The Tibeto-Himalayan Region hosts complex topographical and climatic features and harbors exceptionally high levels of biodiversity and endemism. The “mountain-geobiodiversity hypothesis (MGH)” explores the interaction of topography, climate, and biology in the evolution of mountain biodiversity. We tested this hypothesis in the Himalayas and the Hengduan Mountains on a group of caddisflies that are endemic to this region. We investigated one caddisfly species pair from each mountain respectively, each pair containing one species occupying high elevation and one inhabiting low elevation. We incorporated genomic and ecological evidence to reveal population structure, demographic history, and potential habitat ranges dating back to the last glacial maximum of each species. The results indicated that in both mountains, the high-elevation species showed strong local differentiation, while the low-elevation species were shaped by hydro-morphology. Caddisfly species in the Himalayas generally exhibited an East-West oriented dispersal, while species from the Hengduan Mountains showed greater connectivity on the North-South orientation due to a more extensive area in the North-South orientation. Results of demographic history and species distribution modeling demonstrated that a cold climate leads to an increase in potential habitats, thus causing population expansion. Moreover, most of the divergence and admixture events aligned with the climatic cycles from the middle Pleistocene until the present, suggesting a species-pump effect. Our study demonstrates that, in line with the MGH, mountain topography and climate fluctuations interact and influence the diversification of caddisflies differently in the Himalayas and Hengduan Mountains.

Whole-genome sequencing for generating SNP data is increasingly used in population genetic studies. However, obtaining genomes for massive numbers of samples is still not within the budgets of many researchers. It is thus imperative to select an appropriate reference genome and sequencing coverage to ensure the accuracy of the results for a specific research question, while balancing cost and feasibility. To evaluate the effect of the choice of the reference genome and sequencing coverage on downstream analyses, we used five confamilial reference genomes of variable relatedness and three levels of sequencing coverage (3.5x, 7.5x and 12x) in a population genomic study on two caddisfly species: Himalopsyche digitata and H. tibetana. Using these 30 datasets (five reference genomes × three coverages × two target species), we estimated population genetic indices (inbreeding coefficient, nucleotide diversity, pairwise and genome-wide FST) based on variants and population structure (PCA and admixture) based on genotype likelihood estimates. The results showed that both distantly related reference genomes and lower sequencing coverage lead to degradation of resolution. In addition, choosing a more closely related reference genome may significantly remedy the defects caused by low coverage. Therefore, we conclude that population genetic studies would benefit from closely related reference genomes, especially as the costs of obtaining a high-quality reference genome continue to decrease. However, to determine a cost-efficient strategy for a specific population genomic study, a trade-off between reference genome relatedness and sequencing depth can be considered.