Probability-of-origin maps deduced from stable isotope data are important for inferring broad-scale patterns of animal migration, but few resources and tools for interpreting and validating these maps exist. For example, quantitative tools for comparing multiple probability-of-origin maps do not exist, and many existing approaches for geographic assignment of individuals have not been validated or compared with respect to precision and accuracy. To address these challenges, we created and analyzed probability-of-origin maps using stable hydrogen isotope values from known-origin individuals of three species of migratory bat. We used a metric of spatial overlap to group individuals by areas of origin without a priori knowledge of such regions. The metric of spatial similarity allowed for quantitative comparison of geographic origins and grouping of individuals with similar origins. We then compared four approaches for inferring origins (cumulative-sum, odds-ratio, quantile-only, and quantile-simulation) across a range of thresholds and probable minimum distance traveled. The accuracy of geographic origins and minimum distance traveled varied across species at most threshold values for most approaches. The cumulative-sum and quantile-simulation approaches had generally higher precision at a given level of accuracy than the odds-ratio and quantile-only approaches, and many threshold values were associated with a relatively high degree (> 300 km) of variation in minimum distance traveled. Overall, these results reinforce the importance of validating assignment techniques with known-origin individuals when possible. We present the tools discussed as part of an R package, ‘isocat’ (“Isotope Origin Clustering and Assignment Tools”).
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