model
{
	Tau.noninformative <- 1.0E-2
	Tau.gamma <- 1.0E-3
	Tau.massA <- 10.0
	# Observation
	for (i in 1:N.quad) {
		for (j in 1:N.sp) {
			MassA[i, j] ~ dnorm(massA[i, j], Tau.massA)
			log(massA[i, j]) <- 2 * log.potential[i, j] - log.sum.potential[i]
		}
	}
	# Process
	for (i in 1:N.quad) {
		log.sum.potential[i] <- log(sum(potential[i,]))
		for (j in 1:N.sp) {
			potential[i, j] <- exp(log.potential[i, j])
			log.potential[i, j] ~ dnorm(log.mu[i, j], tau.log.potential)
			log.mu[i, j] <- (
				(betaB[1] + betaS[1, j])
				+ (betaB[2] + betaS[2, j]) * Alt[i]
				+ betaB[3] * Tree[j]
				+ betaB[4] * Evergreen[j]
			)
		}
	}
	# Parameters and hyper parameters
	tau.log.potential ~ dgamma(Tau.gamma, Tau.gamma)
	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)
	}
}