Predicts the spatial distribution of source isotopic values
This function produces the set of isoscapes, i.e. the spatial prediction (i.e. maps) of the distribution of source isotopic values, as well as several variances around such predictions. The predictions are computed using the fitted geostatistical models for each raster cell of a structural raster. All shape files can be exported and loaded into any Geographic Information System (GIS) if needed (see online tutorials).
isoscape(raster, isofit, verbose = interactive())
raster |
The structural raster (RasterLayer) such as an elevation
raster created using |
isofit |
The fitted isoscape created by |
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. |
This function computes the predictions (mean), prediction variances
(mean_predVar), residual variances (mean_residVar) and response
variances (mean_respVar) for the isotopic values at a resolution equal
to the one of the structural raster. It also computes the same information
for the residual dispersion variance (disp_pred, disp_predVar,
disp_residVar, or disp_respVar).
Let us summarize the meaning of mean, mean_predVar,
mean_residVar and mean_respVar (see Courtiol & Rousset 2017 and
Curtiol et al. 2019 for more details):
Our model assumes that that there is a single true unknown isoscape, which is
fixed but which is represented by the mixed-effect model as a random draw
from possible realizations of isoscapes (random draws of the
Matérn-correlated process and of the uncorrelated random effects if
considered). We infer this realized isoscape by fitting the model to a
limited amount of data, with some uncertainty since different random draws of
the unknown isoscape may give the same observed data. There is thus a
conditional distribution of possible true isoscapes given the data. For
linear mixed-effects models, the mean prediction is the mean of this
conditional distribution. The prediction variance is ideally the mean square
difference between the true unknown value of the linear predictor and the
mean prediction at a given location. The residual variance is simply the
prediction of the variance in isotopic value at a given location. Its exact
meaning depends on the aggregation scheme used in prepsources,
but by default, it would correspond to the temporal variation between months
and across years. The response variance estimates the variance of new
observations drawn from the true unknown isoscape at a given location. The
response variance is simply equal to the sum of the prediction variance and
the residual variance (note that the residual variance considered assume that
a single observation is being observed per location).
The isoscape can be plotted using the function plot.ISOSCAPE
(see examples).
This function returns a list of class ISOSCAPE containing a set of all 8 raster layers mentioned above (all being of class RasterLayer), and the location of the sources as spatial points.
Courtiol, A., Rousset, F. (2017). Modelling isoscapes using mixed models. https://www.biorxiv.org/content/10.1101/207662v1
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.
isofit for the function fitting the isoscape
plot.ISOSCAPE for the function plotting the isoscape model
## 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") > 30) {
## We prepare the data
GNIPDataDEagg <- prepsources(data = GNIPDataDE)
## We fit the models
GermanFit <- isofit(data = GNIPDataDEagg,
mean_model_fix = list(elev = TRUE, lat_abs = TRUE))
## We build the isoscapes
GermanScape <- isoscape(raster = ElevRasterDE, isofit = GermanFit)
GermanScape
plot(GermanScape)
## We build more plots
PlotMean <- plot(x = GermanScape, which = "mean", plot = FALSE)
PlotMeanPredVar <- plot(x = GermanScape, which = "mean_predVar", plot = FALSE)
PlotMeanResidVar <- plot(x = GermanScape, which = "mean_residVar", plot = FALSE)
PlotMeanRespVar <- plot(x = GermanScape, which = "mean_respVar", plot = FALSE)
## We display the plots
print(PlotMean, split = c(1, 1, 2, 2), more = TRUE)
print(PlotMeanPredVar, split = c(2, 1, 2, 2), more = TRUE)
print(PlotMeanResidVar, split = c(1, 2, 2, 2), more = TRUE)
print(PlotMeanRespVar, split = c(2, 2, 2, 2), more = FALSE)
## We build a sphere with our isoscape
plot(x = GermanScape, which = "mean", plot = FALSE, sphere = list(build = TRUE))
## We can save a rotating sphere with the isoscape as a .gif-file.
## This file will be located inside your working directory.
## Make sure your current rgl device (from the previous step) is still open
## and that you have both the packages 'rgl' and 'magick' installed.
## The building of the .gif implies to create temporarily many .png
## but those will be removed automatically once the .gif is done.
## Uncomment to proceed (after making sure you have rgl and magick installed)
#if(require("rgl") & require("magick")) {
# movie3d(spin3d(axis = c(0, 0, 1), rpm = 2), duration = 30, dir = getwd())
#}
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.