International Journal of Molecular Ecology and Conservation, 2025, Vol.15, No.2, 54-62 http://ecoevopublisher.com/index.php/ijmec 56 3 Phylogenetic Reconstruction and Analytical Methods 3.1 Sample collection and genome data construction strategies Phylogenetic reconstruction must be done with cautious broad sampling, in which all aspects of taxonomic and geographic diversity of Siniperca species are covered. Multiple samples have been enabled to generate genome-scale data through next-generation sequencing and significantly improved the resolution and power of phylogenetic inference. Approaches entail whole-genome sequencing, enrichment of targeted nuclear coding regions, and strict sequencing quality control of the data to limit errors and achieve maximum coverage (Young and Gillung, 2019; Dissanayake, 2020). 3.2 Identification and alignment of single-copy orthologous genes A critical part of phylogenomics is the identification of single-copy orthologous genes, excellent markers for making inferences about evolution. The process is eased by bioinformatic pipelines that clip gene families so that only those that occur as single copies in every sampled taxon remain. High-quality multiple sequence alignments are then generated within these genes, as the quality of the alignments directly impacts the validity of resulting phylogenetic analysis (Young and Gillung, 2019; Dissanayake, 2020). 3.3 Phylogenetic tree construction and divergence time estimation Phylogenetic trees are also constructed using a number of methods, including maximum likelihood, Bayesian inference, and distance-based approaches. Maximum likelihood and Bayesian methods are applied as they are more precise, especially with genome-scale data, though it is computationally costly. RAxML and IQ-TREE are some of the programs used in tree estimation. Divergence time estimation applies molecular clock models and fossil calibrations to infer divergence timing, putting observed patterns into a background of evolution (Horiike, 2016; Kendall et al., 2018; Young and Gillung, 2019). 3.4 Topological interpretation and taxonomic implications of phylogenetic results Phylogenetic tree topology analysis is the driving force to the realization of evolutionary relationships and to taxonomic recast direction. Well-supported phylogenomic frameworks will resolve past evolutionary disputes, define species limits, and reveal diversification patterns. Phylogenetic results combined with morphological and ecological information enable improved species delimitation and can uncover new taxa or recast those previously recorded. Methodological integrity, including artefact detection and control and model violation, is crucial for ensuring reliability in taxonomic conclusions ( Young and Gillung, 2019; Struck et al., 2023). 4 Comparative Genomics and Detection of Adaptive Evolution Signals 4.1 Gene family expansion/contraction analysis and functional enrichment In Siniperca species, comparative genomics has been used successfully to interpret gene family expansions and contractions—those significant signatures of adaptive evolution. Scientists have found extensive expansions of gene families involved in immune response, sensory perception (e.g., vision and smell), and energy metabolism. These tendencies are most probably adaptations to the predatory life style, environmental heterogeneity, and enhanced prey detection ability in a range of aquatic habitats. Functional enrichment analyses have revealed that the gene families are overrepresented in biological processes such as immune defense, nutrient acquisition, and responding to environmental stimuli, suggesting that gene family evolution is closely linked with ecological specialization and evolutionary innovation (Steffansson et al., 2004; Regev et al., 2015; Csilléry et al., 2018). 4.2 Screening of positively selected genes and identification of rapidly evolving regions Since Siniperca differentiated into various species, certain genes have undergone strong positive selection in adapting to the evolutional environment. Identification of such genes is typically achieved by estimating the nonsynonymous to synonymous substitution ratio (dN/dS) and site-specific evolutionary models. Advances such as Bayesian inference and machine learning techniques such as convolution neural networks and transfer learning have improved the identification of rapidly evolving genomic regions. These approaches are especially powerful in Siniperca to detect candidate genes for the polygenic traits of feeding behavior, aggressiveness, and temperature tolerance where scientists can detect recent positive selection even in polygenic trait scenarios (Csilléry et al., 2018; Lartillot et al., 2020; DeGiorgio et al., 2025).
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