model
{
	Tau.noninformative <- 1.0E-4
	P.gamma <- 1.0E-2
	for (i in 1:N.sector) {
		Disturbance[i] ~ dbin(p[i], N.dist)
		logit(p[i]) <- (
			intcpt
			+ re[i]
			+ b[Tr[i]] * Distance[i]
		)
	}
	re[1:N.sector] ~ car.normal(Adj[], Weights[], Num[], tau)
	intcpt ~ dnorm(0, Tau.noninformative)
	for (k in 1:N.transect) {
		b[k] ~ dnorm(0, Tau.noninformative)
	}
	tau ~ dgamma(P.gamma, P.gamma)
}