The suggested citation for this analytic report is:
Thorley, J.L. and Amies-Galonski E. (2020) Middle Columbia River Fish Indexing Analysis 2019. A Poisson Consulting Analytic Appendix. URL: https://www.poissonconsulting.ca/f/1050384286.
The key management questions to be addressed by the analyses are:
Other objectives include the estimation of species richness and diversity. The year-round minimum flow was implemented in the winter of 2010 at the same time that a fifth turbine was added.
The suggested citation for this analytic appendix is:
Thorley, J.L., Andrusak, G.F., Amies-Galonski, E. and Dalgarno, S.I.J. (2019) Kaslo Bull Trout Productivity 2019. A Poisson Consulting Analytic Appendix. URL: https://www.poissonconsulting.ca/f/168922378.
The Kaslo River and Keen Creek, which is a tributary of the Kaslo River, are important Bull Trout spawning and rearing tributaries on Kootenay Lake. From 2012 to 2019, field crews have night-snorkeled both systems in the fall and recorded all juvenile Bull Trout less than 350 mm in length. Snorkel and electrofishing marking crews have also captured and tagged juvenile Bull Trout for the snorkel crews to resight. Redd counts have been conducted in both systems since 2006.
The primary goal of the current analyses is to answer the following questions:
What is the observer efficiency when night-snorkeling for juvenile Bull Trout in the Kaslo River and Keen Creek?
What are the densities of age-1 Bull Trout in the Kaslo River and Keen Creek?
What is the relationship between the number of redds and the resultant densities of age-1 Bull Trout two years later?
The data were cleaned, tidied and databased using R version 3.6.1 (R Core Team 2015).
Model parameters were estimated using Maximum-Likelihood (ML) and Bayesian methods. The Maximum-Likelihood Estimates (MLE) were obtained using TMB (Kristensen et al. 2016) while the Bayesian estimates were produced using JAGS (Plummer 2015). For additional information on ML and Bayesian estimation the reader is referred to Millar (2011) and McElreath (2016), respectively.
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 ML models, the point estimate is the MLE, the standard deviation is the standard error, the z-score is \(\mathrm{sd}/\mathrm{MLE}\) and the 95% CLs are the \(\mathrm{MLE} \pm 1.96 \cdot \mathrm{sd}\). For Bayesian models, the estimate is the median (50th percentile) of the MCMC samples, the z-score is \(\mathrm{sd}/\mathrm{mean}\) 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.
Model averaging and selection was implemented using an information theoretic approach (Burnham and Anderson 2002). ML models were compared using the marginal Akaike’s Information Criterion corrected for small sample size (AICc, Burnham and Anderson 2002). The support for fixed terms in models with identical random (Kery and Schaub 2011, 75) effects was assessed using the Akaike’s weights (\(w_i\)) (Burnham and Anderson 2002). The best supported model was then refitted as its Bayesian equivalent.
Where relevant, model adequacy was confirmed by examination of residual plots for the full model(s).
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 et al. 2005).
The analyses were implemented using R version 3.6.1
(R Core Team 2015) and the
mbr family of packages.
The annual bias (inaccuracy) and error (imprecision) in observer’s fish length estimates when spotlighting (standing) and snorkeling were quantified from the divergence of their length distribution from the length distribution for JLT and SH (the two most experience snorkelers) in that year. More specifically, the length correction that minimised the Jensen-Shannon divergence (Lin 1991) 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.
All resighted fish with a tag were allocated to the closest unallocated marked fish (with the same colour tag) by fork length and distance. The marked fish were analysed using a logistic regression model. Key assumptions of the full Maximum Likelihood logistic regression include:
Preliminary analysis indicated that river kilometre received similar support to watershed area.
The final Bayesian logistic regression assumed that:
Both systems were broken into 100 m sites by bank. The lineal density at each site was estimated using an over-dispersed Poisson Generalized Linear Mixed Model. Key assumptions of the full Maximum Likelihood GLMM include:
Preliminary analysis indicated that river kilometre was better supported as a predictor of lineal density than watershed area. Preliminary analysis also indicated that autocorrelation (modeled as an AR1 process) was not significant.
The final Bayesian GLMM dropped sinuosity and gradient and took into account the uncertainty in the observer efficiencies as estimated by the observer efficiency model.
The stock-recruitment relationship was estimated using a Bayesian Beverton-Holt stock-recruitment curve. Key assumptions of the final BH SR model include:
.#include <TMB.hpp>
template<class Type>
Type objective_function<Type>::operator() () {
DATA_VECTOR(Observed);
DATA_FACTOR(Observer);
DATA_FACTOR(System);
DATA_VECTOR(Tagged);
DATA_VECTOR(Length);
DATA_VECTOR(Gradient);
DATA_VECTOR(Sinuosity);
DATA_VECTOR(Rkm);
PARAMETER(bSystem);
PARAMETER(bLength);
PARAMETER(bLength2);
PARAMETER(bGradient);
PARAMETER(bSinuosity);
PARAMETER(bRkm);
PARAMETER_VECTOR(bObserver);
vector<Type> eObserved = Observed;
int n = Observed.size();
Type nll = 0.0;
for(int i = 0; i < n; i++){
eObserved(i) = invlogit(bObserver(Observer(i)) + bSystem * System(i) + bLength * Length(i) + bLength2 * pow(Length(i), 2) + bGradient * Gradient(i) + bSinuosity * Sinuosity(i) + bRkm * Rkm(i));
nll -= dbinom(Observed(i), Tagged(i), eObserved(i), true);
return nll;Block 1. Full model.
model{
bIntercept ~ dnorm(0, 5^-2)
bLength ~ dnorm(0, 5^-2)
for(i in 1:length(Observed)){
logit(eObserved[i]) <- bIntercept + bLength * Length[i]
Observed[i] ~ dbern(eObserved[i])
}Block 2. Final model.
.#include <TMB.hpp>
template<class Type>
Type objective_function<Type>::operator() () {
DATA_VECTOR(Count);
DATA_VECTOR(Length);
DATA_VECTOR(Coverage);
DATA_VECTOR(Efficiency);
DATA_VECTOR(Dispersion);
DATA_VECTOR(Gradient);
DATA_VECTOR(Sinuosity);
DATA_VECTOR(Rkm);
DATA_FACTOR(System);
DATA_FACTOR(Site);
DATA_INTEGER(nSite);
DATA_FACTOR(Year);
PARAMETER_MATRIX(bSystemYear);
PARAMETER_VECTOR(bSite);
PARAMETER(bGradient);
PARAMETER(bSinuosity);
PARAMETER(bRkm);
PARAMETER_VECTOR(bDispersion);
DATA_INTEGER(nDispersion);
PARAMETER(log_sDispersion);
PARAMETER(log_sSite);
Type sSite = exp(log_sSite);
Type sDispersion = exp(log_sDispersion);
ADREPORT(sSite);
ADREPORT(sDispersion);
vector<Type> eDensity = Count;
vector<Type> eCount = Count;
vector<Type> eNorm = pnorm(bDispersion, Type(0), Type(1));
vector<Type> eDispersion = qgamma(eNorm, pow(sDispersion, -2), pow(sDispersion, 2));
Type nll = 0.0;
for(int i = 0; i < nSite; i++){
nll -= dnorm(bSite(i), Type(0), sSite, true);
for(int i = 0; i < nDispersion; i++){
nll -= dnorm(bDispersion(i), Type(0), Type(1), true);
eDensity(i) = exp(bSystemYear(System(i), Year(i)) + bGradient * Gradient(i) + bSinuosity * Sinuosity(i) + bRkm * Rkm(i) + bSite(Site(i)));
eCount(i) = eDensity(i) * Length(i) * Coverage(i);
nll -= dpois(Count(i), eCount(i) * Efficiency(i) * eDispersion(i), true);
return nll;Block 3. The full model.
model{
bEfficiency ~ dnorm(Efficiency[1], EfficiencySD[1]^-2) T(0, )
for(i in 1:nSystem) {
for(j in 1:nYear) {
bSystemYear[i,j] ~ dnorm(0, 5^-2)
}
}
log_sSite ~ dnorm(0, 5^-2)
log(sSite) <- log_sSite
for(i in 1:nSite) {
bSite[i] ~ dnorm(0, sSite^-2)
}
bRkm ~ dnorm(0, 5^-2)
log_sDispersion ~ dnorm(0, 5^-2)
log(sDispersion) <- log_sDispersion
for(i in 1:length(Count)) {
log(eDensity[i]) <- bSystemYear[System[i],Year[i]] + bRkm * Rkm[i] + bSite[Site[i]]
eDispersion[i] ~ dgamma(sDispersion^-2, sDispersion^-2)
Count[i] ~ dpois(eDensity[i] * Length[i] * Coverage[i] * bEfficiency * eDispersion[i])
}Block 4. The final model.
model {
log_bAlpha ~ dnorm(5, 5^-2)
log(bAlpha) <- log_bAlpha
for(i in 1:nSystem) {
log_bBeta[i] ~ dnorm(0, 5^-2)
log(bBeta[i]) <- log_bBeta[i]
}
log_sRecruits ~ dnorm(0, 5^-2)
log(sRecruits) <- log_sRecruits
for(i in 1:length(Recruits)){
eRecruits[i] <- bAlpha * Stock[i] / (1 + bBeta[System[i]] * Stock[i])
Recruits[i] ~ dlnorm(log(eRecruits[i]), sRecruits^-2)
}
bCarryingCapacity <- bAlpha / bBetaBlock 5. Final model.
The suggested citation for this analytic appendix is:
Thorley, J.L., Andrusak, G.F. and Amies-Galonski, E. (2019) Duncan Lardeau Juvenile Rainbow Trout Abundance 2019b. A Poisson Consulting Analytic Appendix. URL: https://www.poissonconsulting.ca/f/1245769249.
Rainbow Trout rear in the Lardeau and Lower Duncan rivers. Since 2006 (with the exception of 2015) annual spring snorkel surveys have been conducted to estimate the abundance and distribution of age-1 Rainbow Trout. From 2006 to 2010 the surveys were conducted at fixed index sites. Since 2011 fish observations have been mapped to the river based on their spatial coordinates as recorded by GPS.
The primary aims of the current analyses were to:
The suggested citation for this analytic report is:
Thorley, J.L. (2019) Middle Columbia River Fish Indexing Analysis 2018. A Poisson Consulting Analysis Report. URL: https://www.poissonconsulting.ca/f/1882094716.
The key management questions to be addressed by the analyses are:
Other objectives include the estimation of species richness and diversity. The year-round minimum flow was implemented in the winter of 2010 at the same time that a fifth turbine was added.
The suggested citation for this analytic report is:
Thorley, J.L., Andrusak, G.F., Dalgarno, S.I.J., Amies-Galonski, E. (2018) Kaslo Bull Trout Productivity 2018. A Poisson Consulting Analysis Report. URL: https://www.poissonconsulting.ca/f/1920399815.
The Kaslo River and Keen Creek, which is a tributary of the Kaslo River, are important Bull Trout spawning and rearing tributaries on Kootenay Lake. From 2012 to 2018, field crews have night-snorkeled both systems in the fall and recorded all juvenile Bull Trout less than 350 mm in length. Snorkel and electrofishing marking crews have also captured and tagged juvenile Bull Trout for the snorkel crews to resight. Redd counts have been conducted in both systems since 2006.
The primary goal of the current analyses is to answer the following questions:
What is the observer efficiency when night-snorkeling for juvenile Bull Trout in the Kaslo River and Keen Creek?
What are the densities of age-1 Bull Trout in the Kaslo River and Keen Creek?
What is the relationship between the number of redds and the resultant densities of age-1 Bull Trout two years later?
The data were cleaned, tidied and databased using R version 3.5.1 (R Core Team 2015).
Table 1. Bull Trout length cutoffs by age-class.
| Age Class | Length (mm) |
|---|---|
| Age-0 | 30 - 90 |
| Age-1 | 91 - 140 |
| Age-2+ | 141 - 350 |
Model parameters were estimated using Maximum-Likelihood (ML) and Bayesian methods. The Maximum-Likelihood Estimates (MLE) were obtained using TMB (Kristensen et al. 2016) while the Bayesian estimates were produced using JAGS (Plummer 2015). For additional information on ML and Bayesian estimation the reader is referred to Millar (2011) and McElreath (2016), respectively.
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 ML models, the point estimate is the MLE, the standard deviation is the standard error, the z-score is \(\mathrm{sd}/\mathrm{MLE}\) and the 95% CLs are the \(\mathrm{MLE} \pm 1.96 \cdot \mathrm{sd}\). 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.
Model averaging and selection was implemented using an information theoretic approach (Burnham and Anderson 2002). ML models were compared using the marginal Akaike’s Information Criterion corrected for small sample size (AICc, Burnham and Anderson 2002). The support for fixed terms in models with identical random (Kery and Schaub 2011, 75) effects was assessed using the Akaike’s weights (\(w_i\)) (Burnham and Anderson 2002). The best supported model was then refitted as its Bayesian equivalent.
Where relevant, model adequacy was confirmed by examination of residual plots for the full model(s).
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 et al. 2005).
The analyses were implemented using R version 3.5.1
(R Core Team 2015) and the
mbr family of packages.
The annual bias (inaccuracy) and error (imprecision) in observer’s fish length estimates when spotlighting (standing) and snorkeling were quantified from the divergence of their length distribution from the length distribution for JLT and SH (the two most experience snorkelers) in that year. More specifically, the length correction that minimised the Jensen-Shannon divergence (Lin 1991) 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.
All resighted fish with a tag were allocated to the closest unallocated marked fish (with the same colour tag) by fork length and distance. The marked fish were analysed using a logistic regression model. Key assumptions of the full Maximum Likelihood logistic regression include:
Preliminary analysis indicated that river kilometre received similar support to watershed area.
The final Bayesian logistic regression assumed that:
Both systems were broken into 100 m sites by bank. The lineal density at each site was estimated using an over-dispersed Poisson Generalized Linear Mixed Model. Key assumptions of the full Maximum Likelihood GLMM include:
Preliminary analysis indicated that river kilometre was better supported as a predictor of lineal density than watershed area. Preliminary analysis also indicated that autocorrelation (modeled as an AR1 process) was not significant.
The final Bayesian GLMM dropped sinuosity and gradient and took into account the uncertainty in the observer efficiencies as estimated by the observer efficiency model.
The stock-recruitment relationship was estimated using a Bayesian Beverton-Holt stock-recruitment curve. Key assumptions of the final BH SR model include:
#include <TMB.hpp>
template<class Type>
Type objective_function<Type>::operator() () {
DATA_VECTOR(Observed);
DATA_FACTOR(Observer);
DATA_FACTOR(System);
DATA_VECTOR(Tagged);
DATA_VECTOR(Length);
DATA_VECTOR(Gradient);
DATA_VECTOR(Sinuosity);
DATA_VECTOR(Rkm);
PARAMETER(bSystem);
PARAMETER(bLength);
PARAMETER(bLength2);
PARAMETER(bGradient);
PARAMETER(bSinuosity);
PARAMETER(bRkm);
PARAMETER_VECTOR(bObserver);
vector<Type> eObserved = Observed;
int n = Observed.size();
Type nll = 0.0;
for(int i = 0; i < n; i++){
eObserved(i) = invlogit(bObserver(Observer(i)) + bSystem * System(i) + bLength * Length(i) + bLength2 * pow(Length(i), 2) + bGradient * Gradient(i) + bSinuosity * Sinuosity(i) + bRkm * Rkm(i));
nll -= dbinom(Observed(i), Tagged(i), eObserved(i), true);
return nll;Template 1. Full model.
model{
bIntercept ~ dnorm(0, 5^-2)
bLength ~ dnorm(0, 5^-2)
for(i in 1:length(Observed)){
logit(eObserved[i]) <- bIntercept + bLength * Length[i]
Observed[i] ~ dbern(eObserved[i])
}Template 2. Final model.
#include <TMB.hpp>
template<class Type>
Type objective_function<Type>::operator() () {
DATA_VECTOR(Count);
DATA_VECTOR(Length);
DATA_VECTOR(Coverage);
DATA_VECTOR(Efficiency);
DATA_VECTOR(Dispersion);
DATA_VECTOR(Gradient);
DATA_VECTOR(Sinuosity);
DATA_VECTOR(Rkm);
DATA_FACTOR(System);
DATA_FACTOR(Site);
DATA_INTEGER(nSite);
DATA_FACTOR(Year);
PARAMETER_MATRIX(bSystemYear);
PARAMETER_VECTOR(bSite);
PARAMETER(bGradient);
PARAMETER(bSinuosity);
PARAMETER(bRkm);
PARAMETER_VECTOR(bDispersion);
DATA_INTEGER(nDispersion);
PARAMETER(log_sDispersion);
PARAMETER(log_sSite);
Type sSite = exp(log_sSite);
Type sDispersion = exp(log_sDispersion);
ADREPORT(sSite);
ADREPORT(sDispersion);
vector<Type> eDensity = Count;
vector<Type> eCount = Count;
vector<Type> eNorm = pnorm(bDispersion, Type(0), Type(1));
vector<Type> eDispersion = qgamma(eNorm, pow(sDispersion, -2), pow(sDispersion, 2));
Type nll = 0.0;
for(int i = 0; i < nSite; i++){
nll -= dnorm(bSite(i), Type(0), sSite, true);
for(int i = 0; i < nDispersion; i++){
nll -= dnorm(bDispersion(i), Type(0), Type(1), true);
eDensity(i) = exp(bSystemYear(System(i), Year(i)) + bGradient * Gradient(i) + bSinuosity * Sinuosity(i) + bRkm * Rkm(i) + bSite(Site(i)));
eCount(i) = eDensity(i) * Length(i) * Coverage(i);
nll -= dpois(Count(i), eCount(i) * Efficiency(i) * eDispersion(i), true);
return nll;Template 3. The full model.
model{
bEfficiency ~ dnorm(Efficiency[1], EfficiencySD[1]^-2) T(0, )
for(i in 1:nSystem) {
for(j in 1:nYear) {
bSystemYear[i,j] ~ dnorm(0, 5^-2)
}
}
log_sSite ~ dnorm(0, 5^-2)
log(sSite) <- log_sSite
for(i in 1:nSite) {
bSite[i] ~ dnorm(0, sSite^-2)
}
bRkm ~ dnorm(0, 5^-2)
log_sDispersion ~ dnorm(0, 5^-2)
log(sDispersion) <- log_sDispersion
for(i in 1:length(Count)) {
log(eDensity[i]) <- bSystemYear[System[i],Year[i]] + bRkm * Rkm[i] + bSite[Site[i]]
eDispersion[i] ~ dgamma(sDispersion^-2, sDispersion^-2)
Count[i] ~ dpois(eDensity[i] * Length[i] * Coverage[i] * bEfficiency * eDispersion[i])
}Template 4. The final model.
model {
log_bAlpha ~ dnorm(5, 5^-2)
log(bAlpha) <- log_bAlpha
for(i in 1:nSystem) {
log_bBeta[i] ~ dnorm(0, 5^-2)
log(bBeta[i]) <- log_bBeta[i]
}
log_sRecruits ~ dnorm(0, 5^-2)
log(sRecruits) <- log_sRecruits
for(i in 1:length(Recruits)){
eRecruits[i] <- bAlpha * Stock[i] / (1 + bBeta[System[i]] * Stock[i])
Recruits[i] ~ dlnorm(log(eRecruits[i]), sRecruits^-2)
}
bCarryingCapacity <- bAlpha / bBetaTemplate 5. Final model.
The suggested citation for this analytic report is:
Thorley, J.L., Andrusak, G.F., and Dalgarno, S. (2018) Duncan Lardeau Juvenile Rainbow Trout Abundance 2018. A Poisson Consulting Analysis Report. URL: https://www.poissonconsulting.ca/f/679886444.
Rainbow Trout rear in the Lardeau and Lower Duncan rivers. Since 2006 (with the exception of 2015) annual spring snorkel surveys have been conducted to estimate the abundance and distribution of age-1 Rainbow Trout. From 2006 to 2010 the surveys were conducted at fixed index sites. Since 2011 fish observations have been mapped to the river based on their spatial coordinates as recorded by GPS.
The primary aims of the current analyses were to:
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.
The key management questions to be addressed by the analyses are:
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.
The suggested citation for this analytic report is:
Thorley, J.L., Andrusak, G.F., Muir, C.D., Dalgarno, S. and Hogan P.M. (2017) Duncan Lardeau Juvenile Rainbow Trout Abundance 2017. A Poisson Consulting Analysis Report. URL: https://www.poissonconsulting.ca/f/492093238.
The suggested citation for this analytic report is:
Thorley, J.L. and Muir, C.D. (2017) Lower Columbia River Fish Population Indexing Analysis 2016. A Poisson Consulting Analysis Report. URL: https://www.poissonconsulting.ca/f/589633601.
In the mid 1990s BC Hydro began operating Hugh L. Keenleyside (HLK) Dam to reduce dewatering of Mountain Whitefish and Rainbow Trout eggs.
The primary goal of the Lower Columbia River Fish Population Indexing program is to answer two key management questions:
What are the abundance, growth rate, survival rate, body condition, age distribution, and spatial distribution of subadult and adult Whitefish, Rainbow Trout, and Walleye in the Lower Columbia River?
What is the effect of inter-annual variability in the Whitefish and Rainbow Trout flow regimes on the abundance, growth rate, survival rate, body condition, and spatial distribution of subadult and adult Whitefish, Rainbow Trout, and Walleye in the Lower Columbia River?
The inter-annual variability in the Whitefish and Rainbow Trout flow regimes was quantified in terms of the percent egg dewatering as greater flow variability is associated with more egg stranding.