Infer spatial origins
This function performs the assignment of samples of unknown origins.
isofind(data, isoscape, calibfit = NULL, mask = NA, verbose = interactive())
data |
A dataframe containing the assignment data (see note below) |
isoscape |
The output of the function |
calibfit |
The output of the function |
mask |
A SpatialPolygons of a mask to replace values on all rasters by NA inside polygons (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. |
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.
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.
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.
See AssignDataAlien to know which variables are needed to
perform the assignment and their names.
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.
## 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) +
xyplot(47.48~11.06,
panel = panel.points,
cex = 5, pch = 13, lwd = 2, col = "black")
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