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

TL;DR

This paper shows how different data processing methods and models affect microbial species-area relationship studies, and suggests using DADA2 with a power model for better results.

Contribution

The study reveals incompatibilities between sequence data processing algorithms and fitting models in microbial SAR research.

Findings

Different algorithms affect alpha and beta diversity calculations, altering SAR outcomes.

No single algorithm and model combination is consistently optimal for microbial SAR studies.

DADA2 with a power model is recommended for better compatibility and results.

Abstract

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.

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