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Spatial proximity moderates genotype uncertainty in genetic tagging studies

Cite this dataset

Augustine, Ben; Royle, J. Andrew; Linden, Daniel W.; Fuller, Angela K. (2020). Spatial proximity moderates genotype uncertainty in genetic tagging studies [Dataset]. Dryad.


Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower quality samples cannot be used, and they do not allow for errors in individual identification. We introduce the Genotype Spatial Partial Identity Model (SPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers Pekania pennanti in which 48% of samples were originally discarded because of uncertainty in individual identity. The Genotype SPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected 2 errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63% and 42%, respectively, using 3 PCRs per genetic sample. Further, the simulations showed that the Genotype SPIM model parameters are identifiable with only one PCR per sample, and that accuracy and precision are relatively insensitive to the number of PCRs for high quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high quality samples, but when using the Genotype SPIM, genotyping protocols could be more efficient by devoting more resources to low quality samples.


This data set is part of a manuscript currently under review. See the preprint for the Methods.

Usage notes

 The files can be loaded in Program R using the .R scripts provided. Program R can be found here:

The file contains everything necessary to install the SPIM R package using Program R. The files listed comprise the SPIM R package binary file.

Windows and Mac users most likely want to download the precompiled binaries listed in the upper box, not the source code.

The sources have to be compiled before you can use them. If you do not know what this means, you probably do not want to do it! The latest release (2018-07-02, Feather Spray) R-3