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Yaegashi, S.

Publications and source records attributed to Yaegashi, S..

2 recordsLinked to original sources

Comparison of eDNA, bulk-sample metabarcoding, and morphological approaches: A case study of riverine benthic macroinvertebrate communities

Freshwater biomonitoring is essential for aquatic biodiversity conservation. Advances in high-throughput sequencing allowed parallel sequencing of community samples containing DNA from environmental samples, i.e., metabarcoding. Two approaches of DNA-based method are widely used, bulk-sample metabarcoding the use of bulk tissues such as insects and eDNA the use of environmental samples such as air, water and soil. Despite the novelty of this approach for routine freshwater biomonitoring, questions still need to be answered about its applicability and reliability due to confounding factors, e.g., sample type, laboratory technicalities, and limitations of databases. Hence, studies on direct comparisons are essential to validate the efficiency of these molecular approaches compared to the conventional morphological approach to accurately assessed biodiversity for riverine benthic macroinvertebrate biomonitoring. This study used three approaches to estimate diversity and composition of benthic macroinvertebrates. We also evaluated the relationship between benthic macroinvertebrate communities and environmental factors. We morphologically identified 8,052 individuals from 35 families, 31 genera, and 29 species. eDNA metabarcoding identified 51 families, 84 genera, and 90 species, while 37 families, 55 genera, and 107 species were detected through bulk-sample metabarcoding. We report that bulk-sample metabarcoding showed finer taxonomic resolution than other approaches. Our study highlights the use of bulk-sample metabarcoding for macroinvertebrate biodiversity assessment.

ecology↗

Detection of adaptive divergence in populations of the stream mayfly Ephemera strigata with machine learning

Adaptive divergence is a key mechanism shaping the genetic variation of natural populations. A central question linking ecology with evolutionary biology concerns the role of environmental heterogeneity in determining adaptive divergence among local populations within a species. In this study, we examined adaptive the divergence among populations of the stream mayfly Ephemera strigata in the Natori River Basin in northeastern Japan. We used a genome scanning approach to detect candidate loci under selection and then applied a machine learning method (i.e. Random Forest) and traditional distance-based redundancy analysis (dbRDA) to examine relationships between environmental factors and adaptive divergence at non-neutral loci. We also assessed spatial autocorrelation at neutral loci to quantify the dispersal ability of E. strigata. Our main findings were as follows: 1) random forest shows a higher resolution than traditional statistical analysis for detecting adaptive divergence; 2) separating markers into neutral and non-neutral loci provides insights into genetic diversity, local adaptation and dispersal ability and 3) E. strigata shows altitudinal adaptive divergence among the populations in the Natori River Basin.

evolutionary biology↗