Benchmarking Assembly-Free K-mer Methods for Species Identification in Complex Plant Groups: A Case Study in Populus

Species identification in taxonomically complex plant groups is frequently limited by the inadequacy of organellar markers, whose phylogenetic signal is disrupted by cytonuclear discordance and chloroplast capture. Using the taxonomically complex genus Populus as a model, we evaluated an assembly-free k-mer workflow against a curated SNP reference benchmark. Whole-genome resequencing data from 235 Populus individuals were curated to a 202-individual, 34-species reference dataset in which all retained species are strictly monophyletic in a genome-wide SNP analysis. Independent maximum likelihood analyses further confirmed that the 31 non-hybrid backbone species each maintained high-support monophyly, while taxa of documented reticulate origin showed placement patterns consistent with their reticulate histories. ABBA-BABA D-statistics detected widespread residual allele sharing within the backbone, though the strongest signals did not correspond to the species pairs responsible for the few k-mer identification failures. Against this benchmark, complete plastomes showed limited resolution, recovering only 3.0% species monophyly and 71.1% nearest-neighbor assignment. The optimized k-mer workflow, operating directly on raw reads without assembly or alignment, recovered 91.2% species monophyly, 99.0% nearest-neighbor assignment, and 98.0% group-average assignment. K-mer length was the primary accuracy-controlling parameter, with k = 31 falling within a stable accuracy plateau. Distance-based metrics reached near-saturation at 0.2× sequencing depth, indicating that low-coverage genome skimming can support scalable nuclear genome-based identification with standard computational resources. K-mer distance heatmaps also flagged unusual genomic affinities in hybrid-origin and outlier samples, providing a rapid screen for subsequent population genomic analyses. These results support assembly-free k-mer distances as an efficient tool for reference-based species identification and sample screening in complex plant groups, with residual limitations concentrated near recently diverged species boundaries. Model-based phylogenomic, coalescent, and network analyses remain necessary for resolving deeper species relationships and detailed introgression histories.