# Tau.noninformative = 1.0e-2,
# Tau.gamma = 1.0e-3,
# Err.gamma = 1.0,
model
{
	# Observation
	for (i in 1:N.site) {
		for (j in 1:N.spc) {
			BZP[i, j] ~ dnorm(bzp[i, j], tau.err[1])
			log(bzp[i, j]) <- log.bzp[i, j]
		}
		w.temp[i] ~ dnorm(W.temp[i], tau.err[2]) 
		Fish[i] ~ dbern(p.fish[i])
		p.fish[i] <- 1.0 - exp(-exp(z.fish[i]))
		z.fish[i] ~ dnorm(0, 1) 
	}
	# Process
	for (i in 1:N.site) {
		for (j in 1:N.spc) {
			log.bzp[i, j] ~ dnorm(log.mu[i, j], tau.log.bzp[j])
			log.mu[i, j] <- ( # log(biomass of zoo plankton)
				(betaB[1] + betaS[1, j])
				+ (betaB[2] + betaS[2, j]) * w.temp[i]
				+ (betaB[3] + betaS[3, j]) * z.fish[i]
			)
		}
	}
	# Parameters and hyper parameters
	for (j in 1:N.spc) {
		tau.log.bzp[j] ~ dgamma(Tau.gamma, Tau.gamma)
	}
	for (k in 1:N.beta) {
		betaB[k] ~ dnorm(0, Tau.noninformative)
		for (j in 1:N.spc) {
			betaS[k, j] ~ dnorm(0, tau.betaS[k])
		}
		tau.betaS[k] ~ dgamma(Tau.gamma, Tau.gamma)
	}
	for (k in 1:N.err) {
		tau.err[k] ~ dgamma(Err.gamma, Err.gamma)
	}
}