Data from: Jointly representing long-range genetic similarity and spatially heterogeneous isolation-by-distance

Isolation-by-distance patterns in genetic variation are a widespread
feature of the geographic structure of genetic variation in many species,
and many methods have been developed to illuminate such patterns in
genetic data. However, long-range genetic similarities also exist, often
as a result of rare or episodic long-range gene flow.  Jointly
characterizing patterns of isolation-by-distance and long-range genetic
similarity in genetic data is an open data analysis challenge that, if
resolved, could help produce more complete representations of the
geographic structure of genetic data in any given species.  Here,
we present a computationally tractable method that identifies long-range
genetic similarities in a background of spatially heterogeneous
isolation-by-distance variation. The method uses a coalescent-based
framework, and models long-range genetic similarity in terms of
directional events with source fractions describing the fraction of
ancestry at a location tracing back to a remote source. The method
produces geographic maps annotated with inferred long-range edges, as well
as maps of uncertainty in the geographic location of each source of
long-range gene flow.  We have implemented the method in a
package called FEEMSmix (an extension to FEEMS from Marcus et al 2021),
and validated its implementation using simulations representative of
typical data applications.  We also apply this method to two
empirical data sets. In a data set of over 4,000 humans (Homo sapiens)
across Afro-Eurasia, we recover many known signals of long-distance
dispersal from recent centuries. Similarly, in a data set of over 100 gray
wolves (Canis lupus) across North America, we identify several previously
unknown long-range connections, some of which were attributable to
recording errors in sampling locations. Therefore, beyond identifying
genuine long-range dispersals, our approach also serves as a useful tool
for quality control in spatial genetic studies.