Running head: Multi-species status assessment for salmonids

**A Large-Scale, Multi-Species Status Assessment:**

**Anadromous Salmonids in the Columbia River Basin.**

# Michelle M. McClure

Elizabeth E. Holmes

Beth L. Sanderson

Chris E. Jordan

Northwest Fisheries Science Center

National Marine Fisheries Service

National Oceanographic and Atmospheric Administration

2725 Montlake Blvd. E.

Seattle, WA 98112

Phone: 206-860-3402

Fax: 206-860-3400

e-mail: <michelle.mcclure@noaa.gov>

<eli.holmes@noaa.gov>

<beth.sanderson@noaa.gov>

<chris.jordan@noaa.gov>

**Abstract**

Twelve salmonid evolutionarily significant units (ESUs) throughout the
Columbia River Basin, are currently listed as threatened or endangered
under the Endangered Species Act, and are affected differentially by a
variety of human activities. We present a standardized, quantitative
status and risk assessment for 152 listed salmonid stocks in these ESUs
in the Columbia River Basin and 24 non-listed stocks. Using data from
1980-2000, which represents a time of stable conditions in the Columbia
River hydropower system and a period of ocean conditions generally
regarded as poor for Columbia Basin salmonids, we estimated the status
of these stocks under two different assumptions: that hatchery-reared
spawners were not reproducing during the period of the censuses or that
hatchery-reared spawners were reproducing and thus that reproduction
from hatchery inputs was masking population trends. We repeated the
analyses using a longer time period containing both "good" and "bad"
ocean conditions (1965-2000) as a first step toward determining whether
recent apparent declines are a result of sampling a period of poor ocean
conditions.

All the listed ESUs except Columbia R. chum showed declining trends with
estimated long-term population growth rates (λs) ranging from 0.85 to
1.0, under the assumption that hatchery fish were not reproducing and
not masking the true λ. If hatchery fish were reproducing, the estimated
λs ranged from λ=0.62 to 0.89, indicating extremely low natural
reproduction and survival. For most ESUs, there was no significant
decline in population growth rates calculated for the 1980-2000 versus
1965-2000 time periods, suggesting that the current population status
for most ESUs is not solely a result of changes in ocean conditions, and
that without other changes, risks will persist even during upturns in
ocean conditions. However, estimated population growth rates for the
Snake R. spring/summer chinook salmon and steelhead ESUs were
significantly lower during the longer time period. This difference may
be due to a period of dam building on the Snake River during the 1960s
and 1970s. For 33 stocks and seven ESUs, the probability of extinction
could be estimated. The estimates were generally low for all ESUs with
the exception of Upper Columbia R. spring chinook and Upper Willamette
R. steelhead. The probability of 90% decline could be estimated for all
stocks. The mean probability of 90% decline in 50 years was highest for
Upper Columbia R. spring chinook (95% mean probability across all stocks
within the ESU) and Lower Columbia R. steelhead (80% mean probability).

We estimated the effects of two different management actions on
long-term growth rates for the ESUs. Harvest reductions offer a means to
mitigate risks for ESUs that bear substantial harvest pressure, but they
are unlikely to increase population growth rates enough to produce
stable or increasing trends for all ESUs. Similarly, anticipated
improvements to passage survival through the Snake and mainstem Columbia
hydropower systems may be important, but additional actions are likely
to be necessary to recover affected ESUs.

**Key words**: quantitative risk assessment, extinction risk,
conservation, salmon, steelhead, population growth rates, PVA

Abstract word count: 463

# Introduction

Evaluating the status of multiple species or populations in large
biological systems poses a tremendous challenge to conservation
biologists and managers. Large-scale systems not only typically face a
variety of threats, but also data quality and extent may be inconsistent
across the species or populations of interest. Broad-scale, quantitative
assessments have the potential to play several extremely important roles
in conservation planning in these large systems, especially when
standardized assessments can be conducted given data of variable
quality. First, they can provide the opportunity to prioritize
conservation needs from a biological standpoint, by expressing status in
a common currency across all populations. They can also help prioritize
efforts that include economic or social considerations. Second,
standardized, quantitative status assessments can provide the basis for
subsequent analyses that evaluate the effect of human actions on status.
In particular, they can be used in retrospective analyses that explore
the relationship between population status and environmental conditions
or anthropogenic impacts, or they can provide the starting point from
which to gauge the anticipated effects of actions across species and/or
populations.

In this paper, we conduct a standardized status assessment for
threatened and endangered salmonids in the Columbia River Basin, as an
important first step in recovery planning efforts for these species.
Following Caswell (2000), we adopted the long-term population growth
rate (λ) as the main measure for comparative risk analysis. This is a
critical parameter in viability assessment, not least because most
population extinctions are the result of steady declines, λ \< 1,
(Caughley 1994). We use λ combined with the year-to- year variability to
estimate probabilities of extinction and decline using methods that
require only simple time series of abundance or density and that have
been developed for data sets with high sampling error and age-structure
cycles (Holmes 2001). These methods have been extensively tested using
simulations (Holmes, accompanying ms), and cross-validated with time
series data (Holmes and Fagan 2002). In our analysis, we included
currently threatened and endangered populations as well as several
stocks widely believed to be at low risk. The inclusion of these
non-listed stocks gives us a basis of comparison for interpreting the
estimated status of the more imperiled stocks.

###### Columbia River salmon and steelhead

The Columbia River Basin spans over 640 thousand square kilometers and
encompasses a diverse variety of ecotypes from wetlands to coniferous
forest to shrub steppes. Twelve salmonid Evolutionarily Significant
Units (ESUs) in the Columbia Basin that represent genetically and
demographically independent groups of fish (Waples 1991) have been
listed as threatened or endangered under the United States Endangered
Species Act (ESA). These ESUs, which generally comprise several
populations or stocks, belong to four species of anadromous salmonids:
chinook salmon (*Oncorhynchus tshawytscha*), sockeye salmon (*O.
nerka*), chum salmon (*O. keta*), and steelhead (*O. mykiss*), and are
distributed across the wide variety of habitats found in the Basin.

The Columbia River once supported one of the most productive salmon
fisheries in the world, with an estimated 7-8 million (Chapman 1986) to
15 million (NPPC 1986) anadromous fish returning to spawn each year.
However, the far-ranging distribution of salmon during different parts
of the life cycle has made them vulnerable to a wide variety of
anthropogenic influences in freshwater, estuarine, and ocean habitats.
Heavy fishing pressures initiated a decline beginning in the 1870s in
these populations that has been exacerbated by a variety of factors,
including continuing fishing pressure on some ESUs. Freshwater habitat
throughout the basin has been degraded and lost through agriculture,
ranching, mining, timber harvest and urbanization. Estuarine marshes and
swamps have been diked and drained. The construction and operation of
hydropower and other dams throughout the basin have made dramatic
changes to river systems. In addition, hatchery programs, intended to
improve population status, may have worsened the situation, not only by
increasing harvest rates on wild populations that are part of
mixed-stock fisheries, but also through potential inadvertent negative
genetic and ecological interactions (Thomas 1983, NRC 1996, Williams et
al. 1999). As a result of these many factors, wild coho (*Oncorhynchus
kisutch*), which were once abundant, are now extinct in the interior
basin, Columbia River sockeye, also once abundant, are maintained in a
captive broodstock program, and every sub-basin of the Columbia
currently accessible to anadromous fishes contains at least one
threatened or endangered salmonid ESU (Fig. 1).

However, these human factors are not the only influences on salmon
population status. Recently, decadal-scale changes in ocean conditions
due to climatic cycles (the Pacific Decadal Oscillation or PDO) have
been implicated as a factor affecting Pacific salmon populations (e.g.
Hare and Francis 1995), with Columbia River stocks experiencing 20-30
year periods of "good" ocean conditions associated with cooler
temperatures in the NE Pacific. These alternate with periods of warmer
temperatures in the NE Pacific, which are generally "bad" for Columbia
River salmonids (Mantua et al. 1997, Hare et al. 1999). Other global
climatic events may also affect Pacific salmon populations. In
particular, there are El Nino/Southern Oscillation (ENSO) events, which
are qualitatively similar to the warmer phase of the PDO, and are
correspondingly \"bad\" for Columbia River salmonids. It is anticipated
that these will increase in frequency and intensity in the future
(Johnson 1988, Hare et al. 1999, Meehl et al. 2001). Global warming is
also anticipated to generally worsen conditions for Columbia River
salmonids (Chatters et al. 1995). Clearly, projections of population
status or risks are likely to be affected by any assumptions about
future ocean or climatic conditions.

Although the twelve listed ESUs in the Columbia River Basin have been
the focus of many policy decisions affecting harvest management,
hydropower dam operations, and a variety of other human activities (e.g.
NMFS 1995, NMFS 1999ab, NMFS 2000), few formal population viability
analyses for any Pacific salmon species have been developed (the
exceptions being Ratner et al. 1997 and Botsford and Brittnacher 1998).
The salmonid species throughout the Columbia River Basin share many
habitats and are impacted by many of the same management decisions --
sometimes in differing manners. Consequently, there is a tremendous need
to determine the status of stocks and ESUs throughout the basin, in a
manner that allows comparison between stocks, ESUs and species. Such
comparable quantitative reviews of population status are an important
component of efforts to prioritize populations for recovery and
conservation actions (Allendorf et al. 1997). They can also serve as a
foundation for analytical efforts to determine the magnitude of natural
anthropogenic impacts on population status or the potential of different
restoration actions.

Efforts to determine salmonid population status, however, must deal with
the complicating presence of large numbers of hatchery-reared fish,
which may be reproducing along with wild-born fish. Regardless of
whether the presence of hatchery-reared fish has a negative impact on
wild-born fish, reproduction by hatchery fish presents an accounting
problem which complicates the estimation of population status. This
occurs because the wild population is being supplemented by an external
population (the hatchery). Simply removing the hatchery spawners from
the time series is not sufficient since one must account for the
hatchery fish offspring, their offspring's offspring, etc. Properly
accounting for hatchery fish reproduction requires information on the
relative reproductive success of hatchery fish. While it appears that
hatchery-reared fish that spawn in the wild generally have lower
breeding success than wild-born fish (Fleming 1982, Fleming and Gross
1993, Fleming and Gross 1994, Berejikian 1995), the estimates of their
relative reproductive success are quite variable and range from 10-13%
of that of wild-born spawners, for non-native, domesticated stock across
the entire life-cycle (Chilcote et al. 1986) to 80% of the wild fish
rate, for local stock in the egg to the yearling stage only
(Reisenbichler and McIntyre 1977). In our analyses, we correct for
hatchery reproduction by contrasting two different assumptions. In the
first case, we assume that hatchery fish have not been reproducing. This
gives the most optimistic estimates of population status. In the second
case, we assume that hatchery fish have been reproducing at the same
rate as wild-born spawners. This gives the most pessimistic estimates.
The true rate of hatchery fish reproduction is somewhere between these
extremes.

Using these different assumptions, we then conduct a status assessment
and analysis that focuses on the following: 1) what is the rate of
population decline (or growth) and the associated risk of decline for
listed Columbia River stocks and ESUs under the most recent (poor) ocean
conditions (1980-2000)? 2) how do those estimates change, given the
potential for hatchery fish to reproduce in the wild? and, 3) do
parameter and risk estimates change significantly if data including both
"good" and "bad" ocean conditions (1965-2000) are used in the
assessment? Complete viability analyses will consider other factors in
addition to these strictly demographic ones (Soule and Gilpin 1986).
Genetic diversity, the probability of catastrophes, Allee effects or
depensation, and a variety of other potential factors can all affect
population status. However, many of these concerns, such as the
probability of catastrophe, are difficult or impossible to estimate
(Coulson et al. 2001). In addition, for the majority of stocks in the
region, only the most basic time series data are available. Thus, we
provide these demographic analyses as a first step towards a complete
viability analysis.

**Methods**

We estimated population trends and risk estimates for 152 stocks in
eleven ESUs listed as threatened or endangered throughout the Columbia
River Basin and for 24 stocks in three non-listed ESUs regarded as
"healthy". We did not assess the status of Snake River sockeye, the
12^th^ listed ESU, because this entire ESU is currently maintained in a
captive broodstock program. Estimation of the long-term population
growth rate (λ) was one of the main foci of our analysis. \"Managing for
λ," has been suggested as a strategy of achieving species viability and
productivity (Caswell 2000), since any population with a declining
growth rate (λ \< 1) will eventually go extinct, regardless of initial
size. Populations with a positive trend (λ \> 1) increase in number and
ultimately have a lower extinction risk. In addition, ESUs in the
Columbia River Basin are severely depleted and one current management
objective is to recover these populations to higher levels, which
necessarily entails a λ \> 1.

*Time periods analyzed*

We assessed the status of stocks and ESUs over two time periods:
1980-2000 and 1965-2000. Regime shifts in the Pacific Decadal
Oscillation occurred in 1947 and 1977 (Francis and Hare 1994). Thus, the
1980-2000 time period gave an estimate of population growth rates during
ocean conditions which are considered to have been poor for Columbia
River salmonids (Mantua et al. 1997). Risk estimates projected from
population growth rates using 1980-2000 time series thus carry the
assumption that the warm ocean conditions characteristic of this time
period persist indefinitely into the future. Note that most models of
global climate change suggest that ENSO events (which are superficially
similar to the warm phase of the PDO) will increase in frequency and
intensity (Meehl et al. 2001). Thus, projections using the 1980-2000
period may be a surrogate for continued warm conditions due to global
climate change. The configuration of the Columbia and Snake River
hydropower system (including water storage capacity, which affects the
Columbia plume and estuarine conditions) was also relatively uniform
during this time period. Survival of juvenile chinook from the Snake
River through the hydropower system did improve over these twenty years,
but in comparison with the larger change in passage survival between the
mid-late 1970s and early 1980s, it was relatively constant (Williams et
al. 2001).

The longer time period (1965-2000) encompasses both "good" and "bad"
ocean conditions. Risk estimates or projections of population growth
rates from this time period implicitly incorporate the assumption that
ocean conditions will cycle between these two regimes into the future,
meaning that the poor ocean conditions of the late 20^th^ century will
not persist indefinitely. The 1965-2000 period also includes an episode
of dam construction, particularly focused on the Lower Snake River; risk
estimates for this area therefore also include a substantial
perturbation. The 1965-1980 data were available for approximately half
of the stocks we examined, in all ESUs except Upper Willamette R.
chinook and steelhead, Upper Columbia R. steelhead, Washington Coastal
chinook and Columbia R. chum. However, data prior to 1965 were not
widely available, making pre-1965 analyses for the majority of stocks
impossible.

*Data used in analyses*

Our analyses required at the minimum a time series of spawner abundance.
Spawner abundance data consisted of either direct counts of returning
adults at dams or weirs, index counts of spawner numbers, or estimates
of total returning spawners. Index counts, such "redds per mile" (a redd
is the gravel nest made by spawning female) give a relative index rather
than an absolute count of the total number of spawners. At the stock
level, spawner estimates were typically derived from redd surveys of a
portion of a particular river or creek. Although dam or weir counts were
available for some stocks. For seven ESUs (Snake R. steelhead, fall
chinook and spring/summer chinook, Upper Columbia R. spring chinook and
steelhead, and Upper Willamette R. chinook and steelhead), total spawner
estimates for the entire ESU were available via dam counts at the
downstream end of the ESU. In order to best represent the number of fish
on the spawning grounds, we subtracted fish from the time series that
were harvested in-river or taken into hatcheries upstream, after dam
counts. For three other ESUs (Lower Columbia R. chinook and steelhead,
and Middle Columbia R. steelhead), we created an ESU-level index count
by aggregating all stocks within that ESU for which a total live spawner
time series was available. No ESU-level counts were possible for
Columbia R. chum or the three non-listed ESUs since the majority of time
series within these ESUs were index counts.

Estimates of the proportion of hatchery-reared spawners in the time
series were available for approximately half of the stocks analyzed.
Estimates of the proportion of hatchery fish on the spawning grounds
were based either on direct observations of fin-clipped fish or were
derived from estimated hatchery stray rates. When the proportion of
hatchery and wild spawners was unknown, we conducted our analyses on the
total spawner counts, which include both wild- and hatchery-reared
spawners.

The age at which individuals return to spawn varies by species and
stock, and not all individuals within a given species and stock return
at the same age. The distribution of the spawning age was available for
most ESUs but variably available for individual stocks. A generic
ESU-level spawner age distribution was used for those stocks without
data. The raw spawner count, age and hatchery-fraction data are supplied
in Supplement 1.

*Estimating population-level parameters*

We used time series of spawner counts to estimate population growth
rates and risks by fitting a stochastic exponential decline model,

*N*~t+1~ = *N*~t~ exp(*μ* + *ε*) where *ε* is distributed
Normal(0,*σ*^2^), \[1\]

to the data and then using diffusion approximation methods (Dennis et
al. 1991) to estimate risks. However, the parameter estimation methods
described by Dennis et al. were not appropriate for raw spawner counts
for several reasons. First, spawner counts represent only a single life
stage and are therefore not a representative sample of the entire
population. In addition, salmon life history, particularly iteroparity
and delays between birth and reproduction, make salmon prone to boom and
bust cycles in annual spawner numbers. These cycles confound parameter
estimation. Second, sampling error is likely to be very high in spawner
count data (Hilborn et al. 1999). Large sampling error results in
overestimates of the environmental variance, which lead to
correspondingly poor estimates of any risk metrics that incorporate this
measure of variance (Holmes 2001, Holmes and Fagan 2002). The
development of the parameter estimation methods and tests of their
performance using age-structured salmon models are discussed in detail
by Holmes (accompanying ms). Here we summarize the methods pertaining
directly to this analysis.

First, we used a uniform running-sum of four consecutive counts to
filter out sampling error and age-structure cycles:

$R_{t} = \sum_{j = 1}^{4}S_{t + j - 1}$ \[2\]

We tested the running sum transformed counts for their fit to the
assumptions of the underlying stochastic process: 1) that the
relationship between the variance and the lag, τ, in ln(*R~t+τ ~/R~t~*)
was linear, using the R^2^ of a least-squares fit through the variance
data; 2) that ln($\frac{R_{t + 1}}{R_{t}}$) was distributed normally and
there were no significant outliers (using the dffits statistic \> 2); 3)
that density-dependent processes were not apparent (following Dennis and
Taper 1994); 4) that statistically significant temporal trends in
μ* *were not present (using a method analogous to Dennis and Taper\'s
test for density-dependence); and 5) that there was no significant
serial autocorrelation in the $\frac{R_{t + 1}}{R_{t}}$ ratios (by
de-trending the ratios and using Spearman\'s rank correlation test). All
tests were done at the P\<0.05 significance level with no adjustment for
the fact that multiple tests were conducted. We found a good fit to all
assumptions with the following exceptions: the Upper Columbia spring
chinook ESU-level data showed a downward trend in
$\frac{R_{t + 1}}{R_{t}}$ ratios, as do most of the stocks within that
ESU. This downward trend was also seen in a few stocks in most other
ESUs. It should be kept in mind that simulations (ours and Shenk et al.
1998) indicate that significant trends appear by chance 25-30% of the
time in 20-year samples of stochastic age-structured processes. Several
stocks also showed evidence of density-depensatory or compensatory
processes (Table 1). Risk estimates will tend to be overly optimistic
when there is depensatory density dependence or declining trends in
$\frac{R_{t + 1}}{R_{t}}$ ratios. A handful of stocks and the Upper
Columbia R. summer/fall chinook ESU showed evidence of first order
autocorrelation in $\frac{R_{t + 1}}{R_{t}}$ ratios. When
autocorrelation is present, σ*^2^* is underestimated using our methods,
but μ should be unaffected (Tuljapurkar 1989).

While we have not conducted sensitivity analyses for each of these
factors, a recent cross-validation study of diffusion approximation (DA)
methods (Holmes and Fagan 2002) used long-term salmon time series to
look implicitly at the effects of violations of simple DA model
assumptions, such as no density-dependent processes, low
autocorrelation, and no trends. This study found that DA methods gave
unbiased estimates of λ and of the probability of decline. Only for
rapidly increasing populations were biases in the estimation of λ seen
that were sufficient to cause overestimation of the risk of decline.

We estimated μ for each stock and ESU from the ratios of consecutive
running sums:

${\hat{\mu}}_{\text{run}} = \text{mean}(\text{ln}(\frac{R_{t + 1}}{R_{t}}))$
\[3\]

This method gives an estimate of μ that is resistant to severe
age-structure perturbations and sampling error (Holmes 2001, Holmes,
accompanying ms). We used a slope method to estimate σ^2^:

$\begin{array}{r}
 \\
{\hat{\sigma}}_{\text{slp}}^{2} = \text{slope of }\text{var}\left\lbrack \text{ln}\left( \frac{R_{t + \tau}}{R_{t}} \right) \right\rbrack\ \text{versus}\ \tau
\end{array}$ \[4\]

for τ = 1, 2, 3, and 4. This estimate of σ*^2^* is significantly less
biased in the face of severe sampling error (Holmes and Fagan 2002,
Holmes accompanying ms). These parameter estimation methods have been
cross-validated using a large collection of west-coast salmon time
series by Holmes and Fagan (2002).

Using estimates of μ and σ*^2^*, we calculated the following metrics of
risk to assess the status of these populations.

*Long-term rate of population change.\--* The estimate of the long-term
rate of population change (denoted $\hat{\lambda}$) is:

$\hat{\lambda} = \text{exp}(\hat{\mu})$ \[5\]

Note that we use λ to denote the long-term population growth rate,
defined as
λ=$(\frac{N_{t + \tau}}{N_{t}})^{\text{1/}\tau}\text{ as }\tau \rightarrow \infty$.
If λ is less than 1, the population will go extinct with certainty over
the long-term, and over the short-term λ denotes the median observed
growth rate. Our use of λ follows the concept of the time-averaged
long-term rate of stochastic growth suggested by Caswell (2000). In
Dennis et al. (1991), λ is used indicate the mean (rather than median)
annual growth rate (= exp(μ* *+ σ^2^/2)); however we do not use the mean
as our metric since the mean is not the long-term growth rate nor does
exp(μ* *+ σ^2^/2)) \< 1 indicate extinction with certainty.

A range of underlying stochastic processes (with different μ and σ^2^)
could have produced the observed time series. The 95% confidence
intervals on λ give an estimate of the range of true λs that could have
produced the observed data. From Holmes and Fagan (2002), Equation 4,
the 95% confidence intervals on λ are

$\text{exp}\left( \hat{\mu} - t_{0\text{.}\text{025},\text{df}}\sqrt{\frac{{\hat{\sigma}}^{2}}{\gamma}(n - 4)} \right),\text{   }\text{exp}\left( \hat{\mu} + t_{0\text{.}\text{025},\text{df}}\sqrt{\frac{{\hat{\sigma}}^{2}}{\gamma}(n - 4)} \right)$
\[6\]

where γ is a constant ($1$) and *t~α,~*~df~ is the quantile of a
student's-*t* distribution at probability *α* and degrees of freedom df.
The degrees of freedom for the *t*-distribution are given by the degrees
of freedom of the${\hat{\sigma}}_{\text{slp}}^{2}$: df *=* 0.333 *+*
0.212 *n --* 0.387 *L* where *L* is the number of counts summed together
to form *R~t\ ~*(in our case *L* = 4) and *n* is the time series length
(following Holmes and Fagan 2002).

*Probability of extinction.\--* To estimate extinction probabilities, we
required an estimate of population size. For this, we estimated the
total number of wild-born fish alive at year *t* that do eventually
return to spawn. We denote this *TS~t~*. We can calculate *TS~t~* using
the mean age distribution of returning spawners:

$\text{TS}_{t} = w_{t}S_{t} + (1 - F_{1})w_{t + 1}S_{t + 1} + (1 - F_{1} - F_{2})w_{t + 2}S_{t + 2} + \text{.}\text{.}\text{.}\text{.}$
\[7\]

where *S~t~* is the spawner count at year *t*, *F~i~* is the fraction of
spawners that return at age *i*, and *w~t~* is the fraction of year *t*
spawners that were wild-born (versus hatchery-reared) . Note *F*~0~ = 0,
that is no individuals return to spawn the same year that they are born.

The probability of reaching a given threshold population size, *TS*~e~,
before the end of *t*~e~ years is (Eq. 16 x Eq. 84 in Dennis et al.
1991)

$\begin{array}{r}
G \ast \pi^{'} = \pi^{'} \ast \Phi \\

\end{array}$ \[8\]

The function Φ is the standard normal cumulative distribution function.
The most recent *TS~t~* estimate for each stock is denoted *TS~0~* and
is given in Table 1. For extinction, we used *TS*~e~ =1 and *t*~e~ = 50
years.

*Probability of 90% decline.\--* In many cases, the probability of
extinction could not be calculated, since *TS~t~* requires total spawner
counts rather than index counts, an estimate of the age distribution of
returning spawners, and an estimate of the fraction of spawners in the
time series that are wild-born. Therefore, we also calculated the
probability that the population is 90% lower at the end of *t~e~* years,
= 50 years, (cf. Eq. 6 in Dennis et al. 1991):

$\text{Pr}\left( \frac{\text{TS}_{t_{e}}}{\text{TS}_{0}}\frac{\text{10}}{1} \right) = 1 - \Phi\left( \frac{\text{ln}(\text{10}/1) + \hat{\mu}t_{e}}{\hat{\sigma}\sqrt{t_{e}}} \right)$
\[9\]

This risk metric could be calculated when only index counts were
available or if spawner age data or hatchery fraction data were missing.
The risk of 90% decline also gives another risk perspective for large
populations that have a low extinction probability due to their size
while still having a substantial probability of severe declines due to
underlying dynamics.

We used parametric bootstrapping to estimate the confidence intervals on
the probability of extinction and 90% decline by sampling from the
estimated distributions of $\hat{\mu}$ and ${\hat{\sigma}}^{2}$ (Holmes
and Fagan 2002). The estimated distribution of $\hat{\mu}$ is specified
by
$\hat{\mu} + \sqrt{\frac{{\hat{\sigma}}^{2}}{\gamma}(n - 4)}\begin{matrix}
t_{\text{df}}
\end{matrix}$ where *t*~df~ is a t-distributed random variable with df
degrees of freedom. The estimated distribution of ${\hat{\sigma}}^{2}$
is a chi-squared(df) random variable multiplied by
$\frac{{\hat{\sigma}}^{2}}{\text{df}}$, where df is specified as
discussed for Equation 10. Confidence intervals on these risk metrics
are generally very large. For example, the 95% confidence intervals on
probabilities of 90% decline or extinction are often 0 to 1 (Table 1).
However, cross-validation work suggests that within a collection of
populations, the mean probability of decline gives an unbiased estimate
of the fraction of populations that will decline (Holmes and Fagan\'s
2002) \-- although one does not know which populations will decline. The
variability of the mean is much less than the variability of individual
estimates, and thus we use the mean probability of decline or extinction
of all stocks within an ESU to give us a relatively tight estimate of
the mean risk to those stocks.

*Presenting levels of support for different risk metrics.---* The
bootstrapped confidence intervals indicate how variable the risk
estimates are, but they do not necessarily give a good sense of the
degree to which the data support different conjectures about the risk
levels, for example, whether the true rate of population decline is λ \<
0.95, say. To examine the data support for different risk levels, we
used Bayesian techniques with uniform priors to calculate the
probability that the true risks were above or below certain thresholds.
Bayesian approaches are commonly used in conservation biology to express
risks in this manner (Wade 2000), and Holmes (accompanying ms) gives an
example of algorithms for calculating the probability that λ is less
than some threshold given the observed data and the probability that the
risk of the population declining or going extinct is greater than some
threshold. Using these methods, we estimated the probability that the
stock has a very high extinction risk (VHER) or a very high decline risk
(VHRD). VHER is defined as a greater than 25% probability of extinction
in 50 years. VHRD is defined as a greater than 25% probability of 90%
decline in 50 years. To calculate the probability that a stock falls in
the VHER or VHRD category, we first calculated the posterior probability
distributions of the parameters μ and σ^2^ and then integrated over the
distributions, assuming uniform priors, over those values of μ and σ^2^
which gave a VHER or VHRD. This is shown diagrammatically in Figure 2
and the explicit algorithm is given in Holmes (accompanying ms).

> *Adjusting parameter estimates for inputs from hatchery-origin
> spawners*

The introduction of reproducing hatchery-born spawners (in effect, fish
from another population) confounds the parameter estimates of the
long-term rate of population growth natural reproduction and survival.
If hatchery fish reproduce successfully in-stream, we must account for
these inputs, otherwise μ (and any risk estimates incorporating μ) will
be overestimated. Our adjustment responds to an accounting problem
rather than a negative ecological or genetic effect of the hatchery
fish. Because information on hatchery fish reproductive success is
sparse and variable, we estimated parameters under two assumptions that
taken together, bracket the range of possible situations:

*a*) Hatchery fish were assumed not to reproduce. That is, all wild-born
spawners observed had wild-born parents. Parameters were estimated using
Equations 3 and 4 with hatchery spawners removed from the time series
before analysis. When no estimates of the fraction of hatchery fish were
available, the parameters were estimated using the total spawner or
index count, which may include hatchery-reared spawners. If the
proportion of hatchery fish in the time series does not change
substantially through time, and those hatchery fish do not reproduce,
the resulting estimates of μ and σ^2^ will be the same as if the
hatchery fish had been removed from the time series prior to parameter
estimation.

*b*) Hatchery fish were assumed to reproduce at a rate equal to that of
wild fish, and thus wild spawners in the time series may have had wild-
or hatchery-born parents. Our estimates of μ in this case were (see
Holmes, accompanying ms, for the derivation of this correction):

> $\hat{\mu} = \text{mean}\left\lbrack \frac{1}{T}\text{ln}\left( {\hat{w}}_{t} \right) + \text{ln}\left( S_{t + 1}/S_{t} \right) \right\rbrack$
> (11)
>
> where *w~t~* is the proportion of the spawning population that was
> born in the wild (of wild- or hatchery-reared parents), *S~t~* is the
> total number of spawners (wild plus hatchery-born) at year *t*. Our
> estimates of σ^2^ were not adjusted since simulations indicated that
> ${\hat{\sigma}}_{\text{slp}}^{2}$ corrected for the extra variability
> due to variable hatchery inputs.

*Comparing time periods*

To assess the effect of a parameterization time period that included
cooler, \"good\" ocean conditions, we repeated the analyses for the 83
stocks with data beginning in 1965 (Appendix B), and we then compared
these estimates to the estimates using 1980-2000 data. We compared the
mean λ between the two time periods for stocks within each ESU using a
two-tailed paired t-test. Only the 83 stocks with both 1965-2000 and
1980-2000 data were used in this comparison.

# Results

In many of the listed ESUs, the estimated total spawner population (TS)
showed marked decline since 1980 (Fig. 3). While it is apparent from
these trends alone that these populations are at considerable
demographic risk if such declines continue into the future, a
quantitative assessment of this status allows us to compare formally the
status of listed and unlisted stocks; to estimate the wild population
growth rate with masking from hatchery inputs; and finally, to study
whether these downward trends have been persistent through periods of
both good and bad ocean conditions. When presenting the results, we
contrast three different levels of estimates: a) the ESU-level estimate.
This is the estimate of the risk to the ESU as unit, i.e. the risk
estimated from the total number of spawners within the ESU; b) the
stock-level estimate. This is the risk estimate for a single stock,
generally the fish spawning in a single creek or section of a larger
river; c) the mean risk to stocks within the ESU. This average stock
status is different than the ESU-level risk. For example, the ESU as
whole may appear to be at low risk due to a few large, relatively
healthy, stocks even though the ESU as a whole contains mostly smaller,
rapidly declining, stocks.

*Population trends from 1980-present*

Given the trajectories seen at the ESU level (Fig. 3), it is not
surprising that for most ESUs, the estimated long-term population growth
rate indicated a declining population. We had an ESU-level time series
and thus were able to estimate an ESU-level λ, for ten of the eleven
ESUs; the exception was Columbia R. chum. For nine of these, the point
estimate of λ* *was less than 1.0 (Table 1, Fig. 4a), and for four ESUs,
the estimated λ was less than 0.95. The ESU in the worst apparent
condition was Upper Columbia R. spring chinook, for which the ESU-level
λ was less than 0.9. At the stock-level, the λ estimates were more
variable, and most ESUs contained some stocks with estimated λs greater
than 1.0. However, in all listed ESUs, except Columbia R. chum, the
majority of stock-level λs were less than 1.0. In addition, two ESUs,
Lower Columbia R. steelhead (with 12 stocks), and Upper Columbia R.
spring chinook (with 3 stocks), did not have a single stock with an
estimated λ greater than 1.0, and the Middle Columbia R. steelhead ESU
had only 2 stocks (out of 28) with an estimated λ \> 1.0.

In contrast, the majority of λ estimates for stocks in the unlisted ESUs
were equal to or greater than 1.0 (Table 1), with a mean value of 1.02.
In fact, the population growth rates of the unlisted ESUs and stocks
were significantly higher than those of the listed stocks (one-tailed
t-test, P \< 0.001). The estimated risks faced by these populations were
correspondingly lower (Figs. 4-6).

The confidence intervals on λ estimates were generally wide, primarily
due to our uncertainty in estimation of σ^2^. Thus, we cannot rule out
the possibility that the underlying dynamics in the listed ESUs are
positive (λ \> 1.0) and that the declining trends were observed by
chance as can occur when σ^2^ is large. The consistent declining trend
estimates across the listed ESUs but not in the unlisted ESUs, however,
makes such a conjecture seem unlikely, at least for most of the listed
ESUs. In addition, when we made a quantitative assessment of our
uncertainty, by calculating the probability that λ \< 1.0, we found that
for almost half the listed stocks and seven of the listed ESUs, there
was considerable data support (greater than 60% probability) for a
long-term declining trend (Table 1). For the conjecture that the stocks
and ESUs are undergoing rapid decline, λ \< 0.90, there was generally
low but not negligible data support, roughly a 20% probability for most
ESUs. Upper Columbia R. chinook was the exception with high data support
(72% probability) for a λ \< 0.90. For perspective, populations with a
long-term population growth rate of 0.9 are declining rapidly enough
that the population can be anticipated to halve in less than seven
years.

These low estimates of population growth rate translate into substantial
risks of decline and extinction. At the broad-scale, all ESUs except
Lower Columbia R. steelhead had a probability greater than 50% of VHRD
(Fig. 5, Table 1), indicating that the data gave more support than not
to the possibility that there is a 25% chance of serious decline in the
next 50 years. At the stock level, the picture was similar. For every
ESU except Columbia R. chum, the mean probability of VHRD at the stock
level was also greater than 50% (Fig. 5, Table 1). In addition to the
risk of decline, we were also able to estimate extinction risk for seven
ESUs with total spawner estimates from dam counts. There was high (69%)
support for a greater than 25% chance of extinction (VHER) for the Upper
Columbia R. chinook ESU. However, the probability of VHER for the
remaining ESUs was generally low, ranging from 9 to 37% (Fig. 6, Table
1). Estimates of extinction probability and decline have wide confidence
intervals (Table 1). Rather than focusing on the precise point estimates
for an individual ESU or stock, one should focus on the overall patterns
within the basin across multiple ESUs or of stocks within an ESU. The
mean risk estimated across multiple ESUs or stocks gives a broad picture
of the risk and has much smaller confidence intervals than the
individual point estimates (Figs. 5 and 6).

We also used our estimates of long-term population growth and risk to
determine how much change in population growth rate would be necessary
to mitigate the current risks. At both stock and ESU levels, we
calculated the percent change required to achieve a point estimate of λ
equal to 1.0, as well as the change necessary to reduce the probability
of 90% decline in 50 years to less than 5%. When estimates of total
population size were available, we also calculated the percent increase
in λ necessary to reduce the risk of extinction to less than 5% in 50
years. Although these calculations do not suggest specific management
actions, they can contribute to establishing management goals by giving
rough estimates of the magnitude of changes required. We did not
evaluate the potential for changes in variance to reduce risks of
decline or extinction for these stocks, although this may present
another way in which management actions might alter the status of the
stocks.

To reduce the risk of a 90% decline in 50 years to less than 5%, the
necessary improvements in λ at the stock level ranged from 0 to 53%,
with a mean of 10% (Fig. 7, Table 1). Reducing the long-term risk of
extinction required improvements ranging from 0 to 41%, with a mean of
4% (Table 1). The slightly greater improvements required to avoid
long-term declines are due in part to the fact that larger, less steeply
declining populations can have a low probability of reaching the
extinction threshold over the analyzed time frame, but still have a
reasonably high probability of a substantial decrease in abundance.

There are several considerations for specific stocks or ESUs that are
worth noting when interpreting these results. First, Upper Columbia R.
chinook had the lowest estimated λ (λ = 0.85) by far and the highest
consequent risks. Also the stocks within this ESU appear to have an
increasing rate of decline through time, which will cause both λ and
risk estimates to be overly optimistic. In addition, none of the three
stocks within this ESU had a point estimate of λ corresponding to an
increasing or stable trend. This combination of factors suggests that
the Upper Columbia R. spring chinook ESU may be an ESU that is
disproportionately at risk within the Columbia River Basin. Second when
considering the stock-level estimates in the Snake R. steelhead ESU,
note that stock-level data were available only for "A-run" stocks in the
state of Oregon. The majority of these stocks are experiencing stable
growth trajectories. However, counts at the Lower Granite Dam, which
encompass the entire ESU, and include Idaho and "B-run" stocks, show a
decidedly negative trend. Because the stock-level data from this ESU are
not a representative sample of the ESU, estimates from these data should
be viewed with caution. Given the trends in counts at Lower Granite Dam,
actual risks faced by this ESU are likely to be larger than is apparent
from the stock-level data.

[]{.mark}

Accounting for possible hatchery fish reproduction

We next examined the potential for the true status of the population to
be obscured or masked by hatchery fish reproducing naturally. The effect
we evaluated is not due to an impact of the hatchery fish on wild
populations, although such negative interactions may certainly exist.
Rather, it is a matter of determining the population growth rate due to
wild reproduction alone when the wild population receives an infusion of
fish from another population (namely, the hatchery) each year. If
hatchery fish reproduce, their reproduction effectively masks the
component of population growth due to reproduction and survival in the
wild.

Given the large numbers of hatchery fish in the Columbia River Basin,
population trends and associated risks certainly have the potential to
be substantially masked by hatchery fish reproduction. We had hatchery
fraction data for nine of the eleven listed ESUs. When we corrected for
hatchery fish in the ESU-level time series and assumed that hatchery-
and wild-born fish reproduce at the same rate, the estimated λ's were
less than 0.9 for every ESU and were below 0.8 for four of the nine
(Appendix A, Table 2). For two ESUs with especially high numbers of
hatchery spawners (Upper Willamette R. chinook and Upper Columbia R.
steelhead), the estimated λ's dropped from near 1.0 to 0.62 and 0.69
respectively. Such severely low estimated λ's indicate that if the
hatchery-reared spawners have been reproducing, then the underlying
reproduction and survival in the wild for the listed salmonids in the
Columbia River Basin has been extremely low. The ESUs with the lowest
estimate of long-term growth also shifted with the assumption of 100%
effective hatchery-fish reproduction. Upper Willamette R. chinook stood
out with an especially low estimate (λ = 0.62) while most of the rest of
the ESUs had λ estimates in the range of 0.77-0.89.

Derived risk estimates were similarly changed for the worse when
hatchery reproduction was assumed. Probability of extinction could be
estimated for seven of the ESUs. For six of these, the point estimates
of extinction risk in 50 years increased from near zero, assuming no
hatchery fish reproduction, to an average 62% probability of extinction,
assuming equal hatchery fish reproduction (Appendix A). The probability
of 90% decline in 50 years could be estimated for eight ESUs. The point
estimates indicated a greater than 90% probability of severe decline for
all eight ESUs when hatchery fish were assumed to be reproducing.
Because extinction and decline risk estimates are highly variable, we
present these values to suggest the magnitude of change in risk that is
possible, rather than to provide a precise estimate of that change. The
true rate at which hatchery-born fish spawn in the wild lies between the
two extremes of no reproduction and reproduction equal to wild fish.
Thus the risk estimates shown in Figs. 4-7 and Table 1, which assume no
hatchery fish reproduction, should be viewed as somewhat optimistic and
those in Appendix A, which assume hatchery fish reproduction is
equivalent to wild fish reproduction, should be viewed as somewhat
pessimistic.

When interpreting these low estimates of the "natural" λ and high risk
estimates, it is important to note that our analysis cannot distinguish
between whether the hatchery fish are supporting collapsing wild
populations (playing a positive role) or are instead causing low natural
reproduction and survival (playing a negative role). A relationship
between the natural λ and hatchery fraction cannot be examined since we
were only able to estimate the minimum natural λ by assuming 100%
hatchery fish reproduction. In reality, reproduction by hatchery-reared
fish is not 100% as effective as reproduction by wild-born fish and the
reproductive effectiveness of hatchery-reared fish almost certainly
varies across ESUs and species.

###### Ocean cycles and population status

Finally, we calculated population growth rates and associated risk over
two time periods, 1980-2000 and 1965-2000, that reflect different ocean
conditions for most Columbia River salmonids (Mantua et al. 1997):
(Appendix B). This comparison is a simple test of the sensitivity of our
status and risk estimates to the time period we evaluated. We had
sufficient data from 86 stocks in nine ESUs for this comparison. We did
not have ESU-level data before 1979 in most cases and thus we compared
mean stock-level λ estimates. For four ESUs, the average population
growth rate was slightly higher over the longer time period as might be
expected if pre-1977 years were under "good" ocean conditions. However,
the difference was only significant for Upper Columbia R. spring chinook
(Table 3); this ESU shows a steady declining trend in productivity since
the 1970s. Higher growth rates in the 1980-2000 period (the opposite
expectation based on ocean conditions) were seen in five of the nine
ESUs with significant differences seen in the Snake R. spring/summer
chinook, Snake R. steelhead, and Middle Columbia R. chinook ESUs (Table
3). In addition, Upper Columbia R. summer/fall chinook \"healthy\"
stocks had a lower mean λ over the 1965-2000 time period than during the
1980-2000 time period; this difference was nearly significant (Table 3).

###### Potential for management actions to mitigate risk

Determining whether specific management actions can achieve the changes
necessary to mitigate the risks currently faced by threatened and
endangered populations in the Columbia River Basin is an enormous
challenge, in no small part because the effects of most recovery or
restoration activities on salmon survival are not well quantified.
However, there are two cases in which human-caused salmonid mortality
has been relatively well documented. The first is harvest of adult fish,
in both commercial and sport fisheries. The second is the survival rate
of juveniles and adult spawners migrating through the Columbia River and
Snake River hydropower dams. As a first step toward addressing the
potential for specific management actions to achieve the needed
improvements in population growth rate, λ, we assessed the maximum
possible change to λ that could be achieved by reducing harvest and by
implementing the proposed improvements to fish passage through the dams
outlined in the NMFS Biological Opinion on the Federal Columbia River
Power System (NMFS 2000).

We can use the approximation, λ = *R*~0~^1/T^ where T is the mean
generation time (Caswell 2000), to explore the potential impacts on λ of
changes in harvest rates or survival through the hydropower system.
Exploitation rates for salmon are expressed in terms of the fraction of
spawners that did not return but would have without harvest, e.g. an
exploitation rate of 0.80 indicates that number of returning spawners is
20% of what it would be if there had been no harvest. Exploitation rates
are expressed in this way so that harvest that occurs in-stream versus
in-ocean can be compared via a common currency. Given that the
exploitation rate is expressed this way:

$R_{0} = s_{1}F_{1}(1 - h)R + s_{1}(1 - F_{1})s_{2}F_{2}(1 - h)R + s_{1}(1 - F_{1})s_{2}(1 - F_{2})s_{3}F_{3}(1 - h)R\ldots$
\[12\]

where *h* is the exploitation rate, *s~i~* is the survival from age
*i*-1 to *i*, and *F~i~* is the fraction of spawners that return at age
*i*, *R* is the mean offspring per spawner. Using the relationship
between λ and *R~0~*, we can calculate the proportional change in λ from
a change in *h* alone:

$\frac{\lambda_{\text{new}} - \lambda_{\text{old}}}{\lambda_{\text{old}}} = \left( \frac{R_{0,\text{new}}}{R_{0,\text{old}}} \right)^{\frac{1}{T}} - 1 = \left( \frac{1 - h_{\text{new}}}{1 - h_{\text{old}}} \right)^{\frac{1}{T}} - 1$
\[13\]

To estimate the impact of harvest over the 1980-1999 time period, we
calculated the average total (ocean and in-river) exploitation rates for
each ESU (or component of an ESU that is subject to different harvest
regulations) for those years (Table 4). For most ESUs, there has been a
substantial reduction in harvest in the mid-to-late 1990s in response to
conservation concerns and ESA-listings; the average harvest rates
include this reduction. We then determined the effect on λ of completely
eliminating harvest, *h*~new~ = 0, using Equation 12. We did not assess
the impact of harvest on the Columbia R. chum ESU, since we did not have
total population size estimates, and therefore could not estimate total
harvest rate.

The potential response of ESUs or components of ESUs to this
hypothetical change in harvest management varied with exploitation rate
and current population status. At a broad scale, harvest moratoria had
the largest effect on the Lower Columbia R. chinook, Upper Willamette R.
chinook, and Snake R. fall chinook ESUs, resulting in greater than 15%
increases in λ (Table 4). For context, the optimistic point estimates of
λ* *for these ESUs, which assume no hatchery fish reproduction, require
a 1-5% increase to be equal 1.0 (Table 1) and the pessimistic point
estimates of λ*,* which assume high hatchery fish reproduction, require
a 1-23% increase with most required increases much less than 15%
(Appendix B).

Improving the survival of both juvenile and adult fish migrating through
the Columbia and Snake River dams has been the focus of much effort, and
is another human impact that has been relatively well-quantified. NMFS
(2000) has recently required that agencies operating the Federal
Columbia River Power System implement a variety of activities, including
increased spill, improved passage facilities, and increased
transportation as a means of improving that survival. The dams affect
survival during both downstream migration as juveniles pass through the
hydropower system on their way to the ocean and again during upstream
migration as adults return to their natal streams to spawn. Denoting
adult spawner and juvenile survival through the hydropower system as
*d~s~* and *d~j~* respectively,

$R_{0} = d_{j}s_{1}F_{1}d_{s}R + d_{j}s_{1}(1 - F_{1})s_{2}F_{2}d_{s}R + d_{j}s_{1}(1 - F_{1})s_{2}(1 - F_{2})s_{3}F_{3}d_{s}R\ldots$
\[14\]

If we denote the product of the dam survivals, *d~s~* x *d~j~*, as *d*,
then the proportional change in λ due to change in *d* can be calculated
as:

$\frac{\lambda_{\text{new}} - \lambda_{\text{old}}}{\lambda_{\text{old}}} = \left( \frac{R_{0,\text{new}}}{R_{0,\text{old}}} \right)^{\frac{1}{T}} - 1 = \left( \frac{d_{\text{new}}}{d_{\text{old}}} \right)^{\frac{1}{T}} - 1$
\[15\]

Using Equation 14, anticipated improvements in λ due to the proposed
changes in the hydropower system operation ranged from 1-9% across all
ESUs (Table 5). For most ESUs, the estimated increase in λ represented
one quarter to half of the estimated required increase in λ to achieve λ
= 1, under the optimistic assumption of zero hatchery fish reproduction.
The exception was Snake R. fall chinook for which the estimated increase
in λ was 3-9%, comparing favorably to the estimated required increase of
5%.

# Discussion

###### Columbia River Basin Anadromous Salmonid Status

Regardless of the risk metric chosen, our estimates of the risks faced
by the threatened and endangered salmon populations in the Columbia
River Basin suggest that the majority of these populations unlikely to
be viable. Even under the optimistic assumption of zero hatchery fish
reproduction, nine of the eleven listed ESUs had point estimates of
long-term population growth rate, indicating declining trends. We had
sufficient data to estimate the extinction risk for seven of these ESUs.
The estimated probability of extinction in 50 years was zero or nearly
so for six of the seven, with the striking exception being Upper
Columbia R. chinook with a 54% estimated probability of extinction in 50
years. Despite the predominance of zero point estimates for the risk
extinction, there was much higher estimated risk that the ESUs are
severely (90%) below current levels in 50 years. This risk could be
estimated for ten of the eleven listed ESUs. For six of these, the
probability was a greater than 25% that the ESUs will be one-tenth of
current levels in 50 years. The point estimates give an estimate of the
most likely probability of extinction or severe decline given the
available data. However, both the extinction and decline probability
metrics tend to be highly sensitive to parameter uncertainty. This
parameter uncertainty was high enough in our analyses such that it
cannot not be ruled out that the true risks are high (\> 25%) even when
the point estimates were low.

Most scientific and political attention to date has been focused on the
threatened Snake R. spring/summer chinook stocks. However, our results
suggest that this ESU is not necessarily the management unit most at
risk. Rather, Upper Columbia River stocks and steelhead throughout the
basin had the lowest average long-term population growth rates at the
ESU level. In fact, the Upper Columbia R. chinook and the Lower Columbia
R. steelhead ESUs did not include a single stock with a point estimate
of λ greater than 1.0. The Snake R. spring/summer chinook ESU, on the
other hand, included 16 (out of 38) stocks with point estimates of
population growth rates equal to or greater than one. Obviously, this is
not to say that the Snake R. spring/summer chinook ESU is viable under
current conditions (in fact, the λ* *estimated at ESU-level and for many
of the component stocks is less than 1), but rather that attention
should be directed toward the status of stocks throughout the Columbia
River drainage.

In contrast to the threatened and endangered ESUs in the Columbia River
Basin are the Washington Coastal, Middle Columbia R. spring chinook and
Upper Columbia R. summer/fall chinook stocks widely (and apparently
appropriately) regarded as \"healthy.\" The mean λ value of these stocks
was not only greater than one, with correspondingly low probabilities of
VHER and VHRD, but also significantly greater than the mean λ for listed
stocks. Demographically at least, these unlisted ESUs appear to be more
viable than their listed counterparts.

One important consideration for this, or any other status assessment, is
appropriate population definition. Salmon data have been traditionally
collected on a stream-by-stream basis and treated as de facto
populations. However, little work has been done to verify this
assumption, and fish in multiple streams or rivers may belong to a
single population, or may behave as sources and sinks. Alternatively, a
single stream may contain more than one population. Either case may
complicate the interpretation of adult census data (Brawn and Robinson
1996). Because recovery-planning efforts depend on estimates of the
status of populations, it is critical that demographically independent
populations be defined (McElhany et al. 2000).

*Naturally spawning hatchery fish -- an additional layer of uncertainty*

Our results also suggest that our lack of information about the
reproductive success of hatchery-reared fish spawning in the wild is a
critical information gap for Columbia River salmonids. Our population
growth rates estimates varied widely depending on our assumption about
hatchery fish spawning success. This variation has a cascading effect on
other risk estimates. Without knowing the proportion of hatchery
spawners in a population count and the actual relative reproductive
success of those fish, it is only possible to determine the best and
worst case scenarios for population status, and not the true population
status. Without knowing whether wild populations are stable or
declining, or by how much they are declining, it will be challenging (at
best) to determine appropriate management actions to remedy the
situation. Recently, NMFS (2000) has required that many hatchery fish in
the Columbia River Basin be marked. Although the nature of this marking
program has not yet been established, when implemented, it will enable
managers to determine the proportion of hatchery spawners spawning in
the wild more reliably for many stocks. A complete marking program would
improve our ability to determine population growth rates even further.
However, the reproductive success of hatchery fish is still largely
unknown. It will be important to conduct paternity studies of wild and
hatchery-reared fish spawning in the wild to reduce this uncertainty.

*Ocean conditions and the status of Columbia River salmonids*

Changes in oceanographic conditions are often implicated in salmon
population regulation (Francis and Hare 1994, Mantua et al. 1997). We
evaluated population status across two time periods -- one that included
only years from the warm (\"bad\") phase of the PDO, and a second that
included years from both the warm and cold (\"bad\" and \"good\") phases
of this climatic cycle as one means to assess the possibility that
recent declines are primarily a result of the most recent (post-1977)
downturn in ocean conditions. For most ESUs with sufficient data for
analysis, we found that there was either no significant difference in
the mean population growth rate of stocks between the two time periods
or a significant increase in the growth in the more recent period. There
was hydropower dam building during the 1970s which could have offset the
effects of good ocean conditions during that period, however, the dam
building mainly affected stocks in the Snake River Basin. If we look
only at the ESUs least affected by the dam construction during this
period, namely Lower and Middle Columbia R. ESUs, there is no indication
that the λ estimates using the 1980-2000 period have been skewed lower
due this time period being in a PDO cycle with poor ocean conditions.
Overall our results suggest that the declines seen over last 20 years
are not solely a function of evaluating a period of poor ocean
conditions, but are more likely to be a more long-term phenomenon.
Indeed, salmon in the Columbia River Basin have maintained a steady
decline since the late 1800s (NRC 1996). Regime shifts to more positive
ocean conditions for Columbia River salmon, such as those believed to
have occurred in 1998, will certainly help listed ESUs in this region.
In fact, record runs (including the hatchery component) have been
recorded at Bonneville Dam in 2000 and 2001. However, given our results,
we suggest that recovery is unlikely to be achieved by relying on
improvements in ocean conditions alone.

Snake R. spring/summer chinook and Snake R. steelhead stocks both had
significantly lower population growth rates over the 1965-present time
period than over the more recent 1980-present time period. The single
Snake R. fall chinook stock also experienced a large drop in population
growth rate (0.97 in the recent time period; 0.89 in the longer time
period). The period from 1965-1977 was a period of dramatic change in
the hydropower system for these ESUs, as three major dams on the Snake
and one on the mainstem Columbia were constructed. The lower population
growth rates seen during this period are likely to be at least in part,
a reflection of this large perturbation. Snake R. spring/summer chinook
also experienced an increase in productivity in the early 1980s. This
may have caused the mean population growth rate to be overestimated in
the 1980-present time period. This is not true for Snake R. steelhead,
however, or for Snake R. fall chinook. If a precautionary approach to
status assessment is desired, it may be appropriate to use population
growth rates derived from the longer time periods for the ocean-type
ESUs, and for Snake R. spring/summer chinook (due to the increase in
productivity in the early 1980s that this ESU experienced).

*Mitigating risks*

Using estimates of the potential magnitude of harvest reductions and
increases in survival through the Columbia Basin hydropower system, we
did a coarse evaluation of the potential improvements in λ from these
actions. These analyses suggest that both harvest reduction and
hydropower passage improvements provide biologically viable means of
improving population growth rates. Changes in the harvest levels could
have relatively large effects on the population growth rates of the
Upper Willamette R., Snake R. fall and Lower Columbia R. chinook ESUs.
These three ESUs are subject to harvest both in the ocean and in-river,
resulting in higher overall harvest rates than those seen in other ESUs.
Changes from past harvest levels could also have a moderate (4-7%)
impact on several steelhead ESUs (Table 4). However, it should be kept
in mind that harvest rates have already been reduced recently
(1992-1996) for many ESUs due to conservation concerns, and the average
exploitation rate for the 1980-1999 time period which we used in our
analysis is, in many cases, higher than current exploitation rates. Thus
some portion of the potential improvements in λ from harvest reduction
may have already been achieved via recent reductions.

Improving survival for adults and juveniles as they migrate through the
hydropower corridor is another avenue by which population growth rates
might be increased. In fact, past improvements to the passage system
appear to have been important in increasing overall survival for Snake
R. spring/summer chinook (Kareiva et al. 2000). Anticipated additional
improvements to the hydropower system are less likely to produce large
changes in population growth rates for most ESUs (Table 5). One
exception to this generalization is the Snake R. fall chinook ESU, which
currently has very poor survival through the hydropower system (NMFS
2000). Given the challenge of finding other actions that produce
ESU-wide improvements in survival, these improvements, even if they
result in relatively moderate λ increases, can be an important component
of a suite of actions aimed at recovery.

Unfortunately, evaluating the potential for other management actions to
improve population growth rates is extremely challenging. In particular,
breaching the four lower Snake River dams has been proposed as a
high-profile means of recovering Snake River ESUs. Determining the
magnitude of response to dam breaching is complicated by the potential
for indirect mortality that could be attributable to the hydropower
system (Marmorek et al. 1999). Schaller et al. (1999) compared the
productivity of stocks upstream and downstream of the Snake River dams
and concluded that there was substantial indirect mortality caused by
the dams. However, a more recent analysis comparing those stocks in a
formal BACI design suggests that patterns of productivity in the Snake
River were similar to those seen in the control (downstream) region,
suggesting that dams are not currently causing a substantial reduction
in population growth rates of Snake River salmon stocks (Levin and
Tolimieri 2002). Clearly, evaluating definitively the potential for
removal of the four lower Snake River dams to improve population status
is problematic.

In the hatchery and habitat arenas, there is a dearth of studies that
quantitatively link specific actions or impacts with fish population
responses. In many cases, we lack information even about the
distribution of impacts and efforts to reduce them. Because of this lack
of information, it will be critical for future recovery actions
(including dam breaching, if this option is chosen) to be conducted as
formal experiments. The quantitative and mechanistic links that such
experiments can provide will be a crucial component of conservation
planning for these fishes.

Lack of knowledge about the impact of various management actions is not
the only uncertainty important when mitigating risks to listed ESUs.
Continuing degradation in environmental conditions due to human
activities will affect the magnitude of improvement that is needed to
recover listed ESUs. As human impacts on the landscape increase, they
have the potential to offset benefits achieved through other means, thus
requiring additional improvements. For example, harvest rates on several
ESUs, including the Snake R. spring/summer chinook and Upper Columbia R.
spring chinook were reduced well before 1980. Increased mortality
through the hydropower system, however, may have worked together with a
variety of other factors (such as ocean conditions) to continue the
decline of these ESUs even after harvest was reduced.

*Beyond status assessment---using standardized comparative risk
analyses*

This status assessment is clearly not a final recovery plan for Columbia
Basin salmon and steelhead. Rather it is a first step that quantifies
the status of listed ESUs with respect to a repeatable and standard
metric (λ) that is critical for population viability. It also begins to
assess the impact of two important anthropogenic sources of mortality
across ESUs. However, this type of assessment is also likely to play an
role as a starting point for additional analyses that contribute to a
more complete recovery planning analysis.

For instance, many recovery planning prioritization schemes will include
economic and political considerations as well as biological ones.
Identifying situations where only minor improvements are necessary, and
those where drastic action will be required is important to economic and
political planning. Similarly, many conservation efforts seek to
preserve areas that are currently in the best biological condition, as
these efforts are both biologically and economically expedient
(Allendorf et al. 1997). Having biological criteria in a common currency
allows more ready integration of different considerations, especially
when that common currency incorporates uncertainty, for example the
probability that λ is less than a certain value. In addition, these
standard metrics have the potential to contribute to analyses linking
environmental or other conditions to population status. There are a
variety of efforts in the U.S. Pacific Northwest to quantify the
distribution of geologic and climatic factors as well as the
distribution and magnitude of anthropogenic impacts on anadromous
fishes. Combining these locally explicit assessments of habitat and
other factors with a standard description of population status can
provide the opportunity to begin to link fish population responses to
specific environmental conditions.

Acknowledgements

We thank Tom Cooney, Rich Hinrichsen, Peter Kareiva, Phil Levin, Paul
McElhany, Rich Zabel, Michael Schiewe and two anonymous reviewers for
discussion and comments that greatly improved this manuscript. We also
thank Lisa Holsinger, who created the map of the Columbia River Basin,
and Henry Carson and Jessica Piasecke, who assisted with general
manuscript preparation. The views expressed in this paper are not the
official views of the National Marine Fisheries Service.

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<table style="width:100%;">
<colgroup>
<col style="width: 6%" />
<col style="width: 19%" />
<col style="width: 4%" />
<col style="width: 0%" />
<col style="width: 3%" />
<col style="width: 12%" />
<col style="width: 3%" />
<col style="width: 3%" />
<col style="width: 3%" />
<col style="width: 9%" />
<col style="width: 4%" />
<col style="width: 3%" />
<col style="width: 9%" />
<col style="width: 5%" />
<col style="width: 3%" />
<col style="width: 6%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="16">Table 1. Parameter estimates, risk of extinction and
90% decline in abundance and needed improvements in λ to achieve λ = 1
and to reduce 50-year risk of decline or extinction in to below 5%.
ESU-level estimates are in bold. Estimates were made assuming no
hatchery fish reproduction. When hatchery fraction data was available,
the hatchery input correction was used, otherwise estimates used the
total (wild + hatchery) spawner count data as described in the text.
Population size estimate is an estimate of the total spawner population
as described in the text. First 11 ESUs are listed under the U.S.
Endangered Species Act; the 3 non-listed ESUs (marked with *)
follow.</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td colspan="7">Population Parameter Estimates</td>
<td colspan="3">Risk of Extinction</td>
<td colspan="3">Risk of 90% Decline</td>
<td></td>
</tr>
<tr class="odd">
<td colspan="2" rowspan="3"><strong>ESU</strong> or Stock (Pop. size
est.)</td>
<td colspan="2" rowspan="3">μ</td>
<td rowspan="3">σ<sup>2</sup></td>
<td rowspan="3">λ (95% CI)</td>
<td></td>
<td></td>
<td rowspan="3">%<br />
Inc</td>
<td rowspan="3"><p>50 years</p>
<p>(95% CI)</p></td>
<td rowspan="3"><p>Pr</p>
<p>VHER<sup>1</sup></p></td>
<td rowspan="3">%<br />
Inc</td>
<td rowspan="3"><p>50 years</p>
<p>(95% CI)</p></td>
<td rowspan="3"><p>Pr</p>
<p>VHRD<sup>2</sup></p></td>
<td rowspan="3">%<br />
Inc</td>
<td rowspan="3">Notes<sup>3,4</sup></td>
</tr>
<tr class="even">
<td colspan="2">Pr λ</td>
</tr>
<tr class="odd">
<td>&lt;1.0</td>
<td>&lt;0.9</td>
</tr>
<tr class="even">
<td colspan="2">Lower Columbia R chinook</td>
<td colspan="2"><strong>0.00</strong></td>
<td><strong>0.03</strong></td>
<td><strong>0.99(0.68, 1.44)</strong></td>
<td><strong>0.50</strong></td>
<td><strong>0.27</strong></td>
<td><strong>0</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>0.05(0, 1)</strong></td>
<td><strong>0.51</strong></td>
<td><strong>0</strong></td>
<td><strong>i</strong></td>
</tr>
<tr class="odd">
<td></td>
<td>Abernathy Cr f-t (1587)</td>
<td colspan="2">-0.03</td>
<td>0.02</td>
<td>0.98(0.82, 1.15)</td>
<td>0.58</td>
<td>0.23</td>
<td>3</td>
<td>0(0, 0.43)</td>
<td>0.28</td>
<td>0</td>
<td>0.17(0, 1)</td>
<td>0.55</td>
<td>2</td>
<td>l</td>
</tr>
<tr class="even">
<td></td>
<td>Bear Cr fall</td>
<td colspan="2">-0.14</td>
<td>0.28</td>
<td>0.87(0.45, 1.66)</td>
<td>0.71</td>
<td>0.54</td>
<td>15</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.90(0, 1)</td>
<td>0.77</td>
<td>25</td>
<td>l,h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Big Cr fall</td>
<td colspan="2">-0.06</td>
<td>0.07</td>
<td>0.94(0.76, 1.16)</td>
<td>0.68</td>
<td>0.33</td>
<td>6</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.63(0, 1)</td>
<td>0.69</td>
<td>8</td>
<td>h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Clackamas R fall</td>
<td colspan="2">-0.04</td>
<td>0.04</td>
<td>0.96(0.71, 1.30)</td>
<td>0.59</td>
<td>0.31</td>
<td>4</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.40(0, 1)</td>
<td>0.60</td>
<td>5</td>
<td>t,l,h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Clatskanie R fall</td>
<td colspan="2">-0.02</td>
<td>0.68</td>
<td>0.98(0.50, 1.93)</td>
<td>0.52</td>
<td>0.34</td>
<td>2</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.41(0, 1)</td>
<td>0.60</td>
<td>22</td>
<td>h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Coweman R f-t (2923)</td>
<td colspan="2">0.23</td>
<td>0.19</td>
<td>1.26(0.82, 1.94)</td>
<td>0.14</td>
<td>0.07</td>
<td>0</td>
<td>0(0, 0.13)</td>
<td>0.13</td>
<td>0</td>
<td>0(0, 0.67)</td>
<td>0.18</td>
<td>0</td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td>Cowlitz R f-t (7903)</td>
<td colspan="2">-0.03</td>
<td>0.10</td>
<td>0.97(0.71, 1.33)</td>
<td>0.56</td>
<td>0.29</td>
<td>3</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.33(0, 1)</td>
<td>0.59</td>
<td>6</td>
<td>h</td>
</tr>
<tr class="even">
<td></td>
<td>Cowlitz R spr</td>
<td colspan="2">-0.03</td>
<td>0.03</td>
<td>0.97(0.83, 1.14)</td>
<td>0.61</td>
<td>0.23</td>
<td>3</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.25(0, 1)</td>
<td>0.57</td>
<td>3</td>
<td>l,h</td>
</tr>
<tr class="odd">
<td></td>
<td>Elochoman R f-t</td>
<td colspan="2">0.05</td>
<td>0.40</td>
<td>1.05(0.57, 1.96)</td>
<td>0.40</td>
<td>0.24</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.14(0, 1)</td>
<td>0.47</td>
<td>7</td>
<td>h</td>
</tr>
<tr class="even">
<td></td>
<td>Germany Cr f-t</td>
<td colspan="2">0.01</td>
<td>0.14</td>
<td>1.01(0.67, 1.52)</td>
<td>0.47</td>
<td>0.26</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.15(0, 1)</td>
<td>0.51</td>
<td>4</td>
<td>h</td>
</tr>
<tr class="odd">
<td></td>
<td>Gnat Cr fall</td>
<td colspan="2">-0.03</td>
<td>0.40</td>
<td>0.97(0.56, 1.71)</td>
<td>0.54</td>
<td>0.34</td>
<td>3</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.41(0, 1)</td>
<td>0.61</td>
<td>16</td>
<td>h</td>
</tr>
<tr class="even">
<td></td>
<td>Grays R f-t</td>
<td colspan="2">-0.10</td>
<td>0.42</td>
<td>0.90(0.48, 1.70)</td>
<td>0.65</td>
<td>0.47</td>
<td>11</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.72(0, 1)</td>
<td>0.72</td>
<td>25</td>
<td>h</td>
</tr>
<tr class="odd">
<td></td>
<td>Kalama R spr</td>
<td colspan="2">-0.11</td>
<td>0.22</td>
<td>0.90(0.57, 1.42)</td>
<td>0.69</td>
<td>0.48</td>
<td>11</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.82(0, 1)</td>
<td>0.74</td>
<td>19</td>
<td>l,d,h,f</td>
</tr>
<tr class="even">
<td></td>
<td>Kalama R f-t</td>
<td colspan="2">0.02</td>
<td>0.47</td>
<td>1.02(0.52, 2.00)</td>
<td>0.46</td>
<td>0.29</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.25(0, 1)</td>
<td>0.53</td>
<td>13</td>
<td>h</td>
</tr>
<tr class="odd">
<td></td>
<td>Klickitat R f-t</td>
<td colspan="2">0.08</td>
<td>0.13</td>
<td>1.08(0.73, 1.59)</td>
<td>0.32</td>
<td>0.16</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.01(0, 0.99)</td>
<td>0.36</td>
<td>0</td>
<td>l,h</td>
</tr>
<tr class="even">
<td></td>
<td>Lewis R f-b (34652)</td>
<td colspan="2">-0.02</td>
<td>0.05</td>
<td>0.98(0.79, 1.22)</td>
<td>0.55</td>
<td>0.24</td>
<td>2</td>
<td>0(0, 0.08)</td>
<td>0.19</td>
<td>0</td>
<td>0.19(0, 1)</td>
<td>0.54</td>
<td>3</td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td>Lewis R spr</td>
<td colspan="2">-0.04</td>
<td>0.46</td>
<td>0.96(0.50, 1.85)</td>
<td>0.56</td>
<td>0.38</td>
<td>4</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.49(0, 1)</td>
<td>0.63</td>
<td>19</td>
<td>t,a,h</td>
</tr>
<tr class="even">
<td></td>
<td>Lewis E Fk f-t (853)</td>
<td colspan="2">-0.01</td>
<td>0.02</td>
<td>0.99(0.85, 1.15)</td>
<td>0.53</td>
<td>0.18</td>
<td>1</td>
<td>0(0, 0.27)</td>
<td>0.26</td>
<td>0</td>
<td>0.06(0, 1)</td>
<td>0.48</td>
<td>1</td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td>Mill Cr f-t</td>
<td colspan="2">-0.10</td>
<td>0.26</td>
<td>0.91(0.37, 2.24)</td>
<td>0.64</td>
<td>0.46</td>
<td>10</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.77(0, 1)</td>
<td>0.69</td>
<td>20</td>
<td>l,h</td>
</tr>
<tr class="even">
<td></td>
<td>Plympton Cr fall</td>
<td colspan="2">-0.02</td>
<td>0.11</td>
<td>0.98(0.73, 1.32)</td>
<td>0.54</td>
<td>0.28</td>
<td>2</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.3(0, 1)</td>
<td>0.58</td>
<td>6</td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td>Sandy R fall</td>
<td colspan="2">0.17</td>
<td>0.33</td>
<td>1.19(0.74, 1.90)</td>
<td>0.20</td>
<td>0.10</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.01(0, 0.98)</td>
<td>0.26</td>
<td>0</td>
<td>t,h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Sandy R f-l (3790)</td>
<td colspan="2">0.00</td>
<td>0.02</td>
<td>1.00(0.82, 1.23)</td>
<td>0.47</td>
<td>0.21</td>
<td>0</td>
<td>0(0, 0.01)</td>
<td>0.24</td>
<td>0</td>
<td>0.01(0, 1)</td>
<td>0.44</td>
<td>0</td>
<td>l</td>
</tr>
<tr class="odd">
<td></td>
<td>Sandy R f-t (398)</td>
<td colspan="2">-0.21</td>
<td>0.10</td>
<td>0.81(0.14, 1.78)</td>
<td>0.74</td>
<td>0.61</td>
<td>23</td>
<td>0.98(0, 1)</td>
<td>0.76</td>
<td>19</td>
<td>1(0, 1)</td>
<td>0.78</td>
<td>27</td>
<td>l</td>
</tr>
<tr class="even">
<td></td>
<td>Skamokawa Cr f-t</td>
<td colspan="2">-0.10</td>
<td>0.14</td>
<td>0.90(0.63, 1.30)</td>
<td>0.71</td>
<td>0.47</td>
<td>11</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.85(0, 1)</td>
<td>0.75</td>
<td>16</td>
<td>h</td>
</tr>
<tr class="odd">
<td></td>
<td>Washougal R f-t</td>
<td colspan="2">0.04</td>
<td>0.02</td>
<td>1.04(0.90, 1.21)</td>
<td>0.31</td>
<td>0.11</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0(0, 0.94)</td>
<td>0.30</td>
<td>0</td>
<td>h</td>
</tr>
<tr class="even">
<td></td>
<td>White Salmon R f-t</td>
<td colspan="2">-0.13</td>
<td>0.12</td>
<td>0.88(0.62, 1.23)</td>
<td>0.77</td>
<td>0.55</td>
<td>14</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.96(0, 1)</td>
<td>0.80</td>
<td>19</td>
<td>h</td>
</tr>
<tr class="odd">
<td></td>
<td>Wind R f-t</td>
<td colspan="2">-0.10</td>
<td>0.81</td>
<td>0.90(0.37, 2.18)</td>
<td>0.63</td>
<td>0.46</td>
<td>11</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.67(0, 1)</td>
<td>0.71</td>
<td>36</td>
<td>h</td>
</tr>
<tr class="even">
<td></td>
<td>Wind R spr</td>
<td colspan="2">0.01</td>
<td>0.05</td>
<td>1.01(0.81, 1.27)</td>
<td>0.43</td>
<td>0.18</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.03(0, 1)</td>
<td>0.44</td>
<td>0</td>
<td>h</td>
</tr>
<tr class="odd">
<td></td>
<td>Youngs R fall</td>
<td colspan="2">-0.03</td>
<td>1.21</td>
<td>0.97(0.39, 2.40)</td>
<td>0.53</td>
<td>0.37</td>
<td>3</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.45(0, 1)</td>
<td>0.62</td>
<td>35</td>
<td>h,i</td>
</tr>
<tr class="even">
<td colspan="2"><strong>Up. Columbia R chinook (3381)</strong></td>
<td colspan="2"><strong>-0.16</strong></td>
<td><strong>0.13</strong></td>
<td><strong>0.85(0.62, 1.17)</strong></td>
<td><strong>0.82</strong></td>
<td><strong>0.63</strong></td>
<td><strong>17</strong></td>
<td><strong>0.54(0, 1)</strong></td>
<td><strong>0.65</strong></td>
<td><strong>10</strong></td>
<td><strong>0.99(0.01, 1)</strong></td>
<td><strong>0.89</strong></td>
<td><strong>22</strong></td>
<td><strong>t,d</strong></td>
</tr>
<tr class="odd">
<td></td>
<td>Entiat R spr (168)</td>
<td colspan="2">-0.14</td>
<td>0.04</td>
<td>0.87(0.73, 1.03)</td>
<td>0.86</td>
<td>0.64</td>
<td>15</td>
<td>0.93(0, 1)</td>
<td>0.83</td>
<td>10</td>
<td>1(0.22, 1)</td>
<td>0.88</td>
<td>16</td>
<td>a,d</td>
</tr>
<tr class="even">
<td></td>
<td>Methow R spr (486)</td>
<td colspan="2">-0.14</td>
<td>0.35</td>
<td>0.87(0.51, 1.47)</td>
<td>0.73</td>
<td>0.54</td>
<td>15</td>
<td>0.69(0, 1)</td>
<td>0.76</td>
<td>21</td>
<td>0.87(0, 1)</td>
<td>0.78</td>
<td>28</td>
<td>t,d</td>
</tr>
<tr class="odd">
<td></td>
<td>Wenatchee R spr (1466)</td>
<td colspan="2">-0.17</td>
<td>0.08</td>
<td>0.84(0.65, 1.09)</td>
<td>0.86</td>
<td>0.68</td>
<td>18</td>
<td>0.76(0, 1)</td>
<td>0.76</td>
<td>11</td>
<td>1(0.01, 1)</td>
<td>0.88</td>
<td>21</td>
<td>t,d</td>
</tr>
<tr class="even">
<td colspan="2"><strong>Snake R spr/sum chinook (21683)</strong></td>
<td colspan="2"><strong>-0.03</strong></td>
<td><strong>0.01</strong></td>
<td><strong>0.97(0.89, 1.06)</strong></td>
<td><strong>0.68</strong></td>
<td><strong>0.14</strong></td>
<td><strong>3</strong></td>
<td><strong>0(0, 0)</strong></td>
<td><strong>0.09</strong></td>
<td><strong>0</strong></td>
<td><strong>0.15(0, 1)</strong></td>
<td><strong>0.53</strong></td>
<td><strong>1</strong></td>
<td><strong>l</strong></td>
</tr>
<tr class="odd">
<td></td>
<td>Alturas Lake Cr spr</td>
<td colspan="2">-0.26</td>
<td>0.07</td>
<td>0.77(0.62, 0.96)</td>
<td>0.94</td>
<td>0.86</td>
<td>29</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>1(0.92, 1)</td>
<td>0.96</td>
<td>32</td>
<td>l,h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Bear Valley/Elk Cr (713)</td>
<td colspan="2">0.03</td>
<td>0.16</td>
<td>1.03(0.74, 1.44)</td>
<td>0.42</td>
<td>0.22</td>
<td>0</td>
<td>0.01(0, 0.95)</td>
<td>0.37</td>
<td>0</td>
<td>0.09(0, 1)</td>
<td>0.46</td>
<td>2</td>
<td> </td>
</tr>
<tr class="odd">
<td></td>
<td>Beaver Cr spr</td>
<td colspan="2">-0.14</td>
<td>0.25</td>
<td>0.87(0.53, 1.41)</td>
<td>0.74</td>
<td>0.56</td>
<td>15</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.92(0, 1)</td>
<td>0.79</td>
<td>25</td>
<td>h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Big Cr spr</td>
<td colspan="2">0.00</td>
<td>0.18</td>
<td>1.00(0.69, 1.45)</td>
<td>0.49</td>
<td>0.27</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.22(0, 1)</td>
<td>0.54</td>
<td>6</td>
<td>t,h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Big Sheep Cr spr</td>
<td colspan="2">-0.08</td>
<td>1.77</td>
<td>0.93(0.34, 2.55)</td>
<td>0.59</td>
<td>0.43</td>
<td>8</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.57(0, 1)</td>
<td>0.69</td>
<td>53</td>
<td>i</td>
</tr>
<tr class="even">
<td></td>
<td>Camas Cr spr</td>
<td colspan="2">-0.14</td>
<td>0.12</td>
<td>0.87(0.62, 1.22)</td>
<td>0.71</td>
<td>0.58</td>
<td>15</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.97(0, 1)</td>
<td>0.81</td>
<td>20</td>
<td>i</td>
</tr>
<tr class="odd">
<td></td>
<td>Cape Horn Cr spr</td>
<td colspan="2">0.02</td>
<td>0.22</td>
<td>1.02(0.64, 1.61)</td>
<td>0.34</td>
<td>0.25</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.16(0, 1)</td>
<td>0.81</td>
<td>5</td>
<td>d,i</td>
</tr>
<tr class="even">
<td></td>
<td>Catherine Cr spr</td>
<td colspan="2">-0.10</td>
<td>0.15</td>
<td>0.91(0.68, 1.22)</td>
<td>0.73</td>
<td>0.46</td>
<td>10</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.82(0, 1)</td>
<td>0.77</td>
<td>15</td>
<td>i,h</td>
</tr>
<tr class="odd">
<td></td>
<td>Catherine Cr NF spr</td>
<td colspan="2">-0.06</td>
<td>0.25</td>
<td>0.94(0.58, 1.53)</td>
<td>0.60</td>
<td>0.39</td>
<td>6</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.56(0, 1)</td>
<td>0.66</td>
<td>14</td>
<td>h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Catherine Cr SF spr</td>
<td colspan="2">-0.11</td>
<td>0.86</td>
<td>0.90(0.36, 2.22)</td>
<td>0.63</td>
<td>0.47</td>
<td>11</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.68(0, 1)</td>
<td>0.71</td>
<td>38</td>
<td>i,h</td>
</tr>
<tr class="odd">
<td></td>
<td>Chamberlain Cr spr</td>
<td colspan="2">-0.10</td>
<td>0.09</td>
<td>0.91(0.46, 1.81)</td>
<td>0.66</td>
<td>0.46</td>
<td>10</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.89(0, 1)</td>
<td>0.70</td>
<td>13</td>
<td>t,h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Grande Ronde R spr</td>
<td colspan="2">-0.09</td>
<td>0.18</td>
<td>0.92(0.66, 1.27)</td>
<td>0.70</td>
<td>0.44</td>
<td>9</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.75(0, 1)</td>
<td>0.74</td>
<td>16</td>
<td>i,h</td>
</tr>
<tr class="odd">
<td></td>
<td>Imnaha R spr (610)</td>
<td colspan="2">-0.06</td>
<td>0.06</td>
<td>0.94(0.77, 1.15)</td>
<td>0.67</td>
<td>0.35</td>
<td>6</td>
<td>0.03(0, 1)</td>
<td>0.49</td>
<td>0</td>
<td>0.66(0, 1)</td>
<td>0.69</td>
<td>8</td>
<td> </td>
</tr>
<tr class="even">
<td></td>
<td>Johnson Cr sum (432)</td>
<td colspan="2">0.01</td>
<td>0.05</td>
<td>1.01(0.83, 1.22)</td>
<td>0.45</td>
<td>0.17</td>
<td>0</td>
<td>0(0, 0.59)</td>
<td>0.30</td>
<td>0</td>
<td>0.05(0, 1)</td>
<td>0.44</td>
<td>0</td>
<td> </td>
</tr>
<tr class="odd">
<td></td>
<td>Knapp Cr spr</td>
<td colspan="2">-0.20</td>
<td>0.28</td>
<td>0.82(0.49, 1.36)</td>
<td>0.81</td>
<td>0.66</td>
<td>23</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.98(0.01, 1)</td>
<td>0.85</td>
<td>33</td>
<td>d,h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Lake Cr sum</td>
<td colspan="2">0.03</td>
<td>0.09</td>
<td>1.04(0.78, 1.38)</td>
<td>0.39</td>
<td>0.18</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.03(0, 1)</td>
<td>0.42</td>
<td>0</td>
<td>d,h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Lemhi R spr</td>
<td colspan="2">-0.02</td>
<td>0.32</td>
<td>0.98(0.62, 1.56)</td>
<td>0.53</td>
<td>0.32</td>
<td>2</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.37(0, 1)</td>
<td>0.59</td>
<td>13</td>
<td>h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Lookingglass Cr spr</td>
<td colspan="2">-0.20</td>
<td>0.15</td>
<td>0.82(0.61, 1.10)</td>
<td>0.88</td>
<td>0.73</td>
<td>22</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>1(0.10, 1)</td>
<td>0.91</td>
<td>29</td>
<td>i</td>
</tr>
<tr class="odd">
<td></td>
<td>Loon Cr sum</td>
<td colspan="2">0.00</td>
<td>0.03</td>
<td>1.00(0.88, 1.14)</td>
<td>0.46</td>
<td>0.13</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.02(0, 1)</td>
<td>0.40</td>
<td>0</td>
<td>l,h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Lostine R spr</td>
<td colspan="2">-0.01</td>
<td>0.07</td>
<td>0.99(0.82, 1.21)</td>
<td>0.51</td>
<td>0.19</td>
<td>1</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.14(0, 1)</td>
<td>0.50</td>
<td>3</td>
<td>i,h</td>
</tr>
<tr class="odd">
<td></td>
<td>Marsh Cr spr (286)</td>
<td colspan="2">0.01</td>
<td>0.15</td>
<td>1.01(0.73, 1.39)</td>
<td>0.47</td>
<td>0.25</td>
<td>0</td>
<td>0.03(0, 0.99)</td>
<td>0.46</td>
<td>0</td>
<td>0.17(0, 1)</td>
<td>0.51</td>
<td>4</td>
<td> </td>
</tr>
<tr class="even">
<td></td>
<td>Minam R spr (322)</td>
<td colspan="2">0.01</td>
<td>0.23</td>
<td>1.01(0.68, 1.49)</td>
<td>0.47</td>
<td>0.26</td>
<td>0</td>
<td>0.07(0, 0.99)</td>
<td>0.48</td>
<td>1</td>
<td>0.21(0, 1)</td>
<td>0.52</td>
<td>6</td>
<td> </td>
</tr>
<tr class="odd">
<td></td>
<td>Minam R Up spr</td>
<td colspan="2">0.01</td>
<td>0.12</td>
<td>1.01(0.76, 1.34)</td>
<td>0.45</td>
<td>0.21</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.11(0, 1)</td>
<td>0.48</td>
<td>3</td>
<td>i</td>
</tr>
<tr class="even">
<td></td>
<td>Minam R Low spr</td>
<td colspan="2">0.10</td>
<td>0.31</td>
<td>1.11(0.70, 1.75)</td>
<td>0.30</td>
<td>0.16</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.04(0, 1)</td>
<td>0.36</td>
<td>0</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Poverty Cr (951)</td>
<td colspan="2">0.01</td>
<td>0.08</td>
<td>1.01(0.80, 1.28)</td>
<td>0.45</td>
<td>0.20</td>
<td>0</td>
<td>0(0, 0.84)</td>
<td>0.30</td>
<td>0</td>
<td>0.08(0, 1)</td>
<td>0.47</td>
<td>2</td>
<td> </td>
</tr>
<tr class="even">
<td></td>
<td>Salmon R EF sum</td>
<td colspan="2">-0.06</td>
<td>0.28</td>
<td>0.95(0.61, 1.45)</td>
<td>0.61</td>
<td>0.38</td>
<td>6</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.56(0, 1)</td>
<td>0.67</td>
<td>15</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Salmon R SF sum</td>
<td colspan="2">0.06</td>
<td>0.12</td>
<td>1.07(0.80, 1.41)</td>
<td>0.32</td>
<td>0.14</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.01(0, 0.98)</td>
<td>0.35</td>
<td>0</td>
<td>d,h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Salmon R Upper spr</td>
<td colspan="2">-0.09</td>
<td>0.05</td>
<td>0.91(0.76, 1.08)</td>
<td>0.79</td>
<td>0.45</td>
<td>10</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.94(0.01, 1)</td>
<td>0.80</td>
<td>11</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Salmon R Upper sum</td>
<td colspan="2">-0.11</td>
<td>0.10</td>
<td>0.90(0.66, 1.23)</td>
<td>0.74</td>
<td>0.49</td>
<td>11</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.91(0, 1)</td>
<td>0.77</td>
<td>15</td>
<td>t,h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Secesh R sum</td>
<td colspan="2">-0.02</td>
<td>0.00</td>
<td>0.98(0.94, 1.03)</td>
<td>0.68</td>
<td>0.07</td>
<td>2</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0(0, 0.86)</td>
<td>0.34</td>
<td>0</td>
<td>l,h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Sulphur Cr spr (200)</td>
<td colspan="2">0.03</td>
<td>0.47</td>
<td>1.03(0.59, 1.81)</td>
<td>0.43</td>
<td>0.26</td>
<td>0</td>
<td>0.17(0, 1)</td>
<td>0.55</td>
<td>8</td>
<td>0.21(0, 1)</td>
<td>0.51</td>
<td>11</td>
<td> </td>
</tr>
<tr class="even">
<td></td>
<td>Valley Cr Upper spr</td>
<td colspan="2">0.04</td>
<td>0.63</td>
<td>1.04(0.54, 1.99)</td>
<td>0.43</td>
<td>0.26</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.22(0, 1)</td>
<td>0.51</td>
<td>14</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Valley Cr Upper sum</td>
<td colspan="2">-0.08</td>
<td>0.20</td>
<td>0.92(0.59, 1.43)</td>
<td>0.66</td>
<td>0.43</td>
<td>9</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.72(0, 1)</td>
<td>0.71</td>
<td>16</td>
<td>h,f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Wallowa Cr spr</td>
<td colspan="2">-0.07</td>
<td>0.62</td>
<td>0.93(0.51, 1.69)</td>
<td>0.61</td>
<td>0.43</td>
<td>8</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.6(0, 1)</td>
<td>0.69</td>
<td>28</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Wenaha R SF spr</td>
<td colspan="2">0.03</td>
<td>0.10</td>
<td>1.03(0.81, 1.31)</td>
<td>0.39</td>
<td>0.17</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.05(0, 1)</td>
<td>0.42</td>
<td>0</td>
<td>i,h</td>
</tr>
<tr class="even">
<td></td>
<td>Yankee Fk sum</td>
<td colspan="2">-0.19</td>
<td>0.31</td>
<td>0.83(0.48, 1.43)</td>
<td>0.79</td>
<td>0.63</td>
<td>21</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.96(0, 1)</td>
<td>0.83</td>
<td>33</td>
<td>t,l,h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Yankee W Fk sum</td>
<td colspan="2">0.00</td>
<td>0.24</td>
<td>1.00(0.62, 1.61)</td>
<td>0.49</td>
<td>0.28</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.24(0, 1)</td>
<td>0.54</td>
<td>7</td>
<td>h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Yankee W Fk spr</td>
<td colspan="2">-0.17</td>
<td>0.14</td>
<td>0.84(0.58, 1.22)</td>
<td>0.81</td>
<td>0.63</td>
<td>19</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.99(0, 1)</td>
<td>0.85</td>
<td>24</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td colspan="2"><strong>Snake R fall chinook (1946)</strong></td>
<td colspan="2"><strong>-0.05</strong></td>
<td><strong>0.04</strong></td>
<td><strong>0.95(0.76, 1.18)</strong></td>
<td><strong>0.65</strong></td>
<td><strong>0.32</strong></td>
<td><strong>5</strong></td>
<td><strong>0(0, 0.99)</strong></td>
<td><strong>0.37</strong></td>
<td><strong>0</strong></td>
<td><strong>0.58(0, 1)</strong></td>
<td><strong>0.65</strong></td>
<td><strong>6</strong></td>
<td><strong> </strong></td>
</tr>
<tr class="even">
<td colspan="2"><strong>Up. Willamette R chinook (8770)</strong></td>
<td colspan="2"><strong>-0.01</strong></td>
<td><strong>0.23</strong></td>
<td><strong>0.99(0.65, 1.53)</strong></td>
<td><strong>0.50</strong></td>
<td><strong>0.29</strong></td>
<td><strong>1</strong></td>
<td><strong>0.01(0, 0.99)</strong></td>
<td><strong>0.35</strong></td>
<td><strong>0</strong></td>
<td><strong>0.29(0, 1)</strong></td>
<td><strong>0.56</strong></td>
<td><strong>8</strong></td>
<td><strong> </strong></td>
</tr>
<tr class="odd">
<td></td>
<td>McKenzie R (5112)</td>
<td colspan="2">0.01</td>
<td>0.20</td>
<td>1.01(0.68, 1.51)</td>
<td>0.46</td>
<td>0.25</td>
<td>0</td>
<td>0(0, 0.97)</td>
<td>0.33</td>
<td>0</td>
<td>0.18(0, 1)</td>
<td>0.51</td>
<td>5</td>
<td> </td>
</tr>
<tr class="even">
<td colspan="2">Columbia R chum</td>
<td colspan="2"><h1 id="na">NA</h1></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>i</strong></td>
</tr>
<tr class="odd">
<td rowspan="2"></td>
<td>Grays R WF</td>
<td colspan="2">0.21</td>
<td>0.23</td>
<td>1.23(0.81, 1.88)</td>
<td>0.15</td>
<td>0.07</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0(0, 0.86)</td>
<td>0.26</td>
<td>0</td>
<td>l,i</td>
</tr>
<tr class="even">
<td>Grays R fall</td>
<td colspan="2">-0.03</td>
<td>0.10</td>
<td>0.97(0.73, 1.29)</td>
<td>0.58</td>
<td>0.30</td>
<td>3</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.38(0, 1)</td>
<td>0.61</td>
<td>7</td>
<td>l,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Hardy Cr fall</td>
<td colspan="2">0.04</td>
<td>0.06</td>
<td>1.04(0.86, 1.26)</td>
<td>0.34</td>
<td>0.13</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.01(0, 0.99)</td>
<td>0.35</td>
<td>0</td>
<td>l,i</td>
</tr>
<tr class="even">
<td></td>
<td>Crazy J Cr</td>
<td colspan="2">0.14</td>
<td>0.03</td>
<td>1.15(0.98, 1.34)</td>
<td>0.11</td>
<td>0.05</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0(0, 0.001)</td>
<td>0.13</td>
<td>0</td>
<td>l,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Hamilton Cr fall</td>
<td colspan="2">-0.08</td>
<td>0.05</td>
<td>0.92(0.75, 1.13)</td>
<td>0.74</td>
<td>0.40</td>
<td>8</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.86(0, 1)</td>
<td>0.75</td>
<td>10</td>
<td>i</td>
</tr>
<tr class="even">
<td></td>
<td>Hamilton Springs</td>
<td colspan="2">0.09</td>
<td>0.51</td>
<td>1.10(0.61, 1.97)</td>
<td>0.33</td>
<td>0.19</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.1(0, 1)</td>
<td>0.41</td>
<td>6</td>
<td>t,a,l,i</td>
</tr>
<tr class="odd">
<td colspan="2"><strong>Lower Columbia R steelhead</strong></td>
<td colspan="2"><strong>-0.04</strong></td>
<td><strong>0.00</strong></td>
<td><strong>0.96(0.94, 0.98)</strong></td>
<td><strong>0.84</strong></td>
<td><strong>0.10</strong></td>
<td><strong>4</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>0.05(0, 1)</strong></td>
<td><strong>0.49</strong></td>
<td><strong>0</strong></td>
<td><strong>l</strong></td>
</tr>
<tr class="even">
<td></td>
<td>Clackamas R sum (2155)</td>
<td colspan="2">-0.09</td>
<td>0.09</td>
<td>0.91(0.71, 1.17)</td>
<td>0.73</td>
<td>0.46</td>
<td>10</td>
<td>0.11(0, 1)</td>
<td>0.53</td>
<td>2</td>
<td>0.87(0, 1)</td>
<td>0.76</td>
<td>13</td>
<td>t</td>
</tr>
<tr class="odd">
<td></td>
<td>Clackamas R win (1041)</td>
<td colspan="2">-0.10</td>
<td>0.06</td>
<td>0.91(0.73, 1.13)</td>
<td>0.77</td>
<td>0.46</td>
<td>10</td>
<td>0.13(0, 1)</td>
<td>0.55</td>
<td>2</td>
<td>0.93(0, 1)</td>
<td>0.78</td>
<td>12</td>
<td>t</td>
</tr>
<tr class="even">
<td></td>
<td>Green R win (450)</td>
<td colspan="2">-0.15</td>
<td>0.25</td>
<td>0.86(0.17, 4.46)</td>
<td>0.67</td>
<td>0.52</td>
<td>16</td>
<td>0.73(0, 1)</td>
<td>0.70</td>
<td>18</td>
<td>0.93(0, 1)</td>
<td>0.72</td>
<td>25</td>
<td>t</td>
</tr>
<tr class="odd">
<td></td>
<td>Kalama R sum (6445)</td>
<td colspan="2">-0.04</td>
<td>0.14</td>
<td>0.96(0.67, 1.38)</td>
<td>0.57</td>
<td>0.34</td>
<td>4</td>
<td>0.01(0, 1)</td>
<td>0.39</td>
<td>0</td>
<td>0.42(0, 1)</td>
<td>0.61</td>
<td>8</td>
<td>d</td>
</tr>
<tr class="even">
<td></td>
<td>Kalama R win (4975)</td>
<td colspan="2">-0.02</td>
<td>0.01</td>
<td>0.98(0.87, 1.09)</td>
<td>0.61</td>
<td>0.19</td>
<td>2</td>
<td>0(0, 0)</td>
<td>0.19</td>
<td>0</td>
<td>0.07(0, 1)</td>
<td>0.51</td>
<td>1</td>
<td>l</td>
</tr>
<tr class="odd">
<td></td>
<td>Lewis R EF win</td>
<td colspan="2">-0.17</td>
<td>0.02</td>
<td>0.84(0.15, 4.84)</td>
<td>0.76</td>
<td>0.61</td>
<td>19</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>1(0, 1)</td>
<td>0.78</td>
<td>18</td>
<td>i</td>
</tr>
<tr class="even">
<td></td>
<td>Sandy R win (4535)</td>
<td colspan="2">-0.06</td>
<td>0.03</td>
<td>0.94(0.79, 1.11)</td>
<td>0.71</td>
<td>0.32</td>
<td>6</td>
<td>0(0, 0.71)</td>
<td>0.30</td>
<td>0</td>
<td>0.73(0, 1)</td>
<td>0.70</td>
<td>6</td>
<td> </td>
</tr>
<tr class="odd">
<td></td>
<td>Sandy R sum</td>
<td colspan="2">-0.04</td>
<td>0.09</td>
<td>0.96(0.75, 1.22)</td>
<td>0.62</td>
<td>0.31</td>
<td>5</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.49(0, 1)</td>
<td>0.64</td>
<td>7</td>
<td>t,h</td>
</tr>
<tr class="even">
<td></td>
<td>Toutle R</td>
<td colspan="2">-0.10</td>
<td>0.00</td>
<td>0.91(0.88, 0.93)</td>
<td>0.79</td>
<td>0.43</td>
<td>10</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>1(1, 1)</td>
<td>0.80</td>
<td>6</td>
<td>l,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Trout Cr sum</td>
<td colspan="2">-0.25</td>
<td>0.03</td>
<td>0.78(0.08, 0.95)</td>
<td>0.80</td>
<td>0.69</td>
<td>28</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>1(0, 1)</td>
<td>0.83</td>
<td>28</td>
<td>t,h</td>
</tr>
<tr class="even">
<td></td>
<td>Washougal R sum</td>
<td colspan="2">-0.12</td>
<td>0.01</td>
<td>0.89(0.64, 1.24)</td>
<td>0.73</td>
<td>0.51</td>
<td>12</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>1(0, 1)</td>
<td>0.75</td>
<td>10</td>
<td>l,h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Wind R sum (1218)</td>
<td colspan="2">-0.06</td>
<td>0.00</td>
<td>0.95(0.51, 1.76)</td>
<td>0.64</td>
<td>0.34</td>
<td>6</td>
<td>0(0, 1)</td>
<td>0.38</td>
<td>0</td>
<td>0.91(0, 1)</td>
<td>0.61</td>
<td>3</td>
<td>l</td>
</tr>
<tr class="even">
<td colspan="2"><strong>Middle Columbia R steelhead</strong></td>
<td colspan="2"><strong>-0.06</strong></td>
<td><strong>0.14</strong></td>
<td><strong>0.94(0.69, 1.27)</strong></td>
<td><strong>0.65</strong></td>
<td><strong>0.38</strong></td>
<td><strong>6</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>0.62(0, 1)</strong></td>
<td><strong>0.69</strong></td>
<td><strong>11</strong></td>
<td><strong>i</strong></td>
</tr>
<tr class="odd">
<td></td>
<td>Bear Cr sum</td>
<td colspan="2">-0.08</td>
<td>0.07</td>
<td>0.92(0.69, 1.22)</td>
<td>0.71</td>
<td>0.43</td>
<td>9</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.85(0, 1)</td>
<td>0.73</td>
<td>11</td>
<td>h,f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Beaver Cr NF sum</td>
<td colspan="2">-0.06</td>
<td>0.17</td>
<td>0.95(0.69, 1.29)</td>
<td>0.63</td>
<td>0.36</td>
<td>6</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.57(0, 1)</td>
<td>0.67</td>
<td>12</td>
<td>i</td>
</tr>
<tr class="odd">
<td></td>
<td>Beech Cr sum</td>
<td colspan="2">-0.02</td>
<td>0.17</td>
<td>0.98(0.70, 1.38)</td>
<td>0.54</td>
<td>0.29</td>
<td>2</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.34(0, 1)</td>
<td>0.59</td>
<td>8</td>
<td>f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Beech Cr EF sum</td>
<td colspan="2">-0.02</td>
<td>0.17</td>
<td>0.98(0.71, 1.34)</td>
<td>0.54</td>
<td>0.29</td>
<td>2</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.34(0, 1)</td>
<td>0.59</td>
<td>8</td>
<td>f,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Camp Cr sum</td>
<td colspan="2">-0.01</td>
<td>0.16</td>
<td>0.99(0.73, 1.35)</td>
<td>0.51</td>
<td>0.26</td>
<td>1</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.25(0, 1)</td>
<td>0.55</td>
<td>6</td>
<td>f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Canyon Cr sum</td>
<td colspan="2">-0.03</td>
<td>0.17</td>
<td>0.97(0.71, 1.33)</td>
<td>0.56</td>
<td>0.30</td>
<td>3</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.37(0, 1)</td>
<td>0.60</td>
<td>8</td>
<td>f,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Canyon Cr MF sum</td>
<td colspan="2">-0.06</td>
<td>0.16</td>
<td>0.94(0.69, 1.29)</td>
<td>0.64</td>
<td>0.37</td>
<td>6</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.58(0, 1)</td>
<td>0.68</td>
<td>12</td>
<td>f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Deep Cr sum</td>
<td colspan="2">-0.03</td>
<td>0.18</td>
<td>0.97 (0.70, 1.34)</td>
<td>0.56</td>
<td>0.30</td>
<td>3</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.38(0, 1)</td>
<td>0.60</td>
<td>9</td>
<td>l,f,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Deer Cr sum</td>
<td colspan="2">-0.04</td>
<td>0.40</td>
<td>0.96(0.60, 1.56)</td>
<td>0.56</td>
<td>0.36</td>
<td>4</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.47(0, 1)</td>
<td>0.64</td>
<td>17</td>
<td>f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Deschutes R sum (3052)</td>
<td colspan="2">-0.06</td>
<td>0.16</td>
<td>0.94(0.70, 1.27)</td>
<td>0.64</td>
<td>0.38</td>
<td>6</td>
<td>0.06(0, 0.99)</td>
<td>0.47</td>
<td>1</td>
<td>0.61(0, 1)</td>
<td>0.68</td>
<td>12</td>
<td> </td>
</tr>
<tr class="odd">
<td></td>
<td>Eightmile Cr win</td>
<td colspan="2">-0.08</td>
<td>1.62</td>
<td>0.92(0.15, 5.89)</td>
<td>0.57</td>
<td>0.43</td>
<td>8</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.57(0, 1)</td>
<td>0.66</td>
<td>50</td>
<td>i</td>
</tr>
<tr class="even">
<td></td>
<td>Fields Cr sum</td>
<td colspan="2">-0.11</td>
<td>0.11</td>
<td>0.90(0.70, 1.15)</td>
<td>0.78</td>
<td>0.51</td>
<td>12</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.91(0, 1)</td>
<td>0.80</td>
<td>16</td>
<td>t f,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Fifteen Mile Cr win</td>
<td colspan="2">-0.01</td>
<td>0.03</td>
<td>0.99(0.81, 1.20)</td>
<td>0.54</td>
<td>0.22</td>
<td>1</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.11(0, 1)</td>
<td>0.52</td>
<td>2</td>
<td>h,f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Kahler Cr sum</td>
<td colspan="2">-0.02</td>
<td>0.51</td>
<td>0.98(0.57, 1.68)</td>
<td>0.53</td>
<td>0.34</td>
<td>2</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.41(0, 1)</td>
<td>0.52</td>
<td>19</td>
<td>a,f,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Mcclellan Cr sum</td>
<td colspan="2">-0.05</td>
<td>0.15</td>
<td>0.95(0.70, 1.28)</td>
<td>0.63</td>
<td>0.36</td>
<td>6</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.56(0, 1)</td>
<td>0.67</td>
<td>11</td>
<td>f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Mill Cr sum</td>
<td colspan="2">-0.03</td>
<td>0.00</td>
<td>0.97(0.91, 1.03)</td>
<td>0.69</td>
<td>0.17</td>
<td>3</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.02(0, 1)</td>
<td>0.50</td>
<td>0</td>
<td>l,f,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Murderers Cr sum</td>
<td colspan="2">-0.18</td>
<td>0.25</td>
<td>0.83(0.57, 1.22)</td>
<td>0.82</td>
<td>0.65</td>
<td>20</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.97(0, 1)</td>
<td>0.86</td>
<td>29</td>
<td>f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Olive Cr sum</td>
<td colspan="2">-0.02</td>
<td>0.19</td>
<td>0.98(0.70, 1.37)</td>
<td>0.55</td>
<td>0.30</td>
<td>2</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.36(0, 1)</td>
<td>0.60</td>
<td>9</td>
<td>f,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Parrish Cr sum</td>
<td colspan="2">-0.03</td>
<td>0.51</td>
<td>0.97(0.57, 1.68)</td>
<td>0.54</td>
<td>0.34</td>
<td>3</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.42(0, 1)</td>
<td>0.62</td>
<td>19</td>
<td>f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Ramsey Cr win</td>
<td colspan="2">0.06</td>
<td>1.65</td>
<td>1.06(0.16, 6.83)</td>
<td>0.44</td>
<td>0.30</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.29(0, 1)</td>
<td>0.52</td>
<td>32</td>
<td>i</td>
</tr>
<tr class="odd">
<td></td>
<td>Riley Cr sum</td>
<td colspan="2">-0.06</td>
<td>0.24</td>
<td>0.94(0.61, 1.46)</td>
<td>0.61</td>
<td>0.39</td>
<td>6</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.58(0, 1)</td>
<td>0.67</td>
<td>14</td>
<td>f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Shitike Cr sum</td>
<td colspan="2">-0.06</td>
<td>0.03</td>
<td>0.94(0.80, 1.10)</td>
<td>0.73</td>
<td>0.33</td>
<td>7</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.79(0, 1)</td>
<td>0.71</td>
<td>6</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Tex Cr sum</td>
<td colspan="2">-0.15</td>
<td>0.33</td>
<td>0.86(0.56, 1.34)</td>
<td>0.76</td>
<td>0.57</td>
<td>16</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.89(0, 1)</td>
<td>0.81</td>
<td>28</td>
<td>f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Umatilla R sum (5384)</td>
<td colspan="2">-0.01</td>
<td>0.05</td>
<td>0.99(0.84, 1.17)</td>
<td>0.51</td>
<td>0.19</td>
<td>1</td>
<td>0(0, 0.05)</td>
<td>0.21</td>
<td>0</td>
<td>0.1(0, 1)</td>
<td>0.50</td>
<td>2</td>
<td>d</td>
</tr>
<tr class="odd">
<td></td>
<td>Wall Cr sum</td>
<td colspan="2">-0.02</td>
<td>0.22</td>
<td>0.98(0.69, 1.40)</td>
<td>0.53</td>
<td>0.29</td>
<td>2</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.33(0, 1)</td>
<td>0.58</td>
<td>9</td>
<td>f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Warm Springs sum (729)</td>
<td colspan="2">-0.06</td>
<td>0.08</td>
<td>0.94(0.74, 1.19)</td>
<td>0.66</td>
<td>0.36</td>
<td>6</td>
<td>0.06(0, 1)</td>
<td>0.50</td>
<td>1</td>
<td>0.63(0, 1)</td>
<td>0.68</td>
<td>9</td>
<td> </td>
</tr>
<tr class="odd">
<td></td>
<td>Wind Cr sum</td>
<td colspan="2">-0.06</td>
<td>0.03</td>
<td>0.94(0.83, 1.06)</td>
<td>0.78</td>
<td>0.28</td>
<td>7</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.79(0, 1)</td>
<td>0.74</td>
<td>6</td>
<td>f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Yakima R sum</td>
<td colspan="2">0.14</td>
<td>0.21</td>
<td>1.15(0.59, 2.24)</td>
<td>0.27</td>
<td>0.15</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.01(0, 1)</td>
<td>0.32</td>
<td>0</td>
<td>t</td>
</tr>
<tr class="odd">
<td colspan="2"><strong>Up. Columbia R steelhead (2822)</strong></td>
<td colspan="2"><strong>0.00</strong></td>
<td><strong>0.15</strong></td>
<td><strong>1.00(0.66, 1.52)</strong></td>
<td><strong>0.50</strong></td>
<td><strong>0.27</strong></td>
<td><strong>0</strong></td>
<td><strong>0(0, 1)</strong></td>
<td><strong>0.36</strong></td>
<td><strong>0</strong></td>
<td><strong>0.19(0, 1)</strong></td>
<td><strong>0.53</strong></td>
<td><strong>5</strong></td>
<td><strong>t</strong></td>
</tr>
<tr class="even">
<td colspan="2"><strong>Snake R steelhead (41035)</strong></td>
<td colspan="2"><strong>-0.04</strong></td>
<td><strong>0.03</strong></td>
<td><strong>0.96(0.84, 1.10)</strong></td>
<td><strong>0.65</strong></td>
<td><strong>0.23</strong></td>
<td><strong>4</strong></td>
<td><strong>0(0, 0.003)</strong></td>
<td><strong>0.16</strong></td>
<td><strong>0</strong></td>
<td><strong>0.38(0, 1)</strong></td>
<td><strong>0.61</strong></td>
<td><strong>4</strong></td>
<td><strong> </strong></td>
</tr>
<tr class="odd">
<td></td>
<td>Butte Cr sum A</td>
<td colspan="2">0.06</td>
<td>0.65</td>
<td>1.07(0.57, 1.97)</td>
<td>0.39</td>
<td>0.23</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.17(0, 1)</td>
<td>0.47</td>
<td>12</td>
<td>a,i</td>
</tr>
<tr class="even">
<td></td>
<td>Camp Cr sum A</td>
<td colspan="2">0.02</td>
<td>0.18</td>
<td>1.02(0.73, 1.40)</td>
<td>0.45</td>
<td>0.22</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.15(0, 1)</td>
<td>0.50</td>
<td>4</td>
<td>i</td>
</tr>
<tr class="odd">
<td></td>
<td>Crow Cr sum A</td>
<td colspan="2">0.03</td>
<td>0.25</td>
<td>1.03(0.70, 1.50)</td>
<td>0.42</td>
<td>0.22</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.15(0, 1)</td>
<td>0.49</td>
<td>5</td>
<td>a,i</td>
</tr>
<tr class="even">
<td></td>
<td>Devils Run Cr sum A</td>
<td colspan="2">0.05</td>
<td>0.25</td>
<td>1.05(0.72, 1.55)</td>
<td>0.37</td>
<td>0.19</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.08(0, 1)</td>
<td>0.43</td>
<td>2</td>
<td>t,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Five Points Cr sum A</td>
<td colspan="2">0.00</td>
<td>0.11</td>
<td>1.00(0.78, 1.30)</td>
<td>0.47</td>
<td>0.21</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.15(0, 1)</td>
<td>0.55</td>
<td>3</td>
<td>h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Fly Cr sum A</td>
<td colspan="2">0.00</td>
<td>0.28</td>
<td>1.00(0.67, 1.50)</td>
<td>0.48</td>
<td>0.27</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.26(0, 1)</td>
<td>0.55</td>
<td>9</td>
<td>i</td>
</tr>
<tr class="odd">
<td></td>
<td>Mccoy Cr sum A</td>
<td colspan="2">0.08</td>
<td>0.10</td>
<td>1.09(0.85, 1.39)</td>
<td>0.25</td>
<td>0.10</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0(0, 0.94)</td>
<td>0.28</td>
<td>0</td>
<td>i</td>
</tr>
<tr class="even">
<td></td>
<td>Meadow Cr sum A</td>
<td colspan="2">0.00</td>
<td>0.14</td>
<td>1.00(0.75, 1.33)</td>
<td>0.48</td>
<td>0.23</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.18(0, 1)</td>
<td>0.52</td>
<td>4</td>
<td>i</td>
</tr>
<tr class="odd">
<td></td>
<td>Peavine Cr sum A</td>
<td colspan="2">0.07</td>
<td>0.29</td>
<td>1.07(0.69, 1.67)</td>
<td>0.35</td>
<td>0.19</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.07(0, 1)</td>
<td>0.41</td>
<td>2</td>
<td>i</td>
</tr>
<tr class="even">
<td></td>
<td>Phillips Cr sum A</td>
<td colspan="2">-0.03</td>
<td>0.09</td>
<td>0.97(0.77, 1.23)</td>
<td>0.57</td>
<td>0.26</td>
<td>3</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.32(0, 1)</td>
<td>0.59</td>
<td>6</td>
<td>i</td>
</tr>
<tr class="odd">
<td></td>
<td>Prairie Cr sum A</td>
<td colspan="2">0.16</td>
<td>0.38</td>
<td>1.18(0.74, 1.88)</td>
<td>0.21</td>
<td>0.11</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.02(0, 0.96)</td>
<td>0.28</td>
<td>0</td>
<td>a,h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Snake R A (39585)</td>
<td colspan="2">-0.03</td>
<td>0.03</td>
<td>0.97(0.85, 1.11)</td>
<td>0.60</td>
<td>0.20</td>
<td>3</td>
<td>0(0, 0)</td>
<td>0.14</td>
<td>0</td>
<td>0.21(0, 1)</td>
<td>0.56</td>
<td>3</td>
<td> </td>
</tr>
<tr class="odd">
<td></td>
<td>Snake R B (9115)</td>
<td colspan="2">-0.08</td>
<td>0.07</td>
<td>0.93(0.76, 1.14)</td>
<td>0.68</td>
<td>0.41</td>
<td>8</td>
<td>0(0, 0.94)</td>
<td>0.41</td>
<td>0</td>
<td>0.79(0, 1)</td>
<td>0.71</td>
<td>10</td>
<td> </td>
</tr>
<tr class="even">
<td></td>
<td>Summit Cr sum A</td>
<td colspan="2">0.03</td>
<td>0.36</td>
<td>1.03(0.65, 1.63)</td>
<td>0.43</td>
<td>0.24</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.19(0, 1)</td>
<td>0.50</td>
<td>8</td>
<td>i</td>
</tr>
<tr class="odd">
<td></td>
<td>Swamp Cr sum A</td>
<td colspan="2">0.01</td>
<td>0.11</td>
<td>1.01(0.78, 1.31)</td>
<td>0.45</td>
<td>0.20</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.11(0, 1)</td>
<td>0.47</td>
<td>3</td>
<td>i</td>
</tr>
<tr class="even">
<td></td>
<td>Wallowa R sum A</td>
<td colspan="2">-0.11</td>
<td>0.10</td>
<td>0.89(0.64, 1.26)</td>
<td>0.74</td>
<td>0.50</td>
<td>12</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.93(0, 1)</td>
<td>0.77</td>
<td>15</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td colspan="2">Up Willamette R steelhead (9898)</td>
<td colspan="2"><strong>-0.07</strong></td>
<td><strong>0.05</strong></td>
<td><strong>0.93(0.75, 1.16)</strong></td>
<td><strong>0.70</strong></td>
<td><strong>0.37</strong></td>
<td><strong>7</strong></td>
<td><strong>0(0, 0.97)</strong></td>
<td><strong>0.33</strong></td>
<td><strong>0</strong></td>
<td><strong>0.78(0, 1)</strong></td>
<td><strong>0.71</strong></td>
<td><strong>8</strong></td>
<td><strong> </strong></td>
</tr>
<tr class="even">
<td></td>
<td>Agency Cr win</td>
<td colspan="2">0.00</td>
<td>0.46</td>
<td>1.00(0.52, 1.94)</td>
<td>0.49</td>
<td>0.31</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.31(0, 1)</td>
<td>0.56</td>
<td>14</td>
<td>h,f,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Calapooia R late (196)</td>
<td colspan="2">-0.07</td>
<td>0.21</td>
<td>0.93(0.60, 1.46)</td>
<td>0.63</td>
<td>0.41</td>
<td>7</td>
<td>0.4(0, 1)</td>
<td>0.67</td>
<td>10</td>
<td>0.64(0, 1)</td>
<td>0.68</td>
<td>14</td>
<td> </td>
</tr>
<tr class="even">
<td></td>
<td>Mill Cr win</td>
<td colspan="2">0.04</td>
<td>0.10</td>
<td>1.04(0.76, 1.41)</td>
<td>0.39</td>
<td>0.19</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.03(0, 1)</td>
<td>0.42</td>
<td>0</td>
<td>h,f,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Mollala R late (573)</td>
<td colspan="2">-0.14</td>
<td>0.10</td>
<td>0.87(0.68, 1.11)</td>
<td>0.84</td>
<td>0.61</td>
<td>15</td>
<td>0.69(0, 1)</td>
<td>0.76</td>
<td>11</td>
<td>0.98(0.01, 1)</td>
<td>0.86</td>
<td>19</td>
<td>t</td>
</tr>
<tr class="even">
<td></td>
<td>N. Santiam R late (2286)</td>
<td colspan="2">-0.12</td>
<td>0.05</td>
<td>0.89(0.75, 1.06)</td>
<td>0.84</td>
<td>0.54</td>
<td>12</td>
<td>0.14(0, 0.99)</td>
<td>0.54</td>
<td>2</td>
<td>0.99(0.05, 1)</td>
<td>0.86</td>
<td>14</td>
<td>t</td>
</tr>
<tr class="odd">
<td></td>
<td>S. Santiam R win (1061)</td>
<td colspan="2">0.01</td>
<td>0.02</td>
<td>1.01(0.90, 1.14)</td>
<td>0.39</td>
<td>0.10</td>
<td>0</td>
<td>0(0, 0)</td>
<td>0.15</td>
<td>0</td>
<td>0(0, 0.77)</td>
<td>0.33</td>
<td>0</td>
<td> </td>
</tr>
<tr class="even">
<td></td>
<td>S. Santiam R late (1202)</td>
<td colspan="2">-0.13</td>
<td>0.07</td>
<td>0.88(0.72, 1.08)</td>
<td>0.84</td>
<td>0.58</td>
<td>14</td>
<td>0.4(0, 1)</td>
<td>0.66</td>
<td>6</td>
<td>0.98(0.01, 1)</td>
<td>0.86</td>
<td>16</td>
<td> </td>
</tr>
<tr class="odd">
<td></td>
<td>Willamette R win</td>
<td colspan="2">-0.08</td>
<td>0.08</td>
<td>0.92(0.73, 1.16)</td>
<td>0.72</td>
<td>0.41</td>
<td>8</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.8(0, 1)</td>
<td>0.74</td>
<td>11</td>
<td>h,f</td>
</tr>
<tr class="even">
<td colspan="2">WA Coast chinook*</td>
<td colspan="2"><h1 id="section">0.00</h1></td>
<td><strong>0.10</strong></td>
<td><strong>1.00(0.76, 1.32)</strong></td>
<td><strong>0.47</strong></td>
<td><strong>0.22</strong></td>
<td><strong>0</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>0.12(0, 1)</strong></td>
<td><strong>0.50</strong></td>
<td><strong>2</strong></td>
<td><strong>i</strong></td>
</tr>
<tr class="odd">
<td rowspan="2"></td>
<td>Hoh R fall</td>
<td colspan="2">-0.02</td>
<td>0.03</td>
<td>0.98(0.85, 1.14)</td>
<td>0.57</td>
<td>0.19</td>
<td>2</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.13(0, 1)</td>
<td>0.52</td>
<td>2</td>
<td>h</td>
</tr>
<tr class="even">
<td>Hoh R spr</td>
<td colspan="2">0.00</td>
<td>0.09</td>
<td>1.00(0.79, 1.26)</td>
<td>0.50</td>
<td>0.21</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.16(0, 1)</td>
<td>0.51</td>
<td>3</td>
<td>a,h</td>
</tr>
<tr class="odd">
<td></td>
<td>Queets R fall (16333)</td>
<td colspan="2">0.05</td>
<td>0.04</td>
<td>1.05(0.77, 1.42)</td>
<td>0.28</td>
<td>0.21</td>
<td>0</td>
<td>0 (0, 0.07)</td>
<td>0.23</td>
<td>0</td>
<td>0(0, 1)</td>
<td>0.41</td>
<td>0</td>
<td> </td>
</tr>
<tr class="even">
<td></td>
<td>Willapa R fall</td>
<td colspan="2">0.01</td>
<td>0.05</td>
<td>1.01(0.68, 1.50)</td>
<td>0.47</td>
<td>0.25</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.04(0, 1)</td>
<td>0.49</td>
<td>0</td>
<td>h</td>
</tr>
<tr class="odd">
<td colspan="2">Up Columbia R sum/fall chinook*</td>
<td colspan="2"><h1 id="na-1">NA</h1></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
</tr>
<tr class="even">
<td rowspan="4"></td>
<td>Hanford Reach fall</td>
<td colspan="2">0.04</td>
<td>0.16</td>
<td>1.04(0.70, 1.55)</td>
<td>0.39</td>
<td>0.21</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.06(0, 1)</td>
<td>0.44</td>
<td>1</td>
<td>t,a,h</td>
</tr>
<tr class="odd">
<td>Methow R sum</td>
<td colspan="2">0.01</td>
<td>0.01</td>
<td>1.01(0.92, 1.11)</td>
<td>0.41</td>
<td>0.12</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0(0, 0.69)</td>
<td>0.32</td>
<td>0</td>
<td>l,h,i</td>
</tr>
<tr class="even">
<td>Okanogan R sum</td>
<td colspan="2">0.10</td>
<td>0.15</td>
<td>1.11(0.72, 1.70)</td>
<td>0.29</td>
<td>0.15</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.01(0, 0.96)</td>
<td>0.33</td>
<td>0</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td>Similkameen R sum</td>
<td colspan="2">0.08</td>
<td>0.15</td>
<td>1.09(0.72, 1.65)</td>
<td>0.32</td>
<td>0.16</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.01(0, 0.99)</td>
<td>0.36</td>
<td>0</td>
<td>h,f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Wenatchee R sum</td>
<td colspan="2">0.00</td>
<td>0.02</td>
<td>1.00(0.86, 1.16)</td>
<td>0.47</td>
<td>0.17</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.01(0, 1)</td>
<td>0.43</td>
<td>0</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td colspan="2">Middle Columbia R spr chinook*</td>
<td colspan="2"><h1 id="na-2">NA</h1></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
<td><strong>NA</strong></td>
</tr>
<tr class="even">
<td rowspan="4"></td>
<td>American R</td>
<td>-0.05</td>
<td colspan="2">0.08</td>
<td>0.95(0.72, 1.26)</td>
<td>0.63</td>
<td>0.34</td>
<td>5</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.55(0, 1)</td>
<td>0.65</td>
<td>8</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td>Beaver Cr</td>
<td>-0.05</td>
<td colspan="2">0.06</td>
<td>0.95(0.77, 1.16)</td>
<td>0.67</td>
<td>0.31</td>
<td>5</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.57(0, 1)</td>
<td>0.67</td>
<td>7</td>
<td>l,h,f,i</td>
</tr>
<tr class="even">
<td>Bull Run Cr</td>
<td>0.03</td>
<td colspan="2">0.03</td>
<td>1.03(0.89, 1.18)</td>
<td>0.35</td>
<td>0.10</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.00(0, 0.95)</td>
<td>0.32</td>
<td>0</td>
<td>l,h,f,i</td>
</tr>
<tr class="odd">
<td>Clear Cr</td>
<td>0.01</td>
<td colspan="2">0.07</td>
<td>1.01(0.82, 1.23)</td>
<td>0.46</td>
<td>0.17</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.08(0, 1)</td>
<td>0.46</td>
<td>1</td>
<td>h,f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Granite Cr</td>
<td>0.02</td>
<td colspan="2">0.02</td>
<td>1.02(0.92, 1.14)</td>
<td>0.33</td>
<td>0.08</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.00(0, 0.69)</td>
<td>0.28</td>
<td>0</td>
<td>l,h,f,i</td>
</tr>
<tr class="odd">
<td></td>
<td>John Day R</td>
<td>0.06</td>
<td colspan="2">0.04</td>
<td>1.06(0.92, 1.22)</td>
<td>0.24</td>
<td>0.08</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.00(0, 0.75)</td>
<td>0.24</td>
<td>0</td>
<td>h,i</td>
</tr>
<tr class="even">
<td></td>
<td>John Day R M Fk</td>
<td>0.06</td>
<td colspan="2">0.13</td>
<td>1.07(0.81, 1.40)</td>
<td>0.31</td>
<td>0.13</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.01(0, 0.99)</td>
<td>0.35</td>
<td>0</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>John Day R N Fk</td>
<td>0.05</td>
<td colspan="2">0.07</td>
<td>1.05(0.86, 1.29)</td>
<td>0.31</td>
<td>0.12</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.01(0, 0.94)</td>
<td>0.33</td>
<td>0</td>
<td>h,i</td>
</tr>
<tr class="even">
<td></td>
<td>Klickitat R</td>
<td>0.05</td>
<td colspan="2">0.23</td>
<td>1.06(0.66, 1.69)</td>
<td>0.38</td>
<td>0.21</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.07(0, 1)</td>
<td>0.44</td>
<td>2</td>
<td>h</td>
</tr>
<tr class="odd">
<td></td>
<td>Mill Cr</td>
<td>0.00</td>
<td colspan="2">0.07</td>
<td>1.00(0.80, 1.23)</td>
<td>0.50</td>
<td>0.20</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.13(0, 1)</td>
<td>0.50</td>
<td>2</td>
<td>h,f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Naches R</td>
<td>0.05</td>
<td colspan="2">0.18</td>
<td>1.05(0.39, 2.78)</td>
<td>0.42</td>
<td>0.26</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.06(0, 1)</td>
<td>0.47</td>
<td>1</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Shitike Cr</td>
<td>-0.02</td>
<td colspan="2">0.01</td>
<td>0.98(0.90, 1.07)</td>
<td>0.62</td>
<td>0.12</td>
<td>2</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.05(0, 1)</td>
<td>0.47</td>
<td>1</td>
<td>h,f,i</td>
</tr>
<tr class="even">
<td></td>
<td>Warm Springs R</td>
<td>-0.03</td>
<td colspan="2">0.06</td>
<td>0.97(0.79, 1.19)</td>
<td>0.59</td>
<td>0.25</td>
<td>3</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.32(0, 1)</td>
<td>0.59</td>
<td>5</td>
<td>h,i</td>
</tr>
<tr class="odd">
<td></td>
<td>Wind R</td>
<td>0.01</td>
<td colspan="2">0.05</td>
<td>1.01(0.81, 1.27)</td>
<td>0.43</td>
<td>0.18</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.03(0, 1)</td>
<td>0.44</td>
<td>0</td>
<td>h</td>
</tr>
<tr class="even">
<td></td>
<td>Yakima R</td>
<td>0.00</td>
<td colspan="2">0.00</td>
<td>1.00(0.99, 1.01)</td>
<td>0.48</td>
<td>0.18</td>
<td>0</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>0.55(0, 1)</td>
<td>0.65</td>
<td>0</td>
<td>l,h,f,i</td>
</tr>
<tr class="odd">
<td colspan="16"><sup>1</sup> Very High Extinction Risk - probability
that extinction risk in 50 years is over 25%</td>
</tr>
<tr class="even">
<td colspan="16"><sup>2</sup> Very High Risk of Decline - probability
that risk of 90% decline in 50 years is over 25%</td>
</tr>
<tr class="odd">
<td colspan="16"><sup>3</sup> Tests for underlying assumptions were made
on the running sums of wild spawner only counts where possible;
otherwise total mixed counts were used. The codes designate tests that
failed at (p&lt;.05). Note that a number of the "fails" are false-fails
since the p-value was not adjusted for 152 tests being conducted. If p
value is adjusted (p&lt;.001) to reduce the probability of a false
positive to less than 5%, none of the time series fail the diagnostic
tests.</td>
</tr>
<tr class="even">
<td></td>
<td colspan="12">a. Significant 1st order autocorrelation in
ln(R<sub>t+1</sub>/R<sub>t</sub>) was found.</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td colspan="15">d. A model with depensatory density-dependence fit the
data significantly better than a model with no density-dependence. This
indicates that the risk estimates are pessimistic.</td>
</tr>
<tr class="even">
<td></td>
<td colspan="15">t. A model with a trend in μ fit the data significantly
better than the model with no trend. This indicates that the risk
estimates are optimistic.</td>
</tr>
<tr class="odd">
<td></td>
<td colspan="12">l. The variance versus t plot was non-linear
(R<sup>2</sup> &lt; 0.7) indicating an underestimate of
σ<sup>2</sup>.</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td colspan="16"><sup>4</sup> Reasons for NA in the extinction estimates
column.</td>
</tr>
<tr class="odd">
<td></td>
<td colspan="12">i. Index data; no extinction estimates possible</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td colspan="15">h. No hatchery data; no extinction estimates possible;
risk estimates calculated on (wild+hatchery) spawner count</td>
</tr>
<tr class="odd">
<td></td>
<td colspan="12">f. No age of spawners data; no extinction estimates
possible</td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>

Table 2. Comparison of the estimated in-stream λ under different
assumptions about hatchery fish reproduction. Shown are the mean
stock-level λ estimates -- for those stocks where hatchery fraction and
age of return information was available. Mean λ defined as exp(mean of
stock μ's).

  ------------------------------- -------------------- ------------------
                                  Mean λ estimates     

  ESU                             Hatchery fish do not Hatchery fish
                                  reproduce            reproduce at the
                                                       same rate as
                                                       wild-born fish

  Lower Columbia R. chinook       0.99                 0.95

  Upper Columbia R. spr chinook   0.86                 0.83

  Snake R. spr/sum chinook        0.97                 0.93

  Snake R. fall chinook           0.95                 0.88

  Upper Willamette R. chinook     1.01                 0.86

  Columbia R. chum                1.07                 1.07

  Lower Columbia R. steelhead     0.92                 0.81

  Middle Columbia R. steelhead    0.97                 0.95

  Upper Columbia R. steelhead     1.00                 0.63

  Snake R. steelhead              1.02                 0.96

  Upper Willamette R. steelhead   0.91                 0.85

  WA Coast chinook\*              1.05                 1.03

  Up. Columbia R. sum/fall        no hatchery data     
  chinook\*                                            

  Middle Columbia R. spr          no hatchery data     
  chinook\*                                            
  ------------------------------- -------------------- ------------------

\* Not listed under the U.S. Endangered Species Act.

Table 3. Paired t-test for differences between the mean lambda for
stocks within ESUs from 1965-present and from 1980-present. All stocks
with complete time series for both time periods were included. Mean λ
defined as exp(mean of the stock μ's). Values in bold are significantly
different.

+---------------------------+----------+----------+-----------+-------+
| ESU                       | Number   | λ        | λ         | p-    |
|                           | of       |          |           | value |
|                           | stocks   | 1965     | 198       |       |
|                           |          | -present | 0-present |       |
+---------------------------+----------+----------+-----------+-------+
| Lower Columbia R. chinook | 12       | 0.99     | 1.00      | 0.45  |
+---------------------------+----------+----------+-----------+-------+
| Upper Columbia R. spr.    | 3        | 0.89     | 0.85      | **0   |
| chinook                   |          |          |           | .03** |
+---------------------------+----------+----------+-----------+-------+
| Snake R. spr/sum chinook  | 36       | 0.91     | 0.95      | **0   |
|                           |          |          |           | .01** |
+---------------------------+----------+----------+-----------+-------+
| Snake R. fall chinook     | 1        | 0.89     | 0.97      | n/a   |
+---------------------------+----------+----------+-----------+-------+
| Upper Willamette R.       | No early |          |           |       |
| chinook                   | data     |          |           |       |
|                           | a        |          |           |       |
|                           | vailable |          |           |       |
+---------------------------+----------+----------+-----------+-------+
| Columbia R. chum          | No early |          |           |       |
|                           | data     |          |           |       |
|                           | a        |          |           |       |
|                           | vailable |          |           |       |
+---------------------------+----------+----------+-----------+-------+
| Lower Columbia R.         | 1        | 0.96     | 0.91      | n/a   |
| steelhead                 |          |          |           |       |
+---------------------------+----------+----------+-----------+-------+
| Upper Columbia R.         | No early |          |           |       |
| steelhead                 | data     |          |           |       |
|                           | a        |          |           |       |
|                           | vailable |          |           |       |
+---------------------------+----------+----------+-----------+-------+
| Middle Columbia R.        | 7        | 0.93     | 0.91      | 0.23  |
| steelhead                 |          |          |           |       |
+---------------------------+----------+----------+-----------+-------+
| Snake R. steelhead        | 9        | 0.97     | 1.03      | **0   |
|                           |          |          |           | .02** |
+---------------------------+----------+----------+-----------+-------+
| Upper Willamette R.       | No early |          |           |       |
| steelhead                 | data     |          |           |       |
|                           | a        |          |           |       |
|                           | vailable |          |           |       |
+---------------------------+----------+----------+-----------+-------+
| WA Coast chinook\*        | No early |          |           |       |
|                           | data     |          |           |       |
|                           | a        |          |           |       |
|                           | vailable |          |           |       |
+---------------------------+----------+----------+-----------+-------+
| Up. Columbia R. sum/fall  | 5        | 1.00     | 1.02      | 0.06  |
| chinook\*                 |          |          |           |       |
+---------------------------+----------+----------+-----------+-------+
| Middle Columbia R. spr    | 6        | 1.00     | 1.04      | **0   |
| chinook\*                 |          |          |           | .02** |
+---------------------------+----------+----------+-----------+-------+

\* Not listed under the U.S. Endangered Species Act

Table 4. Harvest rates for ESUs in the Columbia River Basin, from
1980-1999, and expected changes if no fish were harvested. Harvest
impact on chum was not assessed, because the total population size (and
thus harvest rate) was unknown. Exploitation rate data were obtained
from the NMFS Sustainable Fisheries Division, CTC (2001), ODFW
(2001a-e), WDFW (2001), Beamesderfer et al. (1998), Chilcote (2001), and
Cooney (2000). λ estimates for subgroups within an ESU are the mean of
all stocks in that subgroup, or a dam count of that subgroup when
available. The λ calculation assumed zero hatchery fish reproduction. λ
estimates for ESUs without subgroups are the ESU-level λ estimates. Note
that most wild steelhead have been catch-and-release since 1992.
Currently allowable mortality rates on steelhead result from hooking
mortality and incidental take in other fisheries and are generally not
met.

+---------------+-------+------+----+-----+------+--------------+----+
| # ESU         | Sub-  | λ    | Me | M   | \%   | Current      | N  |
|               | group |      | an | ean | incr | allowable^a^ | ot |
|               |       | 1    | re | exp | ease | total        | es |
|               |       | 980- | tu | loi | in λ | exploitation |    |
|               |       | 1999 | rn | tat | with | rate         |    |
|               |       |      | ti | ion | no   |              |    |
|               |       |      | me | r   | har  |              |    |
|               |       |      |    | ate | vest |              |    |
|               |       |      |    | 1   |      |              |    |
|               |       |      |    | 980 |      |              |    |
|               |       |      |    | -99 |      |              |    |
+---------------+-------+------+----+-----+------+--------------+----+
| L. Columbia   | fall  | 0.99 | 3  | 0   | 25   | 0.65         | b  |
| R. chinook    | Tule  |      | .7 | .56 |      |              |    |
+---------------+-------+------+----+-----+------+--------------+----+
|               | fall  | 0.98 | 3  | 0   | 15   | no specific  | c  |
|               | B     |      | .8 | .41 |      | limit        |    |
|               | right |      |    |     |      |              |    |
+---------------+-------+------+----+-----+------+--------------+----+
|               | s     | 0.96 | 3  | 0   | 62   | \<0.15       | d  |
|               | pring |      | .8 | .84 |      |              |    |
+---------------+-------+------+----+-----+------+--------------+----+
| Up. Columbia  |       | 0.86 | 4  | 0   | 2    | \<0.06-0.19  | e  |
| R. spr        |       |      | .3 | .09 |      |              |    |
| chinook       |       |      |    |     |      |              |    |
+---------------+-------+------+----+-----+------+--------------+----+
| Snake R.      | s     | 0.96 | 4  | 0   | 2    | \<0.06-0.19  | e  |
| spr/sum       | pring |      | .5 | .08 |      |              |    |
| chinook       |       |      |    |     |      |              |    |
+---------------+-------+------+----+-----+------+--------------+----+
|               | s     | 0.97 | 4  | 0   | 1    | \<0.06       | f  |
|               | ummer |      | .3 | .03 |      |              |    |
+---------------+-------+------+----+-----+------+--------------+----+
| Snake R. fall |       | 0.94 | 3  | 0   | 30   | \~ 0.50      | g  |
| chinook       |       |      | .7 | .62 |      |              |    |
+---------------+-------+------+----+-----+------+--------------+----+
| Up.           |       | 0.99 | 4  | 0   | 16   | In           | d  |
| Willamette R. |       |      | .4 | .48 |      | development. |    |
| spr chinook   |       |      |    |     |      |              |    |
|               |       |      |    |     |      | Max. likely  |    |
|               |       |      |    |     |      | to be 0.15,  |    |
|               |       |      |    |     |      | Expected     |    |
|               |       |      |    |     |      | 0.09-0.11    |    |
+---------------+-------+------+----+-----+------+--------------+----+
| L. Columbia   | s     | 0.90 | 5  | 0   | 6    | Expected     |    |
| R. steelhead  | ummer |      | .2 | .26 |      | \<0.10       |    |
+---------------+-------+------+----+-----+------+--------------+----+
|               | w     | 0.91 | 4  | 0   | 8    | \<0.10       |    |
|               | inter |      | .5 | .29 |      |              |    |
+---------------+-------+------+----+-----+------+--------------+----+
| Mid. Columbia |       | 0.96 | 4  | 0   | 4    | Max. 0.20    |    |
| R. steelhead  |       |      | .8 | .19 |      |              |    |
|               |       |      |    |     |      | Expected     |    |
|               |       |      |    |     |      | \<0.15       |    |
+---------------+-------+------+----+-----+------+--------------+----+
| Up. Columbia  |       | 1.00 | 3  | 0   | 8    | no specific  |    |
| R. steelhead  |       |      | .8 | .25 |      | limits       |    |
+---------------+-------+------+----+-----+------+--------------+----+
| Snake R.      | A-run | 0.97 | 5  | 0   | 4    | Expected     | h  |
| steelhead     |       |      | .0 | .16 |      | \<0.17       |    |
+---------------+-------+------+----+-----+------+--------------+----+
|               | B-run | 0.93 | 6  | 0   | 7    | 0.17         |    |
|               |       |      | .5 | .36 |      |              |    |
+---------------+-------+------+----+-----+------+--------------+----+
| Up.           |       | 0.94 | 4  | 0   | 3    | \<0.02       |    |
| Willamette R. |       |      | .0 | .12 |      |              |    |
| steelhead     |       |      |    |     |      |              |    |
+---------------+-------+------+----+-----+------+--------------+----+

a\. As set by the National Marine Fisheries Service or state agencies.

b\. Harvest rates on fall-tule stocks have been much lower (0.45) than
allowable limits in recent years. Maximum allowable exploitation rate
likely to be lowered to 0.50.

c\. Fishery managed for 5700 fish escapement to N. F. Lewis River

d\. Fishery selective for marked hatchery fish only

e\. Allowable harvest depends on returns of aggregate upriver Columbia
and wild Snake River stocks.

f\. Harvest rate has been below allowable limits in recent years.

g\. Ocean exploitation has been substantially below allowable levels in
recent years. Current allowable exploitation rate represents a 30%
reduction from 1988-1993 period)

h\. Fishery on A-run managed through limits on B-run fishery

Table 5. Potential impact of anticipated improvements to the hydropower
system, aimed at increasing adult and juvenile migration survival (NMFS
2000). λ estimated assuming hatchery fish do not reproduce. The range of
anticipated improvements for Snake River ESUs includes estimated
indirect mortality attributable to barging.

+-----------------------+------+-------+-------+--------------+-------+
| ESU                   | S    | λ     | Mean  | Anticipated  | \%    |
|                       | ub-g |       | r     | % increase   |       |
|                       | roup | 19    | eturn | in juvenile  | inc   |
|                       |      | 80-99 | time  | and adult    | rease |
|                       |      |       |       | (combined)   |       |
|                       |      |       |       | migration    | in λ  |
|                       |      |       |       | survival     |       |
+-----------------------+------+-------+-------+--------------+-------+
| Lower Columbia R.     | sp   | 0.96  | 3.8   | 5-6          | 1     |
| chinook               | ring |       |       |              |       |
+-----------------------+------+-------+-------+--------------+-------+
|                       | fall | 0.98  | 3.7   | 6-14         | 2-4   |
+-----------------------+------+-------+-------+--------------+-------+
| Upper Columbia R.     |      | 0.86  | 4.3   | 16-21        | 4-5   |
| chinook               |      |       |       |              |       |
+-----------------------+------+-------+-------+--------------+-------+
| Snake R. chinook      | spr  | 0.97  | 4.3   | 5-6          | 1     |
|                       | /sum |       |       |              |       |
+-----------------------+------+-------+-------+--------------+-------+
|                       | fall | 0.95  | 3.7   | 11-39        | 3-9   |
+-----------------------+------+-------+-------+--------------+-------+
| Upper Willamette R.   |      | 0.99  | 4.4   | 0            | 0     |
| chinook               |      |       |       |              |       |
+-----------------------+------+-------+-------+--------------+-------+
| Columbia R. chum      |      | 1.06  | 3.6   | 6-14         | 2-4   |
+-----------------------+------+-------+-------+--------------+-------+
| Lower Columbia R.     |      | 0.96  | 4.7   | 1-4          | 0-1   |
| steelhead             |      |       |       |              |       |
+-----------------------+------+-------+-------+--------------+-------+
| Upper Columbia R.     |      | 1     | 3.8   | 8-17         | 2-4   |
| steelhead             |      |       |       |              |       |
+-----------------------+------+-------+-------+--------------+-------+
| Snake R. steelhead    |      | 0.96  | 5.2   | 6-10         | 1-2   |
+-----------------------+------+-------+-------+--------------+-------+
| Middle Columbia R.    |      | 0.96  | 4.8   | 8-17         | 2-3   |
| steelhead             |      |       |       |              |       |
+-----------------------+------+-------+-------+--------------+-------+
| Upper Willamette R.   |      | 0.94  | 4.1   | 0            | 0     |
| steelhead             |      |       |       |              |       |
+-----------------------+------+-------+-------+--------------+-------+

# Figure Legends

**Figure 1.** Columbia River Basin. Heavy solid lines denote rivers
accessible to anadromous fishes; thin solid lines denote portions of the
Columbia and Snake Rivers blocked by dams. Numbers define regions, with
analyzed salmonid stocks as follows: 1) Washington Coast, non-listed
chinook stocks; 2) Lower Columbia River, steelhead, chum and chinook
salmon listed as threatened; 3) Upper Willamette River, steelhead and
chinook listed as threatened; 4) Middle Columbia River, steelhead listed
as threatened and spring chinook non-listed; 5) Upper Columbia River,
steelhead and spring chinook listed as endangered; 6) Upper Columbia
River, non-listed summer/fall chinook stocks; 7) Snake River, steelhead,
spring/summer chinook and fall chinook listed as threatened, sockeye
salmon listed as endangered.

**Figure 2.** Illustration of the Bayesian risk metrics, the probability
that the true λ is less than 0.9 and the probability that the true risk
of decline or extinction is very high. This requires first calculating
the posterior probability density functions (π) of the parameters. The
surfaces in plots A and B are illustrations of π's. Plot A: The
probability that λ is less than 0.9 is calculated by integrating the π
for μ over those μ for which λ \< 0.9. Plot B: The probability that the
true risk of decline or extinction is very high is calculated by
integrating the joint π's for μ and σ^2^ over those values of μ and σ^2^
for which the probability of 90% decline (VHDR) or probability of
extinction (VHER) is greater than 25%.

**Figure 3.** Time series of *TS~t~*, the estimated total living current
or future spawner population size, for each ESU in the Columbia River
Basin, plus Hanford Reach and coastal chinook. In these plots, *R~t~*
was estimated from total (wild + hatchery origin) spawner count time
series spanning 1980 to 1999.

**Figure 4.** Estimated long-term rate of population decline, λ, at the
individual stock level (circles) and at the ESU level (bar-mark). Plot A
shows the estimates assuming that no masking of the parameter *μ*
occurred due to hatchery fish reproduction (i.e. that hatchery
reproduction = 0). Plot B shows the estimated probability that the
stocks have a true λ of less than 0.9. A λ of less than 0.9 translates
to an average yearly decline of at least 10%. The dotted line in plot B
indicates the level of 50% data support; above 50%, the data give more
support to the conjecture that λ \< 0.9.

**Figure 5.** Histogram of the stock-level estimates of the probability
of 90% decline in 50 years for each ESU including three non-listed ESUs,
WA Coast chinook, Upper Columbia sum/fall chinook, and Middle Columbia
R. spring chinook. Above the histogram bars is shown the mean
probability of 90% decline (diamonds). The 95% confidence intervals on
the mean probabilities, $\overline{x}$, of the *n* stock estimates for
an ESU are shown
($\overline{x} \pm t_{0\text{.}\text{025},n - 1}\frac{s}{\sqrt{n - 1}}$)
where *s* is the unbiased sample variance of the *n* estimates. If *n* =
1 (only one stock estimate in the ESU), the mean probability of 90%
decline was plotted with no error bars. The point estimate for the ESU
as a whole is shown by the cross above the histogram bars.

**Figure 6.** Histogram of the stock-level estimates of the probability
of extinction in 50 years. See Figure 5 for a description of the error
bars.

**Figure 7**. Mean percent increases in
λ ρεθυιρεδ το ρεδυχε τηε ρισκ οφ 90% δεχλινε (Πλοτ Α) ορ εξτινχτιον (Πλοτ Β) ιν 50 ψεαρσ το βελοω 5%.  Τηε ερρορ βαρσ σηοω τηε στανδαρδ ερρορσ.  Νο ερρορ βαρσ ωερε πλοττεδ ωηεν ονλψ ονε εστιματε ωασ αϖαιλαβλε φορ τηε ΕΣΥ.  Ιν Πλοτ Β, τηε ρεσυλτσ ονλψ ινχλυδε τηοσε στοχκσ ανδ ΕΣΥσ φορ ωηιχη α ποπυλατιον σιζε εστιματε ωασ ποσσιβλε (ωηεν τοταλ λιϖε σπαωνερ χουντ ανδ ηατχηερψ φραχτιονσ ωερε αϖαιλαβλε).  Τηε παραμετερσ ωερε εστιματεδ ασσυμινγ τηατ νο μασκινγ οφ τηε παραμετερ μ
occurred due to hatchery fish reproduction (i.e. hatchery reproduction =
0).