An improved method to set significance thresholds for β diversity testing in microbial community comparisons

Area of Science:

• Microbial Ecology
• Bioinformatics
• Environmental Science

Background:

• Measuring microbial community variation (β diversity) between locations is crucial in microbial ecology.
• Current methods lack consensus on significance thresholds for β diversity.
• Technical factors, like subsampling, introduce biases in β diversity estimations.

Purpose of the Study:

• To quantify technical components of β diversity, including subsampling effects.
• To introduce a novel strategy for setting β diversity significance thresholds using rarefaction and a meta-community concept.
• To address the inflation of β diversity caused by intra-sample differences and rarefaction bias.

Main Methods:

• Quantified technical components of β diversity.
• Developed a meta-community-based estimator for β diversity significance thresholds.
• Applied the method to in silico and experimental 16S rRNA pyrosequencing data from rapid sand filters.
• Investigated the impact of microbial community evenness on β diversity estimations.

Main Results:

• Subsampling inflates β diversity due to intra-sample differences, which the proposed method avoids.
• Microbial community evenness significantly affects β diversity estimations, particularly with rarefaction bias.
• The meta-community-based estimator successfully rejects insignificant β diversity values where other methods fail.
• Revealed heterogeneous microbial structures within and across biological rapid sand filters.

Conclusions:

• The proposed meta-community-based method provides a more reliable approach to assess microbial β diversity significance.
• This method overcomes limitations of existing techniques, offering improved accuracy in ecological studies.
• The findings highlight the complex and heterogeneous microbial communities in rapid sand filters.