model
{
	Tau.noninformative <- 1.0E-4
	P.gamma <- 1.0E-4
	for (i in 1:N.sample) {
		Seedling[i] ~ dpois(lambda[i])
		lambda[i] <- exp(log.lambda[i]) * Area[i]
		log.lambda[i] <- (
			flat
			+ flatYLS[Year[i], Line[i], Species[i]]
			+ Edge[i] * (
				bc[1]
				+ bs[1, Species[i]]
				+ bys[1, Year[i], Species[i]]
			)
			+ Removed[i] * (
				bc[2]
				+ bs[2, Species[i]]
				+ bys[2, Year[i], Species[i]]
			)
		)
	}
	for (k in 1:N.b) {
		bc[k] ~ dnorm(0, Tau.noninformative) # non-informative
		for (s in 1:N.species) {
			bs[k, s] ~ dnorm(0, tau.bs[k])
			for (year in 1:N.year) {
				bys[k, year, s] ~ dnorm(0, tau.bys[k])
			}
		}
		tau.bs[k] ~ dgamma(P.gamma, P.gamma)
		tau.bys[k] ~ dgamma(P.gamma, P.gamma)
	}
	flat ~ dnorm(0, Tau.noninformative) # non-informative
	for (s in 1:N.species) {
		for (line in 1:N.line) {
			#flatLS[line, s] ~ dnorm(0, tau[1])
			for (year in 1:N.year) {
				flatYLS[year, line, s] ~ dnorm(0, tau)
			}
		}
	}
	tau ~ dgamma(P.gamma, P.gamma)
}