Alpha diversity is all about the variety within a single sample. Think: how many different species are there (richness)? And how evenly are those species spread out (evenness)? There are a few ways to measure this, depending on what you’re looking for.
Typically, we recommend report results for all calculated metrics (e.g., “CHAO1: p=0.002, Shannon: p=0.042, Simpson: p=0.067”) as they may show different patterns depending on the underlying community changes.

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❓“I want to know how many species are really there (even the rare ones).”

➡ Use: CHAO1 Index
  • Best when your sample has lots of low-abundance organisms (like in microbiome studies).
  • Estimates the total number of species by using the pattern of rare species detection (singletons and doubletons).
  • Useful when you suspect that sequencing depth limitations prevented detection of some species.
  • Based on capture-recapture principles—doesn’t assume any particular statistical distribution for your data.
Takeaway: CHAO1 is your go-to when you’re trying to estimate the true number of species, including the ones that are too rare to be detected reliably in your sample.

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❓“I care about both how many species there are and how evenly they’re spread.”

➡ Use: Shannon Index
  • A balanced metric that considers both richness (how many) and evenness (how equal).
  • Measures the “uncertainty” in predicting species identity when randomly selecting an individual.
  • Sensitive to both common and rare species in the community.
  • Higher values indicate greater diversity through either more species or more even distribution.
Takeaway: Shannon Index gives you a comprehensive picture of community complexity. If your sample has 10 species but one makes up 90% of the total, the Shannon score will reflect that imbalance and show lower diversity.

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❓“I’m mostly interested in the dominant players.”

➡ Use: Simpson Index
  • Focuses heavily on the most abundant species in the community.
  • Measures the probability that two randomly selected individuals belong to different species.
  • Less sensitive to rare or low-abundance species compared to Shannon.
  • Excellent for detecting changes in community dominance structure.
Takeaway: Simpson Index is great when you’re interested in who’s really running the show in your community and how dominance patterns change between samples.

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🧭 So…which one should I use?

Best practice is to calculate and report all three metrics, as they capture different aspects of diversity. However, if focusing on one metric for your analysis:
GoalBest MetricWhy
Estimate total species richnessCHAO1Accounts for undetected rare species
Comprehensive diversity assessmentShannon IndexBalances richness and evenness
Focus on dominance patternsSimpson IndexEmphasizes abundant species
Compare across studiesUse combinationDifferent metrics reveal different patterns

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📈 What About Statistical Testing?

Alpha diversity metrics can be statistically compared between groups using non-parametric tests: Wilcoxon Rank-Sum Test (Mann-Whitney U): For comparing two groups
  • Tests whether the distributions of diversity values differ significantly between groups
  • Appropriate when data may not be normally distributed
Kruskal-Wallis Test: For comparing more than two groups
  • Non-parametric equivalent of one-way ANOVA
  • Follow with post-hoc tests if significant differences are found
Important Considerations:
  • Always visualize your data first (boxplots, violin plots)
  • Check for outliers that might influence results
  • Consider multiple testing corrections when comparing many groups
  • Report effect sizes alongside p-values when possible