Description

IsoriX can be used for building isoscapes using mixed models and inferring the geographic origin of organisms based on their isotopic signature. This package is essentially a simplified interface combining several other packages which implements the statistical framework proposed by Courtiol & Rousset 2017. It uses the package spaMM for fitting and predicting isoscapes, and for performing the assignment. IsoriX also heavily relies on the package rasterVis for plotting the maps produced with raster using the powerful lattice visualization system.

Details

Below, we describe briefly the main steps of the workflow that aims at performing the construction of an isoscape and the assignment of organisms of unknown geographic origin(s) based on their isotopic signature. We advise you to also read the detailed book chapter we wrote (in press), as well as our online documentation, which essentially cover the same material in a more detailed manner. You should also read the dedicated help pages of the functions you are using.

The statistical methods will not be detailed in this document but information on the computation of isoscapes is available in Courtiol & Rousset 2017, and information on the calibration and assignment in the appendix of Courtiol et al. 2019.

1. Fitting the isoscape model with isofit:

   The function isofit fits a geostatistical model, which approximates the relationship between the topographic features of a location and its isotopic signature (see isofit for details). The model fits observations of isotopic delta values at several geographic locations (hereafter, called sources). One common type of sources used in ecology is the delta values for hydrogen in precipitation water collected at weather stations, but one may also use measurements performed on sedentary organisms. In either case, the accuracy of the isoscape (and thereby the accuracy of assignments) increases with the number and spatial coverage of the sources. The function isofit is designed to fit the model on data aggregated per location across all measurements. If instead you want to fit the model on measurements split per time intervals (e.g. per month), within each location, you should use the alternative function isomultifit. Either way the data must be prepared using the function prepsources.

2. Preparing the structural raster with prepraster:

   Building isoscapes and assigning organisms to their origin requires an adequate structural raster, i.e. a matrix representing a spatial grid. The function prepraster allows restricting the extent of the raster to the area covered by isoscape data (in order to avoid extrapolation) and to reduce the resolution of the original structural raster (in order to speed up computation in all following steps). Note that aggregating the raster may lead to different results for the assignment, if the structural raster is used to define a covariate. This is because the values of raster cells changes depending on the aggregation function, which in turn will affect model predictions.

   We provide the function getelev to download an elevation raster for the entire world at a resolution of one altitude per square-km, and other rasters may be used. Such an elevation raster can be used as a structural raster. We have also stored a low resolution raster for Germany in our package (see ElevRasterDE) for users to try things out, but we do not encourage its use for real application.

3. Predicting the isoscape across the area covered by the elevation raster with isoscape:

   The function isoscape generates the isoscapes: it uses the fitted geostatistical models to predict the isotopic values (and several variances associated to those) for each raster cell defined by the structural raster. If the model has been fitted with isomultifit, you should use the alternative function isomultiscape to generate the isoscape.

   Our package allows the production of fine-tuned isoscape figures (using the function plot.ISOSCAPE). Alternatively, the isoscape rasters can be exported as ascii raster and edited in any Geographic Information System (GIS) software (see isoscape and the online documentation for details).

4. Fitting the calibration model with calibfit:

   In most cases, organisms are of another kind than the sources used to build the isoscape (i.e. the isoscape is built on precipitation isotopic values and organisms are not water drops, but e.g. the fur of some bats). In such a case, the hydrogen delta values of the sampled organisms were modulated by their distinct physiology and do not directly correspond to the isotopic signature of the sources. In this situation, one must use sedentary organisms to study the relationship between the isotopic values in organisms and that of their environment. The function calibfit fits a statistical model on such a calibration dataset.

   If the isoscape is directly built from isotopic values of organisms, there is no need to fit a calibration model.

5. Inferring spatial origins of samples with isofind:

   The function isofind tests for each location across the isoscape if it presents a similar isotopic signature than the unknown origin of a given individual(s). This assignment procedure considered the some (but not all, see Courtiol et al. 2019) uncertainty stemming from the model fits (geostatistical models and calibration model). The function plot.ISOFIND then draws such assignment by plotting the most likely origin with the prediction region around it. When several organisms are being assigned, both assignments at the level of each sample and a single assignment for the whole group can be performed.

Note

Please note that the geographic coordinates (latitude, longitude) of any spatial data (locations, rasters) must be given in decimal degrees following the WGS84 spheroid standard.

Author(s)

Alexandre Courtiol alexandre.courtiol@gmail.com,

François Rousset,

Marie-Sophie Rohwaeder,

Stephanie Kramer-Schadt kramer@izw-berlin.de

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.

Courtiol A, Rousset F (2017). "Modelling isoscapes using mixed models." bioRxiv. doi: 10.1101/207662, link.