model
{
	Tau.noninformative <- 1.0E-4
	Tau.err <- 1.0E+4
	sigma.max <- 1.0E+4
	for (i in 1:N) {
		Y[i] ~ dnorm(y[i], Tau.err)
		X[i] ~ dnorm(x[i], Tau.err)
		y[i] <- unit.length[i] * sqrt(rxy[i])
		x[i] <- unit.length[i] / sqrt(rxy[i])
		rxy[i] <- exp(log.rxy[i])
		log.rxy[i] ~ dnorm(mean.log.rxy[i], tau[Spc[i]])
		mean.log.rxy[i] <- (
			bs[1, Spc[i]]
			+ bs[2, Spc[i]] * (unit.length[i] - Mean.ul)
		)
		unit.length[i] <- exp(log.unit.length[i])
		log.unit.length[i] ~ dnorm(0, Tau.noninformative)
	}
	for (k in 1:N.b) {
		for (s in 1:N.spc) {
			bs[k, s] ~ dnorm(0, Tau.noninformative)
		}
	}
	for (s in 1:N.spc) {
		tau[s] <- 1 / (sigma[s] * sigma[s])
		sigma[s] ~ dunif(0, sigma.max)
	}
}