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AYNA_IPM_v5.jags
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AYNA_IPM_v5.jags
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model {
#-------------------------------------------------
# integrated population model for the Gough AYNA population
# - age structured model with 6 age classes
# - adult survival based on CMR ringing data
# - pre breeding census, female-based assuming equal sex ratio & survival
# - productivity based on Area 1 nest monitoring data
# - simplified population process with informed prior for adults skipping breeding and uninformed immatures recruiting
# - FOUR future scenarios to project population growth after eradication, bycatch mitigation or no management
# -------------------------------------------------
#-------------------------------------------------
# 1. PRIORS FOR ALL DATA SETS
#-------------------------------------------------
# -------------------------------------------------
# 1.1. Priors and constraints FOR FECUNDITY
# -------------------------------------------------
for (t in 1:T){
ann.fec[t] ~ dunif(0.3,0.8) ## Informative Priors on fecundity based on Cuthbert et al 2003: dnorm(0.67,50) T(0.01,0.99)
imm.rec[t] ~ dunif(0.01,0.99) ## Informative Priors on annual recruitment based on Cuthbert et al 2003: dnorm(0.28,10) T(0.01,0.99)
skip.prob[t] ~ dunif(0.01,0.99) ## PRIOR FOR ADULT BREEDER SKIPPING PROBABILITY from Cuthbert paper that reported breeding propensity of 0.66: dnorm(0.34,10) T(0.01,0.99)
} #t
# -------------------------------------------------
# 1.2. Priors and constraints FOR POPULATION COUNTS
# -------------------------------------------------
for (s in 1:n.sites){ ### start loop over every study area
## prop.site[s] ~ dnorm(prop[s],100) T(0,1) ### the proportion of the total population that is in 1 of the 11 census areas
for (t in 1:T){ ### start loop over every year
sigma.obs[t,s] ~ dunif(1,100) #Prior for SD of observation process (variation in detectability)
tau.obs[t,s]<-pow(sigma.obs[t,s],-2)
} ## close year loop
} ## close site loop
# -------------------------------------------------
# 1.3. Priors and constraints FOR SURVIVAL
# -------------------------------------------------
### RECAPTURE PROBABILITY
mean.p ~ dunif(0.01, 0.99) # Prior for mean recapture
logit.p <- log(mean.p / (1-mean.p)) # Logit transformation
for (t in 1:T){
logit(p[t]) <- logit.p + capt.raneff[t]
capt.raneff[t] ~ dnorm(0, tau.capt)
}
### SURVIVAL PROBABILITY
for (i in 1:nind){
for (t in f[i]:(T-1)){
logit(phi[i,t]) <- mu[AGEMAT[i,t]] + surv.raneff[AGEMAT[i,t],t] ##+ bycatch*longline[t]
} #t
} #i
## AGE-SPECIFIC SURVIVAL
for (age in 1:2){
beta[age] ~ dunif(0.5, 0.99) # Priors for age-specific survival
mu[age] <- log(beta[age] / (1-beta[age])) # Logit transformation
## RANDOM TIME EFFECT ON SURVIVAL VARIES BY AGE GROUP
for (t in 1:(T-1)){
surv.raneff[age,t] ~ dnorm(0, tau.surv[age])
}
### PRIORS FOR RANDOM EFFECTS
sigma.surv[age] ~ dunif(0, 3) # Prior for standard deviation of survival
tau.surv[age] <- pow(sigma.surv[age], -2)
}
sigma.capt ~ dunif(1, 10) # Prior for standard deviation of capture
tau.capt <- pow(sigma.capt, -2)
### PRIOR FOR BYCATCH EFFECTS
#bycatch ~ dnorm(0,tau.byc)
#sigma.byc ~ dunif(1, 10) # Prior for standard deviation of capture
#tau.byc <- pow(sigma.byc, -2)
#-------------------------------------------------
# 2. LIKELIHOODS AND ECOLOGICAL STATE MODEL
#-------------------------------------------------
# -------------------------------------------------
# 2.1. System process: female based matrix model
# -------------------------------------------------
for (tt in 2:T){
## THE PRE-BREEDING YEARS ##
nestlings[tt] <- round(ann.fec[tt] * 0.5 * Ntot.breed[tt]) ### number of locally produced FEMALE chicks
JUV[tt] ~ dpois(nestlings[tt]) ### need a discrete number otherwise dbin will fail, dpois must be >0
N1[tt] ~ dbin(ann.surv[1,tt-1], max(5,round(JUV[tt-1]))) ### number of 1-year old survivors
N2[tt] ~ dbin(ann.surv[1,tt-1], max(5,round(N1[tt-1]))) ### number of 2-year old survivors
N3[tt] ~ dbin(ann.surv[1,tt-1], max(5,round(N2[tt-1]))) ### number of 3-year old survivors
N4[tt] ~ dbin(ann.surv[1,tt-1], max(5,round(N3[tt-1]))) ### number of 4-year old survivors
N5[tt] ~ dbin(ann.surv[1,tt-1], max(5,round(N4[tt-1]))) ### number of 5-year old survivors
## THE POTENTIAL RECRUITING YEARS ##
N6[tt] ~ dbin(ann.surv[2,tt-1], max(5,round(N5[tt-1]))) ### number of 6-year old survivors that are ready for recruitment - using adult survival
ann.recruits[tt] ~ dbin(imm.rec[tt],max(5,round(N6[tt]+N.notrecruited[tt]))) ### annual recruits are those age 6 or older that haven't recruited yet
non.recruits[tt]<-(N6[tt]+N.notrecruited[tt])-ann.recruits[tt] ## number of birds that do not recruit is the sum of all available minus the ones that do recruit
N.notrecruited[tt] ~ dbin(ann.surv[2,tt-1], round(max(10,non.recruits[tt-1]))) ### number of not-yet-recruited birds surviving from previous year
## THE BREEDING YEARS ##
Ntot.breed[tt] <- Nold.breed[tt] + ann.recruits[tt] ### the annual number of breeding birds is the sum of old breeders and recent recruits
Nold.breed[tt] <- round(N.pot.breed[tt]-N.non.breed[tt]) ### number of old breeders is survivors from previous year minus those that skip a year of breeding
N.pot.breed[tt] ~ dbin(ann.surv[2,tt-1], round(sum(Ntot.breed[tt-1],N.non.breed[tt-1]))) ### number of potential old breeders is the number of survivors from previous year breeders and nonbreeders that return from sabbatical
#N.non.breed[tt] ~ dbin((skip.prob[tt]), max(1,round(N.pot.breed[tt]))) ### number of old nonbreeders (birds that have bred before and skip breeding)
N.non.breed[tt] ~ dbin(skip.prob[tt],N.pot.breed[tt]) ### number of old nonbreeders (birds that have bred before and skip breeding)
} # tt
### INITIAL VALUES FOR COMPONENTS FOR YEAR 1 - based on stable stage distribution from previous model
JUV[1]<-round(Ntot.breed[1]*0.5*ann.fec[1])
N1[1]<-round(Ntot.breed[1]*0.17574058)
N2[1]<-round(Ntot.breed[1]*0.11926872)
N3[1]<-round(Ntot.breed[1]*0.10201077)
N4[1]<-round(Ntot.breed[1]*0.08725001)
N5[1]<-round(Ntot.breed[1]*0.07462511)
non.recruits[1]<-round(Ntot.breed[1]*0.3147774)
Ntot.breed[1] ~ dunif(600,750) # 675 ## sum of pairs observed in year 1 is 675
N.non.breed[1]<- round(Ntot.breed[1]*0.12632740)
# -------------------------------------------------
# 2.2. Observation process for population counts: state-space model of annual counts
# -------------------------------------------------
## State process for entire time series
for (t in 1:T){
#Nobs[t]<-max(1,round(Ntot.breed[t]))
#N.est[t,1:n.sites] ~ dmulti(prop.site, Nobs[t]) # pop in a given study area should be proportion of total breeding population
## Observation process
for (s in 1:n.sites){ ### start loop over every study area
N.est[t,s] <- round(prop[s] * Ntot.breed[t]) # pop in a given study area should be proportion of total breeding population
y.count[t,s] ~ dnorm(max(1,N.est[t,s]), tau.obs[t,s]) # Distribution for random error in observed numbers (counts)
} ## end site loop
} ## end year loop
# -------------------------------------------------
# 2.3. Likelihood for fecundity: Poisson regression from the number of surveyed broods
# -------------------------------------------------
for (t in 1:(T)){ ### T-1 or not
J[t] ~ dpois(rho.fec[t])
rho.fec[t] <- R[t]*ann.fec[t]
} # close loop over every year in which we have fecundity data
# -------------------------------------------------
# 2.4. Likelihood for adult and juvenile survival from CMR
# -------------------------------------------------
# Likelihood
for (i in 1:nind){
# Define latent state at first capture
z[i,f[i]] <- 1
for (t in (f[i]+1):T){
# State process
z[i,t] ~ dbern(mu1[i,t])
mu1[i,t] <- phi[i,t-1] * z[i,t-1]
# Observation process
y[i,t] ~ dbern(mu2[i,t])
mu2[i,t] <- p[t] * z[i,t] * (1-skip.prob[t]) ### added skip.prob[t] because skippers cannot be observed
} #t
} #i
#-------------------------------------------------
# 3. DERIVED PARAMETERS FOR OUTPUT REPORTING
#-------------------------------------------------
## DERIVED SURVIVAL PROBABILITIES PER YEAR
for (t in 1:(T-1)){
for (age in 1:2){
logit(ann.surv[age,t]) <- mu[age] + surv.raneff[age,t]
}
}
## DERIVED POPULATION SIZE PER YEAR
for (t in 1:T){
pop.size[t]<-max(10,sum(N.est[t,1:n.sites])) ## introduced max to prevent this number from being 0 which leads to invalid parent error on Ntot.breed
}
## DERIVED POPULATION GROWTH RATE PER YEAR
for (t in 1:(T-1)){
lambda[t]<-Ntot.breed[t+1]/max(1,Ntot.breed[t]) ## division by 0 creates invalid parent value
} ## end year loop
## DERIVED OVERALL POPULATION GROWTH RATE
#pop.growth.rate <- mean(lambda[1:(T-1)]) # Arithmetic mean for whole time series
## DERIVED MEAN FECUNDITY
mean.fec <- mean(ann.fec)
mean.rec <- mean(imm.rec)
mean.skip <- mean(skip.prob)
#-------------------------------------------------
# 4. PROJECTION INTO FUTURE
#-------------------------------------------------
for (tt in (T+1):FUT.YEAR){
## RANDOMLY DRAW DEMOGRAPHIC RATES FROM PREVIOUS YEARS WHILE AVOIDING THAT INDEX BECOMES 0
#FUT[tt] ~ dunif(1.5,15.5) ### CHANGE FROM 1.5 to 15.5 to only sample from last three years when survival was high
#FUT.int[tt]<-round(FUT[tt])
#fut.fec[tt] ~ dnorm(mean.fec,tau.fec) ### CHANGE FROM mean.fec to 0.69 + 0.16 from Caravaggi et al. 2018 for eradication scenario
# -------------------------------------------------
# 4.1. System process for future
# -------------------------------------------------
## THE PRE-BREEDING YEARS ##
nestlings[tt] <- round(mean.fec* 0.5 * Ntot.breed[tt]) ### number of locally produced FEMALE chicks based on average fecundity - to use just one take ann.fec[FUT.int[tt]]
N1[tt] ~ dbin(beta[1], max(1,round(nestlings[tt-1]))) ### number of 1-year old survivors
N2[tt] ~ dbin(beta[1], round(N1[tt-1])) ### number of 2-year old survivors
N3[tt] ~ dbin(beta[1], round(N2[tt-1])) ### number of 3-year old survivors
N4[tt] ~ dbin(beta[1], round(N3[tt-1])) ### number of 4-year old survivors
N5[tt] ~ dbin(beta[1], round(N4[tt-1])) ### number of 5-year old survivors
## THE POTENTIAL RECRUITING YEARS ##
N6[tt] ~ dbin(beta[2], round(N5[tt-1])) ### number of 6-year old survivors that are ready for recruitment - using adult survival
N.notrecruited[tt] ~ dbin(beta[2], round(max(10,non.recruits[tt-1]))) ### number of not-yet-recruited birds surviving from previous year
non.recruits[tt]<-(N6[tt]+N.notrecruited[tt])-ann.recruits[tt] ### number of birds that do not recruit is the sum of all available minus the ones that do recruit
ann.recruits[tt] ~ dbin(mean.rec,round(N6[tt]+N.notrecruited[tt])) ### new recruits
## THE BREEDING YEARS ##
Ntot.breed[tt] <- Nold.breed[tt] + ann.recruits[tt] ### the annual number of breeding birds is the estimate from the count SSM
Nold.breed[tt] <- N.pot.breed[tt]-N.non.breed[tt] ### number of old breeders is survivors from previous year minus those that skip a year of breeding
N.pot.breed[tt] ~ dbin(beta[2], round(sum(Ntot.breed[tt-1],N.non.breed[tt-1]))) ### number of potential old breeders is the number of survivors from previous year breeders and nonbreeders
N.non.breed[tt] ~ dbin(mean.skip, round(N.pot.breed[tt])) ### number of old nonbreeders (birds that have bred before and skip breeding)
## CALCULATE ANNUAL POP GROWTH RATE ##
fut.lambda[tt-19] <- Ntot.breed[tt]/max(1,Ntot.breed[tt-1]) ### inserted safety to prevent denominator being 0
## RANDOMLY DRAW OBSERVED POPULATION SIZE
NOBS[tt] ~ dnorm(Ntot.breed[tt],mean(tau.obs[,])) ### DRAW RANDOM OBSERVED POP SIZE
} # tt
# -------------------------------------------------
# 4.2. DERIVED POPULATION GROWTH RATE FOR FUTURE
# -------------------------------------------------
## DERIVED FUTURE POPULATION GROWTH RATE
future.growth.rate <- Ntot.breed[(FUT.YEAR)]/max(1,Ntot.breed[T])
#-------------------------------------------------
# 5. FUTURE MONITORING
#-------------------------------------------------
observed.growth.rate <- NOBS[(FUT.YEAR)]/max(1,NOBS[T])
} ## END MODEL LOOP