disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data

We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data. Our neural network performs feature extraction on pairs of genotypes, and uses the geographic information that comes with each sample. These attributes led disperseNN2 to outperform a state-of-the-art deep learning method that does not use explicit spatial information: error was reduced by 33% and 42% using sample sizes of 10 and 100 individuals, respectively. disperseNN2 is particularly useful for non-model organisms or systems with sparse genomic resources, as it uses unphased, single nucleotide polymorphisms as its input. Availability and Implementation Contact chriscs{at}uoregon.edu ### Competing Interest Statement The authors have declared no competing interest.