caribouMetrics

The caribouMetrics R package provides reproducible open source implementations of several models of Boreal woodland caribou (Rangifer tarandus caribou) demography and habitat use. A national two-stage demographic model with density dependence and interannual variability follows Johnson et. al. (2020) with modifications described in Dyson et al. (2022). Demographic rates vary with disturbance as estimated by Johnson et. al. (2020). The package also includes a Bayesian population model designed to integrate prior information from Johnson et al’s national analysis of demographic-disturbance relationships with available local demographic data to reduce uncertainty in population viability projections. The Bayesian population model builds on work by Eacker et al. (2019). The national model can be used to simulate example population trajectories, and combined with a simple observation model and the Bayesian population model to show how monitoring requirements depend on landscape condition. Finally, caribouMetrics contains an implementation of Hornseth and Rempel’s (2016) Ontario boreal caribou resource selection model described in Dyson et al. (2022). Model implementation is intended to be modular and flexible, allowing reuse of components in a variety of contexts including projections of the cumulative effects of disturbance and climate change (e.g. Stewart et al. 2023) and a Shiny app designed to allow allow exploration of user-specified monitoring and disturbance scenarios.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("LandSciTech/caribouMetrics")

Example

This is a basic example demonstrating all the primary functions of the package.

library(caribouMetrics)

pthBase <- system.file("extdata", package = "caribouMetrics")

# load example data
landCoverD <- terra::rast(file.path(pthBase, "landCover.tif")) 
  # convert PLC classes to resource types used in the model 
landCoverD <- reclassPLC(landCoverD)
eskerDras <- terra::rast(file.path(pthBase, "eskerTif.tif"))
eskerDshp <- sf::read_sf(file.path(pthBase, "esker.shp"))
natDistD <- terra::rast(file.path(pthBase, "natDist.tif"))
anthroDistD <-terra::rast(file.path(pthBase, "anthroDist.tif"))
linFeatDras <- terra::rast(file.path(pthBase, "linFeatTif.tif"))
projectPolyD <- sf::read_sf(file.path(pthBase, "projectPoly.shp"))

# calculate disturbance 
disturb <- disturbanceMetrics(landCover = landCoverD,
                              linFeat = linFeatDras,  
                              natDist = natDistD,
                              projectPoly = projectPolyD)
#> cropping landCover to extent of projectPoly
#> cropping linFeat to extent of projectPoly
#> cropping natDist to extent of projectPoly
#> buffering anthropogenic disturbance
#> calculating disturbance metrics

disturb_tbl <- results(disturb)

# Calculate demographic rates
demCoefs <- demographicCoefficients(replicates = 10)

demRates <- demographicRates(covTable = disturb_tbl,
                             popGrowthPars = demCoefs)
#> popGrowthPars contains quantiles so they are used instead of the defaults
#> popGrowthPars contains quantiles so they are used instead of the defaults
demRates
#>   zone   Anthro     Fire Total_dist fire_excl_anthro FID     S_bar   S_stdErr
#> 1    1 39.97933 1.732936   40.56555        0.5862182   0 0.8478733 0.05848277
#>     S_PIlow  S_PIhigh     R_bar   R_stdErr    R_PIlow  R_PIhigh
#> 1 0.7382298 0.9355214 0.1813372 0.08566332 0.05688173 0.3502751

# Simulate population growth
popGrow <- caribouPopGrowth(N = 2000, numSteps = 20, R_bar = demRates$R_bar, 
                            S_bar = demRates$S_bar)

popGrow
#>     N0    lambda   N       R_t        X_t       S_t n_recruits
#> 1 2000 0.9273454 440 0.1822609 0.09113044 0.8359903         27
#>   surviving_adFemales
#> 1                 413

# simulate caribou collar observations
params <- getScenarioDefaults(
  iAnthro = disturb_tbl$Anthro, iFire = disturb_tbl$Fire,
  collarCount = 30, cowMult = 3, 
  obsAnthroSlope = 0, projAnthroSlope = 1, projYears = 10, obsYears = 10
)
simObs <- simulateObservations(params, printPlot = TRUE)


ipm <- caribouBayesianPM(simObs$simSurvObs, simObs$ageRatioOut, 
                          simObs$simDisturbance,
                          # only set to speed up vignette. Normally keep defaults.
                          Niter = 150, Nburn = 100)
#> using Kaplan-Meier survival model

natSim <- getSimsNational(Anthro = unique(simObs$simDisturbance$Anthro))
#> Warning: Setting expected survival S_bar to be between l_S and h_S.

ipmTbls <- getOutputTables(ipm, paramTable = simObs$paramTable, 
                            exData = simObs$exData, 
                            simNational = natSim)

plotRes(ipmTbls, c("Recruitment", "Adult female survival"))
#> $Recruitment

#> 
#> $`Adult female survival`


# Calculate habitat use in Ontario's Churchill range
carHab1 <- caribouHabitat(
  landCover = landCoverD,
  esker = eskerDras, 
  natDist = natDistD, 
  anthroDist = anthroDistD, 
  linFeat = linFeatDras, 
  projectPoly = projectPolyD,
  caribouRange = "Churchill"
)
#> cropping landCover to extent of projectPoly
#> cropping linFeat to extent of projectPoly
#> cropping natDist to extent of projectPoly
#> cropping anthroDist to extent of projectPoly
#> cropping esker to extent of projectPoly
#> resampling linFeat to match landCover resolution
#> resampling esker to match landCover resolution
#> Applying moving window.

# plot the results
plot(carHab1)
#> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
#> which was just loaded, will retire in October 2023.
#> Please refer to R-spatial evolution reports for details, especially
#> https://r-spatial.org/r/2023/05/15/evolution4.html.
#> It may be desirable to make the sf package available;
#> package maintainers should consider adding sf to Suggests:.
#> The sp package is now running under evolution status 2
#>      (status 2 uses the sf package in place of rgdal)

Resources

The package website contains information on the caribouMetrics package. Here you can find documentation for each function and the following articles/vignettes/tutorials:

Demographic Model: Predict demographic rates and population growth based on their relationship to habitat disturbance.
Bayesian Demographic Projection: Project demographic rates and population growth based on the national demographic model and local caribou observations.
Disturbance Metrics: Calculate buffered anthropogenic disturbance and fire disturbance percentages for a given area.
Ontario Habitat Model: Calculate caribou habitat use with Ontario RSF models.
User Interface Help: Instructions for using the SyncroSim user interface for integrating SpaDES, LandR and FireSense projections with caribou habitat and demographic models (WIP)

Getting help

If you have any questions about the caribouMetrics package or suggestions for improving it, please post an issue on the code repository.

References

Dyson, M., Endicott, S., Simpkins C., Turner, J.W., Avery-Gomm S., Johnson, C.A., Leblond, M., Neilson, E.W., Rempel, R., Wiebe, P.A., Baltzer, J.L., Stewart, F.E.C., Hughes, J. 2022. Existing caribou habitat and demographic models are poorly suited for Ring of Fire impact assessment: A roadmap for improving the usefulness, transparency, and availability of models for conservation. bioRxiv 2022.06.01.494350; https://doi.org/10.1101/2022.06.01.494350

Eacker, D.R., Hebblewhite, M., Steenweg, R., Russell, M., Flasko, A. and Hervieux, D., 2019. Web‐based application for threatened woodland caribou population modeling. Wildlife Society Bulletin, 43(1), pp.167-177. https://doi.org/10.1002/wsb.950

ECCC. 2011. Scientific assessment to inform the identification of critical habitat for woodland caribou (Rangifer tarandus caribou), boreal population, in Canada. Canadian Wildlife Service, Ottawa. http://epe.lac-bac.gc.ca/100/200/301/environment_can/2011/scientific_assessment_inform-ef/CW66-296-2011-eng.pdf. Accessed 26 Mar 2021.

Hornseth, M.L. and Rempel, R.S., 2016. Seasonal resource selection of woodland caribou (Rangifer tarandus caribou) across a gradient of anthropogenic disturbance. Canadian Journal of Zoology, 94(2), pp.79-93. https://doi.org/10.1139/cjz-2015-0101

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://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2664.13637

Stewart F.E.C., Micheletti T., Cumming S.G., Barros, C., Chubaty, A. M., Dookie, A. L., Duclos, I., Eddy, I., Haché, S., Hodson, J., Hughes, J., Johnson, C. A., Leblond, M., Schmiegelow, F.K.A., Tremblay, J. A.. McIntire, E.J.B. (2023) Climate-informed forecasts reveal dramatic local habitat shifts and population uncertainty for northern boreal caribou. Ecological Applications 33:e2816. https://doi.org/10.1002/eap.2816

License

caribouMetrics - Terms and Condition of Use

Unless otherwise noted, source code of the caribouMetrics R package is covered under Crown Copyright, Government of Canada, and distributed under the GPL3 license.

Copyright (C) Her Majesty the Queen in Right of Canada as represented by the Minister of the Environment 2021/(C) Sa Majesté la Reine du chef du Canada représentée par le ministre de l’Environnement 2021.

LandSciTech / caribouMetrics

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