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Default scenario parameters.

Source: R/getScenarioDefaults.R
getScenarioDefaults.Rd

getScenarioDefaults(): Returns default parameters for scenarios. Use this function to get a combination of disturbanceDefaults(), timeDefaults(), demographyDefaults(), nationalTrajectoryDefaults(), and monitoringDefaults(). If only one of these sets of parameters is needed consider using the relevant component function instead.

timeDefaults(): Returns default parameter values for scenario durations. See simulateObservations() for additional details.

disturbanceDefaults(): Returns default parameter values for disturbance scenarios. See simulateObservations() for additional details.

demographyDefaults(): Returns default parameter values for simulating any type of demographic trajectories. See trajectoriesFromNational(), trajectoriesFromBayesian(), trajectoriesFromSummary() or simulateObservations() for additional details.

nationalTrajectoryDefaults(): Returns default parameter values for national demographic trajectories. See trajectoriesFromNational() and simulateObservations() for additional details.

monitoringDefaults: Returns default parameter values for monitoring. See simulateObservations() and bayesianScenariosWorkflow() for additional details.

Usage

getScenarioDefaults(
  paramTable = NULL,
  includeDist = T,
  includeTime = T,
  includeDemography = T,
  includeNational = T,
  includeMonitoring = T,
  ...
)

timeDefaults(
  paramTable = NULL,
  projYears = 35,
  obsYears = 15,
  preYears = 0,
  curYear = 2023,
  startYear = NA,
  ...
)

disturbanceDefaults(
  paramTable = NULL,
  iFire = 0,
  iAnthro = 0,
  obsAnthroSlope = 2,
  projAnthroSlope = 2,
  ...
)

demographyDefaults(
  paramTable = NULL,
  N0 = 1000,
  qMin = 0,
  qMax = 0,
  uMin = 0,
  uMax = 0,
  zMin = 0,
  zMax = 0,
  cowMult = 6,
  lQuantile = NA,
  correlateRates = F,
  ...
)

nationalTrajectoryDefaults(
  paramTable = NULL,
  sQuantile = NA,
  rQuantile = NA,
  rSlopeMod = 1,
  sSlopeMod = 1,
  interannualVar = list(eval(formals(caribouPopGrowth)$interannualVar)),
  ...
)

monitoringDefaults(
  paramTable = NULL,
  collarInterval = NA,
  cowCount = NA,
  collarCount = NA,
  ...
)

Arguments

paramTable

a data.frame with column names matching the arguments below. Any columns that are missing will be filled with the default values.

includeDist

logical. Include disturbanceDefaults()?

includeTime

logical. Include timeDefaults()?

includeDemography

logical. Include demographyDefaults()?

includeNational

logical. Include nationalTrajectoryDefaults()?

includeMonitoring

logical. Include monitoringDefaults()?

...

Other parameters passed on to bboutools::bb_fit_survival() and bboutools::bb_fit_recruitment().

projYears

Number of years of projections

obsYears

Number of years of observations

preYears

Number of years before monitoring begins

curYear

year. The current year. All years before are part of the observation period and years after are part of the projection period.

startYear

year. First year in observation period. Optional, if not provided it will be calculated from curYear and obsYears

iFire

number. Initial fire disturbance percentage.

iAnthro

number. Initial anthropogenic disturbance percentage

obsAnthroSlope

number. Percent change in anthropogenic disturbance per year in the observation period

projAnthroSlope

number. Percent change in anthropogenic disturbance per year in the projection period

N0

Number or vector of numbers. Initial population size for one or more sample populations. If NA then population growth rate is \(\lambda_t=S_t*(1+cR_t/s)\).

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 >= 1. The apparent number of adult females per collared animal in composition survey. Set to NA to use cowCount.

lQuantile

number in 0, 1. Lambda quantile

correlateRates

logical. Set TRUE to force correlation between recruitment and survival.

sQuantile

number in 0,1. Survival quantile.

rQuantile

number in 0,1. Recruitment quantile.

rSlopeMod

number. Disturbance-recruitment slope multiplier

sSlopeMod

number. Disturbance-survival slope multiplier

interannualVar

list or logical. List containing interannual variability parameters. These can be either coefficients of variation (R_CV, S_CV), beta precision parameters (R_phi, S_phi), or random effects parameters from a logistic glmm (R_annual, S_annual). Set to FALSE to ignore interannual variability.

collarInterval

number. Optional. Number of years between collar deployments. If missing assumed to be every year

cowCount

Optional. Only used in bayesianScenariosWorkflow() to set the number of cows per year in recruitment survey

collarCount

number >= 1. The target number of collars active each year. Set to NA to use freqStartsPerYear in simulateObservations()

Value

a data.frame of parameter values and for getScenarioDefaults(), a label column that combines all the parameter names and values into a string

Interpretation of Years

Throughout the package a year is treated as the calendar year only if it is in a table that also contains a month column. In all other contexts year is treated as the caribou year which is the annual demographic cycle of caribou, measured from one calving season to the next. By default, April is set as the start of the caribou year.

See also

Caribou demography functions: addMissingYears(), addN0Variation(), bayesianScenariosWorkflow(), bayesianTrajectoryWorkflow(), betaNationalPriors(), caribouPopGrowth(), compareTrajectories(), compositionBiasCorrection(), convertTrajectories(), dataFromSheets(), demographicProjectionApp(), estimateBayesianRates(), estimateNationalRate(), getCaribouYear(), getNationalCoefficients(), plotCompareTrajectories(), plotSurvivalSeries(), plotTrajectories(), popGrowthTableJohnsonECCC, simulateObservations(), trajectoriesFromBayesian(), trajectoriesFromNational(), trajectoriesFromSummary(), trajectoriesFromSummaryForApp()

Examples

getScenarioDefaults()
#> # A tibble: 1 × 24
#>      N0  qMin  qMax  uMin  uMax  zMin  zMax cowMult correlateRates rSlopeMod
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl> <lgl>              <dbl>
#> 1  1000     0     0     0     0     0     0       6 FALSE                  1
#> # ℹ 14 more variables: sSlopeMod <dbl>, interannualVar <list>, iFire <dbl>,
#> #   iAnthro <dbl>, obsAnthroSlope <dbl>, projAnthroSlope <dbl>, hasYear <lgl>,
#> #   projYears <dbl>, obsYears <dbl>, preYears <dbl>, curYear <dbl>,
#> #   startYear <dbl>, ID <int>, label <chr>

# paramTable list takes precedence over argument values
getScenarioDefaults(paramTable = data.frame(iFire = 10, iAnthro = 20, obsYears = 1), obsYears = 5)
#>     N0 qMin qMax uMin uMax zMin zMax cowMult correlateRates rSlopeMod sSlopeMod
#> 1 1000    0    0    0    0    0    0       6          FALSE         1         1
#>     interannualVar iFire iAnthro obsAnthroSlope projAnthroSlope hasYear
#> 1 0.46000, 0.08696    10      20              2               2   FALSE
#>   projYears obsYears preYears curYear startYear ID
#> 1        35        1        0    2023      2023  1
#>                                                                                                                                                                                                                                                    label
#> 1 ID1_startYear2023_curYear2023_preYears0_obsYears1_projYears35_hasYearFALSE_projAnthroSlope2_obsAnthroSlope2_iAnthro20_iFire10_interannualVar0.46_0.08696_sSlopeMod1_rSlopeMod1_correlateRatesFALSE_cowMult6_zMax0_zMin0_uMax0_uMin0_qMax0_qMin0_N01000

timeDefaults()
#> # A tibble: 1 × 6
#>   hasYear projYears obsYears preYears curYear startYear
#>   <lgl>       <dbl>    <dbl>    <dbl>   <dbl>     <dbl>
#> 1 FALSE          35       15        0    2023      2009

disturbanceDefaults()
#> # A tibble: 1 × 10
#>   iFire iAnthro obsAnthroSlope projAnthroSlope hasYear projYears obsYears
#>   <dbl>   <dbl>          <dbl>           <dbl> <lgl>       <dbl>    <dbl>
#> 1     0       0              2               2 FALSE          35       15
#> # ℹ 3 more variables: preYears <dbl>, curYear <dbl>, startYear <dbl>

demographyDefaults()
#> # A tibble: 1 × 10
#>      N0  qMin  qMax  uMin  uMax  zMin  zMax cowMult lQuantile correlateRates
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl> <lgl>     <lgl>         
#> 1  1000     0     0     0     0     0     0       6 NA        FALSE         

nationalTrajectoryDefaults()
#> # A tibble: 1 × 13
#>   rSlopeMod sSlopeMod interannualVar      N0  qMin  qMax  uMin  uMax  zMin  zMax
#>       <dbl>     <dbl> <list>           <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         1         1 <named list [2]>  1000     0     0     0     0     0     0
#> # ℹ 3 more variables: cowMult <dbl>, lQuantile <lgl>, correlateRates <lgl>

monitoringDefaults()
#> # A tibble: 1 × 10
#>      N0  qMin  qMax  uMin  uMax  zMin  zMax cowMult lQuantile correlateRates
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl> <lgl>     <lgl>         
#> 1  1000     0     0     0     0     0     0       6 NA        FALSE