# Mica Dam Expansion Water Temperature and Fish Indexing Study 2020

The suggested citation for this analytic appendix is:

*Thorley, J.L. & Amies-Galonski, E. (2021) Mica Dam Expansion Water
Temperature and Fish Indexing Study 2020. A Poisson Consulting Analysis
Appendix. URL: https://www.poissonconsulting.ca/f/955630057.*

## Background

The Mica Tailrace Fish Indexing Study is a multi-year program to estimate the effects of the addition of two new turbines (Mica 5 and 6) on the ichyofauna and thermal regime in the 2.5 km of the Columbia River downstream of Mica Dam. A single year of fish indexing data (2008) was also available from a previous program. As per the Terms of Reference (TOR) the relative abundance, condition and spatial distribution of the fish populations was assessed.

Mica 5 became operational on January 28th 2015 and Mica 6 became operational on December 22nd 2015.

### Data Preparation

The fish and downstream temperature data were provided by the Ktunaxa Nation. The discharge and elevation data were queried from the Columbia Basin Hydrological Database.

The data were cleaned and tidied using R version 4.0.5 (R Core Team 2020).

### Length Cutoffs

Individuals were classified as fry (age-0), juvenile (age-1 and older subadults) or adult (sexually mature) based on the length cut-offs in Table 1.

### Statistical Analysis

Model parameters were estimated using Bayesian methods. The estimates were produced using JAGS (Plummer 2003). For additional information on Bayesian estimation the reader is referred to McElreath (2016).

Unless stated otherwise, the Bayesian analyses used weakly informative normal and half-normal prior distributions (Gelman, Simpson, and Betancourt 2017). The posterior distributions were estimated from 1500 Markov Chain Monte Carlo (MCMC) samples thinned from the second halves of 3 chains (Kery and Schaub 2011, 38–40). Model convergence was confirmed by ensuring that the potential scale reduction factor \(\hat{R} \leq 1.05\) (Kery and Schaub 2011, 40) and the effective sample size (Brooks et al. 2011) \(\textrm{ESS} \geq 150\) for each of the monitored parameters (Kery and Schaub 2011, 61).

The parameters are summarised in terms of the point *estimate*, *lower*
and *upper* 95% credible limits (CLs) and the surprisal *s-value*
(Greenland 2019). The estimate is the median (50th percentile) of
the MCMC samples while the 95% CLs are the 2.5th and 97.5th percentiles.
The s-value can be considered a test of directionality. More
specifically it indicates how surprising (in bits) it would be to
discover that the true value of the parameter is in the opposite
direction to the estimate. An s-value of 4.3 bits, which is equivalent
to a p-value (Kery and Schaub 2011; Greenland and Poole 2013) of 0.05,
indicates that the surprise would be equivalent to throwing 4.3 heads in
a row. The requirement that non-essential explanatory variables have
s-values \(\geq\) 4.3 bits provides a useful model selection heuristic
(Kery and Schaub 2011).

The results are displayed graphically by plotting the modeled
relationships between particular variables and the response(s) with the
remaining variables held constant. In general, continuous and discrete
fixed variables are held constant at their mean and first level values,
respectively, while random variables are held constant at their typical
values (expected values of the underlying hyperdistributions)
(Kery and Schaub 2011, 77–82). When informative the influence of
particular variables is expressed in terms of the *effect size* (i.e.,
percent or n-fold change in the response variable) with 95% credible
intervals (CIs, Bradford, Korman, and Higgins 2005).

The analyses were implemented using R version 4.0.5
(R Core Team 2020) and the
`mbr`

family of packages.

### Model Descriptions

### Body Condition

The annual variation in condition (body weight when accounting for body length) was estimated from the boat and backpack electrofishing captures using a mass-length model (He et al. 2008).

Key assumptions of the condition model include:

- Weight varies with body length as an allometric relationship, i.e., \(W = \alpha L^{\beta}\).
- \(\alpha\) varies randomly with year.
- \(\alpha\) varies by period (pre versus post Mica 5 and 6).
- The residual variation in weight is log-normally distributed.

Preliminary analyses indicated that site and day of the year were not informative predictors of condition.

### Relative Abundance

The annual variation in relative abundance was estimated from the boat count and catch data using an over-dispersed Poisson model (Kery and Schaub 2011). Lineal densities are by kilometre of river (as opposed to kilometre of bank).

Key assumptions of the relative abundance model include:

- Lineal count density varies by period.
- Lineal count density varies randomly with year.
- Lineal catch efficiency is a fixed multiplier of lineal count efficiency.
- Expected counts (and catches) are the product of the count (catch) density and the length of river (half the length of bank) sampled.
- Observed counts (and catches) are described by a Poisson-gamma distribution.

Preliminary analyses indicated that site and discharge were not informative predictors of the lineal count (or catch) density.

The model does not distinguish between the abundance and observer efficiency, i.e., it estimates the count which is the product of the two. As such it is necessary to assume that changes in observer efficiency by year are negligible in order to interpret the estimates as relative abundance.

### Water Temperature

#### Tailrace

Climatic variation can cause large differences in annual temperatures. Consequently, we explored the data for an effect of the additional turbines on the difference in the water temperature between the right versus left bank and when moving downstream. All apparent systematic differences were within the accuracy of the temperature loggers (\(\pm 0.2^{\circ}\text{C}\)).

#### Forebay and Tailrace

In 2020 the BC Hydro thermistor data for the Mica Dam forebay were analysed using Maximum Likelihood (Millar 2011) and TMB (Kristensen et al. 2016) to determine the extent to which discharge from the turbines and the air temperature influences the water temperature in the tailrace.

Key assumptions of the hourly temperature model include:

- The effective depth of each unit depends on the unit (as a random effect) and its discharge (as a fixed effect).
- The water temperature effect of each unit depends on its effective depth and the stratification in the forebay.
- The water temperature is affected by release from the dam and the difference in air temperature and the water temperature.
- The temporal autocorrelation is described a first order moving average process.
- The residual water temperature at the tailrace temperature logger is normally distributed.

Despite the incorporation of a first order moving average process the residual variation in the hourly tailrace water temperature was strongly autocorrelated. Consequently CIs are not provided for the coefficients or estimates.

### Model Templates

#### Condition

```
.model{
bAlpha ~ dnorm(5, 2^-2)
bBeta ~ dnorm(3, 2^-2)
bAlphaPeriod[1] <- 0
for(i in 2:nPeriod) {
bAlphaPeriod[i] ~ dnorm(0, 2^-2)
}
sAlphaAnnual ~ dnorm(0, 2^-2) T(0,)
for(i in 1:nAnnual) {
bAlphaAnnual[i] ~ dnorm(0, sAlphaAnnual^-2)
}
sWeight ~ dnorm(0, 2^-2) T(0,)
for (i in 1:length(Weight)) {
eAlpha[i] <- bAlpha + bAlphaPeriod[Period[i]] + bAlphaAnnual[Annual[i]]
eBeta[i] <- bBeta
log(eWeight[i]) <- eAlpha[i] + eBeta[i] * Length[i]
Weight[i] ~ dlnorm(log(eWeight[i]), sWeight^-2)
}
```

Block 1.

#### Relative Abundance

```
.model{
bEfficiencyVisitType[1] <- 0
for (i in 2:nVisitType) {
bEfficiencyVisitType[i] ~ dnorm(0, 1^-2)
}
bDensity ~ dnorm(0, 5^-2)
bDensityPeriod[1] <- 0
for(i in 2:nPeriod) {
bDensityPeriod[i] ~ dnorm(0, 2^-2)
}
sDensityAnnual ~ dnorm(0, 2^-2) T(0, )
for(i in 1:nAnnual) {
bDensityAnnual[i] ~ dnorm(0, sDensityAnnual^-2)
}
sDispersion ~ dnorm(0, 2^-2) T(0, )
for (i in 1:length(Annual)) {
log(eEfficiency[i]) <- bEfficiencyVisitType[VisitType[i]]
log(eDensity[i]) <- bDensity + bDensityPeriod[Period[i]] + bDensityAnnual[Annual[i]]
eAbundance[i] <- eDensity[i] * SiteLength[i] / 2
eDispersion[i] ~ dgamma(1 / sDispersion^2, 1 / sDispersion^2)
Count[i] ~ dpois(eAbundance[i] * eEfficiency[i] * eDispersion[i])
}
```

Block 2.

#### Forebay and Tailrace

```
.#include <TMB.hpp>
template<class Type>
Type objective_function<Type>::operator() () {
DATA_VECTOR(Tailrace);
DATA_VECTOR(Intake);
DATA_VECTOR(Forebay);
DATA_VECTOR(Air);
DATA_VECTOR(Unit1);
DATA_VECTOR(Unit2);
DATA_VECTOR(Unit3);
DATA_VECTOR(Unit4);
DATA_VECTOR(Unit5);
DATA_VECTOR(Unit6);
DATA_VECTOR(Spill);
DATA_IVECTOR(Consecutive);
PARAMETER(sDepthUnit);
PARAMETER_VECTOR(bDepthUnit);
PARAMETER(bDepthSpill0);
PARAMETER_VECTOR(bDepthTurbineUnit);
PARAMETER(bRelease);
PARAMETER(bReleaseAir);
PARAMETER(bRho);
PARAMETER(sTailrace);
vector<Type> eTurbineUnit1 = Tailrace;
vector<Type> eTurbineUnit2 = Tailrace;
vector<Type> eTurbineUnit3 = Tailrace;
vector<Type> eTurbineUnit4 = Tailrace;
vector<Type> eTurbineUnit5 = Tailrace;
vector<Type> eTurbineUnit6 = Tailrace;
vector<Type> eDischarge = Tailrace;
vector<Type> eDepthTurbineUnit1 = Tailrace;
vector<Type> eDepthTurbineUnit2 = Tailrace;
vector<Type> eDepthTurbineUnit3 = Tailrace;
vector<Type> eDepthTurbineUnit4 = Tailrace;
vector<Type> eDepthTurbineUnit5 = Tailrace;
vector<Type> eDepthTurbineUnit6 = Tailrace;
vector<Type> eDepthSpill = Tailrace;
vector<Type> eStratification = Tailrace;
vector<Type> eEffectTurbineUnit1 = Tailrace;
vector<Type> eEffectTurbineUnit2 = Tailrace;
vector<Type> eEffectTurbineUnit3 = Tailrace;
vector<Type> eEffectTurbineUnit4 = Tailrace;
vector<Type> eEffectTurbineUnit5 = Tailrace;
vector<Type> eEffectTurbineUnit6 = Tailrace;
vector<Type> eEffectSpill = Tailrace;
vector<Type> eEffectDischarge = Tailrace;
vector<Type> eReleaseEffect = Tailrace;
vector<Type> eOutput = Tailrace;
vector<Type> eRelease = Tailrace;
vector<Type> eTailrace = Tailrace;
Type nll = 0.0;
eTailrace(0) = Tailrace(0);
for(int i = 0; i < 6; i++){
nll -= dnorm(bDepthUnit(i), Type(0), exp(sDepthUnit), true);
for(int i = 1; i < Tailrace.size(); i++){
eTurbineUnit1(i) = Unit1(i) + 0.0001;
eTurbineUnit2(i) = Unit2(i) + 0.0001;
eTurbineUnit3(i) = Unit3(i) + 0.0001;
eTurbineUnit4(i) = Unit4(i) + 0.0001;
eTurbineUnit5(i) = Unit5(i) + 0.0001;
eTurbineUnit6(i) = Unit6(i) + 0.0001;
eDischarge(i) = eTurbineUnit1(i) + eTurbineUnit2(i) + eTurbineUnit3(i) + eTurbineUnit4(i) + eTurbineUnit5(i) + eTurbineUnit6(i) + Spill(i);
eDepthTurbineUnit1(i) = bDepthUnit(0) + bDepthTurbineUnit(0) * eTurbineUnit1(i);
eDepthTurbineUnit2(i) = bDepthUnit(1) + bDepthTurbineUnit(1) * eTurbineUnit2(i);
eDepthTurbineUnit3(i) = bDepthUnit(2) + bDepthTurbineUnit(2) * eTurbineUnit3(i);
eDepthTurbineUnit4(i) = bDepthUnit(3) + bDepthTurbineUnit(3) * eTurbineUnit4(i);
eDepthTurbineUnit5(i) = bDepthUnit(4) + bDepthTurbineUnit(4) * eTurbineUnit5(i);
eDepthTurbineUnit6(i) = bDepthUnit(5) + bDepthTurbineUnit(5) * eTurbineUnit6(i);
eDepthSpill(i) = bDepthSpill0;
eStratification(i) = (Forebay(i) - Intake(i)) / (707 - 690) ;
eEffectTurbineUnit1(i) = eDepthTurbineUnit1(i) * eStratification(i);
eEffectTurbineUnit2(i) = eDepthTurbineUnit2(i) * eStratification(i);
eEffectTurbineUnit3(i) = eDepthTurbineUnit3(i) * eStratification(i);
eEffectTurbineUnit4(i) = eDepthTurbineUnit4(i) * eStratification(i);
eEffectTurbineUnit5(i) = eDepthTurbineUnit5(i) * eStratification(i);
eEffectTurbineUnit6(i) = eDepthTurbineUnit6(i) * eStratification(i);
eEffectSpill(i) = eDepthSpill(i) * eStratification(i);
eEffectDischarge(i) = (eEffectTurbineUnit1(i) * eTurbineUnit1(i) + eEffectTurbineUnit2(i) * eTurbineUnit2(i) + eEffectTurbineUnit3(i) * eTurbineUnit3(i) + eEffectTurbineUnit4(i) * eTurbineUnit4(i) + eEffectTurbineUnit5(i) * eTurbineUnit5(i) + eEffectTurbineUnit6(i) * eTurbineUnit6(i) + eEffectSpill(i) * Spill(i)) / eDischarge(i);
eReleaseEffect(i) = exp(bRelease + bReleaseAir * (Air(i) - Intake(i)));
eOutput(i) = Intake(i) + eEffectDischarge(i);
eRelease(i) = eOutput(i) + eReleaseEffect(i);
eTailrace(i) = eRelease(i) + Consecutive(i) * invlogit(bRho) * (Tailrace(i-1) - eTailrace(i-1));
nll -= dnorm(Tailrace(i), eTailrace(i), exp(sTailrace), true);
return nll;
```

Block 3. Model description.

## Results

### Tables

Table 1. Life-stage fork length cutoffs by species.

Species | Fry | Juvenile |
---|---|---|

Bull Trout | 120 | 400 |

Kokanee | 100 | 250 |

Mountain Whitefish | 120 | 175 |

Rainbow Trout | 120 | 250 |

#### Condition

Table 2. Parameter descriptions.

Parameter | Description |
---|---|

`Annual[i]` |
Year of capture of `i` ^{th} fish as a factor |

`bAlpha` |
Intercept for `eAlpha` |

`bAlphaAnnual[i]` |
Effect of `i` ^{th} `Annual` on `bAlpha` |

`bAlphaPeriod[i]` |
Effect of `i` ^{th} `Period` on `bAlpha` |

`bBeta` |
Intercept for `eBeta` |

`eAlpha[i]` |
Predicted allometric intercept (on centred `log` length)
for `i` ^{th} fish |

`eBeta[i]` |
Predicted allometric slope for `i` ^{th} fish |

`eWeight[i]` |
Predicted `Weight` of `i` ^{th} fish |

`Length[i]` |
Centred `log` Length of `i` ^{th} fish |

`Period[i]` |
The second period is post Mica 5 and 6 |

`sAlphaAnnual` |
SD of `bAlphaAnnual` |

`sWeight` |
SD of residual variation in `log(Weight)` |

`Weight[i]` |
Weight of `i` ^{th} fish |

##### Bull Trout

Table 3. Model coefficients.

term | estimate | lower | upper | svalue |
---|---|---|---|---|

bAlpha | 6.8707736 | 6.5435297 | 7.1957171 | 10.551708 |

bAlphaPeriod[2] | -0.1045242 | -0.6302385 | 0.3420439 | 1.106693 |

bBeta | 3.1078848 | 2.9812488 | 3.2404450 | 10.551708 |

sAlphaAnnual | 0.1389986 | 0.0423917 | 0.6807784 | 10.551708 |

sWeight | 0.1886779 | 0.1665397 | 0.2173000 | 10.551708 |

Table 4. Model summary.

n | K | nchains | niters | nthin | ess | rhat | converged |
---|---|---|---|---|---|---|---|

107 | 5 | 3 | 500 | 1000 | 1174 | 1.004 | TRUE |

##### Mountain Whitefish

Table 5. Model coefficients.

term | estimate | lower | upper | svalue |
---|---|---|---|---|

bAlpha | 5.2794527 | 5.1147382 | 5.4558767 | 10.5517083 |

bAlphaPeriod[2] | -0.0521814 | -0.3096497 | 0.2515648 | 0.9723923 |

bBeta | 3.1365429 | 3.0971546 | 3.1780172 | 10.5517083 |

sAlphaAnnual | 0.0893753 | 0.0339886 | 0.4660849 | 10.5517083 |

sWeight | 0.1167718 | 0.1108145 | 0.1232494 | 10.5517083 |

Table 6. Model summary.

n | K | nchains | niters | nthin | ess | rhat | converged |
---|---|---|---|---|---|---|---|

687 | 5 | 3 | 500 | 1000 | 500 | 1.008 | TRUE |

##### Kokanee

Table 7. Model coefficients.

term | estimate | lower | upper | svalue |
---|---|---|---|---|

bAlpha | 4.7682144 | 4.5271276 | 5.1143568 | 10.5517083 |

bAlphaPeriod[2] | 0.0402219 | -0.4674362 | 0.4473064 | 0.3905764 |

bBeta | 3.2836558 | 3.2152250 | 3.3545707 | 10.5517083 |

sAlphaAnnual | 0.1480292 | 0.0502591 | 0.6328982 | 10.5517083 |

sWeight | 0.2608798 | 0.2398431 | 0.2876929 | 10.5517083 |

Table 8. Model summary.

n | K | nchains | niters | nthin | ess | rhat | converged |
---|---|---|---|---|---|---|---|

240 | 5 | 3 | 500 | 1000 | 1202 | 1.001 | TRUE |

#### Relative Abundance

Table 9. Parameter descriptions.

Parameter | Description |
---|---|

`Annual[i]` |
Year of ith site visit |

`bDensity` |
Intercept for `log(eDensity)` |

`bDensityAnnual[i]` |
Effect of ith `Annual` on `log(eDensity)` |

`bEfficiencyVisitType[i]` |
Value of `log(eEfficiency)` for ith `VisitType` |

`Count[i]` |
Number of fish counted or captured on ith site
visit |

`eAbundance[i]` |
Predicted relative abundance for ith site visit |

`eDensity[i]` |
Predicted relative lineal density for ith site
visit |

`eDispersion[i]` |
Predicted over-dispersion for ith site visit |

`eEfficiency[i]` |
Predicted efficiency relative to counting for
ith site visit |

`sDispersion` |
SD of `eDispersion` |

`SiteLength[i]` |
Length of bank surveyed on ith site visit |

`VisitType[i]` |
Type of ith site visit, i.e., count versus catch |

##### Bull Trout

Table 10. Model coefficients.

term | estimate | lower | upper | svalue |
---|---|---|---|---|

bDensity | 2.4367987 | 1.8728905 | 3.0346808 | 10.5517083 |

bDensityPeriod[2] | 0.1264751 | -0.6351285 | 0.8097706 | 0.5931555 |

bEfficiencyVisitType[2] | -0.7045251 | -1.1770378 | -0.2147932 | 7.0922766 |

sDensityAnnual | 0.1971853 | 0.0080181 | 0.9352467 | 10.5517083 |

sDispersion | 0.6007875 | 0.3949569 | 0.8514637 | 10.5517083 |

Table 11. Model summary.

n | K | nchains | niters | nthin | ess | rhat | converged |
---|---|---|---|---|---|---|---|

57 | 5 | 3 | 500 | 500 | 1302 | 1.001 | TRUE |

##### Mountain Whitefish

Table 12. Model coefficients.

term | estimate | lower | upper | svalue |
---|---|---|---|---|

bDensity | 5.6330267 | 4.0594520 | 6.776410 | 10.5517083 |

bDensityPeriod[2] | -0.1703173 | -1.6307918 | 2.116392 | 0.3434739 |

bEfficiencyVisitType[2] | -1.5643612 | -2.0686241 | -1.030629 | 10.5517083 |

sDensityAnnual | 0.8232030 | 0.3630214 | 2.172976 | 10.5517083 |

sDispersion | 0.8269002 | 0.6811523 | 1.018479 | 10.5517083 |

Table 13. Model summary.

n | K | nchains | niters | nthin | ess | rhat | converged |
---|---|---|---|---|---|---|---|

57 | 5 | 3 | 500 | 500 | 162 | 1.019 | TRUE |

##### Rainbow Trout

Table 14. Model coefficients.

term | estimate | lower | upper | svalue |
---|---|---|---|---|

bDensity | 0.9679847 | -1.3878449 | 2.665015 | 1.565866 |

bDensityPeriod[2] | -1.0680493 | -3.3710270 | 1.336873 | 1.430175 |

bEfficiencyVisitType[2] | -2.6469189 | -3.9097537 | -1.313103 | 10.551708 |

sDensityAnnual | 1.4395039 | 0.4600191 | 3.463163 | 10.551708 |

sDispersion | 0.7985581 | 0.3044938 | 1.694853 | 10.551708 |

Table 15. Model summary.

n | K | nchains | niters | nthin | ess | rhat | converged |
---|---|---|---|---|---|---|---|

57 | 5 | 3 | 500 | 500 | 1178 | 1 | TRUE |

##### Kokanee

Table 16. Model coefficients.

term | estimate | lower | upper | svalue |
---|---|---|---|---|

bDensity | 4.1090603 | 2.4065678 | 5.474832 | 10.5517083 |

bDensityPeriod[2] | -0.2584851 | -2.1020164 | 1.578466 | 0.3855452 |

bEfficiencyVisitType[2] | -1.1210518 | -1.7879443 | -0.497317 | 8.9667458 |

sDensityAnnual | 0.9593140 | 0.3722623 | 2.560715 | 10.5517083 |

sDispersion | 0.9486383 | 0.7594875 | 1.198962 | 10.5517083 |

Table 17. Model summary.

n | K | nchains | niters | nthin | ess | rhat | converged |
---|---|---|---|---|---|---|---|

47 | 5 | 3 | 500 | 500 | 453 | 1.01 | TRUE |

#### Forebay and Tailrace

Table 18. Parameter descriptions.

Parameter | Description |
---|---|

`Air` |
Mean daily air temperature at the Mica weather station (C) |

`bDepthSpill` |
The effective depth of the spill relative to the intake elevation of 690 masl |

`bDepthTurbineUnit[i]` |
The effect of discharge on the effective depth of the `i` ^{th}
unit |

`bDepthUnit[i]` |
The effective depth of the `i` ^{th} unit relative to the intake
elevation of 690 masl |

`bRelease` |
The effect of release on the water temperature |

`bReleaseAir` |
The effect of the difference in the mean daily air temperature
at the Mica weather station and the hourly water temperature at
thermistor in the forebay at 690 masl on the `bRelease` |

`bRho` |
The coefficient for the first order moving average process |

`Consecutive` |
Indicator variable specifying whether the current hour is consecutive |

`Forebay` |
Hourly water temperature at the thermistor in the forebay at 707 masl (C) |

`Intake` |
Hourly water temperature at the thermistor in the forebay at 690 masl (C) |

`sDepthUnit` |
The standard deviation for the variation in `bDepthUnit` |

`Spill` |
Hourly spill (cms) |

`sTailrace` |
The standard deviation for the residual variation in `Tailrace` |

`Tailrace` |
Hourly water temperature in the tail race at 366.3L (C) |

`Unit1` |
Hourly discharge through the 1^{st} unit (cms) |

`Unit2` |
Hourly discharge through the 2^{nd} unit (cms) |

`Unit6` |
Hourly discharge through the 6^{th} unit (cms) |

Table 19. Model coefficients estimates. The confidence limits are not provided as they are considered unreliable due to the extensive autocorrelation in the data.

term | estimate |
---|---|

bDepthSpill0 | 20.1744463 |

bDepthTurbineUnit[1] | 0.0145087 |

bDepthTurbineUnit[2] | 0.0228736 |

bDepthTurbineUnit[3] | 0.0027475 |

bDepthTurbineUnit[4] | 0.0039504 |

bDepthTurbineUnit[5] | 0.0016820 |

bDepthTurbineUnit[6] | 0.0001977 |

bDepthUnit[1] | 10.3067047 |

bDepthUnit[2] | 8.6034577 |

bDepthUnit[3] | 9.6747688 |

bDepthUnit[4] | 9.9977596 |

bDepthUnit[5] | 9.2025899 |

bDepthUnit[6] | 9.1161212 |

bRelease | -1.1280293 |

bReleaseAir | 0.1059494 |

bRho | 1.9785438 |

sDepthUnit | 2.3090611 |

sTailrace | -1.0427099 |

Table 20. Model convergence.

n | K | logLik | IC | converged |
---|---|---|---|---|

54552 | 18 | -20536.39 | 41108.79 | TRUE |

### Figures

#### Discharge

#### Condition

#### Relative Abundance

##### Bull Trout

##### Mountain Whitefish

##### Rainbow Trout

##### Kokanee

#### Backpack Electrofishing

#### Temperature

##### Tailrace

##### Forebay

#### Forebay and Tailrace

#### Maps

##### Sites

##### Relative Distributions

##### Boat Counts

## Acknowledgements

The organisations and individuals whose contributions have made this report possible include:

- BC Hydro
- Trish Joyce
- Jason Watson
- Margo Sadler
- Guy Martel
- Peter McCann
- Fred Katunar
- Alf Leake
- Karen Bray

- Ktunaxa Nation
- Katrina Caley
- Misun Kang
- Joanne Fisher
- Jim Clarricoates
- Bill Green

- Poisson Consulting
- Robyn Irvine
- Seb Dalgarno

- Applied Aquatic Research
- Tom Boag

- Ministry of Forests, Lands and Natural Resource Operations
- Albert Chirico
- Will Warnock

- Jon Bisset
- Mark Thomas
- Charlotte Houston

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