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Science Manual – Using Video Images for Fisheries Monitoring 51

The addition of the IC improves the accuracy and precision of the estimates, but the

degree to which this happens will depend on the relative magnitudes of the three

parameters estimated. This model could be greatly improved by calibrating on a

longer time period, and including river flow as an explanatory variable for counter and

IC efficiency.

Robin Wyatt, 26 Mar 2007

The key part of the model in WinBUGS language is:

model{

fp.rate~dgamma(0.001,0.001)

counter.eff~dbeta(1,1)

vid.eff~dbeta(1,1)

#c.fp.rate <- cut(fp.rate)

#c.counter.eff <- cut(counter.eff)

#c.vid.eff <- cut(vid.eff)

c.fp.rate <- fp.rate

c.counter.eff <- counter.eff

c.vid.eff <- vid.eff

for (i in 1:4){

false.positive[i] ~ dpois(fp.rate)

count.fish[i] ~ dbin(counter.eff, fish[i])

vid[i] ~ dbin(vid.eff, fish[i])

}

#mu.p ~dnorm(0.0, 1.0E-5)

#sd.p ~ dunif(0,50)

#tau.p <- 1/(sd.p*sd.p)

#for (j in 1:4){

#Counter only

#p.fish[j] ~ dnorm(mu.p, tau.p)I(0,100)

#np[j] <- p.fish[j]*c.counter.eff+c.fp.rate

#npq[j] <- p.fish[j]*c.counter.eff*(1-c.counter.eff)+c.fp.rate

#p[j] <- 1-npq[j]/np[j]

#n[j] <- np[j]/p[j]

#counter[j] ~ dbin(p[j],n[j])

#Counter + IC

#p.fish[j] ~ dnorm(mu.p, tau.p)I(0,100)

#np[j] <- p.fish[j]*c.counter.eff+c.fp.rate

#npq[j] <- p.fish[j]*c.counter.eff*(1-c.counter.eff)+c.fp.rate

#p[j] <- 1-npq[j]/np[j]

#n[j] <- np[j]/p[j]

#counter[j] ~ dbin(p[j],n[j])

#vid2[j] ~ dbin(c.vid.eff, p.fish[j])

}

#Data

list(

fish=c(15, 17, 15, 7),

1 2 ... 54 55 56 57 58 59 60 61 62 63 64 ... 74 75

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