model{	for( i in 1:ntree ) { #ntree = number of stems
		SURVIVE[i] ~ dbern(mu[i]) #random error
		mu[i] <-  exp(z[i])/(1+exp(z[i]))

		z[i]  <-  beta.1[species[i]] #species specific intercept
			+ beta.2[species[i]] * (nci[i]) #species specific response to NCI
			+ beta.3[species[i]] * (ncis[i]) #species specific response to NCIS
			+ beta.4[species[i]] * (log.dbh[i]) #species specific response to DBH
			+ beta.5[species[i]] * nci[i] * log.dbh[i] #species specific response to DBH*NCI
			}

	for( j in 1:nspecies ) { #this loop gets species specific parameters for each species from hyperdistributions
		beta.1[j] ~ dnorm(mu.beta[1] + beta.t[1]*trait[j] , tau[1]) #2nd level regression relating trait to growth intercept
		beta.2[j] ~ dnorm(mu.beta[2] + beta.t[2]*trait[j] , tau[2]) #2nd level regression relating trait to NCI response
		beta.3[j] ~ dnorm(mu.beta[2], tau[3]) 
		beta.4[j] ~ dnorm(mu.beta[4], tau[4])
		beta.5[j] ~ dnorm(mu.beta[5], tau[5])
		}

#below are priors
	beta.t[1] ~ dnorm(0, 1E-4)
	beta.t[2] ~ dnorm(0, 1E-4)

	mu.beta[1] ~ dnorm(0, 1E-4)
	mu.beta[2] ~ dnorm(0, 1E-4)
	mu.beta[3] ~ dnorm(0, 1E-4)
	mu.beta[4] ~ dnorm(0, 1E-4)
	mu.beta[5] ~ dnorm(0, 1E-4)

	tau[1] ~ dgamma(1E-3, 1E-3)
	tau[2] ~ dgamma(1E-3, 1E-3)	
	tau[3] ~ dgamma(1E-3, 1E-3)
	tau[4] ~ dgamma(1E-3, 1E-3)	
	tau[5] ~ dgamma(1E-3, 1E-3)	

	sigma <- 1 / sqrt(tau) #converts tau precision parameters to standard deviations for interpretability
	}