Middle Columbia River Fish Indexing Analysis 2017

2018-06-26

The suggested citation for this analytic report is:

Thorley, J.L. (2018) Middle Columbia River Fish Indexing Analysis 2017. A Poisson Consulting Analysis Report. URL: https://www.poissonconsulting.ca/f/77904731.

Background

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

  1. 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?
  2. 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?
  3. 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?
  4. 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 collected by Okanagan Nation Alliance and Golder Associates.

Life-Stage

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

Table 1. Length cutoffs by species and stage.

Species Fry Juvenile
Bull Trout < 120 < 400
Mountain Whitefish < 120 < 175
Rainbow Trout < 120 < 250
Largescale Sucker < 120 < 350

Statistical Analysis

Model parameters were estimated using Bayesian methods. The Bayesian estimates were produced using JAGS (Plummer 2015) and STAN (Carpenter et al. 2017). For additional information on Bayesian estimation the reader is referred to McElreath (2016).

Unless indicated otherwise, the Bayesian analyses used uninformative normal prior distributions (Kery and Schaub 2011, 36). 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 \(\hat{R} \leq 1.1\) (Kery and Schaub 2011, 40) and \(\textrm{ESS} \geq 150\) for each of the monitored parameters (Kery and Schaub 2011, 61). Where \(\hat{R}\) is the potential scale reduction factor and \(\textrm{ESS}\) is the effective sample size.

The parameters are summarised in terms of the point estimate, standard deviation (sd), the z-score, lower and upper 95% confidence/credible limits (CLs) and the p-value (Kery and Schaub 2011, 37, 42). For Bayesian models, the estimate is the median (50th percentile) of the MCMC samples, the z-score is \(\mathrm{mean}/\mathrm{sd}\) and the 95% CLs are the 2.5th and 97.5th percentiles. A p-value of 0.05 indicates that the lower or upper 95% CL is 0.

Where relevant, model adequacy was confirmed by examination of residual plots.

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 change in the response variable) with 95% confidence/credible intervals (CIs, Bradford, Korman, and Higgins 2005).

The analyses were implemented using R version 3.5.0 (R Core Team 2015) and the mbr family of packages.

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:

  • \(k\) can vary with discharge regime and randomly with year.
  • The residual variation in growth is normally distributed.

Mountain Whitefish with a FL \(>\) 250 mm at release were excluded from the growth analysis as they appeared to be undergoing biphasic growth.

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.

Key assumptions of the condition model include:

  • \(\alpha\) can vary with the regime and season and randomly with year.
  • \(\beta\) can vary with the regime and season and randomly with year.
  • The residual variation in weight is log-normally distributed.

Fry were excluded from the condition analysis.

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 and Schaub 2011, 414–18), 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 varies with season.
  • Occupancy varies randomly with site and site within year.
  • The effect of year on occupancy is autoregressive with a lag of one year and varies with discharge regime.
  • 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 by site and year.

Count

The count data were analysed using an overdispersed Poisson model (Kery 2010, pp 168-170; Kery and Schaub 2011, pp 55-56) to provide estimates of the lineal river count density (count/km). The model estimates the expected count which is the product of the abundance and observer efficiency. In order to interpret the estimates as relative densities it is necessary to assume that changes in observer efficiency are negligible.

Key assumptions of the count model include:

  • The count density (count/km) varies as an exponential growth model with the rate of change varying with discharge regime.
  • The count density varies with season.
  • The count density varies randomly with site, year and site within year.
  • The counts are gamma-Poisson distributed.

In the case of suckers the count model replaced the first assumption with

  • The count density varies with discharge regime.

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, fish length and the interaction between season and fish length.
  • Observed site fidelity is Bernoulli distributed.

Fry were excluded from the movement analysis.

Observer Length Correction

The annual bias (inaccuracy) and error (imprecision) in observer’s fish length estimates were quantified from the divergence of the length distribution of their observed fish from the length distribution of the measured fish. More specifically, the percent length correction that minimised the Jensen-Shannon divergence (Lin 1991) between the two distributions provided a measure of the inaccuracy while the minimum divergence (the Jensen-Shannon divergence was calculated with log to base 2 which means it lies between 0 and 1) provided a measure of the imprecision.

Abundance

The catch and geo-referenced count data were analysed using a capture-recapture-based overdispersed gamma-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 full abundance model include:

  • The density (fish/km) varies as an exponential growth model with the rate of change varying with discharge regime.
  • The density varies with season.
  • The density varies randomly with site, year and site within 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.
  • The overdispersion varies by encounter type (count versus capture).

In the case of Adult Suckers the abundance model replaced the first assumption with

  • The density varies with discharge regime.

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 Juvenile and Adult 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 ith species.

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

Distribution

The site within year random effects from the count and abundance models were analysed using a linear mixed model to estimate the distribution.

Key assumptions of the linear mixed model include:

  • The effect varies by river kilometer.
  • The effect of river kilometer varies by discharge regime.
  • The effect of river kilometer varies randomly by year.
  • The effect is normally distributed.

The effects are the predicted site within year random effects after accounting for all other predictors including the site and year random effects. As such an increase in the distribution represents an increase in the relative density of fish closer to Mica Dam. A positive distribution does not however necessarily indicate that the density of fish is higher closer to Mica Dam.

Capture Effect

The effect of capture in a previous year was estimated via an analysis of mass-length relations (He et al. 2008).

Key assumptions of the capture effect model include:

  • \(\alpha\) can vary with recapture and randomly with year.
  • \(\beta\) can vary with recapture and randomly with year.
  • The residual variation in weight is log-normally distributed.

Fry were excluded from the capture effect analysis.

Model Templates

Growth

model {

  bKIntercept ~ dnorm (0, 5^-2)

  bKRegime[1] <- 0
  for(i in 2:nRegime) {
    bKRegime[i] ~ dnorm(0, 5^-2)
  }

  sKAnnual ~ dunif(0, 5)
  for (i in 1:nAnnual) {
    bKAnnual[i] ~ dnorm(0, sKAnnual^-2)
    log(bK[i]) <- bKIntercept + bKRegime[step(i - Threshold) + 1] + bKAnnual[i]
  }

  bLinf ~ dunif(100,1000)
  sGrowth ~ dunif(0, 100)
  for (i in 1:length(Growth)) {

    eGrowth[i] <- (bLinf - LengthAtRelease[i]) * (1 - exp(-sum(bK[Annual[i]:(Annual[i] + Years[i] - 1)])))

    Growth[i] ~ dnorm(eGrowth[i], sGrowth^-2)
  }
  tGrowth <- bKRegime[2]
..

Template 1. The model description.

Condition

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, 1)
  sWeightYearSlope ~ dunif(0, 1)
  for(yr in 1:nYear) {
    bWeightYearIntercept[yr] ~ dnorm(0, sWeightYearIntercept^-2)
    bWeightYearSlope[yr] ~ dnorm(0, sWeightYearSlope^-2)
  }

  sWeight ~ dunif(0, 1)
  for(i in 1:length(Year)) {

    eWeightIntercept[i] <- bWeightIntercept + bWeightRegimeIntercept[Regime[i]] + bWeightSeasonIntercept[Season[i]] + bWeightYearIntercept[Year[i]]

    eWeightSlope[i] <- bWeightSlope + bWeightRegimeSlope[Regime[i]] + bWeightSeasonSlope[Season[i]] + bWeightYearSlope[Year[i]]

    log(eWeight[i]) <- eWeightIntercept[i] + eWeightSlope[i] * LogLength[i]
    Weight[i] ~ dlnorm(log(eWeight[i]) , sWeight^-2)
  }
  tCondition1 <- bWeightRegimeIntercept[2]
  tCondition2 <- bWeightRegimeSlope[2]
..

Template 2. The model description.

Occupancy

model {

  bRate ~ dnorm(0, 5^-2)

  sRateYear ~ dunif(0, 5)
  for(i in 1:nYear) {
    bRateYear[i] ~ dnorm(0, sRateYear^-2)
  }

  bRateRev5 ~ dnorm(0, 5^-2)

  bOccupancyYear[1] ~ dnorm(0, 5^-2)
  for (i in 2:nYear) {
    eRateYear[i-1] <- bRate + bRateYear[i-1] + bRateRev5 * YearRev5[i-1]
    bOccupancyYear[i] <- bOccupancyYear[i-1] + eRateYear[i-1]
  }

  bOccupancySpring ~ dnorm(0, 5^-2)

  sOccupancySite ~ dunif(0, 5)
  sOccupancySiteYear ~ dunif(0, 5)
  for (i in 1:nSite) {
    bOccupancySite[i] ~ dnorm(0, sOccupancySite^-2)
    for (j in 1:nYear) {
      bOccupancySiteYear[i,j] ~ dnorm(0, sOccupancySiteYear^-2)
    }
  }

  for (i in 1:length(Observed)) {
    logit(eObserved[i]) <- bOccupancyYear[Year[i]] + bOccupancySpring * Spring[i] + bOccupancySite[Site[i]] + bOccupancySiteYear[Site[i], Year[i]]
    Observed[i] ~ dbern(eObserved[i])
  }
..

Template 3. The model description.

Count

model {
  bDensity ~ dnorm(0, 5^-2)

  bRate ~ dnorm(0, 5^-2)
  bRateRev5 ~ dnorm(0, 5^-2)

  bTrendYear[1] <- bDensity
  for(i in 2:nYear) {
    bTrendYear[i] <- bTrendYear[i-1] + bRate + bRateRev5 * YearRev5[i-1]
  }

  bDensitySeason[1] <- 0
  for (i in 2:nSeason) {
    bDensitySeason[i] ~ dnorm(0, 5^-2)
  }

  sDensityYear ~ dunif(0, 5)
  for (i in 1:nYear) {
    bDensityYear[i] ~ dnorm(0, sDensityYear^-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)) {

    log(eDensity[i]) <- bTrendYear[Year[i]] + bDensitySeason[Season[i]] + bDensityYear[Year[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])
  }
  tCount <- bRateRev5
..

Template 4. The model description.

Movement

model {
  bMoved ~ dnorm(0, 5^-2)
  bLength ~ dnorm(0, 5^-2)

  bMovedSpring ~ dnorm(0, 5^-5)
  bLengthSpring ~ dnorm(0, 5^-5)

  for (i in 1:length(Moved)) {
    logit(eMoved[i]) <- bMoved + bMovedSpring * Spring[i] + (bLength + bLengthSpring * Spring[i]) * Length[i]
    Moved[i] ~ dbern(eMoved[i])
  }
..

Template 5.

Abundance

model {
  bDensity ~ dnorm(0, 5^-2)

  bRate ~ dnorm(0, 5^-2)
  bRateRev5 ~ dnorm(0, 5^-2)

  bTrendYear[1] <- bDensity
  for(i in 2:nYear) {
    bTrendYear[i] <- bTrendYear[i-1] + bRate + bRateRev5 * YearRev5[i-1]
  }

  bDensitySeason[1] <- 0
  for (i in 2:nSeason) {
    bDensitySeason[i] ~ dnorm(0, 5^-2)
  }

  sDensityYear ~ dunif(0, 5)
  for (i in 1:nYear) {
    bDensityYear[i] ~ dnorm(0, sDensityYear^-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)
    }
  }

  bEfficiency ~ dnorm(0, 5^-2)

  bEfficiencySeason[1] <- 0
  for(i in 2:nSeason) {
    bEfficiencySeason[i] ~ dnorm(0, 5^-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)
      }
    }
  }

  bMultiplier <- 0
  sDispersion ~ dnorm(0, 2^-2)
  bMultiplierType[1] <- 0
  sDispersionType[1] <- 0
  for (i in 2:nType) {
    bMultiplierType[i] ~ dnorm(0, 2^-2)
    sDispersionType[i] ~ dnorm(0, 2^-2)
  }

  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]])
  }

  for (i in 1:length(Year)) {

    logit(eEfficiency[i]) <- bEfficiency + bEfficiencySeason[Season[i]] + bEfficiencySessionSeasonYear[Session[i], Season[i], Year[i]]

    log(eDensity[i]) <- bTrendYear[Year[i]] + bDensitySeason[Season[i]] + bDensityYear[Year[i]] + bDensitySite[Site[i]] + bDensitySiteYear[Site[i],Year[i]]

    log(eMultiplier[i]) <- bMultiplier + bMultiplierType[Type[i]]

    eCatch[i] <- eDensity[i] * SiteLength[i] * ProportionSampled[i] * eEfficiency[i] * eMultiplier[i]

    log(esDispersion[i]) <- sDispersion + sDispersionType[Type[i]]

    eDispersion[i] ~ dgamma(esDispersion[i]^-2 + 0.1, esDispersion[i]^-2 + 0.1)

    Catch[i] ~ dpois(eCatch[i] * eDispersion[i])
  }
  tAbundance <- bRateRev5
..

Template 6. The model description.

Distribution

model {
  bEffect ~ dnorm(0, 1^-2)

  bRkm ~ dnorm(0, 1^-2)
  bRkmRev5 ~ dnorm(0, 1^-2)

  sRkmYear ~ dunif(0, 1)
  for(i in 1:nYear) {
    bRkmYear[i] ~ dnorm(0, sRkmYear^-2)
  }
  sEffect ~ dunif(0, 1)
  for(i in 1:length(Effect)) {
    eEffect[i] <- bEffect + (bRkm + bRkmRev5 * Rev5[i] + bRkmYear[Year[i]]) * Rkm[i]
    Effect[i] ~ dnorm(eEffect[i], sEffect^-2)
  }
tDistribution <- bRkmRev5

Template 7. The model description.

Capture Effect

model {

  bWeightIntercept ~ dnorm(5, 5^-2)
  bWeightSlope ~ dnorm(3, 5^-2)

  bWeightRecaptureIntercept ~ dnorm(0, 5^-2)
  bWeightRecaptureSlope ~ dnorm(0, 5^-2)

  sWeightYearIntercept ~ dunif(0, 1)
  sWeightYearSlope ~ dunif(0, 1)
  for(yr in 1:nYear) {
    bWeightYearIntercept[yr] ~ dnorm(0, sWeightYearIntercept^-2)
    bWeightYearSlope[yr] ~ dnorm(0, sWeightYearSlope^-2)
  }

  sWeight ~ dunif(0, 1)
  for(i in 1:length(Year)) {

    eWeightIntercept[i] <- bWeightIntercept + bWeightRecaptureIntercept * Recapture[i] + bWeightYearIntercept[Year[i]]

    eWeightSlope[i] <- bWeightSlope + bWeightRecaptureSlope * Recapture[i] + bWeightYearSlope[Year[i]]

    log(eWeight[i]) <- eWeightIntercept[i] + eWeightSlope[i] * LogLength[i]
    Weight[i] ~ dlnorm(log(eWeight[i]) , sWeight^-2)
  }
..

Template 8. The model description.

Results

Tables

Growth

Table 2. Parameter descriptions.

Parameter Description
Annual[i] Year
bK[i] Expected growth coefficient in the ith Annual
bKAnnual[i] Effect of ith Annual on bKIntercept
bKIntercept Intercept for log(bK)
bKRegime[i] Effect of ith Regime on bKIntercept
bLinf Mean maximum length
eGrowth[i] Expected Growth of the ith fish
Growth[i] Change in length of the ith fish between release and recapture (mm)
LengthAtRelease[i] Length of the ith fish when released (mm)
sGrowth SD of residual variation about eGrowth
sKAnnual SD of bKAnnual
Threshold Last Annual of the first regime
Years[i] Number of years between release and recapture for the ith fish

Bull Trout

Table 3. Model coefficients.

term estimate sd zscore lower upper pvalue
bKIntercept -1.7956959 0.1284305 -13.9794886 -2.0540450 -1.5396733 0.0007
bKRegime[2] -0.0912346 0.1567313 -0.5988029 -0.4190079 0.2115069 0.5213
bLinf 857.9665612 27.7940935 30.9575116 812.2310329 921.8432641 0.0007
sGrowth 31.4871398 1.3689584 23.0327024 29.0637196 34.2841835 0.0007
sKAnnual 0.2723559 0.0814077 3.4909084 0.1612945 0.4917336 0.0007
tGrowth -0.0912346 0.1567313 -0.5988029 -0.4190079 0.2115069 0.5213

Table 4. Model summary.

n K nchains niters nthin ess rhat converged
281 6 3 500 20 396 1.008 TRUE

Mountain Whitefish

Table 5. Model coefficients.

term estimate sd zscore lower upper pvalue
bKIntercept -1.6162822 0.1443185 -11.1885157 -1.8994488 -1.3339485 7e-04
bKRegime[2] -0.0450868 0.2090726 -0.2378948 -0.4734291 0.3629789 8e-01
bLinf 281.0074443 2.4198200 116.1727774 276.7228666 286.3675546 7e-04
sGrowth 9.4054613 0.2196507 42.8348303 8.9891443 9.8458449 7e-04
sKAnnual 0.3205001 0.1061132 3.1836481 0.1857553 0.6071873 7e-04
tGrowth -0.0450868 0.2090726 -0.2378948 -0.4734291 0.3629789 8e-01

Table 6. Model summary.

n K nchains niters nthin ess rhat converged
935 6 3 500 20 297 1.016 TRUE

Condition

Table 7. Parameter descriptions.

Parameter Description
bWeightIntercept Intercept for eWeightIntercept
bWeightRegimeIntercept[i] Effect of ith Regime on bWeightIntercept
bWeightRegimeSlope[i] Effect of ith Regime on bWeightSlope
bWeightSeasonIntercept[i] Effect of ith Season on bWeightIntercept
bWeightSeasonSlope[i] Effect of ith Season on bWeightSlope
bWeightSlope Intercept for eWeightSlope
bWeightYearIntercept[i] Effect of ith Year on bWeightIntercept
bWeightYearSlope[i] Random effect of ith Year on bWeightSlope
eWeight[i] Expected Weight of the ith fish
eWeightIntercept[i] Intercept for log(eWeight[i])
eWeightSlope[i] Effect of LogLength on eWeightIntercept
LogLength[i] The centered log(Length) of the ith fish
sWeight SD of residual variation about eWeight
sWeightYearIntercept SD of bWeightYearIntercept
sWeightYearSlope SD of bWeightYearSlope
Weight[i] The Weight of the ith fish

Bull Trout

Table 8. Model coefficients.

term estimate sd zscore lower upper pvalue
bWeightIntercept 6.8243606 0.0253906 268.7847230 6.7719023 6.8767147 0.0007
bWeightRegimeIntercept[2] -0.0768944 0.0381404 -2.0031837 -0.1536060 0.0005419 0.0533
bWeightRegimeSlope[2] 0.0428429 0.0539794 0.7708220 -0.0580331 0.1502438 0.4173
bWeightSeasonIntercept[2] 0.0008678 0.0092726 0.1092891 -0.0172720 0.0188264 0.9253
bWeightSeasonSlope[2] 0.0103707 0.0234042 0.4220702 -0.0392083 0.0546331 0.6707
bWeightSlope 3.1662076 0.0361134 87.6425723 3.0926189 3.2310180 0.0007
sWeight 0.1390229 0.0017840 77.9473838 0.1355927 0.1426388 0.0007
sWeightYearIntercept 0.0722051 0.0165045 4.5222876 0.0502368 0.1137593 0.0007
sWeightYearSlope 0.0946811 0.0239326 4.0853900 0.0610342 0.1520444 0.0007
tCondition1 -0.0768944 0.0381404 -2.0031837 -0.1536060 0.0005419 0.0533
tCondition2 0.0428429 0.0539794 0.7708220 -0.0580331 0.1502438 0.4173

Table 9. Model summary.

n K nchains niters nthin ess rhat converged
3258 11 3 500 200 754 1.003 TRUE

Mountain Whitefish

Table 10. Model coefficients.

term estimate sd zscore lower upper pvalue
bWeightIntercept 4.7862432 0.0148585 322.1398836 4.7574469 4.8162260 0.0007
bWeightRegimeIntercept[2] -0.0218116 0.0211631 -1.0238196 -0.0651567 0.0203540 0.2947
bWeightRegimeSlope[2] -0.0201777 0.0254613 -0.7960493 -0.0703478 0.0332107 0.4200
bWeightSeasonIntercept[2] -0.0435541 0.0039948 -10.9416851 -0.0519632 -0.0360428 0.0007
bWeightSeasonSlope[2] -0.1017893 0.0178866 -5.6753994 -0.1363834 -0.0660136 0.0007
bWeightSlope 3.2083898 0.0172953 185.4762398 3.1738205 3.2409857 0.0007
sWeight 0.1008014 0.0007951 126.7651238 0.0993111 0.1024102 0.0007
sWeightYearIntercept 0.0406962 0.0089241 4.7253528 0.0286757 0.0627911 0.0007
sWeightYearSlope 0.0392578 0.0122856 3.3364323 0.0215437 0.0685571 0.0007
tCondition1 -0.0218116 0.0211631 -1.0238196 -0.0651567 0.0203540 0.2947
tCondition2 -0.0201777 0.0254613 -0.7960493 -0.0703478 0.0332107 0.4200

Table 11. Model summary.

n K nchains niters nthin ess rhat converged
7785 11 3 500 200 513 1.007 TRUE

Rainbow Trout

Table 12. Model coefficients.

term estimate sd zscore lower upper pvalue
bWeightIntercept 4.6820357 0.0170798 274.1729503 4.6505567 4.7174297 0.0007
bWeightRegimeIntercept[2] 0.0040558 0.0253178 0.1387943 -0.0507592 0.0507072 0.8613
bWeightRegimeSlope[2] -0.0479584 0.0552192 -0.8786936 -0.1607130 0.0541165 0.3693
bWeightSeasonIntercept[2] -0.0796013 0.0155338 -5.1305247 -0.1086392 -0.0504453 0.0007
bWeightSeasonSlope[2] 0.0190345 0.0410411 0.4682897 -0.0626553 0.0988973 0.6333
bWeightSlope 3.0874338 0.0371693 83.0972535 3.0179776 3.1649307 0.0007
sWeight 0.1151905 0.0034649 33.2655912 0.1083669 0.1221184 0.0007
sWeightYearIntercept 0.0344663 0.0134846 2.6205188 0.0120005 0.0645733 0.0007
sWeightYearSlope 0.0756046 0.0253223 3.1043012 0.0362450 0.1362144 0.0007
tCondition1 0.0040558 0.0253178 0.1387943 -0.0507592 0.0507072 0.8613
tCondition2 -0.0479584 0.0552192 -0.8786936 -0.1607130 0.0541165 0.3693

Table 13. Model summary.

n K nchains niters nthin ess rhat converged
589 11 3 500 200 1304 1.004 TRUE

Largescale Sucker

Table 14. Model coefficients.

term estimate sd zscore lower upper pvalue
bWeightIntercept 6.8162164 0.0288220 236.505860 6.7585239 6.8728955 7e-04
bWeightSeasonIntercept[2] 0.0216586 0.0053503 4.040487 0.0112316 0.0319146 7e-04
bWeightSeasonSlope[2] 0.1666632 0.0455712 3.646674 0.0743200 0.2572852 7e-04
bWeightSlope 2.8822666 0.0927258 31.084418 2.7014079 3.0613701 7e-04
sWeight 0.0840283 0.0012262 68.538023 0.0817101 0.0865267 7e-04
sWeightYearIntercept 0.0683926 0.0269849 2.765798 0.0414743 0.1472769 7e-04
sWeightYearSlope 0.2267932 0.0939117 2.644467 0.1311874 0.4870871 7e-04

Table 15. Model summary.

n K nchains niters nthin ess rhat converged
2426 7 3 500 200 261 1.023 TRUE

Occupancy

Table 16. Parameter descriptions.

Parameter Description
bOccupancySite[i] Effect of ith site on bOccupancyYear
bOccupancySiteYear[i,j] Effect of ith site in jth year on bOccupancyYear
bOccupancySpring Effect of spring on bOccupancyYear
bOccupancyYear[i] Expected Occupancy in ith year
bRate Intercept of eRateYear
bRateRev5[i] Effect of Revelstoke 5 regime on bRate
bRateYear[i] Effect of ith year on biRate
eObserved[i] Probability of observing a species on ith site visit
eRateYear[i] Change in bOccupancyYear between year i-1 and year i
Observed[i] Whether the species was observed on ith site visit
sOccupancySite SD of bOccupancySite
sOccupancySiteYear SD of bOccupancySiteYear
sRateYear SD of bRateYear

Rainbow Trout

Table 17. Model coefficients.

term estimate sd zscore lower upper pvalue
bOccupancySpring -0.0411117 0.2851908 -0.1445481 -0.6069750 0.5131999 0.8933
bRate 0.2379924 0.3928192 0.6185457 -0.5593954 1.0582669 0.4787
bRateRev5 -0.2563198 0.6485588 -0.3825753 -1.5372282 1.0985291 0.6293
sOccupancySite 2.2264129 0.5430082 4.2772283 1.4887948 3.6018555 0.0007
sOccupancySiteYear 0.6653187 0.1947305 3.3943500 0.2568279 1.0403358 0.0007
sRateYear 1.1222808 0.3921133 3.0123116 0.6064728 2.1207664 0.0007

Table 18. Model summary.

n K nchains niters nthin ess rhat converged
1014 6 3 500 1000 435 1.005 TRUE

Burbot

Table 19. Model coefficients.

term estimate sd zscore lower upper pvalue
bOccupancySpring -0.5057937 0.3223352 -1.570036 -1.1209661 0.1464580 0.1173
bRate 0.3841475 0.3898402 1.026790 -0.3835111 1.2055886 0.2627
bRateRev5 -0.7194367 0.6396951 -1.127059 -1.9569717 0.5631416 0.2320
sOccupancySite 0.9911525 0.2835094 3.634356 0.5999947 1.6929255 0.0007
sOccupancySiteYear 0.4851008 0.2244312 2.146587 0.0592532 0.9056742 0.0007
sRateYear 1.1573749 0.3639998 3.306197 0.6403933 2.0551181 0.0007

Table 20. Model summary.

n K nchains niters nthin ess rhat converged
1014 6 3 500 1000 512 1.007 TRUE

Lake Whitefish

Table 21. Model coefficients.

term estimate sd zscore lower upper pvalue
bOccupancySpring -4.8792868 0.7949583 -6.2300809 -6.6843511 -3.5353613 0.0007
bRate 0.2280765 0.5909454 0.3487796 -0.9741561 1.3314284 0.6947
bRateRev5 -0.4705963 0.9543082 -0.4849325 -2.3613754 1.5669143 0.5813
sOccupancySite 0.4622837 0.1803607 2.6677588 0.1831738 0.9018900 0.0007
sOccupancySiteYear 0.2073502 0.1679960 1.4007475 0.0069089 0.6153061 0.0007
sRateYear 1.7337401 0.4500629 3.9998384 1.1293434 2.8731815 0.0007

Table 22. Model summary.

n K nchains niters nthin ess rhat converged
1014 6 3 500 1000 222 1.008 TRUE

Northern Pikeminnow

Table 23. Model coefficients.

term estimate sd zscore lower upper pvalue
bOccupancySpring -2.1728525 0.4525899 -4.827522 -3.1290960 -1.3433385 0.0007
bRate 0.3630713 0.3090265 1.218756 -0.1835884 1.0132314 0.1960
bRateRev5 -0.5877584 0.4778209 -1.242252 -1.5534644 0.3123419 0.1973
sOccupancySite 1.3978357 0.3928139 3.706250 0.8555126 2.3903382 0.0007
sOccupancySiteYear 0.7004825 0.2643190 2.594307 0.1236359 1.1938915 0.0007
sRateYear 0.7594259 0.3034616 2.640950 0.3173491 1.4880743 0.0007

Table 24. Model summary.

n K nchains niters nthin ess rhat converged
1014 6 3 500 1000 786 1.007 TRUE

Redside Shiner

Table 25. Model coefficients.

term estimate sd zscore lower upper pvalue
bOccupancySpring -0.9326677 0.3716394 -2.5384802 -1.6948440 -0.2119687 0.0120
bRate 0.3963091 0.4917673 0.8349994 -0.5738027 1.4075349 0.3733
bRateRev5 -0.4400885 0.8081807 -0.5668822 -2.0794312 1.1450343 0.5467
sOccupancySite 2.1844716 0.5946516 3.8517367 1.4153988 3.6607364 0.0007
sOccupancySiteYear 0.3049922 0.2091674 1.5709232 0.0180595 0.7842232 0.0007
sRateYear 1.4353385 0.4748746 3.1669575 0.7826002 2.6408668 0.0007

Table 26. Model summary.

n K nchains niters nthin ess rhat converged
1014 6 3 500 1000 336 1.006 TRUE

Sculpins

Table 27. Model coefficients.

term estimate sd zscore lower upper pvalue
bOccupancySpring -0.4595474 0.2749237 -1.665046 -1.0020872 0.0935364 0.0960
bRate 0.5311123 0.4401942 1.214043 -0.3183993 1.4311108 0.2120
bRateRev5 -0.8558022 0.6876453 -1.240467 -2.2513058 0.4497523 0.2040
sOccupancySite 1.2980388 0.3129102 4.306438 0.8795545 2.1233750 0.0007
sOccupancySiteYear 0.3318056 0.1960727 1.733583 0.0172489 0.7342655 0.0007
sRateYear 1.2760344 0.3633538 3.657467 0.7833359 2.2107150 0.0007

Table 28. Model summary.

n K nchains niters nthin ess rhat converged
1014 6 3 500 1000 268 1.03 TRUE

Count

Table 29. Parameter descriptions.

Parameter Description
bDensity bTrendYear in the first year
bDensitySeason Effect of season on bTrendYear
bDensitySite[i] Effect of ith site on bTrendYear
bDensitySiteYear[i,j] Effect of ith site in jth year on bDensityTrend
bDensityYear[i] Effect of ith year on bTrendYear
bRate Exponential population growth rate
bRateRev5 Effect of Rev5 on bRate
bTrendYear[i] The intercept for the log(eDensity) in the ith year
Count[i] Count on ith site visit
eCount[i] Expected count on ith site visit
eDensity[i] Expected lineal count density on ith site visit
eDispersion[i] Overdispersion on ith site visit
ProportionSampled[i] Proportion of site sampled on ith site visit
sDensitySite SD of bDensitySite
sDensitySiteYear SD of bDensitySiteYear
sDensityYear SD of bDensityYear
sDispersion[i] SD of eDispersion
SiteLength[i] Length of site on ith site visit
YearRev5[i] Whether the rate of change between the ith and i+1th year is effectd by Rev5

Rainbow Trout

Table 30. Model coefficients.

term estimate sd zscore lower upper pvalue
bDensity -2.8612928 0.6970114 -4.0992721 -4.2818787 -1.4976945 0.0007
bDensitySeason[2] -0.1091911 0.1605998 -0.6741163 -0.4193663 0.2085626 0.4933
bRate 0.2592875 0.0718788 3.6459558 0.1219630 0.4066084 0.0007
bRateRev5 -0.4701834 0.1602989 -2.9593910 -0.7737993 -0.1595690 0.0067
sDensitySite 1.7162579 0.4379115 4.1112994 1.1593217 2.9045018 0.0007
sDensitySiteYear 0.7686949 0.0884909 8.7347477 0.6082854 0.9558580 0.0007
sDensityYear 0.6199756 0.2007822 3.2310252 0.3469152 1.1138997 0.0007
sDispersion 0.8183775 0.0555904 14.7819962 0.7198484 0.9350104 0.0007
tCount -0.4701834 0.1602989 -2.9593910 -0.7737993 -0.1595690 0.0067

Table 31. Model summary.

n K nchains niters nthin ess rhat converged
1014 9 3 500 2000 1098 1.004 TRUE

Burbot

Table 32. Model coefficients.

term estimate sd zscore lower upper pvalue
bDensity -3.2034164 0.7287912 -4.440413 -4.7488911 -1.9144469 0.0007
bDensitySeason[2] -0.7552142 0.2818057 -2.681966 -1.3239397 -0.2138741 0.0080
bRate 0.2346567 0.1037963 2.294250 0.0433189 0.4662045 0.0213
bRateRev5 -0.7258272 0.2543536 -2.875374 -1.2534283 -0.2235450 0.0027
sDensitySite 0.7945730 0.2324992 3.547255 0.4679400 1.3718340 0.0007
sDensitySiteYear 0.4101321 0.1919138 2.072994 0.0375071 0.7548380 0.0007
sDensityYear 0.9986961 0.3129010 3.290181 0.5483730 1.7289211 0.0007
sDispersion 1.1998372 0.1370626 8.780270 0.9424904 1.4752871 0.0007
tCount -0.7258272 0.2543536 -2.875374 -1.2534283 -0.2235450 0.0027

Table 33. Model summary.

n K nchains niters nthin ess rhat converged
1014 9 3 500 2000 1014 1.005 TRUE

Northern Pikeminnow

Table 34. Model coefficients.

term estimate sd zscore lower upper pvalue
bDensity -4.3557727 0.8407470 -5.234004 -6.1851440 -2.8834448 7e-04
bDensitySeason[2] -2.3944693 0.4384267 -5.516285 -3.3346633 -1.6149347 7e-04
bRate 0.3592536 0.1022956 3.581009 0.1916289 0.5831458 7e-04
bRateRev5 -0.6998474 0.2070067 -3.445488 -1.1535332 -0.3364977 7e-04
sDensitySite 1.2799847 0.3761511 3.540258 0.7821973 2.2194469 7e-04
sDensitySiteYear 0.7345422 0.1922025 3.785029 0.3314408 1.0923605 7e-04
sDensityYear 0.6780051 0.2521644 2.834982 0.3235725 1.3141275 7e-04
sDispersion 1.3407643 0.1309785 10.242386 1.0931417 1.6039903 7e-04
tCount -0.6998474 0.2070067 -3.445488 -1.1535332 -0.3364977 7e-04

Table 35. Model summary.

n K nchains niters nthin ess rhat converged
1014 9 3 500 2000 1168 1.005 TRUE

Suckers

Table 36. Model coefficients.

term estimate sd zscore lower upper pvalue
bDensity 1.9658082 0.2534481 7.751190 1.4894203 2.4691617 0.0007
bDensityRev5 0.7222453 0.3159206 2.274950 0.1196108 1.3601837 0.0240
bDensitySeason[2] -0.3241030 0.1006812 -3.248471 -0.5247304 -0.1289312 0.0007
sDensitySite 0.5236664 0.1315783 4.135944 0.3458364 0.8429951 0.0007
sDensitySiteYear 0.5071496 0.0472447 10.735497 0.4187704 0.6020424 0.0007
sDensityYear 0.5781484 0.1352985 4.428347 0.3953560 0.9251016 0.0007
sDispersion 0.7576635 0.0228915 33.138315 0.7139924 0.8059571 0.0007
tCount 0.7222453 0.3159206 2.274950 0.1196108 1.3601837 0.0240

Table 37. Model summary.

n K nchains niters nthin ess rhat converged
1014 8 3 500 2000 894 1.005 TRUE

Movement

Table 38. Parameter descriptions.

Parameter Description
bLength Effect of Length on bMoved
bLengthSpring Effect of Spring on bLength
bMoved Intercept for logit(eMoved)
bMovedSpring Effect of Spring on bMoved
eMoved[i] Probability of different site from previous encounter for ith recaptured fish
Length[i] Length of ith recaptured fish (mm)
Moved[i] Indicates whether ith recaptured fish is recorded at a different site from previous encounter
Spring[i] Whether the ith recaptured is from the spring

Bull Trout

Table 39. Model coefficients.

term estimate sd zscore lower upper pvalue
bLength 0.0050472 0.0016056 3.1326266 0.0018442 0.0082599 0.0013
bLengthSpring 0.0019111 0.0062901 0.3996867 -0.0085459 0.0168717 0.7373
bMoved -2.0057737 0.7198206 -2.7697381 -3.4094842 -0.5923852 0.0053
bMovedSpring 0.0193226 2.7084371 -0.0445004 -6.0079853 4.7805567 0.9960

Table 40. Model summary.

n K nchains niters nthin ess rhat converged
144 4 3 500 100 1314 1.002 TRUE

Mountain Whitefish

Table 41. Model coefficients.

term estimate sd zscore lower upper pvalue
bLength -0.0012623 0.0028729 -0.4149524 -0.0067981 0.0042952 0.6827
bLengthSpring -0.0265964 0.0068067 -3.9631561 -0.0403634 -0.0141731 0.0007
bMoved 0.2354738 0.7347255 0.2992118 -1.1754436 1.6778067 0.7680
bMovedSpring 5.4685838 1.6255916 3.4274672 2.4814080 8.8208538 0.0007

Table 42. Model summary.

n K nchains niters nthin ess rhat converged
459 4 3 500 100 555 1.002 TRUE

Rainbow Trout

Table 43. Model coefficients.

term estimate sd zscore lower upper pvalue
bLength 0.0108561 0.0060665 1.817775 -0.0000668 0.0230225 0.0520
bLengthSpring 0.2187275 0.1219674 1.899689 0.0294910 0.5034065 0.0133
bMoved -3.2979571 1.6582649 -2.013013 -6.8102456 -0.4088259 0.0240
bMovedSpring -66.6287828 36.3791852 -1.944310 -151.3493832 -10.0658893 0.0120

Table 44. Model summary.

n K nchains niters nthin ess rhat converged
25 4 3 500 100 448 1.004 TRUE

Largescale Sucker

Table 45. Model coefficients.

term estimate sd zscore lower upper pvalue
bLength -0.0098648 0.0057419 -1.732708 -0.0209798 0.0003701 0.0613
bLengthSpring -0.1755000 0.0805597 -2.232773 -0.3441619 -0.0406039 0.0053
bMoved 4.1410501 2.4908500 1.656872 -0.3073915 8.9910564 0.0773
bMovedSpring 77.2610292 35.4047315 2.231718 17.1950237 150.8352660 0.0067

Table 46. Model summary.

n K nchains niters nthin ess rhat converged
72 4 3 500 200 162 1.018 TRUE

Abundance

Table 47. Parameter descriptions.

Parameter Description
bDensity Intercept for log(eDensity) in the 1st year
bDensitySeason[i] Effect of ith season on bTrendYear
bDensitySite[i] Effect of ith site on bDensity
bDensitySiteYear[i,j] Effect of ith site in jth year on bDensity
bDensityYear[i] Effect of ith year on bDensity
bEfficiency Intercept for logit(eEfficiency)
bEfficiencySeason[i] Effect of ith season on bEfficiency
bEfficiencySessionSeasonYear[i, j, k] Effect of ith Session in jth Season of kth Year on bEfficiency
bRate Exponential annual population growth rate
bRateRev5[i] Effect of Rev5 on bRate
bTrendYear[i] Intercept for log(eDensity) in the ith year
Catch[i] Number of fish caught on ith site visit
eAbundance[i] Predicted abundance on ith site visit
eDensity[i] Predicted lineal density on ith site visit
eEfficiency[i] Predicted efficiency during ith site visit
Marked[i] Number of marked fish caught in ith river visit
sDensitySite SD of bDensitySite
sDensitySiteYear SD of bDensitySiteYear
sDensityYear SD of bDensityYear
sEfficiencySessionSeasonYear SD of bEfficiencySessionSeasonYear
Tagged[i] Number of fish tagged prior to ith river visit

Bull Trout

Juvenile

Table 48. Model coefficients.

term estimate sd zscore lower upper pvalue
bDensity 2.0317352 0.3790120 5.3455424 1.3033732 2.7645543 0.0007
bDensitySeason[2] 0.2440742 0.3530140 0.7338443 -0.4119758 1.0001333 0.4760
bEfficiency -3.1353008 0.1432960 -21.9225965 -3.4353124 -2.8794709 0.0007
bEfficiencySeason[2] -0.3537353 0.3604264 -1.0139636 -1.0918352 0.3142488 0.3093
bMultiplierType[2] 0.2722326 0.1713857 1.5890810 -0.0464302 0.6271081 0.1053
bRate 0.1491630 0.0465825 3.2319406 0.0643235 0.2508200 0.0013
bRateRev5 -0.1868586 0.1138132 -1.6729114 -0.4362648 0.0249896 0.0760
sDensitySite 0.6001291 0.1445960 4.2988961 0.3989052 0.9574976 0.0007
sDensitySiteYear 0.2773329 0.0584176 4.7249048 0.1550644 0.3873249 0.0007
sDensityYear 0.4398299 0.1276335 3.5747469 0.2472924 0.7536640 0.0007
sDispersion -0.9601223 0.1475537 -6.5768298 -1.2822591 -0.7199343 0.0007
sDispersionType[2] 0.5865248 0.2832811 2.0246163 -0.0040704 1.0888026 0.0507
sEfficiencySessionSeasonYear 0.2361710 0.0521742 4.5320186 0.1365222 0.3407249 0.0007
tAbundance -0.1868586 0.1138132 -1.6729114 -0.4362648 0.0249896 0.0760

Table 49. Model summary.

n K nchains niters nthin ess rhat converged
1104 14 3 500 2000 1132 1.005 TRUE
Adult

Table 50. Model coefficients.

term estimate sd zscore lower upper pvalue
bDensity 4.1205850 0.2637881 15.6250993 3.6079347 4.6517529 0.0007
bDensitySeason[2] -0.1943602 0.3581661 -0.5225600 -0.8562970 0.5597798 0.5560
bEfficiency -3.6315919 0.1165034 -31.1930482 -3.8700865 -3.4163559 0.0007
bEfficiencySeason[2] -0.0857431 0.3557813 -0.2745795 -0.8368120 0.5837932 0.8013
bMultiplierType[2] 0.5222438 0.1493106 3.5334669 0.2390014 0.8266887 0.0007
bRate 0.0272884 0.0291399 0.9630816 -0.0269118 0.0869345 0.2973
bRateRev5 -0.0332167 0.0693881 -0.4786373 -0.1633932 0.1056415 0.6053
sDensitySite 0.5451917 0.1340689 4.2043831 0.3547699 0.8838168 0.0007
sDensitySiteYear 0.4149849 0.0423970 9.7917041 0.3344406 0.5012764 0.0007
sDensityYear 0.2365239 0.0957466 2.4939250 0.0538772 0.4514469 0.0007
sDispersion -0.9190190 0.0925200 -9.9944092 -1.1148555 -0.7559762 0.0007
sDispersionType[2] 0.4416597 0.1880097 2.3195082 0.0221530 0.7859473 0.0387
sEfficiencySessionSeasonYear 0.2178296 0.0428757 5.1497094 0.1426023 0.3132982 0.0007
tAbundance -0.0332167 0.0693881 -0.4786373 -0.1633932 0.1056415 0.6053

Table 51. Model summary.

n K nchains niters nthin ess rhat converged
1104 14 3 500 2000 1174 1.005 TRUE

Mountain Whitefish

Juvenile

Table 52. Model coefficients.

term estimate sd zscore lower upper pvalue
bDensity 5.6170627 0.6690741 8.4116993 4.3434876 6.9734945 0.0007
bDensitySeason[2] 0.4646623 0.6886065 0.6547951 -0.8827184 1.8198348 0.5093
bEfficiency -5.7262786 0.4371984 -13.1478443 -6.6739146 -4.9536452 0.0007
bEfficiencySeason[2] 0.0488543 0.6901959 0.0807100 -1.3351240 1.3632474 0.9413
bMultiplierType[2] 0.5330413 0.2124633 2.5013183 0.1138232 0.9556618 0.0133
bRate 0.0906223 0.1657261 0.5615328 -0.2348045 0.4410380 0.5307
bRateRev5 -0.1350301 0.2425361 -0.5677648 -0.6479221 0.3228641 0.5307
sDensitySite 0.9003265 0.2198041 4.2653817 0.6121254 1.4376721 0.0007
sDensitySiteYear 0.5135963 0.0628418 8.1683567 0.3905679 0.6378851 0.0007
sDensityYear 0.5128644 0.2115669 2.6032595 0.2451370 1.0591084 0.0007
sDispersion -0.5576387 0.0882409 -6.3517525 -0.7379878 -0.3937922 0.0007
sDispersionType[2] 0.5531513 0.1672067 3.3127635 0.2398062 0.8928915 0.0013
sEfficiencySessionSeasonYear 0.2920152 0.0633951 4.6358054 0.1679092 0.4275948 0.0007
tAbundance -0.1350301 0.2425361 -0.5677648 -0.6479221 0.3228641 0.5307

Table 53. Model summary.

n K nchains niters nthin ess rhat converged
875 14 3 500 2000 498 1.005 TRUE
Adult

Table 54. Model coefficients.

term estimate sd zscore lower upper pvalue
bDensity 6.6878660 0.1980319 33.7641948 6.3013714 7.0956635 0.0007
bDensitySeason[2] -0.6542806 0.1223199 -5.3626028 -0.9068309 -0.4196073 0.0007
bEfficiency -4.0065177 0.0662289 -60.5198737 -4.1382706 -3.8784746 0.0007
bEfficiencySeason[2] 0.9042058 0.1217576 7.4409046 0.6770769 1.1561322 0.0007
bMultiplierType[2] 0.7479242 0.1463030 5.1066947 0.4445880 1.0248355 0.0007
bRate 0.0018701 0.0187605 0.1198861 -0.0368802 0.0388578 0.9013
bRateRev5 -0.0270408 0.0446637 -0.6070068 -0.1143601 0.0595042 0.5253
sDensitySite 0.5486478 0.1285072 4.4492360 0.3718853 0.8654625 0.0007
sDensitySiteYear 0.3960126 0.0301615 13.1675431 0.3396973 0.4586579 0.0007
sDensityYear 0.0820778 0.0591381 1.4821590 0.0025397 0.2202992 0.0007
sDispersion -0.7957032 0.0373020 -21.3570001 -0.8702372 -0.7233282 0.0007
sDispersionType[2] 0.4181438 0.1028141 4.0723840 0.2249781 0.6182324 0.0007
sEfficiencySessionSeasonYear 0.2444777 0.0301583 8.1768445 0.1913308 0.3095988 0.0007
tAbundance -0.0270408 0.0446637 -0.6070068 -0.1143601 0.0595042 0.5253

Table 55. Model summary.

n K nchains niters nthin ess rhat converged
1104 14 3 500 2000 927 1.006 TRUE

Rainbow Trout

Adult

Table 56. Model coefficients.

term estimate sd zscore lower upper pvalue
bDensity 0.4137203 0.5939575 0.6771314 -0.8204313 1.5553675 0.4747
bDensitySeason[2] 0.2489691 0.6649070 0.4421001 -0.8825792 1.7504814 0.7053
bEfficiency -2.4743420 0.2592588 -9.5758978 -3.0354726 -2.0359383 0.0007
bEfficiencySeason[2] -0.5358472 0.6659936 -0.8717729 -2.0607134 0.5551417 0.3760
bMultiplierType[2] 0.0452367 2.0116422 0.0226372 -3.8948973 3.9903901 0.9840
bRate -0.0230432 0.1597252 -0.1611414 -0.3559728 0.2931614 0.8747
bRateRev5 0.1443743 0.2352091 0.6007055 -0.3529188 0.6400748 0.5187
sDensitySite 1.2130851 0.3312549 3.8168251 0.7689813 2.0414908 0.0007
sDensitySiteYear 0.5868438 0.1349415 4.3561290 0.3375407 0.8529505 0.0007
sDensityYear 0.4177111 0.2376546 1.8522135 0.0485949 0.9405130 0.0007
sDispersion -1.6241304 1.1805303 -1.6332341 -4.9241527 -0.4965307 0.0007
sDispersionType[2] -0.1364874 2.0229551 -0.0361438 -3.9223618 3.8385929 0.9560
sEfficiencySessionSeasonYear 0.2860454 0.1373747 2.0540970 0.0246497 0.5506224 0.0007
tAbundance 0.1443743 0.2352091 0.6007055 -0.3529188 0.6400748 0.5187

Table 57. Model summary.

n K nchains niters nthin ess rhat converged
785 14 3 500 2000 387 1.005 TRUE

Largescale Sucker

Adult

Table 58. Model coefficients.

term estimate sd zscore lower upper pvalue
bDensity 5.1510705 0.2876970 17.8987169 4.5985206 5.7082043 0.0007
bDensitySeason[2] 0.0157497 0.5749163 0.0847352 -0.9717747 1.2267107 0.9800
bEfficiency -3.4600753 0.1590708 -21.7684830 -3.7810554 -3.1537326 0.0007
bEfficiencySeason[2] -1.1800022 0.5871715 -2.0659784 -2.4420089 -0.1310982 0.0213
bMultiplierType[2] 0.6759784 0.2512915 2.7451933 0.2144580 1.1928307 0.0027
sDensitySite 0.5096088 0.1319270 4.0098108 0.3292363 0.8546882 0.0007
sDensitySiteYear 0.4372796 0.0557146 7.8583799 0.3304168 0.5502250 0.0007
sDensityYear 0.5301892 0.2636274 2.2378175 0.2627932 1.2250619 0.0007
sDispersion -0.7241668 0.0737539 -9.8319012 -0.8701852 -0.5836334 0.0007
sDispersionType[2] 0.3779230 0.1477453 2.5376958 0.0950942 0.6695803 0.0120
sEfficiencySessionSeasonYear 0.5334568 0.0873531 6.2060991 0.3921578 0.7318386 0.0007

Table 59. Model summary.

n K nchains niters nthin ess rhat converged
660 11 3 500 2000 1212 1.005 TRUE

Distribution

Table 60. Parameter descriptions.

Parameter Description
bEffect Intercept for eEffect
bRkm Effect of Rkm on bEffect
bRkmRev5 Effect of Rev5 on bRkm
bRkmYear[i] Effect of ith year on bRkm
eEffect Expected Effect
Effect Estimated site and year effect from the count or abundance model
Rkm Standardised river kilometre
sEffect SD of residual variation in Effect
sRkmYear SD of bRkmYear

Bull Trout

Juvenile

Table 61. Model coefficients.

term estimate sd zscore lower upper pvalue
bEffect 0.0012219 0.0072488 0.1886369 -0.0121164 0.0156917 0.8560
bRkm -0.0009996 0.0040039 -0.2652176 -0.0088620 0.0069872 0.7867
bRkmRev5 0.0026939 0.0060819 0.4406870 -0.0098143 0.0150106 0.6467
sEffect 0.1169748 0.0051791 22.6095410 0.1075126 0.1275491 0.0007
sRkmYear 0.0059504 0.0037622 1.6693618 0.0002967 0.0146578 0.0007
tDistribution 0.0026939 0.0060819 0.4406870 -0.0098143 0.0150106 0.6467

Table 62. Model summary.

n K nchains niters nthin ess rhat converged
255 6 3 500 10 330 1.01 TRUE
Adult

Table 63. Model coefficients.

term estimate sd zscore lower upper pvalue
bEffect 0.0021188 0.0162774 0.1475748 -0.0292853 0.0339879 0.8960
bRkm -0.0101684 0.0085917 -1.1827391 -0.0267713 0.0067268 0.2267
bRkmRev5 0.0313800 0.0136076 2.2969018 0.0036145 0.0581467 0.0187
sEffect 0.2611454 0.0118585 22.0726361 0.2403724 0.2868843 0.0007
sRkmYear 0.0105092 0.0081765 1.4337960 0.0003035 0.0312636 0.0007
tDistribution 0.0313800 0.0136076 2.2969018 0.0036145 0.0581467 0.0187

Table 64. Model summary.

n K nchains niters nthin ess rhat converged
255 6 3 500 10 252 1.005 TRUE

Mountain Whitefish

Adult

Table 65. Model coefficients.

term estimate sd zscore lower upper pvalue
bEffect -0.0009333 0.0183083 -0.0722278 -0.0373985 0.0346882 0.9587
bRkm 0.0006768 0.0110222 0.0354611 -0.0220206 0.0224818 0.9440
bRkmRev5 0.0065547 0.0176821 0.3655735 -0.0282584 0.0411516 0.6773
sEffect 0.2874501 0.0131488 21.8592717 0.2624917 0.3139596 0.0007
sRkmYear 0.0224354 0.0113241 1.9837166 0.0004346 0.0463753 0.0007
tDistribution 0.0065547 0.0176821 0.3655735 -0.0282584 0.0411516 0.6773

Table 66. Model summary.

n K nchains niters nthin ess rhat converged
255 6 3 500 10 322 1.004 TRUE

Rainbow Trout

Table 67. Model coefficients.

term estimate sd zscore lower upper pvalue
bEffect 0.0031770 0.0239032 0.1182777 -0.0425645 0.0496905 0.9187
bRkm -0.0270037 0.0179196 -1.5029346 -0.0611305 0.0083273 0.1360
bRkmRev5 0.0597893 0.0280341 2.0935542 0.0013943 0.1157142 0.0467
sEffect 0.3845771 0.0177854 21.6474494 0.3524732 0.4212615 0.0007
sRkmYear 0.0436899 0.0147730 3.0657064 0.0197199 0.0791968 0.0007
tDistribution 0.0597893 0.0280341 2.0935542 0.0013943 0.1157142 0.0467

Table 68. Model summary.

n K nchains niters nthin ess rhat converged
255 6 3 500 10 900 1.004 TRUE

Burbot

Table 69. Model coefficients.

term estimate sd zscore lower upper pvalue
bEffect 0.0047523 0.0048649 0.9808711 -0.0047884 0.0141454 0.3173
bRkm -0.0007285 0.0030300 -0.2197032 -0.0068651 0.0056757 0.8027
bRkmRev5 0.0027988 0.0047500 0.5716038 -0.0070349 0.0120334 0.5213
sEffect 0.0774573 0.0035538 21.8367590 0.0705242 0.0847139 0.0007
sRkmYear 0.0059140 0.0030531 1.9895850 0.0007050 0.0126025 0.0007
tDistribution 0.0027988 0.0047500 0.5716038 -0.0070349 0.0120334 0.5213

Table 70. Model summary.

n K nchains niters nthin ess rhat converged
255 6 3 500 10 406 1.004 TRUE

Northern Pikeminnow

Table 71. Model coefficients.

term estimate sd zscore lower upper pvalue
bEffect 0.0055556 0.0158735 0.3401192 -0.0264897 0.0365196 0.7293
bRkm -0.0093448 0.0095108 -0.9623197 -0.0280224 0.0096571 0.3240
bRkmRev5 0.0059806 0.0139209 0.4357499 -0.0213419 0.0332630 0.6720
sEffect 0.2487321 0.0117870 21.1714582 0.2280508 0.2739848 0.0007
sRkmYear 0.0167334 0.0088663 1.9325123 0.0015814 0.0361184 0.0007
tDistribution 0.0059806 0.0139209 0.4357499 -0.0213419 0.0332630 0.6720

Table 72. Model summary.

n K nchains niters nthin ess rhat converged
255 6 3 500 10 503 1.013 TRUE

Suckers

Table 73. Model coefficients.

term estimate sd zscore lower upper pvalue
bEffect -0.0020998 0.0201468 -0.1028753 -0.0418827 0.0378313 0.9107
bRkm -0.0127324 0.0142194 -0.8793879 -0.0399750 0.0144496 0.3507
bRkmRev5 0.0146801 0.0218791 0.6713914 -0.0270057 0.0604360 0.4880
sEffect 0.3231260 0.0148205 21.8395680 0.2958326 0.3546773 0.0007
sRkmYear 0.0319111 0.0125226 2.6022866 0.0102629 0.0601587 0.0007
tDistribution 0.0146801 0.0218791 0.6713914 -0.0270057 0.0604360 0.4880

Table 74. Model summary.

n K nchains niters nthin ess rhat converged
255 6 3 500 10 894 1.003 TRUE

Capture Effect

Table 75. Parameter descriptions.

Parameter Description
bWeightIntercept Intercept for eWeightIntercept
bWeightRecaptureIntercept Effect of Recapture on bWeightIntercept
bWeightRecaptureSlope Effect of Recapture on bWeightSlope
bWeightSlope Intercept for eWeightSlope
bWeightYearIntercept[i] Effect of ith Year on bWeightIntercept
bWeightYearSlope[i] Random effect of ith Year on bWeightSlope
eWeight[i] Expected Weight of the ith fish
eWeightIntercept[i] Intercept for log(eWeight[i])
eWeightSlope[i] Effect of LogLength on eWeightIntercept
LogLength[i] The centered log(Length) of the ith fish
Recapture[i] Whether the ith fish was an inter-annual recapture (as indicated by the presence of a PIT tag
sWeight SD of residual variation about eWeight
sWeightYearIntercept SD of bWeightYearIntercept
sWeightYearSlope SD of bWeightYearSlope
Weight[i] The Weight of the ith fish

Bull Trout

Table 76. Model coefficients.

term estimate sd zscore lower upper pvalue
bWeightIntercept 6.9019621 0.0248208 278.069979 6.8520629 6.9501123 0.0007
bWeightRecaptureIntercept 0.0211303 0.0091243 2.316190 0.0039977 0.0399739 0.0187
bWeightRecaptureSlope -0.0724339 0.0321112 -2.236563 -0.1357537 -0.0121291 0.0133
bWeightSlope 3.2142950 0.0279174 115.168276 3.1635580 3.2773244 0.0007
sWeight 0.1408228 0.0019224 73.284413 0.1372138 0.1446884 0.0007
sWeightYearIntercept 0.0767177 0.0217290 3.734924 0.0525973 0.1333774 0.0007
sWeightYearSlope 0.0860442 0.0256628 3.495906 0.0523545 0.1495042 0.0007

Table 77. Model summary.

n K nchains niters nthin ess rhat converged
2679 7 3 500 200 871 1.004 TRUE

Mountain Whitefish

Table 78. Model coefficients.

term estimate sd zscore lower upper pvalue
bWeightIntercept 4.8547644 0.0145183 334.4251915 4.8272645 4.8855516 0.0007
bWeightRecaptureIntercept -0.0012264 0.0035650 -0.3539709 -0.0083452 0.0059806 0.7413
bWeightRecaptureSlope -0.0600122 0.0219132 -2.7327271 -0.1021539 -0.0170139 0.0093
bWeightSlope 3.2033772 0.0170888 187.4572373 3.1688163 3.2380212 0.0007
sWeight 0.1028056 0.0008587 119.6967778 0.1010997 0.1044647 0.0007
sWeightYearIntercept 0.0454747 0.0127240 3.7534633 0.0301905 0.0811690 0.0007
sWeightYearSlope 0.0511438 0.0166262 3.2286722 0.0293022 0.0930370 0.0007

Table 79. Model summary.

n K nchains niters nthin ess rhat converged
6649 7 3 500 200 486 1.008 TRUE

Rainbow Trout

Table 80. Model coefficients.

term estimate sd zscore lower upper pvalue
bWeightIntercept 4.7349166 0.0123842 382.3253043 4.7093353 4.7593201 0.0007
bWeightRecaptureIntercept -0.0173408 0.0321615 -0.5179591 -0.0808138 0.0456678 0.5880
bWeightRecaptureSlope 0.0588460 0.0811028 0.7151062 -0.1036329 0.2156014 0.4827
bWeightSlope 3.0671729 0.0353512 86.8070618 3.0067160 3.1405859 0.0007
sWeight 0.1154397 0.0040074 28.8490126 0.1081938 0.1236207 0.0007
sWeightYearIntercept 0.0309447 0.0122947 2.6057461 0.0109548 0.0595313 0.0007
sWeightYearSlope 0.0849818 0.0342430 2.6414799 0.0386692 0.1746799 0.0007

Table 81. Model summary.

n K nchains niters nthin ess rhat converged
467 7 3 500 200 1198 1.005 TRUE

Figures

Growth

figures/growth/growth.png
Figure 1. Predicted growth curve by species.

Bull Trout

figures/growth/BT/year.png
Figure 2. Predicted von Bertalanffy growth coefficient, k, by year (with 95% CIs).

Mountain Whitefish

figures/growth/MW/year.png
Figure 3. Predicted von Bertalanffy growth coefficient, k, by year (with 95% CIs).

Condition

figures/condition/length.png
Figure 4. Predicted length-mass relationship by species.

Bull Trout

Juvenile
figures/condition/BT/juvenile/year.png
Figure 5. Body condition effect size estimates (with 95% CIs) by year for a 300 mm juvenile Bull Trout.
Adult
figures/condition/BT/adult/year.png
Figure 6. Body condition effect size estimates (with 95% CIs) by year for a 500 mm adult Bull Trout.

Mountain Whitefish

Juvenile
figures/condition/MW/juvenile/year.png
Figure 7. Body condition effect size estimates (with 95% CIs) by year for a 100 mm juvenile Mountain Whitefish.
Adult
figures/condition/MW/adult/year.png
Figure 8. Body condition effect size estimates (with 95% CIs) by year for a 250 mm adult Mountain Whitefish.

Rainbow Trout

Juvenile
figures/condition/RB/juvenile/year.png
Figure 9. Body condition effect size estimates (with 95% CIs) by year for a 150 mm juvenile Rainbow Trout.
Adult
figures/condition/RB/adult/year.png
Figure 10. Body condition effect size estimates (with 95% CIs) by year for a 300 mm adult Rainbow Trout.

Largescale Sucker

Juvenile
figures/condition/CSU/juvenile/year.png
Figure 11. Body condition effect size estimates (with 95% CIs) by year for a 300 mm juvenile Largescale Sucker.
Adult
figures/condition/CSU/adult/year.png
Figure 12. Body condition effect size estimates (with 95% CIs) by year for a 500 mm adult Largescale Sucker.

Occupancy

Rainbow Trout

figures/occupancy/RB/year.png
Figure 13. Estimated occupancy of Rainbow Trout at a typical site by year (with 95% CIs).
figures/occupancy/RB/site.png
Figure 14. Estimated occupancy of Rainbow Trout in 2010 by site (with 95% CIs).

Burbot

figures/occupancy/BB/year.png
Figure 15. Estimated occupancy of Burbot at a typical site by year (with 95% CIs).
figures/occupancy/BB/site.png
Figure 16. Estimated occupancy of Burbot in 2010 by site (with 95% CIs).

Lake Whitefish

figures/occupancy/LW/year.png
Figure 17. Estimated occupancy of Lake Whitefish at a typical site by year (with 95% CIs).
figures/occupancy/LW/site.png
Figure 18. Estimated occupancy of Lake Whitefish in 2010 by site (with 95% CIs).

Northern Pikeminnow

figures/occupancy/NPC/year.png
Figure 19. Estimated occupancy of Northern Pikeminnow at a typical site by year (with 95% CIs).
figures/occupancy/NPC/site.png
Figure 20. Estimated occupancy of Northern Pikeminnow in 2010 by site (with 95% CIs).

Redside Shiner

figures/occupancy/RSC/year.png
Figure 21. Estimated occupancy of Redside Shiner at a typical site by year (with 95% CIs).
figures/occupancy/RSC/site.png
Figure 22. Estimated occupancy of Redside Shiner in 2010 by site (with 95% CIs).

Sculpins

figures/occupancy/CC/year.png
Figure 23. Estimated occupancy of Sculpins at a typical site by year (with 95% CIs).
figures/occupancy/CC/site.png
Figure 24. Estimated occupancy of Sculpins in 2010 by site (with 95% CIs).

Species Richness

figures/richness/year.png
Figure 25. Estimated species richness at a typical site by year (with 95% CIs).
figures/richness/site.png
Figure 26. Estimated species richness in 2010 by year (with 95% CIs).

Count

Rainbow Trout

figures/count/RB/year.png
Figure 27. Estimated lineal river count density of Rainbow Trout by year (with 95% CIs).
figures/count/RB/site.png
Figure 28. Estimated lineal river count density of Rainbow Trout by site in 2010 (with 95% CIs).

Burbot

figures/count/BB/year.png
Figure 29. Estimated lineal river count density of Burbot by year (with 95% CIs).
figures/count/BB/site.png
Figure 30. Estimated lineal river count density of Burbot by site in 2010 (with 95% CIs).

Northern Pikeminnow

figures/count/NPC/year.png
Figure 31. Estimated lineal river count density of Northern Pikeminnow by year (with 95% CIs).
figures/count/NPC/site.png
Figure 32. Estimated lineal river count density of Northern Pikeminnow by site in 2010 (with 95% CIs).

Suckers

figures/count/SU/year.png
Figure 33. Estimated lineal river count density of Sucker by year (with 95% CIs).
figures/count/SU/site.png
Figure 34. Estimated lineal river count density of Sucker by site in 2010 (with 95% CIs).

Movement

Bull Trout

figures/movement/BT/length.png
Figure 35. Probability of recapture at the same site versus a different site by fish length and season (with 95% CIs).

Mountain Whitefish

figures/movement/MW/length.png
Figure 36. Probability of recapture at the same site versus a different site by fish length and season (with 95% CIs).

Rainbow Trout

figures/movement/RB/length.png
Figure 37. Probability of recapture at the same site versus a different site by fish length and season (with 95% CIs).

Largescale Sucker

figures/movement/CSU/length.png
Figure 38. Probability of recapture at the same site versus a different site by fish length and season (with 95% CIs).

Observer Length Correction

figures/observer/observer.png
Figure 39. Length inaccuracy and imprecision by observer, year and species.
figures/observer/uncorrected.png
Figure 40. Observed length density plots by species, year and observer.
figures/observer/corrected.png
Figure 41. Corrected length density plots by species, year and observer.

Abundance

figures/abundance/multiplier.png
Figure 42. Effect of counting (versus capture) on encounter efficiency by species and stage (with 95% CIs).
figures/abundance/dispersion.png
Figure 43. Effect of counting (versus capture) on overdispersion efficiency by species and stage (with 95% CIs).

Bull Trout

Juvenile
figures/abundance/BT/Juvenile/abundance.png
Figure 44. Abundance of Juvenile Bull Trout by year (with 95% CIs).
figures/abundance/BT/Juvenile/site.png
Figure 45. Estimated lineal river count density of Juvenile Bull Trout by site in 2010(with 95% CIs).
figures/abundance/BT/Juvenile/efficiency.png
Figure 46. Capture efficiency for Juvenile Bull Trout by session and year (with 95% CIs).
Adult
figures/abundance/BT/Adult/abundance.png
Figure 47. Abundance of Adult Bull Trout by year (with 95% CIs).
figures/abundance/BT/Adult/site.png
Figure 48. Estimated lineal river count density of Adult Bull Trout by site in 2010(with 95% CIs).
figures/abundance/BT/Adult/efficiency.png
Figure 49. Capture efficiency for Adult Bull Trout by session and year (with 95% CIs).

Mountain Whitefish

Juvenile
figures/abundance/MW/Juvenile/abundance.png
Figure 50. Abundance of Juvenile Mountain Whitefish by year (with 95% CIs).
figures/abundance/MW/Juvenile/site.png
Figure 51. Estimated lineal river count density of Juvenile Mountain Whitefish by site in 2010(with 95% CIs).
figures/abundance/MW/Juvenile/efficiency.png
Figure 52. Capture efficiency for Juvenile Mountain Whitefish by session and year (with 95% CIs).
Adult
figures/abundance/MW/Adult/abundance.png
Figure 53. Abundance of Adult Mountain Whitefish by year (with 95% CIs).
figures/abundance/MW/Adult/site.png
Figure 54. Estimated lineal river count density of Adult Mountain Whitefish by site in 2010(with 95% CIs).
figures/abundance/MW/Adult/efficiency.png
Figure 55. Capture efficiency for Adult Mountain Whitefish by session and year (with 95% CIs).

Rainbow Trout

Adult
figures/abundance/RB/Adult/abundance.png
Figure 56. Abundance of Adult Rainbow Trout by year (with 95% CIs).
figures/abundance/RB/Adult/site.png
Figure 57. Estimated lineal river count density of Adult Rainbow Trout by site in 2010(with 95% CIs).
figures/abundance/RB/Adult/efficiency.png
Figure 58. Capture efficiency for Adult Rainbow Trout by session and year (with 95% CIs).

Largescale Sucker

Adult
figures/abundance/CSU/Adult/abundance.png
Figure 59. Abundance of Adult Largescale Sucker by year (with 95% CIs).
figures/abundance/CSU/Adult/site.png
Figure 60. Estimated lineal river count density of Adult Largescale Sucker by site in 2010(with 95% CIs).
figures/abundance/CSU/Adult/efficiency.png
Figure 61. Capture efficiency for Adult Largescale Sucker by session and year (with 95% CIs).

Species Diversity (Evenness)

figures/evenness/year.png
Figure 62. Estimated species evenness by year (with 95% CIs).
figures/evenness/site.png
Figure 63. Estimated species evenness by site (with 95% CIs).

Distribution

Bull Trout

Juvenile
figures/distribution/BT/Juvenile/year.png
Figure 64. The percent change in the relative upstream density per km (with 95% CIs).
Adult
figures/distribution/BT/Adult/year.png
Figure 65. The percent change in the relative upstream density per km (with 95% CIs).

Mountain Whitefish

Adult
figures/distribution/MW/Adult/year.png
Figure 66. The percent change in the relative upstream density per km (with 95% CIs).

Rainbow Trout

figures/distribution/RB/year.png
Figure 67. The percent change in the relative upstream density per km (with 95% CIs).

Burbot

figures/distribution/BB/year.png
Figure 68. The percent change in the relative upstream density per km (with 95% CIs).

Northern Pikeminnow

figures/distribution/NPC/year.png
Figure 69. The percent change in the relative upstream density per km (with 95% CIs).

Suckers

figures/distribution/SU/year.png
Figure 70. The percent change in the relative upstream density per km (with 95% CIs).

Capture Effect

figures/pittag/pittag.png
Figure 71. The effect of capture in a previous year on body weight by life stage and species.

Effect Size

figures/effect/effect.png
Figure 72. The estimates (with 95% CIs) of the effect of the regime shift by species, analysis and stage. The abundance estimates are the percent change in the annual population growth rate. The distribution estimates are the percent change in the relative upstream density per km.

Significance

Table 82. The significance levels for the management hypotheses tested in the analyses. The Direction column indicates whether significant changes were positive or negative. The estimates and 95% lower and upper credible intervals are the effect sizes.

Analysis Species Stage Significance Direction Estimate Lower Upper
Abundance Bull Trout Juvenile 0.0760 -17 % -35 % 3 %
Abundance Bull Trout Adult 0.6053 -3 % -15 % 11 %
Abundance Mountain Whitefish Juvenile 0.5307 -13 % -48 % 38 %
Abundance Mountain Whitefish Adult 0.5253 -3 % -11 % 6 %
Abundance Rainbow Trout All 0.0067 - -38 % -54 % -15 %
Abundance Rainbow Trout Adult 0.5187 16 % -30 % 90 %
Abundance Burbot All 0.0027 - -52 % -71 % -20 %
Abundance Northern Pikeminnow All 0.0007 - -50 % -68 % -29 %
Condition Bull Trout Juvenile 0.0440 - -9 % -16 % 0 %
Condition Bull Trout Adult 0.0667 -7 % -14 % 0 %
Condition Mountain Whitefish Juvenile 0.8440 -1 % -6 % 5 %
Condition Mountain Whitefish Adult 0.2547 -2 % -7 % 2 %
Condition Rainbow Trout Juvenile 0.5227 2 % -4 % 9 %
Condition Rainbow Trout Adult 0.6573 -1 % -7 % 5 %
Distribution Bull Trout Juvenile 0.6467 0 % -1 % 2 %
Distribution Bull Trout Adult 0.0187 + 3 % 0 % 6 %
Distribution Mountain Whitefish Adult 0.6773 1 % -3 % 4 %
Distribution Rainbow Trout All 0.0467 + 6 % 0 % 12 %
Distribution Sucker All 0.4880 1 % -3 % 6 %
Distribution Burbot All 0.5213 0 % -1 % 1 %
Distribution Northern Pikeminnow All 0.6720 1 % -2 % 3 %
Growth Bull Trout All 0.5213 -9 % -34 % 24 %
Growth Mountain Whitefish All 0.8000 -4 % -38 % 44 %

Acknowledgements

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

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Columbia River Mica Dam Bull Trout Mountain Whitefish Rainbow Trout Growth Condition Occupancy Diversity Abundance Observer Efficiency Distribution