model
{
	Tau.noninformative <- 1.0E-2
	Tau.gamma <- 1.0E-2
	# Observation
	for (i in 1:N.quad) {
		Total.mass[i] ~ dnorm(top.mass[i], tau[1])
		top.mass[i] <- pnpp[i, Top.sp[i]]
		for (j in 1:N.sp) {
			MassA[i, j] ~ dnorm(massA[i, j], tau[1])
			massA[i, j] <- pnpp[i, j] * pnpp[i, j] / sum.pnpp[i]
		}
	}
	# Potential GPP and competitive ability
	for (i in 1:N.quad) {
		sum.pnpp[i] <- sum(pnpp[i,])
		for (j in 1:N.sp) {
			pnpp[i, j] <- exp(log.pnpp[i, j])
			log.pnpp[i, j] ~ dnorm(mu.log.pnpp[i, j], tau[2])
			mu.log.pnpp[i, j] <- (
				betaB[1] + betaB[2] * Alt[i]
				+ betaS[1, j] + betaS[2, j] * Alt[i]
				+ betaB[3] * Tree[j]
				+ betaB[4] * Evergreen[j]
			)
		}
	}
	# Parameters and hyper parameters
	for (k in 1:N.betaB) {
		betaB[k] ~ dnorm(0, Tau.noninformative)
	}
	for (k in 1:N.betaS) {
		for (j in 1:N.sp) {
			betaS[k, j] ~ dnorm(0, tau.betaS[k])
		}
		tau.betaS[k] ~ dgamma(Tau.gamma, Tau.gamma)
	}
	for (k in 1:N.tau) {
		tau[k] ~ dgamma(Tau.gamma, Tau.gamma)
	}
}