Reviews in Fisheries Science, 17(3):348–359, 2009

ISSN: 1064-1262 print

DOI: 10.1080/10641260902716954

Impact of Fine Sediment on

Egg-To-Fry Survival of Pacific Salmon:

A Meta-Analysis of Published Studies

DAVID W. JENSEN,∗ E. ASHLEY STEEL, AIMEE H. FULLERTON,

and GEORGE R. PESS

Northwest Fisheries Science Center, NOAA Fisheries, Seattle, Washington, USA

Egg-to-fry survival of salmonids is tempered by habitat degradation, including increased sediment in streams. To best

manage multiple salmon species and prioritize scarce habitat restoration funds for the benefit of fish recovery, many studies

have described and predicted the relationship between fine sediment deposited in spawning gravels and salmonid egg-to-fry

survival. In this article, we used published studies, agency reports, and university theses (N = 14) to create predictive

relationships between percent fine sediment and egg-to-fry survival of Chinook (Oncorhynchus tshawytscha), coho (O.

kisutch) and chum (O. keta) salmon, and steelhead trout (O. mykiss). In our analysis, coho survival tended to decline more

rapidly per unit sediment increase and chum survival least rapidly. Threshold effects were observed, with survival dropping

rapidly when percent fines less than 0.85 mm was greater than 10%. For other size classes of fines, a threshold was primarily

observed only for eyed egg survival when fines exceeded 25–30%. Our predictive models combine both field and laboratory

data and take into account a variety of conditions; they include estimates of uncertainty in the impact of sediment on

egg-to-fry survival. These models can be used to forecast effects of watershed management practices on salmonids and to

make comparisons between predicted salmonid survival rates under alternative management strategies for conditions where

fine sediment is the limiting factor for survival.

Keywords salmon incubation, salmon spawning, sediment deposition

INTRODUCTION

Since six species of Pacific salmonids were listed by the U.S.

Endangered Species Act (ESA) as threatened or endangered

(Knudsen et al., 2000), research and modeling has focused on

factors that threaten the continued existence of these species.

Habitat degradation is key among these factors (Nehlsen et al.,

1991), including instream sedimentation resulting from human

activities such as timber harvest, agriculture, urban and rural residential development, and road construction (e.g., Bjornn and

Reiser, 1991; Waters, 1995). Increasing amounts of fine sediment (∼2 mm or smaller) in spawning gravels has been shown

to decrease survival of salmonid eggs to emergence (Bjornn and

Reiser, 1991; Everest et al., 1987; Greig et al., 2005a, 2007).

This article is not subject to U.S. copyright law.

∗Current address: 710 W. 27th Ave., Eugene, OR 97405.

Address correspondence to E. Ashley Steel, Northwest Fisheries Science

Center, NOAA Fisheries, 2725 Montlake Blvd East, Seattle, WA 98112. E-mail:

ashley.steel@noaa.gov

Because of the importance of fine sediment deposition to

early salmonid survival, a great deal of research effort has gone

into modeling the processes by which fine sediment enters and

moves through stream systems. Our ability to model mechanisms by which land use increases sediment delivery to streams

(e.g., the WEPP model: Elliot et al., 1995; Elliot and Hall,

1997; the PSYCHIC model: Davison et al., 2008) as well as

our ability to model the mechanisms of transport and storage

through stream networks (e.g., NetStream, Miller, 2003; the

EUROSEM-GRIDSEM model, Botterweg et al., 1998; LISEM,

DeRoo and Wessling, 1996) has become more sophisticated.

Additional research has focused on the empirical evaluation of

these processes (e.g., Collins and Walling, 2004; Heywood and

Walling, 2003; Walling et al., 2008; Cai et al., 2005). As a result,

we are increasingly confident in our predictions of how land use

affects sediment deposition in stream reaches (Nilsson et al.,

2003; Opperman et al., 2005).

However, sediment modeling exercises are often conducted at coarse resolutions across large extents, whereas our

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REVIEW OF EGG-TO-FRY SURVIVAL OF PACIFIC SALMON 349

understanding of how sediment impacts salmonid egg-to-fry

survival comes from laboratory experiments or observations at

the scale of a habitat unit or redd. Extrapolating results of individual survival studies to the larger scales at which we can

restore sediment processes may be inappropriate because of

differences in experimental conditions (i.e., season, geomorphology of stream, water chemistry), methods (i.e., laboratory

vs. in situ studies, genetic origin of fish), and response variables

(i.e., life stage, sediment metric) considered. As well, there are

many factors other than sediment, such as dissolved oxygen, incubation temperatures, and biological controls, that contribute

to the complexity of the spawning environment at the redd scale,

and therefore to salmon survival (Malcolm et al., 2003; Greig

et al., 2005b). Yet, for successful management of watersheds

and salmon populations, managers need a reliable way to link

disparate types of survival analyses.

Managers often rely on complex models to select restoration

projects that will have positive benefits for fish (e.g., EDT, Mobrand Biometrics Inc., 2004). If such models are used, then they

must accurately predict not only which projects are likely to

result in a decrease in fine sediment deposition but, ultimately,

how survival of salmonids will respond to that reduced sediment deposition under a variety of conditions. While there are

more detailed models that include several of the suite of factors present in the natural spawning environment (e.g., Alonso

et al., 1996; Wu, 2000), calibration of these models is a large

task and must be repeated for each new basin. Accurate prediction of fish response over large spatial extents requires models

developed from studies conducted over a range of conditions.

Since landscape scale studies are generally lacking, one way

to address this gap is to pool results from multiple studies,

thus incorporating the variability observed between individual studies into the modeled relationship. Incorporating known

sources of variability is important whether the model has a narrow focus, e.g., the response of egg-to-fry survival to changes

in sediment composition, or is an input to a more complex

model of fish response to changes in multiple environmental

factors.

Development of quantitative relationships linking sediment

and survival that are robust to these differences in experimental conditions, methods, and response variables will improve

our ability to link small-scale observations on fish survival to

large-scale sediment input, routing, and storage models. We

have compiled data from individual published studies relating

egg survival to fine sediment composition, reanalyzed these data

together, and developed models to predict species-specific mean

survival as a function of sediment composition. In this modeling

effort, we assume that it is the sediment quantity and to some

extent the grain size that is primarily controlling survival. We

do not account for the host of non-sediment factors, e.g., the potential impact of poor quality groundwater upwelling (Malcolm

et al., 2003), on salmon survival.

We present a review of existing published data relating sediment to salmon egg-to-fry survival for steelhead trout (Oncorhynchus mykiss) and Chinook (O. tshawytscha) and chum

(O. keta) and coho (O. kisutch) salmon. We explore multiple

early life stages and sediment size classes in freshwater systems of the Pacific Northwest United States to provide a metaanalysis of these existing data. Our meta-analysis is unique

in that it yields models that are robust to the details of particular experiments as well as provides improved quantitative

estimates of model and parameter uncertainty. We compare

these robust models to identify species-specific differences and

gain insight into the structure of the mechanistic relationships

involved. For example, we ask whether the relationships between sediment and survival are linear, or whether they include a threshold above which survival is improbable? These

robust models not only increase our understanding of watershed and salmonid ecology, but also provide powerful management tools by enabling combinations of models and estimates of

uncertainty.

METHODS

Existing Data on Salmonid Egg-to-Fry Survival

We surveyed the primary literature for studies on salmonids

in the Pacific Northwest that investigated the effects of fine

sediment deposition on egg-to-fry survival. Of the 96 papers

identified, most discussed observed or implied effects, but did

not provide data or experimental analysis. Fourteen of these

studies provided data that we could reanalyze, including seven

experiments with Chinook salmon, four with steelhead, six with

coho, and three with chum (Table 1). Some of these investigated

the effects of sediment on survival of wild fish eggs in streams

(e.g., Cederholm and Lestelle, 1974; Hall and Lantz, 1969),

while others (e.g., Reiser and White, 1988; Tappel and Bjornn,

1983) described laboratory experiments, often using hatchery

fish reared at high densities. Because of a general lack of field

studies (e.g., all studies on Chinook salmon were laboratory

studies), and because the laboratory studies have been well cited,

we reasoned that including them would strengthen our dataset. In

addition, our methodology would test whether the conclusions

from field studies differed from those in the lab, and hence

whether combining them was justified statistically. The sieve

size used to quantify the sediment composition varied between

studies, but, within each of the four groups considered here, all

were within 0.2 mm (0.8–0.85 mm, 3.327–3.4 mm, 4.6–4.8 mm,

6.35–6.4 mm). We reanalyzed the data using one or more of five

measures of substrate composition: percent fines < 0.85 mm,

percent fines < 3.4 mm, percent fines < 4.8 mm, percent fines

< 6.4 mm, and geometric mean particle size. The Fredle index

(Lotspeich and Everest, 1981) has been shown to produce higher

correlations with survival than the geometric mean (Chapman,

1988, using Tappel and Bjornn’s (1983) data); however, the

calculation of the index requires additional sediment size data

not available from all papers reporting geometric means. In fact,

we could only add a single study to what Chapman (1988) had

summarized, so we chose not to include this metric.

reviews in fisheries science vol. 17 3 2009
350 D. W. JENSEN ET AL.

Table 1 Pacific salmonid studies evaluated, including whether the study was conducted in an artificial (lab) or natural (field) environment, the

number of redds monitored in each experiment, the life stage at which monitoring began, the sediment metric(s) used, and the slope of the logistic

regression line fit to the original data

Source Type no. reddsa Species Life stage Sediment metric Slope (linear, quadratic)b

Bennett et al., 2003 Lab 8 Chinook Green egg-to-fry % fines < 0.85 mm 0.0602, –0.0032

% fines < 3.4 mm 0.0312, –0.0038

% fines < 4.8 mm 0.0268, –0.0029

% fines < 6.4 mm 0.0243, –0.0022

Geometric mean 0.1527

Bjornn, 1968 Lab 32 Chinook Green egg-to-fry % fines < 6.4 mm –0.1425

Bjornn, 1969 Lab 28 Chinook Green egg-to-fry % fines < 6.4 mm –0.0066, –0.0018

Steelhead Green egg-to-fry % fines < 6.4 mm –0.0795

Cederholm and Lestelle, 1974 Field 11 Steelhead Eyed egg-to-yolk absorption % fines < 0.841 mm —

% fines < 3.36 mm +

Cederholm and Salo, 1979 Lab 31 Coho Green egg-to-fry % fines < 0.85 mm –0.1971

% fines < 3.36 mm –0.0886

Hall and Lantz, 1969 Field 20 Coho Egg-to-fryc % fines < 0.83 mm –0.2166

Hall, 1986 Lab 6 Chinook Eyed egg-to-fry % fines < 0.8 mm –0.1578, –0.0058

Chum Eyed egg-to-fry % fines < 0.8 mm –0.1458

Coho Eyed egg-to-fry % fines < 0.8 mm –0.3328, 0.0046

Koski, 1966 Field 22 Coho Egg-to-fryc % fines < 3.327 mm –0.1203

Koski, 1975 Lab 35 Chum Egg-to-fryc % fines < 3.327 mm 0.2469, –0.0037

Reiser and White, 1988 Lab 15 Chinook Green egg-to-fry % fines < 0.84 mm –0.1447

% fines < 4.6 mm –0.0738

Steelhead Eyed egg-to-fry % fines < 0.84 mm –0.1630

% fines < 4.6 mm +

Steelhead Green egg-to-fry % fines < 0.84 mm –0.1367

% fines < 4.6 mm –0.0550

Reiser and White, 1990 Lab 8 Chinook Eyed egg-to-fry % fines < 0.84 mm —

Geometric mean –0.4870, 0.0119

Chinook Green egg-to-fry % fines < 0.84 mm –0.1458

Geometric mean 0.0710

Scrivener and Brownlee, 1989 Field 13–14 Chum Egg-to-fryc Geometric mean 0.3347

Coho Egg-to-fryc Geometric mean 0.2048

Tagart, 1984 Field 18 Coho Egg-to-fryc % fines < 0.85 mm —

Tappel and Bjornn, 1983 Lab 15 Chinook Eyed egg-to-fry % fines < 0.85 mm –0.2818

% fines < 4.76 mm 0.0411, –0.0034

% fines < 6.35 mm 0.0254, –0.0025

Geometric mean 1.2135, –0.0382

Steelhead Eyed egg-to-fry % fines < 0.85 mm –0.2870

% fines < 4.76 mm –0.1165

% fines < 6.35 mm –0.0969

Geometric mean 1.1284, –0.0363

aNumber of redds (or artificial equivalent) is the same for each experiment unless indicated otherwise.

bAll relationships shown are statistically significant (Wald test) at the 0.05 level. A – indicates the slope is not significant , + indicates the individual

relationship is not statistically significant, but when combined with other observations within the same species and egg stage, a significant relationship

emerges.

cDevelopment stage not identified.

Meta-analysis includes a variety of statistical methods for

synthesizing the results of individual studies (e.g., Hedges and

Olkin, 1985). One requirement for meta-analysis is that there

is a common summary statistic across studies, which is often a

measure of a treatment effect in a series of experiments. Under

certain assumptions, the analysis of the summary statistics is

equivalent to the analysis of the original data (Olkin and Sampson, 1998; Mathew and Nordstrom, 1999). We chose to utilize  ̈

the original data in our meta-analysis since it was generally

available and the published summary statistics were not consistent across studies. Furthermore, we felt that a logistic model

more accurately reflected the relationship between sediment and

survival in that survival is constrained between zero and one.

By using this novel approach, we hoped to improve on past

analyses.

The individual datasets that were used to develop new models

(based on combined datasets) are identified in Table 1. We only

combined studies where either individual relationships with fine

sediment substrate were significant or a significant relationship

was found when combined with other data in the same species

and egg stage class. We included all data that showed any evidence of a statistical relationship between fines and survival

reviews in fisheries science vol. 17 3 2009
REVIEW OF EGG-TO-FRY SURVIVAL OF PACIFIC SALMON 351

and excluded the few datasets without a clear relationship (only

8%).

Statistical Analysis

We used logistic regression models to fit data from multiple sources. The logit of survival was regressed on the various

measures of sediment composition, where

logit(s) = log( s

1−s ), the log of the odds of survival. The relationship between the logit of survival and fines is linear, whereas

the relationship between survival and fines is not. The transformation is survival = 1

1+eβ0 + β1 ∗Sediment . With a single independent

variable that has only a linear component, the odds of survival, ( s

1−s ), has an intuitive interpretation, i.e., the proportional

change in the mean odds of survival with a one-unit change in

sediment.

The reasoning behind these models is that each egg has a

specific probability of surviving to produce a fry, and that probability depends on sediment composition (at least in part; recall

that we did not include other factors influencing survival in our

models). Further, each observation of a group of eggs (e.g., an

experimental tray or a redd in the stream) is considered a binomial experiment with N trials, where N is the number of

eggs in the observational unit. We require an estimate of N,

the number of eggs in the observational unit, in order to fit this

model. In most of the studies, the number of observed eggs

or the estimated number of eggs per female was reported. For

studies without this information, we used species-specific average fecundity values. In a few of the studies, the reported data

were means of two to six observational units; these data were

pooled and treated as one observational unit, but weighted to

compensate for the expected reduction in variability.

We first fit logistic regression models to the data from each experiment independently. This initial fitting indicated that nearly

all of the data were over-dispersed, i.e., the observed variation exceeded that expected under a binomial model. Quasilikelihood procedures (after McCullagh and Nelder, 1989) were

used to fit the models to the data. We used Williams’ method

(Williams, 1982) to estimate the over-dispersion parameter,

which was subsequently used to adjust the standard errors of the

regression parameter estimates. We also checked for quadratic

relationships between sediment metrics and egg-to-fry survival.

Next, we compared the slopes of the relationships between

percent fines and survival among studies using Wald tests (see

McCullagh and Nelder, 1989). If the slopes were significantly

different, we did not try to combine the datasets. Otherwise, a

parallel slopes model was fit to the combined dataset. This combined data model was used to estimate the change in the odds of

egg-to-fry survival as a function of changes in the percentage of

fines in the substrate. We tested whether models with a common

slope shared a common intercept. If not, a weighted average of

the intercepts was calculated.

To construct 95% Wald confidence intervals for the estimated mean survival when a weighted average was used for

the intercept, we used a bootstrap procedure (Efron and Gong,

1983). A bootstrap sample was drawn from each of the groups

having a common slope. We fit a parallel lines model to the

bootstrap sample and used the slope of the lines as an offset in

a subsequent model to estimate the intercept. We repeated this

5000 times and used the output to estimate the variances of the

intercept and slope, as well as the covariance between them.

RESULTS

The datasets included in this meta-analysis are from studies

that represent both lab and field experiments on coho, steelhead,

Chinook, and chum salmon. In 34 out of 39 regressions, when

the data were reanalyzed using our approach, there was a significant linear relationship between the proportion of fines and

(logit) egg-to-fry survival. In 13 of these, a significant quadratic

relationship was also detected, but there was no obvious pattern across species. Two of the five non-significant relationships

were statistically significant when combined with data from

other studies in the same species, even though the studies were

initially modeled with separate regression lines, and, in two additional data sets, there was a second sediment metric that did

produce a significant relationship with survival. Thus, only one

dataset was completely eliminated from the analysis (Tables 1

and 2). The inclusion of non-significant datasets would not have

contributed much to the estimation of the slope of the regression

line, as they all had large 95% confidence intervals; though, by

eliminating them, we have probably underestimated the confidence intervals of the final models for percent fines < 0.85 mm.

There were several groups (e.g., chum salmon and steelhead vs.

geometric mean) with only a single source dataset. These are

included in the tables that follow, even though the parameters

are the same as those in Table 1. The annual survival estimates

reported by Scrivener and Brownlee (1989) were based on calculated egg deposition and enumeration of emergent fry for the

entire study area. Since the other studies monitored individual

redds or some laboratory equivalent, the Scrivener and Brownlee

data were not included in the meta-analysis.

For the smallest sediment size class (percent fines < 0.85

mm), we were able to create a new, combined model for Chinook

and coho salmon together based on five and three datasets,

respectively; however, only one dataset was available for chum

salmon (Table 2, Figure 1). The three steelhead datasets did not

share a common slope.

As mentioned above, the most intuitive way to understand the

impact of sediment on survival is through changes in the odds

of survival. The odds of survival can be transformed into actual

survival, but this relationship is nonlinear. Table 3 describes how

a 1% increase in percent fines (for each size class) translates into

a change in predicted odds of survival by species. For example,

a 1% increase in fines < 0.85 mm will result in a 16.9% (13.3,

20.4) reduction in the odds of survival for Chinook salmon

(Table 3). There was a somewhat more dramatic reduction in

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352 D. W. JENSEN ET AL.

Table 2 Results of comparisons within and across studies where metric is percent fines < 0.85 mm. Groups are based on species and egg stage at

which monitoring began. Bold font indicates parameters of final models developed for this analysis

Comparisons within groups of studies

Species Life stage Source Slopea (SE) p-valueb Interceptc (SE) p-valueb Interceptd (SE)

Chinook Green egg-to-fry Reiser and White, 1988 –0.1853 (0.0211) >0.07 –0.1082 <0.0001 1.3418 (0.1940)

Reiser and White, 1990 1.7497

Bennett et al., 2003 0.5698

Eyed egg-to-fry Hall, 1986 0.9374

Tappel and Bjornn, 1983 2.6680

Reiser and White, 1990 Excluded ————

Steelhead Green egg-to-fry Reiser and White, 1988 –0.1502 >0.48 0.7547 <0.0001 NA

Eyed egg-to-fry Reiser and White, 1988 3.1604

Eyed egg-to-fry Tappel and Bjornn, 1983 –0.2869 3.3137 NA

Eyed egg-to-yolk Cederholm and Lestelle, 1974 Excluded ————

Coho Green egg-to-fry Hall and Lantz, 1969 –0.2024 (0.0345) >0.97 2.2629 0.0002 2.7219 (0.5602)

Green egg-to-fry Cederholm and Salo, 1979 2.1563

Eyed egg-to-fry Hall, 1986 0.4670

Green egg-to-fry Tagart, 1984 Excluded ————

Chum Eyed egg-to-fry Hall, 1986 –0.1458 (0.0288) NA 0.1436 (0.3156) NA NA

All species –0.1846 (0.0125) >0.18 — <0.0001 1.9890 (0.1523)

aThe same estimate within a group signifies that studies were not significantly different from one another (p > 0.05), and different estimates indicate

significant differences within the group (p < 0.05). Standard error is from logistic regression if bootstrapping was unnecessary; otherwise, it was from

bootstrap. Excluded studies were those with non-significant slopes (Table 1).

bp-value refers to the test of whether the parameter is the same for all group members. cIntercept fit using logistic regression. Standard error was included if no bootstrapping was necessary.

d Intercept fit during bootstrap procedure with bootstrap standard error.

survival for coho salmon (18.3%), and a slightly less dramatic

reduction for chum salmon (13.6%). By combining data for all

species, we estimate that, on average, a 1% increase in percent

fines < 0.85 mm will result in about a 17% reduction in the odds

of survival over all species.

Similarly, we were able to provide combined models for percent fines < 3.3–4.8 mm for each species and for pairs of species

with similar survival patterns (Table 4, Figure 2). The studies of

Chinook used 4.8 mm as an upper bound on fine sediment, except for Bennett et al. (2003), who also included measurements

of percent fines < 3.4 mm. We tested for a difference between

the regression lines fit to each of these and found no statistical

difference (slope: p > 0.68, intercept: p > 0.45); hence, only

data on percent fines < 4.8 mm were used, since this size class

corresponded to the other Chinook studies. Because we expect

the mechanisms by which sediment affects egg-to-fry survival

to be similar across species, we used these results for Chinook

salmon to justify combining the two size classes in steelhead,

where a direct test of differences was not possible.

The models for percent fines < 3.3–4.8 mm were based on

three studies with Chinook salmon, four with steelhead, two

with coho salmon, and one with chum. Changes in the odds of

survival for a given increase in fine sediment are much less for

this metric than when the metric is percent fines < 0.85 mm,

with the exception of the eyed egg-to-fry survival in Chinook

(Table 3). The pattern in the modeled odds of survival with each

1% increase in fine sediment was similar to that seen above,

with a lower impact on chum (–6.0), and (with the exception

of eyed Chinook eggs) the largest effect on coho (–9.2). The

impact on green Chinook eggs and steelhead was intermediate.

The large change in the odds of survival with increasing fines

for eyed Chinook eggs (–14.2 per 1% increase in fines) appears

anomalous, but the odds of survival is actually higher for eyed

eggs at any level of percent fines, even though the rate of change

is greater. When evaluating actual survival, this is reflected in

the fact that the impact is constrained to levels of fines above

20%, whereas, in the other groups, there is a smaller change per

sediment increment over a larger range of fines (Figure 1c and

1d). In general, survival at a particular level of fines is higher

when the metric includes larger size classes (Figure 1).

We provide combined models for both Chinook and steelhead

salmon for the largest size class (percent fines < 6.4 mm) and for

a model based on the geometric mean of the sediment grain sizes

(Table 5). Comparisons within and among the studies utilizing

both sediment metrics indicate that the relationship between

Chinook survival and sediment is not consistent. There is a

moderate to strong quadratic component in three of the four

studies for the largest sediment size class, but the evidence for

a quadratic relationship in Bjornn’s (1968) study is weak (p >

0.09). Similarly, only one of the four geometric mean datasets

has a significant quadratic relationship. While a single slope

could be fit to the three studies with a similar relationship, no

common slope model could be fit to all four studies. A common

quadratic model was fit to the steelhead data from both sediment

metrics. The odds of survival is not constant across sediment

composition because of the quadratic relationship, but ranges

from a decrease of 4.1% (–12.0, 4.6), when fines < 6.4 increase

from 10% to 11%, to a decrease of 15.7% (–30.1, 1.5), when

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REVIEW OF EGG-TO-FRY SURVIVAL OF PACIFIC SALMON 353

Figure 1 Relationship between egg-to-fry survival and percent sediment, showing data from the literature used in analysis. (a) Data and modeled egg-to-fry

survival of Chinook and coho salmon vs. percent sediment < 0.85 mm.. (b) Egg-to-fry survival for Chinook, coho, and chum salmon vs. percent sediment < 0.85

mm. Line is the modeled mean survival based on data from all species and includes the 95% confidence interval. (c) Data and modeled green egg-to-fry survival

for Chinook salmon, steelhead, and coho salmon vs. percent fines < 4.8 mm. (d) Data and modeled eyed egg-to-fry survival for Chinook salmon, steelhead, and

chum salmon vs. percent fines < 4.8 mm.

fines increase from 50% to 51%. The odds of survival increase

by 20.8% (9.2, 33.7) when the geometric mean of sediment size

increases from 5 to 5.25 mm, and by 10.3% (–3.1, 25.6) when

the mean increases from 10 to 10.25 mm.

Three field studies, two with coho salmon and one with steelhead, were included in the modeling and shared a common slope

with the lab studies in their group. In both of the coho studies,

the intercept from the field data was larger than those in the lab

Table 3 Comparison of the change in the odds of survival resulting from a 1% increase in fines

Species Metric Change in sediment Egg stage Change in odds of survival (%) 95% Confidence interval

Chinook <0.85 mm +1% Green and eyed −16.9 (–20.4,−13.3)

Coho +1% Green and eyed −18.3 (–23.9,−12.3)

Chum +1% Eyed −13.6 (–18.3,−8.6)

All species +1% Green and eyed −16.9 (–19.1,−14.6)

Chinook <3.4–4.6 mm +1% Green −6.7 (–9.0,−4.4)

+1% Eyed −14.2 (–18.3,−9.8)

Steelhead +1% Green and eyed −6.0 (–8.3,−3.6)

Coho +1% Green and unidentified −9.2 (–12.4,−5.9)

Chum +1% –4.2 (−7.3, −1.0)

All species +1% Green and eyed −7.1 (–8.5,−5.7)

Steelhead <6.4 mm 10–11% Green and eyed −4.1 (–12.0, 4.6)

30–31% −10.1 (–21.6, 3.0)

50–51% −15.7 (–30.1, 1.5)

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354 D. W. JENSEN ET AL.

Table 4 Results of comparisons within and across studies where metric is percent fines < 3.3–4.8 mm. Groups are based on species and egg stage at which

monitoring began. Bold font indicates parameters of final models developed for this analysis

Comparisons between groups of studies

Species Life stage Source Slopea (SE) p-valueb Interceptc (SE) p-valueb Interceptd (SE)

Chinook Green egg-to-fry Bennett et al., 2003 –0.0696 (0.0141) >0.66 0.3336 (0.3096) >0.89 NA

Green egg-to-fry Reiser & White, 1988

Eyed egg-to-fry Tappel and Bjornn, 1983 –0.1528 (0.0265) 5.4962 (0.8859) NA NA

Steelhead Green egg-to-fry Reiser and White, 1988 –0.0615 (0.0126) >0.26 1.0030 (0.4522) NA NA

Eyed egg-to-yolk

absorption

Cederholm and Lestelle, 1974 2.6359 (0.4184) >0.75 NA

Eyed egg-to-fry Tappel and Bjornn, 1983

Eyed egg-to-fry Reiser and White, 1988

Coho Green egg-to-fry Cederholm and Salo, 1979 –0.0966 (0.0152) >0.46 1.9393 0.0303 2.2911 (0.3860)

Egg-to-fry Koski, 1966 2.8783

Chum Egg-to-fry Koski, 1975 –0.0428 (0.0168) NA 1.8774 (0.7206) NA NA

Chinook + Steelhead –0.0737 (0.0115) >0.08 — <0.0001 2.1132 (0.3009)

Coho + Chum –0.0760 (0.0128) >0.25 — <0.0001 2.3753 (0.4512)

All species –0.0746 (0.0087) >0.06 — <0.0001 2.2911 (0.2646)

aThe same estimate within a group signifies that studies were not significantly different from one another (p > 0.05), different estimates indicate significant

differences within a group (p ≤ 0.05). Standard error is from logistic regression if bootstrapping was unnecessary; otherwise, it was from bootstrap. bp-value refers to the test of whether the parameter is the same for all group members. cIntercept fit with logistic regression. Standard error was included if no bootstrap was necessary.

d Intercept fit during bootstrap procedure with bootstrap standard error.

Figure 2 Relationship between egg-to-fry survival and sediment showing data from the literature used in analysis. (a) Chinook salmon data and modeled mean

survival vs. percent sediment < 6.4 mm. (b) Steelhead data and modeled mean survival vs. percent sediment < 6.4 mm. (c) Chinook salmon and steelhead data

vs. geometric mean of sediment size (mm).

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REVIEW OF EGG-TO-FRY SURVIVAL OF PACIFIC SALMON 355

Table 5 Results of comparisons within and across studies where sediment metric is % fines < 6.4 mm or geometric mean (mm). Groups are based on species

and egg stage at which monitoring began. Bold indicates parameters of final models developed for this analysis

Comparisons between groups of studies

Quadratic slopea Linear slopea

Species Life stage Source (SE) p-valueb (SE) p-valueb Interceptc (SE) p-valueb Interceptd (SE)

Sediment metric: Percent fines < 6.4 mm

Chinook Green egg-to-fry Bjornn, 1968 ns — –0.1425 (0.0219) NA 1.5557 (0.3755) NA NA

Green egg-to-fry Bennett et al., 2003 –0.0022 (0.0005) –0.0110 (0.0213) >0.68 0.0932 (0.2875) <0.0001 NA

Green egg-to-fry Bjornn, 1969 0.8530 (0.2294)

Eyed egg-to-fry Tappel and Bjornn, 1983 3.3068 (0.3329)

Steelhead Green egg-to-fry Bjornn, 1969 –0.0016 (0.0006) >0.75 –0.0077 (0.0308) >0.57 0.2376 (0.3126) <0.0001 NA

Eyed egg-to-fry Tappel and Bjornn, 1983 2.8925 (0.4648)

Sediment metric: Geometric mean (mm)

Chinook Green egg-to-fry Bennett et al., 2003 ns — 0.0775 (0.0191) >0.16 –1.6037 (0.2750) <0.0001 NA

Green egg-to-fry Reiser and White, 1990 –0.7847 (0.3020)

Eyed egg-to-fry Reiser and White, 1990 6.6880 (0.5132)

Eyed egg-to-fry Tappel and Bjornn, 1983 –0.0382 (0.0091) NA 1.2135 (0.2236) NA –5.8271 (1.0609) NA NA

Steelhead Eyed egg-to-fry Tappel and Bjornn, 1983 –0.0363 (0.0058) NA 1.1284 (0.1471) NA –5.6557 (0.7317) NA NA

aThe same estimate within a group signifies that studies were not significantly different from one another (p > 0.05), different estimates indicate significant

differences within a group (p < 0.05). Standard error was from logistic regression if bootstrapping was unnecessary; otherwise, it was from bootstrap. bp-value refers to the test of whether the parameter is the same for all group members. cIntercept fit with logistic regression. Standard error was included if no bootstrapping was necessary.

d Intercept fit during bootstrap procedure with bootstrap standard error.

studies, while there was no difference between the intercepts in

the steelhead raised from eyed eggs (Tables 2 and 3).

DISCUSSION

We found evidence in the literature that increasing amounts

of fine sediments in stream substrates reduces egg-to-fry survival for salmonids (see Table 1; all regression equations have a

negative slope or, if quadratic, are generally concave down; the

relationships with the geometric mean are reversed). Using data

from published studies, we were able to construct models that

predict the odds of survival for Chinook, chum, and coho salmon

and steelhead for three fine sediment size classes. Fewer data

were available for chum salmon than the other species analyzed,

and we found no studies that investigated egg-to-fry survival of

pink or sockeye salmon. When the sediment metric was percent

fines < 0.85, 3.4, or 4.8 mm, the relationship with the odds of

survival was generally linear, but when the metric was percent

fines < 6.4 mm or the geometric mean, the relationship was

often quadratic. The fact that the slopes of the modeled relationships were similar within most species and egg stage groups,

despite the differences in intercept, suggests that the change in

the odds of survival is constant and that differences in the environments for specific studies is reflected in the intercepts, i.e.,

the survival curve is simply shifted one way or the other. Adding

more datasets for those groups with few sources of data would

likely help establish a more robust estimate of the survival curve.

Greig et al. (2005a) identified oxygen availability as an important factor in the survival of salmon eggs, but the relationships between the sediment composition and oxygen levels were

complex and varied from site to site as well as within sites.

They found low correlations between various measures of sediment composition and survival. In groundwater-fed streams,

survival may not be related to substrate composition, as the

oxygen content of groundwater can vary independently of substrate characteristics (Sowden and Power, 1985); yet, there is

a clear relationship between survival and dissolved oxygen in

these systems (Sowden and Power, 1985; Malcolm et al., 2003).

In addition, the amount of organic matter in the substrate and

adjacent water column influences the amount of dissolved oxygen available to developing embryos (Greig et al., 2005a). We

did, however, find significant relationships between sediment

composition and egg-to-fry survival in nearly all of the studies

we were able to locate. Perhaps these factors explain some of

the variability we saw in these data. The models incorporated

into this analysis are nonlinear (on the survival scale), and thus

it is possible that the low linear correlations reported by Greig et

al. (2005a) reflect the nonlinearity of the relationship between

sediment and survival. The inter-site variability noted by Greig

et al. (2005a) may correspond to the variability in the intercept

of the regression line found here. The high intra-site variation in

survival found by Greig et al., as well as regional differences in

stream conditions (e.g., their study rivers had higher fine silts,

clays, and organic matter than are typical in Pacific Northwest

streams) highlight the complexity of the spawning environment

and therefore the need to exercise caution when applying models

such as ours to any one particular site.

The act of spawning cleans fine sediment from the gravel

in redds; thus, it is sediment deposited after eggs are laid that

could decrease survival (Chapman, 1988). Multiple mechanisms

have been proposed to explain the negative impacts of this fine

sediment deposition on egg-to-fry survival. First, fine sediment

may impede the flow of oxygenated water through gravel in the

reviews in fisheries science vol. 17 3 2009
356 D. W. JENSEN ET AL.

egg pocket (Chapman, 1988; Lisle 1989), causing suffocation

of eggs or reducing the removal rate of toxic metabolic wastes

(Bennett et al., 2003). These reduced dissolved oxygen levels

may delay embryo development, lead to early emergence (before

yolk sac absorption is complete), and/or decrease emergent fry

size (Chapman, 1988). Second, a layer of fine sediment may

reduce interstitial spaces and physically prevent fry emergence

(Beschta and Jackson, 1979).

Based on proposed mechanisms of how sediment affects survival (i.e., suffocation or entrapment), conventional wisdom suggests that relationships should be strongest when sediment size

classes are smallest. Our analyses confirmed this assumption,

where the odds of survival were lower and decreased faster when

fines were < 0.85 mm than at larger size classes (Table 3). Note,

however, that we did not have data to evaluate possible relationships with finer size classes such as silt, clay, or organic matter.

Work by Greig et al. (2005b, 2007) suggests that these smaller

classes are indeed important in some rivers, and therefore our

models may not be applicable in all cases.

Using size classes available to us, we found four general patterns in the survival curves (Figures 1 and 2). At the smallest

sediment size, percent fines < 0.85 mm, we saw evidence of a

threshold in egg-to-fry survival of Chinook salmon and steelhead. There was a steep decrease in survival, leveling out at less

than 10% when fines were greater than 25%. We found a similar

relationship for eyed eggs when fine sediment was < 4.8 or 6.4

mm, but the threshold occurred at a much higher level of fines

(above 50% for each). Mean survival for eyed eggs of Chinook

or steelhead was generally greater than 90% until the percentage of fines increased beyond 20–25, at which point survival

decreased rapidly. There was less of a threshold effect for green

eggs of Chinook and steelhead for the same fine sediment class.

For green eggs, survival was initially lower than for eyed eggs,

and decreased steadily before leveling off at very low survival

when fines were >30–40%. For the largest sediment size class

(percent fines < 6.4 mm), the relationship with odds of survival

was quadratic. When fine sediment was <10%, survival was

less affected than for other sediment size classes.

While in common use, size class thresholds of, for example,

<0.85 mm are relatively arbitrary. A larger or smaller threshold

might just as well have been used, and the choice of threshold

might impact results and, in particular, perceived differences in

percent fine sediment across regions. The use of the geometric

mean of particle size as the sediment metric was suggested by

Platts et al. (1979) and removes reliance on arbitrary thresholds.

However, in our survey, we found only four studies for which we

could extract the geometric mean. Of those, the sediment size

mixture was not comparable, and survival was highly variable,

especially when the geometric mean was between 7 and 13 mm.

This is a level at which egg survival is expected to decrease, but

the sharp increase is difficult to model due to the variability in

the survival data. We were able to construct some relationships

using these studies; but, before we can develop any meaningful

predictive relationships, more studies are needed that record

geometric mean particle size, particularly in the 7- to 13-mm

range. If the geometric mean can be precisely and accurately

determined, then use of this metric could decrease the variability

in survival among studies because sediment sizes could more

easily be classified on a similar scale.

There were several limitations of the data on our ability to

construct predictive relationships. First, for many of the sediment metrics, we had too few datasets from which to build

representative models. The sparse number of studies could not

represent all (or even most) possible conditions encountered by

fish in natural systems. Furthermore, there was a high amount

of variability among studies. Much of the data we used were

from laboratory studies, where experimental conditions were

highly controlled, yet less realistic than field studies, where

other factors (e.g., temperature, discharge) may interact to reduce egg-to-fry survival. Many of the laboratory studies used

hatchery fish raised at much higher densities than found under

natural conditions. Even within studies, estimates of the intercept were often not consistently precise, reflecting the inherent

variability in survival. Another limitation was that to transform

the predictions from odds of survival to actual survival, the intercept must be estimated even though there is little statistical

justification.

Despite these limitations, our statistical approach complements existing mechanistic modeling (e.g., Alonso et al., 1996;

Wu, 2000) and empirical (e.g., Lisle, 1989) approaches. Such

approaches often cannot be used for conditions other than those

for which they were created due to their complexity and lack

of local empirical data for parameterization. A statistical metaanalysis approach, using data from a wide variety of sources,

overcomes some of these limitations. In cases where researchers

can appropriately match the experimental conditions of individual studies, statistical relationships can be used to estimate

salmonid survival given the amount of fine sediment present

in streams. Environmental conditions are variable within individual studies (e.g., Greig et al., 2005a; Sear, 1993) and our

approach, which includes explicit consideration and calculation of confidence intervals can provide some estimate of the

range of likely responses as well as the probability of particular responses. Across sites, conditions are even more variable.

Land use management decisions and recovery planning decisions for these ESA-listed species are made over very large

scales and across geographically diverse regions. Predictions

from our models are averages with wide confidence intervals,

and they can provide useful estimates of the changes expected

in salmon survival given large-scale changes in land management or suites of proposed watershed restoration actions. For

example, Steel et al. (2008) used earlier versions of these models to compare fisheries’ impacts given six different watershed

restoration strategies. Because they were able to model spatial

variations in expected sediment deposition, they were also able

to predict spatial patterns in salmon survival.

There are significant strengths of our approach. We synthesized information from available studies into simple predictive

relationships that reflect underlying uncertainties in the original datasets. Due to a paucity of comparable rigorous studies,

reviews in fisheries science vol. 17 3 2009
REVIEW OF EGG-TO-FRY SURVIVAL OF PACIFIC SALMON 357

our predictive relationships may not be robust to all conditions

experienced by Pacific salmon species. However, because we

combined data from various sources, the predictive relationships we have developed in this meta-analysis are more robust

to the variables that often plague extrapolation of ecological

relationships from small-scale studies to large-scale models.

As well, they are more robust to geographical or ecological

conditions than the relationships from individual studies of the

relationship between fines and survival. The bootstrapped confidence intervals provide realistic ranges of predicted changes in

salmon survival with changes in in-stream fine sediment. Like

all models, the models presented here are best used with strong

consideration, not just for the point estimate of the most likely

model output, but also for the confidence intervals, the range of

potential modeled outcomes. Like all models, the models presented here are also best used without extrapolating beyond the

range of the data used to build the model. Our meta-analysis

approach has increased the range of data used for any one relationship, but, as pointed out already, there are clearly stream

types and ecological conditions for which no data are available,

and extreme caution should be used if one must make estimates

for stream types and conditions not represented in the original

data.

Given known or modeled levels of fine sediment deposition

associated with different types of land use (e.g., fines in streams

can be much higher in agricultural and developed watersheds;

Opperman et al., 2005), managers can use these predictive relationships to begin to identify areas where salmonid egg-to-fry

survival may be limited. Moreover, our predictive relationships

can be used to forecast potential effects of watershed management practices on salmonids and to make comparisons of relative

predicted survivals under alternative management strategies to

see what set of restoration actions is most likely to improve

egg-to-fry survival (e.g., Steel et al., 2008). These predictive

models should improve our ability to link large-scale models

that predict how much sediment is, or will be, entering streams,

to small-scale observations on fish survival and to more efficiently managing landscapes that contribute to fine sediment

entering streams.

ACKNOWLEDGMENTS

We thank Will Holden for his help in the literature search.

Martin Liermann, Naomi Yoder, and Phil Roni provided constructive comments on earlier drafts of the manuscript. The

editor and an anonymous reviewer also provided helpful suggestions that improved the manuscript.

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