model{
for(r in 1:nRivers){
#performance parameters
ctMax[r]~dnorm(ctMaxMean,ctMaxPrecision)T(tOpt[r],ctUltimate)
tOpt[r]~dnorm(tOptMean,tOptPrecision)T(0,ctUltimate)
sigma[r]~dunif(0,10)
#derivative of the von Bert is linear, intercept and slope(with length) of hourly growth rate
beta1[r]~dnorm(0,100)#T(0,1000)
beta2[r]~dnorm(0,1000)#T(-1000,0)
beta3[r]~dnorm(0,1000)
beta4[r]~dnorm(0,1000)
beta5[r]~dnorm(0,1000)
eps[r]~dunif(0,0.1)
}
#individual random effect on grMax
for(f in 1:nInd){
ranInd[f]~dnorm(0,tauInd)
}
tauInd<-1/pow(sigmaInd,2)
sigmaInd~dunif(0,1)
#performance
for(r in 1:nRivers){
for(t in 1:nTimes){
perf[t,r]<-ifelse(tempDATA[t,r]>tOpt[r],1-((tempDATA[t,r]-tOpt[r])/(tOpt[r]-ctMax[r]))^2,
exp(-((tempDATA[t,r]-tOpt[r])/(2*sigma[r]))^2))
}
}
for(i in 1:nEvalRows){
p[evalRows[i]-1]<-sum(perf[time[evalRows[i]-1]:time[evalRows[i]],riverDATA[evalRows[i]-1]])
grExp[evalRows[i]-1]<-(beta1[riverDATA[evalRows[i]-1]]+
beta2[riverDATA[evalRows[i]-1]]*lengthDATA[evalRows[i]-1]+
beta3[riverDATA[evalRows[i]-1]]*flowDATA[evalRows[i]-1]+
beta4[riverDATA[evalRows[i]-1]]*bktBiomassDATA[evalRows[i]-1]+
beta5[riverDATA[evalRows[i]-1]]*bntBiomassDATA[evalRows[i]-1]+
ranInd[ind[evalRows[i]]])
*p[evalRows[i]-1] #von bert
lengthDATA[evalRows[i]]~dnorm(lengthDATA[evalRows[i]-1]+grExp[evalRows[i]-1],
eps[riverDATA[evalRows[i]-1]]*p[evalRows[i]-1])
}
for(i in 1:nEvalRows){
lengthExp[i]<-lengthDATA[evalRows[i]-1]+grExp[evalRows[i]-1]
}
}
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