The suggested citation for this analytic report is:

*Thorley, J.L., Muir, C.D., Dalgarno, S. and Hogan P.M. (2017) Duncan Lardeau Juvenile Rainbow Trout Abundance 2017. A Poisson Consulting Analysis Report. URL: http://www.poissonconsulting.ca/f/492093238.*

## Background

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 site. 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 are to:

- Estimate the spring abundance of age-1 fish by year.
- Estimate the stock-recruitment relationship between the number of spawners and the abundance of age-1 recruits the following spring.

## Methods

### Data Preparation

The data were provided by the Ministry of Forests, Lands and Natural Resource Operations (MFLNRO). The historical and current snorkel count data were manipulate using R version 3.4.2 (R Core Team 2017) and organised in an SQLite database. Due to the loss of the primary data manager, the estimates should be considered preliminary until additional verification of the restructured data has been conducted. Age-1 individuals were assumed to be those with a fork length \(\geq\) 100 mm.

### Data Analysis

Hierarchical Bayesian models were fitted to the data using R version
3.4.2 (R Core Team 2017), Stan 2.16.0 (Carpenter et al. 2017) and
JAGS 4.2.0 (Plummer 2015) and the
`mbr`

family of packages.

Unless indicated otherwise, the models used prior distributions that
were *vague* in the sense that they did not affect the posterior
distributions (Kery and Schaub 2011, 36). The posterior distributions
were estimated from 2,000 Markov Chain Monte Carlo (MCMC) samples
thinned from the second halves of three chains (Kery and Schaub 2011, 38–40). Model convergence was confirmed by ensuring that \(\hat{R} < 1.1\)
(Kery and Schaub 2011, 40) for each of the monitored parameters in
the model (Kery and Schaub 2011, 61). Where relevant, model adequacy
was confirmed by examination of residual plots.

The posterior distributions of the *fixed* (Kery and Schaub 2011, 75)
parameters are summarised in terms of 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). 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.

Variable selection was achieved by dropping fixed (Kery and Schaub 2011, 77–82) variables with two-sided p-values \(\geq\) 0.05 (Kery and Schaub 2011, 37, 42) and random variables with percent relative errors \(\geq\) 80%.

The results are displayed graphically by plotting the modeled
relationships between particular variables and the response with 95%
credible intervals (CIs) with the remaining variables held constant. In
general, continuous and discrete fixed variables are held constant at
their mean and first level values respectively while random variables
are held constant at their typical values (expected values of the
underlying hyperdistributions) (Kery and Schaub 2011, 77–82). Where
informative the influence of particular variables is expressed in terms
of the *effect size* (i.e., percent change in the response variable)
with 95% CIs (Bradford, Korman, and Higgins 2005).

### Model Descriptions

#### Abundance

The abundance was estimated from the count data using an overdispersed Poisson model (Kery and Schaub 2011, 55–56). The annual abundance estimates represent the total number of fish in the study area.

Key assumptions of the abundance model include:

- The lineal fish density varies with year, useable width and river kilometer as a polynomial, and randomly with site.
- The observer efficiency at marking sites varies by study design (GPS versus Index).
- The observer efficiency also varies by visit type (marking versus count) within study design and randomly by snorkeller.
- The expected count at a site is the expected lineal density multiplied by the site length, the observer efficiency and the proportion of the site surveyed.
- The residual variation in the actual count is gamma-Poisson distributed.

#### Stock-Recruitment

The relationship between the number of spawners in (\(S\)) and the abundance of age-1 individuals the following spring (\(R\)) was estimated using a Beverton-Holt stock-recruitment model (Walters and Martell 2004):

\[ R = \frac{a \cdot S}{1 + b \cdot S} \quad,\]

where \(a\) is the maximum reproductive performance per spawner, and \(b\) is the density dependence.

Key assumptions of the stock-recruitment model include:

- The prior probability of \(a\) is normally distributed with a mean of 500 and a SD of 250; this mean is based on an average of 8,000 eggs per female spawner, a 50:50 sex ratio, 50% egg survival, 50% post-emergence fall survival and 50% overwintering survival.
- The residual variation in the number of recruits is log-multi-normally distributed based on the covariance in the annual age-1 abundance estimates.

The age-1 carrying capacity (\(K\)) is given by:

\[ K = \frac{a}{b} \quad.\]

### Model Templates

### Abundance

```
data {
int<lower=0> nMarked;
int<lower=0> Marked[nMarked];
int<lower=0> Resighted[nMarked];
int<lower=0> IndexMarked[nMarked];
int<lower=0> nObs;
int Marking[nObs];
int Index[nObs];
int<lower=0> nSwimmer;
int<lower=0> Swimmer[nObs];
int<lower=0> nYear;
int<lower=0> Year[nObs];
real Width[nObs];
real Rkm[nObs];
int<lower=0> nSite;
int<lower=0> Site[nObs];
real SiteLength[nObs];
real SurveyProportion[nObs];
int Count[nObs];
}
parameters {
real bEfficiency;
real bEfficiencyIndex;
real bDensity;
vector[nYear] bDensityYear;
real bDensityWidth;
vector[4] bDensityRkm;
real<lower=0> sDensitySite;
vector[nSite] bDensitySite;
real bEfficiencyMarking;
real bEfficiencyMarkingIndex;
real<lower=0,upper=5> sEfficiencySwimmer;
vector[nSwimmer] bEfficiencySwimmer;
real<lower=0> sDispersion;
}
model {
vector[nObs] eDensity;
vector[nObs] eEfficiency;
vector[nObs] eAbundance;
vector[nObs] eCount;
sDispersion ~ gamma(0.01, 0.01);
bDensity ~ normal(0, 2);
bDensityRkm ~ normal(0, 2);
sDensitySite ~ uniform(0, 5);
bDensitySite ~ normal(0, sDensitySite);
bDensityWidth ~ normal(0, 2);
bDensityYear ~ normal(0, 5);
bEfficiency ~ normal(0, 5);
bEfficiencyIndex ~ normal(0, 5);
bEfficiencyMarking ~ normal(0, 5);
bEfficiencyMarkingIndex ~ normal(0, 5);
sEfficiencySwimmer ~ uniform(0, 5);
bEfficiencySwimmer ~ normal(0, sEfficiencySwimmer);
for (i in 1:nMarked) {
target += binomial_lpmf(Resighted[i] | Marked[i],
inv_logit(
bEfficiency +
bEfficiencyIndex * IndexMarked[i] +
bEfficiencyMarking +
bEfficiencyMarkingIndex * IndexMarked[i]
));
}
for (i in 1:nObs) {
eDensity[i] = exp(bDensity +
bDensityRkm[1] * Rkm[i] +
bDensityRkm[2] * pow(Rkm[i], 2.0) +
bDensityRkm[3] * pow(Rkm[i], 3.0) +
bDensityRkm[4] * pow(Rkm[i], 4.0) +
bDensitySite[Site[i]] +
bDensityYear[Year[i]] +
bDensityWidth * log(Width[i]));
eEfficiency[i] = inv_logit(
bEfficiency +
bEfficiencyIndex * Index[i] +
bEfficiencyMarking * Marking[i] +
bEfficiencyMarkingIndex * Index[i] * Marking[i] +
bEfficiencySwimmer[Swimmer[i]]);
eAbundance[i] = eDensity[i] * SiteLength[i];
eCount[i] = eAbundance[i] * eEfficiency[i] * SurveyProportion[i];
}
target += neg_binomial_2_lpmf(Count | eCount, sDispersion);
}
```

Template 1. Abundance model description.

### Stock-Recruitment

```
model {
a ~ dnorm(8000 * 0.5^4, 250^-2) T(0, )
b ~ dunif(0, 0.1)
sScaling ~ dunif(0, 5)
eRecruits <- a * Spawners / (1 + Spawners * b)
for(i in 1:nObs) {
esRecruits[i] <- SDLogRecruits[i] * sScaling
Recruits[i] ~ dlnorm(log(eRecruits[i]), esRecruits[i]^-2)
}
..
```

Template 2. Stock-Recruitment model description.

## Results

### Tables

### Abundance

Table 1. Parameter descriptions.

Parameter | Description |
---|---|

`bDensity` |
Intercept for `log(eDensity)` |

`bDensityRkm[x]` |
`x` ^{th}-order polynomial coefficients of effect of river kilometer on `bDensity` |

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

`bDensityWidth` |
Effect of `Width` on `bDensity` |

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

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

`bEfficiencyIndex` |
Effect of `Index` on `bEfficiency` |

`bEfficiencyMarking` |
Effect of `Marking` on `bEfficiency` |

`bEfficiencyMarkingIndex` |
Effect of `Marking` and `Index` on `bEfficiency` |

`bEfficiencySwimmer[i]` |
Effect of `i` ^{th} `Swimmer` on `bEfficiency` |

`eAbundance[i]` |
Expected abundance of fish at site of `i` ^{th} visit |

`eCount[i]` |
Expected total number of fish at site of `i` ^{th} visit |

`eDensity[i]` |
Expected lineal density of fish at site of `i` ^{th} visit |

`eEfficiency[i]` |
Expected observer efficiency on `i` ^{th} visit |

`Index` |
Whether the `i` ^{th} visit was to an index site |

`Marking[i]` |
Whether the `i` ^{th} visit was to a site with marked fish |

`Rkm[i]` |
River kilometer of `i` ^{th} visit |

`sDensitySite` |
SD of `bDensitySite` |

`sDispersion` |
Overdispersion of `Count[i]` |

`sEfficiencySwimmer` |
SD of `bEfficiencySwimmer` |

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

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

`SurveyProportion[i]` |
Proportion of site surveyed on `i` ^{th} visit |

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

`Width[i]` |
Useable width of site on `i` ^{th} visit |

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

Table 2. Model coefficients.

term | estimate | sd | zscore | lower | upper | pvalue |
---|---|---|---|---|---|---|

bDensity | -0.5609736 | 1.2717405 | -0.4748684 | -3.0208802 | 1.7686930 | 0.6730 |

bDensityRkm\[1\] | -0.2245419 | 0.0970234 | -2.3050645 | -0.4188671 | -0.0320095 | 0.0240 |

bDensityRkm\[2\] | 0.6614224 | 0.1137607 | 5.8202288 | 0.4212796 | 0.8853642 | 0.0005 |

bDensityRkm\[3\] | 0.0309741 | 0.0382207 | 0.8185385 | -0.0419120 | 0.1073823 | 0.4310 |

bDensityRkm\[4\] | -0.3002494 | 0.0383004 | -7.8520963 | -0.3760327 | -0.2241509 | 0.0005 |

bDensityWidth | 0.0816910 | 0.0283956 | 2.8775942 | 0.0254561 | 0.1377320 | 0.0010 |

bDensityYear\[1\] | 0.4807932 | 1.2962360 | 0.4227568 | -1.8357639 | 3.0299739 | 0.7250 |

bDensityYear\[2\] | -0.3464799 | 1.2935789 | -0.2387211 | -2.6933792 | 2.1272273 | 0.7980 |

bDensityYear\[3\] | -0.7047454 | 1.2807716 | -0.4904629 | -3.0051360 | 1.8556464 | 0.6430 |

bDensityYear\[4\] | -0.7545865 | 1.2846435 | -0.5454590 | -3.0895398 | 1.7895746 | 0.6050 |

bDensityYear\[5\] | -0.3468520 | 1.2966775 | -0.2186230 | -2.6968581 | 2.2429440 | 0.8190 |

bDensityYear\[6\] | 0.0395578 | 1.2717297 | 0.0627167 | -2.2843352 | 2.5126749 | 0.9710 |

bDensityYear\[7\] | 0.0103889 | 1.2666607 | 0.0462209 | -2.3013677 | 2.4891637 | 0.9930 |

bDensityYear\[8\] | -0.3197939 | 1.2688035 | -0.2281621 | -2.6648160 | 2.1053966 | 0.8310 |

bDensityYear\[9\] | -0.7803547 | 1.2670294 | -0.5927232 | -3.1533316 | 1.6692387 | 0.5830 |

bDensityYear\[10\] | -1.6874992 | 1.2756542 | -1.2863060 | -4.0218208 | 0.7823581 | 0.2100 |

bDensityYear\[11\] | -1.2588629 | 1.2687599 | -0.9555919 | -3.6158763 | 1.1808410 | 0.3660 |

bEfficiency | -1.7819382 | 0.1703980 | -10.4469313 | -2.1061139 | -1.4321656 | 0.0005 |

bEfficiencyIndex | 0.1934140 | 0.4060052 | 0.4667747 | -0.5683309 | 0.9881197 | 0.6640 |

bEfficiencyMarking | 0.1022605 | 0.1238488 | 0.7975761 | -0.1474622 | 0.3439068 | 0.4510 |

bEfficiencyMarkingIndex | 1.9852189 | 0.3887763 | 5.0685101 | 1.2059535 | 2.7109611 | 0.0005 |

sDensitySite | 0.6598949 | 0.0429870 | 15.3610139 | 0.5757140 | 0.7456404 | 0.0005 |

sDispersion | 1.1754351 | 0.0673789 | 17.4748628 | 1.0549137 | 1.3210813 | 0.0005 |

sEfficiencySwimmer | 0.3535370 | 0.1081607 | 3.4335531 | 0.2132199 | 0.6352534 | 0.0005 |

Table 3. Model summary.

n | K | nsamples | nchains | nsims | rhat | converged |
---|---|---|---|---|---|---|

2916 | 24 | 2000 | 4 | 4000 | 1.01 | TRUE |

Table 4. Summary survey information by year and river.

Year | River | Sites | SurveyLength | SurveyPercent | Fish | Marked | Resighted |
---|---|---|---|---|---|---|---|

2006 | Duncan | 0 | 0.0 | 0 | 0 | 0 | 0 |

2006 | Lardeau | 35 | 2.0 | 1 | 657 | 122 | 79 |

2007 | Duncan | 0 | 0.0 | 0 | 0 | 0 | 0 |

2007 | Lardeau | 47 | 2.7 | 2 | 260 | 0 | 0 |

2008 | Duncan | 0 | 0.0 | 0 | 0 | 0 | 0 |

2008 | Lardeau | 97 | 7.5 | 5 | 618 | 94 | 54 |

2009 | Duncan | 0 | 0.0 | 0 | 0 | 0 | 0 |

2009 | Lardeau | 83 | 5.0 | 4 | 384 | 0 | 0 |

2010 | Duncan | 0 | 0.0 | 0 | 0 | 0 | 0 |

2010 | Lardeau | 48 | 2.5 | 2 | 307 | 0 | 0 |

2011 | Duncan | 32 | 1.8 | 4 | 149 | 0 | 0 |

2011 | Lardeau | 264 | 15.8 | 11 | 1947 | 0 | 0 |

2012 | Duncan | 71 | 3.8 | 7 | 481 | 0 | 0 |

2012 | Lardeau | 257 | 15.2 | 11 | 1691 | 43 | 6 |

2013 | Duncan | 117 | 7.0 | 14 | 828 | 56 | 10 |

2013 | Lardeau | 308 | 18.0 | 13 | 1236 | 145 | 27 |

2014 | Duncan | 77 | 4.9 | 9 | 242 | 70 | 14 |

2014 | Lardeau | 273 | 16.2 | 12 | 928 | 96 | 13 |

2016 | Duncan | 70 | 3.6 | 7 | 137 | 0 | 0 |

2016 | Lardeau | 257 | 14.7 | 10 | 324 | 14 | 2 |

2017 | Duncan | 99 | 6.2 | 12 | 250 | 30 | 5 |

2017 | Lardeau | 604 | 38.4 | 27 | 1326 | 45 | 1 |

Table 5. Summary survey information by year.

Year | Sites | SurveyLength | SurveyPercent | Fish | Marked | Resighted |
---|---|---|---|---|---|---|

2006 | 35 | 2.0 | 1 | 657 | 122 | 79 |

2007 | 47 | 2.7 | 1 | 260 | 0 | 0 |

2008 | 97 | 7.5 | 4 | 618 | 94 | 54 |

2009 | 83 | 5.0 | 3 | 384 | 0 | 0 |

2010 | 48 | 2.5 | 1 | 307 | 0 | 0 |

2011 | 296 | 17.6 | 9 | 2096 | 0 | 0 |

2012 | 328 | 19.0 | 10 | 2172 | 43 | 6 |

2013 | 425 | 25.0 | 13 | 2064 | 201 | 37 |

2014 | 350 | 21.0 | 11 | 1170 | 166 | 27 |

2016 | 327 | 18.3 | 10 | 461 | 14 | 2 |

2017 | 703 | 44.6 | 23 | 1576 | 75 | 6 |

### Stock-Recruitment

Table 6. Parameter descriptions.

Parameter | Description |
---|---|

`a` |
`Recruits` per `Spawner` at low density |

`b` |
Density-dependence |

`eRecruits[i]` |
Expected number of recruits from `i` ^{th} spawn year |

`esRecruits[i]` |
Expected SD of residual variation in `Recruits` |

`Recruits[i]` |
Number of recruits from `i` ^{th} spawn year |

`SDLogRecruits[i]` |
Standard deviation of uncertainty in `log(Recruits[i])` |

`Spawners[i]` |
Number of spawners in `i` ^{th} spawn year |

`sScaling` |
Scaling term for SD of residual variation in `log(eRecruits)` |

Table 7. Model coefficients.

term | estimate | sd | zscore | lower | upper | pvalue |
---|---|---|---|---|---|---|

a | 481.4567932 | 200.8357326 | 2.520724 | 203.6611240 | 944.6992620 | 5e-04 |

b | 0.0040898 | 0.0026784 | 1.705611 | 0.0009107 | 0.0107306 | 5e-04 |

sScaling | 2.3845734 | 0.6332814 | 3.909790 | 1.5157516 | 4.0364412 | 5e-04 |

Table 8. Model summary.

n | K | nsamples | nchains | nsims | rhat | converged |
---|---|---|---|---|---|---|

11 | 3 | 2000 | 4 | 4e+05 | 1 | TRUE |

Table 9. Estimated carry capacity.

estimate | sd | zscore | lower | upper |
---|---|---|---|---|

120849.6 | 48569.61 | 2.660642 | 70856.94 | 234359.9 |

### Figures

### Abundance

### Stock-Recruitment

## Recommendations

- Scan and add all field cards to database.
- Add all GPS tracks to database.
- Verify reorganised data.
- Estimate observer fry length cutoffs based on length-frequency distributions.
- Account for spatial autocorrelation in density estimates.
- Use WAIC to estimate predictive value of habitat variables.

## Acknowledgements

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

- Habitat Conservation Trust Foundation (HCTF) and the anglers, hunters, trappers and guides who contribute to the Trust.
- Fish and Wildlife Compensation Program (FWCP) and its program partners BC Hydro, the Province of BC and Fisheries and Oceans Canada.
- Ministry of Forests, Lands and Natural Resource
Operations (MFLNRO)
- Greg Andrusak

- Gary Pavan
- Stefan Himmer
- Vicky Lipinski
- John Hagen
- Scott Decker
- Jody Schick
- Gillian Sanders
- Jeremy Baxter
- Jeff Berdusco
- Dave Derosa
- Seb Dalgarno

## References

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Carpenter, Bob, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. “*Stan* : A Probabilistic Programming Language.” *Journal of Statistical Software* 76 (1). https://doi.org/10.18637/jss.v076.i01.

Kery, Marc, and Michael Schaub. 2011. *Bayesian Population Analysis Using WinBUGS : A Hierarchical Perspective*. Boston: Academic Press. http://www.vogelwarte.ch/bpa.html.

Plummer, Martyn. 2015. “JAGS Version 4.0.1 User Manual.” http://sourceforge.net/projects/mcmc-jags/files/Manuals/4.x/.

R Core Team. 2017. “R: A Language and Environment for Statistical Computing.” Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Walters, Carl J., and Steven J. D. Martell. 2004. *Fisheries Ecology and Management*. Princeton, N.J: Princeton University Press.