Get prior parameters for Bayesian population model

Returns prior parameter values for the Bayesian population model. The starting point is estimated coefficients from national demographic-disturbance relationships in the table popGrowthTableJohnsonECCC.

getPriors(
  modList = NULL,
  survivalModelNumber = "M1",
  recruitmentModelNumber = "M4",
  rAnthroSlopeSE = 0.006,
  rFireSlopeSE = 0.002,
  sAnthroSlopeSE = 5e-04,
  sIntSE = 0.06,
  sNuMin = 0.01,
  sNuMax = 0.13,
  rIntSE = 0.35,
  rNuMin = 0.01,
  rNuMax = 0.7,
  qMin = 0,
  qMax = 0.6,
  uMin = 0,
  uMax = 0.2,
  zMin = 0,
  zMax = 0.2,
  cowMult = 6,
  populationGrowthTable = caribouMetrics::popGrowthTableJohnsonECCC,
  modelVersion = "Johnson",
  returnValues = TRUE
)

Arguments

modList: a named list of modifiers to use to change the priors. If a modifier is supplied here the corresponding argument below is ignored.
survivalModelNumber, recruitmentModelNumber: character. Which model number to use see popGrowthTableJohnsonECCC for options.
rAnthroSlopeSE: Standard deviation of effect of disturbance on recruitment.
rFireSlopeSE: Standard deviation of effect of fire on recruitment.
sAnthroSlopeSE: Standard deviation of effect of disturbance on survival.
sIntSE: Standard deviation of survival intercept.
sNuMin, sNuMax: Uniform prior for coefficient of variation among years.
rIntSE: Standard deviation of recruitment intercept.
rNuMin, rNuMax: Uniform prior for coefficient of variation among years.
qMin: number in 0, 1. Minimum ratio of bulls to cows in composition survey groups.
qMax: number in 0, 1. Maximum ratio of bulls to cows in composition survey groups.
uMin: number in 0, 1. Minimum probability of misidentifying young bulls as adult females and vice versa in composition survey.
uMax: number in 0, 1. Maximum probability of misidentifying young bulls as adult females and vice versa in composition survey.
zMin: number in 0, 1. Minimum probability of missing calves in composition survey.
zMax: number in 0, 1. Maximum probability of missing calves in composition survey.
cowMult: number. The apparent number of adult females per collared animal in composition survey.
populationGrowthTable: data.frame.popGrowthTableJohnsonECCC is included in the package and should be used in most cases. A custom table of model coefficients and standard errors or confidence intervals can be provided but it must match the column names of popGrowthTableJohnsonECCC. If the table does not contain the standard error it is calculated from the confidence interval.
modelVersion: character. Which model version to use. Currently the only option is "Johnson" for the model used in Johnson et. al. (2020), but additional options may be added in the future.
returnValues: logical. Default is TRUE. If FALSE returns strings for some values showing the initial values and the modifier ie "0.9 * 1.05"

Value

a list with values:

l.R.Prior1: Recruitment intercept
l.R.Prior2: Recruitment intercept standard error times modifier,
beta.Rec.anthro.Prior1: Recruitment anthropogenic disturbance slope,
beta.Rec.anthro.Prior2: Recruitment anthropogenic disturbance standard error times modifier,
beta.Rec.fire.Prior1: Recruitment fire excluding anthropogenic disturbance slope,
beta.Rec.fire.Prior2: Recruitment fire excluding anthropogenic disturbance standard error,
sig.R.Prior1: Mean of the prior distribution of the random effect of year on recruitment,
sig.R.Prior2: Standard deviation of the prior distribution of the random effect of year on recruitment,
l.Saf.Prior1: Adult female survival intercept,
l.Saf.Prior2: Adult female survival intercept standard error times modifier,
beta.Saf.Prior1: Adult female survival anthropogenic disturbance slope,
beta.Saf.Prior2: Adult female survival anthropogenic disturbance standard error times modifier,
sig.Saf.Prior1: Mean of the prior distribution of the random effect of year on adult female survival,
sig.Saf.Prior2: Standard deviation of the prior distribution of the random effect of year on adult female survival,
bias.Prior1: Log-normal mean composition survey bias correction term,
bias.Prior2: Log-normal standard deviation of composition survey bias correction term

Details

Standard errors and random effects of year have been calibrated so that the 95% prior prediction intervals for survival and recruitment from the Bayesian model match the range between the 2.5% and 97.5% quantiles of 1000 survival and recruitment trajectories from the national demographic model caribouPopGrowth(). A log-normal prior for the unknown composition survey bias correction term c is set by specifying an apparent number of adult females per collared animal(cowMult) and minimum and maximum values for each of the ratio of bulls to cows (\(q\)), the probability of misidentifying young bulls as adult females and vice versa (\(u\)), and the probability of missing calves (\(z\)) in composition surveys. See compositionBiasCorrection() and Hughes et al. (2025) for additional details.

References

Hughes, J., Endicott, S., Calvert, A.M. and Johnson, C.A., 2025. Integration of national demographic-disturbance relationships and local data can improve caribou population viability projections and inform monitoring decisions. Ecological Informatics, 87, p.103095. https://doi.org/10.1016/j.ecoinf.2025.103095

Examples

getPriors()
#> $l.R.Prior1
#> [1] -1.023
#> 
#> $l.R.Prior2
#> [1] 0.35
#> 
#> $beta.Rec.anthro.Prior1
#> [1] -0.017
#> 
#> $beta.Rec.anthro.Prior2
#> [1] 0.006
#> 
#> $beta.Rec.fire.Prior1
#> [1] -0.0081
#> 
#> $beta.Rec.fire.Prior2
#> [1] 0.002
#> 
#> $sig.R.Prior1
#> [1] 0.01
#> 
#> $sig.R.Prior2
#> [1] 0.7
#> 
#> $l.Saf.Prior1
#> [1] -0.142
#> 
#> $l.Saf.Prior2
#> [1] 0.06
#> 
#> $beta.Saf.Prior1
#> [1] -8e-04
#> 
#> $beta.Saf.Prior2
#> [1] 5e-04
#> 
#> $sig.Saf.Prior1
#> [1] 0.01
#> 
#> $sig.Saf.Prior2
#> [1] 0.13
#> 
#> $bias.Prior1
#> [1] 0.04594534
#> 
#> $bias.Prior2
#> [1] 0.07631583
#>