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

	# nn: number of neighbor groups
	for (i in 1:N.sample) {
		Nn[i] ~ dpois(mm[i])
		mm[i] <- exp(log.mm[i])
		log.mm[i] ~ dnorm(m[i], tau[2])
		m[i] <- (
			x[P[i]] # random effect
			+ beta[1] * Location[i] 
			+ beta[2] * Water.level[i] 
			+ beta[3] * Copulation[i]
			+ beta[4] * Fruit[i] 
			+ beta[5] * Flower[i] 
			+ beta[6] * Youngleaf[i] 
		)
	}

	# time change of density
	x[1] ~ dnorm(0.0, Tau.noninformative)
	for (j in 2:N.DATE) {
		x[j] ~ dnorm(x[j - 1], tau[1])
	}

	# priors and hyper-priors
	for (k in 1:N.beta) {
		beta[k] ~ dnorm(0.0, Tau.noninformative)
	}
	for (k in 1:N.tau) {
		tau[k] ~ dgamma(Hyper.gamma, Hyper.gamma)
	}
}