model 
	{
	# Prior distributions
		theta~dunif(0,10)
		theta2<-theta*theta
		psi~dunif(0,1)

		for(i in 1:(nind+nz)){
			z[i]~dbern(psi)  # latent indicator variables from data augmentation
			x[i]~dunif(0,4)   # distance is a random variable
			logp[i]<-   -((x[i]*x[i])/theta2)  
			p[i]<-exp(logp[i])
			mu[i]<-z[i]*p[i] 
			y[i]~dbern(mu[i])   # observation model
		}
		N<-sum(z[1:(nind+nz)])
		D<- N/48     # 48 km*km = total area of transects
	}