caribouBayesianPM: Bayesian population model for boreal caribou

In LandSciTech/caribouMetrics: Models and Metrics of Boreal Caribou Demography and Habitat Selection

Bayesian population model for boreal caribou

Description

A Bayesian population model that integrates prior information from Johnson et al.'s (2020) national analysis of demographic-disturbance relationships with local demographic data to project population growth.

Usage

caribouBayesianPM(
  survData = system.file("extdata/simSurvData.csv", package = "caribouMetrics"),
  ageRatio = system.file("extdata/simAgeRatio.csv", package = "caribouMetrics"),
  disturbance = system.file("extdata/simDisturbance.csv", package = "caribouMetrics"),
  betaPriors = "default",
  startYear = NULL,
  endYear = NULL,
  Nchains = 4,
  Niter = 15000,
  Nburn = 10000,
  Nthin = 2,
  N0 = 1000,
  survAnalysisMethod = "Binomial",
  assessmentYrs = 1,
  inputList = list(),
  saveJAGStxt = tempdir(),
  quiet = TRUE
)

Arguments

survData	either a path to a csv file or a dataframe containing the columns "Year", "event", "enter" and "exit". Enter and exit are the beginning and end of the time interval and should be a number from 1 to 12 (December) or 0 (See Details). Event is 0 or 1 where 0 means the animal lived and 1 died. See survival::Surv() for more details.
ageRatio	either a path to a csv file or a dataframe containing the columns "Year","Count", and "Class". Where class can be either "calf" or "cow"
disturbance	either a path to a csv file or a dataframe containing the columns "Anthro","fire_excl_anthro", and "Year".
betaPriors	a list of model priors. See getPriors().
startYear, endYear	year defining the beginning of the observation period and the end of the projection period.
Nchains	Number of chains for the MCMC algorithm.
Niter	Number of iterations for the MCMC algorithm.
Nburn	Length of burn-in for the MCMC algorithm.
Nthin	Thinning rate for the MCMC algorithm.
N0	Initial population size.
survAnalysisMethod	Survival analysis method either "KaplanMeier" or "Binomial". The KaplanMeier method yields slightly biased survival estimates.
assessmentYrs	Number of years over which to assess population growth rate lambda.
inputList	an optional list of inputs with names matching the above. If an argument is included in this list it will override the named argument.
saveJAGStxt	file path. Directory where the JAGS model txt files will be saved. Default is tempdir().
quiet	logical. Should jags run quietly?

Details

The model combines local observations of survival (survData), recruitment based on calf:cow ratios (ageRatio) and anthropogenic disturbance (disturbance) with prior information on the relationship between disturbance and survival and recruitment from the Johnson et al. (2020) national model (getPriors()) to reduce uncertainty and refine parameter estimates.

For a detailed description see the vignette (vignette("BayesianDemographicProjection", package = "caribouMetrics")).

Note: if survData contains values for enter that are > 0 these rows will be dropped to avoid errors when collars are added in the middle of the year. This will reduce the sample size in years when new collars are added.

Value

a list with elements:

• result: an rjags model object see R2jags::jags().

• inData: a list of data that is used as input to the jags model:

  • survDataIn: survival data

  • disturbanceIn: disturbance data

  • ageRatioIn: composition data

See Also

Caribou demography functions: caribouPopGrowth(), compositionBiasCorrection(), demographicCoefficients(), demographicProjectionApp(), demographicRates(), getOutputTables(), getPriors(), getScenarioDefaults(), getSimsNational(), plotRes(), popGrowthTableJohnsonECCC, runScnSet(), simulateObservations()

Examples

# Using observed survival, recruitment and disturbance data
mod <- caribouBayesianPM(
  survData = system.file("extdata/simSurvData.csv",
                         package = "caribouMetrics"),
  ageRatio = system.file("extdata/simAgeRatio.csv",
                         package = "caribouMetrics"),
  disturbance = system.file("extdata/simDisturbance.csv",
                            package = "caribouMetrics"),
  Nchains = 1, Niter = 100, Nburn = 10, Nthin = 2
)
str(mod, max.level = 2)

# Using simulated observation data
scns <- getScenarioDefaults(projYears = 10, obsYears = 10,
                            obsAnthroSlope = 1, projAnthroSlope = 5,
                            collarCount = 20, cowMult = 5)

simO <- simulateObservations(scns)

out <- caribouBayesianPM(survData = simO$simSurvObs, ageRatio = simO$ageRatioOut,
                          disturbance = simO$simDisturbance,
                          startYear = 2014, Nchains = 1, Niter = 100, Nburn = 10,
                          Nthin = 2)

LandSciTech/caribouMetrics documentation built on Feb. 25, 2025, 9:34 a.m.