demographicRates: Sample expected survival and recruitment rates
In LandSciTech/caribouMetrics: Models and Metrics of Boreal Caribou Demography and Habitat Selection

demographicRates

R Documentation

Sample expected survival and recruitment rates

Description

Apply the sampled coefficients to the disturbance covariates to calculate expected recruitment and survival according to the beta regression models estimated by Johnson et al. (2020).demographicRates is a wrapper around sampleRates to sample both survival and recruitment rates based on the result of demographicCoefficients() and using recommended defaults.

Usage

demographicRates(
  covTable,
  popGrowthPars,
  ignorePrecision = FALSE,
  returnSample = FALSE,
  useQuantiles = TRUE,
  predInterval = list(PI_R = c(0.025, 0.975), PI_S = c(0.025, 0.975)),
  transformFns = list(S_transform = function(y) {
     (y * 46 - 0.5)/45
 }, R_transform
    = function(y) {
     y
 })
)

sampleRates(
  covTable,
  coefSamples,
  coefValues,
  modelVersion,
  resVar,
  ignorePrecision,
  returnSample,
  quantilesToUse = NULL,
  predInterval = c(0.025, 0.975),
  transformFn = function(y) {
     y
 }
)

Arguments

`covTable`	data.frame. A table of covariate values to be used. Column names must match the coefficient names in popGrowthTableJohnsonECCC. Each row is a different scenario.
`popGrowthPars`	list. Coefficient values and (optionally) quantiles returned by `demographicCoefficients`.
`ignorePrecision`	logical. Should the precision of the model be used if it is available? When precision is used variation among populations around the National mean responses is considered in addition to the uncertainty about the coefficient estimates.
`returnSample`	logical. If TRUE the returned data.frame has replicates * scenarios rows. If FALSE the returned data.frame has one row per scenario and additional columns summarizing the variation among replicates. See Value for details.
`useQuantiles`	logical or numeric. If it is a numeric vector it must be length 2 and give the low and high limits of the quantiles to use. Only relevant when `ignorePrecision = FALSE`. If `useQuantiles != FALSE`, each replicate population is assigned to a quantile of the distribution of variation around the expected values, and remains in that quantile as covariates change. If `useQuantiles != FALSE` and popGrowthPars contains quantiles, those quantiles will be used. If `useQuantiles = TRUE` and popGrowthPars does not contain quantiles, replicate populations will be assigned to quantiles in the default range of 0.025 and 0.975. If `useQuantiles = FALSE`, sampling is done independently for each combination of scenario and replicate, so the value for a particular replicate population in one scenario is unrelated to the values for that replicate in other scenarios. Useful for projecting impacts of changing disturbance on the trajectories of replicate populations.
`predInterval`	numeric vector with length 2. The default 95% interval is (`c(0.025,0.975)`). Only relevant when `returnSample = TRUE` and `quantilesToUse = NULL`.
`transformFns`	list of functions used to transform demographic rates. The default is `list(S_transform = function(y){(y*46-0.5)/45},R_transform = function(y){y})`. The back transformation is applied to survival rates as in Johnson et al. 2020.
`coefSamples`	matrix. Bootstrapped coefficients with one row per replicate and one column per coefficient
`coefValues`	data.table. One row table with expected values for each coefficient
`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.
`resVar`	character. Response variable, typically "femaleSurvival" or "recruitment"
`quantilesToUse`	numeric vector of length `coefSamples`. See `useQuantiles`.
`transformFn`	function used to transform demographic rates.

Details

Each population is optionally assigned to quantiles of the Beta error distributions for survival and recruitment. Using quantiles means that the population will stay in these quantiles as disturbance changes over time, so there is persistent variation in recruitment and survival among example populations.

A transformation function is also applied to survival to avoid survival probabilities of 1.

A detailed description of the model is available in Hughes et al. (2025)

Value

A data.frame of predictions. The data.frame includes all columns in covTable with additional columns depending on returnSample.

If returnSample = FALSE the number of rows is the same as the number of rows in covTable, additional columns are:

"S_bar" and "R_bar": The mean estimated values of survival and recruitment (calves per cow)
"S_stdErr" and "R_stdErr": Standard error of the estimated values
"S_PIlow"/"S_PIhigh" and "R_PIlow"/"R_PIhigh": If not using quantiles, 95\ minimum values are returned.

If returnSample = TRUE the number of rows is nrow(covTable) * replicates additional columns are:

"scnID": A unique identifier for scenarios provided in covTable
"replicate": A replicate identifier, unique within each scenario
"S_bar" and "R_bar": The expected values of survival and recruitment (calves per cow)

For sampleRates a similar data frame for one response variable

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

Johnson, C.A., Sutherland, G.D., Neave, E., Leblond, M., Kirby, P., Superbie, C. and McLoughlin, P.D., 2020. Science to inform policy: linking population dynamics to habitat for a threatened species in Canada. Journal of Applied Ecology, 57(7), pp.1314-1327. https://doi.org/10.1111/1365-2664.13637

Examples

# get coefficient samples
coefs <- demographicCoefficients(10)

# table of different scenarios to test
covTableSim <- expand.grid(Anthro = seq(0, 90, by = 20),
                           fire_excl_anthro = seq(0, 70, by = 20))
covTableSim$Total_dist = covTableSim$Anthro + covTableSim$fire_excl_anthro

demographicRates(covTableSim, coefs)

cfs <- getCoefs(popGrowthTableJohnsonECCC, "recruitment", "Johnson", "M3")

cfSamps <- sampleCoefs(cfs[[1]], 10)

# disturbance scenarios
distScen <- data.frame(Total_dist = 1:10/10)

# return summary across replicates
sampleRates(distScen, cfSamps$coefSamples, cfSamps$coefValues,
            "Johnson", "recruitment", ignorePrecision = TRUE, 
            returnSample = FALSE)

# return one row per replicate * scenario
sampleRates(distScen, cfSamps$coefSamples, cfSamps$coefValues,
            "Johnson", "recruitment", ignorePrecision = TRUE, 
            returnSample = TRUE)

# return one row per replicate * scenario with replicates assigned to a quantile
sampleRates(distScen, cfSamps$coefSamples, cfSamps$coefValues,
            "Johnson", "recruitment", ignorePrecision = TRUE, 
            returnSample = TRUE, 
            quantilesToUse = quantile(x = c(0, 1),
                                      probs = seq(0.025, 0.975, length.out = 10)))

LandSciTech/caribouMetrics documentation built on June 13, 2025, 8:09 p.m.