Middle Columbia River Fish Indexing Analysis 2013

The main intepretive report by ONA, Golder Associates and Poisson Consulting, is available from BC Hydro.

The suggested citation for this online appendix is:

Thorley, J.L. (2015) Middle Columbia River Fish Indexing Analysis 2013. A Poisson Consulting Ltd. Analysis Report. URL: http://www.poissonconsulting.ca/f/111290438.

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 biomass and the modeling of environmental-fish metric relationships. The year-round minimum flow was implemented in the winter of 2010 at the same time that a fifth turbine was added.

The data were provided by Golder Associates.

Methods

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 < 350

Hierarchical Bayesian models were fitted to the fish indexing data for the MCR 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 (Kery 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 (Kery and Schaub, 2011, pp. 38-40). Model convergence was confirmed by ensuring that Rhat (Kery and Schaub, 2011, p. 40) was less than 1.1 for each of the parameters in the model (Kery and Schaub, 2011, p. 61). Model adequacy was confirmed by examination of residual plots.

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

The results are displayed graphically by plotting the modeled relationships between particular variables and the response (with 95% credible intervals) 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, 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). Plots were produced using the ggplot2 R package (Wickham, 2009).

Occupancy and Species Richness

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. Its 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. The estimated occupancies for multiple species were summed to give the expected species richnesses.

Key assumptions of the occupancy model include:

Count and Species Evenness

The count data were analysed using an overdispersed Poisson model (Kery, 2010; Kery and Schaub, 2011, pp. 168-170,180 and 55-56). 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 model does not distinguish between the abundance and observer efficiency, i.e., it estimates the count which is the product of the two. As such it is necessary to assume that changes in observer efficiency are negligible in order to interpret the estimates as relative abundance. The shannon index of evenness (\(E\)) 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.

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

Key assumptions of the count model include:

Catch

The catch data were analysed using the same overdispersed Poisson model as the count data to provide estimates of relative abundance.

Site Fidelity

The extent to which sites are closed, i.e., fish remain at the same site between sessions, was evaluated from a binomial “t-test” (Kery, 2010, pp. 211-213). The “t-test” estimated the probability that intra-annual recaptures were caught at a different site as previously encountered.

Key assumptions of the site fidelity model include:

Abundance

The catch data were also analysed using a capture-recapture-based binomial mixture model (Kery, 2010; Kery and Schaub, 2011, pp. 253-257 and 134-136, 384-388) 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:

Capture Efficiency

In order to estimate the capture efficiency independent of abundance a recapture-based binomial model (Kery, 2010; Kery and Schaub, 2011, pp. 253-257 and 134-136,384-388) was fitted to just the marked fish. To maximize the number of recaptures the model grouped all the sites into a supersite.

Key assumptions of the efficiency model include:

Growth

Annual growth was estimated from the inter-annual recaptures using the Fabens method (Fabens, 1965) for estimating the von Bertalanffy growth curve (Bertalanffy, 1938).

Key assumptions of the growth model include:

Condition

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

Key assumptions of the condition model include:

Length

Mean length was estimated from the measured lengths.

Key assumptions of the length model include:

Biomass

The biomass was calculated from the posterior distributions for the Length, Condition and Abundance analyses.

Multivariate Analyses

In order to examine the relationships between environmental variables and the fish indexing metrics multivariate analyses were performed. More specifically the trimonthly mean discharge and elevation and the trimonthly mean absolute hourly discharge change were analysed to get their five primary eigenvectors. Next the correlations between the trimonthly environmental time series and the eigenvectors was quantified using a Bayesian model. The same model was also used to quantify the correlations between the eigenvectors and fish indexing time series related to the management hypotheses. Significant relationships were indicated using time series/eigenvector connectivity plots were nodes are connected if the relationship is significant and positive relationships are in black and negative ones in red.

Model Code

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

Occupancy

Variable/Parameter Description
bOccupancy Intercept of logit(eOccupancy)
bOccupancyRegime[i] Effect of ith regime on logit(eOccupancy)
bOccupancySeason[i] Effect of ith season on logit(eOccupancy)
bOccupancySite[i] Effect of ith site on logit(eOccupancy)
bOccupancySiteYear[i, j] Effect of ith site in jth year on logit(eOccupancy)
bOccupancyYear[i] Effect of ith year on logit(eOccupancy)
eObserved[i] Predicted probability of observing species on ith site visit
eOccupancy[i] Predicted occupancy (species presence versus absence) on ith site visit
Observed[i] Whether the species was observed on ith 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)
  sOccupancySiteYear ~ dunif(0, 5)
  for (st in 1:nSite) {
    bOccupancySite[st] ~ dnorm(0, sOccupancySite^-2)
    for (yr in 1:nYear) {
      bOccupancySiteYear[st, yr] ~ dnorm(0, sOccupancySiteYear^-2)
    }
  }

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

    logit(eOccupancy[i]) <- bOccupancy
      + bOccupancyRegime[Regime[i]] + bOccupancySeason[Season[i]] 
      + bOccupancySite[Site[i]] + bOccupancyYear[Year[i]] 
      + bOccupancySiteYear[Site[i],Year[i]]

    eObserved[i] <- eOccupancy[i]

    Observed[i] ~ dbern(eObserved[i])
  }
}

Count

Variable/Parameter Description
bDensity Intercept of log(eDensity)
bDensityRegime[i] Effect of ith regime on log(eDensity)
bDensitySeason[i] Effect of ith season on log(eDensity)
bDensitySite[i] Effect of ith site on log(eDensity)
bDensitySiteYear[i, j] Effect of ith site in jth year on log(eDensity)
bDensityYear[i] Effect of ith year on log(eDensity)
bDispersion Overdispersion parameter
bDistribution Intercept of eDistribution
bDistributionRegime[i] Effect of ith regime on eDistribution
bDistributionSeason[i] Effect of ith season on eDistribution
bDistributionYear[i] Effect of ith year on eDistribution
Count[i] Count on ith site visit
eCount[i] Predicted count on ith site visit
eDensity[i] Predicted lineal count density on ith site visit
eDispersion[i] Predicted dispersion on ith site visit
eDistribution[i] Predicted effect of centred river kilometre on ith site visit on log(eDensity)
Count - Model1
model {

  bDensity ~ dnorm(0, 5^-2)
  bDistribution ~ dnorm(0, 5^-2)

  bDensityRegime[1] <- 0
  bDistributionRegime[1] <- 0 
  for(i in 2:nRegime) {    
    bDensityRegime[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)
  }

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

  bDispersion ~ dgamma(0.1, 0.1)

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

      eDistribution[i] <- bDistribution
      + bDistributionRegime[Regime[i]] + bDistributionSeason[Season[i]]
      + bDistributionYear[Year[i]]

    log(eDensity[i]) <- bDensity 
      + eDistribution[i] * RiverKm[i] 
      + bDensityRegime[Regime[i]] + bDensitySeason[Season[i]] 
      + bDensitySite[Site[i]] + bDensityYear[Year[i]] 
      + bDensitySiteYear[Site[i],Year[i]]

    eCount[i] <- eDensity[i] * SiteLength[i] * ProportionSampled[i] 

    eDispersion[i] ~ dgamma(bDispersion, bDispersion)

    Count[i] ~ dpois(eCount[i] * eDispersion[i])  
  }
  tAbundance <- bDensityRegime[2]
  tDistribution <- bDistributionRegime[2]
}

Catch

Variable/Parameter Description
bDensity Intercept of log(eDensity)
bDensityRegime[i] Effect of ith regime on log(eDensity)
bDensitySeason[i] Effect of ith season on log(eDensity)
bDensitySite[i] Effect of ith site on log(eDensity)
bDensitySiteYear[i, j] Effect of ith site in jth year on log(eDensity)
bDensityYear[i] Effect of ith year on log(eDensity)
bDispersion Overdispersion parameter
bDistribution Intercept of eDistribution
bDistributionRegime[i] Effect of ith regime on eDistribution
bDistributionSeason[i] Effect of ith season on eDistribution
bDistributionYear[i] Effect of ith year on eDistribution
Catch[i] Catch on ith site visit
eCatch[i] Predicted catch on ith site visit
eDensity[i] Predicted lineal catch density on ith site visit
eDispersion[i] Predicted dispersion on ith site visit
eDistribution[i] Predicted effect of centred river kilometre on ith site visit on log(eDensity)
Catch - Model1
model {

  bDensity ~ dnorm(0, 5^-2)
  bDistribution ~ dnorm(0, 5^-2)

  bDensityRegime[1] <- 0
  bDistributionRegime[1] <- 0 
  for(i in 2:nRegime) {    
    bDensityRegime[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)
  }

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

  bDispersion ~ dgamma(0.1, 0.1)

  for (i in 1:length(Year)) {
    eDistribution[i] <- bDistribution
      + bDistributionRegime[Regime[i]] 
      + bDistributionSeason[Season[i]]
      + bDistributionYear[Year[i]]

    log(eDensity[i]) <- bDensity 
      + eDistribution[i] * RiverKm[i] 
      + bDensityRegime[Regime[i]] + bDensitySeason[Season[i]] 
      + bDensitySite[Site[i]] + bDensityYear[Year[i]] 
      + bDensitySiteYear[Site[i],Year[i]]

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

    eDispersion[i] ~ dgamma(bDispersion, bDispersion)

    Catch[i] ~ dpois(eCatch[i] * eDispersion[i])  
  }
  tAbundance <- bDensityRegime[2]
  tDistribution <- bDistributionRegime[2]
}

Site Fidelity

Variable/Parameter Description
bMoved Intercept for logit(eMoved)
bMovedSeason[i] Effect of ith season on logit(eMoved)
eMoved[i] Predicted probability of different site for ith recapture
Moved[i] Was ith recapture recorded at a different site as previously encountered
Site Fidelity - Model1
model {
  bMoved ~ dnorm(0, 5^-2)

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

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

    logit(eMoved[i]) <- bMoved + bMovedSeason[Season[i]]

    Moved[i] ~ dbern(eMoved[i])
  }
}

Abundance

Variable/Parameter Description
bDensity Intercept for log(eDensity)
bDensityRegime[i] Effect of ith regime on log(eDensity)
bDensitySeason[i] Effect of ith season on log(eDensity)
bDensitySite[i] Effect of ith site on log(eDensity)
bDensitySiteYear[i, j] Effect of ith site in jth year on log(eDensity)
bDensityYear[i] Effect of ith year on log(eDensity)
bDistribution Intercept for eDistribution
bDistributionRegime[i] Effect of ith regime on eDistribution
bDistributionSeason[i] Effect of ith season on eDistribution
bDistributionYear[i] Effect of ith year on eDistribution
bEfficiency Intercept for logit(eEfficiency)
bEfficiencySessionSeasonYear[i, j, k] Effect of ith session in jth season of kth year on logit(eEfficiency)
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
eDistribution[i] Predicted effect of centred river kilometre on ith site visit on log(eDensity)
eEfficiency[i] Predicted efficiency during ith site visit
Marked[i] Number of marked fish caught in ith river visit
Tagged[i] Number of fish tagged prior to ith river visit
Abundance - Model1
model {

  bEfficiency ~ dnorm(0, 5^-2)
  bDensity ~ dnorm(0, 5^-2)
  bDistribution ~ dnorm(0, 5^-2)

  bDensityRegime[1] <- 0
  bDistributionRegime[1] <- 0
  for(i in 2:nRegime) {    
    bDensityRegime[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)
  }

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

  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]]

    eDistribution[i] <- bDistribution
      + bDistributionRegime[Regime[i]] 
      + bDistributionSeason[Season[i]]
      + bDistributionYear[Year[i]]

    log(eDensity[i]) <- bDensity 
      + eDistribution[i] * RiverKm[i] 
      + bDensityRegime[Regime[i]] 
      + bDensitySeason[Season[i]] 
      + bDensitySite[Site[i]] 
      + bDensityYear[Year[i]] 
      + bDensitySiteYear[Site[i], Year[i]]

    eSamplingEfficiency[i] <- min(eEfficiency[i] * ProportionSampled[i], 0.9)

    eAbundance[i] <- max(round(eDensity[i] * SiteLength[i]), MinAbundance[i])

    Catch[i] ~ dbin(eSamplingEfficiency[i], eAbundance[i])
  }
  tAbundance <- bDensityRegime[2]
  tDistribution <- bDistributionRegime[2]
}

Capture Efficiency

Variable/Parameter Description
bEFficiency Intercept of logit(eEfficiency)
bEFficiencySeason[i] Effect of ith season on logit(eEfficiency)
bEFficiencySessionSeasonYear[i, j, k] Effect of ith session within jth season and kth year on logit(eEfficiency)
eEfficiency[i] Predicted efficiency during ith vist
Marked[i] Number of marked fish recaught during ith visit
Tagged[i] Number of marked fish tagged prior to ith visit
Capture Efficiency - Model1
model {

  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)
      }
    }
  }

  for(i in 1:length(Year)) {
    logit(eEfficiency[i]) <- bEfficiency 
        + bEfficiencySeason[Season[i]]
        + bEfficiencySessionSeasonYear[Session[i], Season[i], Year[i]]

    Marked[i] ~ dbin(eEfficiency[i], Tagged[i])
  }
}

Growth

Variable/Parameter Description
bK Intercept of log(eK)
bKRegime[i] Effect of ith regime on log(eK)
bKYear[i] Effect of ith year on log(eK)
bLinf Mean maximum length (von Bertalanffy length-at-infinity)
eGrowth[i] Predicted growth (change in length) of the ith recapture between release and recapture
eK[i] Predicted von Bertalanffy growth coefficient for ith year
Growth[i] Growth (change in length) of the ith recapture between release and recapture
LengthAtRelease[i] Length of the ith recapture when released in a previous year
sGrowth SD of residual variation in Growth
Year[i] Year the ith recapture was released
Years[i] Number of years between release and recapture for the ith recapture
Growth - Model1
model {

  bK ~ dnorm (0, 5^-2)

  bKRegime[1] <- 0
  for(i in 2:nThreshold) {
    bKRegime[i] ~ dunif(-100, 100)
  }

  sKYear ~ dunif (0, 5)
  for (i in 1:nYear) {
    bKYear[i] ~ dnorm(0, sKYear^-2)
    log(eK[i]) <- bK + 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(eK[Year[i]:(Year[i] + Years[i] - 1)])))

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

Condition

Variable/Parameter Description
bWeight Intercept for eWeightSlope
bWeightLength Intercept for eWeightIntercept
bWeightRegime[i] Effect of ith regime on eWeightIntercept
bWeightSeason[i] Effect of ith season on eWeightIntercept
bWeightSite[i] Effect of ith site on eWeightIntercept
bWeightSiteYear[i] Effect of ith site in jth year on eWeightIntercept
bWeightYear[i] Effect of ith year on eWeightIntercept
eWeightIntercept[i] Predicted intercept for log(eWeight)
eWeightSlope[i] Predicted effect of centred log length on log(eWeight)
sWeight SD of residual variation in log(Weight)
Weight[i] Weight of ith fish
Condition - Model1
model {

  bWeight ~ dnorm(5, 5^-2)
  bWeightLength ~ dnorm(3, 5^-2)

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

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

  sWeightYear ~ dunif(0, 5)
  for(yr in 1:nYear) {
    bWeightYear[yr] ~ dnorm(0, sWeightYear^-2)
  }  

  sWeightSite ~ dunif(0, 5)
  sWeightSiteYear ~ dunif(0, 5)
  for(st in 1:nSite) {
    bWeightSite[st] ~ dnorm(0, sWeightSite^-2)
    for(yr in 1:nYear) {
      bWeightSiteYear[st, yr] ~ dnorm(0, sWeightSiteYear^-2)
    }
  } 

  sWeight ~ dunif(0, 5)

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

    eWeightIntercept[i] <- bWeight
        + bWeightRegime[Regime[i]] 
        + bWeightSeason[Season[i]]
        + bWeightYear[Year[i]] + bWeightSite[Site[i]] 
        + bWeightSiteYear[Site[i],Year[i]]

    eWeightSlope[i] <- bWeightLength

    log(eWeight[i]) <- eWeightIntercept[i] + eWeightSlope[i] * Length[i]

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

Length

Variable/Parameter Description
bLength Intercept for log(eLength)
bLengthRegime[i] Effect of ith regime on log(eLength)
bLengthSeason[i] Effect of ith season on log(eLength)
bLengthSite[i] Effect of ith site on log(eLength)
bLengthSiteYear[i, j] Effect of ith site in jth year on log(eLength)
bLengthYear[i] Effect of ith year on log(eLength)
eLength[i] Predicted length of ith fish
Length[i] Length of ith fish
sLength SD of residual variation in log(Length)
Length - Model1
model {

  bLength ~ dnorm(5, 5^-2)

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

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

  sLengthYear ~ dunif(0, 5)
  for(yr in 1:nYear) {
    bLengthYear[yr] ~ dnorm(0, sLengthYear^-2)
  }  

  sLengthSite ~ dunif(0, 5)
  sLengthSiteYear ~ dunif(0, 5)
  for(st in 1:nSite) {
    bLengthSite[st] ~ dnorm(0, sLengthSite^-2)
    for(yr in 1:nYear) {
      bLengthSiteYear[st, yr] ~ dnorm(0, sLengthSiteYear^-2)
    }
  } 

  sLength ~ dunif(0, 5)

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

    log(eLength[i]) <- bLength
      + bLengthRegime[Regime[i]] 
      + bLengthSeason[Season[i]] 
      + bLengthYear[Year[i]] + bLengthSite[Site[i]] 
      + bLengthSiteYear[Site[i],Year[i]]

    Length[i] ~ dlnorm(log(eLength[i]), sLength^-2)
  }
}

Multivariate Analysis

Variable/Parameter Description
Growth[i] Growth (change in length) of the ith recapture between release and recapture
Multivariate Analysis - Model1
model {

  sValue ~ dunif(0, 2)

  for(k in 1:nEigen) {
    sWeight[k] ~ dunif(0, 5)
  }

  for (i in 1:nSeries) {
    for (k in 1:nEigen) {
      Weight[i, k] ~ dnorm(0, sWeight[k]^-2)
    }
    for (t in 1:nYear) {
      Fit[i, t] <- inprod(Weight[i,], Eigen[,t])
      Value[i, t] ~ dnorm(Fit[i, t], sValue^-2)
    }
  }
} 

Results

Model Parameters

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

Occupancy - Burbot

Parameter Estimate Lower Upper SD Error Significance
bOccupancy -2.2243 -3.2268 -1.26453 0.4998 44 0.0000
bOccupancyRegime[2] 1.2182 -0.3243 2.78136 0.7831 127 0.1158
bOccupancySeason[2] -0.7246 -1.3942 -0.06423 0.3401 92 0.0299
sOccupancySite 0.9846 0.5684 1.58687 0.2708 52 0.0000
sOccupancySiteYear 0.6540 0.3292 1.03038 0.1851 54 0.0000
sOccupancyYear 1.1530 0.5801 2.03171 0.3927 63 0.0000
Rhat Iterations
1.05 10000

Occupancy - Kokanee

Parameter Estimate Lower Upper SD Error Significance
bOccupancy 2.0280 0.85462 3.3224 0.6438 61 0.0039
bOccupancyRegime[2] -1.6457 -4.03807 0.5482 1.1634 139 0.1604
bOccupancySeason[2] -2.5954 -3.28585 -1.9448 0.3472 26 0.0000
sOccupancySite 0.6502 0.34196 1.1364 0.2036 61 0.0000
sOccupancySiteYear 0.2155 0.01424 0.5630 0.1410 127 0.0000
sOccupancyYear 1.8386 1.06723 3.2947 0.5555 61 0.0000
Rhat Iterations
1.07 20000

Occupancy - Lake Whitefish

Parameter Estimate Lower Upper SD Error Significance
bOccupancy -1.2322 -2.21867 -0.3010 0.4829 78 0.0213
bOccupancyRegime[2] 0.1281 -2.00776 1.9421 0.9928 1542 0.8193
bOccupancySeason[2] -3.9651 -5.84419 -2.5271 0.8241 42 0.0000
sOccupancySite 0.5363 0.13484 0.9556 0.2106 77 0.0000
sOccupancySiteYear 0.2410 0.01188 0.6532 0.1696 133 0.0000
sOccupancyYear 1.3843 0.82480 2.4161 0.4094 57 0.0000
Rhat Iterations
1.08 20000

Occupancy - Northern Pikeminnow

Parameter Estimate Lower Upper SD Error Significance
bOccupancy -2.3587 -3.89496 -1.014 0.7355 61 0.0019
bOccupancyRegime[2] 0.3746 -1.63687 2.452 1.0427 546 0.6800
bOccupancySeason[2] -2.1280 -3.19415 -1.163 0.5191 48 0.0000
sOccupancySite 1.7535 1.03826 2.905 0.4919 53 0.0000
sOccupancySiteYear 0.5397 0.05011 1.068 0.2827 94 0.0000
sOccupancyYear 1.3796 0.66794 2.636 0.5143 71 0.0000
Rhat Iterations
1.05 40000

Occupancy - Rainbow Trout

Parameter Estimate Lower Upper SD Error Significance
bOccupancy -1.3013 -2.9073 0.1926 0.7922 119 0.0798
bOccupancyRegime[2] 1.4558 -0.2357 3.0956 0.8237 114 0.0878
bOccupancySeason[2] -0.2133 -0.8535 0.4149 0.3288 297 0.5250
sOccupancySite 2.4533 1.5918 3.8068 0.5722 45 0.0000
sOccupancySiteYear 0.6737 0.3027 1.0381 0.1939 55 0.0000
sOccupancyYear 1.0899 0.5154 2.0154 0.4117 69 0.0000
Rhat Iterations
1.1 10000

Occupancy - Redside Shiner

Parameter Estimate Lower Upper SD Error Significance
bOccupancy -2.3716 -4.23982 -0.7949 0.8313 73 0.0057
bOccupancyRegime[2] 0.7752 -1.06899 2.9853 1.0379 261 0.4406
bOccupancySeason[2] -0.8266 -1.63757 -0.1026 0.3955 93 0.0190
sOccupancySite 2.3121 1.45116 3.7791 0.6041 50 0.0000
sOccupancySiteYear 0.3160 0.03113 0.7517 0.1896 114 0.0000
sOccupancyYear 1.4292 0.72702 2.5563 0.5003 64 0.0000
Rhat Iterations
1.03 40000

Occupancy - Sculpin

Parameter Estimate Lower Upper SD Error Significance
bOccupancy -0.1261 -2.08105 1.5862 0.8813 1454 0.8663
bOccupancyRegime[2] 1.6179 -1.04718 4.2916 1.3942 165 0.2535
bOccupancySeason[2] -0.4067 -1.03214 0.2153 0.3243 153 0.2076
sOccupancySite 1.3817 0.89407 2.2541 0.3405 49 0.0000
sOccupancySiteYear 0.3106 0.05895 0.6656 0.1605 98 0.0000
sOccupancyYear 2.1245 1.25940 3.3939 0.5627 50 0.0000
Rhat Iterations
1.04 10000

Count - Bull Trout

Parameter Estimate Lower Upper SD Error Significance
bDensity 2.03514 1.782146 2.30310 0.12550 13 0.0000
bDensityRegime[2] -0.15827 -0.478551 0.15737 0.16100 201 0.2914
bDensitySeason[2] 0.09524 -0.082556 0.26270 0.08883 181 0.2854
bDispersion 3.18282 2.731092 3.66853 0.24956 15 0.0000
bDistribution 0.01406 -0.067522 0.09333 0.04073 572 0.7425
bDistributionRegime[2] -0.01865 -0.088326 0.06215 0.03880 403 0.5788
bDistributionSeason[2] 0.14192 0.080945 0.19839 0.02885 41 0.0000
sDensitySite 0.38367 0.237572 0.61871 0.10004 50 0.0000
sDensitySiteYear 0.28498 0.209772 0.36443 0.04005 27 0.0000
sDensityYear 0.18516 0.069146 0.34949 0.07042 76 0.0000
sDistributionYear 0.02571 0.001211 0.07043 0.01961 135 0.0000
tAbundance -0.15827 -0.478551 0.15737 0.16100 201 0.2914
tDistribution -0.01865 -0.088326 0.06215 0.03880 403 0.5788
Rhat Iterations
1.03 1e+05

Count - Burbot

Parameter Estimate Lower Upper SD Error Significance
bDensity -2.12106 -3.07045 -1.3163 0.4515 41 0.0000
bDensityRegime[2] 1.16720 -0.33222 2.6961 0.7478 130 0.1058
bDensitySeason[2] -0.90226 -1.47500 -0.3325 0.2903 63 0.0040
bDispersion 0.72088 0.45354 1.1193 0.1762 46 0.0000
bDistribution -0.08595 -0.33491 0.1597 0.1213 288 0.4232
bDistributionRegime[2] 0.02611 -0.32944 0.3514 0.1719 1304 0.8283
bDistributionSeason[2] 0.06740 -0.15507 0.2853 0.1134 327 0.5649
sDensitySite 0.83747 0.43729 1.3956 0.2585 57 0.0000
sDensitySiteYear 0.43676 0.04984 0.8185 0.1989 88 0.0000
sDensityYear 1.03620 0.48952 1.8225 0.3377 64 0.0000
sDistributionYear 0.15921 0.01340 0.4136 0.1051 126 0.0000
tAbundance 1.16720 -0.33222 2.6961 0.7478 130 0.1058
tDistribution 0.02611 -0.32944 0.3514 0.1719 1304 0.8283
Rhat Iterations
1.05 1e+05

Count - Mountain Whitefish

Parameter Estimate Lower Upper SD Error Significance
bDensity 4.20214 3.894438 4.52789 0.16448 8 0.0000
bDensityRegime[2] -0.06864 -0.456495 0.30495 0.18422 555 0.7063
bDensitySeason[2] 0.21478 0.059809 0.37173 0.07941 73 0.0079
bDispersion 2.95799 2.640761 3.29659 0.16811 11 0.0000
bDistribution 0.04481 -0.062326 0.15218 0.05280 239 0.3651
bDistributionRegime[2] 0.01093 -0.067755 0.10829 0.04469 806 0.8373
bDistributionSeason[2] -0.04340 -0.093992 0.01247 0.02662 123 0.0992
sDensitySite 0.54857 0.355798 0.86988 0.13526 47 0.0000
sDensitySiteYear 0.37655 0.307390 0.45473 0.03812 20 0.0000
sDensityYear 0.21846 0.098504 0.38661 0.07674 66 0.0000
sDistributionYear 0.03029 0.001631 0.09321 0.02307 151 0.0000
tAbundance -0.06864 -0.456495 0.30495 0.18422 555 0.7063
tDistribution 0.01093 -0.067755 0.10829 0.04469 806 0.8373
Rhat Iterations
1.04 4e+05

Count - Northern Pikeminnow

Parameter Estimate Lower Upper SD Error Significance
bDensity -2.61448 -3.879746 -1.6147 0.5558 43 0.0000
bDensityRegime[2] -0.46532 -2.366936 1.6239 1.0200 429 0.6707
bDensitySeason[2] -2.14169 -4.330803 -0.4254 1.0217 91 0.0100
bDispersion 0.57842 0.382244 0.8508 0.1203 41 0.0000
bDistribution -0.43927 -0.712322 -0.2342 0.1245 54 0.0000
bDistributionRegime[2] -0.30751 -0.741164 0.1251 0.2190 141 0.1277
bDistributionSeason[2] 0.09878 -0.459409 0.6001 0.2709 536 0.7385
sDensitySite 0.45656 0.022681 1.0036 0.2517 107 0.0000
sDensitySiteYear 0.68946 0.229965 1.1685 0.2291 68 0.0000
sDensityYear 1.26637 0.594101 1.9173 0.3654 52 0.0000
sDistributionYear 0.15787 0.006018 0.4540 0.1214 142 0.0000
tAbundance -0.46532 -2.366936 1.6239 1.0200 429 0.6707
tDistribution -0.30751 -0.741164 0.1251 0.2190 141 0.1277
Rhat Iterations
1.02 1e+05

Count - Rainbow Trout

Parameter Estimate Lower Upper SD Error Significance
bDensity -1.71862 -2.68787 -0.7926 0.48508 55 0.0020
bDensityRegime[2] 1.21956 0.11475 2.3342 0.58085 91 0.0279
bDensitySeason[2] -0.07506 -0.48545 0.2991 0.19700 523 0.6866
bDispersion 1.44187 1.06036 1.9316 0.21918 30 0.0000
bDistribution -0.53060 -0.80835 -0.2836 0.13418 49 0.0000
bDistributionRegime[2] 0.20183 -0.04976 0.4657 0.13583 128 0.1018
bDistributionSeason[2] 0.02819 -0.09323 0.1431 0.06079 419 0.6307
sDensitySite 1.25142 0.76876 2.0409 0.33948 51 0.0000
sDensitySiteYear 0.54744 0.37034 0.7580 0.10009 35 0.0000
sDensityYear 0.84055 0.35428 1.5612 0.30746 72 0.0000
sDistributionYear 0.13308 0.02558 0.3071 0.06994 106 0.0000
tAbundance 1.21956 0.11475 2.3342 0.58085 91 0.0279
tDistribution 0.20183 -0.04976 0.4657 0.13583 128 0.1018
Rhat Iterations
1.04 1e+05

Count - Suckers

Parameter Estimate Lower Upper SD Error Significance
bDensity 2.00579 1.647942 2.36984 0.17535 18 0.0000
bDensityRegime[2] 0.66181 0.234254 1.08487 0.20776 64 0.0060
bDensitySeason[2] -0.51602 -0.752171 -0.27660 0.12388 46 0.0000
bDispersion 1.53388 1.329000 1.73733 0.10764 13 0.0000
bDistribution -0.14356 -0.264984 -0.04295 0.05643 77 0.0080
bDistributionRegime[2] 0.05502 -0.062730 0.20398 0.06801 242 0.3752
bDistributionSeason[2] -0.13029 -0.214682 -0.05513 0.04183 61 0.0020
sDensitySite 0.48648 0.284109 0.82393 0.14317 55 0.0000
sDensitySiteYear 0.44806 0.331650 0.56767 0.05974 26 0.0000
sDensityYear 0.26785 0.064797 0.53524 0.11556 88 0.0000
sDistributionYear 0.05421 0.004028 0.14324 0.03714 128 0.0000
tAbundance 0.66181 0.234254 1.08487 0.20776 64 0.0060
tDistribution 0.05502 -0.062730 0.20398 0.06801 242 0.3752
Rhat Iterations
1.03 1e+05

Catch - Adult BT

Parameter Estimate Lower Upper SD Error Significance
bDensity 0.78732 0.457109 1.149900 0.17129 44 0.0000
bDensityRegime[2] -0.26182 -0.656633 0.122127 0.19566 149 0.1697
bDensitySeason[2] -0.21097 -0.431215 0.004242 0.10908 103 0.0619
bDispersion 4.49381 3.209098 6.261625 0.76510 34 0.0000
bDistribution 0.04665 -0.061424 0.157822 0.05351 235 0.3653
bDistributionRegime[2] 0.02317 -0.084402 0.130666 0.05409 464 0.6826
bDistributionSeason[2] 0.16037 0.077826 0.245413 0.04080 52 0.0000
sDensitySite 0.48746 0.295988 0.775417 0.12735 49 0.0000
sDensitySiteYear 0.38464 0.279302 0.500791 0.05477 29 0.0000
sDensityYear 0.22193 0.029268 0.437854 0.10279 92 0.0000
sDistributionYear 0.03751 0.002177 0.096973 0.02580 126 0.0000
tAbundance -0.26182 -0.656633 0.122127 0.19566 149 0.1697
tDistribution 0.02317 -0.084402 0.130666 0.05409 464 0.6826
Rhat Iterations
1.02 1e+05

Catch - Adult CSU

Parameter Estimate Lower Upper SD Error Significance
bDensity 1.11165 -0.34196 2.93510 0.73401 147 0.0995
bDensityRegime[2] 0.79642 -1.47291 2.69679 0.94959 262 0.3005
bDensitySeason[2] -1.81915 -2.08174 -1.55303 0.13415 15 0.0000
bDispersion 2.89590 2.04700 4.06981 0.51552 35 0.0000
bDistribution -0.11689 -0.41797 0.16425 0.14137 249 0.2846
bDistributionRegime[2] 0.06797 -0.34436 0.44484 0.18312 581 0.5891
bDistributionSeason[2] -0.17724 -0.26282 -0.09096 0.04482 48 0.0000
sDensitySite 0.43466 0.22877 0.76316 0.13558 61 0.0000
sDensitySiteYear 0.32996 0.14991 0.51288 0.09726 55 0.0000
sDensityYear 0.83043 0.29912 1.85495 0.40558 94 0.0000
sDistributionYear 0.15358 0.01124 0.54801 0.13879 175 0.0000
tAbundance 0.79642 -1.47291 2.69679 0.94959 262 0.3005
tDistribution 0.06797 -0.34436 0.44484 0.18312 581 0.5891
Rhat Iterations
1.08 2e+05

Catch - Adult MW

Parameter Estimate Lower Upper SD Error Significance
bDensity 2.72254 2.409243 3.052607 0.15567 12 0.0000
bDensityRegime[2] -0.08813 -0.371264 0.177327 0.14015 311 0.5170
bDensitySeason[2] 0.35130 0.212342 0.500749 0.07282 41 0.0000
bDispersion 4.23060 3.640658 4.880453 0.30392 15 0.0000
bDistribution 0.09529 -0.012867 0.211395 0.05517 118 0.0878
bDistributionRegime[2] 0.02350 -0.098944 0.153396 0.06555 537 0.6946
bDistributionSeason[2] -0.05805 -0.111677 -0.003328 0.02630 93 0.0379
sDensitySite 0.52305 0.340636 0.815730 0.12414 45 0.0000
sDensitySiteYear 0.34517 0.276420 0.420760 0.03622 21 0.0000
sDensityYear 0.12532 0.009733 0.286938 0.07014 111 0.0000
sDistributionYear 0.07254 0.016579 0.142725 0.03123 87 0.0000
tAbundance -0.08813 -0.371264 0.177327 0.14015 311 0.5170
tDistribution 0.02350 -0.098944 0.153396 0.06555 537 0.6946
Rhat Iterations
1.05 1e+05

Catch - Adult RB

Parameter Estimate Lower Upper SD Error Significance
bDensity -2.39475 -3.256340 -1.60981 0.4150 34 0.0000
bDensityRegime[2] 0.78385 -0.036726 1.57844 0.4011 103 0.0539
bDensitySeason[2] -0.48276 -1.032504 0.06893 0.2704 114 0.0758
bDispersion 3.87290 1.160833 12.62729 2.9172 148 0.0000
bDistribution -0.34522 -0.635832 -0.03433 0.1561 87 0.0319
bDistributionRegime[2] 0.21841 -0.104383 0.56106 0.1681 152 0.1457
bDistributionSeason[2] -0.01177 -0.222400 0.17702 0.1015 1697 0.9242
sDensitySite 1.07260 0.604075 1.91570 0.3374 61 0.0000
sDensitySiteYear 0.43798 0.041438 0.84705 0.2099 92 0.0000
sDensityYear 0.29111 0.006331 0.92494 0.2477 158 0.0000
sDistributionYear 0.13233 0.007455 0.42741 0.1138 159 0.0000
tAbundance 0.78385 -0.036726 1.57844 0.4011 103 0.0539
tDistribution 0.21841 -0.104383 0.56106 0.1681 152 0.1457
Rhat Iterations
1.09 1e+05

Catch - Juvenile BT

Parameter Estimate Lower Upper SD Error Significance
bDensity -0.41950 -1.146359 0.20692 0.32877 161 0.1796
bDensityRegime[2] 0.52515 -0.503355 1.66320 0.54884 206 0.3194
bDensitySeason[2] 0.04370 -0.189475 0.28269 0.11955 540 0.7465
bDispersion 5.42367 3.431977 8.88822 1.34990 50 0.0000
bDistribution -0.02235 -0.154525 0.11542 0.06625 604 0.7046
bDistributionRegime[2] -0.03931 -0.187350 0.09012 0.06723 353 0.4930
bDistributionSeason[2] 0.02573 -0.049026 0.10159 0.03930 293 0.5190
sDensitySite 0.65279 0.406477 1.07973 0.16925 52 0.0000
sDensitySiteYear 0.13048 0.003729 0.28469 0.08052 108 0.0000
sDensityYear 0.78113 0.445743 1.33116 0.22610 57 0.0000
sDistributionYear 0.05395 0.002564 0.18482 0.04897 169 0.0000
tAbundance 0.52515 -0.503355 1.66320 0.54884 206 0.3194
tDistribution -0.03931 -0.187350 0.09012 0.06723 353 0.4930
Rhat Iterations
1.05 1e+05

Catch - Juvenile MW

Parameter Estimate Lower Upper SD Error Significance
bDensity 0.16276 -0.78433 1.24596 0.49411 624 0.7166
bDensityRegime[2] -0.36635 -1.92367 1.72488 0.84939 498 0.5210
bDensitySeason[2] 0.97123 0.73845 1.19147 0.11588 23 0.0000
bDispersion 3.55936 2.50852 5.13174 0.67879 37 0.0000
bDistribution 0.08046 -0.14256 0.29649 0.11146 273 0.4491
bDistributionRegime[2] 0.03091 -0.18802 0.26359 0.11392 731 0.7645
bDistributionSeason[2] -0.09298 -0.17787 -0.01611 0.04195 87 0.0120
sDensitySite 0.90675 0.58063 1.47701 0.21766 49 0.0000
sDensitySiteYear 0.48345 0.32964 0.64415 0.08038 33 0.0000
sDensityYear 0.86916 0.40454 1.75268 0.35388 78 0.0000
sDistributionYear 0.10919 0.01236 0.28871 0.08381 127 0.0000
tAbundance -0.36635 -1.92367 1.72488 0.84939 498 0.5210
tDistribution 0.03091 -0.18802 0.26359 0.11392 731 0.7645
Rhat Iterations
1.09 1e+05

Site Fidelity - Adult BT

Parameter Estimate Lower Upper SD Error Significance
bMoved 0.6329 0.2214 1.080 0.2153 68 0.0027
bMovedSeason[2] 1.6167 0.1320 3.456 0.8519 103 0.0373
Rhat Iterations
1.01 1000

Site Fidelity - Adult CSU

Parameter Estimate Lower Upper SD Error Significance
bMoved -0.3889 -1.005 0.1484 0.2928 148 0.1787
bMovedSeason[2] -0.4122 -2.394 1.4496 0.9652 466 0.6853
Rhat Iterations
1.02 1000

Site Fidelity - Adult MW

Parameter Estimate Lower Upper SD Error Significance
bMoved 0.03725 -0.1455 0.2122 0.09477 480 0.664
bMovedSeason[2] -1.23878 -1.5866 -0.9104 0.17062 27 0.000
Rhat Iterations
1.02 1000

Site Fidelity - Adult RB

Parameter Estimate Lower Upper SD Error Significance
bMoved -0.412 -1.654 0.6798 0.6029 283 0.5053
bMovedSeason[2] -1.522 -4.689 1.0500 1.4011 188 0.2573
Rhat Iterations
1.02 1000

Site Fidelity - Juvenile BT

Parameter Estimate Lower Upper SD Error Significance
bMoved -0.3666 -0.8919 0.1367 0.2549 140 0.1373
bMovedSeason[2] -0.2816 -1.4484 0.8074 0.5836 400 0.6440
Rhat Iterations
1.03 1000

Site Fidelity - Juvenile MW

Parameter Estimate Lower Upper SD Error Significance
bMoved 1.0002 -0.6411 2.943 0.9167 179 0.2480
bMovedSeason[2] 0.1488 -2.2370 2.673 1.2608 1650 0.8907
Rhat Iterations
1.02 1000

Abundance - Adult BT

Parameter Estimate Lower Upper SD Error Significance
bDensity 4.20835 3.821262 4.56973 0.19401 9 0.0000
bDensityRegime[2] -0.21596 -0.651413 0.21110 0.21768 200 0.3114
bDensitySeason[2] -0.31832 -0.901175 0.27665 0.29307 185 0.2615
bDistribution 0.04286 -0.056668 0.14114 0.05116 231 0.3952
bDistributionRegime[2] 0.02290 -0.075820 0.12272 0.05129 433 0.6707
bDistributionSeason[2] 0.17070 0.109112 0.23041 0.03079 36 0.0000
bEfficiency -3.44160 -3.657811 -3.23149 0.10937 6 0.0000
bEfficiencySeason[2] 0.05735 -0.490728 0.59937 0.28234 950 0.8204
sDensitySite 0.48441 0.289877 0.80582 0.12856 53 0.0000
sDensitySiteYear 0.45317 0.377749 0.53767 0.04308 18 0.0000
sDensityYear 0.20907 0.020809 0.46557 0.11601 106 0.0000
sDistributionYear 0.03979 0.001965 0.09882 0.02755 122 0.0000
sEfficiencySessionSeasonYear 0.28829 0.210698 0.38018 0.04264 29 0.0000
tAbundance -0.21596 -0.651413 0.21110 0.21768 200 0.3114
tDistribution 0.02290 -0.075820 0.12272 0.05129 433 0.6707
Rhat Iterations
1.02 1e+05

Abundance - Adult CSU

Parameter Estimate Lower Upper SD Error Significance
bDensity 4.7654 3.37994 6.093932 0.66594 28 0.0000
bDensityRegime[2] 0.8763 -0.83660 2.535281 0.81215 192 0.2282
bDensitySeason[2] -0.9872 -1.85660 -0.020303 0.45862 93 0.0476
bDistribution -0.1368 -0.52594 0.177661 0.18025 257 0.3452
bDistributionRegime[2] 0.1175 -0.32821 0.716513 0.23558 444 0.5159
bDistributionSeason[2] -0.2217 -0.29178 -0.155417 0.03443 31 0.0000
bEfficiency -3.7923 -4.12324 -3.470488 0.16789 9 0.0000
bEfficiencySeason[2] -0.9270 -1.91299 -0.004846 0.46478 103 0.0516
sDensitySite 0.4030 0.15509 0.715174 0.13647 69 0.0000
sDensitySiteYear 0.4896 0.35508 0.656606 0.07685 31 0.0000
sDensityYear 0.7971 0.21464 1.831250 0.41390 101 0.0000
sDistributionYear 0.1868 0.02085 0.634588 0.16930 164 0.0000
sEfficiencySessionSeasonYear 0.3684 0.25294 0.532247 0.07165 38 0.0000
tAbundance 0.8763 -0.83660 2.535281 0.81215 192 0.2282
tDistribution 0.1175 -0.32821 0.716513 0.23558 444 0.5159
Rhat Iterations
1.03 4e+05

Abundance - Adult MW

Parameter Estimate Lower Upper SD Error Significance
bDensity 6.59232 6.165380 6.92823 0.18858 6 0.0000
bDensityRegime[2] -0.06525 -0.393118 0.24133 0.15677 486 0.6806
bDensitySeason[2] -0.54611 -0.771560 -0.33029 0.11430 40 0.0000
bDistribution 0.08668 -0.014721 0.19071 0.05271 118 0.0998
bDistributionRegime[2] 0.03421 -0.075782 0.13989 0.05447 315 0.5010
bDistributionSeason[2] -0.06937 -0.089339 -0.04591 0.01097 31 0.0000
bEfficiency -3.91010 -4.036179 -3.78523 0.06366 3 0.0000
bEfficiencySeason[2] 0.88554 0.647967 1.10707 0.12039 26 0.0000
sDensitySite 0.54654 0.344551 0.91054 0.14730 52 0.0000
sDensitySiteYear 0.42053 0.369420 0.48258 0.03015 13 0.0000
sDensityYear 0.11325 0.009933 0.27993 0.07255 119 0.0000
sDistributionYear 0.06342 0.010889 0.13389 0.03010 97 0.0000
sEfficiencySessionSeasonYear 0.26836 0.218023 0.32712 0.02823 20 0.0000
tAbundance -0.06525 -0.393118 0.24133 0.15677 486 0.6806
tDistribution 0.03421 -0.075782 0.13989 0.05447 315 0.5010
Rhat Iterations
1.09 1e+05

Abundance - Juvenile MW

Parameter Estimate Lower Upper SD Error Significance
bDensity 5.90978 4.626464 7.19876 0.63866 22 0.0000
bDensityRegime[2] -0.48239 -1.786131 0.88208 0.68033 277 0.4199
bDensitySeason[2] 0.27648 -0.794851 1.41687 0.54572 400 0.6129
bDistribution 0.08705 -0.119295 0.30528 0.10663 244 0.3900
bDistributionRegime[2] 0.02051 -0.194031 0.26127 0.12256 1110 0.8279
bDistributionSeason[2] -0.08247 -0.137233 -0.02828 0.02876 66 0.0080
bEfficiency -5.80413 -6.623554 -5.00042 0.41590 14 0.0000
bEfficiencySeason[2] 0.68501 -0.422483 1.74098 0.55216 158 0.2030
sDensitySite 0.91820 0.567997 1.46611 0.23071 49 0.0000
sDensitySiteYear 0.54555 0.424362 0.69262 0.07128 25 0.0000
sDensityYear 0.83364 0.396244 1.78407 0.34205 83 0.0000
sDistributionYear 0.10433 0.009736 0.29296 0.07384 136 0.0000
sEfficiencySessionSeasonYear 0.28712 0.194558 0.41464 0.05631 38 0.0000
tAbundance -0.48239 -1.786131 0.88208 0.68033 277 0.4199
tDistribution 0.02051 -0.194031 0.26127 0.12256 1110 0.8279
Rhat Iterations
1.04 2e+05

Capture Efficiency - Adult BT

Parameter Estimate Lower Upper SD Error Significance
bEfficiency -3.5116 -3.8080 -3.2567 0.1388 8 0.000
bEfficiencySeason[2] 0.2349 -0.3585 0.8615 0.3315 260 0.501
sEfficiencySessionSeasonYear 0.4639 0.1393 0.8035 0.1690 72 0.000
Rhat Iterations
1.03 10000

Capture Efficiency - Adult CSU

Parameter Estimate Lower Upper SD Error Significance
bEfficiency -3.8896 -4.33522 -3.5500 0.2038 10 0.0000
bEfficiencySeason[2] -0.5687 -1.51340 0.3157 0.4809 161 0.2196
sEfficiencySessionSeasonYear 0.3879 0.05404 0.9453 0.2347 115 0.0000
Rhat Iterations
1.06 10000

Capture Efficiency - Adult MW

Parameter Estimate Lower Upper SD Error Significance
bEfficiency -3.7867 -3.9458 -3.6476 0.07355 4 0
bEfficiencySeason[2] 0.7650 0.4848 1.0510 0.14681 37 0
sEfficiencySessionSeasonYear 0.3148 0.1928 0.4749 0.06982 45 0
Rhat Iterations
1.02 10000

Capture Efficiency - Adult RB

Parameter Estimate Lower Upper SD Error Significance
bEfficiency -3.0347 -3.75934 -2.367 0.3660 23 0.000
bEfficiencySeason[2] 0.3818 -0.80690 1.601 0.6098 315 0.523
sEfficiencySessionSeasonYear 0.5147 0.04226 1.337 0.3395 126 0.000
Rhat Iterations
1.04 10000

Capture Efficiency - Juvenile BT

Parameter Estimate Lower Upper SD Error Significance
bEfficiency -3.0780 -3.4822 -2.7502 0.1916 12 0.0000
bEfficiencySeason[2] -0.3587 -1.0863 0.4153 0.3878 209 0.3573
sEfficiencySessionSeasonYear 0.5802 0.1161 1.0220 0.2216 78 0.0000
Rhat Iterations
1.04 10000

Capture Efficiency - Juvenile MW

Parameter Estimate Lower Upper SD Error Significance
bEfficiency -5.9845 -7.200744 -5.081 0.5538 18 0.0000
bEfficiencySeason[2] 0.8390 -0.640380 2.294 0.7142 175 0.2136
sEfficiencySessionSeasonYear 0.6398 0.007024 1.884 0.5057 147 0.0000
Rhat Iterations
1.05 10000

Growth - Bull Trout

Parameter Estimate Lower Upper SD Error Significance
bK -1.7666 -2.0179 -1.5165 0.1326 14 0.0000
bKRegime[2] 0.0338 -0.4980 0.5531 0.2547 1555 0.8802
bLinf 849.2730 798.1868 912.9158 29.5070 7 0.0000
sGrowth 31.6647 28.9252 34.9028 1.5398 9 0.0000
sKYear 0.2958 0.1398 0.5902 0.1136 76 0.0000
tGrowth 0.0338 -0.4980 0.5531 0.2547 1555 0.8802
Rhat Iterations
1.02 10000

Growth - Mountain Whitefish

Parameter Estimate Lower Upper SD Error Significance
bK -2.68292 -3.0977 -2.3186 0.1961 15 0.0000
bKRegime[2] 0.03225 -0.7417 0.7602 0.3759 2329 0.9507
bLinf 362.43462 343.4124 383.7954 10.4440 6 0.0000
sGrowth 10.73578 10.2272 11.2958 0.2761 5 0.0000
sKYear 0.42473 0.2098 0.8103 0.1561 71 0.0000
tGrowth 0.03225 -0.7417 0.7602 0.3759 2329 0.9507
Rhat Iterations
1.03 20000

Condition - Adult BT

Parameter Estimate Lower Upper SD Error Significance
bWeight 7.570905 7.5268284 7.616426 0.023215 1 0.0000
bWeightLength 3.043657 3.0026155 3.088092 0.022015 1 0.0000
bWeightRegime[2] -0.098010 -0.1844099 -0.002311 0.045849 93 0.0444
bWeightSeason[2] -0.005243 -0.0384774 0.026434 0.016585 619 0.7459
sWeight 0.159374 0.1539146 0.165422 0.002901 4 0.0000
sWeightSite 0.014131 0.0009545 0.033180 0.008411 114 0.0000
sWeightSiteYear 0.027094 0.0116366 0.041598 0.007510 55 0.0000
sWeightYear 0.065032 0.0378199 0.112969 0.019071 58 0.0000
tCondition -0.098010 -0.1844099 -0.002311 0.045849 93 0.0444
Rhat Iterations
1.03 20000

Condition - Adult MW

Parameter Estimate Lower Upper SD Error Significance
bWeight 4.95081 4.932520 4.96848 0.0089447 0 0
bWeightLength 3.17967 3.168084 3.19180 0.0060579 0 0
bWeightRegime[2] -0.05747 -0.086057 -0.02757 0.0149570 51 0
bWeightSeason[2] -0.03719 -0.042511 -0.03186 0.0028525 14 0
sWeight 0.09210 0.091005 0.09323 0.0005731 1 0
sWeightSite 0.00932 0.003791 0.01603 0.0032253 66 0
sWeightSiteYear 0.01652 0.013224 0.02026 0.0017949 21 0
sWeightYear 0.02498 0.015077 0.04013 0.0066885 50 0
tCondition -0.05747 -0.086057 -0.02757 0.0149570 51 0
Rhat Iterations
1.03 20000

Condition - Adult RB

Parameter Estimate Lower Upper SD Error Significance
bWeight 6.08831 6.0465283 6.1392945 0.023214 1 0.0000
bWeightLength 3.05947 2.9767795 3.1402146 0.041085 3 0.0000
bWeightRegime[2] -0.01781 -0.1024085 0.0558622 0.040135 444 0.6647
bWeightSeason[2] -0.05670 -0.1115305 -0.0009062 0.027670 98 0.0479
sWeight 0.09705 0.0844802 0.1105521 0.006745 13 0.0000
sWeightSite 0.01873 0.0006613 0.0532621 0.014162 140 0.0000
sWeightSiteYear 0.02132 0.0013503 0.0544168 0.014839 124 0.0000
sWeightYear 0.04241 0.0071652 0.0920718 0.022475 100 0.0000
tCondition -0.01781 -0.1024085 0.0558622 0.040135 444 0.6647
Rhat Iterations
1.04 10000

Condition - Juvenile BT

Parameter Estimate Lower Upper SD Error Significance
bWeight 5.51577 5.472969 5.56097 0.022088 1 0.0000
bWeightLength 3.09918 3.070663 3.12740 0.014678 1 0.0000
bWeightRegime[2] -0.06585 -0.147559 0.02538 0.043816 131 0.1140
bWeightSeason[2] -0.01355 -0.034853 0.00768 0.010913 157 0.2127
sWeight 0.10405 0.099267 0.10885 0.002481 5 0.0000
sWeightSite 0.01530 0.003232 0.03056 0.007003 89 0.0000
sWeightSiteYear 0.01092 0.001207 0.02512 0.006562 109 0.0000
sWeightYear 0.05737 0.031146 0.09996 0.018806 60 0.0000
tCondition -0.06585 -0.147559 0.02538 0.043816 131 0.1140
Rhat Iterations
1.04 40000

Condition - Juvenile MW

Parameter Estimate Lower Upper SD Error Significance
bWeight 3.5604707 3.5419949 3.57926 0.009583 1 0.0000
bWeightLength 2.9480321 2.8633107 3.03211 0.042966 3 0.0000
bWeightRegime[2] -0.0164309 -0.0519889 0.02120 0.018460 223 0.3772
bWeightSeason[2] 0.0001869 -0.0181120 0.01736 0.008920 9490 0.9800
sWeight 0.1075213 0.1036648 0.11151 0.001978 4 0.0000
sWeightSite 0.0080554 0.0003498 0.02137 0.005733 130 0.0000
sWeightSiteYear 0.0221623 0.0097315 0.03285 0.005753 52 0.0000
sWeightYear 0.0213545 0.0080408 0.04229 0.008759 80 0.0000
tCondition -0.0164309 -0.0519889 0.02120 0.018460 223 0.3772
Rhat Iterations
1.02 10000

Length - Adult BT

Parameter Estimate Lower Upper SD Error Significance
bLength 6.31237 6.274086 6.35021 0.019502 1 0.0000
bLengthRegime[2] -0.03744 -0.094524 0.02297 0.029616 157 0.2053
bLengthSeason[2] 0.01916 -0.017943 0.05331 0.018530 186 0.3051
sLength 0.18176 0.175810 0.18817 0.003132 3 0.0000
sLengthSite 0.05599 0.035750 0.08868 0.013542 47 0.0000
sLengthSiteYear 0.01889 0.001564 0.03685 0.009515 93 0.0000
sLengthYear 0.03375 0.014809 0.06153 0.011823 69 0.0000
Rhat Iterations
1.04 80000

Length - Adult CSU

Parameter Estimate Lower Upper SD Error Significance
bLength 6.05062 5.947679 6.10140 0.034319 1 0.000
bLengthRegime[2] 0.03445 -0.031591 0.13290 0.038724 239 0.258
bLengthSeason[2] -0.03244 -0.044380 -0.02093 0.006127 36 0.000
sLength 0.07415 0.071669 0.07673 0.001293 3 0.000
sLengthSite 0.01454 0.005480 0.02644 0.005181 72 0.000
sLengthSiteYear 0.01180 0.003967 0.01943 0.003912 66 0.000
sLengthYear 0.03429 0.007222 0.11049 0.032254 151 0.000
Rhat Iterations
1.14 80000

Length - Adult MW

Parameter Estimate Lower Upper SD Error Significance
bLength 5.48669 5.45668 5.51324 0.0147550 1 0.0000
bLengthRegime[2] -0.03665 -0.09747 0.01260 0.0272310 150 0.1557
bLengthSeason[2] -0.02503 -0.03259 -0.01674 0.0041721 32 0.0000
sLength 0.13994 0.13844 0.14145 0.0007813 1 0.0000
sLengthSite 0.03247 0.02058 0.04995 0.0077833 45 0.0000
sLengthSiteYear 0.02351 0.01965 0.02802 0.0021776 18 0.0000
sLengthYear 0.03824 0.02355 0.06395 0.0101290 53 0.0000
Rhat Iterations
1.08 40000

Length - Adult RB

Parameter Estimate Lower Upper SD Error Significance
bLength 5.86147 5.753889 5.97072 0.05609 2 0.0000
bLengthRegime[2] -0.04416 -0.188676 0.09999 0.07169 327 0.4748
bLengthSeason[2] 0.04535 -0.056329 0.15494 0.05317 233 0.4122
sLength 0.18965 0.168068 0.21685 0.01261 13 0.0000
sLengthSite 0.14707 0.073783 0.25242 0.04582 61 0.0000
sLengthSiteYear 0.03295 0.001001 0.08619 0.02295 129 0.0000
sLengthYear 0.06814 0.003175 0.17548 0.04721 126 0.0000
Rhat Iterations
1.05 40000

Length - Juvenile BT

Parameter Estimate Lower Upper SD Error Significance
bLength 5.72996 5.666876 5.79228 0.030834 1 0.0000
bLengthRegime[2] -0.03411 -0.101135 0.02221 0.031239 181 0.2515
bLengthSeason[2] -0.09342 -0.141262 -0.04458 0.024248 52 0.0000
sLength 0.22462 0.214459 0.23563 0.005537 5 0.0000
sLengthSite 0.09459 0.059296 0.14945 0.023731 48 0.0000
sLengthSiteYear 0.04641 0.012123 0.07137 0.013963 64 0.0000
sLengthYear 0.02496 0.002694 0.05352 0.014497 102 0.0000
Rhat Iterations
1.07 10000

Length - Juvenile MW

Parameter Estimate Lower Upper SD Error Significance
bLength 5.031096 5.017517 5.045676 0.006890 0 0.00
bLengthRegime[2] -0.030737 -0.055184 -0.008117 0.011552 77 0.01
bLengthSeason[2] 0.069682 0.060453 0.079195 0.004897 13 0.00
sLength 0.062730 0.060460 0.064986 0.001130 4 0.00
sLengthSite 0.011718 0.005767 0.020016 0.003797 61 0.00
sLengthSiteYear 0.007552 0.001896 0.012831 0.002744 72 0.00
sLengthYear 0.014119 0.007248 0.025400 0.004771 64 0.00
Rhat Iterations
1.04 10000

Multivariate Analysis - Environmental

Parameter Estimate Lower Upper SD Error Significance
sValue 0.7031 0.6080 0.812 0.05315 15 0
sWeight[1] 1.8070 1.0478 2.948 0.48500 53 0
sWeight[2] 0.8926 0.2040 1.723 0.37029 85 0
sWeight[3] 0.7793 0.1167 1.518 0.35490 90 0
sWeight[4] 0.8787 0.2163 1.674 0.37193 83 0
sWeight[5] 1.3199 0.6853 2.234 0.40753 59 0
Rhat Iterations
1.02 10000

Multivariate Analysis - Indexing

Parameter Estimate Lower Upper SD Error Significance
sValue 0.8144 0.73454 0.9015 0.04201 10 0
sWeight[1] 1.4652 0.88325 2.2488 0.34749 47 0
sWeight[2] 0.4808 0.03215 1.0852 0.29986 110 0
sWeight[3] 0.4277 0.01620 0.9354 0.24915 107 0
sWeight[4] 0.8071 0.15416 1.4737 0.33987 82 0
sWeight[5] 0.8335 0.25003 1.4688 0.29258 73 0
Rhat Iterations
1.07 10000

Figures

Species Richness

figures/richness/year.png
Figure 1.
figures/richness/site.png
Figure 2.

Species Evenness

figures/evenness/year.png
Figure 3.
figures/evenness/site.png
Figure 4.

Occupancy - Burbot

figures/occupancy/BB/year.png
Figure 5.
figures/occupancy/BB/site.png
Figure 6.

Occupancy - Kokanee

figures/occupancy/KO/year.png
Figure 7.
figures/occupancy/KO/site.png
Figure 8.

Occupancy - Lake Whitefish

figures/occupancy/LW/year.png
Figure 9.
figures/occupancy/LW/site.png
Figure 10.

Occupancy - Northern Pikeminnow

figures/occupancy/NPC/year.png
Figure 11.
figures/occupancy/NPC/site.png
Figure 12.

Occupancy - Rainbow Trout

figures/occupancy/RB/year.png
Figure 13.
figures/occupancy/RB/site.png
Figure 14.

Occupancy - Redside Shiner

figures/occupancy/RSC/year.png
Figure 15.
figures/occupancy/RSC/site.png
Figure 16.

Occupancy - Sculpin

figures/occupancy/CC/year.png
Figure 17.
figures/occupancy/CC/site.png
Figure 18.

Count - Bull Trout

figures/count/BT/year.png
Figure 19.
figures/count/BT/site.png
Figure 20.
figures/count/BT/distribution.png
Figure 21.

Count - Burbot

figures/count/BB/year.png
Figure 22.
figures/count/BB/site.png
Figure 23.
figures/count/BB/distribution.png
Figure 24.

Count - Mountain Whitefish

figures/count/MW/year.png
Figure 25.
figures/count/MW/site.png
Figure 26.
figures/count/MW/distribution.png
Figure 27.

Count - Northern Pikeminnow

figures/count/NPC/year.png
Figure 28.
figures/count/NPC/site.png
Figure 29.
figures/count/NPC/distribution.png
Figure 30.

Count - Rainbow Trout

figures/count/RB/year.png
Figure 31.
figures/count/RB/site.png
Figure 32.
figures/count/RB/distribution.png
Figure 33.

Count - Suckers

figures/count/SU/year.png
Figure 34.
figures/count/SU/site.png
Figure 35.
figures/count/SU/distribution.png
Figure 36.

Catch - Adult BT

figures/catch/Adult BT/year.png
Figure 37.
figures/catch/Adult BT/site.png
Figure 38.
figures/catch/Adult BT/distribution.png
Figure 39.

Catch - Adult CSU

figures/catch/Adult CSU/year.png
Figure 40.
figures/catch/Adult CSU/site.png
Figure 41.
figures/catch/Adult CSU/distribution.png
Figure 42.

Catch - Adult MW

figures/catch/Adult MW/year.png
Figure 43.
figures/catch/Adult MW/site.png
Figure 44.
figures/catch/Adult MW/distribution.png
Figure 45.

Catch - Adult RB

figures/catch/Adult RB/year.png
Figure 46.
figures/catch/Adult RB/site.png
Figure 47.
figures/catch/Adult RB/distribution.png
Figure 48.

Catch - Juvenile BT

figures/catch/Juvenile BT/year.png
Figure 49.
figures/catch/Juvenile BT/site.png
Figure 50.
figures/catch/Juvenile BT/distribution.png
Figure 51.

Catch - Juvenile MW

figures/catch/Juvenile MW/year.png
Figure 52.
figures/catch/Juvenile MW/site.png
Figure 53.
figures/catch/Juvenile MW/distribution.png
Figure 54.

Site Fidelity - Adult BT

figures/movement/Adult BT/season.png
Figure 55.

Site Fidelity - Adult CSU

figures/movement/Adult CSU/season.png
Figure 56.

Site Fidelity - Adult MW

figures/movement/Adult MW/season.png
Figure 57.

Site Fidelity - Adult RB

figures/movement/Adult RB/season.png
Figure 58.

Site Fidelity - Juvenile BT

figures/movement/Juvenile BT/season.png
Figure 59.

Site Fidelity - Juvenile MW

figures/movement/Juvenile MW/season.png
Figure 60.

Abundance - Adult BT

figures/abundance/Adult BT/abundance.png
Figure 61.
figures/abundance/Adult BT/efficiency.png
Figure 62.
figures/abundance/Adult BT/year.png
Figure 63.
figures/abundance/Adult BT/site.png
Figure 64.
figures/abundance/Adult BT/distribution.png
Figure 65.

Abundance - Adult CSU

figures/abundance/Adult CSU/abundance.png
Figure 66.
figures/abundance/Adult CSU/efficiency.png
Figure 67.
figures/abundance/Adult CSU/year.png
Figure 68.
figures/abundance/Adult CSU/site.png
Figure 69.
figures/abundance/Adult CSU/distribution.png
Figure 70.

Abundance - Adult MW

figures/abundance/Adult MW/abundance.png
Figure 71.
figures/abundance/Adult MW/efficiency.png
Figure 72.
figures/abundance/Adult MW/year.png
Figure 73.
figures/abundance/Adult MW/site.png
Figure 74.
figures/abundance/Adult MW/distribution.png
Figure 75.

Abundance - Juvenile MW

figures/abundance/Juvenile MW/abundance.png
Figure 76.
figures/abundance/Juvenile MW/efficiency.png
Figure 77.
figures/abundance/Juvenile MW/year.png
Figure 78.
figures/abundance/Juvenile MW/site.png
Figure 79.
figures/abundance/Juvenile MW/distribution.png
Figure 80.

Capture Efficiency - Adult BT

figures/efficiency/Adult BT/efficiency.png
Figure 81.

Capture Efficiency - Adult CSU

figures/efficiency/Adult CSU/efficiency.png
Figure 82.

Capture Efficiency - Adult MW

figures/efficiency/Adult MW/efficiency.png
Figure 83.

Capture Efficiency - Adult RB

figures/efficiency/Adult RB/efficiency.png
Figure 84.

Capture Efficiency - Juvenile BT

figures/efficiency/Juvenile BT/efficiency.png
Figure 85.

Capture Efficiency - Juvenile MW

figures/efficiency/Juvenile MW/efficiency.png
Figure 86.

Growth - Bull Trout

figures/growth/BT/year.png
Figure 87.

Growth - Mountain Whitefish

figures/growth/MW/year.png
Figure 88.

Condition - Adult BT

figures/condition/Adult BT/year.png
Figure 89.
figures/condition/Adult BT/site.png
Figure 90.

Condition - Adult MW

figures/condition/Adult MW/year.png
Figure 91.
figures/condition/Adult MW/site.png
Figure 92.

Condition - Adult RB

figures/condition/Adult RB/year.png
Figure 93.
figures/condition/Adult RB/site.png
Figure 94.

Condition - Juvenile BT

figures/condition/Juvenile BT/year.png
Figure 95.
figures/condition/Juvenile BT/site.png
Figure 96.

Condition - Juvenile MW

figures/condition/Juvenile MW/year.png
Figure 97.
figures/condition/Juvenile MW/site.png
Figure 98.

Biomass - Adult BT

figures/biomass/Adult BT/year.png
Figure 99.
figures/biomass/Adult BT/site.png
Figure 100.
figures/biomass/Adult BT/year-weight.png
Figure 101.

Biomass - Adult MW

figures/biomass/Adult MW/year.png
Figure 102.
figures/biomass/Adult MW/site.png
Figure 103.
figures/biomass/Adult MW/year-weight.png
Figure 104.

Biomass - Juvenile MW

figures/biomass/Juvenile MW/year.png
Figure 105.
figures/biomass/Juvenile MW/site.png
Figure 106.
figures/biomass/Juvenile MW/year-weight.png
Figure 107.

Multivariate Analysis - Eigen Vector

figures/fda/eigen/eigen.png
Figure 108.

Multivariate Analysis - Environmental

figures/fda/env/environmental.png
Figure 109.
figures/fda/env/relationships.png
Figure 110.

Multivariate Analysis - Indexing

figures/fda/index/relationships.png
Figure 111.

Significance

The following table summarises the significance levels for the management hypotheses tested in the analyses.

Parameter Analysis Species Stage Significance Direction
Growth Growth Bull Trout All 0.8802
Growth Growth Mountain Whitefish All 0.9507
Condition Condition Bull Trout Juvenile 0.1140
Condition Condition Bull Trout Adult 0.0444 -
Condition Condition Mountain Whitefish Juvenile 0.3772
Condition Condition Mountain Whitefish Adult 0.0000 -
Condition Condition Rainbow Trout Adult 0.6647
Abundance Count Bull Trout All 0.2914
Abundance Abundance Bull Trout Adult 0.3114
Abundance Count Mountain Whitefish All 0.7063
Abundance Abundance Mountain Whitefish Juvenile 0.4199
Abundance Abundance Mountain Whitefish Adult 0.6806
Abundance Count Rainbow Trout All 0.0279 +
Abundance Count Burbot All 0.1058
Abundance Count Northern Pikeminnow All 0.6707
Abundance Abundance Sucker (Largescale) Adult 0.2282
Abundance Count Sucker All 0.0060 +
Distribution Count Bull Trout All 0.5788
Distribution Abundance Bull Trout Adult 0.6707
Distribution Count Mountain Whitefish All 0.8373
Distribution Abundance Mountain Whitefish Juvenile 0.8279
Distribution Abundance Mountain Whitefish Adult 0.5010
Distribution Count Rainbow Trout All 0.1018
Distribution Count Burbot All 0.8283
Distribution Count Northern Pikeminnow All 0.1277
Distribution Abundance Sucker (Largescale) Adult 0.5159
Distribution Count Sucker All 0.3752

References

Acknowledgements

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


blog comments powered by Disqus