The Biological Effects of Suspended and Bedded Sediment (SABS) in Aquatic Systems:

A Review

Internal Report

August 20, 2003

by

Walter Berry, Norman Rubinstein, and Brian Melzian

United States Environmental Protection Agency

Office of Research and Development

National Health and Environmental Effects Laboratory

Atlantic Ecology Division

27 Tarzwell Drive

 Narragansett, RI 02882

Brian Hill

United States Environmental Protection Agency

Office of Research and Development

National Health and Environmental Effects Laboratory

Midcontinent Ecology Division

6201 Congdon Boulevard

 Duluth, MN 55804
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Executive Summary

A review was conducted of the available literature on the biological effects of suspended

and bedded sediment (SABS), and the current state standards for SABS to assess the feasability

of developing national scientifically-defensible SABS criteria using the traditional

“toxicological” dose-response approach. The review has the following take home messages:

1) Some useful models for the biological effects of SABS exist and others are under

development. As the water clarity criteria for the protection of SAV in the Chesapeake show,

the traditional toxicological dose-response approach can be used if a specific species from a

particular habitat is to be protected and the required dose-response data are available. 

Generalizations are difficult because biological response to both increased suspended sediment

and increased bedded sediment varies with species and sediment characteristics.

2) After additional research it may be possible to develop national scientifically-defensible

SABS criteria using the traditional “toxicological” dose-response approach. These criteria will

presumably have to incorporate some habitat-specificity in order to be widely applicable.

3) Some habitats that have not been well studied (in terms of their sensitivity to SABS) deserve

more study, especially those habitats with moderate and variable amounts of SABS.

4) Many states have set standards for SABS, but there is little consistency among them.
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Table of Contents

Executive Summary................................................................................................................2

Introduction.............................................................................................................................4

Approaches to setting numerical targets.................................................................................5

Conceptual model of the biological effects of SABS.............................................................7

Effects of SABS.....................................................................................................................10

Summaries of the Effects of SABS............................................................................10

Effects on invertebrates..............................................................................................11

Effects on corals.........................................................................................................12

Effects on plants.........................................................................................................13

Effects on fish.............................................................................................................14

Effects on wildlife.......................................................................................................16

Modeling the effects of increased SABS................................................................................16

Current criteria for SABS.......................................................................................................17

“State of the Science” for SABS criteria setting....................................................................18

Take home messages..............................................................................................................20

Acknowledgments..................................................................................................................20

References...............................................................................................................................22

Tables and Figures..................................................................................................................30

Appendix A: “Channel Suspended Sediment and Fisheries: A Concise Guide to Impacts”

(Newcombe, 1997)

Appendix B: Model of the effects of turbidity on fishes (C.P. Newcombe, Personal

Communication)

Appendix C: Available data on the effects of suspended sediments on biota

Appendix D: Revised model of the effects of suspended sediments on estuarine fishes (C.P.

Newcombe, Personal Communication) 

Appendix E: Summary figures from Wilber and Clarke (2001)

Appendix F: Summary of state standards for suspended and bedded sediments. Prepared by

Kate Sullivan for the U.S.EPA, Office of Water. 
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Introduction

Imbalance in loading of suspended and bedded sediment (SABS) to aquatic systems is

now considered one of the greatest causes of water quality impairment in the Nation (U.S.EPA,

2003a). Turbidity, suspended solids, sediment, and siltation have been consistently listed in

305(b) Water Quality Reports in rivers and streams, lakes, reservoirs, ponds, wetlands, and

ocean shoreline waters ( Table 1). In 1998, approximately 40% of assessed river miles in the

U.S. had problems arising from sediment stress (U.S.EPA, 2000). The effects of sediments on

receiving water ecosystems are complex and multi-dimensional, and further compounded by the

fact that sediment flux is a natural and vital process for aquatic systems. We use the term

sediment imbalance here to connote significant changes in normal sediment loading to aquatic

systems, changes that typically result in increases in sedimentation but can also result in

reductions in sedimentation when compared to natural patterns. Sediment stress results from a

change in sediment load originating from within the watershed, ultimately compromising the

ecological integrity of the aquatic environment (Nietch and Borst, 2001). 

Although the lack of sediment supply due to dam construction, bank modification, water

diversion, and sea-level rise is a serious problem in some areas, leading to loss of wetlands (e.g.,

Boesch et al., 1994), lack of sediment was considered more of a physical than biological concern

for the purpose of this review. Since very few studies have found organisms with a need or even

a preference for increased suspended sediment or sedimentation in the field or laboratory (Cyrus

and Blaber, 1987a,b) this paper will concentrate on the deleterious effects of increases in

sediment supply to watersheds.

The impacts of suspended and bedded sediment in surface and coastal waters have been

reviewed by a number of authors. Recent reviews of sedimentation in aquatic systems include 

Waters (1995), Naiman and Baily (1998), Reid and Dunne (1996), Wilber and Clarke (2001),

and Nietch and Borst (2001). The vast majority of information presented in these reviews

pertains to sources and exposure regimes of sediments as a function of geomorphology,

erosional processes, catchment basin properties, and other geophysical factors (e.g., Leopold et

al., 1964).

In this review we focus on direct and indirect biological effects of sediment (suspended

and bedded) imbalance in aquatic systems. The literature on suspended sediment is larger and

better summarized than that for bedded sediment, and that is reflected in the greater emphasis on

the effects of suspended sediment in this review. We further restrict this review to “clean”

(uncontaminated) sediments and do not address biological effects caused by chemical toxicants

associated with sediments. In addition to toxicants associated directly with sediments and the

sediments themselves, animals in a natural environment are exposed to mixtures of chemical and

physical stressors which can combine to cause adverse effects that may not be observed when a
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stressor (like SABS) is considered individually (Herbranson et al., 2003a). This problem will be

considered in the modeling section.

We focus on those studies that describe quantitative dose-response relationships of

aquatic organisms exposed to suspended and bedded sediments. One of the goals of this review

is to provide a simple, practical compilation of referenced sediment-effects (dose-response)

information useful for development of sediment Total Maximum Daily Loads (TMDLs) for

receiving waters (see U.S.EPA, 1999 for a description of the TMDL process) and suspended and

bedded sediment criteria.

Many of the reviews of the clean sediment literature have been listed above. Most of

them have been limited in scope to a particular habitat (e.g., Waters, 1995, dealt with streams) or

taxonomic group (e.g., Newcombe and Jensen, 1996, dealt with fishes). This paper is not

intended to be an exhaustive review of the primary literature, rather it summarizes the existing

literature and models of the biological effects of suspended and bedded sediments on a wide

range of organisms from various habitats. We also provide some useful tools for resource

managers by providing summaries of existing models for the biological effects of suspended and

bedded sediments, providing a table of existing data on the biological effects of suspended

sediment, and a table of the current criteria and standards for bedded and suspended sediment.

Approaches to setting numerical targets

 Within the regulatory community, the terms “guidelines”, “criteria”, and “standards” all

have specific regulatory meaning. Guidelines do not necessarily have any regulatory authority. 

Criteria are set by U.S.EPA as recommendations, which have the force of law when adopted by

states and tribes as standards. The term “criteria” will be used throughout this review to take the

place of all three of these terms. 

The U.S.EPA’s Office of Water is presently considering how to develop criteria for

SABS. The potential approaches for criteria development that U.S.EPA’s Office of Water is

considering investigating in the Strategy for Developing Water Quality Criteria for SABS

include the following:

1. State-by-State Reference Condition Criteria Derivation Approach

2. Conditional Probability Approach to Establishing Thresholds 

3. Toxicological Dose-Response Approach

4. Relative Bed Stability and Sedimentation Approach

5. Rosgen Geomorphological Approach

6. Water Body Use Functional Approach

7. Combinations of above approaches

The purpose of this document is to review the data available to support the development

of criteria for SABS using the toxicological dose-response approach. This should help provide a
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basis for deciding if it will be possible to develop SABS criteria using this approach. 

Furthermore, an understanding of the mechanisms of action of SABS on biota complements the

inferential data generated via the field data-associative approaches (Suter et al., 2002). This

understanding has been referred to as “the missing link” between excess sediment and the

designated use of a water body (Kuhnle and Simon, 2000; Kuhnle et al., 2001).

We propose the following steps, which correspond with the initial steps for TMDL

development, for setting SABS criteria using the toxicological approach: 

1) Develop a conceptual model outlining the ecological processes affected by SABS for a

particular class of water body,

2) Choose the ecological processes, species or groups of species, and designated uses

deemed desirable for protection, and

3) Develop numerical targets for protecting the ecological processes, species or groups of

species, and designated uses deemed desirable for protection based on the correlations

between SABS and biotic response.

 There is a need for habitat classification in order for a program attempting to develop

criteria or TMDLs for SABS to be successful because different sites have different processes

involving SABS, and different tolerance levels depending on the habitat. The amount of

suspended sediment tolerated in a mountain stream may be much different from that tolerated in

the Mississippi River. Even within habitats there may be great variation in the effect of SABS. 

This need is discussed in detail in the Aquatic Stressors Framework document (U.S.EPA,

2002a), and was a continuing theme in the peer review comments on the Framework. A very

general example of a conceptual model of the biological effects of SABS is presented later in

this document. It may be that with a better understanding of the effects of SABS in the

environment, the need for site-specificity in conceptual models for SABS will not be as great as

previously thought.

The second step in the process, deciding which species or designated uses to protect, is

largely a management decision, and outside the purview of this document. The simplest

approach is to “protect everything”, that is, to set the criteria or TMDL at a level protective of

the most sensitive aquatic organisms. This is roughly equivalent to making sure that SABS do

not exceed the background levels used in the reference approach. Another approach is to

“protect most everything”, as is done for the water quality criteria, which attempt to be

protective of 95% of the genera tested (Stephan et al., 1985). An alternate approach is to choose

the most sensitive of the biota which are deemed important. This requires a value judgment. 

The role of science is to determine which parts of the ecosystem are the most sensitive, and to

develop the information that can be used to establish target levels with the desired level of

protection and uncertainty associated with them.

An investigation into the science required for the third step in the process, developing

effects-based target levels for protection from SABS, forms the bulk of this review. SABS have
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many impacts in aquatic ecosystems, and effects on biota vary considerably among habitats. 

However, there are dose-response models for some species in some habitats, and criteria have

been developed for their protection (e.g., British Columbia Guidelines in Caux et al., 1997,

Chesapeake Bay Water Clarity Guidelines in U.S.EPA, 2003b). Whether the science behind

these criteria is adequate is the subject of debate. Some investigators (e.g., Newcombe and

Jensen, 1996) maintain that there are empirical models presently available that can be used to

predict the effects of SABS, and thus to develop effects-based guidelines. Others maintain that

the models and data that are now available are not adequate for effects-based criteria

development, with the possible exception of salmonid protection in streams (e.g., Wilber and

Clarke, 2001) and SAV protection in the Chesapeake (U.S.EPA, 2003b). 

Conceptual model of the biological effects of SABS

Organizing a broad review of the data on the effects of SABS is difficult because SABS

can have effects in a wide range of habitats, including streams, rivers, lakes, estuaries, wetlands,

coral reefs, and beaches. Some of these habitats are more well-studied than others. There are

many studies involving streams, for example, while there are very few studies of the biological

effects of SABS on beaches. Within each of these habitats live many types of animals and

plants, many of which are vulnerable to effects from SABS. In this section we will present a

general conceptual model. Later sections of this review will be organized by taxonomic groups:

plants, benthic invertebrates, fish, and aquatic-dependent wildlife. 

 A conceptual model of the movement and effects of SABS is shown in Figure 1. In this

model, sediments enter waterways through a wide variety of transport mechanisms, including

surface water transport, bank sloughing, and atmospheric deposition. Once in the system,

resuspension and deposition can “recycle” sediments, reintroducing them into the water column

where they can exert water column effects, and then redepositing them where they can have

further effects on the benthos. Anthropogenic activities which enhance erosional processes 

(e.g., forestry, mining, urban development, agriculture, dam construction) are among the most

pervasive causes of sediment imbalance in aquatic systems (Waters, 1995, Nietch and Borst,

2001). Dredging activities can also lead to increased suspended sediment and deposition, both at

the dredging site and the disposal site (Wilber and Clarke, 2001).

The biological effects of SABS on estuarine environments were reviewed by Wilber and

Clarke (2001) and will only briefly be summarized here. Further discussion will be found later

in the sections dealing with effects on the various taxonomic groups. Excessive sediments in

aquatic systems contribute to increased turbidity leading to altered light regimes which can

directly impact primary productivity, species distribution, behavior, feeding, reproduction, and

survival of aquatic biota. Reduced light can reduce production of phytoplankton, submerged

aquatic vegetation, and the zooxanthellae in corals. Reduced light and increased turbidity can

also affect the feeding ability and movements of fish, especially larval fish. Larger fish may be

able to reduce some of these effects by avoiding low visibility water. Wildlife may also have

trouble hunting in turbid water, but like some fish they may be able to avoid some short -term

turbidity events by relocating. Humans are also affected by the lack of water clarity - turbid
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water is generally not as aesthetically pleasing as clean for swimming or other recreational

activities, or for drinking water.

Other direct effects of increased SABS include physical abrasion, and clogging of

filtration and respiratory organs. The concentrations of suspended sediment required to cause

these sorts of effects are generally very high, but may occur in certain situations such as near

dredges (Wilber and Clarke, 2001). In extreme cases, excess SABS can cause burial and

smothering of infaunal or epibenthic organisms. Most estuarine benthic organisms are adapted

to living in an environment subject to periodic resuspension of sediment and can dig out from

under a small amount of sediment (Maurer, 1986). Demersal eggs may be particularly

vulnerable, however, as only a few millimeters of deposited sediment may prevent them from

hatching (D. Nelson, personal communication).

Some of the most important indirect effects of SABS in estuarine and marine habitats

relate to loss of primary and secondary production. Reductions in primary production effects

primary consumers, which in turn effects secondary consumers, and on up the food chain. 

Eventually these effects reach even the top predators, such as eagles and humans.

 The effects of SABS in streams were reviewed by Waters (1995). SABS have two

major avenues of action in streams and rivers: 1) direct effects on biota and 2) direct effects on

physical habitat, which results in indirect effects on biota. Examples of direct effects on biota

include suppression of photosynthesis by shading primary producers; increased drifting of, and

consequent predation on, benthic invertebrates; and shifts to turbidity-tolerant fish communities. 

Indirect effects on biota will occur as the biotic assemblages that rely upon aquatic habitat for

reproduction, feeding, and cover are adversely affected by habitat loss or degradation of this

habitat. A noteworthy example of indirect effects of SABS in streams and rivers is the loss of

spawning habitat for salmonid fishes by an increase in embeddedness, caused by the entrapment

of fine material in the gravel. Increased sedimentation can limit the amount of oxygen in the

spawning beds which can reduce hatching success, or trap the fry in the sediment after hatching.

The effects of SABS in streams and rivers span the scales of biota. The biological

responses to this stressor at a site are related to site-specific effects (turbidity, shading, substrate

embeddedness) and to the cumulative loadings of sediments from the catchment above the site. 

Additionally, the effects of these biological responses at sites are cumulative for the entire

catchment, such that catchment-wide assessments of impacts are possible based on the

cumulative nature of the stressor. These cumulative effects might show a threshold response, a

multiplicative response, or other patterns, when acting on habitats important and unimportant for

the various life history stages of a species. There might also be a threshold effect in the case of

an extremely mobile fish species, or one that depends upon habitat refugia that are relatively

rare.

A widely applicable model of the effects of SABS might be expected to have parameters

for different habitats and species which could be plugged in for specific situations. In fact there

is little hard evidence in the literature that species from different habitats have different SABS

requirements. This is largely because there have been very few studies that compare species
9

from different habitats in the same study, and given the wide range of experimental designs used

in the literature it is very difficult to make comparisons between studies. 

One way to conduct a between-habitat comparison would be to use the models in

Appendix A and D from Newcombe and Jensen (1996) and compare the models from the adult

salmonids (which we might assume to be the most sensitive of the groups of adult fishes: Model

2) with the adult freshwater nonsalmonids (which might be assumed to have an intermediate

sensitivity: Model 6) and the adult estuarine fishes (which might be assumed to be the least

sensitive of the three: Model 5). However, if the empirical data that have been used to generate

the models are compared (Newcombe and Jensen, 1996: Figures 2a, 5a and 6a), it is clear that

there are not enough data to make a rigorous comparison between the models. 

As stated above, another way to make a comparison between habitats is to expose

organisms that live in different habitats to suspended sediments using identical experimental

protocols. These types of experiments have been conducted at least twice. McFarland and

Peddicord (1980) exposed a number of organisms to varying levels of kaolin in suspension. 

They found that the organisms restricted to muddy bottoms were very insensitive to high

suspended clay concentrations. Some open water fish, fouling organisms, and sandy bottom

epifauna were relatively sensitive. However, there were tolerant species identified from both

groups. One particularly interesting comparison was between two members of the same genus. 

Mytilus californianus, a mussel from rocky coastal environments was more sensitive than the

closely related blue mussel, Mytilus edulis, usually found in bays and harbors, which may be

more turbid.

Cyrus and Blaber (1987a) examined, in the laboratory, the turbidity preferences of the

juveniles of 10 species of fish which inhabit a large estuarine system in southeastern Africa. 

They compared these preferences to the field abundances of the same species in habitats with

different turbidities. They found that the turbidity preferences of the fishes varied from species

to species. Species which were typically found in highly turbid habitats generally preferred

turbid water in the laboratory. Species which were found in clearer water in the field generally

preferred clearer water in the laboratory. Both of these studies seem to indicate that the expected

relationship between habitat and SABS tolerance exists. However, much more of this sort of

experimental work needs to be done if this toxicological approach is to be used. In particular, it

would be useful to conduct additional studies of closely related species that live in habitats with

different levels of SABS. 

Another reason that it is difficult to compare the effects of SABS between habitats is that

most of the research on the effects of SABS has been done in streams. This is because some of

the most obvious consequences of elevated SABS are in stream systems, often leading to

complete loss of salmonid fisheries. Many miles of streams in the U.S. are listed as not meeting

designated uses (303(b) reports), but other habitats are also impacted by increased SABS (Table

1). Of these coral reefs are among the most studied (Appendix C and Coral Reef section of this

document). There have been far fewer studies done in freshwater river and lake habitats and

estuaries, but several indicate that the biota in these habitats can be very sensitive to increases in

SABS.
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Some lake species, like the Bear Lake sculpin for example, require large cobble and

boulders for spawning (Ruzycki et al, 1998). Gravel and sand embedded habitats are not

suitable for spawning. An increase of the supply of sand to this system could further restrict the

habitat of this endemic species. One additional problem in lake habitats is that it might take

them much longer to recover from increased sedimentation than it takes for a flashy stream to

recover. Spring freshets can resuspend fine materials from streams, and move them down

stream, thus restoring a coarse-grained bottom. This is less likely to happen in a lake. 

Effects of SABS

Summaries of the Effects of SABS

Summarizing effects data for SABS is difficult for several reasons. One reason is that

there is not one agreed-upon measurement for SABS. Caux et al. (1997) provide an excellent

discussion of the various methods of measuring suspended sediments. Suspended sediments

contribute to turbidity and thus affect light transmission through the water column (Waters,

1995). Turbidity is an optical property of water resulting in a decrease in light transmission due

to absorption and scattering. Consequently turbidity is a key water quality parameter in aquatic

systems in that it has a predominant influence on the compensation point (the depth at which

photosynthesis equals respiration in plants) and is therefore a critical determinant in the

distribution of submerged aquatic vegetation (SAV) (Batuik, et al., 1992). The correlation of

turbidity with concentrations of suspended solids (mg/L) is impractical because the size, shape,

and refractive index of particulate material affect turbidity but are not directly related to the

concentration of suspended solids (Caux et al., 1997), and thus the correlation is site-specific. 

Various measurements are used for bedded sediments as well. These include depth of deposition

within a given time period, percent fines, geometric mean diameter, and Fredle number (Caux et

al., 1997). (Fredle number is an index of permeability that has been found to correlate well with

survival-to-emergence of salmon and trout (Lotspeich and Everest, 1981)).

Another reason summarizing effects data for SABS is difficult is that there are no

standard durations for SABS effects testing. Both the duration (Newcombe and MacDonald,

1991) and frequency (Shaw and Richardson, 2001) of sediment exposures are important. For

example, some species are able to recolonize between sediment events, while some other species

may not be able to recover before the next event (Yount and Nimmi, 1990). 

 Newcombe and MacDonald (1991) recognized that the appropriate way to report data

for the effects of suspended sediment on aquatic organisms was to include information on

duration of exposure, as well as exposure concentration. Up until that point, the importance of

duration of exposure had been largely overlooked. They summarized, in graphical and tabular

form, much of the available data on the effects of SABS on fish and invertebrates. Newcombe

and Jensen (1996) presented an extensive data table of the effects of SABS on fish, and went a

step further developing empirical models of the effects of SABS on fish. Newcombe also

developed a model for the effects of SABS on aquatic invertebrates and flora (Newcombe, 1997)
11

and another dealing with the effects of diminished water clarity on fish (Newcombe, 2003). 

These models are included in Appendices A and B.

A recent review of the biological effects of suspended sediments on fish and shellfish

was conducted by Wilber and Clarke (2001). Their paper synthesized the results of studies that

report the dose-response relationships of estuarine aquatic organisms to suspended sediments

and then related those findings to sediment conditions associated with dredging projects. Doseresponse graphs were modified from Newcombe and Jensen (1996) to provide an easy reference

for estimating biological responses to suspended sediments. Wilber and Clarke (2001) also

provide tables that depict biological response as a function of suspended sediment exposure

(sediment concentration and duration). Biological response categories reported by Wilber and

Clarke (2001) include: no effect, behavioral, sub-lethal, and lethal effects. In this review

(Appendix C) we have expanded the tables of Wilber and Clarke (2001) using data from other

studies to include fresh water species, corals, and aquatic plants. Studies which did not include

measurements of total suspended solids (TSS) were excluded from the tables. For a recent

review of the effects of turbidity on fishes, see Newcombe (2003).

Effects on invertebrates

Elevated levels of SABS have been shown to have wide ranging effects on both pelagic

and benthic invertebrates (Cordone and Kelly 1961; Maurer et al., 1986; Peddicord,1980; 

Waters, 1995; Wilber and Clarke, 2001). Effects can be classified as having a direct impact on

the organism due to abrasion, clogging of filtration mechanisms thereby interfering with

ingestion and respiration, and in extreme cases smothering and burial resulting in mortality.

Indirect effects stem primarily from light attenuation leading to changes in feeding efficiency

and behavior (i.e., drift and avoidance) and alteration of habitat stemming from changes in

substrate composition, affecting the distribution of infaunal and epibenthic species (Donahue and

Irvine, 2003; Waters, 1995; Zweig and Rabeni, 2001).

Increased levels of suspended sediment were shown to impair ingestion rates of

freshwater mussels in laboratory studies (Aldridge et al., 1987). However, Box and Mossa

(1999) reviewed the literature on the effects of sedimentation on freshwater mussels and

concluded that the relative significance of human activities to sediment production, and their

susbsequent effects on freshwater mussels, is difficult to evaluate. Reduced feeding activity as a

response to increased levels of suspended sediments has also been reported for copepods (Tester

and Turner, 1988; Sherk et al., 1976) and daphnids (Arruda et al., 1983). Invertebrate drift is

directly affected by increased suspended sediment load in freshwater streams and lakes. 

Increases in suspended sediments (e.g., 120 mg/l) can result in increased drift, significantly

altering the distribution of benthic invertebrates in streams (Herbert and Merkens, 1961).

Waters (1995) considers the effects of increased deposition of sediments on benthic

invertebrates as one of the most important concerns within the sediment pollution issue,

especially in regards to the dependence of freshwater fisheries on benthic productivity. Waters

(1995) identifies three major relationships between benthic invertebrate communities and
12

sediment deposition in streams: 1) correlation between abundance and substrate particle size, 2)

embeddedness of substrate and loss of interstitial space, and 3) change in species composition

with change in type of habitat (substrate composition).

 Alteration in the quality and quantity of deposited sediments can affect the structure and

function of benthic macrofaunal communities by increasing substrate embeddedness and altering

substrate particle size distributions (Erman and Erman, 1984). Increased embeddedness can

result in decreases in aquatic insect densities and small increases in siltation can directly affect

caddisfly pupa survival. Zweig and Rabeni (2001) examined the response of benthic infauna to

deposited fine sediments in four Missouri streams. Five biomonitoring metrics were significantly

correlated with deposited sediments across streams. Deposited-sediment tolerance values were

developed representing responses to deposited sediments for 30 taxa. Tolerance values where

then used to develop the Deposited Sediment Biotic Index (DSBI). The DSBI was calculated to

characterize sediment impairment in the four streams. DSBI values for each site examined were

highly correlated with depth and degree of embeddedness of deposited sediment.

Several studies have examined the effects of the burial of estuarine invertebrates. Maurer

et al. (1986) found that species responded differently to burial by 36-40 cm of sediment, and that

some organisms were able to migrate more easily up through sandy sediment, while other

organisms were able to migrate better through muddy sediment. Hinchey et al. (in review) found

that species-specific response to burial by sediments varied as a function of motility, living

position and inferred physiological tolerance of anoxic conditions while buried. Their study

compared responses of five estuarine invertebrate species (3 infaunal and 2 epifaunal) to clean

sediment burial in laboratory experiments. Hinchey et al. (in review) suggested that effective

overburden stress, which incorporates both the bulk density of the sediment as well as the depth

of burial (Richards et al., 1974), was a better measure of the force exerted on organisms by

sediment burial than depth of sediment alone.

Effects on Corals 

The increased sedimentation resulting from coastal development is a major source of

coral reef degradation (Rogers, 1983, 1990; Torres, 2001). Excessive sedimentation can

adversely affect the structure and function of the coral reef ecosystem by altering physical and

biological processes (Rogers, 1990). High sediment loads can smother tissue resulting in

bleaching in the short-term and death in the long-term (Rogers, 1983). 

Cortes and Risk (1985) reported reduced growth rates in Montastraea annularis living in

waters with average sedimentation rates between 20-1,000 mg cm-2 d-1. Reduced growth rates

and temporary bleaching in M. annularis were also reported by Dodge et al. (1974). In a

subsequent study, Torres (2001) showed that growth rates of M. annularis were significantly

lower and negatively related with sediment deposition rates and percentages of terrigenous

sediments deposited on a coral reef on the south coast of Puerto Rico. Nemeth and Nowlis

(2001) reported bleaching of coral colonies at sediment deposition rates between 10 and 14 mg

cm-2d-1. Their study indicated that stress from sedimentation may lead to a decline in living

coral. An indirect effect of increased suspended sediment load was an increase in turbidity,
13

which caused a corresponding decrease in light penetration that limited the photosynthetic

capacity of symbiotic zooxanthellae, and furthered the decline in coral populations. 

Excessive sedimentation can affect the complex food web associated with coral reefs,

killing not only corals but other reef dwelling organisms (e.g., sponges) which serve as food for

commercially important fish and shellfish (Rogers, 1990). Declines in tropical reef fisheries in

the Carribean and the Pacific are believed to be partially due to increased sedimentation rates

(Rogers, 1985; Dahl, 1985). Increased sedimentation is also one of several factors which affect

coral recruitment. Coral larvae will not settle and establish themselves in shifting sediments.

Consequently, increases in sedimentation rates can alter the distribution of corals and their

associated reef constituents by influencing the ability of coral larvae to settle and survive

(Rogers, 1990).

Effects on Aquatic Plants

Some populations of aquatic macrophytes have experienced dramatic losses over the past

two decades, a decline largely attributed to changes in underwater light climate due to increases

in suspended sediment concentrations (Best et al., 2001). Turbidity limits the growth and

distribution of aquatic plants by reducing available light. The large-scale declines of submerged

aquatic vegetation (SAV) reported in Chesapeake Bay are believed to be directly related to

increasing amounts of nutrients and sediments entering the Bay (Batiuk et al., 1992, 2000;

Dennison et al., 1993). To address the unacceptable Bay-wide decline in SAV the U.S.EPA

Chesapeake Bay Program office established water clarity criteria. Water clarity criteria are based

on the light requirements for SAV growth and survival. The criteria take total suspended solids

(particulate matter and chlorophyll a) into account, as well as epiphytic growth and salinity

regime. Water clarity criteria are used in Chesapeake Bay because it is assumed that they will

result in achievement of clarity/solids levels that would not impair other habitats/organisms

(with the exception that the water clarity criteria may not fully protect "smothering" of bottom

soft or hard bottom habitats with larger sized sediment particles from sources that "bypass"/don't influence shallow water habitats), since the SAV represent one of the components of

the Chesapeake Bay ecosystem that is most sensitive to increases in SABS. A detailed

explanation of the derivation of Chesapeake Bay water clarity criteria can be found in U.S. EPA

(2003b). 

SAV are also subject to burial, although different species have different tolerances for

sediment accretion, and different sediment entrainment qualities (Fonseca and Fisher, 1986).

These different tolerances can result in changes in species composition in addition to overall loss

of SAV as a result of increased siltation (Terrados et al., 1998). It is not always possible to

separate out the effects of burial from the other effects of increased sediment input, e.g. reduced

light penetration (Terrados et al., 1998).

Effects on fish

Of all of the taxonomic groups, fishes, particularly salmonids, have received the most

attention from SABS researchers. This is because of the commercial and recreational
14

importance of salmonids, and the obvious impact that logging and other land use activities have

had on salmonid fisheries, particularly in the Pacific northwest (Waters, 1995). There are three

major effects of SABS on fishes: 1) direct physiological effects of suspended sediment, such as

suffocation, 2) effects due to decreases in water clarity, and 3) effects due to sediment

deposition, leading to increased embeddedness or burial of eggs and larvae (Waters, 1995;

Wilber and Clarke, 2001).

The conventional wisdom (at least since the publication of Newcombe and MacDonald,

1991) is that both the degree of exposure (measured as TSS or turbidity, or decreased water

clarity) and the duration of the exposure are important. It follows that the longer the duration

and the greater the exposure, the more severe the effects. Therefore, it is expected that the first,

mild, primarily behavioral effects would be seen with low intensity, short-term exposures. As the

duration of exposure and intensity of exposure increase, sublethal effects are manifested, and

lethal effects begin to be expressed at more intense exposures of longer duration (Figure 2). The

timing of exposure to suspended sediment is also very important, as it may affect different lifehistory stages in different ways. Different life-history stages of the same species may also have

differing abilities to avoid exposure.

Effects of Suspended Sediment on Fish

Newcombe and Jensen (1996) summarized much of the available data on the effects of

suspended sediment on fishes, and fit the data into empirical models in the form of data

“triplets”, with matched biological effect, concentration and duration information. The effects

were scored on a qualitative “severity of ill effect” (SEV) scale, that included responses ranging

from no behavioral effects (lowest on the scale) to behavioral effects (low on the scale), to

sublethal effects (higher on the scale), to lethal effects (highest on the scale). Different models

were developed for different age groups of fishes: juvenile and adult salmonids together, adult

salmonids, juvenile salmonids, eggs and larvae of salmonids and non-salmonids, adult estuarine

non-salmonids, and adult freshwater non-salmonids. The models were presented both in visual

form (as 3-dimensional response surfaces) and as linear regression equations, and were also used

to interpolate and extrapolate from the empirical data. The tabular forms of the models are

presented in Appendices A and D. They are taken from Newcombe (1997) and Newcombe

(personal communication). Appendix A also includes an empirical model for the effects of

suspended sediments on invertebrates as well as an empirical model for plants. Appendix D

corrects the error in the estuarine adult fish model from Newcombe and Jensen (1996) identified

by Wilber and Clarke (2001). Although the visual presentations in Newcombe and Jensen

(1996) of the models look complete, it is evident from the figures of the “empirical data”

(Appendix A) that there are not enough data for the various groups of organisms (with the

possible exception of the salmonids) to fill in the idealized model of fish response to increased

suspended sediments shown in Figure 2. This is because there are not enough data, and because

of the great variability in the data. 

Wilber and Clarke (2001) published another review of the data on the effects of SABS on

fish, focusing on impacts of dredging on estuarine organisms. They added to the data from

Newcombe and Jensen (1996) and provided a useful way of plotting the empirical data, such that

all of it can be seen and compared with expected exposure concentrations (in their case, from
15

dredging operations). These figures are presented in Appendix E. This display of data provides

a powerful tool for the estimation of expected effects from a given suspended sediment exposure

scenario. When looking at the figure from Wilber and Clarke (2001) describing the data for

salmonid fishes (Figure 2 in Appendix E), there does appear to be enough data from studies with

adult salmonids to begin to visualize the idealized pattern seen in Figure 2. However, Wilber

and Clarke (2001) also plotted the adult estuarine fish data separately from the freshwater and

salmonid data, to show how little data there were for the fishes, and that most of those data were

from short duration tests at very high exposures (Figure 4 in Appendix E).

Effects of Decreased Water Clarity on Fish

Wilber and Clarke (2001) also summarized the effects of increased turbidity and reduced

water clarity on the feeding of fishes, but did not include the data in their tables or figures,

because most of them are reported in turbidity units which are difficult to convert to suspended

solids concentrations (Caux et al., 1997). It is very difficult to make generalizations about these

data. Some fishes are able to hunt better as suspended solids increase, at least up to a point,

because of increased contrast between the prey and the surrounding water. Some larval fish, like

striped bass, seem to be able to feed under extremely turbid conditions, or even complete

darkness. This ability could be very important for a fish that follows the turbidity maximum for

its abundant food (Chesney, 1993). 

Centrarchids (e.g., smallmouth and largemouth bass), on the other hand, may be severely

impacted in their ability to feed by even small increases in turbidity (J. Sweeten, personal

communication). Suspended sediment has little if any effect on the nests of centrarchids due to

their nesting behavior of "fanning" eggs (J. Sweeten, personal communication). However, low

concentrations of suspended sediment caused reduced growth in smallmouth bass (Micropterus

dolomieui). The inhibition concentration (IC) 25 value for a one day exposure was only 11.4

mg/L suspended bentonite (Sweeten and McCreedy, 2002). The authors concluded that even

low concentrations of suspended sediment at this early life-stage may strongly affect year class

strength. Other fish may be excluded from desirable habitat because of increased turbidity

(Ponton and Fortier, 1992).

Despite the difficulties in putting together the data on the effects of turbidity on fishes,

Newcombe (2003) has developed an impact model for clear water fishes exposed to excessively

cloudy water. This is discussed in the modeling section below.

Effects of Increased Sedimentation on Fish

The effects of increased SABS resulting in increased embeddedness, on salmonids in

particular, have been well documented (e.g., Waters, 1995). An increased supply of fine

sediment to a stream can cause the gravel interstices of a stream bed to be filled in. This process

can cause reduced hatching due to the reduction in flow through the stream bed and the resulting

decrease in dissolved oxygen. It can also cause reduced larval survival because of armoring of

the sediment surface which traps the larvae. Increased sedimentation in other habitats (e.g.,

estuaries) can cause burial of eggs (Wilber and Clarke, 2001). Even a small amount of deposited

sediment can cause a problem. Winter flounder eggs, for example, will suffer reduced hatching

success if buried to only one half an egg diameter (D. Nelson, NMFS, unpublished data). 
16

Effects on Wildlife

There are very few published reports on the effects of SABS on aquatic-dependent

wildlife (i.e., birds and mammals). For the most part, aquatic-dependent wildlife are more

mobile than the fish, invertebrates and plants discussed above, and therefore aquatic-dependent

wildlife can avoid most of the direct effects of increased SABS. A heron or an osprey, for

example, can avoid more turbid areas, and choose areas of clearer water. If and when the water

clears in the area, the bird can return. If increases in SABS are wide-spread and long-term,

however, they might cause a problem for aquatic-dependent wildlife that consume aquatic prey. 

A bear, for example, may have to abandon part of its range if there is failure of a salmon run. 

Loons are thought to require clear water for fishing, and may avoid nesting areas with

inadequate water clarity (McIntyre, 1988).

Most of the studies of the relationship between turbidity and aquatic-dependent wildlife

involve field studies with birds. Van Eeerden and Voslamber (1995) describe a mass (group)

fishing behavior of cormorants, which was apparently developed as a response to an increase in

the turbidity of a lake in the Netherlands. Stevens et al. (1997) found that waterbirds were most

abundant on the clear and variably turbid segments of the Colorado River and least abundant on

the more turbid lower segment, providing evidence that turbidity makes it difficult for birds to

forage effectively. Another study in British Columbia ponds, however, found that the abundance

of dabbling ducks was positively correlated with turbidity and total dissolved nitrogen, and

negatively correlated with percent of forested shoreline, percent of marsh, and chloride (Savard

et al., 1994). The authors had no explanation for these relationships, and felt that their results

highlighted the problems associated with interpreting correlative-type studies, especially the

difficulties in assessing the biological significance of the observed correlations.

Modeling the Effects of Increased SABS 

The preceding discussion indicates that the effects of SABS on aquatic life are

complicated, and unraveling them may be difficult. However, at least one expert in the field

feels that we are well on our way to developing models that can predict the effects of SABS on

fish, at least in streams. Newcombe (2000) presents a primer with information on water quality

and sediment quality models for assessing the impact of excess stream channel sediment, and

provides a framework for their use. The principle is simple: if the SABS problem in a stream is

related to suspended sand and silt, a suspended sediment model should be used; if the problem in

the stream relates to suspended clay particles, a water clarity model should be used; and if the

problem relates to sediment deposition, a sediment quality model should be used. The models

for the effects on fish in streams have the most data and are the most complete, but by extension

they may be used in other habitats. For suspended sand and silt problems, models like those in

Newcombe and Jensen (1996) should be used. A model for the effects on invertebrates is also

included in Appendix A. The model for estuarine fishes has been corrected, as suggested by

Wilber and Clarke (2001) (Appendix D). For excess clay a turbidity model, such as the draft

model in Appendix B, can be used (Newcombe, 2003). There are three sediment quality models

presented in Newcombe (2000). These models are from Crouse et al. (1981); Kondolf (1997),
17

and Tappel and Bjornn (1983). Additional sediment models are reviewed in Caux et al. (1997). 

The variables most often used to assess the composition of streambed sediments are percent

fines, geometric mean diameter, and Fredle number (Caux et al., 1997). All of these variables

can be used to develop empirical models of salmonid hatching as a function of sediment

composition.

The models described above consider the effects of SABS as a single stressor, but

organisms in nature are exposed to multiple chemical and physical stressors. Most work

considering the interaction of increased sediment and chemical contaminants has addressed the

effects that suspended sediment can have on the bioavailability of contaminants. Increased

suspended sediment can decrease the bioavailability of hydrophobic contaminants by reducing

dissolved concentrations in the water column (Schrap and Opperhuzien, 1990). 

A few studies, however, have examined the interaction of suspended sediment and

toxicants. Herbrandson and colleagues found that increased suspended sediment load could

decrease the EC50 concentration of carbofuran to Daphnia by a factor of five (Herbrandson et

al., 2003a). They developed a model of the combined effects of suspended sediment and

carbofuran. These effects were more than additive (i.e., the measured EC50s were lower than

would be predicted by an additive model). They hypothesized that this reduction was due to a

reduction in feeding efficiency of the test organisms in the presence of increased suspended

sediment (Herbrandson et al., 2003b). The possibility of interactive effects is a real problem for

SABS criteria-setting based on laboratory testing, because of the huge number of possible

interactions between SABS and other stressors. Herbrandson et al. (2003a) found that there was

an interaction of carbofuran and suspended sediment, even though there was no increased

mortality due to very high levels of suspended sediment in the absence of carbofuran. 

Current Criteria for SABS

One of the best available summaries of the current criteria for SABS is in the Technical

Appendix to the Ambient Water Quality Guidelines (Criteria) for Turbidity, Suspended and

Benthic Sediments (Caux et al., 1997), prepared for the British Columbia Ministry of

Environment, Land and Parks. British Columbia has SABS criteria for a number of water uses,

varying from drinking water to aquatic life use to industrial water supply. Caux et al. (1997)

outline the criteria for each, and provides the scientific rationale. Caux et al. (1997) build on an

earlier review of available criteria by Singleton (1985). A more recent review of current SABS

criteria was done by K. Sullivan for the Office of Water (Appendix F.).

There is a wide range of criteria in current use in the United States (Appendix F). Some

states use numerical criteria, some use narrative criteria, some use both, and some states have no

criteria for SABS. Many states have different criteria for different stream channel substrate

types. When they are differentiated, states typically have more stringent criteria for streams with

hard substrates (gravel, cobble, bedrock) and less stringent criteria for streams with soft

substrates (sand, silt, clay). Hawaii has a separate criteria for reefs. Cold water fisheries typically

have more stringent criteria than do warm water fisheries in states that differentiate between the

two. A few states use biocriteria (e.g., biotic indices), and at least one uses soil loss as a
18

criterion. Several states provide criteria for an averaging period (e.g., 30 days) as well as an

allowed daily maximum concentration.

Most states with numerical criteria use turbidity as a measure. Some use exceedances

over background (e.g., “Not greater than 50 NTU over background”, or “not more than 10%

above background”), while some use absolute values (e.g., “Not greater than 100 NTU”). Only a

few states use suspended solids as a criterion. Suspended sediment criteria values vary from 30

mg/L up to 158 mg/L. At least one state uses transparency (> 90% of background) as a standard. 

A number of states have criteria based on sediment deposited over a time period, or during a

storm event. Values are typically 5 mm during an individual event (e.g., during the 24 hours

following a heavy rainstorm) for streams with hard substrates bottoms and 10 mm for streams

with soft bottoms. Hawaii's reef criterion is 2 mm deposited sediment after an event.

The Chesapeake Bay Program’s unique criterion is discussed in the plant section above. 

The criterion is based on suspended particulate matter and chlorophyll a, and takes into account

epiphytic growth and salinity in its calculation of water clarity (U.S.EPA, 2003b). 

Many states have narrative criteria for SABS in addition to, or in lieu of, numerical

criteria. These criteria most frequently pertain to turbidity or appearance of the water (e.g., “free

of substances that change color or turbidity”). Others refer to undesirable biological effects

(e.g., “no adverse effects” or “no actions which will impair or alter the communities”). Given the

wide range of measures (e.g., turbidity, TSS, color) used to measure SABS and the wide range of

values within a given measure, it is difficult to evaluate what appropriate criteria should be,

especially because the rationale for the criteria are not always readily available. The British

Columbia SABS criteria for aquatic life are one of the few examples of criteria explicitly

supported by a scientific rationale (Caux et al., 1997). Idaho's Guide to Selection of Targets for

Use in Idaho TMDLs (Idaho DEQ, 2003) is another good example of a document outlining

SABS criteria with explicit biological justification.

Upon first glance, the channel substrate and fisheries specificity in the criteria from

across the U.S. (Appendix F), might lead one to believe that the observed variation in criteria is

related to regional variation in SABS across the country. However, as is noted in other parts of

this review, it does not appear that the data are sufficient to back up this contention. 

“State of the Science” for SABS criteria setting

A full “state of the science review” might review all of these approaches for setting

criteria for SABS and the steps needed to implement them, but that is beyond the scope of this

review. Here we will focus on some of the gaps in our understanding of the effects of SABS,

using the conceptual model in Figure 1 as an outline for the SABS processes affecting aquatic

and aquatic-dependent life.

Our understanding of the physical processes controlling the input of sediments to aquatic

systems is better developed than our understanding of the effects of these inputs. The input of

sediment to streams from watershed activities is probably the most studied. Changing land use
19

can often result in greatly increased sediment loading to streams (U.S.EPA, 1999; Leopold et al.,

1964; Rosgen, 1996). Deposition due to dredged material disposal has been largely predicted

using the “ADDAMS” models developed by the US Army Corps of Engineers (Schroeder and

Palermo, 1995). Resuspension and deposition (both from barge overflow and resuspension) at

the dredging site will be modeled with a new model, SSFATE (Suspended Sediment Fate, D.

Clarke, personal communication). Deposition and resuspension from natural processes are

outside of the scope of this review because the organisms in different habitats have evolved to

survive in the resuspension and deposition regime native to their habitat, although a good

estimate of natural resuspension and deposition helps to put anthropogenic increases in these

phenomena into context. Erosion is primarily a concern in marsh habitats (Boesch,et al., 1994),

and results in part from a decrease of sediment supply to some waterbodies.

Among the biological effects due to suspended sediments the most important are

smothering (and abrasion), shading, and reduced feeding due to increased turbidity. Of these,

the shading of SAV has been the most heavily studied, and is probably the best understood; in

fact, criteria based on models of shading of SAV are available (U.S.EPA, 2003b). Models are

also available for the prediction of the direct effects of suspended sediment (smothering and

abrasion) on fish and invertebrates (Appendices A and D; Newcombe and Jensen, 1996), but

most of the data used to support these models come from unrealistically high and short-term

exposures. Also, there has been little field validation of these models. Further work with longer

term and more environmentally realistic exposures will be required before the real effects of

suspended sediment on fishes in the environment can be understood (Wilber and Clarke, 2001). 

We know less about the effects of suspended sediment on other groups of organisms, including

zooplankton and aquatic-dependent wildlife.

Studies of the effects of suspended sediments on feeding have been done primarily with

larval fish, and a model is available (Appendix B; Newcombe, 2003), but there has been little

field validation of the model. We know less about the effects of suspended sediment on the

feeding of other groups of organisms.

Wilber et al. (in review) conducted a review of the effects of burial associated with

dredging as a followup to Wilber and Clarke's (2001) review of the effects of suspended

sediment. They concluded that “Overall, the literature available to determine whether elevated

sedimentation rates, hypothetically linked to dredging and disposal, can result in impacts to

sensitive resources and other biota is scant and varies widely between habitats. Very thin

veneers of sediment are known to adversely affect both settlement and recruitment of bivalve

larvae. Some quantitative data are available for eggs of demersal fish, both for cover and

changes to particle size of the substratum composition. Although there are documented,

unambiguous, adverse effects of sedimentation on fishes, seagrasses and submerged aquatic

vegetation, available data have not been and are insufficient to be transformed into target

values.”

Wilber et al. (in review) further concluded that “documentation of how either natural or

dredging-induced sedimentation rates affect targeted biological communities is needed. There

are insufficient data for all habitat types investigated to establish dose-response models
20

(particularly with parameters appropriate to dredging) as would be required for predicting

potentially harmful rates of sedimentation or establishing technically defensible guidelines for

their protection. Work to date relating sedimentation to impacts on resources can generally be

classified as either (1) manipulative experiments in which varying amounts of sediment are

added to a targeted system, or (2) a posteriori determinations of causes and effects following

major sedimentation events (e.g., dredging operation, storm). The latter retrospective approach

suffers from confounding factors acting synergistically with or independently from

sedimentation, such as elevated suspended sediment load, changes in nutrient supply, or other

related environmental perturbations. Unfortunately, most reports of sedimentation impacts fall

into the latter category.”

The general conclusion from the analysis in this review is that, as the water clarity

criteria for the protection of SAV in the Chesapeake show, the toxicological approach can be

used if a species or group of species from a particular habitat is to be protected and the required

dose-response data are available. Currently models of the biological effects of SABS are in use

for the effects of shading on SAV, the direct effects of suspended sediments on fishes, the effects

of water clarity on larval fishes, and the effects of embeddedness on the hatching of salmonids. 

When these models have received more field validation and are made more generalizable it may

be possible to set national criteria for suspended or bedded sediment using the traditional

“toxicological” dose-response approach. These criteria will presumably have to incorporate

some habitat-specificity in order to be widely applicable.

Take home messages

1) Some useful models for the biological effects of SABS exist and others are under

development. As the water clarity criteria for the protection of SAV in the Chesapeake show,

the approach can be used if a specific species from a particular habitat is to be protected and the

required dose-response data are available. Generalizations are difficult because biological

response to both increased suspended sediment and increased bedded sediment varies with

species and sediment characteristics.

2) After additional research it may be possible to develop national scientifically-defensible

SABS criteria using the traditional “toxicological” dose-response approach. These criteria will

presumably have to incorporate some habitat-specificity in order to be widely applicable.

3) Some habitats that have not been well studied (in terms of their sensitivity to SABS) deserve

more study, especially those habitats with moderate and variable amounts of SABS.

4) Many states have set standards for SABS, but there is little consistency among them.

Acknowledgements

A number of people have contributed greatly to this review. C.P. Newcombe provided much

insight into the problems of SABS, as well as providing the unpublished material for several of

the Appendices. Dara Wilber provided valuable suggestions, and provided the figures for
21

Appendix E. The review also profited from discussions with Don MacDonald, Doug Clarke, and

Jerry Sweeten. Thanks to Timothy Gleason, Elizabeth Hinchey, Phil Kaufman, Lesa Meng,

Wayne Munns, and John Paul for the critical and constructive reviews that they provided. The

conceptual model cartoon was modified from one provided by Wayne Munns. The research

described in this paper has been funded by the U.S. Environmental Protection Agency. This

paper has not been subjected to Agency review. Therefore, it does not necessarily reflect the

views of the Agency. Mention of trade names or commercial products does not constitute

endorsement or recommendation for use. This is contribution number AED-02-103.
22

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30

List of Tables

Table 1: Suspended Solids & Bedded Sediment, [Comparisons of 305(b) National Water

Quality Inventory Reports] Data from U.S.EPA (2003a.)

List of Figures

Figure 1: Conceptual model of biological effects of suspended and bedded sediments.

Figure 2. Idealized model of fish response to increased suspended sediments. Source of

above figure is unknown; it is a generic, un-calibrated impact assessment model based on 

Newcombe, C. P., and J. O. T. Jensen. 1996. Channel suspended sediment and fisheries:

a synthesis for quantitative assessment of risk and impact. North American Journal of

Fisheries Management. 16: 693-727. Reprinted, with permission, from:

http://wow.nrri.umn.edu/wow/under/parameters/turbidity.html.
31

Table 1

RANKING OF AQUATIC STRESSORS: SUSPENDED SOLIDS & BEDDED

SEDIMENTS

[Comparisons of 305(b) National Water Quality Inventory Reports (U.S.EPA, 2003a)]

RIVERS & STREAMS:

1994 (7 P/S* Listed) 1996 (8 P/S Listed) 1998 (8 P/S Listed) 2000 (8 P/S Listed)

Siltation (2)** Siltation (1) Siltation (1) Siltation (2)

Suspended Solids (7) Suspended Solids ( 7)

NOTES: *P/S = Pollutants/Stressors

** As an example, Siltation was ranked No. 2 out of the seven (7)

Pollutants/Stressors found on the Table for Rivers & Streams in the 1994

Report.

LAKES, PONDS, & RESERVOIRS:

1994 (7 P/S Listed) 1996 (7 P/S Listed) 1998 (7 P/S Listed) 2000 (7 P/S Listed)

Siltation (2) Siltation (3) Siltation (3) Siltation (3)

Suspended Solids (5) Suspended Solids (6) Suspended Solids (5)

WETLANDS:

1994 (9 P/S Listed) 1996 (8 P/S Listed) 1998 (7 P/S Listed) 2000 (6 P/S Listed)

Sediment (1) Sedimentation & Sedimentation & Sedimentation &

Siltation (1) Siltation (1) Siltation (1)

ESTUARIES:

1994 (7 P/S Listed) 1996 (7 P/S Listed) 1998 (7 P/S Listed) 2000 (7 P/S Listed)

Note that Siltation, Suspended Solids, Sediment, and Turbidity were not found on these

Lists for 1994, 1996, 1998, and the 2000 305(b) Reports.

OCEAN SHORELINE WATERS:

1994 (7 P/S Listed) 1996 (8 P/S Listed) 1998 (7 P/S Listed) 2000 (7 P/S Listed)

Turbidity (4) Turbidity (2) Turbidity (2) Turbidity (3)

Siltation (5) Suspended Solids (5) Suspended Solids (4) Suspended Solids (4)

Siltation (5)
32

Figure 1: Conceptual model of biological effects of suspended and bedded sediments in estuaries.
33

Figure 2. Idealized model of fish response to increased suspended sediments. Schematic

source of above figure is unknown; it is a generic, un-calibrated impact assessment

model based on Newcombe, C. P., and J. O. T. Jensen. 1996. Channel suspended

sediment and fisheries: a synthesis for quantitative assessment of risk and impact. North

American Journal of Fisheries Management. 16: 693-727. Reprinted, with permission,

from: http://wow.nrri.umn.edu/wow/under/parameters/turbidity.html.
34

Appendix A

“Channel Suspended Sediment and Fisheries: A Concise Guide to Impacts”

By

Charles P. Newcombe

Ministry of Environment, Land, and Parks

Victoria, British Columbia

Not included, available upon request
35

Appendix B

Model of the effects of turbidity on fishes 

(C.P. Newcombe, Personal Communication)

Reprinted with permission of the author

This is similar to the model in Newcombe (2003)

Not included, available upon request
36

Appendix C

Available data on the effects of suspended sediments on biota. Data take from the

original literature (unless otherwise noted) or Newcombe and Jensen (1996: “N&J”)

Key:

Life Stage: A = Adult, J = Juvenile, L = Larval

Concentration: Material is listed if known: k = kaolin, ns = natural sediment

Source: Original data consulted unless otherwise noted. N&J = Newcombe and Jensen,

1996.

Duration: Duration is in hours unless otherwise noted. d = days. f = field studies.
37

SPECIES

Life Stage

Concentrationin mg/l

Duration

in

Hours

EFFECT (Response) REFERENC

E

Source

MOLLUSCA

Eastern oyster

Crassostrea virginica

L 400 12 d 10% mortality Davis and

Hidu 1969

 “ ” L 500 12 d 18% mortality “ ”

L 750 12 d reduced growth

L 750 12 d 30% mortality

L 1000 12 d 40% mortality

L 1500 12 d 58 % mortality

L 2000 12 d 75% mortality

L 3000 12 d 99 % mortality

Pacific Oyster

Cassostrea gigas

L $1200 2 d abnormal shell

development

Cardwell et.

al. 1976

L $800 2 d 50% mortality

Hard Clam

Mercenaria

mercenaria

L $750 10 d 10% mortality Davis and

Hidu 1969

L 3000 10 d 15% mortality

L 4000 11 d 30% mortality

Eastern Oyster

Crassostrea virginica

A $1000 2 d reduced pumping Loosanoff,

1962

Soft Shell Clam

Mya arenaria

A 100 35 d reduced growth Grant and

Murphy 1985

Hard Clam 

Mercenaria

mercenaria

A 27 14 d reduced growth Murphy, 1985

“ ” A 100 2 d reduced growth Turner and

Miller, 1991

“ ” J 44 21 d reduced growth Bricelj et

al.,1984

Coast Mussels

Mytilus californiamus

A 8100 17 d 10% mortality Peddicord,

1980

“ ” J 15500 16 d 20-14% mortality “ ”

“ ” A 80000 11 d 50% mortality “ ”

“ ” A 85000 9 d 50% mortality “ ”

Blue Mussel

Mytilus edulis

A 15000 8 d 0-20% mortality Peddicord,

1976
38

“ ” J 100000 5 d 10% mortality McFarland

and

Peddicors,

1980

A 60000 10 d 10% mortality Wakeman et

al., 1975

Surf Clam

Spisula solidissima

A 500 21 d reduced growth Robinson et

al.,1984

Bay Scallop

Argopecten irradians

A 500 7 d increased respiration Morre, 1978

“ ” A 1000 7 d increased respiration “ ” “

CRUSTACEA

Sand Shrimp

Crangon

nirgomaculata

16000 8 d 10%mortality Mc Farland

and Peddicord

1980

“ ” 50000 8 d 50% mortality “ ”

Grass Shrimp

Paleomon

macrodactylus

24000 (k) 10 d 10% mortality “ ”

“ ” 77000 (k) 8 d 20% mortality “ ”

Dungeness Crab

Cancer magister

9200 (ns) 8 d 5% mortality Peddicord and

McFarland,

1976

“ ” 11700

(ns)

7 d 20% mortality “ ”

“ ” juvenile J 15900

(ns)

9 d 15% mortality “ ”

“ ” “ J 18900

(ns)

4 d 20% mortality “ ”

“ ” adult A 10000 (k) 8 d 10% mortality McFarland

and

Peddicord,

1980

“ ” “ A 32000 (k) 8 d 50% mortality “ ”

Kuruma Prawn

Penaeus japonicus

J 180 (ns) 21 d 10% mortality Lin et al.,

1992

“ ” J 370 (ns) 21 d 32% mortality “ ”

Black-tailed Sand

Shrimp

Cragnon nigrocauda

11900

(ns)

5 d 10% mortality Peddicord,

1990

“ ” 4300 (ns) 3 d 5% mortality “ ”

“ ” 9000 (b) 10 d 10% mortality Wakeman et

al. 1975

Mysid Shrimp

Mysidopsis bahia

230 (ns) 28 d 40 % mortality Nimmo et al.

1982
39

“ ” 1020 (ns) 28 d 60-80% mortality “ ”

Copepod

Eurytmora affinis

>350 (ns) f reduced population

growth

Sellner and

Bundy, 1986

Copepod >100 (ns) f reduced vertical

migration

Daborn and

Brylinsky,

1981

Copepod

Acartia tonsa

>95 (ns) f reduced feeding Tester and

Turner, 1988

Copepod

A.tonsa, E. affinis

>250 f reduced feeding Sherk et al.,

1976

Daphnids 50-100

(ns)

<18 d reduced feeding Arruda et al.,

1983

Benthic Algae 2.0-4.2 f decrease in biomass,

growth

Wilson et al.,

1999

Freshwater Mussels 600-750

(ns)

f decreased filter clearance Aldridge et

al., 1987

Red Algae

Lemanea

5000 (ns) 21 d reduced primary

production

Thirb and

Benson-Evans

1985

“ ”

Egeria

30-40

(ns)

40 d reduced growth Tanner et al.

1993

Oyster

Crassostrea virginica

100 f reduced pumping Sherk et al.

1975

FISH

Adult salmonids and

rainbow smelt

Grayling (Arctic) A 100 0.10 Fish avoided turbid water Suchanek et

al. (1984a,

1984b)

N & J

A 100 1,008 Fish had decreased

resistance to

environmental stress

McLeay et al.

(1984)

N & J

A 100 1,008 Impaired feeding N & J

Salmon A 25 4 Feeding activity reduced Phillips

(1970)

N & J

A 16.5 24 Feeding behavior

apparently educed

Townsend

(1983); Ott

(1984)

N & J

A 1,650 240 Loss of habitat caused by

excessive sediment

transport

Coats et al.

(1985)

N & J

Salmon (Atlantic) A 2,500 24 Increased risk of

predation

Gibson (1933) N & J
40

Salmon (chinook) A 650 168 No histological signs of

damage to olfactory

epithelium

Brannon et al.

(1981)

N & J

Salmon (chinook) A 350 0.17 Home water preference

disrupted

Whitman et

al. (1982)

N & J

Salmon (chinook) A 650 168 Homing behavior

normal, but fewer test

fish returned

Whitman et

al. (1982)

N & J

Salmon (chinook) A 39,300 24 No mortality (VA, <5-

100 um; median, <15

um)

Newcomb and

Flagg (1983)

N & J

Salmon (chinook) A 82,400 6 Mortality rate 60% (VA,

<5-100 um)

Newcomb and

Flagg (1983)

N & J

Salmon (chinook) A 207,000 1 Mortality rate 100%

(VA, <5-100 um)

Newcomb and

Flagg (1983)

N & J

Salmon (Pacific) A 525 588 No mortality (other end

points not investigated)

Griffin (1938) N & J

Salmon (sockeye) A 500 96 Plasma glucose levels

increased 39%

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) A 1,500 96 Plasma glucose levels

increased 150%

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) A 39,300 24 No mortality (VA, <5-

100 um; median, <15

um)

Newcomb and

Flagg (1983) 

N & J

Salmon (sockeye) A 82,400 6 Mortality rate 60% (VA,

<5-100 um; median, <15

um)

Newcomb and

Flagg (1983)

N & J

Salmon (sockeye) A 207,000 1 Mortality rate 100%

(VA)

Newcomb and

Flagg (1983)

N & J

Smelt (rainbow) A 3.5 168 Increased vulnerability to

predation

Swenson

(1978)

N & J

 

Steelhead A 500 3 Signs of sublethal stress

(VA)

Redding and

Schreck

(1982)

N & J

 

Steelhead A 1,650 240 Loss of habitat caused by

excessive sediment

transport

Coats et al.

(1985)

N & J

 

Steelhead A 500 9 Blood cell count and

blood chemistry change

Redding and

Schreck

(1982)

N & J

 

Trout A 16.5 24 Feeding behavior

apparently reduced

Townsend

(1983); Ott

(1984)

N & J

 

Trout A 75 168 Reduced quality of

rearing habitat

Slaney et al.

(1977b) 

N & J

 
41

Trout A 270 312 Gill tissue damaged Herbert and

Merkens

(1961)

N & J

 

Trout A 525 588 No mortality (other end

points not investigated)

Griffin (1938) N & J

 

Trout A 300 720 Decrease in population

size

Peters (1967) N & J

 

Trout (brook) A 4.5 168 Fish more active and less

dependent on cover

Gradall and

Swenson

(1982)

N & J

 

Trout (brown) A 1,040 17,520 Gill lamellae thickened

(VFSS)

Herbert et al.

(1961)

N & J

 

Trout (brown) A 1,210 17,520 Some gill lamellae

became fused (VFSS)

Herbert et al.

(1961)

 N& J

 

Trout (brown) A 18 720 Abundance reduced Peters (1967) N & J

Trout (brown) A 100 720 Population reduced Scullion and

Edwards

(1980)

N & J

Trout (brown) A 1,040 8.760 Population one-seventh

of expected size (River

Fal)

Herbert et al.

(1961)

N & J

Trout (brown) A 5,838 8,760 Fish numbers oneseventh of expected size 

(River Par)

Herbert et al.

(1961)

N & J

Trout (cutthroat) A 35 2 Feeding ceased; fish

sought cover

Cordone and

Kelly (1961)

N & J

Trout (lake) A 3.5 168 Fish avoided turbid areas Swenson

(1978)

N & J

Trout (rainbow) A 66 1 Avoidance behavior

manifested part of the

time

Lawrence and

Scherer

(1974)

N & J

Trout (rainbow) A 665 1 Fish attracted to turbidity Lawrence and

Scherer

(1974)

N & J

Trout (rainbow) A 100 0.10 Fish avoided turbid water

(avoidance behavior)

Suchanek et

al. (1984a,

1984b)

N & J

Trout (rainbow) A 100 0.25 Rate of coughing

increased (FSS)

Hughes

(1975)

N & J

Trout (rainbow) A 250 0.25 Rate of coughing

increased (FSS)

Hughes

(1975)

N & J

Trout (rainbow) A 810 504 Gills of fish that survived

had thickened epithelium

Herbert and

Merkens

(1961)

N & J
42

Trout (rainbow) A 17,500 168 Fish survived: gill

epithelium proliferated

and thickened

Slanina

(1962)

N & J

Trout (rainbow) A 50 960 Rate of weight gain

reduced (CWS)

Herbert and

Richards

(1963)

N & J

Trout (rainbow) A 50 960 Rate of weight gain

reduced (WF)

Herbert and

Richards

(1963)

N & J

Trout (rainbow) A 810 504 Some fish died Herbert and

Merkens

(1961)

N & J

Trout (rainbow) A 270 3,240 Survival rate reduced Herbert and

Merkens

(1961)

N & J

Trout (rainbow) A 200 24 Test fish began to die on

the first day (WF)

Herbert and

Richards

(1963)

N & J

Trout (rainbow) A 80,000 24 No mortality D. Herbert,

personal

communicatio

n to Alabaster

and Lloyd

(1980)

N & J

Trout (rainbow) 

 

A 18 720 Abundance reduced Peters (1967) N & J

Trout (rainbow) A 59 2,232 Habitat damage; reduced

porosity of gravel

Slaney et al.

(1977b) 

N & J

Trout (rainbow) A 4,250 588 Mortality rate 50% (CS) Herbert and

Wakeford

(1962)

N & J

Trout (rainbow) A 49,838 96 Mortality rate 50% (DM) Lawrence and

Scherer

(1974)

N & J

Trout (rainbow) A 3,500 1,488 Catastrophic reduction in

population size

Herbert and

Merkens

(1961)

N & J

Trout (rainbow) A 160,000 24 Mortality rate 100% D. Herbert,

personal

communicatio

n to Alabaster

and Lloyd

(1980)

N & J

Trout (sea) A 210 24 Fish abandoned

traditional spawning

habitat

Hamilton

(1961)

N & J

Whitefish (lake) A 0.66 1 Swimming behavior

changed 

Lawrence and

Scherer

(1974)

N & J
43

Whitefish (lake) A 16,613 96 Mortality rate 50% (DM) Lawrence and

Scherer

(1974)

N & J

Whitefish (mountain) A 10,000 24 Fish died; silt-clogged

gills

Langer (1980) N & J

JUVENILE

SALMONIDS

Grayling (Arctic) U 20 24 Fish avoided parts of the

stream

Birtwell et al.

(1984)

N & J

Grayling (Arctic) U 10,000 96 Fish swam near the

surface

McLeay et al.

(1987)

N & J

Grayling (Arctic) J 86 0.42 78% of fish avoided

turbid water (NTU, <20)

Scannell

(1988)

N & J

Grayling (Arctic) U 100 1 Catch rate reduced

(unfamiliar prey:

drosophila)

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 100 1 Catch rate reduced

(unfamiliar prey:

tubificids)

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 300 1 Catch rate reduced

(unfamiliar prey:

drosophila)

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 1,000 1 Feeding rate reduced

(unfamiliar prey:

tubificids)

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 1,000 1 Feeding rate reduced

(unfamiliar prey:

drosophila)

McLeay et al.

(1987)

N & J

Grayling (Arctic) YY 3,810 144 Food intake severely

limited

Simmons

(1982)

N & J

Grayling (Arctic) U 100 12 Reduced ability to

tolerate high

temperatures

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 100 756 Fish moved out of the

test

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 1,000 1,008 Fish had frequent

misstrikes while feeding

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 1,000 1,008 Fish responded very

slowly to prey

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 300 1,008 Rate of feeding reduced McLeay et al.

(1987)

N & J

Grayling (Arctic) U 1,000 840 Rate of feeding reduced McLeay et al.

(1987)

N & J

Grayling (Arctic) U 1,000 1,008 Fish failed to consume

all prey

McLeay et al.

(1987)

N & J
44

Grayling (Arctic) U 300 840 Serious impairment of

feeding 

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 300 1,008 Respiration rate

increased (FSS)

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 300 1,008 Fish less tolerant of

pentachlorophenol

McLeay et al.

(1987)

N & J

Grayling (Arctic) YY 3,810 144 Mucus and sediment

accumulated in the gill

lamellae

Simmons

(1982)

N & J

Grayling (Arctic) YY 3,810 144 Fish displayed many

signs of poor condition

Simmons

(1982)

N & J

Grayling (Arctic) YY 1,250 48 Moderate damage to gill

tissue

Simmons

(1982)

N & J

Grayling (Arctic) YY 1,388 96 Hyperplasia and

hypertrophy of gill tissue

Simmons

(1982)

N & J

Grayling (Arctic) U 100 1,008 Growth rate reduced McLeay et al.

(1984)

N & J

Grayling (Arctic) U 100 840 Fish responded less

rapidly to drifting food

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 300 1,008 Weight gain reduced McLeay et al.

(1987)

N & J

Grayling (Arctic) U 1,000 1,008 Weight gained reduced

by 33%

McLeay et al.

(1987)

N & J

Grayling (Arctic) U 300 756 Fish displaced from their

habitat

McLeay et al.

(1987))

N & J

Grayling (Arctic) U 100,000 168 No changes in gill

histology (not an end

point)

McLeay et al.

(1983)

N & J

Salmon (chinook) S 943 72 Tolerance to stress

reduced (VA)

Stober et al.

(1981)

N & J

Salmon (chinook) J 6 1,440 Growth rate reduced

(LNFH)

MacKinley et

al. (1987)

N & J

Salmon (chinook) J 1,400 36 Mortality rate 50% Newcomb and

Flagg (1983)

N & J

Salmon (chinook) J 9,400 36 Mortality rate 50% Newcomb and

Flagg (1983)

N & J

Salmon (chinook) S 488 96 Mortality rate 50% Stober et al.

(1981)

N & J

Salmon (chinook) S 11,000 96 Mortality rate 50% Stober et al.

(1981)

N & J

Salmon (chinook) S 19,364 96 Mortality rate 50% Stober et al.

(1981)

N & J

Salmon (chinook) J 39,400 36 Mortality rate 90% (VA) Newcomb and

Flagg (1983)

N & J

Salmon (chum) J 28,000 96 Mortality rate 50% Smith (1940) N & J
45

Salmon (chum) J 55,000 96 Mortality rate 50%

(winter)

Smith (1940) N & J

Salmon (coho) J 53.5 0.02 Alarm reaction Berg (1983) N & J

Salmon (coho) J 88 0.02 Alarm reaction Bisson and

Bilby (1982)

N & J

Salmon (coho) U 20 0.05 Cough frequency not

increased

Servizi and

Martens

(1992)

N & J

Salmon (coho) J 53.5 12 Changes in territorial

behavior

Berg and

Northcote

(1985)

N & J

Salmon (coho) J 88 0.08 Avoidance behavior Bisson and

Bilby (1982)

N & J

Salmon (coho) J 6,000 1 Avoidance behavior Noggle

(1978)

N & J

Salmon (coho) U 300 0.17 Avoidance behavior

within minutes

Servizi and

Martens

(1992)

N & J

Salmon (coho) J 25 1 Feeding rate decreased Noggle

(1978)

N & J

Salmon (coho) J 100 1 Feeding rate decreased to

55% of maximum

Noggle

(1978)

N & J

Salmon (coho) J 250 1 Feeding rate decreased to

10% of maximum

Noggle

(1978)

N & J

Salmon (coho) J 300 1 Feeding ceased Noggle

(1978)

N & J

Salmon (coho) U 2,460 0.05 Coughing behavior

manifest within minutes

Servizi and

Martens

(1992)

N & J

Salmon (coho) J 53.5 12 Increased physiological

stress

Berg and

Northcote

(1985)

N & J

Salmon (coho) U 2,460 1 Cough frequency greatly

increased

Servizi and

Martens

(1992)

N & J

Salmon (coho) U 240 24 Cough frequency

increased more than 5-

fold

Servizi and

Martens

(1992)

N & J

Salmon (coho) U 530 96 Blood glucose levels

increased

Servizi and

Martens

(1992)

N & J

Salmon (coho) J 1,547 96 Gill damage Noggle

(1978)

N & J

Salmon (coho) U 2,460 24 Fatigue of the cough

reflex

Servizi and

Martens

(1992)

N & J
46

Salmon (coho) U 3,000 48 High level sublethal

stress; avoidance

Servizi and

Martens

(1992)

N & J

Salmon (coho) J 102 336 Growth rate reduced (FC,

BC)

Sigler et al.

(1984)

N & J

Salmon (coho) U 8,000 96 Mortality rate 1% Servizi and

Martens

(1991)

N & J

Salmon (coho) J 1,200 96 Mortality rate 50% Noggle

(1978)

N & J

Salmon (coho) J 35,000 96 Mortality rate 50% Noggle

(1978)

N & J

Salmon (coho) U 22,700 96 Mortality rate 50% Servizi and

Martens

(1991)

N & J

Salmon (coho) F* 8,100 96 Mortality rate 50% Servizi and

Martens

(1991)

N & J

Salmon (coho) PS 18,672 96 Mortality rate 50% Stober et al.

(1981)

N & J

Salmon (coho) S 509 96 Mortality rate 50% Stober et al.

(1981)

N & J

Salmon (coho) S 1,217 96 Mortality rate 50% (VA) Stober et al.

(1981)

N & J

Salmon (coho) S 28,184 96 Mortality rate 50% (VA) Stober et al.

(1981)

N & J

Salmon (coho) S 29,580 96 Mortality rate 50% Stober et al.

(1981)

N & J

Salmon (sockeye) S 1,261 96 Body moisture content

reduced

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) S 7,447 96 Plasma chloride levels

increased slightly

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 1,465 96 Hypertrophy and

necrosis of gill tissue

(CSS)

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 3,143 96 Hypertrophy and

necrosis of gill tissue

(FSS)

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 9,851 96 Hypertrophy and

necrosis of gill tissue

(MCSS)

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 17,560 96 Hypertrophy and

necrosis of gill tissue

(FSS)

Servizi and

Martens

(1987)

N & J
47

Salmon (sockeye) U 23,790 96 Hypertrophy and

necrosis of gill tissue

(FSS)

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 2,688 96 Hypertrophy and

necrosis of gill tissue

(MCSS)

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 2,100 96 No fish died (MFSS) Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 9,000 96 No mortality Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 13,900 96 Mortality rate 10% (FSS) Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 9,850 96 Gill hyperplasia,

hypertrophy, separation,

necrosis (MFSS)

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) J 1,400 36 Mortality rate 50% Newcomb and

Flagg (1983)

N & J

Salmon (sockeye) J 9,400 36 Mortality rate 50% Newcomb and

Flagg (1983)

N & J

Salmon (sockeye) U 1,700 96 Mortality rate 50% (CSS) Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 4,850 96 Mortality rate 50%

(MCSS)

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 8,200 96 Mortality rate 50%

(MFSS)

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 17,560 96 Mortality rate 50% (FSS) Servizi and

Martens

(1987)

N & J

Salmon (sockeye) J 39,400 36 Mortality rate 90% (VA) Newcomb and

Flagg (1983)

N & J

Salmon (sockeye) U 13,000 96 Mortality rate 90%

(MFSS)

Servizi and

Martens

(1987)

N & J

Salmon (sockeye) U 23,900 96 Mortality rate 90% (FSS) Servizi and

Martens

(1987)

N & J

Steelhead J 102 336 Growth rate reduced (FC,

BC)

Sigler et al.

(1984)

N & J

Trout (brook) FF 12 5,880 Growth rates declined Sykora et al.

(1972)

N & J

Trout (brook) FF 24 5,208 Growth rate reduced

(LNFH)

Sykora et al.

(1972)

N & J
48

Trout (brook) FF* 100 1,176 Test fish weighed 16% of

controls (LNFH)

Sykora et al.

(1972)

N & J

Trout (brook) FF 50 1,848 Growth rates declined

(LNFH)

Sykora et al.

(1972)

N & J

Trout (rainbow) FF 1,750 480 Mortality rate 57%

(controls 5%)

Campbell

(1954)

N & J

Trout (rainbow) J 4,887 384 Hyperplasia of gill tissue Goldes (1983) N & J

Trout (rainbow) J 4,887 384 Parasitic infection of gill

tissue

Goldes (1983) N & J

Trout (rainbow) J 171 96 Particles penetrated cells

of branchial epithelium

Goldes (1983) N & J

Trout (rainbow) Y 90 456 Mortality rates 0-20%

(DE)

Herbert and

Merkens

(1961)

N & J

Trout (rainbow) Y 90 456 Mortality rates 0-15%

(KC)

Herbert and

Merkens

(1961)

N & J

Trout (rainbow) Y 270 456 Mortality rates 10-35%

(KC)

Herbert and

Merkens

(1961)

N & J

Trout (rainbow) Y 810 456 Mortality rates 35-85%

(DE)

Herbert and

Merkens

(1961)

N & J

Trout (rainbow) Y 810 456 Mortality rates 5-80%

(KC)

Herbert and

Merkens

(1961)

N & J

Trout (rainbow) Y 270 456 Mortality rates 25-80%

(DE)

Herbert and

Merkens

(1961)

N & J

Trout (rainbow) Y 7,433 672 Mortality rate 40% (CS) Herbert and

Wakeford

(1962)

N & J

Trout (rainbow) Y 4,250 672 Mortality rate 50% Herbert and

Wakeford

(1962)

N & J

Trout (rainbow) Y 2,120 672 Mortality rate 100% Herbert and

Wakeford

(1962)

N & J

Trout (rainbow) J 4,315 57 Mortality rate ~ 100%

(CSS)

Newcombe et

al. (1995)

N & J

SALMONID EGGS

AND LARVAE
49

Grayling (Arctic) SF 25 24 Mortality rate 5.7% J. LaPerriere

(personal

communication)

N & J

Grayling (Arctic) SF 22.5 48 Mortality rate 14.0% J. LaPerriere

(personal

communication)

N & J

Grayling (Arctic) SF 65 24 Mortality rate 15.0% J. LaPerriere

(personal

communication)

N & J

Grayling (Arctic) SF 21.7 72 Mortality rate 14.7% J. LaPerriere

(personal

communication)

N & J

Grayling (Arctic) SF 20 96 Mortality rate 13.4% J. LaPerriere

(personal

communication)

N & J

Grayling (Arctic) SF 142.5 48 Mortality rate 26% J. LaPerriere

(personal

communication)

N & J

Grayling (Arctic) SF 185 72 Mortality rate 41.3% J. LaPerriere

(personal

communication)

N & J

Grayling (Arctic) SF 230 96 Mortality rate of 47% J. LaPerriere

(personal

communication)

N & J

Salmon E 117 960 Mortality; deterioration

of spawning gravel

Cederholm et

al. (1981)

N & J

Salmon (chum) E 97 2,808 Mortality rate 77%

(controls, 6%)

Langer (1980) N & J

Salmon (coho) E 157 1,728 Mortality rate 100%

(controls, 16.2%)

Shaw and

Maga (1943)

N & J

Steelhead E 37 1,488 Hatching success 42%

(controls, 63%)

Slaney et al.

(1977b)

N & J

Trout E 117 960 Mortality; deterioration

of spawning gravel

Cederholm et

al. (1981)

N & J

Trout (rainbow) EE 1,750 144 Mortality rate greater

than controls (controls,

6%)

Campbell

(1954)

N & J

Trout (rainbow) E 6.6 1,152 Mortality rate 40% Slaney et al.

(1977b)

N & J

Trout (rainbow) E 57 1,488 Mortality rate 47%

(controls, 32%)

Slaney et al.

(1977b)

N & J
50

Trout (rainbow) E 120 384 Mortality rates 60-70%

(controls, 38.6%)

Erman and

Lignon (1988)

N & J

Trout (rainbow) E 20.8 1,152 Mortality rate 72% Slaney et al.

(1977a)

N & J

Trout (rainbow) E 46.6 1,152 Mortality rate 100% Slaney et al.

(1977b)

N & J

Trout (rainbow) E 101 1,440 Mortality rate 98%

(controls, 14.6%)

Turnpenny

and Williams

(1980)

N & J

NONSALMONID

EGGS AND

LARVAE

Bass (striped) L 200 0.42 Feeding rate reduced

40%

Breitburg

(1988)

N & J

Bass (striped) E 800 24 Development rate slowed

significantly

Morgan et al.

(1983)

N & J

Bass (striped) E 100 24 Hatching delayed Schubel and

Wang (1973)

N & J

Bass (striped) E 1,000 168 Reduced hatching

success

Auld and

Schubel

(1978)

N & J

Bass (striped) L 1,000 68 Mortality rate 35%

(controls, 16%)

Auld and

Schubel

(1978)

N & J

Bass (striped) L 500 72 Mortality rate 42%

(controls, 17%)

Auld and

Schubel

(1978)

N & J

Bass (striped) L 485 24 Mortality rate 50% Morgan et al.

(1973)

N & J

Herring L 10 3 Depth preference

changed

Johnson and

Wildish

(1982)

N & J

Herring (lake) L 16 24 Depth preference

changed

Swenson and

Matson

(1976)

N & J

Herring (Pacific) L 2,000 2 Feeding rate reduced Boehlert and

Morgan

(1985)

N & J

Herring (Pacific) L 1,000 24 Mechanical damage to

epidermis

Boehlert

(1984)

N & J

Herring (Pacific) L 4,000 24 Epidermis punctured;

microridges less distinct

Boehlert

(1984)

N & J

Perch (white) E 800 24 Egg development slowed

significantly

Morgan et al.

(1983)

N & J

Perch (white) E 100 24 Hatching delayed Schubel and

Wang (1973)

N & J
51

Perch (white) E 1,000 168 Reduced hatching

success

Auld and

Schubel

(1978)

N & J

Perch (white) L 155 48 Mortality rate 50% Morgan et al.

(1973)

N & J

Perch (white) L 373 24 Mortality rate 50% Morgan et al.

(1973)

N & J

Perch (white) L 280 48 Mortality rate 50% Morgan et al.

(1973)

N & J

Perch (yellow) L 500 96 Mortality rate 37%

(controls, 7%)

Auld and

Schubel

(1978)

N & J

Perch (yellow) L 1,000 96 Mortality rate 38%

(controls, 7%)

Auld and

Schubel

(1978)

N & J

Shad (American) L 100 96 Mortality rate 18%

(controls, 5%)

Auld and

Schubel

(1978)

N & J

Shad (American) L 500 96 Mortality rate 36%

(controls, 4%)

Auld and

Schubel

(1978)

N & J

Shad (American) L 1,000 96 Mortality rate 34%

(controls, 5%)

Auld and

Schubel

(1978)

N & J

ADULT

NONSALMONIDS

Anchovy (bay) A 231 24 Mortality rate 10% (FE) Sherk et al.

(1975)

N & J

Anchovy (bay) A 471 24 Mortality rate 50% (FE) Sherk et al.

(1975)

N & J

Anchovy (bay) A 960 24 Mortality rate 90% Sherk et al.

(1975)

N & J

Bass (striped A 1,500 336 Haematocrit increased

(FE)

Sherk et al.

(1975)

N & J

Bass (striped) A 1,500 336 Plasma osmolality

increased (FE)

Sherk et al.

(1975)

N & J

Cunner A 28,000 24 Mortality rate 50% (20.0-

25.0° C)

Rogers (1969) N & J

Cunner A 133,000 12 Mortality rate 50%

(15°C)

Rogers (1969) N & J

Cunner A 100,000 24 Mortality rate 50% 

(15°C) 

Rogers (1969) N & J

Cunner A 72,000 48 Mortality rate 50%

(15°C)

Rogers (1969) N & J

Fish A 3,000 240 Fish died Kemp (1949) N & J
52

Herring (Atlantic) A 20 3 Reduced feeding rate Johnson and

Wildish

(1982)

N & J

Hogchoker A 1,240 24 Energy utilization

increased

Sherk et al.

(1975)

N & J

Hogchoker A 1,240 120 Erythrocyte count

increased

Sherk et al.

(1975)

N & J

Hogchoker A 1,240 120 Haematocrit increased Sherk et al.

(1975)

N & J

Killifish (striped) A 960 120 Haematocrit increased Sherk et al.

(1975)

N & J

Killifish (striped) A 3,277 24 Mortality rate 10% (FE) Sherk et al.

(1975)

N & J

Killifish (striped) A 9,720 24 Mortality rate 10% Sherk et al.

(1975)

N & J

Killifish (striped) A 3,819 24 Mortality rate 50% Sherk et al.

(1975)

N & J

Killifish (striped) A 12,820 24 Mortality rate 50% Sherk et al.

(1975)

N & J

Killifish (striped) A 16,930 24 Mortality rate 90% Sherk et al.

(1975)

N & J

Killifish (striped) A 6,136 24 Mortality rate 90% Sherk et al.

(1975)

N & J

Menhaden (Atlantic) A 154 24 Mortality rate 10% (FE) Sherk et al.

(1975)

N & J

Menhaden (Atlantic) A 247 24 Mortality rate 50% (FE) Sherk et al.

(1975)

N & J

Menhaden (Atlantic) A 396 24 Mortality rate 90% (FE) Sherk et al.

(1975)

N & J

Minnow (sheepshead) A 200,000 24 Mortality rate 10%

(15°C)

Rogers (1969) N & J

Minnow (sheepshead) A 300,000 24 Mortality rate 30%

(10°C)

Rogers (1969) N & J

Minnow (sheepshead) A 100,000 24 Mortality rate 90%

(19°C)

Rogers (1969) N & J

Mummichog A 300,000 24 No mortality (15°C) Rogers (1969) N & J

Mummichog A 2,447 24 Mortality rate 10% (FE) Sherk et al.

(1975)

N & J

Mummichog A 3,900 24 Mortality rate 50% (FE) Sherk et al.

(1975)

N & J

Mummichog A 6,217 24 Mortality rate 90% Sherk et al.

(1975)

N & J

Perch (white) A 650 120 Haematocrit increased Sherk et al.

(1975)

N & J
53

Perch (white) A 650 120 Erythrocyte count

increased

Sherk et al.

(1975)

N & J

Perch (white) A 650 120 Hemoglobin

concentration increased

Sherk et al.

(1975)

N & J

Perch (white) A 305 120 Gill tissue may have

been damaged

Sherk et al.

(1975)

N & J

Perch (white) A 650 120 Histological damage to

gill tissue

Sherk et al.

(1975)

N & J

Perch (white) A 305 24 Mortality rate 10% (FE) Sherk et al.

(1975)

N & J

Perch (white) A 985 24 Mortality rate 50% Sherk et al.

(1975)

N & J

Perch (white) A 3,181 24 Mortality rate 90% (FE) Sherk et al.

(1975)

N & J

Rasbora (harlequin) A 40,000 24 Fish died (BC) Alabaster and

Lloyd (1980)

N & J

Rasbora (harlequin) A 6,000 168 No mortality Alabaster and

Lloyd (1980)

N & J

Shad (American) A 150 0.25 Change in preferred

swimming depth

Dadswell et

al. (1983)

N & J

Silverside (Atlantic) A 58 24 Mortality rate 10% (FE) Sherk et al.

(1975)

N & J

Silverside (Atlantic) A 250 24 Mortality rate 50% (FE) Sherk et al.

(1975)

N & J

Silverside (Atlantic) A 1,000 24 Mortality rate 90% (FE) Sherk et al.

(1975)

N & J

Spot A 114 48 Mortality rate 10% (FE) Sherk et al.

(1975)

N & J

Spot A 1,309 24 Mortality rate 10% (FE) Sherk et al.

(1975)

N & J

Spot A 6,875 24 Mortality rate 10% Sherk et al.

(1975)

N & J

Spot A 189 48 Mortality rate 50% (FE) Sherk et al.

(1975)

N & J

Spot A 2,034 24 Mortality rate 50% Sherk et al.

(1975)

N & J

Spot A 8,800 24 Mortality rate 50% Sherk et al.

(1975)

N & J

Spot A 317 48 Mortality rate 90% (FE) Sherk et al.

(1975)

N & J

Spot A 11,263 24 Mortality rate 90% Sherk et al.

(1975)

N & J

Stickleback (four

spine)

A 100 24 Mortality rate <1% (IA) Rogers (1969) N & J
54

Stickleback (four

spine)

A 10,000 24 No mortality (KS: 10-

12°C)

Rogers (1969) N & J

Stickleback (four

spine)

A 300 24 Mortality rate ~50% (IA) Rogers (1969) N & J

Stickleback (four

spine)

A 18,000 24 Mortality rate 50% (15.0-

16.0°C)

Rogers (1969) N & J

Stickleback (four

spine)

A 50,000 24 Mortality rate 50% (KS) Rogers (1969) N & J

Stickleback (four

spine)

A 53,000 24 Mortality rate 50% (10-

12°C)

Rogers (1969) N & J

Stickleback (four

spine)

A 330,000 24 Mortality rate 50% (9.0-

9.5°C)

Rogers (1969) N & J

Stickleback (four

spine)

A 500 24 Mortality rate 100% Rogers (1969) N & J

Stickleback (four

spine)

A 200,000 24 Mortality rate 95% (KS) Rogers (1969) N & J

Stickleback (three

spine)

A 28,000 96 No mortality in test

designed to identify

lethal threshold

LeGore and

DesVoigne

(1973)

N & J

Toadfish (oyster) A 3,360 1 Oxygen consumption

more variable in

prestressed fish

Neumann et

al. (1975)

N & J

Toadfish (oyster) A 14,600 72 Fish largely unaffected,

but developed latent ill

effects

Neumann et

al. (1975)

N & J

Toadfish (oyster) A 11,090 72 Latent ill effects

manifested in subsequent

test at low SS

Neumann et

al. (1975)

N & J

ADULT

NONSALMONIDS

Bass (largemouth) A 62.5 720 Weight gain reduced ~

50%

Buck (1956) N & J

Bass (largemouth) A 144.5 720 Growth retarded Buck (1956) N & J

Bass (largemouth) A 144.5 720 Fish unable to reproduce Buck (1956) N & J

Bluegill A 423 0.05 Rate of feeding reduced Gardner

(1981)

N & J

Bluegill A 15 1 Reduced capacity to

locate prey

Vinyard and

O’Brien

(1976)

N & J

Bluegill A 144.5 720 Growth retarded Buck (1956) N & J

Bluegill A 62.5 720 Weight gain reduced ~

50%

Buck (1956) N & J

Bluegill A 144.5 720 Fish unable to reproduce Buck (1956) N & J
55

 Carp (common) A 25,000 336 Some mortality (MC) Wallen (1951) N & J

Darters A 2,045 8,760 Darters absent Vaughan

(1979);

Vaughan et al.

(1982)

N & J

Fish A 120 384 Density of fish reduced Erman and

Lignon (1988)

N & J

Fish A 620 48 Fish kills downstream

from sediment source

Hesse and

Newcomb

(1982)

N & J

Fish A 900 720 Fish absent or markedly

reduced in abundance

Herbert and

Richards

(1963)

N & J

Fish A 2,045 8,760 Habitat destruction; fish

populations smaller than

expected

Vaughan

(1979);

Vaughan et al.

(1982)

N & J

Fish (warm water) A 100,000 252 Some fish died; most

survived

Wallen (1951) N & J

Fish (warm water) A 200,000 1,125 Fish died; opercular

cavities and gill filaments

clogged

Wallen (1951) N & J

Fish (warm water) A 22 8,760 Fish populations

destroyed

Menzel et al.

(1984)

N & J

Goldfish A 25,000 336 Some mortality (MC) Wallen (1951) N & J

Sunfish (green) A 9,600 1 Rate of ventilation

increased

Horkel and

Pearson

(1976)

N & J

Sunfish (red ear) A 62.5 720 Weight gain reduced ~

50% compared to

controls

Buck (1956) N & J

Sunfish (red ear) A 144.5 720 Growth retarded Buck (1956) N & J

Sunfish (red ear) A 144.5 720 Fish unable to reproduce Buck (1956) N & J
56

Appendix D

Revised model of the effects of suspended sediments on estuarine fishes 

(C.P. Newcombe, Personal Communication)

Reprinted with permission of the author

Not included, available upon request
57

Appendix E.

Summary figures from Wilber and Clarke, 2001

Reprinted with permission of the authors

Not included, available upon request
58

Appendix F

Summary of state standards for suspended and bedded sediments. 

Prepared by Kathleen Sullivan for the U.S.EPA, Office of Water. 

Reprinted with permission of the U.S.EPA Office of Water (Chris Zabawa and Bill Swietlik)

See Appendix 4 of SABS Consultation Document

Available upon request