Interpreting Microbial Species–Area Relationships: Effects of Sequence Data Processing Algorithms and Fitting Models

• Published in: Microorganisms (Basel) Vol. 13; no. 3; p. 635
• Main Authors: Qi, Fu-Liang, Deng, Wei, Cheng, Yi-Ting, Yang, Xiao-Yan, Li, Na, Xiao, Wen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.03.2025; MDPI
• Subjects: Algorithms; Biodiversity; Biological diversity; Compatibility; Data processing; Data structures; Datasets; Electronic data processing; Health aspects; high-throughput sequencing; Li Na; microbial diversity patterns; Microorganisms; model fitting; Next-generation sequencing; Quality control; Social aspects; species–area relationship; microbial diversity patterns; model fitting; high-throughput sequencing; species–area relationship
• ISSN: 2076-2607; 2076-2607
• DOI: 10.3390/microorganisms13030635

In the study of Species–Area Relationships (SARs) in microorganisms, outcome discrepancies primarily stem from divergent high-throughput sequencing data processing algorithms and their combinations with different fitting models. This paper investigates the impacts and underlying causes of using diverse sequence data processing algorithms in microbial SAR studies, as well as compatibility issues that arise between different algorithms and fitting models. The findings indicate that the balancing strategies employed by different algorithms can result in variations in the calculations of alpha and beta diversity, thereby influencing the SARs of microorganisms. Crucially, incompatibilities exist between algorithms and models, with no consistently optimal combination identified. Based on these insights, we recommend prioritizing the use of the DADA2 algorithm in conjunction with a power model, which demonstrates greater compatibility. This study serves as a comprehensive comparison and reference for fundamental methods in microbial SAR research. Future microbial SAR studies should carefully select the most appropriate algorithms and models based on specific research objectives and data structures.