysical Data Center (1986) to determine the “primary texture” of samples. Overall,

the low resolution scheme effectively pools the original granulometric and field-description data,

albeit at the expense of some detail for samples analyzed in the laboratory, and nevertheless

maximizes the number (n = 2,457) and spatial coverage (Fig. 9) of samples that are available for

habitat analysis.

Primary Assumptions

We have made two assumptions while assembling the EBSSED database for fish habitat

studies. First, in keeping with the time scale of sedimentary processes, we assume that neither new

deposition nor scouring have changed textural properties on the EBS shelf significantly over the

sampling period represented in the database (1934-present; Table 1). Knebel (1974) estimates

average deposition rates for recent surface strata on the east-central Bering shelf at 8 to 70 cm per

millennium. At these rates, sediment accumulation over a 65-year period would measure 0.52 to
13

4.5 cm thick. This represents a fraction of the minimum 10 cm surface interval which, as

previously indicated, most of the sample descriptions represent. Also, evidence from each of eight

piston cores from Bristol Bay show that the upper 1.5 m were deposited under physical conditions

similar to the present (Sharma et al. 1972). Together with the degree of depositional equilibrium

indicated by the overall size grading by depth, these observations suggest that present surficial

sediments likely do not differ substantially from those occurring when the original samples were

collected.

Secondly, classifications with our low resolution scheme included samples that were

originally analyzed in the lab as well as those described more qualitatively in the field. This

grouping assumes that assignments of the laboratory-analyzed samples based on our gravel-sandmud ternary diagram were consistent with grain-size limits and minimum-composition thresholds

determining size-grade inclusion in field descriptions. Strictly speaking, this probably is not the

case, given the continuous nature of the grain size variable and the relative imprecision and

subjectivity of field description methods. However, we do not consider this a significant source of

error. The ternary diagram that we used to describe the texture of lab samples was a culmination

of efforts by Folk (1954) and others (Wentworth 1922, Krumbein 1934) to create a standard

classification system that was consistent with de facto practical limits and definitions used in the

field, thus minimizing any discrepancies between the two methods of description.

DISCUSSION

Observations From The Data

To reveal patterns and characteristics in the spatial variability of surficial sediment over the
14

study area, we have produced maps showing values of textural variables at sample locations. 

Maps are of mean N, sorting coefficient, and high and low resolution textural classes (Figs. 4, 5, 8,

and 9, respectively). Skewness and kurtosis are not included due to the heterogeneity of the

indexes used in the original studies. The data based on granulometric analysis are from

approximately 1,000 sample locations constituting relatively fine-scale, even representation of the

study area (e.g., high-resolution class, Fig. 8). The combined (low resolution) data set is even

more extensive (n = 2,457) and maximizes the number of locations represented (Fig. 9). However,

the majority of the additional data are from a single source (SI-NMNH 1994; n = 1,459) describing

samples confined to a portion of northern Bristol Bay, resulting in a less even distribution. The

database design allows selective elimination of such a locally concentrated subset for more uniform

coverage.

The ternary plot of 903 specific sample compositions (Fig. 7) indicates that, overall, sand is

the greatest constituent of the surficial sediment in the study area, with less mud and much less

gravel present. Also, major components within samples containing more than one usually include

sand and mud or sand and gravel, seldom both the extreme size classes (gravel and mud) together. 

This produces a distribution of points concentrated along the base and right side of the triangle,

revealing at least some degree of sorting to be common.

As have previous studies, our data reveal a trend of gradually decreasing average grain size

with increasing depth and distance from the mainland shore. This is well illustrated in Figure 4,

which shows the spatial distribution of sample mean grain size (n = 994) expressed according to

seven divisions of the range of calculated values, designed to facilitate visualization of spatial

variability. The pattern is especially discernible in Bristol Bay and on the adjacent outer shelf, but
15

is less compelling farther north. 

Sample sorting values expressed according to Folk’s (1974) levels (Fig. 5; n = 1,013) do

not show distribution patterns quite as consistently related to features such as depth or distance

offshore as does mean grain size. From north to south across the mouth of Bristol Bay is a swath

of very well to moderately well sorted samples, although these are interspersed with a few poorly

and very poorly sorted ones. From this area, sorting generally becomes poorer both toward the

head of the bay and southwestward toward the outer shelf. Northwest of the bay, a wide band of

very well sorted and well sorted samples parallels the coast offshore from Cape Newenham on

across the mouth of adjacent Kuskokwim Bay to Nunivak Island. Again, from this region sorting

grades to very poor and extremely poor across the shelf to the outer edge.

In addition to mean N, high and low resolution textural classes (Figs. 8, 9) also reveal the

overall fining of sediment with increased distance offshore. The latter scheme with its set of fewer

classes more effectively illustrates regional trends: The area of concentrated sampling in Togiak

Bay and vicinity shows extensive fine-scale variation, with considerable representation of all types

except gravel/sand/mud and gravel/mud. Near the Pribilof Islands, textures also vary

considerably, from gravel/sand to sand/mud, due possibly to local erosion and variation in depth

and current. Overall, however, samples reveal some large-scale patterns. The inner shelf (0-50 m)

has isolated areas of gravel and somewhat more gravel/sand near shore, changing to sand medially,

with sand/mud occurring occasionally farther offshore. The middle shelf (50-100 m) is largely

sand/mud extending in a broad band from southeast to northwest and generally following the

bathymetry. This pattern is disrupted by indications of a belt of sand along a line extending from

the Pribilof Islands to the western tip of Nunivak Island. Surficial sediments of the outer shelf
16

(100-200 m) are, again, largely sand/mud, disrupted by localized variation around the Pribilofs. 

Mud is common along the northwest margin of the middle and outer shelf portions of the study

area, from around St. Matthew Island westward.

Previous EBS Shelf Descriptions

Allowing for differences among sample classification methods, the data are in general

agreement with the large-scale characterizations of the eastern Bering shelf by Sharma (1974b) and

Naidu (1988; Fig. 2), particularly regarding the tendency of decreasing grain size with increasing

distance from the mainland shore. However, those studies presented generalizations of the

distribution of textural characteristics in the form of maps which spatially averaged sample data. 

Also, with regard to our study area, they were based on a smaller, more sparsely distributed set of

samples. Our data for individual sample locations naturally describe considerably finer spatial

detail and reveal, within average regional grading, textural variability such as that near the Pribilof

Islands or as observed by Johnson (1983) in shallow nearshore areas of Bristol Bay.

Advantages Accruing from the Database

In summary, the database presents a fairly complete, detailed representation of the

distribution of surficial sediment textural characteristics in the study area. This is expressed in

terms of a number of individual variables each likely reflected in some way in biological processes. 

Included in these data, we have specified sediment textural type according to each of two

classification schemes differentiating grain size distributions at different respective levels of

resolution. One level expresses greater detail in textural variation, while the other, although losing

some detail to data standardization, describes the greatest number of samples by a single textural
17

variable. Compared with previous large-scale studies, textural data are reported for a significantly

greater number of different sample locations in the study area. In addition, we have not spatially

averaged or smoothed data but instead present the values at the individual sample locations. This

expresses the greatest known detail in spatial variation while allowing flexibility in the design of

investigations of relationships with biological variables. These considerations all compellingly

support use of the EBSSED database for such purpose.

ACKNOWLEDGMENTS

We are indebted to those who provided us with unpublished original data and documents. 

In particular, Michael Brett-Surman, National Museum of Natural History, Smithsonian

Institution.; James V. Gardner, U.S.G.S. Menlo Park; and Robin Warnken, National Geophysical

Data Center, NOAA, provided considerable unpublished data. In addition, a number of people

assisted us greatly with information augmenting and explaining the data in these and other sources

and with suggestions on accessing existing pertinent data not already acquired: Janet L.

Armstrong, School of Fisheries, University of Washington; Richard T. Bachman, Space and Naval

Warfare Systems Center, San Diego, CA; Chirk Chu, School of Fisheries and Ocean Sciences,

University of Alaska, Fairbanks; Paul A. Fishman, Fishman Environmental Services, Portland, OR;

Mary Hollinger, National Oceanic Data Center, NOAA; Carla Moore, National Geophysical Data

Center, NOAA; and A. Sathy Naidu, School of Fisheries and Ocean Sciences, University of

Alaska, Fairbanks.
6

Includes those for literature referred to in the “Param. measures” field of database records.

18

CITATIONS6

Allen, J. R. L. 1985. Principles of Physical Sedimentology. George Allen & Unwin, London, UK. 

272 p.

Armstrong, D. A., J. L. Armstrong, G. Jensen, R. Palacios, and G. Williams. 1987. Distribution,

abundance, and biology of blue king and Korean hair crabs around the Pribilof Islands. U.S.

Dep. Commer., NOAA, OCSEAP Final Rep. 67:1-278.

Barnes, C. A., and T. G. Thompson. 1938. Physical and chemical investigations in the Bering Sea

and portions of the North Pacific Ocean. University of Washington Publications in

Oceanography 3(2):35-79 and Appendix 1-164. Seattle, WA.

Boyce, R. E. 1967. Electrical resistivity of modern marine sediment from the Bering Sea. Tech.

Pub. 6, U.S.N. Naval Undersea Warfare Cent., Marine Env. Div., San Diego, CA. 106 p.

Brenninkmeyer, B. M. 1982. Moment measures. Pages 555-557, in M. L. Schwartz (editor).

Encyclopedia of Beaches and Coastal Environments, Encyclopedia of Earth Sciences vol.

15. Hutchinson Ross, Stroudsberg, PA.

Buffington, E. C., A. J. Carsola, and R. S. Dietz. 1950. Oceanographic cruise to the Bering and

Chukchi Seas, summer 1949. Part 1. Sea floor studies. Rep. 204, U.S.N. Electronics

Lab., Applied Res. Div., San Diego, CA. 26 p.
19

Cimberg, R. L., D. P. Costa, and P. A. Fishman. 1986. Ecological characterization of shallow

subtidal habitats in the North Aleutian shelf. U.S. Dep. Commer., NOAA, OCSEAP Final

Rep. 44:437-646.

Emery, K. O., and H. Gould. 1948. A code for expressing grain size distribution. J. Sed.

Petrology 18(1):14-23.

Folk, R. L. 1954. The distinction between grain size and mineral composition in sedimentary rock

nomenclature. J. Geology 62:344-359. 

Folk, R. L. 1966. A review of grain-size parameters. Sedimentology 6:74-93.

Folk, R. L. 1974. Petrology of Sedimentary Rocks. Hemphill Publishing, Drawer M, University

Station, Austin, TX. 182 p.

Folk, R. L., and W. C. Ward. 1957. Brazos River Bar: a study in the significance of grain size

parameters. J. Sed. Petrology 27(1):3-26.

GeoSea Consulting (Canada) Ltd. 1999. NOAA: Bering Sea Particle Size and Shell Content

Analysis. Unpublished ms. Brentwood Bay, BC, Canada. 110 p. Available from the

authors, NMFS Alaska Fish. Sci. Cent., 7600 Sand Point Way NE., Seattle, WA 98115-

0070

Goddard, P., and G. Walters. 1998. 1994 Bottom trawl survey of the eastern Bering Sea

continental shelf. AFSC Processed Rep. 98-03, 153 p. NMFS Alaska Fish. Sci. Cent.,

7600 Sand Point Way NE., Seattle WA 98115-0070.

Horn, D. R., B. M. Horn, and M. N. Delach. 1967. Acoustic provinces of the North Pacific based

on deep-sea cores: a preliminary survey. Tech. Rep. 3, CU-3-67 NAVSHIPS NOOO24-

67-C-1186, Lamont Geological Observatory, Columbia Univ., Palisades, NY. 55 p.
20

Hoskin, C. M. 1977a. Unpublished data, Cruise OCS808 of NOAA ship Discoverer. Univ. of

Alaska Fairbanks. Archived as MGGID#41015001, Marine Geol. Inventory, Digital Grain

Size Database, U.S. Dep. Commer., NOAA, Natl. Geophys. Data Cent., Boulder, CO.

Hoskin, C. M. 1977b. Unpublished data, Cruise OCS818 of NOAA ship Miller Freeman. Univ.

of Alaska Fairbanks. Archived as MGGID#41025001, Marine Geol. Inventory, Digital

Grain Size Database, U.S. Dep. Commer., NOAA, Nat’l Geophys. Data Cent., Boulder,

CO.

Inman, D. L. 1952. Measures for describing the size distribution of sediments. J. Sed. Petrology

22(3):125-145.

Johnson, E. A. 1983. Textural and compositional sedimentary characteristics of the Southeastern

Bristol Bay continental shelf, Alaska. M.S. thesis. California State University, Northridge,

CA. 142 p.

Karl, H. A., P. R. Carlson, K. H. Johnson, and D. M. Rearic. 1987. Grain-size parameters and

constituent grain composition of surface sediment in Navarin Basin province, Bering Sea.

Open-File Rep. 87-64, U.S. Geol. Surv., Menlo Park, CA. 32 p.

Knebel, H. J. 1974. Holocene sedimentary framework, east-central Bering Sea continental shelf.

Pages 157-172, in Y. Herman (editor). Marine geology and oceanography of the Arctic

seas. Springer-Verlag, New York.

Krumbein, W. C. 1934. Size frequency distributions of sediments. J. Sed. Petrology 4:65-77.

Krumbein, W. C., and F. J. Pettijohn. 1938. Manual of Sedimentary Petrography. AppletonCentury-Crofts, Inc., New York. 549 p.
21

LaFond, E. C., R. S. Dietz, and D. W. Pritchard. 1949. Oceanographic measurements from the

U.S.S. Nereus on a cruise to the Bering and Chukchi Seas, 1947. Interim Report 91,

U.S.N. Electronics Lab., Research Div., San Diego, CA. 95 p.

Lisitsyn, A. P. 1959. Bottom sediments of the Bering Sea.. Pages 65-179, in P. L. Bezrukov

(editor). Geographical Description of the Bering Sea: Bottom Relief and Sediments. 

Acad. of Sci. of U.S.S.R., Transactions of the Oceanological Institute vol. 29. Moskow. 

Israel Prog. for Sci. Transl., Jerusalem, 1964.

Lisitsyn, A. P. 1966. Recent Sedimentation in the Bering Sea. Acad. of Sci. of U.S.S.R.,

Moskow. Israel Prog. for Sci. Transl., Jerusalem, 1969. 614 p.

Lotspeich, F. B. and F. H. Everest. 1981. A new method for reporting and interpreting textural

composition of spawning gravel. Res. Note PNW-369. Pacific Northwest Forest and Range

Exp. Sta., U.S. Forest Ser., Corvallis, OR. 11 p. 

McCammon, R. B. 1962. Efficiency of percentile measures for describing the mean size and

sorting of sedimentary particles. J. Geology 70:453-465.

McManus, D. A, V. Kolla, D. M. Hopkins, and C. H. Nelson. 1977. Distribution of Bottom

Sediments on the Continental Shelf, Northern Bering Sea. U.S. Geol. Surv. Prof. Paper

759-C. U.S. Govt. Printing Office, Washington, D.C. 31p. 

McMurray, G., A. H. Vogel, P. A. Fishman, D. A. Armstrong, and S. C. Jewett. 1984. 

Distribution of larval and juvenile red king crabs (Paralithodes camtschatica) in Bristol

Bay. U.S. Dep. Commer., NOAA, OCSEAP Final Rep. 53:267-477.
22

Naidu, A. S. 1985. Unpublished data. Univ. Alaska Fairbanks. Archived as MGGID#41995002,

Marine Geol. Inventory, Digital Grain Size Database, NOAA Nat’l Geophys. Data Cent.,

Boulder, CO.

Naidu, A. S. 1988. Marine surficial sediments. Section 1.4, in C. N. Ehler, D. J. Basta, T. F.

LaPointe, and G. C. Ray (editors). Bering, Chukchi, and Beaufort Seas coastal and ocean

zones strategic assessment: Data atlas. U.S. Dep. Commer., NOAA, Nat’l Ocean Ser.,

Off. Oceanog. and Mar. Assess., Ocean Assess. Div., Str. Assess. Br. Silver Spring, MD.

National Geophysical Data Center (NGDC), NOAA. 1986. Index to Marine Geological Samples. 

Data Announcement No. 86MGG10. Boulder, CO.

National Geophysical Data Center (NGDC) 1994a. 8/18/94, Index to Marine Geological Samples, 

http://www.ngdc.noaa.gov/mgg/curator/curator.html. Data contributed by Univ. 

Washington, Seattle, WA (Cruises BB269, TT051).

National Geophysical Data Center (NGDC) 1994b. 8/18/94, Index to Marine Geological Samples, 

http://www.ngdc.noaa.gov/mgg/curator/curator.html. Data contr. by USGS, Menlo Park,

CA, (Cruises D381BS, S476BS, S677BS).

Oshite, K., and G. D. Sharma. 1974. Contemporary depositional environment of the eastern

Bering Sea. Part 2. Distribution of recent diatoms on the eastern Bering shelf. Pages 541-

552, in D. W. Hood and E. J. Kelley (editors). Oceanography of the Bering Sea, with

emphasis on renewable resources. Inst. Mar. Sci., Univ. Alaska, Fairbanks.

Plumley, W. J., and D. H. Davis. 1956. Estimation of recent sediment size parameters from a

triangular diagram. J. Sed. Petrology 26(2):140-155.
23

Roberts, R. W. 1976. Bottom sediment granulometric data for the continental margins of the

Bering, Chukchi, East Siberian, Laptev and Beaufort Seas. Spec. Rep. No. 70., Dept. of

Oceanog., Univ. Washington, Seattle, WA. 228 p.

Selley, R. C. 1988. Applied Sedimentology. Academic Press, San Diego, CA. 446 p.

Sharma, G. D. 1974a. Geological oceanography of the Bering shelf. Pages 141-156, in Y.

Herman (editor). Marine Geology and Oceanography of the Arctic Seas. Springer-Verlag,

New York.

Sharma, G. D. 1974b. Contemporary depositional environment of the eastern Bering Sea. Part 1. 

Contemporary sedimentary regimes of the eastern Bering sea. Pages 517-540 and 551-

552, in D. W. Hood and E. J. Kelley (editors). Oceanography of the Bering Sea, with

emphasis on renewable resources. Inst. Mar. Sci., Univ. Alaska, Fairbanks.

Sharma, G. D. 1976. Unpublished data. Univ. Alaska Fairbanks. Archived as MGGID#41005001,

Marine Geol. Inventory, NOAA Nat’l Geophys. Data Cent., Boulder, CO.

Sharma, G. D., A. S. Naidu, and D. W. Hood. 1972. Bristol Bay: A model contemporary graded

shelf. Am. Assoc. Petrol. Geol. Bull. 56:2000-2012.

Shepard, F. P. 1954. Nomenclature based on sand-silt-clay ratios. J. Sed. Petr. 24(3):151-158.

Trefethen, J. M. 1950. Classification of Sediments. Am. J. Sci. 248(1):55-62.

Umbach, M. J. 1976. Hydrographic Manual. Fourth Edition. U.S. Dep. Commer., NOAA,

National Ocean Survey. Rockville, MD. 324 p.
24

U.S.N. Hydrographic Office (USN-H). 1955. Arctic Northwind Bering Sea. TR-46. Laboratory

analysis of bottom sediment samples collected by the U.S.Ss. Northwind, Burton Island,

and Edisto (Cruises 31501, 31505 and 31506), winter and spring 1955, unpub. ms. 335 p. 

Archived as MGGID#09995040, Marine Geol. Inventory, NOAA Nat’l Geophys. Data

Cent., Boulder, CO. 

U.S. Naval Oceanographic Office (USN-O), Marine Sciences Dept., Marine Surveys Div.,

Oceanographic Br., Washington, D.C. 1964. Oceanographic data report, Arctic 1960.

IMR-0-62-63, unpub. ms. 148 p.

Wentworth, C. K. 1922. A scale of grade and class terms for clastic sediments. J. Geology 

30:377-392.

Word, J. Q. 1977. An evaluation of benthic invertebrate sampling devices for investigating

feeding habits of fish. Pages 43-55, in C. A. Simenstad and S. J. Lipovsky (editors). Fish

Food Habits Studies. 1st Pacific Northwest Technical Workshop. Workshop Proceedings,

Astoria, Oregon, Oct. 13-15, 1976, WSG-WO 77-2. Washington Sea Grant, Div. Mar.

Res., Univ. Washington HG-30. Seattle, WA.
7Bachman, R.T. Personal comm. 3/7/95. Sediment sample grain size statistics, unpublished data

set “Ber 70". U.S.N. Naval Command, Control, and Ocean Surveil. Cent.; Research, Devel., Test, and

Eval. Div.; San Diego, CA.

8Gardner, J.V. Personal comm. 11/21/94. Sediment sample grain size statistics, unpublished data,

Cruises S4-76 and S6-77. U.S. Geol. Surv., Menlo Park, CA.

25

Table 1. Data sources used to assemble the EBSSED database of surficial sediment textures of the eastern

Bering Sea shelf. Complete citations for sources are in the Citations section or footnotes.

Source Year of

Sampling

Sampling Gear Total

Samples Grab Core Dredge Unknown

Armstrong et al. (1987) 1983 81 81

Bachman (1995)71970 1 1

Barnes and Thompson

(1938)

1934 31 31

Boyce (1967) 1965 3 2 5

Buffington et al. (1950) 1949 6 2 7 1 16

Cimberg et al. (1986) 1982 30 30

Gardner (1994)81976-1977 26 60 86

GeoSea Consulting Ltd.

(1999)

1997 114 114

Horn et al. (1967) 1965 1 1

Hoskin (1977a) 1975 31 31

Hoskin (1977b) 1975 39 39

Johnson (1983) 1980-1981 130 130

Karl et al. (1987) 1980-1982 14 54 31 99

NGDC (1994a) 1960-1970 2 4 6

NGDC (1994b) 1976-1981 2 9 11

LaFond et al. (1949) 1947 6 4 10

McMurray et al. (1984) 1983 53 53

Naidu (1985) 1976

N.A.

7

22

7

22
Source Year of

Sampling

Sampling Gear Total

Samples Grab Core Dredge Unknown

9Smithsonian Institution, Nat’l Museum of Nat. Hist., Washington, D.C. (SI-NMNH). 1994. 

Paleobiology Sediments Master File Database, via pers. comm. 11/29/94 with Michael Brett-Surman,

Dept. of Paleobiology.

10Scripps Institution of Oceanography, Geological Collections (SIO-GC). 1995. Sample

descriptions from cruises MUKB on R/V Baird and MUKH on R/V Horizon, via pers. comm. 1/30/95

with Warren Smith, Geological Collections. San Diego, CA.

26

SI-NMNH (1994)91985-1991 1458 1 1459

Oshite and Sharma (1974) 1960 17 17

Roberts (1976) 1961-1970 140 79 219

SIO-GC (1995)10 1957 2 2 4

Sharma (1976) 1968 80 80

USN-H (1955) 1955 31 31

USN-O (1964) 1960 4 4

Totals 2300 217 7 63 2587
27

Table 2. Scale by Wentworth (1922) classifying sediment particles according to the diameter expressed in

units of N (phi, the negative log2 of the diameter in millimeters).

Major Grade

Phi (N) limits Wentworth

 size class Lower Upper

gravel

<-8 -8 boulder

 -8 -6 cobble

 -6 -2 pebble

 -2 -1 granule

sand

 -1 0 very coarse sand

 0 1 coarse sand

 1 2 medium sand

 2 3 fine sand

 3 4 very fine sand

mud

 4 5 coarse silt

 5 6 medium silt

 6 7 fine silt

 7 8 very fine silt

 8 >8 clay
28

Table 3. Frequency of occurrence of high resolution textural classes among 903 sediment samples in the

eastern Bering Sea study area. One of 15 class names was unambiguously assigned to each

sample having detailed granulometric data, according to gravel-sand-mud composition as shown

on Folk’s (1954) ternary diagram (Figs. 6a, 7).

High resolution class (code) Frequency

gravel (G) 5

sandy gravel (sG) 42

muddy sandy gravel (msG) 10

muddy gravel (mG) 1

gravelly sand (gS) 34

gravelly muddy sand (gmS) 11

gravelly mud (gM) 3

slightly gravelly sand ([g]S) 115

slightly gravelly muddy sand ([g]mS) 43

slightly gravelly sandy mud ([g]sM) 15

slightly gravelly mud ([g]M) 2

sand (S) 155

muddy sand (mS) 203

sandy mud (sM) 218

mud (M) 46
29

Table 4. Frequency of occurrence of low resolution textural classes among 2457 samples in the eastern

Bering Sea study area. One of 7 class names was unambiguously assigned to each sample having

data sufficient for low-resolution classification, irrespective of the original method of description. 

Classification was based on the (significant) presence of gravel, sand, or mud (see text), illustrated

by a modified version of Folk’s (1954) ternary diagram (Fig. 6b).

Low resolution class (code) Frequency

gravel (1) 174

gravel/sand (2) 401

gravel/sand/mud (3) 52

gravel/mud (4) 10

sand (5) 873

sand/mud (6) 759

mud (7) 188
30

Appendix. Data dictionary for the EBSSED database of surficial sediments of the eastern Bering 

Sea.

Field Name No. of

Records

Comments

ID 2587 unique record identifier (Ref. # + Cruise/Field # + Sta. # +

Sample #)

Latitude 2587 decimal degree format (±dd.dd)

Longitude 2587 decimal degree format (±ddd.dd)

Depth (m) 2478 water depth, meters

Gear 2524 sampling gear

Depth in core (cm) 270 distance of core subsample below sediment surface (cm)

Source 2587 original data source (see Table 1 and “Citations”)

Institution 2587 sponsoring institution

Reference # 1459 NOAA/NOS hydrographic survey # (SI-NMNH, 1994)

Cruise/Field # 2300 cruise or operation identifier assigned by investigator

Station # 2106 station identifier assigned by investigator

Sample # 821 sample identifier assigned by investigator

Ship 2477 ship serving as sampling platform

Date 2554 date sample collected (yymmdd; “99” for month or day

indicates “no data”.)

Comment 498 pertinent information regarding sample, by original

investigators or EBSSED database authors

Mean N 994 mean grain diameter (N units) 

Sorting 1013 sorting coefficient (standard deviation of grain diameter in

N units)

Skewness 823 index of symmetry of grain diameter distribution

Kurtosis 681 index of peakedness of grain diameter distribution

Param. measures 1013 measures used as grain size parameters

% coarse 1033 weight fraction of gravel and sand

% gravel 903 weight fraction
Field Name No. of

Records

Comments

31

% sand 903 weight fraction

% mud 1033 weight fraction of silt and clay

% silt 866 weight fraction

% clay 866 weight fraction

Sediment type 1555 field description of texture, determining low resolution class

when grain sizes not measured in laboratory

High res. code 903 See Table 4 and Figure 2 for classes, codes.

Low res. code 2457 See Table 3 and Figure 2 for classes, codes.
32

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34

Figure 2. Surficial sediment textural characteristics according to Naidu (1988) for the portion of the continental shelf which is the focus of the

EBSSED database. The number-code of each polygon indicates Folk’s (1954) sorting level from key.
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Figure 4. Mean N at 994 sample locations, shown according to 7 divisions of the range of sample values illustrating the spatial

variation. Inset is larger-scale view of St. George and St. Paul Pribilof Islands and vicinity.
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Figure 5. Degree of sorting at 1,013 sample locations, according to Folk’s (1966) sorting levels. Inset is larger-scale view of St.

George and St. Paul Pribilof Islands and vicinity.
Figure 6. Ternary diagrams illustrating classification of sediment samples into descriptive textural

classes according to gravel-sand-mud composition. Sample composition by a size grade equals 100% at

the respective designated vertex of the triangle, thence decreases as the distance perpendicular from the

opposite side (0%). a. High resolution classes: Folk’s (1954) 15 standard textural classes used to assign

high resolution class names to 903 samples originally analyzed by quantitative laboratory methods. 

Labels refer to class names in Table 3. b. Low resolution classes: A simplified version of Folk’s standard

ternary diagram, used in assigning low resolution class names to 2,457 samples in the EBSSED database.

Some internal boundaries have been removed to accomodate samples described using less detailed

visual/tactile field methods, resulting in 7 distinct classes. Table 4 gives class names. 
39

Sand : Mud Ratio

Percent of Gravel

1 : 9 9 : 1

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Figure 7. Plot of 903 grain size analysis samples on Folk’s (1954) standard 15-class

gravel-sand-mud composition diagram. Because composition (%) is represented to

scale, the boundary line (at 0.01% gravel) differentiating slightly gravelly sediment

types from those with essentially no gravel cannot be distinguished from the 0%-gravel

base of the diagram.
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$T mud

Figure 8. High resolution textural class of grain size analysis samples (n = 903). Inset is larger-scale view of St. George and St.

Paul Pribilof Islands and vicinity.
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Textural Classes

gravel

#S

#S gravel & sand

#S gravel, sand, mud

gravel & mud

#S

#S sand

#S sand & mud

mud

#S

Figure 9. Low resolution textural class of grain size analysis and field description samples (n = 2,457). Inset is larger-scale view

of Togiak Bay and vicinity.
RECENT TECHNICAL MEMORANDUMS

Copies of this and other NOAA Technical Memorandums are available from the

National Technical Information Service, 5285 Port Royal Road, Springfield, VA 22167

(web site: www.ntis.gov). Paper and microfiche copies vary in price. 

AFSC103 RUGH, D. J., M. M. MUTO, S. E. MOORE, and D. P. DEMASTER. 1999. Status review of the eastern

north Pacific stock of gray whales, 93 p. NTIS No. PB99-172678.

102 BUCKLEY, T. W., G. E. TYLER, D. M. SMITH, and P. A. LIVINGSTON. 1999. Food habits of some

commercially important groundfish off the coasts of California, Oregon, Washington, and British

Columbia, 173 p. NTIS No. PB99-166563.

101 MCELDERRY, H., W. A. KARP, J. TWOMEY, M. MERKLEIN, V. CORNISH, and M. SAUNDERS. 1999.

Proceedings of the first biennial Canada/U.S. observer program workshop, 113 p. NTIS No. PB99-

146482. 

100 SEASE, J. L., and T. R. LOUGHLIN. 1999. Aerial and land-based surveys of Steller sea lions

(Eumetopias jubatus) in Alaska, June and July 1997 and 1998, 61 p. NTIS No. PB99-140618.

99 SEASE, J. L., J. M. STRICK, R. L. MERRICK, and J. P. LEWIS. 1999. Aerial and land-based surveys of

Steller sea lions (Eumetopias jubatus) in Alaska, June and July 1996, 43 p. NTIS No. PB99-134462.

98 LAUTH, R. R. 1999. The 1997 Pacific West Coast upper continental slope trawl survey of groundfish

resources off Washington, Oregon, and California: Estimates of distribution, abundance, and length

composition, 284 p. NTIS No. PB99-133043.

97 HILL, P. S., and D. P. DEMASTER. 1998. Alaska marine mammal stock assessments, 1998, 166 p. NTIS

No. PB99-130791.

96 WING, B. L., M. M. MASUDA, C. M. GUTHRIE III, and J. H. HELLE. 1998. Some size relationships and

genetic variability of Atlantic salmon (Salmo salar) escapees captured in Alaska fisheries, 1990-95, 32 p.

NTIS No. PB99-118697.

95 ORR, J. W., M. A. BROWN, and D. C. BAKER. 1998. Guide to rockfishes (Scorpaenidae) of the genera

Sebastes, Sebastolobus, and Adelosebastes of the northeast Pacific Ocean, 46 p. NTIS No. PB99-

114217.

94 THROWER, F., R. MARTIN, and R. HEINTZ. 1998. Effect of seawater entry date on 24-hour plasma

sodium concentration and survival of juvenile spring chinook salmon (Oncorhynchus tshawytscha) reared

in marine net-pens, 18 p. NTIS No. PB98-173545.

93 MURPHY, J. M., N. E. MALONEY, and B. L. WING. 1998. Distribution and abundance of zooplankton

in the north Pacific Subarctic Frontal Zone and adjacent water masses, 31 p. NTIS No. PB98-159163.

92 FRITZ, L. W., and S. A. LOWE. 1998. Seasonal distributions of Atka mackerel ( Pleurogrammus

monopterygius) in commercially-fished areas of the Aleutian Islands and Gulf of Alaska, 29 p. NTIS No.

PB98-153703.

91 WING, B. L., and J. J. PELLA. 1998. Time series analyses of climatological records from Auke Bay,

Alaska, 90 p. NTIS No. PB98-149206.

90 PACUNSKI, R. E., P. A. LIVINGSTON, and B. S. MILLER. 1998. Food of flathead sole Hippoglossoides

elassodon in the eastern Bering Sea, 27 p. NTIS No. PB98-148679. 

89 WILKINS, M. E., M. ZIMMERMANN, and K. L. WEINBERG. 1998. The 1995 Pacific west coast bottom

trawl survey of groundfish resources: Estimates of distribution, abundance, and length and age

composition, 138 p. plus Appendices. NTIS No. PB98-136252.