model
{
	Tau.noninformative <- 1.0E-2
	P.gamma <- 1.0E-2
	for (i in 1:N.site) {
		for (j in 1:4) {
			Y[i, j] ~ dpois(mean[i, j])
			log(mean[i, j]) <- beta[j] + re[j, i]
		}
		for (j in 1:2) {
			re[j, i] ~ dnorm(0.0, tau[j])
		}
	}
	re[3, 1:N.site] ~ car.normal(Adj[], Weights[], Num[], tau[3])
	re[4, 1:N.site] ~ car.normal(Adj[], Weights[], Num[], tau[4])
	for (j in 1:4) {
		beta[j] ~ dnorm(0, Tau.noninformative)
		tau[j] ~ dgamma(P.gamma, P.gamma)
	}
}