Kootenay Lake Exploitation Study 2013

The main intepretive report by Redfish Consulting and Poisson Consulting, which was prepared for the Habitat Conservation Trust Foundation and the Fish and Wildlife Compensation Program, is available from ResearchGate.

The suggested citation for this online appendix is:

Thorley, J.L. (2014) Kootenay Lake Exploitation Study 2013. A Poisson Consulting Analysis. URL: http://www.poissonconsulting.ca/f/1088509848.

Background

The Kootenay Lake Exploitation Study is a multi-year fish tagging program to estimate the proportion of large rainbow and bull trout that are caught by anglers versus die of natural causes on Kootenay Lake.

Methods

Data

The data were collected by releasing large (\(\ge\) 500 mm) rod caught rainbow trout and bull trout with acoustic tags and T-bar reward tags. An acoustic receiver array detected movements of the fish while anglers reported recaptures.

Species Year Tagged 2009 2010 2011 2012 2013
Bull Trout 2008 3 1 1 0 0 0
Bull Trout 2009 23 1 6 3 0 1
Bull Trout 2010 26 0 4 1 2 0
Bull Trout 2011 21 0 0 0 0 2
Rainbow Trout 2008 20 1 0 0 0 0
Rainbow Trout 2009 31 5 1 2 0 0
Rainbow Trout 2010 30 0 2 2 1 0
Rainbow Trout 2011 37 0 0 3 1 0

The back-calculation of length-at-age from scale samples was conducted by Greg Andrusak of Redfish Consulting Ltd. The Ministry of Forests, Lands and Natural Resource Operations (MFLNRO) provided spawner escapement estimates for kokanee in Meadow Creek spawning channel and rainbow trout at Gerrard as well as data from the Kootenay Lake Rainbow Trout (KLRT) mailout survey.

Statistical Analysis

Hierarchical Bayesian models were fitted to the rainbow and bull trout tagging, acoustic detection and angler return data for Kootenay Lake using R version 3.0.3 (Team, 2013) and JAGS 3.3.0 (Plummer, 2012) which interfaced with each other via jaggernaut 1.7 (Thorley, 2014). 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) pages 41-44.

Unless specified, the models assumed vague (low information) prior distributions (Kéry and Schaub, 2011, p. 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 (Kéry and Schaub, 2011, pp. 38-40). Model convergence was confirmed by ensuring that Rhat (Kéry and Schaub, 2011, p. 40) was less than 1.1 for each of the parameters in the model (Kéry and Schaub, 2011, p. 61). When possible model adequacy was confirmed by examination of residual plots.

The posterior distributions of the fixed (Kéry and Schaub, 2011, p. 75) parameters are summarised below 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 (Kéry and Schaub, 2011, p. 37,42).

When none of the candidate models included random effects, model selection was achieved by choosing the model with the lower Deviance Information Criterion (DIC) value. Otherwise model selection was achieved by percent relative error-based backwards stepwise variable selection (Kéry and Schaub, 2011, p. 42) from the full model until all the variables had a percent relative error less than 100. This is approximately equivalent to dropping insignificant (p > 0.05) fixed variables and uninformative random (Kéry and Schaub, 2011, pp. 77-82) variables.

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) (Kéry and Schaub, 2011, pp. 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% credible intervals (Bradford et al. 2005). Through the report bull trout data and estimates are plotted in black while rainbow trout are plotted in red. Plots were produced using the ggplot2 R package (Wickham, 2009).

Body Condition

Temporal variation in condition (body weight when accounting for body length) was estimated from the initial captures using a mass-length model (He et al. 2008).

Key assumptions of the full condition model include:

Competing models were fitted without year and/or day of the year.

Short-Term Hooking Mortality

Short-term (pre-release) hooking mortality was estimated from the initial captures using a two-way ANOVA (Kéry and Schaub, 2011, pp. 100-106) with an interaction and a binomial (logistic) response.

Key assumptions of the hooking mortality model include:

Competing models were fitted without the fish size and/or hook type effects and their interaction.

T-bar Tag Loss

T-bar tag loss was estimated from the number of tags reported from recaught double-tagged individuals (Fabrizio et al. 1999).

Key assumptions of the tag loss model include:

Preliminary analyses indicated that there were insufficient recaptures to include days at large or fish length as explanatory variables.

Harvest Rates

Harvest rates were estimated from the number of fish reported caught and harvested in the KLRT mailout survey since 1999 using a logistic ANOVA.

Key assumptions of the full harvest model include:

Competing models were fitted without the weight effect.

Annual Growth

Annual growth was estimated from the initial capture’s scale-based back-calculated lengths using the Fabens form of the Von Bertalanffy growth model (He and Bence, 2007).

Key assumptions of the full growth model include:

Competing models were fitted without the annual and/or individual variation in k.

Natural and Fishing Mortality

Natural and fishing mortality was estimated from the acoustic detection and tag reporting data using a Cormack-Jolly-Seber state-space model (Kéry and Schaub, 2011, pp. 175-177).

Key assumptions of the mortality model include:

Preliminary analyses indicated that length and season were not significant predictors and year was not an informative predictor of capture probability.

Model Code

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

Body Condition

Variable/Parameter Description
bWeight Intercept for log(eWeight)
bWeightDayte Effect of standardized day of the year on log(eWeight)
bWeightDayte2 Quadratic effect of standardized day of the year on log(eWeight)
bWeightLength Effect of centered log length on log(eWeight)
bWeightYear[i] Effect of ith year on log(eWeight)
Dayte[i] Standardised day of year on which the ith fish was caught
eWeight[i] Expected weight of ith fish
Length[i] Centered log length of ith fish
sWeight SD of log-normally distributed residual weight
Weight[i] Weight of ith fish
Body Condition - Model1
model {

  bWeight ~ dnorm(0, 2^-2)
  bWeightLength ~ dnorm(3, 2^-2)
  bWeightDayte ~ dnorm(0, 2^-2)
  bWeightDayte2 ~ dnorm(0, 2^-2)

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

  sWeight ~ dunif(0, 2)

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

    log(eWeight[i]) <- bWeight 
        + bWeightLength * Length[i] 
        + bWeightDayte * Dayte[i] 
        + bWeightDayte2 * Dayte[i]^2 
        + bWeightYear[Year[i]]

    Weight[i] ~ dlnorm(log(eWeight[i]), sWeight^-2)
  }
}
Body Condition - Model2
model {

  bWeight ~ dnorm(0, 2^-2)
  bWeightLength ~ dnorm(3, 2^-2)

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

  sWeight ~ dunif(0, 2)

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

    log(eWeight[i]) <- bWeight 
      + bWeightLength * Length[i] 
      + bWeightYear[Year[i]]

    Weight[i] ~ dlnorm(log(eWeight[i]), sWeight^-2)
  }
}
Body Condition - Model3
model {

  bWeight ~ dnorm(0, 2^-2)
  bWeightLength ~ dnorm(3, 2^-2)
  bWeightDayte ~ dnorm(0, 2^-2)
  bWeightDayte2 ~ dnorm(0, 2^-2)

  sWeight ~ dunif(0, 2)

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

    log(eWeight[i]) <- bWeight 
      + bWeightLength * Length[i] 
      + bWeightDayte * Dayte[i] 
      + bWeightDayte2 * Dayte[i]^2 

    Weight[i] ~ dlnorm(log(eWeight[i]), sWeight^-2)
  }
}
Body Condition - Model4
model {

  bWeight ~ dnorm(0, 2^-2)
  bWeightLength ~ dnorm(3, 2^-2)

  sWeight ~ dunif(0, 2)

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

    log(eWeight[i]) <- bWeight 
      + bWeightLength * Length[i] 

    Weight[i] ~ dlnorm(log(eWeight[i]), sWeight^-2)
  }
}

Short-Term Hooking Mortality

Variable/Parameter Description
bMortality Intercept for logit(eMortality[i])
bMortalityHookType[i] Effect of ith hook type on logit(eMortality[i])
bMortalitySize[i] Effect of ith fish size on logit(eMortality[i])
bMortalitySizeHookType[i, j] Effect of interaction between ith fish size and jth hook type on logit(eMortality[i])
eMortality[i] Predicted probability of a hooking mortality for ith fish
Mortality[i] Observed hooking mortality of ith fish
Short-Term Hooking Mortality - Model1
model{

  bMortality ~ dnorm(0, 5^-2)

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

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

  for (j in 1:nHookType) {
    bMortalitySizeHookType[1, j] <- 0
  }

  for (i in 2:nSize) {
      bMortalitySizeHookType[i, 1] <- 0
      for (j in 2:nHookType) {
          bMortalitySizeHookType[i, j] ~ dnorm(0, 5^-2)
      }
  }

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

    logit(eMortality[i]) <- bMortality 
      + bMortalitySize[Size[i]] 
      + bMortalityHookType[HookType[i]]
      + bMortalitySizeHookType[Size[i], HookType[i]]

    Mortality[i] ~ dbern(eMortality[i])
  }
}
Short-Term Hooking Mortality - Model2
model{

  bMortality ~ dnorm(0, 5^-2)

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

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

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

    logit(eMortality[i]) <- bMortality 
      + bMortalitySize[Size[i]] 
      + bMortalityHookType[HookType[i]]

    Mortality[i] ~ dbern(eMortality[i])
  }
}
Short-Term Hooking Mortality - Model3
model{

  bMortality ~ dnorm(0, 5^-2)

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

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

  logit(eMortality[i]) <- bMortality 
    + bMortalitySize[Size[i]] 

  Mortality[i] ~ dbern(eMortality[i])
  }
}
Short-Term Hooking Mortality - Model4
model{

  bMortality ~ dnorm(0, 5^-2)

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

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

    logit(eMortality[i]) <- bMortality 
      + bMortalityHookType[HookType[i]]

    Mortality[i] ~ dbern(eMortality[i])
  }
}
Short-Term Hooking Mortality - Model5
model{

  bMortality ~ dnorm(0, 5^-2)

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

  logit(eMortality[i]) <- bMortality 

  Mortality[i] ~ dbern(eMortality[i])
  }
}

T-Bar Tag Loss

Variable/Parameter Description
bTagLossIntercept Intercept for logit(eTagLoss)
eTagLoss[i] Predicted probability of single tag loss for ith recapture
TagsRecap[i] Tags remaining on ith recapture
T-Bar Tag Loss - Model1
model{
  bTagLossIntercept ~ dnorm(0, 2^-2)

  for (i in 1:length(TagsRecap)) {
    logit(eTagLoss[i]) <- bTagLossIntercept

    TagsRecap[i] ~ dbin(1 - eTagLoss[i], 2) T(1, )
  }
}

Harvest Rates

Variable/Parameter Description
bHarvest Intercept for logit(eHarvest)
bHarvestWeight[i] Effect of ith weight class on logit(eHarvest)
Catch[i] Number of fish in ith year-weight class caught
eHarvest[i] Predicted harvest rate for ith year-weight class
Harvest[i] Number of fish in ith year-weight class harvested
Harvest Rates - Model1
model{

  bHarvest ~ dnorm(0, 2^-2)

  bHarvestWeight[1] <- 0
  for(i in 2:nWeight) {
    bHarvestWeight[i] ~ dnorm(0, 2^-2)
  }

  sDispersion ~ dunif(0, 2)
  for(i in 1:length(Harvest)) {

    eDispersion[i] ~ dnorm(0, sDispersion^-2)

    logit(eHarvest[i]) <- bHarvest
      + bHarvestWeight[Weight[i]]
      + eDispersion[i]

    Harvest[i] ~ dbin(eHarvest[i], Catch[i])
  }
}
Harvest Rates - Model2
model{

  bHarvest ~ dnorm(0, 2^-2)

  sDispersion ~ dunif(0, 2)
  for(i in 1:length(Harvest)) {

    eDispersion[i] ~ dnorm(0, sDispersion^-2)

    logit(eHarvest[i]) <- bHarvest
      + eDispersion[i]

    Harvest[i] ~ dbin(eHarvest[i], Catch[i])
  }
}

Growth

Variable/Parameter Description
bKFish[i] Effect of ith fish on eK
bKYear[i] Effect of ith year on eK
eGrowth[i] Predicted growth for ith annulus
eK[i] Predicted growth coefficient for ith annulus
Growth[i] Back-calculated growth for ith annulus
kIntercept Intercept for eK
Length[i] Back-calculated length in previous year for ith annulus
Linf Mean maximum length (length-at-infinity)
sGrowth SD of residual growth
Growth - Model1
model{

  kIntercept ~ dunif (0, 1)
  Linf <- 1000

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

  sKFish ~ dunif(0, 1)
  for (i in 1:nFish) {
    bKFish[i] ~ dnorm(0, sKFish^-2)
  }

  sGrowth ~ dunif(0, 100)

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

    eK[i] <- kIntercept
      + bKYear[Year[i]] 
      + bKFish[Fish[i]]

    eGrowth[i] <- (Linf - Length[i]) * (1 - exp(-eK[i]))
    Growth[i] ~ dnorm(eGrowth[i], sGrowth^-2)
  }
}
Growth - Model2
model{

  kIntercept ~ dunif (0, 1)
  Linf <- 1000

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

  sGrowth ~ dunif(0, 100)

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

  eK[i] <- kIntercept
    + bKYear[Year[i]] 

  eGrowth[i] <- (Linf - Length[i]) * (1 - exp(-eK[i]))
  Growth[i] ~ dnorm(eGrowth[i], sGrowth^-2)
  }
}
Growth - Model3
model {

  kIntercept ~ dunif (0, 1)
  Linf <- 1000

  sKFish ~ dunif(0, 1)
  for (i in 1:nFish) {
    bKFish[i] ~ dnorm(0, sKFish^-2)
  }

  sGrowth ~ dunif(0, 100)

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

    eK[i] <- kIntercept
    + bKFish[Fish[i]]

    eGrowth[i] <- (Linf - Length[i]) * (1 - exp(-eK[i]))
    Growth[i] ~ dnorm(eGrowth[i], sGrowth^-2)
  }
}
Growth - Model4
model {

  kIntercept ~ dunif (0, 1)
  Linf <- 1000

  sGrowth ~ dunif(0, 100)

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

    eK[i] <- kIntercept

    eGrowth[i] <- (Linf - Length[i]) * (1 - exp(-eK[i]))
    Growth[i] ~ dnorm(eGrowth[i], sGrowth^-2)
  }
}

Natural And Fishing Mortality

Variable/Parameter Description
Alive[i,j] Whether ith fish was alive in jth interval
bAngled Log-odds quarterly probability of being angled
bHandlingM Log-odds probability of handling mortality if angled
bHarvest Log-odds probability of being harvested if angled
bNaturalM Log-odds quarterly probability of in-lake natural mortality
bNaturalMSpawning Effect of spawning on bNaturalM
bSpawned Log-odds annual probability of spawning
bSpawnedLength Effect of centred length on bSpawned
bTagLoss Log-odds probability of single tag loss
Harvested[i,j] Whether ith fish was harvested in jth interval
Monitored[i] The interval in which the ith fish was initially captured
NonReporting[1:2] Lower and upper limits for pNonReporting
pNonReporting Probability of non-reporting of capture by anglers
pTagLoss[i] Probability of complete tag loss with i tags
Reported[i,j] Whether ith fish was reported caught in jth interval
Spawned[i,j] Whether ith fish spawned in jth interval
TagLoss[1] Mean of bTagLoss
TagLoss[2] SD of bTagLoss
Natural And Fishing Mortality - Model1
model {

  bHandlingM ~ dnorm(0, 2^-2)
  bAngled ~ dnorm(0, 2^-2)
  bHarvest ~ dnorm(0, 2^-2)
  bNaturalM ~ dnorm(0, 2^-2)
  bSpawned ~ dnorm(0, 2^-2)

  bTagLoss ~ dnorm(TagLoss[1], TagLoss[2]^-2)
  logit(pTagLoss[1]) <- bTagLoss
  for (i in 2:nTags) {
    pTagLoss[i] <- pTagLoss[1]^2
  }

  pNonReporting ~ dunif(NonReporting[1], NonReporting[2])

  bNaturalMSpawning ~ dnorm(0, 2^-2)

  bSpawnedLength ~ dnorm(0, 2^-2)
  bNaturalMLength ~ dnorm(0, 2^-2)

  for (i in 1:nFish) {

    logit(eHandlingM[i, Monitored[i]]) <- bHandlingM
    logit(eAngled[i, Monitored[i]]) <- bAngled 
    logit(eHarvest[i, Monitored[i]]) <- bHarvest
    logit(eSpawned[i, Monitored[i]]) <- bSpawned 
                                      + bSpawnedLength * Length[i, Monitored[i]]

    dAliveM[i] ~ dbern((1-eHandlingM[i, Monitored[i]]))

    Spawned[i, Monitored[i]] ~ dbern(dAliveM[i] * eSpawned[i, Monitored[i]]
                                     * equals(Season[Monitored[i]], SpawningSeason))

    logit(eNaturalM[i, Monitored[i]]) <- 
                        (bNaturalM + bNaturalMLength * Length[i, Monitored[i]]) 
                            * (1-Spawned[i, Monitored[i]])
                        + bNaturalMSpawning * Spawned[i, Monitored[i]]

    dAngled[i, Monitored[i]] ~ dbern(dAliveM[i] * eAngled[i, Monitored[i]])

    Reported[i, Monitored[i]] ~ dbern(
      dAngled[i, Monitored[i]] * (1 - pTagLoss[Tags[i]]) * (1 - pNonReporting)
    )

    Harvested[i, Monitored[i]] ~ dbern(
      dAngled[i, Monitored[i]] * eHarvest[i, Monitored[i]]
    )

    Alive[i, Monitored[i]] ~ dbern(
      dAliveM[i] * (1-eNaturalM[i, Monitored[i]]) 
        * (1 - (dAngled[i, Monitored[i]] * Harvested[i, Monitored[i]]
        + dAngled[i, Monitored[i]] * (1-Harvested[i, Monitored[i]]) 
            * eHandlingM[i, Monitored[i]]))
    )

    for (j in (Monitored[i]+1):nInterval) {

      logit(eHandlingM[i, j]) <- bHandlingM
      logit(eAngled[i, j]) <- bAngled

      logit(eHarvest[i, j]) <- bHarvest
      logit(eSpawned[i, j]) <- bSpawned
                                  + bSpawnedLength * Length[i, j]

      Spawned[i, j] ~ dbern(
        Alive[i, j-1] * eSpawned[i, j] * equals(Season[j],SpawningSeason)
      )

      logit(eNaturalM[i, j]) <- (bNaturalM + bNaturalMLength * Length[i, j]) 
                                  * (1-Spawned[i, j])
                                + bNaturalMSpawning * Spawned[i, j]

      dAngled[i, j] ~ dbern(Alive[i, j-1] * eAngled[i, j])

      Reported[i, j] ~ dbern(
          dAngled[i, j] * (1 - pTagLoss[Tags[i]]) * (1 - pNonReporting)
      )

    Harvested[i, j] ~ dbern(
      dAngled[i, j] * eHarvest[i, j]
    )
      Alive[i,j] ~ dbern(Alive[i, j-1] * (1-eNaturalM[i,j]) 
                         * (1 - (dAngled[i, j] * Harvested[i, j]
                                 + dAngled[i, j] * (1-Harvested[i, j]) 
                                 * eHandlingM[i, j])))
    }
  }
}

Results

Model Parameters

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

Body Condition - Bull Trout

Parameter Estimate Lower Upper SD Error Significance
bWeight 0.7818 0.7570 0.8074 0.012664 3 0
bWeightLength 3.0594 2.8721 3.2508 0.095355 6 0
sWeight 0.1352 0.1189 0.1553 0.009404 13 0
Rhat Iterations
1 1e+05

Body Condition - Rainbow Trout

Parameter Estimate Lower Upper SD Error Significance
bWeight 0.5794 0.5621 0.5968 0.008903 3 0
bWeightLength 3.3922 3.3204 3.4591 0.034967 2 0
sWeight 0.1401 0.1287 0.1527 0.006206 9 0
Rhat Iterations
1.01 1e+05

Short-Term Hooking Mortality - Bull Trout

Model DIC Dbar pD
Model4 19.25 17.96 1.2951
Model1 20.46 18.19 2.2722
Model2 20.47 18.32 2.1456
Model5 21.89 20.96 0.9265
Model3 22.63 20.68 1.9557
Parameter Estimate Lower Upper SD Error Significance
bMortality -3.416 -5.06 -2.1097 0.785 43 0.0000
bMortalityHookType[2] -4.547 -11.39 -0.1858 2.904 123 0.0279
Rhat Iterations
1.01 10000

Short-Term Hooking Mortality - Rainbow Trout

Model DIC Dbar pD
Model3 116.9 114.8 2.1365
Model5 117.7 116.8 0.8873
Model2 118.6 115.5 3.0921
Model1 119.9 116.3 3.6083
Model4 120.0 117.9 2.1574
Parameter Estimate Lower Upper SD Error Significance
bMortality -2.434 -3.144 -1.83544 0.3442 27 0.0000
bMortalitySize[2] -1.030 -2.207 0.02916 0.5739 109 0.0559
Rhat Iterations
1.01 10000

T-Bar Tag Loss - Bull Trout

Parameter Estimate Lower Upper SD Error Significance
bTagLossIntercept -1.37 -2.196 -0.6104 0.4031 58 0.002
Rhat Iterations
1.04 1e+05

T-Bar Tag Loss - Rainbow Trout

Parameter Estimate Lower Upper SD Error Significance
bTagLossIntercept -2.745 -3.997 -1.743 0.5691 41 0
Rhat Iterations
1.02 1e+05

Harvest Rates - Bull Trout

Parameter Estimate Lower Upper SD Error Significance
bHarvest 0.21843 0.007344 0.3855 0.10142 87 0.0444
bHarvestWeight[2] -0.07433 -0.396126 0.2308 0.15263 422 0.5952
bHarvestWeight[3] -0.45131 -0.770514 -0.1446 0.16267 69 0.0058
sDispersion 0.42592 0.327153 0.5572 0.05849 27 0.0000
Rhat Iterations
1.08 20000

Harvest Rates - Rainbow Trout

Parameter Estimate Lower Upper SD Error Significance
bHarvest -0.5900 -0.74798 -0.4115 0.08518 29 0.0000
bHarvestWeight[2] 0.2436 -0.01304 0.4686 0.12287 99 0.0580
bHarvestWeight[3] 0.3511 0.12425 0.5698 0.11462 63 0.0058
sDispersion 0.3098 0.24421 0.3886 0.03731 23 0.0000
Rhat Iterations
1.09 20000

Growth - Rainbow Trout

Parameter Estimate Lower Upper SD Error Significance
kIntercept 1.925e-01 1.507e-01 2.353e-01 0.022194 22 0
Linf 1.000e+03 1.000e+03 1.000e+03 0.000000 0 0
sGrowth 3.654e+01 3.312e+01 4.050e+01 1.891000 10 0
sKFish 3.222e-02 1.636e-02 4.549e-02 0.007269 45 0
sKYear 6.213e-02 3.363e-02 1.180e-01 0.023236 68 0
Rhat Iterations
1.05 10000

Natural And Fishing Mortality - Bull Trout

Parameter Estimate Lower Upper SD Error Significance
bAngled -3.05897 -3.490523 -2.65905 0.21531 14 0.000
bHandlingM -3.59204 -5.760142 -2.16526 0.94717 50 0.000
bHarvest 1.69134 0.657668 2.94487 0.59117 68 0.000
bNaturalM -2.23804 -2.599325 -1.87233 0.18970 16 0.000
bNaturalMLength 0.94182 0.356751 1.57961 0.32285 65 0.006
bNaturalMSpawning -4.15079 -5.939903 -2.83463 0.80372 37 0.000
bSpawned 1.27432 0.859646 1.74744 0.23302 35 0.000
bSpawnedLength 1.37758 0.484302 2.35057 0.47007 68 0.002
bTagLoss -1.39614 -2.209410 -0.68899 0.38923 54 0.000
pNonReporting 0.04795 0.002963 0.09672 0.02793 98 0.000
pTagLoss[1] 0.20545 0.098909 0.33426 0.06207 57 0.000
pTagLoss[2] 0.04606 0.009783 0.11173 0.02809 111 0.000
Rhat Iterations
1.03 10000

Natural And Fishing Mortality - Rainbow Trout

Parameter Estimate Lower Upper SD Error Significance
bAngled -3.373519 -3.8769710 -2.91605 0.246090 14 0.0000
bHandlingM -1.582469 -2.1545979 -1.07334 0.276490 34 0.0000
bHarvest 0.723766 -0.2254920 1.80020 0.514690 140 0.1417
bNaturalM -2.289401 -2.6590167 -1.91491 0.184520 16 0.0000
bNaturalMLength 1.033763 0.2979054 1.81834 0.378470 74 0.0080
bNaturalMSpawning 0.632821 0.1089234 1.18880 0.289340 85 0.0240
bSpawned -0.990649 -1.3994545 -0.58147 0.209010 41 0.0000
bSpawnedLength 2.988020 2.0726796 4.20661 0.538530 36 0.0000
bTagLoss -2.676541 -3.7984381 -1.41907 0.586980 44 0.0000
pNonReporting 0.051508 0.0032784 0.09778 0.028855 92 0.0000
pTagLoss[1] 0.073682 0.0219147 0.19481 0.042621 117 0.0000
pTagLoss[2] 0.007244 0.0004803 0.03795 0.009932 259 0.0000
Rhat Iterations
1.01 10000

Figures

Escapement

figures/spawners/spawners.png
Figure 1. Spawner escapement estimates for kokanee in Meadow Creek spawning channel and rainbow trout at Gerrard.

Fishery

figures/fishery/catch-rate.png
Figure 2. Catch rates from the KLRT mailout survey.
figures/fishery/harvest.png
Figure 3. Harvest rates from the KLRT mailout survey.

Age - Rainbow Trout

figures/age/backcalc-age.png
Figure 4. Scale-based back-calculated lengths-at-age for rainbow trout.

Detections

figures/timeline/timeline.png
Figure 5. Fish capture, recapture and detection histories. Initial capture is indicated by a black diamond, recapture and release by a red circle, recapture and harvest by a red triangle, tag life by a grey band and in-lake detections by black stripes.

Body Condition

figures/condition/species.png
Figure 6. Estimated weight-length relationship (with 95% CRIs).

Short-Term Hooking Mortality

figures/hooking/hooking.png
Figure 7. Estimated short-term hooking mortality (with 95% CRIs).

T-Bar Tag Loss

figures/tagloss/tag-loss.png
Figure 8. Estimated T-bar tag-loss (with 95% CRIs).

Harvest Rates

figures/harvest/harvest.png
Figure 9. Estimated harvest rate (with 95% CRIs).

Growth - Rainbow Trout

figures/growth/Rainbow Trout/growth.png
Figure 10. Estimated growth curve for rainbow trout (with 95% CRIs).

Natural And Fishing Mortality

figures/mortality/spawnLength.png
Figure 11. Estimated spawning probability by length (with 95% CRIs).
figures/mortality/innaturalLength.png
Figure 12. Estimated in-lake natural mortality by length (with 95% CRIs).
figures/mortality/naturalLength.png
Figure 13. Estimated natural mortality by length (with 95% CRIs).
figures/mortality/components.png
Figure 14. Estimated probabilities of life-history events and parameters (with 95% CRIs) for a 700 mm individual in a typical year where Capture is the annual interval capture probability; T-Bar Tag Loss is for a single T-bar tag between capture and recapture; Non-Reporting is the non-reporting of a captured T-bar tagged fish; Harvest Rate is the probability of harvest of a captured fish; Handling Mortality is the short-term mortality rate of a released fish; Natural In-lake Mortality is the natural mortality for a fish that doesn’t spawn in that year; Natural Spawner Mortality is the natural mortality for a fish that does spawn in that year; Spawning is the probability of spawning; Fishing Mortality is the annual interval fishing mortality probability and Natural Mortality is the annual interval natural mortality probability.

Acknowledgements

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

References


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