modelCountDataJAGS {MigConnectivity}

Estimates population-level relative abundance from count data

Description

Uses a Bayesian hierarchical model to estimate relative abundance of regional populations from count-based data (e.g., Breeding Bird Survey)

Usage

modelCountDataJAGS(count_data, ni = 20000, nt = 5, nb = 5000, nc = 3)

Arguments

count_data

List containing the following elements: '

C

nYears by nRoutes matrix containing the observed number of individuals counted at each route in each year.

strat

Vector of length nRoutes indicating the population/region in which each route is located.

routesPerStrata

Vector of length 1 or nStrata containing the number of routes (i.e. counts) per population. If length(routesPerStrata) == 1, number of routes is identical for each population.

ni

Number of MCMC iterations. Default = 20000.

nt

Thinning rate. Default = 5.

nb

Number of MCMC iterations to discard as burn-in. Default = 5000.

nc

Number of chains. Default = 3.

Value

modelCountDataJAGS returns an mcmc object containing posterior samples for each monitored parameter.

References

Cohen, E. B., J. A. Hostetler, M. T. Hallworth, C. S. Rushing, T. S. Sillett, and P. P. Marra. 2018. Quantifying the strength of migratory connectivity. Methods in Ecology and Evolution 9: 513-524. doi:10.1111/2041-210X.12916

Link, W. A. and J. R. Sauer. 2002. A hierarchical analysis of population change with application to Cerulean Warblers. Ecology 83: 2832-2840. doi:10.1890/0012-9658(2002)083[2832:AHAOPC]2.0.CO;2

Examples

set.seed(150)

### Set parameters for simulation ----

# Number of populations
nStrata. <- 4
# Number of routes w/i each population (assumed to be balanced)
routePerStrat. <- 30 # reduced from 90 for example speed
# Number of years
nYears. <- 5 # reduced from 10 for example speed
# log(Expected number of birds counted at each route)
alphaStrat. <- 1.95
# standard deviation of normal distribution assumed for route/observer random
# effects
sdRoute. <- 0.6
# standard deviation of normal distribution assumed for year random effects
sdYear. <- 0.18


# Number of simulated datasets to create and model
nsims <- 50 # reduced from 100 for example speed
# Number of MCMC iterations
ni. <- 1000 # reduced from 15000 for example speed
# Number of iterations to thin from posterior (reduced from 5)
nt. <- 1
# Number of iterations to discard as burn-in
nb. <- 500 # reduced from 5000 for example speed
# Number of MCMC chains
nc. <- 1 # reduced from 3 for example speed

### Create empty matrix to store model output ---
sim_in <- vector("list", nsims)
sim_out <- vector("list", nsims)


# Simulation ---

system.time(for(s in 1:nsims){
  cat("Simulation",s,"of",nsims,"\n")

  # Simulate data
  sim_data <- simCountData(nStrata = nStrata., routesPerStrata = routePerStrat.,
                           nYears = nYears., alphaStrat = alphaStrat.,
                           sdRoute = sdRoute., sdYear = sdYear.)
  sim_in[[s]] <- sim_data


  # Estimate population-level abundance
  out_mcmc <- modelCountDataJAGS(count_data = sim_data, ni = ni., nt = nt.,
                                 nb = nb., nc = nc.)

  # Store model output
  sim_out[[s]] <- out_mcmc
  remove(out_mcmc)

})


### Check that relative abundance is, on average, equal for each population
prop.table(sapply(sim_in, function(x) return(rowsum(colSums(x$C), x$strat))), 2)

rel_names <- paste0('relN[', 1:nStrata., ']')
rel_abund1 <- data.frame(sim=1:nsims,
                         ra1.mean=NA, ra2.mean=NA, ra3.mean=NA, ra4.mean=NA,
                         ra1.low=NA, ra2.low=NA, ra3.low=NA, ra4.low=NA,
                         ra1.high=NA, ra2.high=NA, ra3.high=NA, ra4.high=NA,
                         ra1.cover=0, ra2.cover=0, ra3.cover=0, ra4.cover=0)
for (s in 1:nsims) {
  rel_abund1[s, 2:5] <- summary(sim_out[[s]])$statistics[rel_names, "Mean"]
  rel_abund1[s, 6:9] <- summary(sim_out[[s]])$quantiles[rel_names, 1]
  rel_abund1[s, 10:13] <- summary(sim_out[[s]])$quantiles[rel_names, 5]
}
rel_abund1 <- transform(rel_abund1,
                        ra1.cover = (ra1.low<=0.25 & ra1.high>=0.25),
                        ra2.cover = (ra2.low<=0.25 & ra2.high>=0.25),
                        ra3.cover = (ra3.low<=0.25 & ra3.high>=0.25),
                        ra4.cover = (ra4.low<=0.25 & ra4.high>=0.25))

summary(rel_abund1)