The suggested citation for this analytic report is:

*Thorley, J.L. and Beliveau, A. (2016) Middle Columbia River Fish Indexing Analysis 2015. A Poisson Consulting Analysis Report. URL: http://www.poissonconsulting.ca/f/1492264933.*

## Background

The key management questions to be addressed by the analyses are:

- Is there a change in abundance of adult life stages of fish using the Middle Columbia River (MCR) that corresponds with the implementation of a year-round minimum flow?
- Is there a change in growth rate of adult life stages of the most common fish species using the MCR that corresponds with the implementation of a year-round minimum flow?
- Is there a change in body condition (measured as a function of relative weight to length) of adult life stages of fish using the MCR that corresponds with the implementation of a year-round minimum flow?
- Is there a change in spatial distribution of adult life stages of fish using the MCR that corresponds with the implementation of a year-round minimum flow?

Other objectives include the estimation of species richness, species diversity (evenness) and the modeling of environmental-fish metric relationships and scale age data. The year-round minimum flow was implemented in the winter of 2010 at the same time that a fifth turbine was added.

## Methods

### Data Preparation

The data were provided by Golder Associates.

#### Life-Stage

The four primary fish species were categorized as fry, juvenile or adult based on their lengths.

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

Bull Trout | <120 | <400 |

Mountain Whitefish | <120 | <175 |

Rainbow Trout | <120 | <250 |

Largescale Sucker | <120 | <350 |

### Statistical Analysis

Hierarchical Bayesian models were fitted to the count data using R version 3.3.0 (Team 2013) and JAGS 4.1.0 (Plummer 2015) which interfaced with each other via jaggernaut 2.3.3 (Thorley 2013). For additional information on hierarchical Bayesian modelling in the BUGS language, of which JAGS uses a dialect, the reader is referred to Kery and Schaub (2011, 41–44).

Unless specified, the models assumed vague (low information) prior distributions (Kery and Schaub 2011, 36). The posterior distributions were estimated from a minimum of 1,000 Markov Chain Monte Carlo (MCMC) samples thinned from the second halves of three chains of at least 10,000 iterations in length (Kery and Schaub 2011, 38–40). Model convergence was confirmed by ensuring that Rhat (Kery and Schaub 2011, 40) was less than 1.1 for each of the parameters in the model (Kery and Schaub 2011, 61). Model adequacy was confirmed by examination of residual plots.

The posterior distributions of the *fixed*
(Kery and Schaub 2011, 75) parameters
are summarised in terms of a *point* estimate (mean),
*lower* and *upper* 95% credible limits (2.5th
and 97.5th percentiles), the standard deviation (*SD*), percent relative *error* (half the 95% credible interval as a percent of the point estimate) and *significance* (Kery and Schaub 2011, 37, 42).

Variable selection was achieved by dropping *insignificant*
(Kery and Schaub 2011, 37, 42) fixed (Kery and Schaub 2011, 77–82) variables
and *uninformative* random variables.
A fixed variables was considered to be
insignificant if its significance was \(\geq\) 0.05 while a random variable was
considered to be uninformative if its percent relative error was \(\geq\) 80%.

The results are displayed
graphically by plotting the modeled relationships between particular
variables and the response with 95%
credible intervals (CRIs) 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).
Where informative the influence of particular variables is expressed in terms
of the *effect size* (i.e., percent change in the response variable) with
95% CRIs
(Bradford, Korman, and Higgins 2005).

#### Growth

Annual growth was estimated from the inter-annual recaptures using the Fabens method (Fabens 1965) for estimating the von Bertalanffy (VB) growth curve (von Bertalanffy 1938). The VB curves is based on the premise that

\[ \frac{dl}{dt} = k (L_{\infty} - l)\]

where \(l\) is the length of the individual, \(k\) is the growth coefficient and \(L_{\infty}\) is the mean maximum length.

Integrating the above equation gives

\[ l_t = L_{\infty} (1 - e^{-k(t - t0)})\]

where \(l_t\) is the length at time \(t\) and \(t0\) is the time at which the individual would have had no length.

The Fabens form allows

\[ l_r = l_c + (L_{\infty} - l_c) (1 - e^{-kT})\]

where \(l_r\) is the length at recapture, \(l_c\) is the length at capture and \(T\) is the time at large.

Key assumptions of the growth model include:

- \(L_{\infty}\) is constant.
- \(k\) can vary with discharge regime.
- \(k\) can vary randomly with year.
- The residual variation in growth is independently and identically normally distributed.

#### Condition

Condition was estimated via an analysis of mass-length relations (He et al. 2008).

More specifically the model was based on the allometric relationship

\[ W = \alpha L^{\beta}\]

where \(W\) is the weight (mass), \(\alpha\) is the coefficent, \(\beta\) is the exponent and \(L\) is the length.

To improve chain mixing the relation was log-transformed, i.e.,

\[ \log(W) = \log(\alpha) + \beta \log(L)\]

and the logged lengths centered, i.e., \(\log(L) - \overline{\log(L)}\), prior to model fitting.

Preliminary analyses indicated that the variation in the exponent \(\beta\) with respect to year was not informative.

Key assumptions of the final condition model include:

- The expected weight varies with length as an allometric relationship.
- The intercept of the log-transformed allometric relationship is described by a linear mixed model.
- The intercept of the log-transformed allometric relationship varies with discharge regime and season.
- The intercept of the log-transformed allometric relationship varies randomly with year, site and the interaction between year and site.
- The slope of the log-transformed allometric relationship is described by a linear mixed model.
- The slope of the log-transformed allometric relationship varies with discharge regime and season.
- The slope of the log-transformed allometric relationship varies randomly with year.
- The residual variation in weight for the log-transformed allometric relationship is independently and identically normally distributed.

#### Occupancy

Occupancy, which is the probability that a particular species was present at a site, was estimated from the temporal replication of detection data (Kery, 2010; Kery and Schaub, 2011, pp. 238-242 and 414-418), i.e., each site was surveyed multiple times within a season. A species was considered to have been detected if one or more individuals of the species were caught or counted. It is important to note that the model estimates the probability that the species was present at a given (or typical) site in a given (or typical) year as opposed to the probability that the species was present in the entire study area. We focused on Northern Pikeminnow, Burbot, Lake Whitefish, Rainbow Trout, Redside Shiner and Sculpins because they were low enough density to not to be present at all sites at all times yet were encounted sufficiently often to provide information on spatial and temporal changes.

Key assumptions of the occupancy model include:

- Occupancy (probability of presence) is described by a generalized linear mixed model with a logit link.
- Occupancy varies with discharge regime and season.
- Occupancy varies randomly with site and year.
- Sites are closed, i.e., the species is present or absent at a site for all the sessions in a particular season of a year.
- Observed presence is described by a bernoulli distribution, given occupancy.

#### Species Richness

The estimated probabilities of presence for the six species considered in the occupany analyses were summed to give the expected species richnesses at a given (or typical) site in a given (or typical) year.

#### Count

The count data were analysed using an overdispersed Poisson model (Kery, 2010; Kery and Schaub, 2011, pp. 168-170,180 and 55-56) to provide estimates of the lineal river count density (count/km) by year and site. Unlike Kery (2010) and Kery and Schaub (2011), which used a log-normal distribution to account for the extra-Poisson variation, the current model used a gamma distribution with identical shape and scale parameters because it has a mean of 1 and therefore no overall effect on the expected count. The count data does not enable us to estimate abundance nor observer efficiency, but it enables us to estimate an expected count, which is the product of the two. As such it is necessary to assume that changes in observer efficiency are negligible in order to interpret the estimates as relative density.

Key assumptions of the abundance model include:

- Lineal density (fish/km) is described by an autoregressive generalized linear mixed model with a logarithm link.
- Lineal density (fish/km) varies with season.
- Lineal density (fish/km) varies randomly with year, river kilometer, site and the interaction between site and year.
- The effect of year on lineal density (fish/km) is autoregressive with a lag of one year and varies with discharge regime.
- The regression coefficient of river kilometre is described by a linear mixed model.
- The effect of river kilometre on lineal density (distribution of density along the river) varies with discharge regime and season.
- The effect of river kilometre on lineal density (distribution of density along the river) varies randomly with year.
- The counts are gamma-Poisson distributed, given the mean count.

#### Movement

The extent to which sites are closed, i.e., fish remain at the same site between sessions, was evaluated from a logistic ANCOVA (Kery 2010). The model estimated the probability that intra-annual recaptures were caught at the same site versus a different one.

Key assumptions of the site fidelity model include:

- Site fidelity varies with season, length and the interaction between season and length.
- Observed site fidelity is Bernoulli distributed.

#### Observer Length Correction

The bias (accuracy) and error (precisions) in observer’s fish length estimates were quantified using a model with a categorical distribution that compared the proportions of fish in different length-classes for each observer to the equivalent proportions for the measured fish.

Key assumptions of the observer length correction model include:

- The expected length bias can vary by observer.
- The expected length error can vary by observer.
- The residual variation in length is independently and identically normally distributed.

The observed fish lengths were corrected for the estimated length biases.

#### Abundance

The catch data were analysed using a capture-recapture-based overdispersed Poisson model to provide estimates of capture efficiency and absolute abundance. To maximize the number of recaptures the model grouped all the sites into a supersite for the purposes of estimating the number of marked fish but analysed the total captures at the site level.

Key assumptions of the abundance model include:

- Lineal density (fish/km) varies with year, season and river km.
- Lineal density (fish/km) varies randomly with site and the interaction between site and year.
- The effect of year on lineal density (fish/km) is autoregressive with a lag of one year and varies with discharge regime.
- The effect of river km on lineal density (fish/km) (distribution) varies with discharge regime and season.
- The effect of river km on lineal density (fish/km) (distribution) varies randomly with year.
- Efficiency (probability of capture) varies by season and method (capture versus count).
- Efficiency varies randomly by session within season within year.
- Marked and unmarked fish have the same probability of capture.
- There is no tag loss, migration (at the supersite level), mortality or misidentification of fish.
- The number of fish caught is gamma-Poisson distributed.

Note that the analysis did not converge for Largescale Suckers. Excluding the 2001 data from the analysis (there was no data for this species from 2002 to 2009) did not improve convergence.

#### Species Evenness

The site and year estimates of the lineal bank
count densities from the count model for Rainbow Trout,
Suckers, Burbot and Northern Pikeminnow were combined with the
equivalent count estimates for Bull Trout and Adult Mountain Whitefish from
the abundance model to calculate the shannon index of evenness \((E)\).
The index was calculated using the following formula where \(S\) is the number of species
and \(p_i\) is the proportion of the total count belonging to the i*th* species.

\[ E = \frac{-\sum p_i \log(p_i)}{\log(S)}\]

#### Long-Term Trends

The long-term congruence between the yearly fall fish metrics (Grw = Estimated growth \(k\),
Con = Estimated condition effect, Blank = Estimated population growth rate, Rkm = Estimated effect of centred river kilometre on log density) by life stage
(Juv = Juvenile, AD = Adult) and a range
of environmental variables including the tri-monthly (JaMa = January - March,
ApJn = April - June, JlSe = July - September, OcDe = October - December)
discharge from Revelstoke Dam (Q10, QMu = Mean, Q90, QDlt = Mean absolute difference)
and the elevation (Ele),
the Pacific Decadal Oscillation (PDO), the chlorophyll-a (ChlA) and
invertebrate production (Inv) and the kokanee abundance (KO) was examined using
Dynamic Factor Analysis (Zuur, Tuck, and Bailey 2003). Due
to the *rotation problem* the underlying trends
were indeterminate (Abmann, Boysen-Hogrefe, and Pape 2014).
The October to December discharge and elevation values
were lead (opposite of lagged) by one year to account for the fact that
they occurred after sampling.

Key assumptions of the Dynamic Factor Analysis (DFA) model include:

- The time series are described by three underlying trends.
- The random walk processes in the trends are normally distributed.
- The residual variation in the standardised variables is normally distributed.

The similarities were represented visually by using non-metric multidimensional scaling (NMDS) to map the distances onto two-dimensional space. The more similar two time series are the closer they will tend to be in the resultant NMDS plot.

#### Short-Term Correlations

To assess the short-term congruence between the yearly fish metrics and the environmental variables, the pair-wise distances between the residuals from the DFA model were calculated as \(1 - abs(cor(x, y))\) where \(cor\) is the Pearson correlation, \(abs\) the absolute value and \(x\) and \(y\) are the two time series being compared.

The short-term similarities were represented visually by using NMDS to map the distances onto two-dimensional space.

#### Scale Age

The age of Mountain Whitefish was estimated from scales by two independent agers. The scale aging process was repeated twice per ager, leading to two observations per ager per fish encounter (Hurlbert 1984). The actual age was determined from the length of each fish at first capture based on the stage cutoffs by season. The scale ages were analysed using a linear mixed model.

Key assumptions of the scale age model include:

- The scale age varies by ager.
- The scale age varies randomly with fish encounter and ager within fish encounter.
- The scale age varies linearly with age.
- The effect of age on scale age varies by ager.
- The random effects are normally distributed.
- The residual variation in the scale ages (replicate within ager within encounter) is described by a zero-truncated normal distribution.

### Model Code

The JAGS model code, which uses a series of naming conventions, is presented below.

#### Growth

Variable/Parameter | Description |
---|---|

`bK[i]` |
Expected growth coefficient in the `i` th year |

`bKIntercept` |
Intercept for `log(bK)` |

`bKRegime[i]` |
Effect of `i` ^{th} regime on `log(bK)` |

`bKYear[i]` |
Random effect of `i` ^{th} `Year` on `log(bK)` |

`bLinf` |
Mean maximum length |

`eGrowth[i]` |
Expected `Growth` of the `i` ^{th} recapture |

`Growth[i]` |
Change in length of the `i` ^{th} fish between release and recapture |

`LengthAtRelease[i]` |
Length of the `i` ^{th} recapture when released |

`nRegime[i]` |
Number of regimes |

`sGrowth` |
SD of residual variation in `Growth` |

`sKYear[i]` |
SD of effect of `Year` on `log(bK)` |

`Threshold` |
Last year of the first regime |

`Year[i]` |
Year the `i` ^{th} recapture was released |

`Years[i]` |
Number of years between release and recapture for the `i` ^{th} recapture |

##### Growth - Model1

```
model {
bKIntercept ~ dnorm (0, 5^-2)
bKRegime[1] <- 0
for(i in 2:nRegime) {
bKRegime[i] ~ dnorm(0, 5^-2)
}
sKYear ~ dunif (0, 5)
for (i in 1:nYear) {
bKYear[i] ~ dnorm(0, sKYear^-2)
log(bK[i]) <- bKIntercept + bKRegime[step(i - Threshold) + 1] + bKYear[i]
}
bLinf ~ dunif(100, 1000)
sGrowth ~ dunif(0, 100)
for (i in 1:length(Year)) {
eGrowth[i] <- (bLinf - LengthAtRelease[i]) * (1 - exp(-sum(bK[Year[i]:(Year[i] + Years[i] - 1)])))
Growth[i] ~ dnorm(eGrowth[i], sGrowth^-2)
}
tGrowth <- bKRegime[2]
}
```

#### Condition

Variable/Parameter | Description |
---|---|

`bWeightIntercept` |
Intercept for `eWeightIntercept` |

`bWeightRegimeIntercept[i]` |
Effect of `i` ^{th} regime on `bWeightIntercept` |

`bWeightRegimeSlope[i]` |
Effect of `i` ^{th} regime on `bWeightSlope` |

`bWeightSeasonIntercept[i]` |
Effect of `i` ^{th} season on `bWeightIntercept` |

`bWeightSeasonSlope[i]` |
Effect of `i` ^{th} season on `bWeightSlope` |

`bWeightSiteIntercept[i]` |
Random effect of `i` ^{th} site on `bWeightIntercept` |

`bWeightSiteYearIntercept[i,j]` |
Random effect of `i` ^{th} site in `j` ^{th} year on `bWeightIntercept` |

`bWeightSlope` |
Intercept for `eWeightSlope` |

`bWeightYearIntercept[i]` |
Random effect of `i` ^{th} year on `bWeightIntercept` |

`bWeightYearSlope[i]` |
Random effect of `i` ^{th} year on `bWeightSlope` |

`eWeight[i]` |
Expected weight of the `i` ^{th} fish |

`eWeightIntercept[i]` |
Intercept for `log(eWeight[i])` |

`eWeightSlope[i]` |
Slope for `log(eWeight[i])` |

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

`sWeight` |
Residual SD of `Weight` |

`sWeightSiteIntercept` |
SD for the effect of site on `bWeightIntercept` |

`sWeightSiteYearIntercept` |
SD for the effect of the combination of site and year on `eWeightIntercept` |

`sWeightYearIntercept` |
SD of the effect of year on `bWeightIntercept` |

`sWeightYearSlope` |
SD for the random effect of year on `eWeightSlope` |

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

##### Condition - Model1

```
model {
bWeightIntercept ~ dnorm(5, 5^-2)
bWeightSlope ~ dnorm(3, 5^-2)
bWeightRegimeIntercept[1] <- 0
bWeightRegimeSlope[1] <- 0
for(i in 2:nRegime) {
bWeightRegimeIntercept[i] ~ dnorm(0, 5^-2)
bWeightRegimeSlope[i] ~ dnorm(0, 5^-2)
}
bWeightSeasonIntercept[1] <- 0
bWeightSeasonSlope[1] <- 0
for(i in 2:nSeason) {
bWeightSeasonIntercept[i] ~ dnorm(0, 5^-2)
bWeightSeasonSlope[i] ~ dnorm(0, 5^-2)
}
sWeightYearIntercept ~ dunif(0, 5)
sWeightYearSlope ~ dunif(0, 5)
for(yr in 1:nYear) {
bWeightYearIntercept[yr] ~ dnorm(0, sWeightYearIntercept^-2)
bWeightYearSlope[yr] ~ dnorm(0, sWeightYearSlope^-2)
}
sWeightSiteIntercept ~ dunif(0, 5)
sWeightSiteYearIntercept ~ dunif(0, 5)
for(st in 1:nSite) {
bWeightSiteIntercept[st] ~ dnorm(0, sWeightSiteIntercept^-2)
for(yr in 1:nYear) {
bWeightSiteYearIntercept[st, yr] ~ dnorm(0, sWeightSiteYearIntercept^-2)
}
}
sWeight ~ dunif(0, 5)
for(i in 1:length(Year)) {
eWeightIntercept[i] <- bWeightIntercept
+ bWeightRegimeIntercept[Regime[i]]
+ bWeightSeasonIntercept[Season[i]]
+ bWeightYearIntercept[Year[i]]
+ bWeightSiteIntercept[Site[i]]
+ bWeightSiteYearIntercept[Site[i],Year[i]]
eWeightSlope[i] <- bWeightSlope
+ bWeightRegimeSlope[Regime[i]]
+ bWeightSeasonSlope[Season[i]]
+ bWeightYearSlope[Year[i]]
log(eWeight[i]) <- eWeightIntercept[i] + eWeightSlope[i] * Length[i]
Weight[i] ~ dlnorm(log(eWeight[i]) , sWeight^-2)
}
tCondition1 <- bWeightRegimeIntercept[2]
tCondition2 <- bWeightRegimeSlope[2]
}
```

#### Occupancy

Variable/Parameter | Description |
---|---|

`bOccupancy` |
Intercept of `logit(eOccupancy)` |

`bOccupancyRegime[i]` |
Effect of `i` ^{th} regime on `logit(eOccupancy)` |

`bOccupancySeason[i]` |
Effect of `i` ^{th} season on `logit(eOccupancy)` |

`bOccupancySite[i]` |
Effect of `i` ^{th} site on `logit(eOccupancy)` |

`bOccupancyYear[i]` |
Effect of `i` ^{th} year on `logit(eOccupancy)` |

`eObserved[i]` |
Probability of observing species on `i` ^{th} site visit |

`eOccupancy[i]` |
Predicted occupancy (species presence versus absence) on `i` ^{th} site visit |

`nRegime` |
Number of regimes in the dataset (2) |

`nSeason` |
Number of seasons in the dataset (2) |

`nSite` |
Number of sites in the dataset |

`nYear` |
Number of years of data |

`Observed[i]` |
Whether the species was observed on `i` ^{th} site visit (0 or 1) |

`Regime[i]` |
Regime of`i` ^{th} site visit |

`Season[i]` |
Season of `i` ^{th} site visit |

`Site[i]` |
Site of `i` ^{th} site visit |

`sOccupancySite` |
SD parameter for the distribution of `bOccupancySite[i]` |

`sOccupancyYear` |
SD parameter for the distribution of `bOccupancyYear[i]` |

`Year[i]` |
Year of `i` ^{th} site visit |

##### Occupancy - Model1

```
model {
bOccupancy ~ dnorm(0, 5^-2)
bOccupancySeason[1] <- 0
for(i in 2:nSeason) {
bOccupancySeason[i] ~ dnorm(0, 5^-2)
}
bOccupancyRegime[1] <- 0
for(i in 2:nRegime) {
bOccupancyRegime[i] ~ dnorm(0, 5^-2)
}
sOccupancyYear ~ dunif(0, 5)
for (yr in 1:nYear) {
bOccupancyYear[yr] ~ dnorm(0, sOccupancyYear^-2)
}
sOccupancySite ~ dunif(0, 5)
for (st in 1:nSite) {
bOccupancySite[st] ~ dnorm(0, sOccupancySite^-2)
}
for (i in 1:length(Year)) {
logit(eOccupancy[i]) <- bOccupancy
+ bOccupancyRegime[Regime[i]] + bOccupancySeason[Season[i]]
+ bOccupancySite[Site[i]] + bOccupancyYear[Year[i]]
eObserved[i] <- eOccupancy[i]
Observed[i] ~ dbern(eObserved[i])
}
}
```

#### Count

Variable/Parameter | Description |
---|---|

`bDensitySeason[i]` |
Effect of `i` ^{th} season on `log(eDensity)` |

`bDensitySite[i]` |
Effect of `i` ^{th} site on `log(eDensity)` |

`bDensitySiteYear[i, j]` |
Effect of `i` ^{th} site in `j` ^{th} year on `log(eDensity)` |

`bDensityYear[i]` |
Random effect of `i` ^{th} year on `log(eDensity)` |

`bDistribution` |
Intercept of `eDistribution` |

`bDistributionRegime[i]` |
Effect of `i` ^{th} regime on `eDistribution` |

`bDistributionSeason[i]` |
Effect of `i` ^{th} season on `eDistribution` |

`bDistributionYear[i]` |
Effect of `i` ^{th} year on `eDistribution` |

`bRate` |
Baseline rate of change (relative to the previous year) in `eDensity` due to year effect |

`bRateRegime[i]` |
Deviate from `bRate` due to regime effect in the `i` ^{th} year |

`bRateYear[i]` |
Random deviate from `bRate` due to year effect in the `i` ^{th} year |

`Count[i]` |
Count on `i` ^{th} site visit |

`eCount[i]` |
Expected count on `i` ^{th} site visit |

`eDensity[i]` |
Lineal density on `i` ^{th} site visit |

`eDispersion[i]` |
Overdispersion factor on `i` ^{th} site visit |

`eDistribution[i]` |
Effect of centred river kilometre on `i` ^{th} site visit on `log(eDensity)` |

`eRateYear[i]` |
Rate of change in year effect between the `(i-1)` ^{th} and `i` ^{th} year |

`ProportionSampled[i]` |
Proportion of `i` ^{th} site that was sampled |

`sDispersion[i]` |
SD of the overdispersion factor distribution |

`SiteLength[i]` |
Length of `i` ^{th} site |

`sRateYear` |
SD of the distribution of `bRateYear` |

##### Count - Model1

```
model {
bDistribution ~ dnorm(0, 5^-2)
bRateRegime[1] <- 0
bDistributionRegime[1] <- 0
for(i in 2:nRegime) {
bRateRegime[i] ~ dnorm(0, 5^-2)
bDistributionRegime[i] ~ dnorm(0, 5^-2)
}
bDensitySeason[1] <- 0
bDistributionSeason[1] <- 0
for(i in 2:nSeason) {
bDensitySeason[i] ~ dnorm(0, 5^-2)
bDistributionSeason[i] ~ dnorm(0, 5^-2)
}
bRate ~ dnorm(0, 5^-2)
sRateYear ~ dunif(0, 5)
for(i in 1:nYear) {
bRateYear[i] ~ dnorm(0, sRateYear^-2)
}
bDensityYear[1] ~ dnorm(0, 5^-2)
for (i in 2:nYear) {
eRateYear[i-1] <- bRate + bRateYear[i-1] + bRateRegime[YearRegime[i-1]]
bDensityYear[i] <- bDensityYear[i-1] + eRateYear[i-1]
}
sDistributionYear ~ dunif(0, 2)
for (i in 1:nYear) {
bDistributionYear[i] ~ dnorm(0, sDistributionYear^-2)
}
sDensitySite ~ dunif(0, 5)
sDensitySiteYear ~ dunif(0, 2)
for (i in 1:nSite) {
bDensitySite[i] ~ dnorm(0, sDensitySite^-2)
for (j in 1:nYear) {
bDensitySiteYear[i, j] ~ dnorm(0, sDensitySiteYear^-2)
}
}
sDispersion ~ dunif(0, 5)
for (i in 1:length(Year)) {
eDistribution[i] <- bDistribution
+ bDistributionRegime[Regime[i]]
+ bDistributionSeason[Season[i]]
+ bDistributionYear[Year[i]]
log(eDensity[i]) <- bDensityYear[Year[i]]
+ eDistribution[i] * RiverKm[i]
+ bDensitySeason[Season[i]]
+ bDensitySite[Site[i]]
+ bDensitySiteYear[Site[i],Year[i]]
eCount[i] <- eDensity[i] * SiteLength[i] * ProportionSampled[i]
eDispersion[i] ~ dgamma(1 / sDispersion^2, 1 / sDispersion^2)
Count[i] ~ dpois(eCount[i] * eDispersion[i])
}
tAbundance <- bRateRegime[2]
tDistribution <- bDistributionRegime[2]
}
```

#### Movement

Variable/Parameter | Description |
---|---|

`bLength` |
Coefficient for the effect of `Length` on `logit(eMoved)` |

`bLengthSeason[i]` |
Coefficient for the effect of the interaction between `Length` and `Season` on `logit(eMoved)` |

`bMoved` |
Intercept for `logit(eMoved)` |

`bMovedSeason[i]` |
Effect of `i` ^{th} season on `logit(eMoved)` |

`eMoved[i]` |
Probability of different site from previous encounter for `i` ^{th} recapture |

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

`Moved[i]` |
Indicates whether `i` ^{th} recapture is recorded at a different site from previous encounter |

`nSeason` |
Number of seasons in the study (2) |

`Season[i]` |
Season of `i` ^{th} recapture |

##### Movement - Model1

```
model {
bMoved ~ dnorm(0, 5^-2)
bLength ~ dnorm(0, 5^-2)
bMovedSeason[1] <- 0
bLengthSeason[1] <- 0
for(i in 2:nSeason) {
bMovedSeason[i] ~ dnorm(0, 5^-5)
bLengthSeason[i] ~ dnorm(0, 5^-5)
}
for (i in 1:length(Season)) {
logit(eMoved[i]) <- bMoved + bMovedSeason[Season[i]] + (bLength + bLengthSeason[Season[i]]) * Length[i]
Moved[i] ~ dbern(eMoved[i])
}
}
```

#### Observer Length Correction

Variable/Parameter | Description |
---|---|

`bLength[i]` |
Relative inaccuracy of the`i` ^{th} `Observer` |

`ClassLength` |
Mean `Length` of fish belonging to the `i` ^{th} class |

`dClass[i]` |
Prior value for the relative proportion of fish in the `i` ^{th} class |

`eClass[i]` |
Expected class of the `i` ^{th} fish |

`eLength[i]` |
Expected `Length` of the `i` ^{th} fish |

`eSLength[i]` |
Expected SD of the residual variation in `Length` for the `i` ^{th} |

`Length[i]` |
Observed fork length of the `i` ^{th} fish |

`Observer[i]` |
Observer of the `i` ^{th} fish where the first observer used a length board |

`pClass[i]` |
Proportion of fish in the `i` ^{th} class |

`sLength[i]` |
Relative imprecision of the `i` ^{th} `Observer` |

##### Observer Length Correction - Model1

```
model {
for(i in 1:nClass) {
dClass[i] <- 1
}
pClass[1:nClass] ~ ddirch(dClass[])
bLength[1] <- 1
sLength[1] <- 1
for(i in 2:nObserver) {
bLength[i] ~ dunif(0.5, 2)
sLength[i] ~ dunif(1, 50)
}
for(i in 1:length(Length)) {
eClass[i] ~ dcat(pClass[])
eLength[i] <- bLength[Observer[i]] * ClassLength[eClass[i]]
eSLength[i] <- sLength[Observer[i]] * ClassSD
Length[i] ~ dnorm(eLength[i], eSLength[i]^-2)
}
}
```

#### Abundance

Variable/Parameter | Description |
---|---|

`bDensitySeason[i]` |
Effect of `i` ^{th} `Season` on `bDensity` |

`bDensitySite[i]` |
Random effect of `i` ^{th} `Site` on `bDensity` |

`bDensitySiteYear[i, j]` |
Effect of `i` ^{th} `Site` in `j` ^{th} year on `bDensity` |

`bDensityYear[i]` |
Random effect of `i` ^{th} `Year` on `bDensity` |

`bDistribution` |
Intercept for `eDistribution` |

`bDistributionRegime[i]` |
Effect of `i` ^{th} `Regime` on `bDistribution` |

`bDistributionSeason[i]` |
Effect of `i` ^{th} `Season` on `bDistribution` |

`bDistributionYear[i]` |
Random effect of `i` ^{th} `Year` on `bDistribution` |

`bEfficiency` |
Intercept for `logit(eEfficiency)` |

`bEfficiencySessionSeasonYear[i, j, k]` |
Effect of `i` ^{th} `Session` in `j` ^{th} `Season` of `k` ^{th} `Year` on `bEfficiency` |

`bRate` |
Baseline rate of change (relative to the previous year) in `eDensity` due to year effect |

`bRateRegime[i]` |
Deviate from `bRate` due to regime effect in the `i` ^{th} year |

`bRateYear[i]` |
Random deviate from `bRate` due to year effect in the `i` ^{th} year |

`Catch[i]` |
Number of fish caught on `i` ^{th} site visit |

`eAbundance[i]` |
Predicted abundance on `i` ^{th} site visit |

`eDensity[i]` |
Predicted lineal density on `i` ^{th} site visit |

`eDistribution[i]` |
Predicted relationship between centred river kilometre and `i` ^{th} site visit on `bDensity` |

`eEfficiency[i]` |
Predicted efficiency during `i` ^{th} site visit |

`eRateYear[i]` |
Rate of change in year effect between the `(i-1)` ^{th} and `i` ^{th} year |

`Marked[i]` |
Number of marked fish caught in `i` ^{th} river visit |

`sRateYear` |
SD of the distribution of `bRateYear` |

`Tagged[i]` |
Number of fish tagged prior to `i` ^{th} river visit |

##### Abundance - Model1

```
model {
bEfficiency ~ dnorm(0, 5^-2)
bDistribution ~ dnorm(0, 5^-2)
bRateRegime[1] <- 0
bDistributionRegime[1] <- 0
for(i in 2:nRegime) {
bRateRegime[i] ~ dnorm(0, 5^-2)
bDistributionRegime[i] ~ dnorm(0, 5^-2)
}
bEfficiencySeason[1] <- 0
bDensitySeason[1] <- 0
bDistributionSeason[1] <- 0
for(i in 2:nSeason) {
bEfficiencySeason[i] ~ dnorm(0, 5^-2)
bDensitySeason[i] ~ dnorm(0, 5^-2)
bDistributionSeason[i] ~ dnorm(0, 5^-2)
}
bRate ~ dnorm(0, 5^-2)
sRateYear ~ dunif(0, 5)
for(i in 1:nYear) {
bRateYear[i] ~ dnorm(0, sRateYear^-2)
}
bDensityYear[1] ~ dnorm(0, 5^-2)
for (i in 2:nYear) {
eRateYear[i-1] <- bRate + bRateYear[i-1] + bRateRegime[YearRegime[i-1]]
bDensityYear[i] <- bDensityYear[i-1] + eRateYear[i-1]
}
sDistributionYear ~ dunif(0, 2)
for (i in 1:nYear) {
bDistributionYear[i] ~ dnorm(0, sDistributionYear^-2)
}
sDensitySite ~ dunif(0, 5)
sDensitySiteYear ~ dunif(0, 2)
for (i in 1:nSite) {
bDensitySite[i] ~ dnorm(0, sDensitySite^-2)
for (j in 1:nYear) {
bDensitySiteYear[i, j] ~ dnorm(0, sDensitySiteYear^-2)
}
}
sEfficiencySessionSeasonYear ~ dunif(0, 5)
for (i in 1:nSession) {
for (j in 1:nSeason) {
for (k in 1:nYear) {
bEfficiencySessionSeasonYear[i, j, k] ~ dnorm(0, sEfficiencySessionSeasonYear^-2)
}
}
}
bType[1] <- 1
for (i in 2:nType) {
bType[i] ~ dunif(0, 10)
}
for(i in 1:length(EffIndex)) {
logit(eEff[i]) <- bEfficiency
+ bEfficiencySeason[Season[EffIndex[i]]]
+ bEfficiencySessionSeasonYear[Session[EffIndex[i]],
Season[EffIndex[i]],
Year[EffIndex[i]]]
Marked[EffIndex[i]] ~ dbin(eEff[i], Tagged[EffIndex[i]])
}
sDispersion ~ dunif(0, 5)
for (i in 1:length(Year)) {
logit(eEfficiency[i]) <- bEfficiency
+ bEfficiencySeason[Season[i]]
+ bEfficiencySessionSeasonYear[Session[i], Season[i], Year[i]]
eDistribution[i] <- bDistribution
+ bDistributionRegime[Regime[i]]
+ bDistributionSeason[Season[i]]
+ bDistributionYear[Year[i]]
log(eDensity[i]) <- bDensityYear[Year[i]]
+ eDistribution[i] * RiverKm[i]
+ bDensitySeason[Season[i]]
+ bDensitySite[Site[i]]
+ bDensityYear[Year[i]]
+ bDensitySiteYear[Site[i], Year[i]]
eCatch[i] <- eDensity[i] * SiteLength[i] * ProportionSampled[i] * eEfficiency[i] * bType[Type[i]]
eDispersion[i] ~ dgamma(1 / sDispersion^2, 1 / sDispersion^2)
Catch[i] ~ dpois(eCatch[i] * eDispersion[i])
}
tAbundance <- bRateRegime[2]
tDistribution <- bDistributionRegime[2]
}
```

#### Long-Term Trends

Variable/Parameter | Description |
---|---|

`bDistance[i,j]` |
Euclidean distance between `i` ^{th} and `j` ^{th} `Variable` |

`bTrendYear[t,y]` |
Expected value for `t` ^{th} trend in `y` ^{th} `Year` |

`eValue[v,y,t]` |
Expected standardised value for `v` ^{th} `Variable` in `y` ^{th} `Year` considering `t` ^{th} trends |

`sTrend` |
SD in trend random walks |

`sValue` |
SD for residual variation in `Value` |

`Value[i]` |
Standardised value for `i` ^{th} data point |

`Variable[i]` |
Variable for `i` ^{th} data point |

`Year[i]` |
Year of `i` ^{th} data point |

`Z[v,y]` |
Expected weighting for `v` ^{th} `Variable` in `y` ^{th} `Year` |

##### Long-Term Trends - Model1

```
model{
sTrend ~ dunif(0, 1)
for (t in 1:nTrend) {
bTrendYear[t,1] ~ dunif(-1,1)
for(y in 2:nYear){
bTrendYear[t,y] ~ dnorm(bTrendYear[t,y-1], sTrend^-2)
}
}
for(v in 1:nVariable){
for(t in 1:nTrend) {
Z[v,t] ~ dunif(-1,1)
}
for(y in 1:nYear){
eValue[v,y,1] <- Z[v,1] * bTrendYear[1,y]
for(t in 2:nTrend) {
eValue[v,y,t] <- eValue[v,y,t-1] + Z[v,t] * bTrendYear[t,y]
}
}
}
sValue ~ dunif(0, 1)
for(i in 1:length(Value)) {
Value[i] ~ dnorm(eValue[Variable[i], Year[i], nTrend], sValue^-2)
}
for(i in 1:nVariable) {
for(j in 1:nVariable) {
bDistance[i,j] <- sqrt(sum((Z[i,]-Z[j,])^2))
}
}
}
```

#### Scale Age

Variable/Parameter | Description |
---|---|

`Ager[i]` |
`i` th ager |

`bIntercept` |
Intercept of `eScaleAge` |

`bInterceptAger[i]` |
Effect of `Ager[i]` on the intercept of `eScaleAge` |

`bInterceptEncounterID[i]` |
Random effect of `EncounterID[i]` on the intercept of `eScaleAge` |

`bInterceptEncounterIDAger[i,j]` |
Random effect of the interaction of `EncounterID[i]` and `Ager[j]` on the intercept of `eScaleAge` |

`BirthYear[i]` |
BirthYear of the `i` th fish |

`bSlope` |
Intercept on the slope of the effect of `eAge` on `eScaleAge` |

`bSlopeAger[i]` |
Effect of `Ager[i]` on the slope of the effect of `eAge` on `eScaleAge` |

`dYear[i]` |
`i` th parameter of the Dirichlet distribution on `pYear` |

`eAge[i]` |
Expected true age of the fish on the `i` th scale age observation |

`EncounterID[i]` |
ID of the `i` th encounter |

`eScaleAge[i]` |
Expected scale age on the `i` th scale age observation |

`FishID[i]` |
Fish ID of the `i` th scale age |

`pYear[i]` |
Probability that a fish is born in the `i` th year, starting with 1993 |

`ScaleAge[i]` |
`i` th scale age observation |

`sInterceptEncounterID` |
SD of `bInterceptEncounterID` |

`sInterceptEncounterIDAger` |
SD of `bInterceptEncounterIDAger` |

`sSD[i]` |
SD of the `i` th ager due to pseudo-replication |

`Year[i]` |
Encounter year of the `i` th scale age observation |

##### Scale Age - Model1

```
model {
for(i in 1:nBirthYear) {
dYear[i] <- 1
}
pYear ~ ddirch(dYear[])
for(i in 1:nFishID){
BirthYear[i] ~ dcat(pYear)
}
mBirthYear <- BirthYear
bIntercept ~ dnorm(0, 2^-2)
bSlope ~ dnorm(1, 2^-2)
for(i in 1:nAger) {
sSD[i] ~ dunif(0, 5)
}
bInterceptAger[1] <- 0
bSlopeAger[1] <- 0
for(i in 2:nAger) {
bInterceptAger[i] ~ dnorm(0, 2^-2)
bSlopeAger[i] ~ dnorm(0, 2^-2)
}
sInterceptEncounterID ~ dunif(0, 2)
sInterceptEncounterIDAger ~ dunif(0, 2)
for(i in 1:nEncounterID) {
bInterceptEncounterID[i] ~ dnorm(0, sInterceptEncounterID^-2)
for(j in 1:nAger) {
bInterceptEncounterIDAger[i,j] ~ dnorm(0, sInterceptEncounterIDAger^-2)
}
}
for(i in 1:length(ScaleAge)){
eAge[i] <- Year[i] - BirthYear[FishID[i]]
eScaleAge[i] <- bIntercept + bInterceptAger[Ager[i]] + (bSlope + bSlopeAger[Ager[i]]) * eAge[i] + bInterceptEncounterID[EncounterID[i]] + bInterceptEncounterIDAger[EncounterID[i], Ager[i]]
ScaleAge[i] ~ dnorm(eScaleAge[i], sSD[Ager[i]]^-2) T(0,)
}
}
```

## Results

### Model Parameters

The posterior distributions for the *fixed* (Kery and Schaub 2011 p. 75) parameters in each model are summarised below.

#### Growth - Bull Trout

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bKIntercept | -1.7953 | -2.0693 | -1.4706 | 0.1498 | 17 | 0.0010 |

bKRegime[2] | -0.0322 | -0.4536 | 0.3114 | 0.1864 | 1200 | 0.8823 |

bLinf | 845.9000 | 795.4000 | 906.2000 | 28.0000 | 7 | 0.0010 |

sGrowth | 31.5580 | 28.7940 | 34.6740 | 1.5170 | 9 | 0.0010 |

sKYear | 0.2909 | 0.1444 | 0.5245 | 0.0990 | 65 | 0.0010 |

tGrowth | -0.0322 | -0.4536 | 0.3114 | 0.1864 | 1200 | 0.8823 |

Convergence | Iterations |
---|---|

1.01 | 10000 |

#### Growth - Mountain Whitefish

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bKIntercept | -2.7289 | -3.0555 | -2.4222 | 0.1593 | 12 | 0.0010 |

bKRegime[2] | 0.1727 | -0.2611 | 0.6196 | 0.2248 | 260 | 0.4152 |

bLinf | 360.9900 | 344.6300 | 379.3400 | 8.9600 | 5 | 0.0010 |

sGrowth | 10.9370 | 10.4620 | 11.4710 | 0.2590 | 5 | 0.0010 |

sKYear | 0.3762 | 0.2046 | 0.6533 | 0.1174 | 60 | 0.0010 |

tGrowth | 0.1727 | -0.2611 | 0.6196 | 0.2248 | 260 | 0.4152 |

Convergence | Iterations |
---|---|

1.05 | 10000 |

#### Growth - Rainbow Trout

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bKIntercept | -1.733 | -3.287 | -0.468 | 0.700 | 81 | 0.0155 |

bKRegime[2] | -0.282 | -1.671 | 1.251 | 0.698 | 520 | 0.4851 |

bLinf | 587.900 | 439.400 | 918.000 | 124.400 | 41 | 0.0010 |

sGrowth | 26.500 | 17.040 | 43.690 | 6.930 | 50 | 0.0010 |

sKYear | 0.525 | 0.018 | 1.980 | 0.546 | 190 | 0.0010 |

tGrowth | -0.282 | -1.671 | 1.251 | 0.698 | 520 | 0.4851 |

Convergence | Iterations |
---|---|

1.04 | 20000 |

#### Condition - Bull Trout

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bWeightIntercept | 6.837430 | 6.801240 | 6.874430 | 0.018880 | 1 | 0.0010 |

bWeightRegimeIntercept[2] | -0.101100 | -0.170100 | -0.037300 | 0.032800 | 66 | 0.0060 |

bWeightRegimeSlope[2] | 0.036900 | -0.089000 | 0.172800 | 0.065000 | 360 | 0.5015 |

bWeightSeasonIntercept[2] | 0.000790 | -0.018610 | 0.018940 | 0.009420 | 2400 | 0.9354 |

bWeightSeasonSlope[2] | 0.013460 | -0.035760 | 0.060910 | 0.024330 | 360 | 0.5294 |

bWeightSlope | 3.162200 | 3.082400 | 3.236900 | 0.039300 | 2 | 0.0010 |

sWeight | 0.138387 | 0.134908 | 0.142259 | 0.001916 | 3 | 0.0010 |

sWeightSiteIntercept | 0.012160 | 0.001970 | 0.025340 | 0.005750 | 96 | 0.0010 |

sWeightSiteYearIntercept | 0.018060 | 0.006630 | 0.027260 | 0.005230 | 57 | 0.0010 |

sWeightYearIntercept | 0.053750 | 0.033750 | 0.087910 | 0.013480 | 50 | 0.0010 |

sWeightYearSlope | 0.107600 | 0.063400 | 0.181900 | 0.030700 | 55 | 0.0010 |

tCondition1 | -0.101100 | -0.170100 | -0.037300 | 0.032800 | 66 | 0.0060 |

tCondition2 | 0.036900 | -0.089000 | 0.172800 | 0.065000 | 360 | 0.5015 |

Convergence | Iterations |
---|---|

1.02 | 2e+05 |

#### Condition - Mountain Whitefish

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bWeightIntercept | 4.786910 | 4.768410 | 4.804940 | 0.008900 | 0 | 0.0010 |

bWeightRegimeIntercept[2] | -0.043120 | -0.071130 | -0.015720 | 0.014670 | 64 | 0.0040 |

bWeightRegimeSlope[2] | -0.024300 | -0.084600 | 0.036500 | 0.031000 | 250 | 0.4152 |

bWeightSeasonIntercept[2] | -0.044090 | -0.051720 | -0.036180 | 0.003900 | 18 | 0.0010 |

bWeightSeasonSlope[2] | -0.102480 | -0.134870 | -0.068010 | 0.017360 | 33 | 0.0010 |

bWeightSlope | 3.209610 | 3.171200 | 3.243630 | 0.018540 | 1 | 0.0010 |

sWeight | 0.099091 | 0.097426 | 0.100709 | 0.000824 | 2 | 0.0010 |

sWeightSiteIntercept | 0.007000 | 0.001500 | 0.013070 | 0.003050 | 83 | 0.0010 |

sWeightSiteYearIntercept | 0.013931 | 0.010117 | 0.018004 | 0.001942 | 28 | 0.0010 |

sWeightYearIntercept | 0.024910 | 0.015480 | 0.040400 | 0.006430 | 50 | 0.0010 |

sWeightYearSlope | 0.046360 | 0.025280 | 0.081230 | 0.014410 | 60 | 0.0010 |

tCondition1 | -0.043120 | -0.071130 | -0.015720 | 0.014670 | 64 | 0.0040 |

tCondition2 | -0.024300 | -0.084600 | 0.036500 | 0.031000 | 250 | 0.4152 |

Convergence | Iterations |
---|---|

1.01 | 1e+05 |

#### Condition - Rainbow Trout

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bWeightIntercept | 4.57739 | 4.54525 | 4.60890 | 0.01567 | 1 | 0.0010 |

bWeightRegimeIntercept[2] | -0.00140 | -0.04721 | 0.04137 | 0.02276 | 3200 | 0.9761 |

bWeightRegimeSlope[2] | -0.03850 | -0.17230 | 0.07870 | 0.06480 | 330 | 0.5430 |

bWeightSeasonIntercept[2] | -0.07612 | -0.10812 | -0.04496 | 0.01571 | 41 | 0.0010 |

bWeightSeasonSlope[2] | 0.00600 | -0.07290 | 0.09230 | 0.04230 | 1400 | 0.9082 |

bWeightSlope | 3.09470 | 3.01990 | 3.17530 | 0.03920 | 3 | 0.0010 |

sWeight | 0.11211 | 0.10536 | 0.11998 | 0.00381 | 7 | 0.0010 |

sWeightSiteIntercept | 0.02045 | 0.00158 | 0.04556 | 0.01160 | 110 | 0.0010 |

sWeightSiteYearIntercept | 0.01720 | 0.00161 | 0.04015 | 0.01067 | 110 | 0.0010 |

sWeightYearIntercept | 0.02082 | 0.00119 | 0.05355 | 0.01412 | 130 | 0.0010 |

sWeightYearSlope | 0.08440 | 0.04140 | 0.15060 | 0.02910 | 65 | 0.0010 |

tCondition1 | -0.00140 | -0.04721 | 0.04137 | 0.02276 | 3200 | 0.9761 |

tCondition2 | -0.03850 | -0.17230 | 0.07870 | 0.06480 | 330 | 0.5430 |

Convergence | Iterations |
---|---|

1.03 | 1e+05 |

#### Condition - Largescale Sucker

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bWeightIntercept | 6.912700 | 6.776400 | 7.111200 | 0.081000 | 2 | 0.0010 |

bWeightRegimeIntercept[2] | -0.114800 | -0.335800 | 0.030600 | 0.089500 | 160 | 0.1171 |

bWeightRegimeSlope[2] | -0.379000 | -1.066000 | 0.339000 | 0.339000 | 180 | 0.2044 |

bWeightSeasonIntercept[2] | 0.021750 | 0.011600 | 0.032570 | 0.005440 | 48 | 0.0010 |

bWeightSeasonSlope[2] | 0.180600 | 0.083400 | 0.280600 | 0.048900 | 55 | 0.0010 |

bWeightSlope | 3.166000 | 2.484000 | 3.796000 | 0.316000 | 21 | 0.0010 |

sWeight | 0.083378 | 0.080859 | 0.085947 | 0.001342 | 3 | 0.0010 |

sWeightSiteIntercept | 0.006850 | 0.000430 | 0.015580 | 0.004040 | 110 | 0.0010 |

sWeightSiteYearIntercept | 0.010050 | 0.002090 | 0.016570 | 0.003550 | 72 | 0.0010 |

sWeightYearIntercept | 0.079300 | 0.033000 | 0.222700 | 0.051300 | 120 | 0.0010 |

sWeightYearSlope | 0.273800 | 0.112000 | 0.724100 | 0.166300 | 110 | 0.0010 |

tCondition1 | -0.114800 | -0.335800 | 0.030600 | 0.089500 | 160 | 0.1171 |

tCondition2 | -0.379000 | -1.066000 | 0.339000 | 0.339000 | 180 | 0.2044 |

Convergence | Iterations |
---|---|

1.07 | 4e+05 |

#### Occupancy - Rainbow Trout

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bOccupancy | -1.224 | -2.474 | 0.109 | 0.684 | 100 | 0.0819 |

bOccupancyRegime[2] | 0.613 | -0.653 | 1.882 | 0.664 | 210 | 0.3414 |

bOccupancySeason[2] | -0.039 | -0.616 | 0.501 | 0.288 | 1400 | 0.8983 |

sOccupancySite | 2.221 | 1.461 | 3.349 | 0.498 | 43 | 0.0010 |

sOccupancyYear | 1.172 | 0.684 | 1.969 | 0.337 | 55 | 0.0010 |

Convergence | Iterations |
---|---|

1.02 | 10000 |

#### Occupancy - Burbot

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bOccupancy | -2.217 | -3.299 | -1.223 | 0.545 | 47 | 0.0010 |

bOccupancyRegime[2] | 0.260 | -1.191 | 1.824 | 0.775 | 580 | 0.7705 |

bOccupancySeason[2] | -0.520 | -1.174 | 0.118 | 0.325 | 120 | 0.1138 |

sOccupancySite | 0.995 | 0.597 | 1.633 | 0.270 | 52 | 0.0010 |

sOccupancyYear | 1.418 | 0.820 | 2.406 | 0.422 | 56 | 0.0010 |

Convergence | Iterations |
---|---|

1.02 | 10000 |

#### Occupancy - Lake Whitefish

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bOccupancy | -1.143 | -2.1020 | -0.1420 | 0.5160 | 86 | 0.0360 |

bOccupancyRegime[2] | 0.834 | -0.6250 | 2.4020 | 0.7670 | 180 | 0.2535 |

bOccupancySeason[2] | -5.048 | -6.9420 | -3.6790 | 0.8420 | 32 | 0.0010 |

sOccupancySite | 0.502 | 0.1994 | 0.9398 | 0.1829 | 74 | 0.0010 |

sOccupancyYear | 1.377 | 0.8490 | 2.2610 | 0.3780 | 51 | 0.0010 |

Convergence | Iterations |
---|---|

1.01 | 10000 |

#### Occupancy - Northern Pikeminnow

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bOccupancy | -2.154 | -3.316 | -1.088 | 0.563 | 52 | 0.0010 |

bOccupancyRegime[2] | 0.342 | -1.050 | 1.834 | 0.719 | 420 | 0.6288 |

bOccupancySeason[2] | -2.080 | -3.144 | -1.166 | 0.480 | 48 | 0.0010 |

sOccupancySite | 1.552 | 0.901 | 2.673 | 0.448 | 57 | 0.0010 |

sOccupancyYear | 1.127 | 0.575 | 2.080 | 0.379 | 67 | 0.0010 |

Convergence | Iterations |
---|---|

1.02 | 10000 |

#### Occupancy - Redside Shiner

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bOccupancy | -2.426 | -4.201 | -0.647 | 0.854 | 73 | 0.0060 |

bOccupancyRegime[2] | 0.128 | -1.614 | 1.920 | 0.901 | 1400 | 0.8863 |

bOccupancySeason[2] | -1.050 | -1.767 | -0.307 | 0.368 | 69 | 0.0020 |

sOccupancySite | 2.268 | 1.389 | 3.751 | 0.592 | 52 | 0.0010 |

sOccupancyYear | 1.571 | 0.894 | 2.655 | 0.457 | 56 | 0.0010 |

Convergence | Iterations |
---|---|

1.02 | 10000 |

#### Occupancy - Sculpins

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bOccupancy | -0.123 | -1.429 | 1.380 | 0.713 | 1100 | 0.8424 |

bOccupancyRegime[2] | 0.721 | -1.444 | 2.758 | 1.066 | 290 | 0.4871 |

bOccupancySeason[2] | -0.515 | -1.095 | 0.038 | 0.283 | 110 | 0.0699 |

sOccupancySite | 1.348 | 0.867 | 2.120 | 0.322 | 46 | 0.0010 |

sOccupancyYear | 1.974 | 1.260 | 3.115 | 0.481 | 47 | 0.0010 |

Convergence | Iterations |
---|---|

1.03 | 10000 |

#### Count - Rainbow Trout

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bDensitySeason[2] | -0.0729 | -0.4452 | 0.2967 | 0.1887 | 510 | 0.7126 |

bDistribution | -0.5171 | -0.8011 | -0.2393 | 0.1389 | 54 | 0.0010 |

bDistributionRegime[2] | 0.2913 | 0.0833 | 0.5151 | 0.1117 | 74 | 0.0180 |

bDistributionSeason[2] | -0.0467 | -0.1532 | 0.0554 | 0.0549 | 220 | 0.4032 |

bRate | 0.2990 | -0.0574 | 0.6268 | 0.1567 | 110 | 0.0779 |

bRateRegime[2] | -0.6380 | -1.3960 | 0.1270 | 0.3640 | 120 | 0.0839 |

sDensitySite | 1.3050 | 0.8160 | 2.0700 | 0.3340 | 48 | 0.0010 |

sDensitySiteYear | 0.6810 | 0.5026 | 0.8982 | 0.1000 | 29 | 0.0010 |

sDispersion | 0.8675 | 0.7429 | 0.9837 | 0.0626 | 14 | 0.0010 |

sDistributionYear | 0.1374 | 0.0269 | 0.2819 | 0.0617 | 93 | 0.0010 |

sRateYear | 0.5470 | 0.0860 | 1.2360 | 0.3130 | 100 | 0.0010 |

tAbundance | -0.6380 | -1.3960 | 0.1270 | 0.3640 | 120 | 0.0839 |

tDistribution | 0.2913 | 0.0833 | 0.5151 | 0.1117 | 74 | 0.0180 |

Convergence | Iterations |
---|---|

1.1 | 1e+05 |

#### Count - Burbot

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bDensitySeason[2] | -0.7060 | -1.2800 | -0.1550 | 0.2790 | 80 | 0.0040 |

bDistribution | -0.0528 | -0.3122 | 0.2233 | 0.1409 | 510 | 0.6587 |

bDistributionRegime[2] | 0.0570 | -0.3251 | 0.4114 | 0.1845 | 650 | 0.6707 |

bDistributionSeason[2] | 0.0832 | -0.1266 | 0.3084 | 0.1118 | 260 | 0.4691 |

bRate | 0.5050 | -0.2470 | 1.3300 | 0.4420 | 160 | 0.2615 |

bRateRegime[2] | -1.4850 | -3.2230 | -0.0200 | 0.8270 | 110 | 0.0440 |

sDensitySite | 0.8660 | 0.4830 | 1.5310 | 0.2560 | 60 | 0.0010 |

sDensitySiteYear | 0.3672 | 0.0034 | 0.7553 | 0.2088 | 100 | 0.0010 |

sDispersion | 1.2176 | 0.9491 | 1.4882 | 0.1373 | 22 | 0.0010 |

sDistributionYear | 0.2304 | 0.0267 | 0.5579 | 0.1437 | 120 | 0.0010 |

sRateYear | 1.3340 | 0.6920 | 2.3310 | 0.4350 | 61 | 0.0010 |

tAbundance | -1.4850 | -3.2230 | -0.0200 | 0.8270 | 110 | 0.0440 |

tDistribution | 0.0570 | -0.3251 | 0.4114 | 0.1845 | 650 | 0.6707 |

Convergence | Iterations |
---|---|

1.09 | 1e+05 |

#### Count - Northern Pikeminnow

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bDensitySeason[2] | -2.4190 | -3.7240 | -1.2750 | 0.6320 | 51 | 0.0010 |

bDistribution | -0.4532 | -0.7562 | -0.1982 | 0.1397 | 62 | 0.0020 |

bDistributionRegime[2] | 0.0663 | -0.3287 | 0.4841 | 0.1996 | 610 | 0.7446 |

bDistributionSeason[2] | 0.0370 | -0.3462 | 0.3919 | 0.1882 | 1000 | 0.8264 |

bRate | 0.3850 | -0.2370 | 1.0400 | 0.2980 | 170 | 0.1897 |

bRateRegime[2] | -0.3050 | -1.5380 | 0.8490 | 0.5700 | 390 | 0.5290 |

sDensitySite | 0.4466 | 0.0358 | 0.9717 | 0.2490 | 100 | 0.0010 |

sDensitySiteYear | 0.6536 | 0.1812 | 1.0738 | 0.2182 | 68 | 0.0010 |

sDispersion | 1.3827 | 1.1355 | 1.6656 | 0.1374 | 19 | 0.0010 |

sDistributionYear | 0.2462 | 0.0352 | 0.5179 | 0.1290 | 98 | 0.0010 |

sRateYear | 0.8740 | 0.3700 | 1.7000 | 0.3360 | 76 | 0.0010 |

tAbundance | -0.3050 | -1.5380 | 0.8490 | 0.5700 | 390 | 0.5290 |

tDistribution | 0.0663 | -0.3287 | 0.4841 | 0.1996 | 610 | 0.7446 |

Convergence | Iterations |
---|---|

1.09 | 1e+05 |

#### Count - Suckers

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bDensitySeason[2] | -0.47310 | -0.66710 | -0.29070 | 0.09560 | 40 | 0.0010 |

bDistribution | -0.14720 | -0.26050 | -0.04500 | 0.05440 | 73 | 0.0040 |

bDistributionRegime[2] | 0.13150 | 0.01800 | 0.26160 | 0.06150 | 93 | 0.0239 |

bDistributionSeason[2] | -0.15980 | -0.21930 | -0.09940 | 0.03130 | 38 | 0.0010 |

bRate | 0.04800 | -0.06700 | 0.13790 | 0.04860 | 210 | 0.3235 |

bRateRegime[2] | 0.27540 | 0.01720 | 0.54480 | 0.12980 | 96 | 0.0417 |

sDensitySite | 0.42070 | 0.25750 | 0.66980 | 0.11070 | 49 | 0.0010 |

sDensitySiteYear | 0.45150 | 0.34430 | 0.56380 | 0.05650 | 24 | 0.0010 |

sDispersion | 0.77174 | 0.72422 | 0.82041 | 0.02447 | 6 | 0.0010 |

sDistributionYear | 0.08510 | 0.02460 | 0.15850 | 0.03550 | 79 | 0.0010 |

sRateYear | 0.19820 | 0.01140 | 0.46250 | 0.11780 | 110 | 0.0010 |

tAbundance | 0.27540 | 0.01720 | 0.54480 | 0.12980 | 96 | 0.0417 |

tDistribution | 0.13150 | 0.01800 | 0.26160 | 0.06150 | 93 | 0.0239 |

Convergence | Iterations |
---|---|

1.06 | 4e+05 |

#### Movement - Bull Trout

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bLength | 0.004532 | 0.001354 | 0.007935 | 0.001719 | 73 | 0.0040 |

bLengthSeason[2] | 0.002620 | -0.008590 | 0.015860 | 0.006530 | 470 | 0.7226 |

bMoved | -1.794000 | -3.224000 | -0.329000 | 0.755000 | 81 | 0.0160 |

bMovedSeason[2] | -0.140000 | -5.830000 | 4.830000 | 2.770000 | 3700 | 0.9721 |

Convergence | Iterations |
---|---|

1.03 | 10000 |

#### Movement - Mountain Whitefish

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bLength | -0.00080 | -0.00736 | 0.00587 | 0.00350 | 830 | 0.8484 |

bLengthSeason[2] | -0.02673 | -0.04152 | -0.01343 | 0.00754 | 53 | 0.0010 |

bMoved | 0.08200 | -1.60200 | 1.77700 | 0.89300 | 2100 | 0.9681 |

bMovedSeason[2] | 5.56500 | 2.29000 | 9.09300 | 1.80200 | 61 | 0.0010 |

Convergence | Iterations |
---|---|

1.02 | 10000 |

#### Movement - Rainbow Trout

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bLength | 0.00815 | -0.00441 | 0.02242 | 0.0069 | 160 | 0.2256 |

bLengthSeason[2] | 0.24690 | 0.04820 | 0.46450 | 0.1124 | 84 | 0.0010 |

bMoved | -2.77500 | -6.33500 | 0.38600 | 1.7480 | 120 | 0.0919 |

bMovedSeason[2] | -75.20000 | -140.20000 | -15.50000 | 33.9000 | 83 | 0.0010 |

Convergence | Iterations |
---|---|

1.06 | 10000 |

#### Movement - Largescale Sucker

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bLength | -0.01028 | -0.02261 | 0.00193 | 0.00626 | 120 | 0.0969 |

bLengthSeason[2] | -0.15750 | -0.30300 | -0.03170 | 0.06730 | 86 | 0.0190 |

bMoved | 4.27000 | -0.97000 | 9.69000 | 2.74000 | 120 | 0.1140 |

bMovedSeason[2] | 69.20000 | 13.50000 | 132.30000 | 29.70000 | 86 | 0.0209 |

Convergence | Iterations |
---|---|

1.03 | 40000 |

#### Observer Length Correction - Bull Trout

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bLength[1] | 1.0000 | 1.0000 | 1.0000 | 0.0000 | 0 | 0.001 |

bLength[2] | 0.9529 | 0.8811 | 1.0310 | 0.0444 | 8 | 0.001 |

bLength[3] | 0.8416 | 0.7569 | 0.9150 | 0.0371 | 9 | 0.001 |

bLength[4] | 0.8173 | 0.7504 | 0.8900 | 0.0364 | 9 | 0.001 |

bLength[5] | 0.9343 | 0.8278 | 1.0011 | 0.0583 | 9 | 0.001 |

bLength[6] | 0.9813 | 0.8819 | 1.0818 | 0.0518 | 10 | 0.001 |

sLength[1] | 1.0000 | 1.0000 | 1.0000 | 0.0000 | 0 | 0.001 |

sLength[2] | 3.9900 | 1.0000 | 12.2800 | 3.2400 | 140 | 0.001 |

sLength[3] | 4.5200 | 1.0700 | 11.2200 | 2.9300 | 110 | 0.001 |

sLength[4] | 5.0100 | 1.2700 | 11.7600 | 2.8600 | 100 | 0.001 |

sLength[5] | 1.4870 | 1.0030 | 4.9560 | 1.2520 | 130 | 0.001 |

sLength[6] | 8.4000 | 1.5000 | 18.9200 | 4.6800 | 100 | 0.001 |

Convergence | Iterations |
---|---|

1.84 | 80000 |

#### Observer Length Correction - Mountain Whitefish

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bLength[1] | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0 | 0.001 |

bLength[2] | 1.01345 | 0.99375 | 1.03306 | 0.00997 | 2 | 0.001 |

bLength[3] | 0.92466 | 0.91076 | 0.93896 | 0.00729 | 2 | 0.001 |

bLength[4] | 0.67133 | 0.65696 | 0.68561 | 0.00714 | 2 | 0.001 |

bLength[5] | 0.97418 | 0.95437 | 0.99450 | 0.01016 | 2 | 0.001 |

bLength[6] | 0.84852 | 0.81775 | 0.87775 | 0.01551 | 4 | 0.001 |

sLength[1] | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0 | 0.001 |

sLength[2] | 4.12000 | 3.29000 | 5.02700 | 0.44600 | 21 | 0.001 |

sLength[3] | 1.84650 | 1.43940 | 2.33420 | 0.23590 | 24 | 0.001 |

sLength[4] | 3.07800 | 2.52100 | 3.66900 | 0.28500 | 19 | 0.001 |

sLength[5] | 4.30400 | 3.65300 | 4.99500 | 0.33400 | 16 | 0.001 |

sLength[6] | 6.53600 | 5.22200 | 7.90300 | 0.69700 | 21 | 0.001 |

Convergence | Iterations |
---|---|

1 | 10000 |

#### Observer Length Correction - Suckers

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bLength[1] | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 0 | 0.001 |

bLength[2] | 0.964830 | 0.944890 | 0.973280 | 0.007820 | 1 | 0.001 |

bLength[3] | 0.923171 | 0.919766 | 0.926995 | 0.001886 | 0 | 0.001 |

bLength[4] | 0.729610 | 0.719260 | 0.740340 | 0.005370 | 1 | 0.001 |

bLength[5] | 0.928880 | 0.907080 | 0.951390 | 0.011590 | 2 | 0.001 |

bLength[6] | 0.712590 | 0.674280 | 0.752620 | 0.019830 | 5 | 0.001 |

sLength[1] | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 0 | 0.001 |

sLength[2] | 1.495000 | 1.001000 | 3.733000 | 0.863000 | 91 | 0.001 |

sLength[3] | 1.032900 | 1.001100 | 1.107700 | 0.030200 | 5 | 0.001 |

sLength[4] | 3.650000 | 2.569000 | 4.794000 | 0.573000 | 30 | 0.001 |

sLength[5] | 6.432000 | 5.010000 | 7.941000 | 0.755000 | 23 | 0.001 |

sLength[6] | 12.384000 | 10.485000 | 14.527000 | 1.014000 | 16 | 0.001 |

Convergence | Iterations |
---|---|

1.19 | 80000 |

#### Abundance - Bull Trout - Adult

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bDensitySeason[2] | -0.32600 | -0.89700 | 0.30100 | 0.30000 | 180 | 0.2675 |

bDistribution | 0.05450 | -0.04210 | 0.14180 | 0.04760 | 170 | 0.2436 |

bDistributionRegime[2] | 0.02520 | -0.06020 | 0.10260 | 0.04140 | 320 | 0.5090 |

bDistributionSeason[2] | 0.12230 | 0.06390 | 0.18040 | 0.03090 | 48 | 0.0010 |

bEfficiency | -3.51470 | -3.73420 | -3.30420 | 0.11430 | 6 | 0.0010 |

bEfficiencySeason[2] | 0.05600 | -0.57500 | 0.61200 | 0.29600 | 1100 | 0.8164 |

bRate | 0.01960 | -0.04720 | 0.09630 | 0.03480 | 370 | 0.5290 |

bRateRegime[2] | -0.03780 | -0.18640 | 0.10830 | 0.07360 | 390 | 0.4951 |

bType[1] | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0 | 0.0010 |

bType[2] | 1.90700 | 1.26200 | 2.71400 | 0.37300 | 38 | 0.0010 |

sDensitySite | 0.45890 | 0.29000 | 0.73340 | 0.11460 | 48 | 0.0010 |

sDensitySiteYear | 0.41680 | 0.32860 | 0.51030 | 0.04560 | 22 | 0.0010 |

sDispersion | 0.44050 | 0.36840 | 0.50760 | 0.03590 | 16 | 0.0010 |

sDistributionYear | 0.02941 | 0.00142 | 0.07863 | 0.02098 | 130 | 0.0010 |

sEfficiencySessionSeasonYear | 0.22390 | 0.13410 | 0.31680 | 0.04520 | 41 | 0.0010 |

sRateYear | 0.10440 | 0.01550 | 0.22910 | 0.05610 | 100 | 0.0010 |

tAbundance | -0.03780 | -0.18640 | 0.10830 | 0.07360 | 390 | 0.4951 |

tDistribution | 0.02520 | -0.06020 | 0.10260 | 0.04140 | 320 | 0.5090 |

Convergence | Iterations |
---|---|

1.08 | 1e+05 |

#### Abundance - Bull Trout - Juvenile

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bDensitySeason[2] | 0.3250 | -0.2070 | 0.9690 | 0.2990 | 180 | 0.2767 |

bDistribution | -0.0224 | -0.1562 | 0.1261 | 0.0719 | 630 | 0.7304 |

bDistributionRegime[2] | 0.0390 | -0.0850 | 0.1588 | 0.0631 | 310 | 0.4836 |

bDistributionSeason[2] | -0.0182 | -0.0898 | 0.0518 | 0.0358 | 390 | 0.6428 |

bEfficiency | -3.0385 | -3.3095 | -2.7879 | 0.1351 | 9 | 0.0010 |

bEfficiencySeason[2] | -0.3260 | -0.9470 | 0.2120 | 0.2960 | 180 | 0.2588 |

bRate | 0.1141 | -0.0321 | 0.2608 | 0.0724 | 130 | 0.1015 |

bRateRegime[2] | -0.1990 | -0.4652 | 0.0552 | 0.1285 | 130 | 0.1155 |

bType[1] | 1.0000 | 1.0000 | 1.0000 | 0.0000 | 0 | 0.0010 |

bType[2] | 0.9151 | 0.5661 | 1.4327 | 0.2327 | 47 | 0.0010 |

sDensitySite | 0.6658 | 0.4148 | 1.0565 | 0.1616 | 48 | 0.0010 |

sDensitySiteYear | 0.1787 | 0.0191 | 0.3345 | 0.0839 | 88 | 0.0010 |

sDispersion | 0.4089 | 0.3034 | 0.5218 | 0.0561 | 27 | 0.0010 |

sDistributionYear | 0.0801 | 0.0094 | 0.1677 | 0.0407 | 99 | 0.0010 |

sEfficiencySessionSeasonYear | 0.2608 | 0.1593 | 0.3706 | 0.0540 | 41 | 0.0010 |

sRateYear | 0.2023 | 0.1045 | 0.3697 | 0.0678 | 66 | 0.0010 |

tAbundance | -0.1990 | -0.4652 | 0.0552 | 0.1285 | 130 | 0.1155 |

tDistribution | 0.0390 | -0.0850 | 0.1588 | 0.0631 | 310 | 0.4836 |

Convergence | Iterations |
---|---|

1.05 | 2e+05 |

#### Abundance - Mountain Whitefish - Adult

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bDensitySeason[2] | -0.59550 | -0.80400 | -0.40300 | 0.10200 | 34 | 0.0010 |

bDistribution | 0.08950 | -0.00590 | 0.19980 | 0.05180 | 120 | 0.0699 |

bDistributionRegime[2] | 0.02880 | -0.05830 | 0.12410 | 0.04480 | 320 | 0.4991 |

bDistributionSeason[2] | -0.07079 | -0.10931 | -0.03343 | 0.02035 | 54 | 0.0010 |

bEfficiency | -3.92200 | -4.03810 | -3.81420 | 0.05740 | 3 | 0.0010 |

bEfficiencySeason[2] | 0.90380 | 0.70870 | 1.09890 | 0.10080 | 22 | 0.0010 |

bRate | 0.00245 | -0.02132 | 0.03157 | 0.01406 | 1100 | 0.9601 |

bRateRegime[2] | -0.02810 | -0.11030 | 0.03890 | 0.03710 | 270 | 0.4152 |

bType[1] | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0 | 0.0010 |

bType[2] | 1.74900 | 1.27000 | 2.36900 | 0.28900 | 31 | 0.0010 |

sDensitySite | 0.49770 | 0.31460 | 0.75820 | 0.11930 | 45 | 0.0010 |

sDensitySiteYear | 0.35470 | 0.28930 | 0.42410 | 0.03400 | 19 | 0.0010 |

sDispersion | 0.47594 | 0.44493 | 0.51210 | 0.01728 | 7 | 0.0010 |

sDistributionYear | 0.05650 | 0.01160 | 0.11480 | 0.02600 | 91 | 0.0010 |

sEfficiencySessionSeasonYear | 0.21350 | 0.16180 | 0.27210 | 0.02890 | 26 | 0.0010 |

sRateYear | 0.04550 | 0.00180 | 0.13840 | 0.03630 | 150 | 0.0010 |

tAbundance | -0.02810 | -0.11030 | 0.03890 | 0.03710 | 270 | 0.4152 |

tDistribution | 0.02880 | -0.05830 | 0.12410 | 0.04480 | 320 | 0.4991 |

Convergence | Iterations |
---|---|

1.04 | 1e+05 |

#### Abundance - Mountain Whitefish - Juvenile

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bDensitySeason[2] | 0.1560 | -1.0370 | 1.4590 | 0.6190 | 800 | 0.7676 |

bDistribution | 0.0930 | -0.0945 | 0.2948 | 0.0982 | 210 | 0.3423 |

bDistributionRegime[2] | 0.0361 | -0.1095 | 0.1815 | 0.0733 | 400 | 0.5599 |

bDistributionSeason[2] | -0.0951 | -0.1650 | -0.0307 | 0.0352 | 71 | 0.0040 |

bEfficiency | -5.8280 | -6.7450 | -5.0770 | 0.4330 | 14 | 0.0010 |

bEfficiencySeason[2] | 0.4730 | -0.8140 | 1.6800 | 0.6130 | 260 | 0.3917 |

bRate | 0.0511 | -0.5181 | 0.4359 | 0.2192 | 930 | 0.6924 |

bRateRegime[2] | -0.1370 | -0.7800 | 0.6200 | 0.3340 | 510 | 0.5579 |

bType[1] | 1.0000 | 1.0000 | 1.0000 | 0.0000 | 0 | 0.0010 |

bType[2] | 1.0560 | 0.6190 | 1.6830 | 0.2770 | 50 | 0.0010 |

sDensitySite | 0.8865 | 0.5648 | 1.4141 | 0.2272 | 48 | 0.0010 |

sDensitySiteYear | 0.4598 | 0.3276 | 0.6026 | 0.0701 | 30 | 0.0010 |

sDispersion | 0.5700 | 0.4733 | 0.6671 | 0.0488 | 17 | 0.0010 |

sDistributionYear | 0.0711 | 0.0053 | 0.1853 | 0.0452 | 130 | 0.0010 |

sEfficiencySessionSeasonYear | 0.2757 | 0.1593 | 0.4130 | 0.0627 | 46 | 0.0010 |

sRateYear | 0.4129 | 0.1731 | 0.8990 | 0.1981 | 88 | 0.0010 |

tAbundance | -0.1370 | -0.7800 | 0.6200 | 0.3340 | 510 | 0.5579 |

tDistribution | 0.0361 | -0.1095 | 0.1815 | 0.0733 | 400 | 0.5599 |

Convergence | Iterations |
---|---|

1.06 | 8e+05 |

#### Abundance - Rainbow Trout - Adult

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bDensitySeason[2] | -0.0380 | -1.3610 | 1.5310 | 0.7370 | 3800 | 0.8983 |

bDistribution | -0.3427 | -0.6279 | -0.0794 | 0.1429 | 80 | 0.0140 |

bDistributionRegime[2] | 0.2387 | -0.0323 | 0.5276 | 0.1453 | 120 | 0.0739 |

bDistributionSeason[2] | -0.0573 | -0.2244 | 0.1262 | 0.0922 | 310 | 0.5130 |

bEfficiency | -2.9030 | -3.5880 | -2.2780 | 0.3350 | 23 | 0.0010 |

bEfficiencySeason[2] | -0.2070 | -1.8490 | 1.0870 | 0.7410 | 710 | 0.8324 |

bRate | 0.0488 | -0.2321 | 0.3751 | 0.1449 | 620 | 0.7006 |

bRateRegime[2] | -0.1289 | -0.6186 | 0.3176 | 0.2283 | 360 | 0.5150 |

bType[1] | 1.0000 | 1.0000 | 1.0000 | 0.0000 | 0 | 0.0010 |

bType[2] | 5.0620 | 0.2300 | 9.7350 | 2.8290 | 94 | 0.0010 |

sDensitySite | 1.0860 | 0.6050 | 1.9230 | 0.3370 | 61 | 0.0010 |

sDensitySiteYear | 0.4393 | 0.0342 | 0.8017 | 0.1972 | 87 | 0.0010 |

sDispersion | 0.4867 | 0.0754 | 0.8710 | 0.2142 | 82 | 0.0010 |

sDistributionYear | 0.1307 | 0.0088 | 0.3499 | 0.0919 | 130 | 0.0010 |

sEfficiencySessionSeasonYear | 0.2623 | 0.0129 | 0.5585 | 0.1515 | 100 | 0.0010 |

sRateYear | 0.2637 | 0.0283 | 0.6476 | 0.1647 | 120 | 0.0010 |

tAbundance | -0.1289 | -0.6186 | 0.3176 | 0.2283 | 360 | 0.5150 |

tDistribution | 0.2387 | -0.0323 | 0.5276 | 0.1453 | 120 | 0.0739 |

Convergence | Iterations |
---|---|

1.05 | 1e+05 |

#### Long-Term Trends

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

sTrend | 0.47770 | 0.3383 | 0.63530 | 0.07700 | 31 | 0.001 |

sValue | 0.74922 | 0.7042 | 0.79973 | 0.02495 | 6 | 0.001 |

Convergence | Iterations |
---|---|

1.03 | 1e+05 |

#### Scale Age

Parameter | Estimate | Lower | Upper | SD | Error | Significance |
---|---|---|---|---|---|---|

bIntercept | 1.3317 | 1.1054 | 1.5972 | 0.1246 | 18 | 0.0007 |

bInterceptAger[2] | -0.4677 | -0.6772 | -0.2784 | 0.1003 | 43 | 0.0007 |

bSlope | 0.7363 | 0.6397 | 0.8130 | 0.0427 | 12 | 0.0007 |

bSlopeAger[2] | -0.0145 | -0.0745 | 0.0566 | 0.0327 | 450 | 0.6040 |

sInterceptEncounterID | 0.5542 | 0.4557 | 0.6493 | 0.0501 | 17 | 0.0007 |

sInterceptEncounterIDAger | 0.2977 | 0.1659 | 0.4226 | 0.0685 | 43 | 0.0007 |

sSD[1] | 0.7120 | 0.6384 | 0.7999 | 0.0427 | 11 | 0.0007 |

sSD[2] | 0.4980 | 0.4521 | 0.5526 | 0.0256 | 10 | 0.0007 |

Convergence | Iterations |
---|---|

1.06 | 2000 |

### Figures

#### Growth

#### Growth - Bull Trout

#### Growth - Mountain Whitefish

#### Growth - Rainbow Trout

#### Condition

#### Condition - Bull Trout - Juvenile

#### Condition - Bull Trout - Adult

#### Condition - Mountain Whitefish - Juvenile

#### Condition - Mountain Whitefish - Adult

#### Condition - Rainbow Trout - Juvenile

#### Condition - Rainbow Trout - Adult

#### Condition - Largescale Sucker - Juvenile

#### Condition - Largescale Sucker - Adult

#### Occupancy - Rainbow Trout

#### Occupancy - Burbot

#### Occupancy - Lake Whitefish

#### Occupancy - Northern Pikeminnow

#### Occupancy - Redside Shiner

#### Occupancy - Sculpins

#### Species Richness

#### Count - Rainbow Trout

#### Count - Burbot

#### Count - Northern Pikeminnow

#### Count - Suckers

#### Movement - Bull Trout

#### Movement - Mountain Whitefish

#### Movement - Rainbow Trout

#### Movement - Largescale Sucker

#### Observer Length Correction

#### Abundance

#### Abundance - Bull Trout - Adult

#### Abundance - Bull Trout - Juvenile

#### Abundance - Mountain Whitefish - Adult

#### Abundance - Mountain Whitefish - Juvenile

#### Abundance - Rainbow Trout - Adult

#### Species Diversity (Evenness)

#### Long-Term Trends

#### Short-Term Correlations

#### Scale Age

### Significance

The following table summarises the significance levels for the management
hypotheses tested in the analyses where *Condition1* is the effect
of the regime change on weight for big and small fish and *Condition2* is
the effect on big relative to small fish. The *Direction* column
indicates whether significant changes were positive or negative where
a positive change for *Distribution* indicates a shift towards the dam.

Test | Species | Stage | Significance | Direction |
---|---|---|---|---|

Growth | Mountain Whitefish | 0.4152 | ||

Growth | Rainbow Trout | 0.4851 | ||

Growth | Bull Trout | 0.8823 | ||

Condition1 | Mountain Whitefish | 0.0040 | - | |

Condition1 | Rainbow Trout | 0.9761 | ||

Condition1 | Bull Trout | 0.0060 | - | |

Condition1 | Largescale Sucker | 0.1171 | ||

Condition2 | Mountain Whitefish | 0.4152 | ||

Condition2 | Rainbow Trout | 0.5430 | ||

Condition2 | Bull Trout | 0.5015 | ||

Condition2 | Largescale Sucker | 0.2044 | ||

Abundance | Mountain Whitefish | Juvenile | 0.5579 | |

Abundance | Mountain Whitefish | Adult | 0.4152 | |

Abundance | Rainbow Trout | 0.0839 | ||

Abundance | Rainbow Trout | Adult | 0.5150 | |

Abundance | Bull Trout | Juvenile | 0.1155 | |

Abundance | Bull Trout | Adult | 0.4951 | |

Abundance | Sucker | 0.0417 | + | |

Abundance | Burbot | 0.0440 | - | |

Abundance | Northern Pikeminnow | 0.5290 | ||

Distribution | Mountain Whitefish | Juvenile | 0.5599 | |

Distribution | Mountain Whitefish | Adult | 0.4991 | |

Distribution | Rainbow Trout | 0.0180 | + | |

Distribution | Rainbow Trout | Adult | 0.0739 | |

Distribution | Bull Trout | Juvenile | 0.4836 | |

Distribution | Bull Trout | Adult | 0.5090 | |

Distribution | Sucker | 0.0239 | + | |

Distribution | Burbot | 0.6707 | ||

Distribution | Northern Pikeminnow | 0.7446 |

## Acknowledgements

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

- BC Hydro
- Guy Martel
- Karen Bray
- Jason Watson

- Okanagan National Alliance
- Amy Duncan
- Michael Zimmer
- Charlotte Whitney

- Golder Associates
- David Roscoe
- Dustin Ford
- Sima Usvyatsov
- Dana Schmidt
- Larry Hildebrand

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