model
{
	Tau.noninformative <- 1.0E-4 # for non-informative dnorm()
	Hyper.gamma <- 1.0E-2 # non-informative 
	for (i in 1:N.sample) {
		# observations
		N.seeds[i] ~ dpois(seeds[i])
		seeds[i] <- exp(log.seeds[i])
		# latent variables
		log.seeds[i] ~ dnorm(mean.log.seeds[i], tau[1]) # re of flowers
		mean.log.seeds[i] <- (
			beta[1]
			+ beta[2] * N.flowers[i]
			+ beta[3] * PositionM[i]
			+ beta[4] * PositionT[i]
			+ beta[5] * MorphG[i]
			+ beta[6] * Hand[i]
			+ beta[7] * N.flowers[i] * PositionM[i]
			+ beta[8] * N.flowers[i] * PositionT[i]
			+ beta[9] * N.flowers[i] * MorphG[i]
			+ beta[10] * N.flowers[i] * Hand[i]
			+ beta[11] * Hand[i] * MorphG[i]
			+ re[Individual[i]]
		)
	}

	# parameters
	for (j in 1:N.individuals) {
		re[j] ~ dnorm(0.0, tau[2])
	}
	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) # non-informative
	}

}