model
{
	Tau.noninformative <- 1.0E-4 # SD = 100
	Tau.err <- 1.0E+4 # measurement error, SD = 0.01 (cm)
	P.gamma <- 1.0E-4 # non-informative, SD = 100
	# DBH growth
	for (i in 1:N.sample) {
		# dbh1
		Dbh1[i] ~ dnorm(dbh1[i], Tau.err) # measurement error
		dbh1[i] <- exp(log.dbh1[i])
		log.dbh1[i] ~ dnorm(0.0, Tau.noninformative)
		# dbh2
		Dbh2[i] ~ dnorm(dbh2[i], Tau.err) # measurement error
		dbh2[i] <- dbh1[i] * exp(r[i] * Year[i]) # growth model
		r[i] ~ dnorm(mean.r[i], tau) # random effects of tree
		mean.r[i] <- (
			b[1] + b.spc[1, Spc[i]] # intercept
			+ (b[2] + b.spc[2, Spc[i]]) * (log(dbh1[i]) - Mean.log.dbh1) # size 
			+ (b[3] + b.spc[3, Spc[i]]) * LocalBA[i]  # local BA, centralized
		)
	}
	# parameters
	for (j in 1:N.b) {
		b[j] ~ dnorm(0.0, Tau.noninformative)
		for (k in 1:N.spc) {
			b.spc[j, k] ~ dnorm(0.0, tau.b[j])
		}
		tau.b[j] ~ dgamma(P.gamma, P.gamma)
	}
	tau ~ dgamma(P.gamma, P.gamma)
}