model
{
	Tau.noninformative <- 1.0E-3
	Hyper.gamma <- 1.0E-2
	Tau.measurement <- 100

	for (i in 1:N.sample) {
		# measurements
		X.measurement[i] ~ dnorm(x[i], Tau.measurement)
		Y.measurement[i] ~ dnorm(y[i], Tau.measurement)
		# weight allocation between root and shoot
		x[i] <- exp(log.totalw[i]) * (1.0 - p[i])
		y[i] <- exp(log.totalw[i]) * (p[i])
		logit(p[i]) <- logit.p[i]
		logit.p[i] ~ dnorm(mean.logit.p[i], tau.tree)
		mean.logit.p[i] <- (
			bb[1] + bs[1, Spc[i]]
			+ (bb[2] + bs[2, Spc[i]]) * (log.totalw[i] - Mean.log.totalw)
		)
		# total weight
		log.totalw[i] ~ dnorm(Mean.log.totalw, Tau.noninformative)
	}

	# parameters
	tau.tree ~ dgamma(Hyper.gamma, Hyper.gamma) # individual effects
	for (k in 1:N.b) {
		bb[k] ~ dnorm(0.0, Tau.noninformative) # common part
		for (s in 1:N.spc) {	# differences among species
			bs[k, s] ~ dnorm(0.0, tau.bs[k])
		}
		tau.bs[k] ~ dgamma(Hyper.gamma, Hyper.gamma) # for species
	}
}