Prepared by Blake Feist and Ashley Steel

SUMMARY

As of 28 January 2003, all spatial and statistical analyses in the John
Day, Wenatchee, and Yakima subbasins have been completed.

DATA

 *Redd Counts* -- Redd count data were obtained from StreamNet (2002)
initially, and supplemental data were obtained from other sources as
necessary (including the NWFSC spawner database, unpublished data). To
our knowledge, we worked with comprehensive datasets. All index reach
segments were mapped to either 1:24k or 1:100k USGS stream networks.

 *Geospatial* -- Most geospatial datalayers were readily available from
previous SWAM analyses done in the Willamette and Salmon River basins
(see Table 1). However, the following had to be generated or extensively
manipulated: 1:24k stream network and stream junctions for the John Day;
10 m DEM mosaicking and error correcting; watershed flow accumulations;
and explicit maps of index reach segments.

ANALYSIS

 *Spatial* -- Jeff Cowen, with the NWFSC Data Management Team, created
a tool in ArcView that greatly accelerated running the spatial component
of SWAM. It also improved accuracy and reduced the likelihood of errors.
Spatial analyses were completed in three subbasins for chinook, and also
for steelhead in one of the subbasins (Table 2, and Figure 1). Analyses
were run at three areas of influence (AOI) or spatial scales: reach (500
m buffer around index reach); HUC 6 buffer (HUC 6 catchments directly
contacting index reach); and watershed (entire catchment flowing into
given index reach). Output from the spatial overlays at the three
different scales, with the various GIS habitat data layers, was
summarized in a table, created using the aforementioned ArcView tool.
This out put was then used for the statistical portion of the modeling.

 *Statistical* -- Statistical analyses have been completed for each of
the four SWAM runs described in the Table 2. David Jensen applied a
mixed models approach at each of the three areas of influence described
above and for each of the four SWAM runs independently. The mixed models
approach allows for temporally correlated observations within a reach
and makes use of all the available data. It was not necessary to average
redd counts within a reach or ignore reaches with missing data. We had
access to excellent habitat data, which enabled several new variables
(not available for earlier SWAM runs) that describe habitat change. For
each of area of influence and each run, a set of best models was
developed using a modified all subsets routine. All models were tested
to check than no one observation was driving the results using Cook's
distance, modified for use with mixed models. Each potential model has
also been tested for prediction accuracy using a leave-one-out
cross-validation approach. The final selection of best models has not
been made because we are in the process of double-checking results. The
best set will be selected based on statistical significance of the
predictors, model fit using the Bayesian information criteria (BIC),
correlations between habitat variables in the model, robustness of the
model, and predictive success in the cross-validation procedure. All
results and conclusions should be considered preliminary; and, of
course, the correlations identified using SWAM should not be used to
infer cause and effect.

GENERAL RESULTS

 *Chinook salmon in the Wenatchee and John Day Rivers* -- For each of
these two SWAM runs, there were only 6 index reaches. Model fitting was
therefore limited. The best results were achieved for the watershed area
of influence. The density of dams

and channel slope were the most useful variables for predicting the
distribution of chinook salmon redds in the Wenatchee River. Cattle
grazing allotments were the best predictor of chinook salmon redd
density in the John Day River.

 *Chinook salmon in the Yakima River and steelhead in the John Day
River* -- There were large numbers of index reaches available for both
of these SWAM runs and the modeling was successful for all three areas
of influence. Correlations between observed and predicted redd densities
were often as high as 0.9. Because there were a high number of good
models, we expect to select a set of best models for each run and area
of influence and average the predictions from that set of models. We
used that same approach in the Salmon and Willamette basins. Attached
figures show the fit between observed redd density and predicted redd
density for each of three models using habitat variables at the
watershed area of influence for both SWAM runs. These habitat models
will not necessarily be the final set of best models. Overall, it
appears that channel slope, vegetation, and dam density are strong
predictors on chinook redd density on the Yakima River. Hillslope and
surrounding topography, precipitation, and vegetation are among the best
predictors of steelhead redd density on the John Day river.

SPECIFIC RESULTS

**John Day Subbasin Steelhead**

Covariance Structure

A number of possible mixed models were evaluated in order to determine
which would be the most appropriate for these particular data. Models
with each of the potential covariates were fit using four different
covariance structures. An autoregressive-moving average model
(ARMA(1,1)), autoregressive covariance structure of order 1 (AR(1)),
compound symmetry (all correlations between years equal) and
independence between years. Thus, for each AOI, there were 16 possible
covariance/random coefficients combinations.

The results of the fitting of the one-variable models were similar for
all AOI's. The models with random intercept and slope, both correlated
and independent, did not fit well. Generally, 25% or more of the
covariance matrices for the random components were not positive definite
(precluding hypothesis testing of the parameter estimates), and
typically 50% or more of the models had variance components that were
not different from zero (p \> 0.05), yielding very few viable models.
Thus, the random slope was dropped from consideration.

Of the models with two intercepts, only the models with the AR(1)
covariance structure and the independent observations fit well. For the
ARMA(1,1) and compound symmetry models, the variance component
associated with reach was not different from zero in many cases (p \>
0.05) when 2 random intercepts were fit.

There were six potential model structures that produced valid models for
all potential predictor variables: ARMA(1,1) with 1 random intercept
(year), AR(1) with 2 random intercepts (year and reach), AR(1) with 1
random intercept (year), compound symmetry with 1 random intercept
(year), and independent observations with both 1 and 2 random
intercepts. Each of these models was fit to each of the potential
predictor variables and for each AOI, the results ranked by BIC. For
each of the AOIs and all predictors, the ARMA(1,1) model with a random
intercept for year was selected by the BIC. This structure was used for
all subsequent modeling.

Variable Selection

The first step in the variable selection procedure entailed fitting the
model to each of the potential explanatory variables separately for each
AOI. For the HUC AOI, there was some evidence that 14 might be useful in
explaining the variation in redd density (defined as a p-value \< 0.20);
for the watershed AOI, 13 of the variables met this criteria; for the
buffer AOI, 8 variables were selected. Table 3 identifies the variables
by AOI.

Two variable models were generated by fitting all combinations of these
variables for each AOI. From these, all of the models for which both
p-values were less than 0.10 were selected for three-variable modeling,
as described below. This included 12 HUC models, 10 watershed models and
8 buffer models.

For each of the two-variable models selected above, the remaining
predictors were added singly to form three-variable models. Each of
these was retained only if the p-value for all three fixed effects was
less than 0.10. This resulted in eleven 3-variable HUC models, 14
3-variable watershed models and 16 3-variable buffer models. This
procedure was repeated to select the best set of 4-variable models; each
remaining variable was added to each of the 3-variable models, and those
with all p-values less than 0.10 were retained resulting in eleven
4-variable HUC models, nine 4-variable watershed models and 33
4-variable buffer models. Repeating this procedure again added two
5-variable HUC models, four 5-variable watershed models and 27
5-variable buffer models to the pool. The procedure was halted at the
five-variable stage since it was not resulting in much improvement in
BIC, and many of the larger models had collinearity problems (see
below).

At the end of the procedure outlined above, all of the models in which
p-values for any of the parameters was greater than 0.05 were
eliminated. Almost all of the potential explanatory variables are
correlated with each of the other variables to some degree. Since
collinearity is a problem that will need to be addressed anyway, those
models in which any of the pairwise correlations exceeded 0.6 in
absolute value were eliminated from consideration. The next step was the
calculation of the condition index for each matrix of explanatory
variables. The condition index is the ratio of each singular value to
the maximum singular value and provides a measure of the stability of
the solution (Belsey, Kuh & Welsh 1980). \[The singular values are the
square root of the eigen values\]. Belsey, Kuh and Welsh suggest that
condition indices greater than 10 indicate that collinearity may be
severe enough to have an effect. All models that exceeded this
recommendation were eliminated.

Cook's distance measures the shift in the parameter estimates when a
single observation is excluded, and is useful for flagging influential
sites (Cook 1977). This is a fairly standard part of statistical
software fitting least squares models, but has only recently begun to be
applied to mixed models (e.g. Christensen et al. 1992). Since the
explanatory variables are the same for each of the 43 sites, it was
feasible to refit each model 43 times, leaving out one site each time,
and calculate Cook's D. Models with values of Cook's D greater than 1.0
for at least one site were deemed unstable and eliminated from the
analysis. A value of Cook's D between 0.8 and 1.0 indicates a shift to
near the 50^th^ percentile of the confidence ellipsoid around β and is
cause for concern (Cook 1977).

The next stage in model selection was a cross-validation procedure,
which assessed the ability of the models to predict redd counts for
sites not sampled. The data were divided into a training set, used to
fit the model, and a validation set, used to assess the fit of the model
with data not used in model fitting, by randomly selecting 10% of the
observations for validation. To assess the fit, the correlation between
the values predicted for the validation set and the measured redd
densities was calculated, as was the root mean squared prediction error
(RMSPE). This was repeated 1000 times for each model, resulting in 1000
correlation and RMSPE values. This is an external validation technique,
and as such gives a better indication of how the model would perform for
sites without redd count data (Harrell et al. 1996). It is the same idea
as the 'leave-one-out' approach, but using a validation set of size=1 is
conservative in the sense that it tends to select an unnecessarily large
model (Shao 1993), and by repeating the data splitting procedure
multiple times, the results are not dependent on a particular split,
which results in less variation in the accuracy measurements (Harrell et
al. 1996). The results were evaluated by looking at the mean correlation
and RMSPE from the 1000 runs (a measure of how well the model
predictions fit the observations) and the standard deviation of these
metrics (a measure of the precision of the predictions). Smaller
variation was taken as an indication of a more stable predictive model.
As expected, these measures coincided fairly well. Table 4 lists the
criteria used for model evaluation including a summary of the
cross-validation results. Definitions of the variable names used in the
table follows; the change in vegetation was calculated by subtracting
historic coverage from current coverage. Percentages refer to the
percentage of the AOI in which that feature is present.

For the final model selection, the procedure outlined above was applied.
Four models were selected for each AOI. These models are summarized
below (Table 5).

**John Day Subbasin Chinook Salmon**

Covariance Structure

A number of possible mixed models were evaluated in order to determine
which would be the most appropriate for these particular data. Models
with each of the potential covariates were fit using four different
covariance structures. An autoregressive-moving average model
(ARMA(1,1)), autoregressive covariance structure of order 1 (AR(1)),
compound symmetry (all correlations between years equal) and
independence between years. Thus, for each AOI, there were 16 possible
covariance/random coefficients combinations.

The results of the fitting of the one-variable models were similar for
all AOI's. The models with random intercept and slope, both correlated
and independent, did not fit well. Generally, 25% or more of the
covariance matrices for the random components were not positive definite
(precluding hypothesis testing of the parameter estimates), and
typically 50% or more of the models had variance components that were
not different from zero (p \> 0.05), yielding very few viable models.
Thus, the random slope was dropped from consideration. For the models
with two intercepts, none of the variance components associated with
reach was different from zero (p \> 0.08 in all cases), so this
structure was dropped as well.

The four potential covariate structures fit with a random intercept for
year produced valid models for all potential predictor variables. Each
of these was fit to each of the potential predictor variables and for
each AOI and the results ranked by BIC. For each of the AOIs and all
predictors, the ARMA(1,1) model was selected by the BIC. This structure
was used for all subsequent modeling.

Variable Selection

The first step in the variable selection procedure entailed fitting the
model to each of the potential explanatory variables separately for each
AOI. For both the HUC and the buffer AOIs, only one variable (cattle)
was correlated with redd density (defined as a p-value \< 0.20); for the
watershed AOI, eight of the variables met this criteria. In light of
these results, two variable models were generated by fitting all
combinations of the original set of variables for each AOI. From these,
all of the models for which both p-values were less than 0.10 were
selected for three-variable modeling, as described below. This included
one HUC model, 12 watershed models and a single buffer model. At this
stage, the covariance structure was not fitting well, with the estimate
for the gamma parameter at the boundary of the parameter space for some
models. The model fitting was then repeated with an AR(1) covariance,
which resulted in a much greater number of potential models. For the
models in which the covariance structure fit well, the ARMA(1,1) model
had a smaller BIC (on the order of 5-8%), otherwise the AR(1) model had
the smaller BIC. Since the ARMA(1,1) covariance structure fit better
(except in the situation noted above), it was used initially in the
3-variable modeling.

For each of the two-variable models with the ARMA(1,1) covariance
structure selected above, the remaining predictors were added singly to
form three-variable models. Each of these was retained only if the
p-value for all three fixed effects was less than 0.10. All of the
3-variable models that met this criteria had the problem with the
ill-fitting covariance parameter mentioned above, so the AR(1)
covariance structure was used instead. This resulted in five 3-variable
HUC models, 52 3-variable watershed models and three 3-variable buffer
models. The procedure was halted at the three-variable stage since there
were only 6 index reaches.

At the end of the procedures outlined above, all of the models in which
p-values for any of the parameters was greater than 0.05 were
eliminated. For models fit to two covariance structures, only the model
with the lower BIC was kept. Almost all of the potential explanatory
variables are correlated with each of the other variables to some
degree. Since collinearity is a problem that will need to be addressed
anyway, those models in which any of the pairwise correlations exceeded
0.6 in absolute value were eliminated from consideration. The next step
was the calculation of the condition index for each matrix of
explanatory variables. The condition index is the ratio of each singular
value to the maximum singular value and provides a measure of the
stability of the solution (Belsey, Kuh & Welsh 1980). \[The singular
values are the square root of the eigen values\]. Belsey, Kuh and Welsh
suggest that condition indices greater than 10 indicate that
collinearity may be severe enough to have an effect. All models that
exceeded this recommendation were eliminated.

Cook's distance measures the shift in the parameter estimates when a
single observation is excluded, and is useful for flagging influential
sites (Cook 1977). This is a fairly standard part of statistical
software fitting least squares models, but has only recently begun to be
applied to mixed models (e.g. Christensen et al. 1992). Since the
explanatory variables are the same for each of the 6 sites, it was
feasible to refit each model 6 times, leaving out one site each time,
and calculate Cook's D. Models with values of Cook's D greater than 1.0
for at least one site were deemed unstable and eliminated from the
analysis. A value of Cook's D between 0.8 and 1.0 indicates a shift to
near the 50^th^ percentile of the confidence ellipsoid around β and is
cause for concern (Cook 1977).

The final model selection criteria was cross-validation, which assessed
the ability of the models to predict redd counts for sites not sampled.
The data were divided into a training set, used to fit the model, and a
validation set, used to assess the fit of the model with data not used
in model fitting, by randomly selecting 10% of the observations for
validation. To assess the fit, the correlation between the values
predicted for the validation set and the measured redd densities was
calculated, as was the root mean squared prediction error (RMSPE). This
was repeated 1000 times for each model, resulting in 1000 correlation
and RMSPE values. This is an external validation technique, and as such
gives a better indication of how the model would perform for sites
without redd count data (Harrell et al. 1996). It is the same idea as
the 'leave-one-out' approach, but using a validation set of size=1 is
conservative in the sense that it tends to select an unnecessarily large
model (Shao 1993), and by repeating the data splitting procedure
multiple times, the results are not dependent on a particular split,
which results in less variation in the accuracy measurements (Harrell et
al. 1996). The results were evaluated by looking at the mean correlation
and RMSPE from the 1000 runs (a measure of how well the model
predictions fit the observations) and the standard deviation of these
metrics (a measure of the precision of the predictions). Smaller
variation was taken as an indication of a more stable predictive model.
As expected, these measures coincided fairly well.

Only three models met the selection criteria with respect to
correlation, Cook's distance and p-value. The cross validation analysis
was applied to these models, Table 6 summarizes the results. Definitions
of the variable names used in the table follows; the change in
vegetation was calculated by subtracting historic coverage from current
coverage. Percentages refer to the percentage of the AOI in which that
feature is present. Since there were only three models, the final
selection criteria outlined in the introduction were not applied, though
both the correlation and RMSPE were within the guidelines specified
there. Thus, these three models were selected as the final models. They
are summarized in Table 7.

**Wenachee Subbasin Chinook Salmon**

Covariance Structure

A number of possible mixed models were evaluated in order to determine
which would be the most appropriate for these particular data. Models
with each of the potential covariates were fit using four different
covariance structures. An autoregressive-moving average model
(ARMA(1,1)), autoregressive covariance structure of order 1 (AR(1)),
compound symmetry (all correlations between years equal) and
independence between years. Thus, for each AOI, there were 16 possible
covariance/random coefficients combinations.

The results of the fitting of the one-variable models were similar for
all AOI's. The models with random intercept and slope, both correlated
and independent, did not fit well. Generally, 25% or more of the
covariance matrices for the random components were not positive definite
(precluding hypothesis testing of the parameter estimates), and up to
50% of the models had variance components that were not different from
zero (p \> 0.05). Thus, the random slope was dropped from consideration.
In all of the models with two intercepts, there was no evidence that the
variance of the intercept associated with reach was different from zero
(p \> 0.08).

The model with a random intercept for year was combined with each of the
four covariance structures described above and fit to each of the
potential predictor variables. For each AOI, the resulting models were
ranked by BIC. For each of the AOIs and all predictors, the model with
the ARMA(1,1) covariance was selected. This structure was used for all
subsequent modeling.

Variable Selection

The first step in the variable selection procedure entailed fitting the
model to each of the potential explanatory variables separately for each
AOI. For the HUC AOI, there was some evidence that 3 might be useful in
explaining the variation in redd density (defined as a p-value \< 0.20);
for the watershed AOI, 8 of the variables met this criteria; for the
buffer AOI, two variables were selected. Table 8 identifies the
variables by AOI.

Since the pool of potential predictors was small for two of the three
AOIs, two variable models were generated by fitting all combinations of
all predictors for each AOI. From these, all of the models for which
both p-values were less than 0.10 were selected for three-variable
modeling, as described below. This included 49 HUC models, 50 watershed
models and 32 buffer models.

For each of the two-variable models selected above, the remaining
predictors were added singly to form three-variable models. Each of
these was retained only if the p-value for all three fixed effects was
less than 0.10. This resulted in 143 3-variable HUC models, 249
3-variable watershed models and 92 3-variable buffer models. This
procedure was halted at the 3-variable stage because there were only six
index reaches.

At the end of the procedure outlined above, all of the models in which
p-values for any of the parameters was greater than 0.05 were
eliminated. This left 61 HUC models, 215 watershed models and 86 buffer
models. Almost all of the potential explanatory variables are correlated
with each of the other variables to some degree. Since collinearity is a
problem that will need to be addressed anyway, those models in which any
of the pairwise correlations exceeded 0.6 in absolute value were dropped
from consideration. Most of the remaining models were eliminated at this
step, including 52 HUC models, 31 watershed models (only the top 30
3-variable models were evaluated) and 78 buffer models. The next step
was the calculation of the condition index for each matrix of
explanatory variables. The condition index is the ratio of each singular
value to the maximum singular value and provides a measure of the
stability of the solution (Belsey, Kuh & Welsh 1980). \[The singular
values are the square root of the eigen values\]. Belsey, Kuh and Welsh
suggest that condition indices greater than 10 indicate that
collinearity may be severe enough to have an effect. The four buffer
models that exceeded this recommendation were eliminated.

Cook's distance measures the shift in the parameter estimates when a
single observation is excluded, and is useful for flagging influential
sites (Cook 1977). This is a fairly standard part of statistical
software fitting least squares models, but has only recently begun to be
applied to mixed models (e.g. Christensen et al. 1992). Since the
explanatory variables are the same for each of the six sites, it was
feasible to refit each model six times, leaving out one site each time,
and calculate Cook's D. Models with large values of Cook's D for at
least one site were deemed unstable and eliminated from the analysis.
Generally, 1.0 is used for the cutoff, but since the pool of potential
models was so small several models with Cook's distances slightly
greater than 1.0 were retained. A value of Cook's D between 0.8 and 1.0
indicates a shift to near the 50^th^ percentile of the confidence
ellipsoid around β and is cause for concern (Cook 1977).

The next phase of model selection was cross-validation, which assessed
the ability of the models to predict redd counts for sites not sampled.
The data were divided into a training set, used to fit the model, and a
validation set, used to assess the fit of the model with data not used
in model fitting, by randomly selecting 10% of the observations for
validation. To assess the fit, the correlation between the values
predicted for the validation set and the measured redd densities was
calculated, as was the root mean squared prediction error (RMSPE). This
was repeated 1000 times for each model, resulting in 1000 correlation
and RMSPE values. This is an external validation technique, and as such
gives a better indication of how the model would perform for sites
without redd count data (Harrell et al. 1996). It is the same idea as
the 'leave-one-out' approach, but using a validation set of size=1 is
conservative in the sense that it tends to select an unnecessarily large
model (Shao 1993), and by repeating the data splitting procedure
multiple times, the results are not dependent on a particular split,
which results in less variation in the accuracy measurements (Harrell et
al. 1996). The results were evaluated by looking at the mean correlation
and RMSPE from the 1000 runs (a measure of how well the model
predictions fit the observations) and the standard deviation of these
metrics (a measure of the precision of the predictions). Smaller
variation was taken as an indication of a more stable predictive model.
As expected, these measures coincided fairly well.

Only thirteen models met the criteria regarding p-values, correlation
and Cook's distance (2 HUC models, 10 watershed models and 1 buffer
model). Table 9 lists the criteria used for model evaluation and
includes a summary of the cross-validation results. Definitions of the
variable names used in the table follows; the change in vegetation was
calculated by subtracting historic coverage from current coverage.
Percentages refer to the percentage of the AOI in which that feature is
present.

The final model selection described above were then applied to select
the final group of models for the watershed AOI; there was only one
candidate for the buffer AOI and two candidate for the HUC AOI, all of
which performed adequately in the cross-validation. The models selected
are summarized below (Table 10).

**Yakima Subbasin Chinook Salmon**

Covariance Structure

A number of possible mixed models were evaluated in order to determine
which would be the most appropriate for these particular data. Models
with each of the potential covariates were fit using four different
covariance structures. An autoregressive-moving average model
(ARMA(1,1)), autoregressive covariance structure of order 1 (AR(1)),
compound symmetry (all correlations between years equal) and
independence between years. Thus, for each AOI, there were 16 possible
covariance/random coefficients combinations.

The results of the fitting of the one-variable models were similar for
all AOI's. The models with random intercept and slope, both correlated
and independent, did not fit well. Generally, 10-20% of the models did
not converge, 25% or more of the covariance matrices for the random
components were not positive definite (precluding hypothesis testing of
the parameter estimates), and typically 50% of the models had variance
components that were not different from zero (p \> 0.05). Thus, the
random slope was dropped from consideration.

Of the models with two intercepts, only the models with the AR(1)
covariance structure and the independent observations fit well. For the
ARMA(1,1) model, the gamma parameter was not different from zero (p \>
0.09 in all cases) when 2 random intercepts were fit, and for the
compound symmetry model, in roughly 25% of the models both the reach
variance and the correlation among observations were not different from
zero (p \> 0.49).

There were six potential model structures that produced valid models for
all potential predictor variables: ARMA(1,1) with 1 random intercept
(year), AR(1) with 2 random intercepts (year and reach), AR(1) with 1
random intercept (year), compound symmetry with 1 random intercept
(year), and independent observations with both 1 and 2 random
intercepts. Each of these models was fit to each of the potential
predictor variables and for each AOI, the results ranked by BIC. For
each of the AOIs and for all predictors, the AR(1) model with 2 random
intercepts was selected by the BIC. This structure was used for all
subsequent modeling.

Variable Selection

The first step in the variable selection procedure entailed fitting the
model to each of the potential explanatory variables separately for each
AOI. For the HUC AOI, there was some evidence that 17 might be useful in
explaining the variation in redd density (defined as a p-value \< 0.20);
for the watershed AOI, 18 of the variables met this criteria; for the
buffer AOI, 11 variables were selected. Table 11 identifies the
variables by AOI.

Two variable models were generated by fitting all combinations of these
variables for each AOI. From these, all of the models for which both
p-values were less than 0.10 were selected for three-variable modeling,
as described below. This included 27 HUC models, 35 watershed models and
23 buffer models.

For each of the two-variable models selected above, the remaining
predictors were added singly to form three-variable models. Each of
these was retained only if the p-value for all three fixed effects was
less than 0.10. This resulted in 50 3-variable HUC models, 80 3-variable
watershed models and 75 3-variable buffer models. This procedure was
repeated to select the best set of 4-variable models; each remaining
variable was added to each of the 3-variable models, and those with all
p-values less than 0.10 were retained. The procedure was halted at the
four-variable stage since it was not resulting in much improvement in
BIC, and many of the larger models had collinearity problems (see
below).

At the end of the procedure outlined above, all of the models with
p-values for any of the parameters greater than 0.05 were eliminated.
Almost all of the potential explanatory variables are correlated with
each of the other variables to some degree. Since collinearity is a
problem that will need to be addressed anyway, those models in which any
of the pairwise correlations exceeded 0.6 in absolute value were
eliminated from consideration. The next step was the calculation of the
condition index for each matrix of explanatory variables. The condition
index is the ratio of each singular value to the maximum singular value
and provides a measure of the stability of the solution (Belsey, Kuh &
Welsh 1980). \[The singular values are the square root of the eigen
values\]. Belsey, Kuh and Welsh suggest that condition indices greater
than 10 indicate that collinearity may be severe enough to have an
effect. All models that exceeded this recommendation were eliminated.

Cook's distance measures the shift in the parameter estimates when a
single observation is excluded, and is useful for flagging influential
sites (Cook 1977). This is a fairly standard part of statistical
software fitting least squares models, but has only recently begun to be
applied to mixed models (e.g. Christensen et al. 1992). Since the
explanatory variables are the same for each of the 20 sites, it was
feasible to refit each model 20 times, leaving out one site each time,
and calculate Cook's D. Models with values of Cook's D greater than 1.0
for at least one site were deemed unstable and eliminated from the
analysis. A value of Cook's D between 0.8 and 1.0 indicates a shift to
near the 50^th^ percentile of the confidence ellipsoid around β and is
cause for concern (Cook 1977).

The final model selection criteria was cross-validation, which assessed
the ability of the models to predict redd counts for sites not sampled.
The data were divided into a training set, used to fit the model, and a
validation set, used to assess the fit of the model with data not used
in model fitting, by randomly selecting 10% of the observations for
validation. To assess the fit, the correlation between the values
predicted for the validation set and the measured redd densities was
calculated, as was the root mean squared prediction error (RMSPE). This
was repeated 1000 times for each model, resulting in 1000 correlation
and RMSPE values. This is an external validation technique, and as such
gives a better indication of how the model would perform for sites
without redd count data (Harrell et al. 1996). It is the same idea as
the 'leave-one-out' approach, but using a validation set of size=1 is
conservative in the sense that it tends to select an unnecessarily large
model (Shao 1993), and by repeating the data splitting procedure
multiple times, the results are not dependent on a particular split,
which results in less variation in the accuracy measurements (Harrell et
al. 1996). The results were evaluated by looking at the mean correlation
and RMSPE from the 1000 runs (a measure of how well the model
predictions fit the observations) and the standard deviation of these
metrics (a measure of the precision of the predictions). Smaller
variation was taken as an indication of a more stable predictive model.
As expected, these measures coincided fairly well. Table 12 lists the
criteria used for model evaluation including a summary of the
cross-validation results. Definitions of the variable names used in the
table follows; the change in vegetation was calculated by subtracting
historic coverage from current coverage. Percentages refer to the
percentage of the AOI in which that feature is present.

For the final model selection, the procedure outlined above was applied.
Four models were selected for each AOI. These models are summarized
below (Table 13).

Several predictor variables were created by summing the percentages of
two or more related categories within a given data layer. This was done
both to reduce the pool of potential predictors, and to increase the
number of index reaches with non-zero values for a given predictor. If
there were fewer than three index reaches with non-zero values for a
predictor, then that predictor was eliminated from the pool outright; it
was preferable that most of the index reaches had non-zero values. The
categories combined to form the new predictor variables are listed below
(Table 14). The change in land cover variables were created be
subtracting current coverage from historic coverage. Thus, zero
indicates no change, positive values indicate an increase in coverage,
while negative values represent a decrease from historic conditions. The
open water variable was derived from the land use/land cover data layer
rather than the lithology layer.

Summary of Model Fitting Procedure

Mixed models were utilized to explore the relationships between redd
density and the candidate explanatory variables. Mixed models are those
that have both fixed effects and random effects. One advantage in using
mixed models is that they can accommodate correlated responses and
heterogeneous variances. Since these data were collected over time, it
is likely that the measurements within each site are serially
autocorrelated. Using a mixed model, this autocorrelation can be modeled
and the fit of various covariance structures tested. The Bayesian
Information Criterion (BIC) was used to select the most appropriate
covariance structure, as well as to compare models (Littell et al.
1996). The BIC is essentially twice the log likelihood value (a measure
of well the model fits) plus a penalty for the number of parameters
estimated. Smaller values indicate a better fit. All statistical
analyses were generated using SAS/STAT software, Version 8.2 of the SAS
System for Windows. Copyright © 1999-2001 SAS Institute Inc. SAS and all
other SAS Institute Inc. product or service names are registered
trademarks or trademarks of SAS Institute Inc., Cary, NC, USA. Details
regarding the selection of an appropriate covariance structure for each
subbasin are given in the sections corresponding to each subbasin.

Four random coefficients models were evaluated. The slope and intercept
were initially modeled as random effects. In this scenario, the slope
and intercept for each year is allowed to vary randomly from an average
slope and intercept. These are also referred to as hierarchical linear
models (Byrk & Raudenbush 1992). The appropriateness of these models was
assessed by noting whether the variance of the slope and/or intercept
was significantly different from zero, which was assessed with Wald
tests (Casela & Berger 1990), as well as by comparing the BICs of the
various models. The intercept was also modeled as a random function of
both year and reach, i.e. the observed intercept depends on both year
and location. Lastly, the intercept was allowed to vary randomly by year
only.

There were three different areas of influence (AOI) used for the
calculation of the covariate data. The watershed AOI included all area
in the watershed above the index reach, the HUC AOI included the area in
each 6^th^ field HUC adjacent to the index reach, and the buffer AOI
included a 500-meter strip an either side of the index reach. Models
were fit to the covariates from each AOI independently, and the best
models from each are compared at the end of the model selection process.

At the end of the variable selection process, there were generally
upwards of 25 candidate models. The initial variable selection process
is described in each subbasin section. The remainder of the selection
process primarily identified those models with undesirable properties;
these models were subsequently eliminated. Among the criteria and
methods used to evaluate models were high pairwise correlation, large
Cook's distance, multicollinearity and a cross-validation procedure.
Details about these procedures are also included in the report sections
for each subbasin.

**Final model selection**

The final model selection procedure involved a ranking of the remaining
models by several criteria. All of the remaining models appeared to
perform well; the goal was to identify at most four models for each AOI
within a given subbasin that could be considered best. A secondary
consideration included the selection of models with a diversity of
predictor variables. Selection occurred in the following sequence, with
the process halted as soon as the pool of models dropped to four or
fewer. In most cases, it was not necessary to proceed through the entire
list before the pool of models was decreased sufficiently.

1\. Rank all remaining models by BIC.

2\. Eliminate models where either the correlation between the observed
and predicted response in the cross-validation is less than 99% of the
correlation from the best model, or the root mean squared prediction
error (RMSPE) is more than 2% greater than the model with the lowest
prediction error.

3\. Eliminate models in which the standard deviation of either the
correlation or the RMSPE from the cross-validation is more than 10%
greater than the model with the smallest standard deviation.

4\. If two models are identical except that one includes a current
habitat variable and the other includes the change in that variable,
eliminate the model with the largest BIC.

5\. Eliminate models where the elimination of one or more predictors
increases the BIC.

6\. Eliminate models in which all predictors are non-habitat variables.

7\. Starting with the model with the largest BIC, eliminate models where
all of the predictor variables are included in at least one model with a
lower BIC.

8\. Identify jumps in BIC that separate the remaining models into groups
and select whole groups that result in four or fewer models being
selected. If the models fall into groups such that all one-variable
models are in one group and larger models in another with a larger BIC,
then select models from each of these groups.

The final group of models is summarized below (Table 15). Further
details about the models, including parameter estimates and significance
levels, are presented in the subbasin sections of this document.
Predictions of redd density for each subbasin could be made by averaging
the predictions from several models. The BIC can be used to compare
models from the various AOIs within a subbasin, though cannot compare
models from different subbasins because in order to do so, the set of
response variables must be identical.

**References**

Belsey, D.A., Kuh, E. and Welsch, R.E. 1982. Regression Diagnostics:
Identifying Influential Data and Sources of Collinearity. John Wiley,
New York. 292 pp.

Byrk, A.S. and Raudenbush, S.W. 1992. Hierarchical Linear Models. Sage
Publications, Newberry Park, CA. 265 pp.

Casella, G. and Berger, R.L. 1990. Statistical Inference. Duxbury Press,
Belmont, CA 650 pp.

Christensen, R., Pearson, L.M. and Johnson, W. 1992. Case-Deletion
Diagnostics for Mixed Models. Technometrics 34:38-45.

Cook, R.D. 1977. Detection of Influential Observation in Linear
Regression. Technometrics 19:15-18.

Daly, C., Neilson, R. P. & Phillips, D. L. 1994. A
statistical-topographic model for mapping climatological precipitation
over mountainous terrain. Journal of Applied Meteorology. 33:140-158.

Harrell, F.E. Jr., Lee, K.L. and Mark, D.B. 1996. Multivariable
Prognostic Models: Issues in Developing Models, Evaluating Assumptions
and Adequacy, and Measuring and Reducing Errors. Statistics in Medicine
15:361-387.

ICBEMP (Interior Columbia Basin Ecosystem Management Project) Spatial
Data. 1999. Portland, OR: ICBEMP, October 1999. \[July 1999\]. URL:
\<http://www.icbemp.gov/\>.

Johnson, D.H. and T.A. O\'Neil. 2001. *Wildlife-Habitat Relationships in
Oregon and Washington*. Oregon State University Press. Eugene, OR. 768
p.

Littell, R.C., Milliken, G.A., Stroup, W.W. and Wolfinger, R.D. 1996.
SAS® System for Mixed Models. SAS Institute Inc., Cary, NC. 633 pp.

Northwest Habitat Institute (NHI) Spatial Data. 2000. Eugene, OR: NHI,
August 1999. \[July 1999\]. URL: \<http://www.nwhi.org\>.

Shao, J. 1993. Linear Model Selection by Cross-Validation. Journal of
the American Statistical Association 88:486-494.

StreamNet Spatial Data. 2002. Current anadromous chinook salmon and
steelhead distribution at a 1:100,000 scale. Pacific States Marine
Fisheries Commission. URL: \<http://www.streamnet.org/\>.

Thornton, P. E., Running, S. W. & White, M. A. 1997. Generating surfaces
of daily meteorological variables over large regions of complex terrain.
Journal of Hydrology 190:214-251.

Table 15. Summary of Model Selection Process.

  --------------- ----------- -------------------------------------- ---------
  **Subbasin**    **AOI**     **Model**                              **BIC**

  John Day        Buffer      sheep grazing                          2122.2
  (steelhead)                                                        

                              precipitation                          2126.7

                              AOI area + ∆ conifer forest            2136.3

                              precipitation + ponderosa pine         2138.3

                  Watershed   ∆ alpine veg.                          2119.1

                              sheep grazing                          2120.8

                              cattle grazing                         2124.3

                              precipitation                          2127.3

                  HUC         successional forest                    2117.5

                              ∆ wetlands                             2119.3

                              ∆ alpine veg.                          2119.5

                              successional forest + ∆ conifer        2130.8
                              forest + screens                       

  John Day        Buffer      cattle grazing                         516.83
  (chinook)                                                          

                  Watershed   cattle grazing                         517.52

                              sedimentary geology + urbanization     514.95

  Yakima          Buffer      ch. slope + riparian                   696.37
  (chinook)                                                          

                              ch. slope + riparian + successional    699.34
                              forest                                 

                              ch. slope + riparian + succ. forest +  705.39
                              wetlands                               

                              mines + riparian                       707.02

                  Watershed   ch. slope + riparian                   693.88

                              dams                                   696.57

                              agriculture + ch. slope                698.62

                              ch. slope + riparian + ∆ alpine        698.92

                  HUC         ch. slope + riparian                   699.31

                              ch. slope + agriculture                704.27

                              ch. slope + cattle grazing             704.79

                              ch. slope + dams + alpine veg. + ∆     705.03
                              arid veg.                              

  Wenachee        Buffer      alluvium + open water                  552.74
  (chinook)                                                          

                  Watershed   ch. slope + dams + ∆ open water        519.77

                              ch. slope + dams + ∆ ponderosa pine    523.82

                              alluvium + mines                       531.33

                              hill slope + ∆ successional forest     531.54

                  HUC         hill slope + mines                     522.06

                              mines + ∆ open water                   530.33
  --------------- ----------- -------------------------------------- ---------

Table 14. Predictor variables for geology and land use and land cover
(LULC) that were created by combining categories within data layers.
Note: not all categories were present for all subbasins or all AOIs
within a subbasin.

+------------------+---------------------------------------------------+
| Predictor        | Original Categories                               |
| Variable         |                                                   |
+------------------+---------------------------------------------------+
| Igneous          | -   Calc-alkaline Intrusive                       |
| (Geology)        |                                                   |
|                  | -   Calc-alkaline Meta-volcanic                   |
|                  |                                                   |
|                  | -   Calc-alkaline Volcanoclastic                  |
|                  |                                                   |
|                  | -   Felsic Pyroclastic                            |
|                  |                                                   |
|                  | -   Felsic Volcanic Flow                          |
|                  |                                                   |
|                  | -   Mafic Intrusive                               |
|                  |                                                   |
|                  | -   Mafic Meta-volcanic                           |
|                  |                                                   |
|                  | -   Mafic Volcanic Flow                           |
|                  |                                                   |
|                  | -   Tuff                                          |
|                  |                                                   |
|                  | -   Ultramafic                                    |
+------------------+---------------------------------------------------+
| Sedimentary      | -   Sandstone                                     |
| (Geology)        |                                                   |
|                  | -   Siltstone                                     |
|                  |                                                   |
|                  | -   Mixed Eugeosynclinal                          |
+------------------+---------------------------------------------------+
| Metamorphic      | -   Granitic Gneiss                               |
| (Geology)        |                                                   |
|                  | -   Interlayered Meta-sedimentary                 |
|                  |                                                   |
|                  | -   Mafic Gneiss                                  |
|                  |                                                   |
|                  | -   Meta-sedimentary Phyllite and Schist          |
|                  |                                                   |
|                  | -   Argillite and Slate                           |
|                  |                                                   |
|                  | -   Carbonate                                     |
|                  |                                                   |
|                  | -   Shale And Mudstone                            |
+------------------+---------------------------------------------------+
| Conifer Forest   | -   Montane Mixed Conifer Forest                  |
| (LULC)           |                                                   |
|                  | -   Eastside (Interior) Mixed Conifer Forest      |
|                  |                                                   |
|                  | -   Lodgepole Pine Forest and Woodlands           |
+------------------+---------------------------------------------------+
|                  | -                                                 |
+------------------+---------------------------------------------------+
| Alpine           | -   Subalpine Parklands                           |
| Vegetation       |                                                   |
| (LULC)           | -   Alpine Grasslands and Shrublands              |
+------------------+---------------------------------------------------+
| Arid Vegetation  | -   Western Juniper and Mountain Mahogany         |
| (LULC)           |     Woodlands                                     |
|                  |                                                   |
|                  | -   Eastside (Interior) Grasslands                |
|                  |                                                   |
|                  | -   Shrub-steppe                                  |
|                  |                                                   |
|                  | -   Dwarf Shrub-steppe                            |
|                  |                                                   |
|                  | -   Desert Playa and Salt Scrub                   |
+------------------+---------------------------------------------------+
| Wetland          | -   Herbaceous Wetlands                           |
| Vegetation       |                                                   |
| (LULC)           | -   Montane Coniferous Wetlands                   |
+------------------+---------------------------------------------------+

Table 1: Geographic information system (GIS) datalayers used in habitat
analysis. All data layers were generated by other entities (such as
federal state, and academic institutions), with the exception of
hillslope, channel slope, and stream junctions, which we generated
ourselves. The "k" after all of the map scales is an abbreviation for
"kilo" or 1,000. Therefore, a map scale of 1:100k is equal to 1:100,000.
"Gridcell Size" is the size of each individual pixel or gridcell for
raster based datalayers. Gridcell size is separated from map scale by a
horizontal dotted line for clarity.

+-----------+---------+-----------------------------------------------+
| Datalayer | [Map    | Description                                   |
|           | Scal    |                                               |
|           | e]{.und |                                               |
|           | erline} |                                               |
|           |         |                                               |
|           | G       |                                               |
|           | ridcell |                                               |
|           | Size    |                                               |
+-----------+---------+-----------------------------------------------+
| Hillslope | 1:24k   | Hillslope gradient generated from USGS 30 m   |
|           |         | Digital Elevation Model (DEM), using          |
|           | \-      | ARC/INFO. Calculated the slope of every 30 m  |
|           | \-\-\-\ | gridcell in the DEM. Hillslope for any given  |
|           | -\-\-\- | index reach is then calculated by summing all |
|           | \-\-\-- | of the 30 m DEM gridcells contained in any    |
|           |         | index reaches' associated watershed or reach  |
|           | 30 m    | scale area of influence that has a slope less |
|           |         | than 6% (for steelhead) or 1.5% (for          |
|           |         | chinook).                                     |
+-----------+---------+-----------------------------------------------+
| Channel   | 1:24k   | Calculated from USGS 1:24k 30 m digital       |
| Slope     |         | elevation models (DEM). Defined as rise       |
|           | \-      | (upstream elevation minus downstream          |
|           | \-\-\-\ | elevation of index reach) over run (river km  |
|           | -\-\-\- | length of index reach) multiplied by 100      |
|           | \-\-\-- |                                               |
|           |         |                                               |
|           | 30 m    |                                               |
+-----------+---------+-----------------------------------------------+
| Stream    | 1:24k   | Used ARC/INFO to calculate the density of     |
| Junctions |         | stream junctions for each of the three areas  |
|           | \-      | of influence using the USGS 1:24k stream      |
|           | \-\-\-\ | network data layer. Used for steelhead only   |
|           | -\-\-\- | in the John Day subbasin.                     |
|           | \-\-\-- |                                               |
|           |         |                                               |
|           | N/A     |                                               |
+-----------+---------+-----------------------------------------------+
| Air       | N/A     | Mean annual temperature for 1989 (Thornton et |
| Te        |         | al., 1997, acquired from ICBEMP, 1999). 1989  |
| mperature | \-      | was considered a "normal" year. Expressed as  |
|           | \-\-\-\ | an area weighted mean, where each gridcell is |
|           | -\-\-\- | multiplied by its temperature value, summed   |
|           | \-\-\-- | over all gridcells, and then divided by the   |
|           |         | appropriate area of influence (reach, HUC6 or |
|           | 2 km    | watershed).                                   |
+-----------+---------+-----------------------------------------------+
| Prec      | N/A     | Total annual precipitation for 1989,          |
| ipitation |         | considered a "normal" year from Precipitation |
|           | \-      | Elevation Regressions on Independent Slopes   |
|           | \-\-\-\ | Model (PRISM) (Daly et al. 1994), acquired    |
|           | -\-\-\- | from the Interior Columbia Basin Ecosystem    |
|           | \-\-\-- | Management Project (ICBEMP) in 1999.          |
|           |         | Expressed as an area weighted mean, where     |
|           | 500 m   | each gridcell is multiplied by its            |
|           |         | precipitation value, summed over all          |
|           |         | gridcells, and then divided by the            |
|           |         | corresponding area of influence.              |
+-----------+---------+-----------------------------------------------+
| Major     | 1:500k  | USGS classification of geologic map units     |
|           |         | according to major lithology.                 |
| lithology | \-      |                                               |
|           | \-\-\-\ |                                               |
|           | -\-\-\- |                                               |
|           | \-\-\-- |                                               |
|           |         |                                               |
|           | N/A     |                                               |
+-----------+---------+-----------------------------------------------+
| Land Use  | 1:100k  | Change in land use and land cover calculated  |
| and Land  | & 1000k | using Northwest Habitat Institute (2000) GIS  |
| Cover     |         | data layers of historical (ca. 1850) and      |
| Change    | \-      | current (ca. 2000) wildlife-habitat types.    |
|           | \-\-\-\ | Raster historical map was "subtracted" from   |
|           | -\-\-\- | the current map using ESRI ARC/INFO in order  |
|           | \-\-\-- | to calculate percent change in each of the    |
|           |         | land use and land cover categories.           |
|           | 25 m &  | Wildlife-habitat types maps originally        |
|           | 1 km    | published in Johnson and O\'Neil (2001)       |
+-----------+---------+-----------------------------------------------+
| W         | 1:24 -  | Designated wilderness areas on Forest         |
| ilderness | 1:500k  | Service, Bureau of Land Management and        |
|           |         | National Park lands. Acquired from and        |
|           | \-      | compiled by ICBEMP (1999).                    |
|           | \-\-\-\ |                                               |
|           | -\-\-\- |                                               |
|           | \-\-\-- |                                               |
|           |         |                                               |
|           | N/A     |                                               |
+-----------+---------+-----------------------------------------------+
| Livestock | 1:24 -  | Livestock grazing allotments. U.S. Forest     |
|           | 1:126k  | Service, and BLM delineations of areas where  |
|           |         | livestock can graze. Used only on sheep and   |
|           | \-      | cattle categories. Acquired from ICBEMP       |
|           | \-\-\-\ | (1999).                                       |
|           | -\-\-\- |                                               |
|           | \-\-\-- |                                               |
|           |         |                                               |
|           | N/A     |                                               |
+-----------+---------+-----------------------------------------------+
| Roads     | 1:100k  | Polyline representation of road networks.     |
|           |         | Expressed as linear km of road per unit area  |
|           | \-      | of appropriate AOI. Source material includes  |
|           | \-\-\-\ | USGS DLG, USGS road/street maps, field        |
|           | -\-\-\- | compilation, survey data, and Census Bureau   |
|           | \-\-\-- | TIGER/Line files as provided by Wessex Corp.  |
|           |         | Acquired from and compiled by ICBEMP (1999).  |
|           | N/A     |                                               |
+-----------+---------+-----------------------------------------------+
| Mines     | 1:24 -  | Mining related hazard potential sites (ICBEMP |
|           | 1:100k  | 1999). Compiled by ICBEMP (1999) from 7.5\' & |
|           |         | 15\' USGS paper quads (site investigation     |
|           | \-      | field maps), published and unpublished        |
|           | \-\-\-\ | literature, mining company records, and       |
|           | -\-\-\- | public land records.                          |
|           | \-\-\-- |                                               |
|           |         |                                               |
|           | N/A     |                                               |
+-----------+---------+-----------------------------------------------+
| Dams      | N/A     | Dams with greater than 50 acre feet storage   |
|           |         | capacity. Acquired from ICBEMP (1999) and     |
|           |         | derived from the National Inventory of Dams,  |
|           |         | U.S. Army Corps of Engineers, and State Water |
|           |         | Resource Department Dam Safety Divisions.     |
+-----------+---------+-----------------------------------------------+
| D         | 1:100k  | U.S. Forest Service database of water         |
| iversions |         | irrigation diversions, screens, ladders, and  |
|           | \-      | pumps, supplemented by BPA, and State Fish    |
|           | \-\-\-\ | and Game data. Only used diversions for our   |
|           | -\-\-\- | analyses (screened, unscreened, and unknown). |
|           | \-\-\-- | Acquired from and compiled by ICBEMP (1999).  |
|           |         |                                               |
|           | N/A     |                                               |
+-----------+---------+-----------------------------------------------+
| Streams   | 1:100k  | Polyline representation of the stream         |
|           |         | networks, generated by USGS.                  |
|           | \-      |                                               |
|           | \-\-\-\ |                                               |
|           | -\-\-\- |                                               |
|           | \-\-\-- |                                               |
|           |         |                                               |
|           | N/A     |                                               |
+-----------+---------+-----------------------------------------------+
| Watershed | 1:24k   | Polygon representation of total area upslope  |
| AOI       |         | of the downstream end of any given index      |
|           | \-      | reach. Generated from a USGS 30 m DEM.        |
|           | \-\-\-\ |                                               |
|           | -\-\-\- |                                               |
|           | \-\-\-- |                                               |
|           |         |                                               |
|           | N/A     |                                               |
+-----------+---------+-----------------------------------------------+
| HUC6 AOI  | 1:24k   | Polygon representation of all 6^th^ field     |
|           |         | hydrologic units (HU's) that touch a given    |
|           | \-      | index reach. Generated using ICBEMP (1999)    |
|           | \-\-\-\ | sixth field hydrologic units (HU's).          |
|           | -\-\-\- |                                               |
|           | \-\-\-- |                                               |
|           |         |                                               |
|           | N/A     |                                               |
+-----------+---------+-----------------------------------------------+
| Reach AOI | 1:24k   | Polygon representation of area within 500 m   |
|           |         | of any given index reach. Generated around    |
|           | \-      | each index reach in ESRI ARC/INFO using       |
|           | \-\-\-\ | BUFFER command.                               |
|           | -\-\-\- |                                               |
|           | \-\-\-- |                                               |
|           |         |                                               |
|           | N/A     |                                               |
+-----------+---------+-----------------------------------------------+

Table 2. Summary table of subbasins and species of salmon for which SWAM
was used.

  ------------------- ---------------- ------------------ -----------------
                      **John Day**     **Wenatchee**      **Yakima**

  **Chinook**         X                X                  X

  **Steelhead**       X                                   
  ------------------- ---------------- ------------------ -----------------

Table 3. Predictor Variables Selected for 2-variable All-subsets
Modeling

  -----------------------------------------------------------------------
  Variable            HUC AOI         Watershed AOI     Buffer AOI
  ------------------- --------------- ----------------- -----------------
  Agriculture                         X                 

  Alpine Vegetation   X               X                 

  ∆ Alpine Vegetation X               X                 

  Area                X                                 X

  1989 Average Temp   X               X                 X

  Cattle              X               X                 X

  Successional Forest X               X                 

  Conifer                                               X

  ∆ Conifer           X               X                 X

  Hill Slope          X                                 

  Ponderosa Pine      X               X                 X

  ∆ Ponderosa Pine    X               X                 

  Precipitation       X               X                 X

  Sheep               X               X                 X

  Open Water                          X                 

  ∆ Wetland           X                                 
  Vegetation                                            

  Wetland Vegetation  X               X                 
  -----------------------------------------------------------------------

Table 4a. Model Evaluation Results for HUC AOI.

+-----------------+------+----+-----+------+------+-----+-----+-----+
| MODEL           | BIC  | m  | max | max  | corr | s   | RM  | std |
|                 |      | ax |     |      |      | tdo | SPE |     |
|                 |      |    | c   | p-v  | (o   | rr) |     | RM  |
|                 |      | \| | ond | alue | bs,p |     |     | SPE |
|                 |      | co | \#  |      | red) |     |     |     |
|                 |      | rr |     |      |      |     |     |     |
|                 |      | \| |     |      |      |     |     |     |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| success         | 211  | NA | NA  | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
|                 | 7.52 |    |     | 0236 | 6949 | 573 | 612 | 738 |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| wetlands        | 211  | NA | NA  | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
|                 | 8.72 |    |     | 0140 | 6873 | 595 | 697 | 737 |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| wetland2        | 211  | NA | NA  | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
|                 | 9.27 |    |     | 0195 | 6902 | 584 | 651 | 730 |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| alpine          | 211  | NA | NA  | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
|                 | 9.47 |    |     | 0043 | 6896 | 594 | 732 | 759 |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| alpine2         | 211  | NA | NA  | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
|                 | 9.49 |    |     | 0043 | 6899 | 576 | 691 | 748 |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| sheep           | 212  | NA | NA  | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
|                 | 1.54 |    |     | 0020 | 6932 | 567 | 673 | 721 |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| cattle          | 212  | NA | NA  | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
|                 | 3.57 |    |     | 0046 | 6853 | 587 | 727 | 760 |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| precip          | 212  | NA | NA  | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
|                 | 7.55 |    |     | 0100 | 6884 | 575 | 695 | 754 |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| area            | 212  | NA | NA  | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
|                 | 8.06 |    |     | 0256 | 6913 | 565 | 695 | 729 |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| success-c       | 213  | 0  | 3   | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
| onifer2-screens | 0.78 | .3 | .36 | 0134 | 6900 | 568 | 682 | 723 |
|                 |      | 83 |     |      |      |     |     |     |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| area-success    | 213  | 0  | 5   | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
|                 | 1.78 | .0 | .06 | 0245 | 6955 | 600 | 630 | 739 |
|                 |      | 43 |     |      |      |     |     |     |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| succes          | 213  | 0  | 3   | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
| s-pine2-screens | 2.08 | .2 | .30 | 0331 | 6899 | 584 | 665 | 756 |
|                 |      | 53 |     |      |      |     |     |     |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| success-pine2-  | 214  | 0  | 3   | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
| screens-alpine2 | 0.32 | .2 | .44 | 0318 | 6911 | 590 | 712 | 748 |
|                 |      | 53 |     |      |      |     |     |     |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| success-pine2   | 214  | 0  | 3   | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
| -screens-alpine | 0.99 | .2 | .45 | 0422 | 6924 | 580 | 643 | 740 |
|                 |      | 53 |     |      |      |     |     |     |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| area-conifer2   | 214  | 0  | 5   | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
|                 | 1.11 | .0 | .25 | 0467 | 6851 | 607 | 718 | 766 |
|                 |      | 62 |     |      |      |     |     |     |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| success-pine    | 214  | 0  | 3   | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
| 2-screens-sheep | 2.90 | .2 | .41 | 0381 | 6877 | 622 | 726 | 810 |
|                 |      | 53 |     |      |      |     |     |     |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| suc             | 214  | 0  | 5   | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
| cess-area-pine2 | 5.14 | .0 | .86 | 0487 | 6875 | 592 | 721 | 783 |
|                 |      | 43 |     |      |      |     |     |     |
+-----------------+------+----+-----+------+------+-----+-----+-----+
| success-conife  | 214  | 0  | 6   | 0.   | 0.   | 0.0 | 0.4 | 0.0 |
| r2-area-screens | 5.60 | .3 | .31 | 0320 | 6875 | 592 | 721 | 783 |
|                 |      | 83 |     |      |      |     |     |     |
+-----------------+------+----+-----+------+------+-----+-----+-----+

Table 4b. Model Evaluation Results for Watershed AOI.

+-----------------+-----+----+-----+-----+-------+-----+------+------+
| COL1            | BIC | m  | max | max | corr  | std | R    | std  |
|                 |     | ax |     |     |       |     | MSPE |      |
|                 |     |    | c   | p   | (obs, | c   |      | R    |
|                 |     | \| | ond | -va | pred) | orr |      | MSPE |
|                 |     | co | \#  | lue |       |     |      |      |
|                 |     | rr |     |     |       |     |      |      |
|                 |     | \| |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| alpine2         | 2   | NA | NA  | 0.0 | 0.693 | 0.0 | 0    | 0.   |
|                 | 119 |    |     | 037 |       | 591 | .465 | 0751 |
|                 | .09 |    |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| alpine          | 2   | NA | NA  | 0.0 | 0.692 | 0.0 | 0    | 0.   |
|                 | 119 |    |     | 046 |       | 597 | .468 | 0737 |
|                 | .50 |    |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| sheep           | 2   | NA | NA  | 0.0 | 0.695 | 0.0 | 0    | 0.   |
|                 | 120 |    |     | 011 |       | 550 | .463 | 0713 |
|                 | .77 |    |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| cattle          | 2   | NA | NA  | 0.0 | 0.692 | 0.0 | 0    | 0.   |
|                 | 124 |    |     | 067 |       | 556 | .470 | 0731 |
|                 | .27 |    |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| conifer2        | 2   | NA | NA  | 0.0 | 0.690 | 0.0 | 0    | 0.   |
|                 | 125 |    |     | 232 |       | 580 | .473 | 0756 |
|                 | .96 |    |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| pine2           | 2   | NA | NA  | 0.0 | 0.688 | 0.0 | 0    | 0.   |
|                 | 126 |    |     | 216 |       | 604 | .473 | 0765 |
|                 | .02 |    |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| success-sheep   | 2   | 0  | 2   | 0.0 | 0.692 | 0.0 | 0    | 0.   |
|                 | 126 | .1 | .01 | 355 |       | 604 | .465 | 0758 |
|                 | .02 | 12 |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| precip          | 2   | NA | NA  | 0.0 | 0.692 | 0.0 | 0    | 0.   |
|                 | 127 |    |     | 102 |       | 537 | .465 | 0725 |
|                 | .31 |    |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| success-ag      | 2   | 0  | 5   | 0.0 | 0.690 | 0.0 | 0    | 0.   |
| -sheep-ch_slope | 128 | .1 | .64 | 271 |       | 585 | .473 | 0754 |
|                 | .62 | 44 |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| ag-             | 2   | 0  | 6   | 0.0 | 0.689 | 0.0 | 0    | 0.   |
| avgtemp-success | 133 | .3 | .22 | 487 |       | 603 | .472 | 0764 |
|                 | .50 | 74 |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| sediment        | 2   | 0  | 5   | 0.0 | 0.688 | 0.0 | 0    | 0.   |
| -sheep-success- | 133 | .1 | .95 | 316 |       | 606 | .472 | 0785 |
|                 | .68 | 44 |     |     |       |     |      |      |
| ch_slope-ag     |     |    |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| success         | 2   | 0  | 2   | 0.0 | 0.690 | 0.0 | 0    | 0.   |
| -sheep-sediment | 138 | .1 | .29 | 468 |       | 610 | .467 | 0760 |
|                 | .38 | 17 |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+
| success-ag      | 2   | 0  | 2   | 0.0 | 0.691 | 0.0 | 0    | 0.   |
| -sheep-sediment | 142 | .1 | .64 | 490 |       | 591 | .467 | 0777 |
|                 | .79 | 17 |     |     |       |     |      |      |
+-----------------+-----+----+-----+-----+-------+-----+------+------+

Table 4c. Model Evaluation Results for Buffer AOI.

+------------------+-----+----+----+-----+-------+-----+-----+-----+
| COL1             | BIC | m  | m  | max | corr  | std | RM  | std |
|                  |     | ax | ax |     |       |     | SEP |     |
|                  |     |    |    | p   | (obs, | c   |     | RM  |
|                  |     | \| | c  | -va | pred) | orr |     | SEP |
|                  |     | co | on | lue |       |     |     |     |
|                  |     | rr | d# |     |       |     |     |     |
|                  |     | \| |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+
| sheep            | 2   | NA |    | 0.0 | 0.691 | 0.0 | 0.  | 0.0 |
|                  | 122 |    |    | 027 |       | 588 | 467 | 742 |
|                  | .19 |    |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+
| precip           | 2   | NA |    | 0.0 | 0.690 | 0.0 | 0.  | 0.0 |
|                  | 126 |    |    | 059 |       | 591 | 471 | 751 |
|                  | .68 |    |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+
| area-sheep       | 2   | 0  | 4. | 0.0 | 0.691 | 0.0 | 0.  | 0.0 |
|                  | 130 | .0 | 93 | 360 |       | 602 | 470 | 763 |
|                  | .53 | 63 |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+
| area-c           | 2   | 0  | 8. | 0.0 | 0.685 | 0.0 | 0.  | 0.0 |
| h_slope-conifer2 | 131 | .4 | 69 | 051 |       | 596 | 476 | 737 |
|                  | .85 | 09 |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+
| area             | 2   | 0  | 9. | 0.0 | 0.689 | 0.0 | 0.  | 0.0 |
| -ch_slope-cattle | 133 | .4 | 30 | 145 |       | 618 | 471 | 784 |
|                  | .23 | 09 |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+
| area-conifer2    | 2   | 0  | 5. | 0.0 | 0.690 | 0.0 | 0.  | 0.0 |
|                  | 136 | .1 | 15 | 354 |       | 574 | 470 | 708 |
|                  | .32 | 60 |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+
| area-cattle      | 2   | 0  | 6. | 0.0 | 0.688 | 0.0 | 0.  | 0.0 |
|                  | 136 | .0 | 05 | 380 |       | 606 | 471 | 758 |
|                  | .60 | 60 |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+
| pine-precip      | 2   | 0  | 9. | 0.0 | 0.692 | 0.0 | 0.  | 0.0 |
|                  | 138 | .2 | 72 | 134 |       | 601 | 466 | 763 |
|                  | .28 | 84 |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+
| area-ch_slop     | 2   | 0  | 8. | 0.0 | 0.690 | 0.0 | 0.  | 0.0 |
| e-conifer2-sheep | 143 | .4 | 79 | 280 |       | 592 | 472 | 780 |
|                  | .14 | 09 |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+
| area-conife      | 2   | 0  | 9. | 0.0 | 0.689 | 0.0 | 0.  | 0.0 |
| r-pine2-ch_slope | 149 | .4 | 67 | 494 |       | 582 | 472 | 736 |
|                  | .11 | 09 |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+
| ar               | 2   | 0  | 6. | 0.0 | 0.691 | 0.0 | 0.  | 0.0 |
| ea-conifer-pine2 | 150 | .3 | 30 | 402 |       | 583 | 466 | 755 |
|                  | .56 | 95 |    |     |       |     |     |     |
+------------------+-----+----+----+-----+-------+-----+-----+-----+

Table 4d. Definitions of the variable names used above.

  ------------ ----------------------------------------------------------
  ag           \% Agriculture

  alpine       \% Alpine & Subalpine Vegetation

  alpine2      Change in % Alpine/Subalpine Vegetation

  avgtemp      1989 Average Temperature (° C)

  cattle       \% Cattle Grazing

  ch_slope     Channel Gradient

  conifer      \% Mixed Conifer & Lodgepole Pine Forest

  conifer2     change in % Mixed Conifer & Lodgepole Pine Forest

  pine         \% Ponderosa Pine

  pine2        Change in % Ponderosa Pine

  precip       Precipitation (mm)

  screens      Density of Screened Diversions

  sediment     \% Sedimentary Geology

  sheep        \% Sheep Grazing

  success      \% Successional Forest

  wetlands     \% Wetland Vegetation

  wetland2     Change in % Wetlands
  ------------ ----------------------------------------------------------

**Table 5a. Summary of Models for Buffer AOI.**

  ---------- ------------------ --------- -------- ----- ------- ----------
             Variable           Coeff.    se       df    t       p-value

  Model 1    Intercept          1.056     0.094    41    11.21   \< 0.0001

             Sheep Grazing      -0.0074   0.0025   892   -3.00   0.003

  Model 2    Intercept          1.679     0.257    41    6.53    \<.0001

             Precipitation      -0.0013   0.0005   892   -2.76   0.006

  Model 3    Intercept          1.259     0.157    41    8.04    \< 0.0001

             Area               -0.052    0.022    891   -2.32   0.020

             ∆ Conifer Forest   0.0049    0.0023   891   2.11    0.035

  Model 4    Intercept          1.604     0.246    41    6.51    \< 0.0001

             Precipitation      -0.0015   0.0005   891   -3.34   0.0009

             Ponderosa Pine     0.0051    0.0020   891   2.41    0.013
  ---------- ------------------ --------- -------- ----- ------- ----------

**Table 5b. Summary of Models for Watershed AOI.**

  ---------- ---------------- ---------- ---------- ----- ------- ----------
             Variable         Coeff.     se         df    t       p-value

  Model 1    Intercept        1.059      0.0949     41    11.17   \< 0.0001

             ∆ Alpine Veg.    -0.042     0.0146     892   -2.91   0.004

  Model 2    Intercept        1.067      0.0941     41    11.34   \< 0.0001

             Sheep Grazing    -0.0078    0.00239    892   -3.28   0.001

  Model 3    Intercept        0.629      0.171      41    3.67    0.0007

             Cattle Grazing   0.00496    0.00183    892   2.72    0.007

  Model 4    Intercept        1.853      0.338      41    5.48    \< 0.0001

             Precipitation    -0.0014    0.00055    892   -2.57   0.010
  ---------- ---------------- ---------- ---------- ----- ------- ----------

**Table 5c. Summary of Models for HUC AOI.**

  --------- -------------------- -------- -------- ----- ------- ----------
            Variable             Coeff.   se       df    t       p-value

  Model 1   Intercept            1.122    0.106    41    10.63   \< 0.0001

            Successional Forest  -0.348   0.153    892   -2.27   0.024

  Model 2   Intercept            1.087    0.099    41    10.98   \< 0.0001

            ∆ Wetlands           -0.130   0.055    892   -2.34   0.020

  Model 3   Intercept            1.071    0.095    41    11.23   \< 0.0001

            ∆ Alpine Veg.        -0.040   0.014    892   -2.86   0.004

  Model 4   Intercept            1.052    0.122    41    8.66    \< 0.0001

            Successional Forest  -0.425   0.147    890   -2.90   0.004

            ∆ Conifer Forest     0.0077   0.0029   890   2.67    0.008

            Diversion Screens    -2.568   1.036    890   -2.48   0.013
  --------- -------------------- -------- -------- ----- ------- ----------

Table 6a. Model Evaluation Results.

+---------+-------------+------+------+-------+------+------+------+
| AOI     | Model       | BIC  | max  | Corr  | std  | R    | std  |
|         |             |      |      |       |      | MSPE |      |
|         |             |      | p-v  |       | corr |      | R    |
|         |             |      | alue |       |      |      | MSPE |
+---------+-------------+------+------+-------+------+------+------+
| Buffer  | Cattle      | 51   | 0.   | 0.754 | 0.   | 0    | 0.   |
|         |             | 6.83 | 0127 |       | 1038 | .577 | 1032 |
+---------+-------------+------+------+-------+------+------+------+
| Wa      | Cattle      | 51   | 0.   | 0.761 | 0.   | 0    | 0.   |
| tershed |             | 7.52 | 0330 |       | 0936 | .570 | 0977 |
+---------+-------------+------+------+-------+------+------+------+
| Wa      | sedime      | 51   | 0.   | 0.756 | 0.   | 0    | 0.   |
| tershed | ntary-urban | 4.95 | 0303 |       | 0975 | .574 | 1025 |
+---------+-------------+------+------+-------+------+------+------+

Table 6b. Definitions of the variable names used above.

  ---------------------- ------------------------------------------------
  Cattle                 \% cattle grazing

  sedimentary            \% sedimentary geology

  Urban                  \% urbanization/development
  ---------------------- ------------------------------------------------

Table 7. Summary of Final Models.

  -------- ----------- ---------------- --------- --------- ----- ------- ---------
           AOI         Variable         Coeff.    se        df    t       p-value

  Model 1  Buffer      Intercept        2.0368    0.2563    41    7.95    \<.0001

                       Cattle Grazing   -0.0129   0.0051    201   -2.51   0.013

  Model 2  Watershed   Intercept        2.402     0.4276    41    5.62    \<.0001

                       Cattle Grazing   -0.0133   0.00617   201   -2.15   0.033

  Model 3  Watershed   Intercept        0.471     0.4417    41    1.07    0.29

                       Sedimentary Rock 0.084     0.03376   200   2.49    0.014

                       Urbanization     3.090     1.416     200   2.18    0.030
  -------- ----------- ---------------- --------- --------- ----- ------- ---------

Table 8. Predictor Variables Selected for 2-variable All-subsets
Modeling

  -----------------------------------------------------------------------
  Variable            HUC AOI         Watershed AOI     Buffer AOI
  ------------------- --------------- ----------------- -----------------
  Area                                X                 X

  ∆ Successional                      X                 
  Forest                                                

  1.5% Hill Slope                     X                 

  Mines               X               X                 

  Riparian Vegetation                 X                 

  Roads (km)                          X                 X

  Sandstone                           X                 

  Open Water          X               X                 

  ∆ Open Water        X                                 
  -----------------------------------------------------------------------

Table 9a. Model Evaluation Results.

<table>
<colgroup>
<col style="width: 10%" />
<col style="width: 18%" />
<col style="width: 7%" />
<col style="width: 6%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 9%" />
<col style="width: 7%" />
<col style="width: 8%" />
<col style="width: 8%" />
</colgroup>
<tbody>
<tr class="odd">
<td>AOI</td>
<td>MODEL</td>
<td>BIC</td>
<td><p>max</p>
<p>corr</p></td>
<td><p>max</p>
<p>cond #</p></td>
<td><p>max</p>
<p>p-value</p></td>
<td><p>max</p>
<p>D &gt; 1</p></td>
<td><p>corr</p>
<p>(obs,pred)</p></td>
<td><p>std</p>
<p>corr</p></td>
<td>RMSPE</td>
<td><p>std</p>
<p>RMSPE</p></td>
</tr>
<tr class="even">
<td>Watershed</td>
<td>dams-ch_slope-water</td>
<td>519.73</td>
<td>0.461</td>
<td>7.20</td>
<td>0.0043</td>
<td>slightly</td>
<td>0.836</td>
<td>0.0591</td>
<td>0.323</td>
<td>0.0974</td>
</tr>
<tr class="odd">
<td>Watershed</td>
<td>dams-ch_slope-water2</td>
<td>519.77</td>
<td>0.496</td>
<td>7.44</td>
<td>0.0112</td>
<td></td>
<td>0.834</td>
<td>0.0608</td>
<td>0.324</td>
<td>0.1028</td>
</tr>
<tr class="even">
<td>HUC</td>
<td>h_slope-mines</td>
<td>522.06</td>
<td>0.436</td>
<td>4.29</td>
<td>0.0000</td>
<td></td>
<td>0.833</td>
<td>0.0612</td>
<td>0.322</td>
<td>0.1023</td>
</tr>
<tr class="odd">
<td>Watershed</td>
<td>dams-ch_slope-pine2</td>
<td>523.82</td>
<td>0.417</td>
<td>5.65</td>
<td>0.0024</td>
<td></td>
<td>0.832</td>
<td>0.0605</td>
<td>0.326</td>
<td>0.1029</td>
</tr>
<tr class="even">
<td>Watershed</td>
<td>water-dams</td>
<td>528.97</td>
<td>0.240</td>
<td>3.70</td>
<td>0.0142</td>
<td>slightly</td>
<td>0.832</td>
<td>0.0607</td>
<td>0.325</td>
<td>0.1018</td>
</tr>
<tr class="odd">
<td>HUC</td>
<td>mines-water2</td>
<td>530.33</td>
<td>0.252</td>
<td>4.11</td>
<td>0.0000</td>
<td></td>
<td>0.834</td>
<td>0.0639</td>
<td>0.327</td>
<td>0.1016</td>
</tr>
<tr class="even">
<td>Watershed</td>
<td>alluv-mines</td>
<td>531.33</td>
<td>0.574</td>
<td>4.76</td>
<td>0.0000</td>
<td></td>
<td>0.832</td>
<td>0.0622</td>
<td>0.327</td>
<td>0.0998</td>
</tr>
<tr class="odd">
<td>Watershed</td>
<td>success2-h_slope</td>
<td>531.54</td>
<td>0.040</td>
<td>4.90</td>
<td>0.0476</td>
<td></td>
<td>0.834</td>
<td>0.0586</td>
<td>0.323</td>
<td>0.0995</td>
</tr>
<tr class="even">
<td>Watershed</td>
<td>mines-wetlands</td>
<td>532.11</td>
<td>0.597</td>
<td>5.19</td>
<td>0.0097</td>
<td>slightly</td>
<td>0.836</td>
<td>0.0600</td>
<td>0.322</td>
<td>0.1021</td>
</tr>
<tr class="odd">
<td>Watershed</td>
<td>dams-water2-success2</td>
<td>532.27</td>
<td>0.382</td>
<td>4.57</td>
<td>0.0029</td>
<td>slightly</td>
<td>0.831</td>
<td>0.0649</td>
<td>0.326</td>
<td>0.1016</td>
</tr>
<tr class="even">
<td>Watershed</td>
<td>dams-water-success2</td>
<td>532.44</td>
<td>0.240</td>
<td>4.16</td>
<td>0.0029</td>
<td></td>
<td>0.830</td>
<td>0.0615</td>
<td>0.329</td>
<td>0.0964</td>
</tr>
<tr class="odd">
<td>Watershed</td>
<td>arid_veg-water-mines</td>
<td>536.82</td>
<td>0.506</td>
<td>5.68</td>
<td>0.0034</td>
<td></td>
<td>0.831</td>
<td>0.0606</td>
<td>0.327</td>
<td>0.1021</td>
</tr>
<tr class="even">
<td>Buffer</td>
<td>alluv-water</td>
<td>552.74</td>
<td>0.497</td>
<td>3.61</td>
<td>0.0468</td>
<td>slightly</td>
<td>0.833</td>
<td>0.0603</td>
<td>0.321</td>
<td>0.0994</td>
</tr>
</tbody>
</table>

Table 9b. Definitions of the variable names used above.

  ---------- ------------------------------------------------------------
  alluv      \% alluvium

  arid_veg   \% Grassland, Shrub-steppe, Juniper Woodland, Desert Scrub

  ch_slope   Channel Gradient

  dams       Dam Density

  h_slope    \% of AOI with \< 1.5% hill slope

  mines      Mine Density

  pine2      Change in % Ponderosa Pine

  success2   Change in % Successional Forest

  water      \% Open Water

  water2     Change in % Open Water

  wetlands   \% Wetland Vegetation
  ---------- ------------------------------------------------------------

Table 10. Summary of Final Models.

  -------- ----------- ------------------- -------- ------- ----- ------- ---------
           AOI         Variable            Coeff.   se      df    t       p-value

  Model 1  Buffer      Intercept           1.245    0.335   42    3.72    0.0006

                       Alluvium            0.012    0.006   208   2.00    0.047

                       Open Water          -0.103   0.047   208   -2.19   0.030

  Model 2  Watershed   Intercept           0.873    0.392   42    2.23    0.031

                       Dam Density         114.73   24.50   207   4.68    \< 0.0001

                       Ch. Gradient        189.01   67.06   207   2.82    0.005

                       ∆ Open Water        -0.988   0.386   207   -2.56   0.011

  Model 3  Watershed   Intercept           0.179    0.258   42    0.69    0.49

                       Dam Density         126.25   23.00   207   5.49    \< 0.0001

                       Ch. Gradient        327.77   59.76   207   5.48    \< 0.0001

                       ∆ Ponderosa Pine    0.093    0.030   207   3.08    0.002

  Model 4  Watershed   Intercept           0.838    0.209   42    4.02    0.0002

                       Alluvium            -0.210   0.040   208   -5.22   \< 0.0001

                       Mine Density        24.44    4.31    208   5.68    \< 0.0001

  Model 5  Watershed   Intercept           2.021    0.401   42    5.04    \< 0.0001

                       ∆ Successional      -0.350   0.176   208   -1.99   0.048
                       Forest                                             

                       Hill Slope          -46.93   17.85   208   -2.63   0.009

  Model 6  HUC         Intercept           1.29     0.20    42    6.51    \< 0.0001

                       Hill Slope          -13.72   2.34    208   -5.86   \< 0.0001

                       Mine Density        13.48    2.15    208   6.28    \< 0.0001

  Model 7  HUC         Intercept           1.11     0.19    42    5.75    \< 0.0001

                       Mine Density        10.90    2.05    208   5.33    \< 0.0001

                       ∆ Open Water        -0.288   0.051   208   -5.65   \< 0.0001
  -------- ----------- ------------------- -------- ------- ----- ------- ---------

Table 11. Predictor Variables Selected for 2-variable All-subsets
Modeling

  -----------------------------------------------------------------------
  Variable          HUC AOI           Watershed AOI     Buffer AOI
  ----------------- ----------------- ----------------- -----------------
  Agriculture       X                 X                 

  Alluvium                                              X

  Area              X                 X                 X

  Arid Vegetation   X                 X                 

  ∆ Arid Vegetation X                                   X

  Cattle            X                 X                 

  Channel Slope     X                 X                 X

  ∆ Clearcut        X                                   

  Clearcut          X                                   

  Conifer           X                 X                 

  ∆ Conifer         X                 X                 X

  Dams              X                 X                 X

  Metamorphic       X                 X                 

  Mines             X                                   X

  Ponderosa Pine                      X                 

  ∆ Ponderosa Pine                    X                 

  Precipitation                       X                 

  Riparian          X                 X                 X

  Roads (km)        X                 X                 X

  Sedimentary                                           X

  Open Water                          X                 

  ∆ Open Water                        X                 

  ∆ Wetlands        X                 X                 

  Wetlands          X                 X                 X
  -----------------------------------------------------------------------

Table 12a. Model Evaluation Results for HUC AOI.

+------------------+-----+----+-----+-----+-------+-----+------+------+
| MODEL            | BIC | m  | max | max | corr  | std | R    | std  |
|                  |     | ax |     |     |       |     | MSEP |      |
|                  |     |    | con | p   | (obs, | c   |      | R    |
|                  |     | \| | d.# | -va | pred) | orr |      | MSEP |
|                  |     | co |     | lue |       |     |      |      |
|                  |     | rr |     |     |       |     |      |      |
|                  |     | \| |     |     |       |     |      |      |
+==================+=====+====+=====+=====+=======+=====+======+======+
| dams             | 697 | NA | NA  | 0.0 | 0     | 0.0 | 0.   | 0.   |
|                  | .73 |    |     | 049 | .9249 | 256 | 4930 | 0667 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| c                | 699 | 0. | 3   | 0.0 | 0     | 0.0 | 0.   | 0.   |
| h_slope-riparian | .31 | 11 | .08 | 087 | .9249 | 240 | 4911 | 0637 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| ch_slope         | 699 | NA | NA  | 0.0 | 0     | 0.0 | 0.   | 0.   |
|                  | .68 |    |     | 363 | .9265 | 229 | 4874 | 0625 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| dams-riparian    | 699 | 0. | 2   | 0.0 | 0     | 0.0 | 0.   | 0.   |
|                  | .83 | 16 | .20 | 401 | .9228 | 256 | 4979 | 0691 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| ag-ch_slope      | 704 | 0. | 3   | 0.0 | 0     | 0.0 | 0.   | 0.   |
|                  | .27 | 28 | .21 | 018 | .9261 | 227 | 4867 | 0626 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| Cattle-ch_slope  | 704 | 0. | 3   | 0.0 | 0     | 0.0 | 0.   | 0.   |
|                  | .79 | 04 | .05 | 401 | .9249 | 246 | 4920 | 0657 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| ch_slope-a       | 705 | 0. | 4   | 0.0 | 0     | 0.0 | 0.   | 0.   |
| rid2-dams-alpine | .03 | 51 | .21 | 208 | .9259 | 237 | 4875 | 0630 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| arid2-dams       | 705 | 0. | 1   | 0.  | 0     | 0.0 | 0.   | 0.   |
|                  | .42 | 03 | .82 | 013 | .9234 | 246 | 4971 | 0658 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| arid2-ch_slope   | 705 | 0. | 2   | 0.0 | 0     | 0.0 | 0.   | 0.   |
|                  | .72 | 15 | .74 | 047 | .9264 | 239 | 4879 | 0642 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| ag-dams          | 706 | 0. | 2   | 0.0 | 0     | 0.0 | 0.   | 0.   |
|                  | .49 | 06 | .63 | 354 | .9224 | 253 | 5010 | 0701 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| ch_slope-conifer | 706 | 0. | 4   | 0.0 | 0     | 0.0 | 0.   | 0.   |
|                  | .77 | 31 | .15 | 044 | .9267 | 237 | 4877 | 0626 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| wetlands         | 706 | NA | NA  | 0.  | 0     | 0.0 | 0.   | 0.   |
|                  | .92 |    |     | 004 | .9249 | 237 | 4921 | 0653 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| ch_slope         | 708 | 0. | 3   | 0.0 | 0     | 0.0 | 0.   | 0.   |
| -arid2-dams-wild | .48 | 45 | .88 | 302 | .9260 | 230 | 4908 | 0609 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| conifer2         | 708 | NA | NA  | 0.0 | 0     | 0.0 | 0.   | 0.   |
|                  | .56 |    |     | 004 | .9230 | 244 | 4961 | 0643 |
+------------------+-----+----+-----+-----+-------+-----+------+------+
| metamor          | 710 | NA | NA  | 0.0 | 0     | 0.0 | 0.   | 0.   |
|                  | .47 |    |     | 041 | .9238 | 271 | 4933 | 0729 |
+------------------+-----+----+-----+-----+-------+-----+------+------+

Table 12b. Model Evaluation Results for Watershed AOI.

+----------------+----+----+----+------+-------+----+------+------+
| MODEL          | B  | m  | m  | max  | corr  | s  | R    | std  |
|                | IC | ax | ax |      |       | td | MSEP |      |
|                |    |    |    | p-v  | (obs, |    |      | R    |
|                |    | \| | co | alue | pred) | co |      | MSEP |
|                |    | co | nd |      |       | rr |      |      |
|                |    | rr | .# |      |       |    |      |      |
|                |    | \| |    |      |       |    |      |      |
+================+====+====+====+======+=======+====+======+======+
| ch_            | 69 | 0. | 2. | 0.   | 0     | 0. | 0.   | 0.   |
| slope-riparian | 3. | 26 | 84 | 0008 | .9257 | 02 | 4873 | 0638 |
|                | 88 |    |    |      |       | 41 |      |      |
+----------------+----+----+----+------+-------+----+------+------+
| dams           | 69 | NA | NA | 0.   | 0     | 0. | 0.   | 0.   |
|                | 6. |    |    | 0071 | .9252 | 02 | 4915 | 0633 |
|                | 57 |    |    |      |       | 40 |      |      |
+----------------+----+----+----+------+-------+----+------+------+
| ag-ch_slope    | 69 | 0. | 3. | 0.   | 0     | 0. | 0.   | 0.   |
|                | 8. | 43 | 31 | 0001 | .9277 | 02 | 4836 | 0626 |
|                | 62 |    |    |      |       | 35 |      |      |
+----------------+----+----+----+------+-------+----+------+------+
| ch_slope-ri    | 69 | 0. | 4. | 0.   | 0     | 0. | 0.   | 0.   |
| parian-alpine2 | 8. | 56 | 52 | 0266 | .9259 | 02 | 4906 | 0633 |
|                | 92 |    |    |      |       | 28 |      |      |
+----------------+----+----+----+------+-------+----+------+------+
| ch_slope       | 69 | NA | NA | 0.   | 0     | 0. | 0.   | 0.   |
|                | 9. |    |    | 0363 | .9267 | 02 | 4869 | 0643 |
|                | 68 |    |    |      |       | 41 |      |      |
+----------------+----+----+----+------+-------+----+------+------+
| ari            | 70 | 0. | 3. | 0.   | 0     | 0. | 0.   | 0.   |
| d_veg-ch_slope | 4. | 21 | 03 | 0026 | .9264 | 02 | 4885 | 0636 |
|                | 14 |    |    |      |       | 37 |      |      |
+----------------+----+----+----+------+-------+----+------+------+
| riparian       | 70 | NA | NA | 0.   | 0     | 0. | 0.   | 0.   |
|                | 6. |    |    | 0388 | .9228 | 02 | 4969 | 0774 |
|                | 03 |    |    |      |       | 83 |      |      |
+----------------+----+----+----+------+-------+----+------+------+
| ch             | 70 | 0. | 7. | 0.   | 0     | 0. | 0.   | 0.   |
| _slope-conifer | 6. | 28 | 53 | 0083 | .9244 | 02 | 4922 | 0637 |
|                | 64 |    |    |      |       | 34 |      |      |
+----------------+----+----+----+------+-------+----+------+------+
| water          | 70 | NA | NA | 0.   | 0     | 0. | 0.   | 0.   |
|                | 8. |    |    | 0038 | .9244 | 02 | 4918 | 0626 |
|                | 06 |    |    |      |       | 45 |      |      |
+----------------+----+----+----+------+-------+----+------+------+
| water2         | 70 | NA | NA | 0.   | 0     | 0. | 0.   | 0.   |
|                | 8. |    |    | 0092 | .9247 | 02 | 4908 | 0639 |
|                | 58 |    |    |      |       | 44 |      |      |
+----------------+----+----+----+------+-------+----+------+------+
| metamor        | 71 | NA | NA | 0.   | 0     | 0. | 0.   | 0.   |
|                | 1. |    |    | 0298 | .9236 | 02 | 4950 | 0642 |
|                | 95 |    |    |      |       | 45 |      |      |
+----------------+----+----+----+------+-------+----+------+------+
| arid_veg       | 71 | NA | NA | 0.   | 0     | 0. | 0.   | 0.   |
|                | 4. |    |    | 0367 | .9219 | 02 | 4992 | 0783 |
|                | 89 |    |    |      |       | 92 |      |      |
+----------------+----+----+----+------+-------+----+------+------+

Table 12c. Model Evaluation Results for Buffer AOI.

+---------------------+----+---+---+-----+------+----+-----+-----+
| MODEL               | B  | m | m | max | corr | s  | RM  | std |
|                     | IC | a | a |     |      | td | SEP |     |
|                     |    | x | x | p   | (o   |    |     | RM  |
|                     |    |   |   | -va | bs,p | co |     | SEP |
|                     |    | \ | c | lue | red) | rr |     |     |
|                     |    | | | o |     |      |    |     |     |
|                     |    | c | n |     |      |    |     |     |
|                     |    | o | d |     |      |    |     |     |
|                     |    | r |   |     |      |    |     |     |
|                     |    | r |   |     |      |    |     |     |
|                     |    | \ |   |     |      |    |     |     |
|                     |    | | |   |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| ch_slope-riparian   | 69 | 0 | 3 | 0.0 | 0    | 0. | 0.4 | 0.0 |
|                     | 6. | . | . | 005 | .924 | 02 | 943 | 679 |
|                     | 37 | 2 | 0 |     |      | 56 |     |     |
|                     |    | 0 | 5 |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| ch_slo              | 69 | 0 | 4 | 0.0 | 0    | 0. | 0.4 | 0.0 |
| pe-riparian-success | 9. | . | . | 044 | .924 | 02 | 958 | 680 |
|                     | 34 | 3 | 1 |     |      | 54 |     |     |
|                     |    | 8 | 5 |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| ch_slope            | 69 | N | N | 0.0 | 0    | 0. | 0.4 | 0.0 |
|                     | 9. | A | A | 363 | .924 | 02 | 963 | 674 |
|                     | 68 |   |   |     |      | 45 |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| ch_slop             | 70 | 0 | 3 | 0.0 | 0    | 0. | 0.5 | 0.0 |
| e-riparian-sediment | 2. | . | . | 087 | .920 | 03 | 048 | 881 |
|                     | 23 | 2 | 3 |     |      | 41 |     |     |
|                     |    | 0 | 9 |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| riparian-wetlan     | 70 | 0 | 5 | 0.0 | 0    | 0. | 0.5 | 0.0 |
| ds-success-ch_slope | 5. | . | . | 167 | .922 | 02 | 010 | 704 |
|                     | 39 | 3 | 7 |     |      | 68 |     |     |
|                     |    | 8 | 8 |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| arid2-ch_slope      | 70 | 0 | 2 | 0.0 | 0    | 0. | 0.4 | 0.0 |
|                     | 5. | . | . | 031 | .922 | 02 | 968 | 708 |
|                     | 70 | 1 | 6 |     |      | 71 |     |     |
|                     |    | 7 | 9 |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| ch_slope-sediment   | 70 | 0 | 2 | 0.0 | 0    | 0. | 0.5 | 0.0 |
|                     | 6. | . | . | 426 | .920 | 03 | 062 | 859 |
|                     | 81 | 0 | 8 |     |      | 17 |     |     |
|                     |    | 4 | 5 |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| mines-riparian      | 70 | 0 | 3 | 0.0 | 0    | 0. | 0.4 | 0.0 |
|                     | 7. | . | . | 159 | .922 | 02 | 981 | 701 |
|                     | 02 | 0 | 3 |     |      | 69 |     |     |
|                     |    | 1 | 6 |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| dams-riparian       | 70 | 0 | 2 | 0.0 | 0    | 0. | 0.5 | 0.0 |
|                     | 7. | . | . | 426 | .921 | 03 | 012 | 802 |
|                     | 12 | 0 | 2 |     |      | 08 |     |     |
|                     |    | 5 | 1 |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| arid                | 70 | 0 | 3 | 0.0 | 0    | 0. | 0.5 | 0.0 |
| 2-ch_slope-sediment | 8. | . | . | 010 | .920 | 03 | 042 | 892 |
|                     | 80 | 1 | 1 |     |      | 35 |     |     |
|                     |    | 7 | 0 |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| riparian            | 70 | N | N | 0.0 | 0    | 0. | 0.4 | 0.0 |
|                     | 9. | A | A | 058 | .924 | 02 | 937 | 708 |
|                     | 00 |   |   |     |      | 64 |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| wetlands            | 71 | N | N | 0.0 | 0    | 0. | 0.5 | 0.0 |
|                     | 0. | A | A | 049 | .922 | 02 | 010 | 765 |
|                     | 51 |   |   |     |      | 83 |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| ch_slope-roads_km   | 71 | 0 | 3 | 0.0 | 0    | 0. | 0.4 | 0.0 |
|                     | 1. | . | . | 235 | .924 | 02 | 940 | 662 |
|                     | 03 | 0 | 1 |     |      | 56 |     |     |
|                     |    | 2 | 3 |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+
| min                 | 71 | 0 | 3 | 0.0 | 0    | 0. | 0.4 | 0.0 |
| es-riparian-success | 1. | . | . | 147 | .923 | 02 | 968 | 710 |
|                     | 45 | 3 | 6 |     |      | 66 |     |     |
|                     |    | 8 | 8 |     |      |    |     |     |
+---------------------+----+---+---+-----+------+----+-----+-----+

Table 12d. Definitions of the variable names used above.

  ----------- -----------------------------------------------------------
  alpine      \% Alpine & Subalpine Vegetation

  alpine2     Change in % Alpine/Subalpine Vegetation

  arid_veg    \% Grassland, Shrub-steppe, Juniper Woodland, Desert Scrub

  arid2       Change in % Arid Vegetation

  ag          \% Agriculture

  cattle      \% Cattle Grazing

  ch_slope    Channel Gradient

  conifer     \% Mixed Conifer & Lodgepole Pine Forest

  conifer2    change in % Mixed Conifer & Lodgepole Pine Forest

  dams        Dam Density

  metamor     \% Metamorphic Geology

  mines       Mine Density

  riparian    \% Riparian Vegetation

  roads_km    Road Length in kilometers

  sediment    \% Sedimentary Geology

  success     \% Successional Forest

  water       \% Open Water

  water2      Change in % Open Water

  wetland     \% Wetland Vegetation

  wild        \% Designated Wilderness
  ----------- -----------------------------------------------------------

Table 13a. Summary of Final Models for the Buffer AOI.

  ---------- --------------------- -------- ------- ----- ------- ----------
             Variable              Coeff.   se      df    t       p-value

  Model 1    Intercept             1.98     0.32    17    6.25    \< 0.0001

             Channel Gradient      -93.55   26.74   340   -3.50   0.0005

             Riparian Veg.         -0.339   0.081   340   -4.16   \< 0.0001

  Model 2    Intercept             2.54     0.34    16    7.56    \< 0.0001

             Channel Gradient      -106.4   23.4    340   -4.54   \< 0.0001

             Riparian Veg.         -0.431   0.075   340   -5.74   \< 0.0001

             Successional Forest   -0.150   0.052   340   -2.87   0.004

  Model 3    Intercept             2.01     0.371   16    5.43    \< 0.0001

             Channel Gradient      -88.10   22.14   339   -3.98   \< 0.0001

             Riparian Veg.         -0.363   0.072   339   -5.07   \< 0.0001

             Successional Forest   -0.183   0.049   339   -3.77   0.0002

             Wetlands              0.069    0.027   339   2.40    0.017

  Model 4    Intercept             0.615    0.362   17    1.70    0.11

             Mine Density          3.51     1.45    340   2.42    0.016

             Riparian Veg.         -0.286   0.090   340   -3.19   0.002
  ---------- --------------------- -------- ------- ----- ------- ----------

Table 13b. Summary of Final Models for the Watershed AOI.

  ---------- ------------------- --------- ------- ----- ------- ----------
             Variable            Coeff.    se      df    t       p-value

  Model 1    Intercept           1.90      0.34    17    5.58    \< 0.0001

             Channel Gradient    -99.49    29.49   340   -3.37   0.0008

             Riparian Veg.       -4.61     1.35    340   -3.42   0.0007

  Model 2    Intercept           0.095     0.368   18    0.26    0.80

             Dam Density         142.38    52.58   340   2.71    0.007

  Model 3    Intercept           2.23      0.36    17    6.11    \< 0.0001

             Channel Gradient    -123.27   30.02   340   -4.11   \< 0.0001

             Agriculture         -0.161    0.041   340   -3.91   0.0001

  Model 4    Intercept           1.41      0.38    16    3.70    0.002

             Channel Gradient    -137.73   31.95   340   -4.31   \< 0.0001

             Riparian Veg.       -3.81     1.27    340   -3.01   0.003

             ∆ Alpine Veg.       0.142     0.064   340   2.23    0.027
  ---------- ------------------- --------- ------- ----- ------- ----------

Table 13c. Summary of Final Models for the HUC AOI.

  ---------- ------------------- -------- -------- ----- ------- ----------
             Variable            Coeff.   se       df    t       p-value

  Model 1    Intercept           1.75     0.35     18    4.95    0.0001

             Channel Gradient    -81.07   30.74    339   -2.64   0.009

             Riparian Veg.       -1.06    0.39     339   -2.70   0.007

  Model 2    Intercept           2.10     0.39     17    5.39    \< 0.0001

             Channel Gradient    -98.91   30.41    340   -3.25   0.001

             Agriculture         -0.041   0.013    340   -3.15   0.002

  Model 3    Intercept           1.58     0.36     17    4.41    0.0004

             Channel Gradient    -69.60   32.54    340   -2.14   0.033

             Cattle Grazing      -0.172   0.083    340   -2.06   0.040

  Model 4    Intercept           0.525    0.447    15    1.17    0.26

             Channel Gradient    -79.23   29.90    340   -2.65   0.008

             ∆ Arid Veg.         0.024    0.0096   340   2.55    0.011

             Dam Density         58.48    19.58    340   2.99    0.003

             Alpine Veg.         0.098    0.042    340   2.32    0.021
  ---------- ------------------- -------- -------- ----- ------- ----------

![](media/image1.jpeg){width="5.999305555555556in"
height="6.684722222222222in"}

Figure 1. Three subbasins in the Columbia River basin where SWAM
analyses have been ran for BPA.