IsoriX (version 0.9.3)

isofind: Infer spatial origins

Description

This function performs the assignment of samples of unknown origins.

Usage

isofind(
  data,
  isoscape,
  calibfit = NULL,
  mask = NA,
  neglect_covPredCalib = TRUE,
  verbose = interactive()
)

Value

This function returns a list of class ISOFIND containing itself three lists (sample, group, and sp_points) storing all rasters built during assignment and the spatial points for sources, calibration and assignments. The list

sample contains three set of raster layers: one storing the value of the test statistic ("stat"), one storing the value of the variance of the test statistic ("var") and one storing the p-value of the test ("pv"). The list

group contains one raster storing the p-values of the assignment for the group. The list

sp_points contains two spatial point objects: sources and calibs.

Arguments

data

A dataframe containing the assignment data (see note below)

isoscape

The output of the function isoscape

calibfit

The output of the function calibfit (This argument is not needed if the isoscape had been fitted using isotopic ratios from sedentary animals.)

mask

A polygon of class SpatVector representing a mask to replace values on all rasters by NA inside polygons (see details)

neglect_covPredCalib

A logical indicating whether to neglect the covariance between the uncertainty of predictions from the isoscape mean fit and the uncertainty in predictions from the calibration fit (default = TRUE). See Details.

verbose

A logical indicating whether information about the progress of the procedure should be displayed or not while the function is running. By default verbose is TRUE if users use an interactive R session and FALSE otherwise.

Details

An assignment is a comparison, for a given organism, of the predicted isotopic source value at its location of origin and the predicted isotopic source value at each location of the isoscape. The difference between these two values constitute the statistic of the assignment test. Under the null hypothesis (the organism is at a location with the same isotopic value than its original location), the test statistics follows a normal distribution with mean zero and a certain variance that stems from both the isoscape model fits and the calibration fit. The function isofind computes the map of p-value for such an assignment test (i.e. the p-values in all locations of the isoscape) for all samples in the dataframe data. The function also performs a single assignment for the entire group by combining the p-value maps of all samples using the Fisher's method (Fisher 1925). Significant p-values are strong evidence that the sample do NOT come from the candidate location (and not the opposite!). For statistical details about this procedure as well as a discussion of which uncertainties are captured and which are not, please refer to Courtiol et al. 2019.

Details on parameters:

• neglect_covPredCalib: as long as the calibration method used in calibfit is "wild", a covariance is expected between the uncertainty of predictions from the isoscape mean fit and the uncertainty in predictions from the calibration fit. This is because both the isoscape and the calibration use in part the same data. By default this term is omitted (i.e. the value for the argument neglect_covPredCalib is TRUE) since in practice it seems to affect the results only negligibly in our trials and the computation of this term can be quite computer intensive. We nonetheless recommend to set neglect_covPredCalib to FALSE in your final analysis. If the calibration method used in calibfit is not "wild", this parameter has no effect.

• mask: a mask can be used so to remove all values falling in the mask. This can be useful for performing for example assignments on lands only and discard anything falling in large bodies of water (see example). By default our OceanMask is considered. Setting mask to NULL allows to prevent this automatic behaviour.

References

Courtiol A, Rousset F, Rohwäder M, Soto DX, Lehnert L, Voigt CC, Hobson KA, Wassenaar LI & Kramer-Schadt S (2019). Isoscape computation and inference of spatial origins with mixed models using the R package IsoriX. In Hobson KA & Wassenaar LI (eds.), Tracking Animal Migration with Stable Isotopes, second edition. Academic Press, London.

Fisher, R.A. (1925). Statistical Methods for Research Workers. Oliver and Boyd (Edinburgh). ISBN 0-05-002170-2.

Examples

## The examples below will only be run if sufficient time is allowed
## You can change that by typing e.g. options_IsoriX(example_maxtime = XX)
## if you want to allow for examples taking up to ca. XX seconds to run
## (so don't write XX but put a number instead!)

if (getOption_IsoriX("example_maxtime") > 200) {
  ## We fit the models for Germany
  GNIPDataDEagg <- prepsources(data = GNIPDataDE)

  GermanFit <- isofit(
    data = GNIPDataDEagg,
    mean_model_fix = list(elev = TRUE, lat_abs = TRUE)
  )


  ## We build the isoscape
  GermanScape <- isoscape(
    raster = ElevRasterDE,
    isofit = GermanFit
  )


  ## We fit the calibration model
  CalibAlien <- calibfit(
    data = CalibDataAlien,
    isofit = GermanFit
  )

  ## We perform the assignment on land only
  AssignmentDry <- isofind(
    data = AssignDataAlien,
    isoscape = GermanScape,
    calibfit = CalibAlien
  )

  ## perform the assignment on land and water
  Assignment <- isofind(
    data = AssignDataAlien,
    isoscape = GermanScape,
    calibfit = CalibAlien,
    mask = NULL
  )

  ## We plot the group assignment
  plot(Assignment, who = "group", mask = list(mask = NULL))

  plot(AssignmentDry, who = "group", mask = list(mask = NULL))

  ## We plot the assignment for the 8 first samples
  plot(AssignmentDry,
    who = 1:8,
    sources = list(draw = FALSE),
    calibs = list(draw = FALSE)
  )

  ## We plot the assignment for the sample "Alien_10"
  plot(AssignmentDry, who = "Alien_10")


  ### Other example without calibration:
  ### We will try to assign a weather station
  ### in the water isoscape

  ## We create the assignment data taking
  ## GARMISCH-PARTENKIRCHEN as the station to assign
  GPIso <- GNIPDataDEagg[GNIPDataDEagg$source_ID == "GARMISCH-PARTENKIRCHEN", "mean_source_value"]
  AssignDataGP <- data.frame(
    sample_value = GPIso,
    sample_ID = "GARMISCH-PARTENKIRCHEN"
  )

  ## We perform the assignment
  AssignedGP <- isofind(
    data = AssignDataGP,
    isoscape = GermanScape,
    calibfit = NULL
  )
  ## We plot the assignment and
  ## show where the station really is (using lattice)
  plot(AssignedGP, plot = FALSE) +
    xyplot(47.48 ~ 11.06,
      cex = 5, pch = 13, lwd = 2, col = "black"
    )
}