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

348REVIEW 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 atthe 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 ofdifferences 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 aremany 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 restorationprojects 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 largetask 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 knownsources 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 sedimentand 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. Wedo 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 multipleearly 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 uniquein 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 relationshipsinvolved. For example, we ask whether the relationships between sediment and survival are linear, or whether they include a threshold above which survival is improbable? Theserobust 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 2009350 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

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

b All 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.

c Development 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 isequivalent to the analysis of the original data (Olkin and Sampson, 1998; Mathew and Nordstr  ̈ om, 1999). We chose to utilize the original data in our meta-analysis since it was generallyavailable 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 speciesand 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 2009REVIEW 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, whereasthe relationship between survival and fines is not. The transformation is survival = 1 1 + e β0 + β 1 ∗ Sediment 

### . With a single independentvariable 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 aspecific 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., anexperimental 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. Forstudies 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 nearlyall 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, aparallel 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, whenthe 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 quadraticrelationship 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 wereinitially 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, byeliminating 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 estimatesreported 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

reviews in fisheries science vol. 17 3 2009352 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)

a The 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).

b p -value refers to the test of whether the parameter is the same for all group members.

c Intercept 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 ofChinook 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

reviews in fisheries science vol. 17 3 2009REVIEW 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)



### reviews in fisheries science vol. 17 3 2009354 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)

a The 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.

b p -value refers to the test of whether the parameter is the same for all group members.

c Intercept 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).



### reviews in fisheries science vol. 17 3 2009REVIEW 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

a The 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.

b p -value refers to the test of whether the parameter is the same for all group members.

c Intercept 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 amountsof 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 wasoften 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 theoxygen 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 andadjacent 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 2009356 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 athreshold 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 Chinookor 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 toconstruct 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, whereother 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 undernatural conditions. Even within studies, estimates of the intercept were often not consistently precise, reflecting the inherent variability in survival. Another limitation was that to transformthe 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 lackof 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 researcherscan 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 ourapproach, which includes explicit consideration and calculation of confidence intervals can provide some estimate of therange 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 expectedin salmon survival given large-scale changes in land management or suites of proposed watershed restoration actions. Forexample, 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 predictiverelationships that reflect underlying uncertainties in the original datasets. Due to a paucity of comparable rigorous studies,

reviews in fisheries science vol. 17 3 2009REVIEW 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 wecombined 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 therelationship 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 ofpotential 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-analysisapproach 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 relationshipscan 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. Theeditor and an anonymous reviewer also provided helpful suggestions that improved the manuscript.

REFERENCES

Alonso, C. V., F. D. Theurer, and D. W. Zachmann. Sediment Intrusion

and Dissolved Oxygen Transport Model-SIDO . Technical Report No. 5. USDA, Washington, DC (1996). Bennett, D. H., W. P. Connor, and C. A. Eaton. Substrate composition and emergence success of fall Chinook salmon in the Snake River.
NW Sci

., 77: 93–99 (2003). Beschta, R. L., and W. L. Jackson. The intrusion of fine sediments

into a stable gravel bed. J. Fish. Res. Board Can.

, 36: 204–210 (1979).Bjornn, T. C. Survival and emergence of trout and salmon fry in various gravel-sand mixtures. In: Logging and Salmon: Proceedings of a Forum, pp. 80–88. Juneau, AK: American Institute of Fishery Biologists, Alaska District (1968). Bjornn, T. C. Embryo Survival and Emergence Studies. Salmon

and Steelhead Investigations . Job Completion Report, Project F49-R-7. Idaho Fish and Game Department, Boise, ID, 17 pp. (1969). Bjornn, T. C., and D. W. Reiser. Habitat requirements of salmonids

in streams. In: Influences of Forest and Rangeland Management on
Salmonid Fishes and Their Habitat (W. R. Meehan, Ed.). Bethesda, MD: American Fisheries Society (1991). Botterweg, P., R. Leek, E. Romstad, and A. Vatn. The

EUROSEM-GRIDSEM modeling system for erosion analyses under different natural and economic conditions. Ecolog. Model.,
108: 115–129 (1998). Available at http://gcmd.gsfc.nasa.gov
/records/EUROSEM.html. Cai, Q. G., H. Wang, D. Curtin, and Y. Zhu. Evaluation of

the EUROSEM model with single event data on Steeplands in the Three Gorges Reservoir Areas, China. Catena, 59: 19–33 (2005). Cederholm, C. J., and L. C. Lestelle. Observations on the Effects

of Landslide Siltation on the Salmon and Trout Resources of theClearwater River, Jefferson County, Washington, 1972–1973: Final Report, Part I. Seattle: Fisheries Research Institute, College of Fisheries, University of Washington (1974). Cederholm, C. J., and E. O. Salo. The Effects of Logging Road Landslide Siltation on the Salmon and Trout Spawning Gravels of Stequaleho Creek and the Clearwater River Basin, Jefferson County, Washington, 1972–1978: Final Report, Part III. Seattle: Fisheries Research Institute, College of Fisheries, University of Washington (1979). Chapman, D. W. Critical review of variables used to define effects of

fines in redds of large salmonids. Trans. Am. Fish. Soc.

, 117: 1–21 (1988). Collins, A. L., and D. E. Walling. Documenting catchment suspended sediment sources: Problems, approaches and prospects. Progr. Phys. Geog., 28: 159–196 (2004). Davison, P. S., P. J. A. Withers, E. I. Lord, M. J. Betson, and J.
Str  ̈ omqvist. PSYCHICflA process-based model of phosphorus and sediment mobilisation and delivery within agricultural catchments. Part 1: Model description and parameterisation. J. Hydrol., 350: 290–302 (2008). De Roo, A. P. J., and C

. G. Wessling. LISEM: A single-event physically based hydrological and soil erosion model for drainage basins. I: Theory, input and output. Hydrol

.Process

., 10: 1107–1117 (1996). Efron, B., and G. Gong. A leisurely look at the bootstrap, the jackknife,
and cross-validation. Am. Stat.

, 37: 36–48 (1983). Elliot, W. J., R. B. Foltz, and C. H. Luce. Validation of Water erosion

prediction project (WEPP) model for low-volume forest roads. Minneapolis, MN: Proceedings of the Sixth International Conference on
Low-Volume Roads

, 1: 178–186 (1995).



### reviews in fisheries science vol. 17 3 2009358 D. W. JENSEN ET AL.

Elliot, W. J., and D. E. Hall. Water Erosion Prediction Project (WEPP)

Forest Applications . Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station (1997). Everest, F. L., R. L. Beschta, J. C. Scrivener, K. V. Koski, J. R.Sedell, and C. J. Cederholm. Fine sediment and salmonid productionflA paradox. In: Streamside Management: Forestry and Fishery Interactions (E. O. Salo and T. W. Cundy, Eds., pp. 98–142). Seattle: College of Forest Resources, University of Washington (1987). Greig, S. M., D. A. Sear, and P. A. Carling. The impact of fine sedimentaccumulation on the survival of incubating salmon progeny: Implications for sediment management. Sci. Total Environ.

, 344: 241–258 (2005a). Greig S. M., D. A. Sear, D. Smallman, and P. A. Carling. Impact of

clay particles on cutaneous exchange of oxygen across the chorion of Atlantic salmon eggs. J. Fish Biol.

, 66: 1681–1691 (2005b).Greig, S. M., D. A. Sear, and P. A. Carling. Review of factors influencing the availability of dissolved oxygen to incubating salmon embryos. Hydrol

.Process

., 21: 323–324 (2007). Hall, J. D., and R. L. Lantz. Effects of logging on the habitat of coho
salmon and cutthroat trout in coastal streams. In: Symposium on Salmon and Trout in Streams: A Symposium Held at the University of British Columbia, February 22–24

, 1968 (T. G. Northcote, Ed., pp. 355–375). Vancouver: Institute of Fisheries, University of British Columbia (1969). Hall, T. J. A Laboratory Study of the Effects of Fine Sediments on

Survival of Three Species of Pacific Salmon from Eyed Egg-to-Fry Emergence . New York: National Council of the Paper Industry for Air and Stream Improvements Technical Bulletin No. 482, 36 pp. (1986). Hedges, L. V., and I. Olkin. Statistical Methods for Meta-Analysis

. Orlando, FL: Academic Press (1985). Heywood, M. J. T., and D. E. Walling. Suspended sediment fluxes in chalk streams in the Hampshire Avon catchment, UK. Hydrobiologia, 494: 111–117 (2003). Knudsen, E. E., C. R. Steward, D. D. MacDonal, J. E. Williams, and

D. W. Reiser. Sustainable Fisheries Management: Pacific Salmon

. Boca Raton, FL: Lewis Publishers (2000). Koski, K. V. The Survival of Coho Salmon (Oncorhynchus kisutch)

from Egg Deposition to Emergence in Three Oregon Coastal
Streams . M.S. Thesis, Oregon State University, Corvallis, 84 pp. (1966). Koski, K. V. The Survival and Fitness of Two Stocks of Chum Salmon (Oncorhynchus keta) from Egg Deposition to Emergence in a Controlled-Stream Environment at Big Beef Creek. Ph.D. Thesis. University of Washington, Seattle, 212 pp. (1975).Lisle, T. Sediment transport and resulting deposition in spawning gravels, North Coastal California. Water Resour. Res., 25: 1303–1319 (1989). Lotspeich, F. B., and F. H. Everest. A new method for reporting and

interpreting textural composition of spawning gravel. U.S. Forest Service Research Note PNW-139 (1981). Malcolm, I. A., A. Youngson, and C. Soulsby. Survival ofsalmonid eggs in gravel bed streams: effects of groundwatersurface water interactions. River Res. Applications

, 19: 303–316 (2003). Mathew, T., and K. Nordstr  ̈ om. On the equivalence of meta-analysis using literature and using individual patient data. Biometrics

, 55: 1221–1223 (1999). McCullagh, P., and J. A. Nelder. Monographs on statistics and applied probability, 37. In: Generalized Linear Models, 2nd ed.. London: Chapman and Hall (1989). Miller, D. J. Landscape Dynamics and Forest Management. GeneralTechnical Report RMRS, GTR-101-CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station (2003).
Mobrand Biometrics Inc. EDT On-line User Manual. Available at www.mobrand.com/MBI/pdfs/edt web docs.pdf (2004). Nehlsen, W., J. E. Williams, and J. A. Lichatowich. Pacific salmon at the crossroads: Stocks at risk in California, Oregon, Idaho, and Washington. Fisheries

, 16: 4–21 (1991). Nilsson, C., J. E. Pizzuto, G. E. Moglen, M. A. Palmer, E. H. Stanley,

N. E. Bockstae, and L. C. Thompson. Ecological forecasting and theurbanization of stream ecosystems: Challenges for economists, hydrologists, geomorphologists, and ecologists. Ecosystems

, 6: 659– 674 (2003).Olkin, I., and A. Sampson. Comparison of meta-analysis versus analysis of variance of individual patient data. Biometrics

, 54: 317

–322 (1998). Opperman, J. J., K. A. Lohse, C. Brooks, N. M. Kelly, and A. M.

Merenlender. Influence of land use on fine sediment in salmonid spawning gravels within the Russian River Basin, California. Can. J. Fish. Aquat. Sci.

, 62: 2740–2751 (2005). Platts, W. S., M. A. Shirazi, and D. H. Lewis. Sediment Particle Sizes Used by Salmon for Spawning with Methods for Evaluation . Corvallis, OR: Environmental Protection Agency, Office of Research and Development, Corvallis Environmental Research Laboratory (1979). Reiser, D. W., and R. G. White. Effects of two sediment size classes on survival of steelhead and Chinook salmon eggs. N. Am. J. Fish. Manage.

, 8: 432–437 (1988). Reiser, D. W., and R. G. White. Effects of streamflow reduction on Chinook salmon egg incubation and fry quality. Rivers

, 1: 110–118 (1990).Scrivener, J. C., and M. J. Brownlee. Effects of forest harvesting on spawning gravel and incubation survival of chum ( Oncorhynchus keta) and coho salmon ( O. kisutch) in Carnation Creek, British Columbia. Can. J. Fish. Aquat. Sci.

, 46: 681–696 (1989). Sear, D. A. Fine sediment infiltration into gravel spawning

within a regulated river experiencing floods: Ecological implications for salmonids. Reg. Rivers Res. Manage., 8: 373–390 (1993).Sowden, T. K., and G. Power. Prediction of rainbow trout survival in relation to groundwater seepage and particle size of spawning substrates. Trans. Am. Fish. Soc.

, 114: 804–812 (1985). Steel, E. A., A. Fullerton, Y. Caras, M. B. Sheer, P. Olson, D.

Jensen, J. Burke, M. Maher, and P. McElhany. A spatially explicit decision support system for watershed-scale management of
salmon. Ecol. Soc.

, 13: 50. Online at http://www.ecologyandsociety. org/vol13/iss2/art50/ (2008).Tagart, J. V. Coho salmon survival from egg deposition to fry emergence. In: Proceedings of the Olympic Wild Fish Conference, March 23–25, 1983 (J. M. Walton and D. B. Houston, Eds., pp. 173–182).Port Angeles, WA: Fisheries Technology Program, Peninsula College (1984).



### reviews in fisheries science vol. 17 3 2009REVIEW OF EGG-TO-FRY SURVIVAL OF PACIFIC SALMON 359

Tappel, P. D., and T. C. Bjornn. A new method of relating size of spawning gravel to salmonid embryo survival. N. Am. J. Fish. Manage.

, 3: 123–135 (1983).Walling, D. E., A. L. Collins, and R. W. Stroud. Tracing suspended sediment and particulate phosphorus sources in catchments. J. Hydrol.,
350: 274–289 (2008). Waters, T. F. Effects of sediment on fish. In: Sediment in Streams: Sources, Biological Effects, and Control (T. F. Waters, Ed., pp. 79–118). Bethesda, MD: American Fisheries Society Monograph 7 (1995). Williams, D. A. Extra-binomial variation in logistic linear models.

Appl. Stat.

, 31: 144–148 (1982).Wu, F. Modelling embryo survival affected by sediment deposition into salmonid spawning gravels: Application to flushing flow principles. Water Resour. Res., 36: 1595–1603 (2000).



### reviews in fisheries science vol. 17 3 2009

## Copyright of Reviews in Fisheries Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.