Alpha Diversity — Within-Sample
Alpha diversity measures the diversity within a single sample. Three dimensions:
- Richness (how many species): Chao1, ACE, observed OTUs/ASVs.
- Evenness (how evenly distributed): Pielou's J.
- Combined (richness + evenness): Shannon index (most commonly reported in this wiki), Simpson index (probability two random sequences are different species).
Clinical pattern: Reduced alpha diversity is the most consistent microbiome finding across disease states — IBD, CRC, depression, schizophrenia, ASD, obesity, CVD, and endometriosis all show lower Shannon indices compared to healthy controls. Oral alpha diversity is inversely associated with breast cancer risk (OR 0.86 per SD) [1]. However, reduced diversity is not always pathological — the healthy vaginal microbiome is naturally low-diversity (Lactobacillus-dominant), and high vaginal diversity indicates dysbiosis.
Beta Diversity — Between-Sample
Beta diversity measures how different two microbial communities are from each other:
- Bray-Curtis dissimilarity: Based on abundance differences (ignores phylogeny).
- UniFrac (weighted/unweighted): Incorporates phylogenetic distances between taxa.
- Jaccard index: Presence/absence only (ignores abundance).
Visualized via PCoA, NMDS, or PERMANOVA. Disease vs. control groups typically show significant beta-diversity separation (PERMANOVA p < 0.05), indicating distinct community structures.
Limitations
- Alpha/beta diversity are summary statistics — they tell you the community is different but not how or why. Two communities with identical Shannon indices can have completely different species compositions.
- 16s rrna sequencing primer choice affects diversity estimates.
- Relative abundance data (standard in 16S) can inflate apparent diversity changes when a single dominant taxon shifts.
Cross-References
- 16s rrna sequencing — primary method generating diversity metrics
- shotgun metagenomics — provides functional diversity beyond taxonomic
- dysbiosis — reduced alpha diversity as hallmark