Improving Malaria Parasite Gene Flow Inference Under Sparse Spatial Sampling.

Estimating malaria parasite migration is crucial for informing elimination strategies, particularly by identifying regions with higher parasite migration that may serve as transmission sources and intervention targets. Methods that infer spatial variations in gene flow from georeferenced genetic data, including Estimated Effective Migration Surfaces (EEMS) and related approaches such as estimating Migration And Population-size Surfaces (MAPS), have become widely used to visualize barriers and corridors of organism movement. However, when sampling locations are sparse or unevenly distributed, spatial gene-flow maps can contain artefacts that are difficult to interpret, and methods such as EEMS provide posterior probability summaries that indicate whether inferred migration rates are statistically supported by the genomic data. However, these summaries do not directly capture whether a spatially inferred feature, such as a migration barrier or corridor, lies in a region with sufficient sampling locations to be geographically reliable. A high posterior probability in a sparsely sampled region reflects model confidence given available data, not spatial sampling adequacy. In this study, we developed a sample location-aware (SLA) filtering workflow that applies topological skeletons and kernel density estimation to assess sample density for each contour and filter out areas with lower sample density. This workflow can be applied to any gene-flow inference approach that produces spatially explicit migration features (e.g., contours, surfaces, or high/low-migration regions) from genotype data and sample coordinates. Using simulated genomic data and different barrier configurations under systematic site subsampling scenarios, we show that posterior-only filtering can retain spurious barrier features, whereas SLA filtering consistently increases the precision of inferred low-migration regions across sample sites. In an empirical case study of Plasmodium falciparum from Cambodia, SLA filtering produced more stable estimates of parasite migration patterns under location subsampling than posterior probability filtering alone. These results demonstrate that incorporating an explicit measure of geographic sampling density around each inferred migration feature improves the reliability and interpretability of spatial gene-flow migration patterns for malaria parasites and provides a general diagnostic and filtering approach for a broad class of georeferenced population-genetic migration mapping methods.